From 9715e2bab011b7fc1f7c4410190f206113cc0e02 Mon Sep 17 00:00:00 2001 From: Brandon Beltz - NOAA Affiliate <136381970+BBeltz1@users.noreply.github.com> Date: Thu, 29 Feb 2024 16:39:23 -0500 Subject: [PATCH 1/8] change catalog urls to match ecodata names adjusted make rmd function to set the url of each catalog page to its ecodata data name. built catalog with new urls. added catalog page for rec hms. --- R/make_rmd.R | 4 +- chapters/HMS_species_distribution.rmd | 10 +-- chapters/SAV.rmd | 2 +- chapters/abc_acl.rmd | 2 +- chapters/aggregate_biomass.rmd | 2 +- chapters/aquaculture.rmd | 2 +- chapters/bennet.rmd | 2 +- chapters/bottom_temp.rmd | 4 +- chapters/bottom_temp_comp.rmd | 4 +- chapters/bottom_temp_seasonal_gridded.rmd | 2 +- chapters/calanus_variation.rmd | 4 +- chapters/ch_bay_sal.rmd | 4 +- chapters/ch_bay_temp.rmd | 2 +- chapters/ches_bay_sst.rmd | 5 +- chapters/ches_bay_synthesis.rmd | 2 +- chapters/ches_bay_wq.rmd | 6 +- chapters/chl_pp.rmd | 20 +++-- chapters/cold_pool.rmd | 2 +- chapters/comdat.rmd | 2 +- chapters/commercial_div.rmd | 2 +- chapters/condition.rmd | 2 +- chapters/energy_density.rmd | 6 +- chapters/engagement.rmd | 6 +- chapters/exp_n.rmd | 2 +- chapters/forage_index.rmd | 4 +- chapters/gom_salmon.rmd | 4 +- chapters/grayseal.rmd | 2 +- chapters/gsi.rmd | 2 +- chapters/habitat_diversity.rmd | 2 +- chapters/habs.rmd | 2 +- chapters/harborporpoise.rmd | 2 +- chapters/heatwave.rmd | 2 +- chapters/heatwave_year.rmd | 2 +- chapters/hms_cpue.rmd | 2 +- chapters/hms_landings.rmd | 4 +- chapters/hms_stock_status.rmd | 2 +- chapters/long_term_sst.rmd | 2 +- chapters/mab_inshore_survey.rmd | 2 +- chapters/mass_inshore_survey.rmd | 2 +- chapters/narw.rmd | 12 +-- chapters/ne_inshore_survey.rmd | 2 +- chapters/observation_synthesis.rmd | 2 +- chapters/ocean_acidification.rmd | 8 +- chapters/osw_survey_impact.rmd | 4 +- chapters/persistent_hotspots.rmd | 2 +- chapters/phyto_size.rmd | 2 +- chapters/ppr.rmd | 2 +- chapters/productivity_anomaly.rmd | 5 +- chapters/rec_hms.rmd | 91 +++++++++++++++++++++++ chapters/recdat.rmd | 2 +- chapters/seabird_ne.rmd | 2 +- chapters/seal_pups.rmd | 2 +- chapters/seasonal_oisst_anom.rmd | 2 +- chapters/seasonal_sst_anomaly_gridded.rmd | 2 +- chapters/slopewater.rmd | 4 +- chapters/spawn_timing.rmd | 2 +- chapters/species_dist.rmd | 2 +- chapters/species_groupings.rmd | 2 +- chapters/stock_status.rmd | 2 +- chapters/thermal_habitat_area.rmd | 2 +- chapters/thermal_habitat_persistence.rmd | 2 +- chapters/timing_shifts.rmd | 2 +- chapters/trans_dates.rmd | 2 +- chapters/wbts_mesozooplankton.rmd | 4 +- chapters/wcr.rmd | 2 +- chapters/wind_dev_speed.rmd | 2 +- chapters/wind_port.rmd | 2 +- chapters/wind_revenue.rmd | 2 +- chapters/zoo_abundance_anom.rmd | 2 +- chapters/zoo_diversity.rmd | 2 +- 70 files changed, 202 insertions(+), 109 deletions(-) create mode 100644 chapters/rec_hms.rmd diff --git a/R/make_rmd.R b/R/make_rmd.R index b8471472..46147a56 100644 --- a/R/make_rmd.R +++ b/R/make_rmd.R @@ -21,8 +21,8 @@ make_rmd <- function(listobject){ # start to create the Rmd #cat(paste0("# ",stringr::str_to_title(indicator_name)),append=T,fill=T,file=con) ### DESCRIPTION, CONTRIBUTORS, AFFILIATION, FAMILY -# cat(paste0("# ",listobject$dataname," {#",listobject$indicatorname,"}"),append=T,fill=T,file=con) - cat(paste0("# ",listobject$dataname),append=T,fill=T,file=con) + cat(paste0("# ",listobject$dataname," {#",listobject$indicatorname,"}"),append=T,fill=T,file=con) + #cat(paste0("# ",listobject$dataname),append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space cat(paste0("**Description**: ",listobject$description),append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space diff --git a/chapters/HMS_species_distribution.rmd b/chapters/HMS_species_distribution.rmd index 1ab391b8..1841a343 100644 --- a/chapters/HMS_species_distribution.rmd +++ b/chapters/HMS_species_distribution.rmd @@ -1,6 +1,6 @@ -# Cetacean Distribution Shifts +# Cetacean Distribution Shifts {#HMS_species_distribution} -**Description**: The data presented here are the locations of the center of core habitat for cetacean by season as documented in 2010 versus 2017. +**Description**: The data presented here are the locations of the center of core habitat for cetaceans by season as documented in 2010 versus 2017. **Indicator family**: @@ -16,7 +16,7 @@ knitr::opts_chunk$set(echo = F) library(ecodata) ``` ## Introduction to Indicator -Marine species are being affected by global climate changes, where and in most cases the documented responses include distribution shifts from their historical habitat. In addition, human-caused drivers such as the noise and physical disturbances from oil and gas exploration, fishing, boat traffic and infrastructure such as offshore renewable energy developments, as well as other maritime activities could also result in shifts. [@chavez-rosales_detection_2022] used Northwest Atlantic cetacean location data collected in its changing environment to investigate if their habitats are changing, and if so, to what extent. +Marine species are being affected by global climate changes, and in most cases the documented responses include distribution shifts from their historical habitat. In addition, human-caused drivers such as the noise and physical disturbances from oil and gas exploration, fishing, boat traffic and infrastructure such as offshore renewable energy developments, as well as other maritime activities could also result in shifts. [@chavez-rosales_detection_2022] used Northwest Atlantic cetacean location data collected in its changing environment to investigate if their habitats are changing, and if so, to what extent. A climate vulnerability assessment is published for Atlantic and Gulf of Mexico marine mammal populations [@lettrich_vulnerability_2023]. @@ -57,8 +57,8 @@ Shifting species distributions alter both species interactions and fishery inter **Variable definitions** -1) Time=time period of centroid location. 2) species=cetacean species. 3) season. 4) wlat=latitude of centroid. -5) wlon=longitude of centroid. +1) Time=time period of centroid location. 2) species=cetacean species. 3) season. +4) wlat=latitude of centroid. 5) wlon=longitude of centroid. ```{r vars_HMS_species_distribution} # Pull all var names diff --git a/chapters/SAV.rmd b/chapters/SAV.rmd index f351c6cd..88f9a014 100644 --- a/chapters/SAV.rmd +++ b/chapters/SAV.rmd @@ -1,4 +1,4 @@ -# Submerged Aquatic Vegetation +# Submerged Aquatic Vegetation {#SAV} **Description**: The data provided here are the 1984-2022 area distribution and percent coverage of submerged aquatic vegetation in the Chesapeake Bay and its tributaries that area measured and calculated from photo-interpreted aerial imagery taken during surveys conducted in the growing season. diff --git a/chapters/abc_acl.rmd b/chapters/abc_acl.rmd index f73d7052..770e2560 100644 --- a/chapters/abc_acl.rmd +++ b/chapters/abc_acl.rmd @@ -1,4 +1,4 @@ -# ABC or ACL for Managed Stocks +# ABC or ACL for Managed Stocks {#abc_acl} **Description**: Mid-Atlantic Council catch limits (e.g., ABC or ACL) and associated total catch estimate by year for each species and sector (commercial or recreational, as appropriate). diff --git a/chapters/aggregate_biomass.rmd b/chapters/aggregate_biomass.rmd index b45b3565..4da76b6b 100644 --- a/chapters/aggregate_biomass.rmd +++ b/chapters/aggregate_biomass.rmd @@ -1,4 +1,4 @@ -# Aggregate Survey Biomass +# Aggregate Survey Biomass {#aggregate_biomass} **Description**: Aggregate biomass from Northeast Fisheries Science Center (NEFSC) bottom trawl survey. diff --git a/chapters/aquaculture.rmd b/chapters/aquaculture.rmd index 46d25d75..ff7a2731 100644 --- a/chapters/aquaculture.rmd +++ b/chapters/aquaculture.rmd @@ -1,4 +1,4 @@ -# Aquaculture Production +# Aquaculture Production {#aquaculture} **Description**: Oyster production: number of oysters harvested from aquaculture. diff --git a/chapters/bennet.rmd b/chapters/bennet.rmd index 4c38224b..94f17699 100644 --- a/chapters/bennet.rmd +++ b/chapters/bennet.rmd @@ -1,4 +1,4 @@ -# Bennet Indicator +# Bennet Indicator {#bennet} **Description**: The data presented here are changes in revenue ($ real) split into a price indicator and a volume indicator. The sum of the price and the volume indicator is equal to the revenue change relative to a base year, which is 1982. diff --git a/chapters/bottom_temp.rmd b/chapters/bottom_temp.rmd index b40fe514..5447647a 100644 --- a/chapters/bottom_temp.rmd +++ b/chapters/bottom_temp.rmd @@ -1,4 +1,4 @@ -# Bottom Temperature - in situ +# Bottom Temperature - in situ {#bottom_temp} **Description**: The data presented here are time series of regional average bottom temperature anomalies from ship-based measurements made on the Northeast Continental Shelf. @@ -45,7 +45,7 @@ Temporal scale: Annual **Synthesis Theme**: - +- [X] Multiple System Drivers ```{r autostats_bottom_temp} diff --git a/chapters/bottom_temp_comp.rmd b/chapters/bottom_temp_comp.rmd index 0aef2074..87b3cb2c 100644 --- a/chapters/bottom_temp_comp.rmd +++ b/chapters/bottom_temp_comp.rmd @@ -1,4 +1,4 @@ -# Bottom temperature - Seasonal Anomaly +# Bottom temperature - Seasonal Anomaly {#bottom_temp_comp} **Description**: The data are seasonal bottom temperature anomaly time series for each EPU @@ -7,7 +7,7 @@ - [X] Oceanographic -**Contributor(s)**: Joseph Caracappa, Hubert duPontavice, Vincent Saba, Zhuomin Chen +**Contributor(s)**: Joseph Caracappa, Hubert du Pontavice, Vincent Saba, Zhuomin Chen **Affiliations**: NEFSC diff --git a/chapters/bottom_temp_seasonal_gridded.rmd b/chapters/bottom_temp_seasonal_gridded.rmd index b699df01..074807aa 100644 --- a/chapters/bottom_temp_seasonal_gridded.rmd +++ b/chapters/bottom_temp_seasonal_gridded.rmd @@ -1,4 +1,4 @@ -# Bottom temperature - Seasonal Gridded +# Bottom temperature - Seasonal Gridded {#bottom_temp_seasonal_gridded} **Description**: Seasonal mean bottom temperatures on the Northeast Continental Shelf between 1959 and 2023 in a 1/12° grid. diff --git a/chapters/calanus_variation.rmd b/chapters/calanus_variation.rmd index eecf65c7..a3bb60db 100644 --- a/chapters/calanus_variation.rmd +++ b/chapters/calanus_variation.rmd @@ -1,4 +1,4 @@ -# Seasonal Variation of Calanus finmarchicus +# Seasonal Variation of Calanus finmarchicus {#calanus_variation} **Description**: Abundance of late copepodid stages of the planktonic copepod, Calanus finmarchicus, measured during seasonal surveys between 1977 and 2019. Data from NOAA EcoMon/MARMAP program @@ -26,8 +26,6 @@ Historically, the high abundance of C. finmarchicus in the GOM combined with the This phenology indicator shows the change in abundance of the planktonic copepod, Calanus finmarchicus over a mean annual cycle in Wilkinson Basin, the primary overwintering habitat of this species in the western Gulf of Maine. The data are provided by the NOAA EcoMon/MARMAP survey, which has sampled stations along the Northeast U.S. Shelf, including the Gulf of Maine, seasonally (2-6 times per year) in nearly all years since 1977. The 333 µm mesh plankton nets used by the survey quantitatively capture only the late copepodid stages (C3-adult) of C. finmarchicus, but these stages nevertheless are representative of the seasonal variation in abundance of the population. This indicator serves as a baseline that can be used to interpret future changes in wGoM C. finmarchicus abundance. ## Key Results and Visualizations -Calanus finmarchicus phenology figure (uploaded with data) here - Seasonal abundance (number m-3) of C. finmarchicus late copepodid stages (mostly stages CIII-CVI) in Wilkinson Basin. X-axis represents time of year, from 1 January (yearday 0) to 31 December (yearday 365). Background gray circles show individual MARMAP/EcoMon abundance data points in Wilkinson Basin between 1977-2019. Solid black line shows the seasonal pattern in mean abundance from the MARMAP/EcoMon data; dotted lines show 2x (top) and ½ (bottom) of the mean abundance. Colored horizontal lines show conceptual model of seasonally variable predominant drivers. Predominant drivers in winter (Jan-Mar: days 1-100) suggested to be a combination of predation mortality and advective loss. The abundance of late stage Calanus finmarchicus in the western Gulf of Maine is seasonally variable. The highest abundances are observed in May-June, the result of reproduction, the magnitude of which depends on the timing of food availability to females (Stage CVI) in late-winter through spring. By late summer, most of the C. finmarchicus population is present as Stage CV, which overwinters at depth in a dormant state. The number of stage CV and hence the overall population abundance dwindles depending on net losses from advection and vertebrate and invertebrate predators. The abundance reaches its nadir in February-March, when the population is in stage CV or newly molted adult females and males. Note the difference between the late winter and late spring mean abundances is about three orders of magnitude. diff --git a/chapters/ch_bay_sal.rmd b/chapters/ch_bay_sal.rmd index 4ddca77d..e87302a0 100644 --- a/chapters/ch_bay_sal.rmd +++ b/chapters/ch_bay_sal.rmd @@ -1,4 +1,4 @@ -# Chesapeake Bay Salinity +# Chesapeake Bay Salinity {#ch_bay_sal} **Description**: This data is collected from the CBIBS buoy system. @@ -49,7 +49,7 @@ The changes in the temperature and salinity have implications in the habitat ## Get the data -**Point of contact**: [Charles Pellerin (charles.pellerin@noaa.gov](mailto:Charles Pellerin (charles.pellerin@noaa.gov){.email} +**Point of contact**: [Charles Pellerin (charles.pellerin@noaa.gov)](mailto:Charles Pellerin (charles.pellerin@noaa.gov)){.email} **ecodata name**: `ecodata::ch_bay_sal` diff --git a/chapters/ch_bay_temp.rmd b/chapters/ch_bay_temp.rmd index a0cd3867..a9973420 100644 --- a/chapters/ch_bay_temp.rmd +++ b/chapters/ch_bay_temp.rmd @@ -1,4 +1,4 @@ -# Chesapeake Bay Temperature +# Chesapeake Bay Temperature {#ch_bay_temp} **Description**: This data is collected from the CBIBS buoy system. diff --git a/chapters/ches_bay_sst.rmd b/chapters/ches_bay_sst.rmd index 1acc9459..94887830 100644 --- a/chapters/ches_bay_sst.rmd +++ b/chapters/ches_bay_sst.rmd @@ -1,4 +1,4 @@ -# Chesapeake Bay Seasonal Sea Surface Temperature Anomaly +# Chesapeake Bay Seasonal Sea Surface Temperature Anomaly {#ches_bay_sst} **Description**: Chesapeake Bay Seasonal Sea Surface Temperature Anomaly @@ -61,7 +61,8 @@ In the fall season, there were warmer-than-average temperatures in the Western S **Variable definitions** -1) sst: sea surface temperature 2023, Celsius 2) sst_climatol: sea surface temperature climatology 2007-2022, Celsius +1) sst: sea surface temperature 2023, Celsius +2) sst_climatol: sea surface temperature climatology 2007-2022, Celsius 3) sst_anomaly: sea surface temperature anomaly 2023 minus 2007-2022, Celsius ```{r vars_ches_bay_sst} diff --git a/chapters/ches_bay_synthesis.rmd b/chapters/ches_bay_synthesis.rmd index 6013173e..3c3ea0b1 100644 --- a/chapters/ches_bay_synthesis.rmd +++ b/chapters/ches_bay_synthesis.rmd @@ -1,4 +1,4 @@ -# Chesapeake Bay 2023 Synthesis +# Chesapeake Bay 2023 Synthesis {#ches_bay_synthesis} **Description**: Synthesis of Chesapeake Bay 2023 habitat conditions with implications for managed species diff --git a/chapters/ches_bay_wq.rmd b/chapters/ches_bay_wq.rmd index 44c9ea36..25096ff0 100644 --- a/chapters/ches_bay_wq.rmd +++ b/chapters/ches_bay_wq.rmd @@ -1,4 +1,4 @@ -# Chesapeake Bay Water Quality Standards Attainment +# Chesapeake Bay Water Quality Standards Attainment {#ches_bay_wq} **Description**: Chesapeake Bay Water Quality Attainment Indicator @@ -61,8 +61,8 @@ Patterns of attainment of individual designated uses are variable (Figure 2). Ac **Variable definitions** -Period: Assessment period Year 1: Starting year of the assessment period Year 2: Ending year of the assessment period -Total: The overall attainment indicator +Period: Assessment period Year 1: Starting year of the assessment period +Year 2: Ending year of the assessment period Total: The overall attainment indicator MSN-DO: Estimated attainment of the dissolved oxygen criterion for the migratory spawning and nursery designated use OW-DO: Estimated attainment of the dissolved oxygen criterion for the open water designated use DW-DO: Estimated attainment of the dissolved oxygen criterion for the deep water designated use diff --git a/chapters/chl_pp.rmd b/chapters/chl_pp.rmd index a6b98fd5..736be584 100644 --- a/chapters/chl_pp.rmd +++ b/chapters/chl_pp.rmd @@ -1,8 +1,6 @@ -# Chlorophyll and Primary Production +# Chlorophyll and Primary Production {#chl_pp} -**Description**: Satellite derived phytoplankton data including chlorophyll concentration, phytoplankton size class, and primary production for the Northeast Continental Shelf and ecological production units. - -(To be expanded) +**Description**: Satellite derived phytoplankton data including chlorophyll concentration, phytoplankton size class, and primary production for the Northeast Continental Shelf and ecological production units. **Indicator family**: @@ -18,12 +16,18 @@ knitr::opts_chunk$set(echo = F) library(ecodata) ``` ## Introduction to Indicator -Phytoplankton are the foundation of the marine food web and are the primary food source for zooplankton and filter feeders such as shellfish. Numerous environmental and oceanographic factors interact to drive the abundance, composition, spatial distribution, and productivity of phytoplankton. Satellite derived measurements of chlorophyll, the dominant photosynthetic pigment in phytoplankton, are used to estimate phytoplankton biomass. Phytoplankton growth depends on the availability of carbon dioxide, sunlight and nutrients and their growth rates can be influenced by water temperature, water depth, wind, and grazing pressure. Primary productivity is a measure of the amount of carbon produced by phytoplankton. The seasonal cycle of phytoplankton size distribution are typically dominated by larger-celled microplankton during the winter-spring and fall bloom periods, while smaller-celled nanoplankton dominate during the warmer summer months. +Phytoplankton are key biological regulators of the structure and function of most marine ecosystems. They are the foundation of the marine food web and are the primary food source for zooplankton and filter feeders such as shellfish. Numerous environmental and oceanographic factors interact to drive the abundance, composition, spatial distribution, seasonal timing and productivity of phytoplankton. Satellite derived measurements of chlorophyll, the dominant photosynthetic pigment in phytoplankton, are used to estimate total phytoplankton biomass. The size structure of the phytoplankton community influences important biogeochemical and ecological processes, including transfer of energy through the marine food web. Phytoplankton growth depends on the availability of carbon dioxide, sunlight and nutrients and their growth rates can be influenced by water temperature, water depth, wind, and grazing pressure. Primary productivity is a measure of the amount of carbon produced by phytoplankton. -(To be expanded) +The unique physical characteristics of the Northeast U.S. continental shelf help make it among the most productive continental shelf systems in the world influenced by both bottom-up (e.g. nutrient concentrations, light availability, and mixing/stratification) and top-down (e.g. grazing) controls. Phytoplankton biomass, composition, and productivity all have high spatial, seasonal and interannual variability. The most pronounced spatial pattern is the decrease in phytoplankton biomass from the coast to the shelf break. Georges Bank and Nantucket Shoals are shallow regions that are well mixed by tides. This mixing supplies sufficient nutrients to support phytoplankton growth throughout the year. In other regions, blooms of large diatom species occur on a seasonal cycle when growing conditions are ideal. ## Key Results and Visualizations -(In development) +The seasonal cycles of phytoplankton size distribution are typically dominated by larger-celled microplankton during the winter-spring and fall bloom periods, while smaller-celled nanoplankton dominate during the warmer summer months. In 2023, MAB total chlorophyll was below average in early spring, near average through the summer and above average throughout the fall. A peak in primary production occurred in summer, followed by an above average productivity associated with the early fall bloom. Phytoplankton size class distributions were near average for most of the year, except during the early fall bloom. + +Total chlorophyll concentrations on Georges Bank were above average for most of the year. The early winter bloom was most likely associated with diatoms, however the above average chlorophyll, primary production and microplankton fraction from April through August can be attributed to the dinoflagellate _Tripos muelleri_. + +Total chlorophyll concentrations in the Gulf of Maine were above average for most of the year. The early winter bloom was most likely associated with diatoms, however the record high chlorophyll, primary production and microplankton fraction from April through August can be attributed to the dinoflagellate _Tripos muelleri_. + +There is high interannual variability of the seasonal phytoplankton cycle. At the monthly scale, MAB chlorophyll and primary production are increasing during January and there has been a decrease in September chlorophyll, likely due to extension of the [summer stratification](https://noaa-edab.github.io/catalog/transition-dates.html) and delayed fall turnover. Fall and winter chlorophyll and primary production are increasing on Georges Bank and Gulf of Maine. ### MidAtlantic @@ -117,7 +121,7 @@ Temporal scale: Daily, weekly, monthly, annual, climatology (1998 to current yea ``` ## Implications -(In development) +Phytoplankton abundance, productivity, diversity, cell size, phenology, and carbon fluxes are regulated by the local physical and chemical environment and grazing. Interannual and climatological changes in temperature, freshwater inputs (due to ice sheet melting and/or enhanced river discharge), wind direction, and wind speed can alter the circulation patterns, upwelling conditions, and nutrient fluxes, directly affecting the timing, location, species composition of phytoplankton blooms in the NES. As the NES responds to warming, changing phenologies, changing chemistry, and changes in circulation patterns, we must understand how varying biophysical interactions control phytoplankton and subsequently affect fisheries, their habitats and the people, businesses and communities that depend on them. ## Get the data diff --git a/chapters/cold_pool.rmd b/chapters/cold_pool.rmd index 1e85d77c..3e50c09b 100644 --- a/chapters/cold_pool.rmd +++ b/chapters/cold_pool.rmd @@ -1,4 +1,4 @@ -# Cold Pool Index +# Cold Pool Index {#cold_pool} **Description**: Three annual cold pool indices (and standard error) for ss1959 through 2023 diff --git a/chapters/comdat.rmd b/chapters/comdat.rmd index bb00988e..0b7e58a3 100644 --- a/chapters/comdat.rmd +++ b/chapters/comdat.rmd @@ -1,4 +1,4 @@ -# Commercial Landings and Revenue +# Commercial Landings and Revenue {#comdat} **Description**: Commercial landings and revenue from dealer reports diff --git a/chapters/commercial_div.rmd b/chapters/commercial_div.rmd index 77cf2384..b3df79c4 100644 --- a/chapters/commercial_div.rmd +++ b/chapters/commercial_div.rmd @@ -1,4 +1,4 @@ -# Commercial Catch and Fleet Diversity +# Commercial Catch and Fleet Diversity {#commercial_div} **Description**: Permit-level species diversity and Council-level fleet diversity. diff --git a/chapters/condition.rmd b/chapters/condition.rmd index 9edd292f..6451817a 100644 --- a/chapters/condition.rmd +++ b/chapters/condition.rmd @@ -1,4 +1,4 @@ -# Relative condition +# Relative condition {#condition} **Description**: NEFSC fall bottom trawl survey relative condition diff --git a/chapters/energy_density.rmd b/chapters/energy_density.rmd index 5f96d5e2..82959e2b 100644 --- a/chapters/energy_density.rmd +++ b/chapters/energy_density.rmd @@ -1,6 +1,6 @@ -# Forage Fish Energy Density +# Forage Fish Energy Density {#energy_density} -**Description**: Energy density of alewife, butterfish, sand lance, and Atlantic mackerel varies seasonally, with seasonal estimates both higher and lower than estimates from previous decades. The data presented are the seasonal (Spring and Fall) energy density (kJ/g) for eight important forage species; Alewife, Atlantic Herring, Silver Hake, Northern Sand Lance, Atlantic Mackerel, Butterfish, Northern Shortfin Squid, and Inshore Longfin Squid. Samples are obtained from the NEFSC seasonal bottom trawl surveys and processed int he lab to estimate energy content. +**Description**: Energy density of alewife, butterfish, sand lance, and Atlantic mackerel varies seasonally, with seasonal estimates both higher and lower than estimates from previous decades. The data presented are the seasonal (Spring and Fall) energy density (kJ/g) for eight important forage species; Alewife, Atlantic Herring, Silver Hake, Northern Sand Lance, Atlantic Mackerel, Butterfish, Northern Shortfin Squid, and Inshore Longfin Squid. Samples are obtained from the NEFSC seasonal bottom trawl surveys and processed in the lab to estimate energy content. **Indicator family**: @@ -77,7 +77,7 @@ Source data are NOT publicly available. ## Accessibility and Constraints -Email mark.wuenschel@noaa.gov for further information. Data tables are beign created to make this readily available soon. +Email mark.wuenschel@noaa.gov for further information. Data tables are being created to make this readily available soon. **tech-doc link** diff --git a/chapters/engagement.rmd b/chapters/engagement.rmd index fcde7642..9a22cfaf 100644 --- a/chapters/engagement.rmd +++ b/chapters/engagement.rmd @@ -1,4 +1,4 @@ -# Engagement, Reliance, and Environmental Justice in Top Fishing Communities +# Engagement, Reliance, and Environmental Justice in Top Fishing Communities {#engagement} **Description**: The data presented here are 2021 environmental justice indicators in top commercial and top recreational communities in Mid-Atlantic and New England regions, respectively. @@ -16,7 +16,7 @@ knitr::opts_chunk$set(echo = F) library(ecodata) ``` ## Introduction to Indicator -We report the top ten communities most engaged in, and/or reliant upon, commercial and recreational fisheries and the degree to which these communities may be vulnerable to environmental justice issues (i.e., Poverty, Population Composition, and Personal Disruption). To select and present these communities we developed indicators (or indices that inform the importance of fishing and relative social conditions in each community. +We report the top ten communities most engaged in, and/or reliant upon, commercial and recreational fisheries and the degree to which these communities may be vulnerable to environmental justice issues (i.e., Poverty, Population Composition, and Personal Disruption). To select and present these communities we developed indicators (or indices) that inform the importance of fishing and relative social conditions in each community. The engagement and reliance indices demonstrate the importance of commercial and recreational fishing to a given community relative to other coastal communities in a region. Similarly, the environmental justice indices characterize different facets and levels of social vulnerability in a given community relative to other coastal communities in a region. @@ -145,7 +145,7 @@ It is also important to note that factor scores and their associated categorical ## Get the data -**Point of contact**: [Lisa Colburn (lisa.l.colburn@noaa.gov)](mailto:Lisa Colburn (lisa.l.colburn@noaa.gov)){.email} +**Point of contact**: [Lisa Colburn (lisa.l.colburn@noaa.gov); Changhua Weng (changhua.weng@noaa.gov)](mailto:Lisa Colburn (lisa.l.colburn@noaa.gov); Changhua Weng (changhua.weng@noaa.gov)){.email} **ecodata name**: `ecodata::engagement` diff --git a/chapters/exp_n.rmd b/chapters/exp_n.rmd index 133be309..7c00323b 100644 --- a/chapters/exp_n.rmd +++ b/chapters/exp_n.rmd @@ -1,4 +1,4 @@ -# Expected Number of Species +# Expected Number of Species {#exp_n} **Description**: Diversity metric from the Northeast Fisheries Science Center (NEFSC) Bottom Trawl Surveys. diff --git a/chapters/forage_index.rmd b/chapters/forage_index.rmd index 6cb9c44f..444fdb5e 100644 --- a/chapters/forage_index.rmd +++ b/chapters/forage_index.rmd @@ -1,4 +1,4 @@ -# Forage Fish Index +# Forage Fish Index {#forage_index} **Description**: Aggregate forage fish biomass index from fish stomach contents @@ -75,7 +75,7 @@ Changes in the distribution of forage biomass also affects predator distribution ## Get the data -**Point of contact**: [Sarah.Gaichas@noaa.gov](mailto:Sarah.Gaichas@noaa.gov){.email} +**Point of contact**: [Sarah Gaichas (Sarah.Gaichas@noaa.gov)](mailto:Sarah Gaichas (Sarah.Gaichas@noaa.gov)){.email} **ecodata name**: `ecodata::forage_index` diff --git a/chapters/gom_salmon.rmd b/chapters/gom_salmon.rmd index e1696cdd..bf11b9a0 100644 --- a/chapters/gom_salmon.rmd +++ b/chapters/gom_salmon.rmd @@ -1,6 +1,6 @@ -# Gulf of Maine Atlantic salmon +# Gulf of Maine Atlantic salmon {#gom_salmon} -**Description**: The data presented here are time series of documented Atlantic salmon returns to Gulf of Maine Rivers since 1972 and return rates for two sea winter returns from hatchery smolt stockings. +**Description**: The data presented here are time series of documented Atlantic salmon returns to Gulf of Maine rivers since 1972 and return rates for two sea winter returns from hatchery smolt stockings. **Indicator family**: diff --git a/chapters/grayseal.rmd b/chapters/grayseal.rmd index 6f7fdb2a..7664886e 100644 --- a/chapters/grayseal.rmd +++ b/chapters/grayseal.rmd @@ -1,4 +1,4 @@ -# Gray Seal Bycatch +# Gray Seal Bycatch {#grayseal} **Description**: The data presented here are time series of the species specific estimates of bycatch from U.S. North Atlantic commercial fisheries. diff --git a/chapters/gsi.rmd b/chapters/gsi.rmd index c9a4c10b..22db3f7d 100644 --- a/chapters/gsi.rmd +++ b/chapters/gsi.rmd @@ -1,4 +1,4 @@ -# Gulf Stream Index +# Gulf Stream Index {#gsi} **Description**: The monthly Gulf Stream North Wall Index presented here are based on the gridded EN.4.2.2 analyses dataset from 1954 to 2022 (https://www.metoffice.gov.uk/hadobs/en4/), calculated following @joyce_relationship_2009. diff --git a/chapters/habitat_diversity.rmd b/chapters/habitat_diversity.rmd index d899b5e5..58136898 100644 --- a/chapters/habitat_diversity.rmd +++ b/chapters/habitat_diversity.rmd @@ -1,4 +1,4 @@ -# Species Richness +# Species Richness {#habitat_diversity} **Description**: Abundance data were extracted from the NEFSC’s SVDBS database using Survdat for 55 fish species regularly sampled on spring and fall NEFSC bottom trawl surveys (see SOE Tech Doc for a list). Data were converted to presence/absence for species richness modeling. diff --git a/chapters/habs.rmd b/chapters/habs.rmd index 6a40d855..5a1b83c2 100644 --- a/chapters/habs.rmd +++ b/chapters/habs.rmd @@ -1,4 +1,4 @@ -# Harmful Algal Blooms +# Harmful Algal Blooms {#habs} **Description**: These data represent annual estimated abundance of Alexandrium catanella cysts in the Gulf of Maine and the presence of PSP toxins in blue mussels at coastal sites in the Gulf of Maine (MA, NH, ME), and shellfishery closures (MA). diff --git a/chapters/harborporpoise.rmd b/chapters/harborporpoise.rmd index 7ac0c754..5f912cbf 100644 --- a/chapters/harborporpoise.rmd +++ b/chapters/harborporpoise.rmd @@ -1,4 +1,4 @@ -# Harbor Porpoise Bycatch +# Harbor Porpoise Bycatch {#harborporpoise} **Description**: The data presented here are time series of the species specific estimates of bycatch from U.S. North Atlantic commercial fisheries. diff --git a/chapters/heatwave.rmd b/chapters/heatwave.rmd index 2b8b66a3..8b95f286 100644 --- a/chapters/heatwave.rmd +++ b/chapters/heatwave.rmd @@ -1,4 +1,4 @@ -# Annual Heatwave Intensity +# Annual Heatwave Intensity {#heatwave} **Description**: Surface and bottom MHWs for 2023. diff --git a/chapters/heatwave_year.rmd b/chapters/heatwave_year.rmd index 887cf8bc..c62c587d 100644 --- a/chapters/heatwave_year.rmd +++ b/chapters/heatwave_year.rmd @@ -1,4 +1,4 @@ -# Marine Heatwave Events +# Marine Heatwave Events {#heatwave_year} **Description**: Surface and bottom MHWs for 2023. diff --git a/chapters/hms_cpue.rmd b/chapters/hms_cpue.rmd index 40ffa20a..9a2979a6 100644 --- a/chapters/hms_cpue.rmd +++ b/chapters/hms_cpue.rmd @@ -1,4 +1,4 @@ -# Highly Migratory Species POP Catch Per Unit Effort +# Highly Migratory Species POP Catch Per Unit Effort {#hms_cpue} **Description**: CPUE from pelagic observer program (POP) observed hauls, presented as number of fish per haul, is provided for the northeast (i.e., the Northeast Coastal and Mid-Atlantic Bight fishing areas) by year/species from 1992-2022. diff --git a/chapters/hms_landings.rmd b/chapters/hms_landings.rmd index b618380d..2951c0bd 100644 --- a/chapters/hms_landings.rmd +++ b/chapters/hms_landings.rmd @@ -1,6 +1,6 @@ -# Highly Migratory Species Landings +# Highly Migratory Species Landings {#hms_landings} -**Description**: Aggregated Atlantic HMS landings data prepared for the Fisheries of the United States (FUS) report, spanning 2015-2022. +**Description**: Aggregated Atlantic highly migratory species landings data prepared for the Fisheries of the United States (FUS) report, spanning 2015-2022. **Indicator family**: diff --git a/chapters/hms_stock_status.rmd b/chapters/hms_stock_status.rmd index 52fffecf..8f1c0932 100644 --- a/chapters/hms_stock_status.rmd +++ b/chapters/hms_stock_status.rmd @@ -1,4 +1,4 @@ -# Highly Migratory Species Stock Status +# Highly Migratory Species Stock Status {#hms_stock_status} **Description**: Summary of the most recent stock assessment results for each assessed Highly Migratory Species. diff --git a/chapters/long_term_sst.rmd b/chapters/long_term_sst.rmd index 062e76ce..7ea1ebe3 100644 --- a/chapters/long_term_sst.rmd +++ b/chapters/long_term_sst.rmd @@ -1,4 +1,4 @@ -# NE Shelf Annual Sea Surface Temperature (SST) +# NE Shelf Annual Sea Surface Temperature (SST) {#long_term_sst} **Description**: Average annual sea-surface temperatures from the NOAA extended reconstructed sea surface temperature data set (ERSST V5) on the Northeast Continental Shelf. diff --git a/chapters/mab_inshore_survey.rmd b/chapters/mab_inshore_survey.rmd index 5865331a..bf00ede5 100644 --- a/chapters/mab_inshore_survey.rmd +++ b/chapters/mab_inshore_survey.rmd @@ -1,4 +1,4 @@ -# Inshore Survey (Mid Atlantic) +# Inshore Survey (Mid Atlantic) {#mab_inshore_survey} **Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: diff --git a/chapters/mass_inshore_survey.rmd b/chapters/mass_inshore_survey.rmd index b4ab517c..e5cdd184 100644 --- a/chapters/mass_inshore_survey.rmd +++ b/chapters/mass_inshore_survey.rmd @@ -1,4 +1,4 @@ -# Inshore Survey (Massachusetts) +# Inshore Survey (Massachusetts) {#mass_inshore_survey} **Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: diff --git a/chapters/narw.rmd b/chapters/narw.rmd index 70215fa9..7b89df1d 100644 --- a/chapters/narw.rmd +++ b/chapters/narw.rmd @@ -1,4 +1,4 @@ -# Right Whale Abundance +# Right Whale Abundance {#narw} **Description**: The data presented here are time series of the North Atlantic right whale population abundance estimates and calf abundance estimates. @@ -73,7 +73,7 @@ Temporal scale: Annual 1990 - 2022 ``` ## Implications -Strong evidence exists to suggest that interactions between right whales and both the fixed gear fisheries in the U.S. and Canada and vessel strikes in the U.S. are contributing substantially to the decline of the species [@hayes_north_2018]. Further, right whale distribution has changed since 2010. New research suggests that recent climate driven changes in ocean circulation have resulted in right whale distribution changes driven by increased warm water influx through the Northeast Channel, which has reduced the primary right whale prey (Calanus fnmarchicus) in the central and eastern portions of the Gulf of Maine [@hayes_north_2018; @record_rapid_2019; @sorochan_north_2019]. Additional potential stressors include offshore wind development, which overlaps with important habitat areas used year-round by right whales, including mother and calf migration corridors and foraging habitat [@quintana-rizzo_residency_2021; @schick_striking_2009]. This area is also a primary right whale winter foraging habitat. Additional information can be found in the offshore wind section. Turbine presence and extraction of energy from the system could alter local oceanography @christiansen_emergence_2022. persistent foraging hotspots of right whales and seabirds overlap on Nantucket Shoals, where unique hydrography aggregates enhanced prey densities @white_spatial_2020 ; @sorochan_north_2019. +Strong evidence exists to suggest that interactions between right whales and both the fixed gear fisheries in the U.S. and Canada and vessel strikes in the U.S. are contributing substantially to the decline of the species [@hayes_north_2018]. Further, right whale distribution has changed since 2010. New research suggests that recent climate driven changes in ocean circulation have resulted in right whale distribution changes driven by increased warm water influx through the Northeast Channel, which has reduced the primary right whale prey (Calanus finmarchicus) in the central and eastern portions of the Gulf of Maine [@hayes_north_2018; @record_rapid_2019; @sorochan_north_2019]. Additional potential stressors include offshore wind development, which overlaps with important habitat areas used year-round by right whales, including mother and calf migration corridors and foraging habitat [@quintana-rizzo_residency_2021; @schick_striking_2009]. This area is also a primary right whale winter foraging habitat. Additional information can be found in the offshore wind section. Turbine presence and extraction of energy from the system could alter local oceanography @christiansen_emergence_2022. Persistent foraging hotspots of right whales and seabirds overlap on Nantucket Shoals, where unique hydrography aggregates enhanced prey densities @white_spatial_2020 ; @sorochan_north_2019. The UMEs are under investigation and are likely the result of multiple drivers. For all large whale UMEs, human interaction appears to have contributed to increased mortalities, although investigations are not complete. @@ -85,12 +85,14 @@ The UMEs are under investigation and are likely the result of multiple drivers. **Variable definitions** -"Palka_NARW_abundance_2023_10_02.csv 1) Year. 2) lower95 = lower 95% confidence interval value in number of animals. +"Palka_NARW_abundance_2023_10_02.csv 1) Year. +2) lower95 = lower 95% confidence interval value in number of animals. 3) Median=median estimate of right whale abundance in number of animals. 4) Upper95= upper 95% confidence interval value in number of animals. 5) Mean= mean estimate of right whale abundance in number of animals. -6) SD=standard deviation of estimate of right whale abundance in number of animals. Palka_NARW_Calves_1980_2023.csv -1) Year. 2) Tot.Calves = total number of right whale calves born that year in number of animals. " +6) SD=standard deviation of estimate of right whale abundance in number of animals. +Palka_NARW_Calves_1980_2023.csv 1) Year. +2) Tot.Calves = total number of right whale calves born that year in number of animals. " ```{r vars_narw} # Pull all var names diff --git a/chapters/ne_inshore_survey.rmd b/chapters/ne_inshore_survey.rmd index abf1fd5f..16359d22 100644 --- a/chapters/ne_inshore_survey.rmd +++ b/chapters/ne_inshore_survey.rmd @@ -1,4 +1,4 @@ -# Inshore Survey (New England) +# Inshore Survey (New England) {#ne_inshore_survey} **Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: diff --git a/chapters/observation_synthesis.rmd b/chapters/observation_synthesis.rmd index 6a54e411..1b0d084a 100644 --- a/chapters/observation_synthesis.rmd +++ b/chapters/observation_synthesis.rmd @@ -1,4 +1,4 @@ -# 2023 Observation Synthesis +# 2023 Observation Synthesis {#observation_synthesis} **Description**: Synthesis of multiple anomalous and extreme conditions observed in 2023 that should be noted and considered in future analyses. diff --git a/chapters/ocean_acidification.rmd b/chapters/ocean_acidification.rmd index dd9b665b..3f15769a 100644 --- a/chapters/ocean_acidification.rmd +++ b/chapters/ocean_acidification.rmd @@ -1,4 +1,4 @@ -# Ocean Acidification and Other Stressors +# Ocean Acidification and Other Stressors {#ocean_acidification} **Description**: Maps and variability of regional carbonate chemistry and other oceanographic properties @@ -102,10 +102,8 @@ The seasonal level resolution of data collected in the Mid-Atlantic Bight in 202 **Variable definitions** -1) depth_interpolated meters -2) temperature degrees Celsius 3) chlorophyll_a µg L-1 -4) oxygen_concentration_shifted_mgL mg L-1 5) pH_shifted -6) aragonite_saturation_state +1) depth_interpolated meters 2) temperature degrees Celsius 3) chlorophyll_a µg L-1 +4) oxygen_concentration_shifted_mgL mg L-1 5) pH_shifted 6) aragonite_saturation_state No Data diff --git a/chapters/osw_survey_impact.rmd b/chapters/osw_survey_impact.rmd index dc0d48c9..c6cc76bd 100644 --- a/chapters/osw_survey_impact.rmd +++ b/chapters/osw_survey_impact.rmd @@ -1,4 +1,4 @@ -# Survey Impacts from Offshore Wind Development +# Survey Impacts from Offshore Wind Development {#osw_survey_impact} **Description**: Offshore wind development is expected to have several impacts on federal and state surveys. @@ -43,7 +43,7 @@ Proposed wind development areas interact with the region’s federal scientific 3) Alteration of benthic and pelagic habitats, and airspace in and around the wind energy development, requiring new designs and methods to sample new habitats; and, 4) Reduced sampling productivity through navigation impacts of wind energy infrastructure on aerial and vessel survey operations. -Increased vessel transit between stations may decrease data collections that are already limited by annual days-at-sea day allocations. The total survey area overlap ranges from 1-70% for all Greater Atlantic federal surveys. The Gulf of Maine Cooperative Research Bottom Longline Survey (41%) and the Shrimp Survey (70%) have the largest percent overlap with the draft Gulf of Maine Wind Energy Areas. The remaining surveys range from 1-16% overlap. Individual survey strata have significant interaction with wind, including the sea scallop survey (up to 96% of individual strata) and the bottom trawl survey (BTS, up to 60% strata overlap). Additionally, up to 50% of the southern New England North Atlantic right whale survey’s area overlaps with proposed project areas and A region-wide survey mitigation program is underway @northeast_fisheries_science_center_us_fall_2022 +Increased vessel transit between stations may decrease data collections that are already limited by annual days-at-sea day allocations. The total survey area overlap ranges from 1-70% for all Greater Atlantic federal surveys. The Gulf of Maine Cooperative Research Bottom Longline Survey (41%) and the Shrimp Survey (70%) have the largest percent overlap with the draft Gulf of Maine Wind Energy Areas. The remaining surveys range from 1-16% overlap. Individual survey strata have significant interaction with wind, including the sea scallop survey (up to 96% of individual strata) and the bottom trawl survey (BTS, up to 60% strata overlap). Additionally, up to 50% of the southern New England North Atlantic right whale survey’s area overlaps with proposed project areas and a region-wide survey mitigation program is underway @northeast_fisheries_science_center_us_fall_2022 ## Get the data diff --git a/chapters/persistent_hotspots.rmd b/chapters/persistent_hotspots.rmd index edb24428..1460b43f 100644 --- a/chapters/persistent_hotspots.rmd +++ b/chapters/persistent_hotspots.rmd @@ -1,4 +1,4 @@ -# Persistent annual hotspots +# Persistent annual hotspots {#persistent_hotspots} **Description**: Integrated persistent annual hotspots derived from at-sea observations of seabirds, cetaceans and sea turtles collected on systematic ship and aerial surveys diff --git a/chapters/phyto_size.rmd b/chapters/phyto_size.rmd index d056526f..4ac108e1 100644 --- a/chapters/phyto_size.rmd +++ b/chapters/phyto_size.rmd @@ -1,4 +1,4 @@ -# Phytoplankton Size Class +# Phytoplankton Size Class {#phyto_size} **Description**: Satellite derived phytoplankton data including chlorophyll concentration, phytoplankton size class, and primary production for the Northeast Continental Shelf and ecological production units. diff --git a/chapters/ppr.rmd b/chapters/ppr.rmd index 5537367d..56d03e89 100644 --- a/chapters/ppr.rmd +++ b/chapters/ppr.rmd @@ -1,4 +1,4 @@ -# Ecosytem overfishing +# Ecosytem overfishing {#ppr} **Description**: Ecosystem overfishing is an ecological, and not legal, term that ultimately evaluates how much fish are caught in an ecosystem relative to how much can be produced. Several indices are used to evaluate ecosystem overfishing, the Ryther index, the Fogarty index, and primary production required. diff --git a/chapters/productivity_anomaly.rmd b/chapters/productivity_anomaly.rmd index 3db53874..b3b56c2d 100644 --- a/chapters/productivity_anomaly.rmd +++ b/chapters/productivity_anomaly.rmd @@ -1,4 +1,4 @@ -# Fish Productivity Indicators +# Fish Productivity Indicators {#productivity_anomaly} **Description**: Amount of small fish produced per large fish biomass over time @@ -92,7 +92,8 @@ The apparent decline in productivity across multiple managed species in the MAB, Variable names are organized using this format: [region] stock name - variable type and source Variables ending with "_Survey" are survey derived recruits/spawner anomalies Variables ending with "-Assessment" are assessment derived quantities Survey stock names are in ALL CAPS -NE LME prepended to a survey stock name means the anomalies are coastwide Assessment stock names are in Sentence case +NE LME prepended to a survey stock name means the anomalies are coastwide +Assessment stock names are in Sentence case Units for survey variables are the Z score of (number of recruits in year+1/biomass of adults in year) Units for plotted assessment variables are the Z score of (numbers of recruits per kg spawning biomass with recruits aligned to spawning biomass year using age at recruitment) Other variables are available in the assessment derived dataset but are not plotted. To be added later. diff --git a/chapters/rec_hms.rmd b/chapters/rec_hms.rmd new file mode 100644 index 00000000..a9a74ff3 --- /dev/null +++ b/chapters/rec_hms.rmd @@ -0,0 +1,91 @@ +# Recreational HMS {#rec_hms} + +**Description**: Recreational shark landings pulled from the Marine Recreational Information Program (MRIP). + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Brandon Beltz; Kim Bastille + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Recreational shark landings pulled from the MRIP database + +## Key Results and Visualizations +Recreational landings of sharks are plotted. Sharks are categorized as large coastal, pelagic, prohibited and small coastal. + +### MAB + +```{r plot_rec_hmsMAB} +# Plot indicator +ggplotObject <- ecodata::plot_rec_hms(report='MidAtlantic') +ggplotObject +``` + +### NE + +```{r plot_rec_hmsNE} +# Plot indicator +ggplotObject <- ecodata::plot_rec_hms(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: by EPU + +Temporal scale: annually from 1981 to 2022 + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_rec_hms} +# Either from Contributor or ecodata +``` + +## Implications +Sharks are landed recreationally in quantities that are relevant to fisheries management. These landings should be considered when assessing the populations of sharks. + +## Get the data + +**Point of contact**: [Brandon Beltz (brandon.beltz@noaa.gov)](mailto:Brandon Beltz (brandon.beltz@noaa.gov)){.email} + +**ecodata name**: `ecodata::rec_hms` + +**Variable definitions** + +See variables below + +```{r vars_rec_hms} +# Pull all var names +vars <- ecodata::rec_hms |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/recdat.rmd b/chapters/recdat.rmd index c49d70b9..9d939830 100644 --- a/chapters/recdat.rmd +++ b/chapters/recdat.rmd @@ -1,4 +1,4 @@ -# Recreational Fishing Indicators +# Recreational Fishing Indicators {#recdat} **Description**: A variety of indicators derived from MRIP Recreational Fisheries Statistics, including total recreational catch, total angler trips by region, annual diversity of recreational fleet effort, and annual diversity of managed species. diff --git a/chapters/seabird_ne.rmd b/chapters/seabird_ne.rmd index 693a70a5..703be0bf 100644 --- a/chapters/seabird_ne.rmd +++ b/chapters/seabird_ne.rmd @@ -1,4 +1,4 @@ -# Seabird diet and productivity - New England +# Seabird diet and productivity - New England {#seabird_ne} **Description**: Common tern annual diet and productivity at seven Gulf of Maine colonies managed by the National Audubon Society’s Seabird Restoration Program diff --git a/chapters/seal_pups.rmd b/chapters/seal_pups.rmd index 63964fde..5757eab4 100644 --- a/chapters/seal_pups.rmd +++ b/chapters/seal_pups.rmd @@ -1,4 +1,4 @@ -# Gray Seal Pups +# Gray Seal Pups {#seal_pups} **Description**: The data presented here are counts of gray seal pups at 4 U.S. haulout sites from 1988 to 2021. diff --git a/chapters/seasonal_oisst_anom.rmd b/chapters/seasonal_oisst_anom.rmd index 77a64aa5..29da4a45 100644 --- a/chapters/seasonal_oisst_anom.rmd +++ b/chapters/seasonal_oisst_anom.rmd @@ -1,4 +1,4 @@ -# Sea-surface temperature anomaly +# Sea-surface temperature anomaly {#seasonal_oisst_anom} **Description**: Seasonal sea surface temperature anomaly diff --git a/chapters/seasonal_sst_anomaly_gridded.rmd b/chapters/seasonal_sst_anomaly_gridded.rmd index f7400da1..9a5e80c5 100644 --- a/chapters/seasonal_sst_anomaly_gridded.rmd +++ b/chapters/seasonal_sst_anomaly_gridded.rmd @@ -1,4 +1,4 @@ -# Seasonal OISST Anomaly Map +# Seasonal OISST Anomaly Map {#seasonal_sst_anomaly_gridded} **Description**: Mapped seasonal sea surface temperature anomaly diff --git a/chapters/slopewater.rmd b/chapters/slopewater.rmd index 58b91b59..f7ef256c 100644 --- a/chapters/slopewater.rmd +++ b/chapters/slopewater.rmd @@ -1,4 +1,4 @@ -# Slopewater Proportions +# Slopewater Proportions {#slopewater} **Description**: This index gives the relative proportions of watermass type observed in the deep Northeast Channel (150-200 m water depth). @@ -37,7 +37,7 @@ Temporal scale: Annual **Synthesis Theme**: - +- [X] Multiple System Drivers ```{r autostats_slopewater} diff --git a/chapters/spawn_timing.rmd b/chapters/spawn_timing.rmd index 36918550..0a8a778b 100644 --- a/chapters/spawn_timing.rmd +++ b/chapters/spawn_timing.rmd @@ -1,4 +1,4 @@ -# Spawning Timing +# Spawning Timing {#spawn_timing} **Description**: Maturity information for groundfish is used to evaluate changes in spawning seasonality. diff --git a/chapters/species_dist.rmd b/chapters/species_dist.rmd index ca51163a..241c8825 100644 --- a/chapters/species_dist.rmd +++ b/chapters/species_dist.rmd @@ -1,4 +1,4 @@ -# Species Distribution Indicators +# Species Distribution Indicators {#species_dist} **Description**: Species mean depth, along-shelf distance, and distance to coastline diff --git a/chapters/species_groupings.rmd b/chapters/species_groupings.rmd index b7673922..50b5823b 100644 --- a/chapters/species_groupings.rmd +++ b/chapters/species_groupings.rmd @@ -1,4 +1,4 @@ -# Feeding guilds by management bodies +# Feeding guilds by management bodies {#species_groupings} **Description**: Classification of species guild membership by management bodies. diff --git a/chapters/stock_status.rmd b/chapters/stock_status.rmd index b56ce3f8..54029ca8 100644 --- a/chapters/stock_status.rmd +++ b/chapters/stock_status.rmd @@ -1,4 +1,4 @@ -# Stock Status +# Stock Status {#stock_status} **Description**: Summary of the most recent stock status results for each assessed species diff --git a/chapters/thermal_habitat_area.rmd b/chapters/thermal_habitat_area.rmd index 7794bd4f..9f807c08 100644 --- a/chapters/thermal_habitat_area.rmd +++ b/chapters/thermal_habitat_area.rmd @@ -1,4 +1,4 @@ -# Thermal Habitat Area +# Thermal Habitat Area {#thermal_habitat_area} **Description**: Calculates the proportion of each EPU that exceeds temperature thresholds as a daily time series from 1993 – 2023 diff --git a/chapters/thermal_habitat_persistence.rmd b/chapters/thermal_habitat_persistence.rmd index dce5b829..96bbc5c9 100644 --- a/chapters/thermal_habitat_persistence.rmd +++ b/chapters/thermal_habitat_persistence.rmd @@ -1,4 +1,4 @@ -# Thermal Habitat Persistence +# Thermal Habitat Persistence {#thermal_habitat_persistence} **Description**: The number of days per year per 1/12 degree cell that exceeds a temperature threshold. diff --git a/chapters/timing_shifts.rmd b/chapters/timing_shifts.rmd index f117d103..85341857 100644 --- a/chapters/timing_shifts.rmd +++ b/chapters/timing_shifts.rmd @@ -1,4 +1,4 @@ -# Timing shifts: Risks to Seasonal Management +# Timing shifts: Risks to Seasonal Management {#timing_shifts} **Description**: Shifts in the timing of life-cycle events are a risk to meeting seasonal and temporal management objectives. diff --git a/chapters/trans_dates.rmd b/chapters/trans_dates.rmd index c108d8f7..c67e6bf7 100644 --- a/chapters/trans_dates.rmd +++ b/chapters/trans_dates.rmd @@ -1,4 +1,4 @@ -# Transition Dates +# Transition Dates {#trans_dates} **Description**: The date that cool winter conditions transition to warm stratified summer conditions. diff --git a/chapters/wbts_mesozooplankton.rmd b/chapters/wbts_mesozooplankton.rmd index 0db7a14f..2fa0bb4f 100644 --- a/chapters/wbts_mesozooplankton.rmd +++ b/chapters/wbts_mesozooplankton.rmd @@ -1,4 +1,4 @@ -# Mesozooplankton Biomass at Wilkinson Basin +# Mesozooplankton Biomass at Wilkinson Basin {#wbts_mesozooplankton} **Description**: Mesozooplankton biomass at the Wilkinson Basin Time Series Station (WBTS): 2005-2022 @@ -21,8 +21,6 @@ The Wilkinson Basin Time Series Station (WBTS: 257 m depth), located in the nort Data collected at the WBTS station include CTD- rosette measurements of salinity, temperature and chlorophyll a concentration, microscopic enumeration of phytoplankton species, bacterial and microplankton measurements using flow cytometer, eDNA measurements, measurement of total mesozooplankton biomass and microscopic enumeration of zooplankton species collected with a 0.75 m, 200µm ring net towed from near bottom to the surface. Only the mesozooplankton biomass data are reported for the 2024 SOE; a fuller reporting of the time series data awaits further vetting and publication of the data in the primary literature. ## Key Results and Visualizations -Note: mesozooplankton biomass figure uploaded in data folder (not sure where to upload it) - Planktonic copepods typically constituted the great majority of catch of the vertically integrated ring net tow. Larger microzooplankton, like euphausids and jellyfish, are underrepresented. Chaetognaths, round tentaculate ctenophores, notably Pleurobrachia, and salps were captured, although the latter tend to degrade in formaldehyde over time and are likely underrepresented. Given these limitations, the mesozooplankton dry mass data allow comparison of biomass across pelagic ecosystems where similar measurements have been taken. Notably, at WBTS, the copepodid stages of Calanus finmarchicus typically make up 50% or more of the total mesozooplankton biomass in spring through fall. Following the seasonal life cycle of C. finmarchicus, mesozooplankton biomass is lowest in late winter and highest in summer. Biomass levels of 10-20 g m-2 observed in 2005-2008 in summer and winter were among the highest observed across the subarctic North Atlantic Ocean, including the Gulf of St. Lawrence (@de_lafontaine_pelagic_1991; @sorochan_north_2019), Scotian Shelf (@casault_optical_2022) and the Norwegian and Barents Seas (@melle_north_2014; @skjoldal_size-fractioned_2022). The mesozooplankton biomass collected at WBTS in late summer (Aug-Oct) and winter (Nov-Mar) has since declined significantly, by about 50%, between the start of the time series in 2005-2008 and 2021-2022 (see figure). The summer and winter biomass levels reflect the predominance of the larger, lipid rich late stage C. finmarchicus (CIV-CVI) as compared to spring, which is dominated by younger C. finmarchicus stages CI-CIV. diff --git a/chapters/wcr.rmd b/chapters/wcr.rmd index 360dd4d7..d3867f21 100644 --- a/chapters/wcr.rmd +++ b/chapters/wcr.rmd @@ -1,4 +1,4 @@ -# Warm Core Rings +# Warm Core Rings {#wcr} **Description**: Number of warm core rings produced annually by the Gulf Stream off the Northeast US diff --git a/chapters/wind_dev_speed.rmd b/chapters/wind_dev_speed.rmd index b5ea7f29..da782ad9 100644 --- a/chapters/wind_dev_speed.rmd +++ b/chapters/wind_dev_speed.rmd @@ -1,4 +1,4 @@ -# Speed and Scale of Offshore Wind Development in the Northeast +# Speed and Scale of Offshore Wind Development in the Northeast {#wind_dev_speed} **Description**: The footprint and timeline of offshore wind development in the Northeast by 2030 diff --git a/chapters/wind_port.rmd b/chapters/wind_port.rmd index e208f139..f47dbb11 100644 --- a/chapters/wind_port.rmd +++ b/chapters/wind_port.rmd @@ -1,4 +1,4 @@ -# Community Port Landings and Revenue from Wind Energy Areas (WEAs) +# Community Port Landings and Revenue from Wind Energy Areas (WEAs) {#wind_port} **Description**: NA diff --git a/chapters/wind_revenue.rmd b/chapters/wind_revenue.rmd index 5a034e6b..b3a46860 100644 --- a/chapters/wind_revenue.rmd +++ b/chapters/wind_revenue.rmd @@ -1,4 +1,4 @@ -# Fishery Impacts from Offshore Wind Development +# Fishery Impacts from Offshore Wind Development {#wind_revenue} **Description**: The data presented here include landings and revenue of managed species within existing offshore wind lease areas, Central Atlantic Bight final wind energy areas, and the Gulf of Maine draft wind energy area. diff --git a/chapters/zoo_abundance_anom.rmd b/chapters/zoo_abundance_anom.rmd index 008a4b9f..04858f06 100644 --- a/chapters/zoo_abundance_anom.rmd +++ b/chapters/zoo_abundance_anom.rmd @@ -1,4 +1,4 @@ -# Zooplankton Abundance Anomalies +# Zooplankton Abundance Anomalies {#zoo_abundance_anom} **Description**: Abundance anomalies for 20 zooplankton taxa diff --git a/chapters/zoo_diversity.rmd b/chapters/zoo_diversity.rmd index 4339873d..53208e9a 100644 --- a/chapters/zoo_diversity.rmd +++ b/chapters/zoo_diversity.rmd @@ -1,4 +1,4 @@ -# Zooplankton Diversity +# Zooplankton Diversity {#zoo_diversity} **Description**: Effective Shannon diversity calculated using 42 zooplankton taxa collected from EcoMon cruises From 5d52a6f88e58e3cdef674c29dec219e44f672055 Mon Sep 17 00:00:00 2001 From: Brandon Beltz - NOAA Affiliate <136381970+BBeltz1@users.noreply.github.com> Date: Fri, 1 Mar 2024 10:39:54 -0500 Subject: [PATCH 2/8] turned on rec_hms page in bookdown yml added rec_hms to the bookdown yml file to activate the page. --- _bookdown.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/_bookdown.yml b/_bookdown.yml index ff49595e..72f9044f 100644 --- a/_bookdown.yml +++ b/_bookdown.yml @@ -79,6 +79,7 @@ rmd_files: - "chapters/commercial_div.rmd" - "chapters/ppr.rmd" - "chapters/recdat.rmd" + - "chapters/rec_hms.rmd" - "chapters/abc_acl.rmd" - "chapters/bennet.rmd" - "chapters/stock_status.rmd" From c5d692b74d3ec3107b878166a1237c2d65879653 Mon Sep 17 00:00:00 2001 From: Brandon Beltz - NOAA Affiliate <136381970+BBeltz1@users.noreply.github.com> Date: Mon, 4 Mar 2024 10:31:42 -0500 Subject: [PATCH 3/8] build engagement edits pulled edits from engagement github issues for inclusion in catalog --- chapters/engagement.rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/chapters/engagement.rmd b/chapters/engagement.rmd index 9a22cfaf..a8056ed4 100644 --- a/chapters/engagement.rmd +++ b/chapters/engagement.rmd @@ -33,9 +33,9 @@ In the 2023 report, we presented environmental justice vulnerability as a dichot ##### Commercial -Barnegat Light, NJ, is the only community that scored high for both commercial engagement and reliance based on 2021 data. Cape May, NJ ranked high for both commercial engagement and commercial reliance based on 2020 data but decreased to medium-high for its commercial reliance in 2021. Reedville, VA ranked high for both commercial engagement and commercial reliance based on 2020 data but decreased to medium-high and medium, respectively, in 2021. Reedville, VA; Hatteras and Hobucken, NC are no longer listed as top ten commercial fishing communities, replaced by Hampton, VA; Swan Quarter, NC; Bowers and Little Creek, DE. +Barnegat Light, NJ, and Reedville, VA are the only communities that scored high for both commercial engagement and reliance based on 2021 data. Cape May, NJ ranked high for both commercial engagement and commercial reliance based on 2020 data but decreased to medium-high for its commercial reliance in 2021. Hatteras and Hobucken, NC are no longer listed as top ten commercial fishing communities, replaced by Hampton, VA; Swan Quarter, NC; Bowers and Little Creek, DE. -Communities that ranked medium-high or above for one or more of the environmental justice indicators are highlighted in bright orange, including Hampton Bays/Shinnecock, NY; Atlantic City, NJ; Newport News, VA; Swan Quarter and Columbia, NC; Bower and Little Creek, DE. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Point Pleasant Beach, NJ; Hampton, VA; Beaufort and Wilmington, NC. +Communities that ranked medium-high or above for one or more of the environmental justice indicators are highlighted in bright orange, including Hampton Bays/Shinnecock, NY; Atlantic City, NJ; Swan Quarter and Columbia, NC; Bower and Little Creek, DE. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Point Pleasant Beach, NJ; Hampton, VA; Beaufort and Wilmington, NC. Detailed scores of the three environmental justice indicators for the same communities plotted in spider plots. Communities are plotted clockwise in a descending order of commercial engagement scores from high to low, with the most highly engaged community, Cape May, NJ, listed on the top. Among these communities, ranked medium-high or above for environmental justice vulnerability, Atlantic City, NJ scored high for all of the three environmental justice indicators. From c873a187a088041bff23f3f26b09a5c622764a49 Mon Sep 17 00:00:00 2001 From: andybeet <22455149+andybeet@users.noreply.github.com> Date: Fri, 8 Mar 2024 13:55:07 -0500 Subject: [PATCH 4/8] updated references in bib file --- bibliography/StateOftheEcosystem.bib | 118 +++++++++++++++++++++++++++ 1 file changed, 118 insertions(+) diff --git a/bibliography/StateOftheEcosystem.bib b/bibliography/StateOftheEcosystem.bib index e37fdb67..28da2284 100644 --- a/bibliography/StateOftheEcosystem.bib +++ b/bibliography/StateOftheEcosystem.bib @@ -3275,3 +3275,121 @@ @article{de_lafontaine_pelagic_1991 year = {1991}, pages = {99--124.}, } + +@article{colburn_indicators_2016, + title = {Indicators of climate change and social vulnerability in fishing dependent communities along the {Eastern} and {Gulf} {Coasts} of the {United} {States}}, + volume = {74}, + issn = {0308-597X}, + url = {https://www.sciencedirect.com/science/article/pii/S0308597X16302123}, + doi = {10.1016/j.marpol.2016.04.030}, + abstract = {Changing climatic conditions are affecting the relationship between fishing communities and the marine resources they depend on. This shift will require an adaptive response on the part of policy makers and fishery managers. In the U.S., the National Oceanic and Atmospheric Administration (NOAA) established, in its fisheries agency (NOAA Fisheries), a set of social indicators of fishing community vulnerability and resilience to evaluate the impacts of changes in fishery management regimes. These indicators enhance the analytical capabilities within NOAA Fisheries for conducting fisheries social impact assessments and informing ecosystem-based fishery management. Building on the existing Community Social Vulnerability Indicators (CSVIs), new measures of climate change vulnerability are defined for the U.S. Eastern and Gulf coasts. These new indicators are used to assess the impact of sea level rise on critical commercial fishing infrastructure and the dependence of communities on species identified as vulnerable to the effects of climate change. Examples are provided in this article to demonstrate the utility of these new indicators to policy makers and the NOAA strategic goal for building resilient coastal communities that are environmentally and economically sustainable. Integration of CSVIs and the new climate change vulnerability indices highlight community needs for unique solutions in order to adapt to environmental and social changes and maintain their well-being.}, + urldate = {2024-02-16}, + journal = {Marine Policy}, + author = {Colburn, Lisa L. and Jepson, Michael and Weng, Changhua and Seara, Tarsila and Weiss, Jeremy and Hare, Jonathan A.}, + month = dec, + year = {2016}, + keywords = {Climate change, Indicators, Fishing communities, Social vulnerability}, + pages = {323--333}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\TU6U2J9P\\Colburn et al. - 2016 - Indicators of climate change and social vulnerabil.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\J6S2IF4Q\\S0308597X16302123.html:text/html}, +} + +@article{okeefe_forming_2013, + title = {Forming a {Partnership} to {Avoid} {Bycatch}}, + volume = {38}, + issn = {0363-2415}, + url = {https://doi.org/10.1080/03632415.2013.838122}, + doi = {10.1080/03632415.2013.838122}, + abstract = {Bycatch of Yellowtail Flounder in the U.S. Sea Scallop Fishery is a constraint to achieving optimum yield of scallops. Between 2000 and 2009, in-season bycatch closures of prime scallop grounds resulted in economic losses over US{\textbackslash}100 million. To address this constraint, we collaborated with the scallop fishing industry to implement a bycatch avoidance program in the Nantucket Lightship harvest area in 2010. Vessels shared near real-time location information about bycatch amounts during fishing activities. We compiled the information, identified bycatch hotspots, and provided daily advisories to vessels on the fishing grounds. Catch per tow of Yellowtail and fishing effort in high bycatch regions significantly declined after the fleet received the advisories. The fleet harvested the target scallop allocation worth US{\textbackslash}40 million while catching only 32\% of the Yellowtail bycatch limit. This program continues as a collaborative, iterative approach to bycatch reduction that balances fleet objectives with conservation constraints.}, + number = {10}, + urldate = {2024-03-08}, + journal = {Fisheries}, + author = {O'Keefe, Catherine E. and DeCelles, Gregory R.}, + month = nov, + year = {2013}, + note = {Publisher: Taylor \& Francis +\_eprint: https://doi.org/10.1080/03632415.2013.838122}, + pages = {434--444}, +} + +@article{okeefe_forming_2013-1, + title = {Forming a {Partnership} to {Avoid} {Bycatch}}, + volume = {38}, + issn = {0363-2415}, + url = {https://doi.org/10.1080/03632415.2013.838122}, + doi = {10.1080/03632415.2013.838122}, + abstract = {Bycatch of Yellowtail Flounder in the U.S. Sea Scallop Fishery is a constraint to achieving optimum yield of scallops. Between 2000 and 2009, in-season bycatch closures of prime scallop grounds resulted in economic losses over US{\textbackslash}100 million. To address this constraint, we collaborated with the scallop fishing industry to implement a bycatch avoidance program in the Nantucket Lightship harvest area in 2010. Vessels shared near real-time location information about bycatch amounts during fishing activities. We compiled the information, identified bycatch hotspots, and provided daily advisories to vessels on the fishing grounds. Catch per tow of Yellowtail and fishing effort in high bycatch regions significantly declined after the fleet received the advisories. The fleet harvested the target scallop allocation worth US{\textbackslash}40 million while catching only 32\% of the Yellowtail bycatch limit. This program continues as a collaborative, iterative approach to bycatch reduction that balances fleet objectives with conservation constraints.}, + number = {10}, + urldate = {2024-03-08}, + journal = {Fisheries}, + author = {O'Keefe, Catherine E. and DeCelles, Gregory R.}, + month = nov, + year = {2013}, + note = {Publisher: Taylor \& Francis +\_eprint: https://doi.org/10.1080/03632415.2013.838122}, + pages = {434--444}, +} + +@article{pdf, + title = {Seasonal trends and phenology shifts in sea surface temperature on the {North} {American} northeastern continental shelf}, + volume = {5}, + doi = {10.1525/journal.elementa.240}, + abstract = {The northeastern North American continental shelf from Cape Hatteras to the Scotian Shelf is a region of globally extreme positive trends in sea surface temperature (SST). Here, a 33-year (1982–2014) time series of daily satellite SST data was used to quantify and map spatial patterns in SST trends and phenology over this shelf. Strongest trends are over the Scotian Shelf ({\textgreater}0.6°C decade–1) and Gulf of Maine ({\textgreater}0.4°C decade–1) with weaker trends over the inner Mid-Atlantic Bight ({\textasciitilde}0.3°C decade–1). Winter (January–April) trends are relatively weak, and even negative in some areas; early summer (May–June) trends are positive everywhere, and later summer (July–September) trends are strongest ({\textasciitilde}1.0°C decade–1). These seasonal differences shift the phenology of many metrics of the SST cycle. The yearday on which specific temperature thresholds (8° and 12°C) are reached in spring trends earlier, most strongly over the Scotian Shelf and Gulf of Maine ({\textasciitilde} –0.5 days year–1). Three metrics defining the warmest summer period show significant trends towards earlier summer starts, later summer ends and longer summer duration over the entire study region. Trends in start and end dates are strongest ({\textasciitilde}1 day year–1) over the Gulf of Maine and Scotian Shelf. Trends in increased summer duration are {\textgreater}2.0 days year–1 in parts of the Gulf of Maine. Regression analyses show that phenology trends have regionally varying links to the North Atlantic Oscillation, to local spring and summer atmospheric pressure and air temperature and to Gulf Stream position. For effective monitoring and management of dynamically heterogeneous shelf regions, the results highlight the need to quantify spatial and seasonal differences in SST trends as well as trends in SST phenology, each of which likely has implications for the ecological functioning of the shelf}, + journal = {Elementa: Science of the Anthropocene}, + author = {Thomas, A. C. and Pershing, A. J. and Friedland, K. D. and Nye, J. A. and Mills, K. E. and Alexander, M. A. and Record, N. R. and Weatherbee, R. A. and Henderson, M. E.}, + year = {2017}, + pages = {48--65}, + file = {Thomas_etal-Elem_2017 Seasonal trends and phen:C\:\\Users\\andrew.beet\\Zotero\\storage\\RC8HLQVZ\\Thomas_etal-Elem_2017 Seasonal trends and phen.pdf:application/pdf}, +} + +@article{pdf, + title = {Trends and change points in surface and bottom thermal environments of the {US} {Northeast} {Continental} {Shelf} {Ecosystem}}, + volume = {n/a}, + issn = {1054-6006}, + doi = {10.1111/fog.12485}, + abstract = {Abstract Temperature is an important factor in defining the habitats of marine resource species. While satellite sensors operationally measure ocean surface temperatures, we depend on in situ measurements to characterize benthic habitats. Ship-based measurements were interpolated to develop a time series of gridded spring and fall, surface and bottom temperature fields for the US Northeast Shelf. Surface and bottom temperatures have increased over the study period (1968?2018) at rates between 0.18?0.31°C per decade and over a shorter time period (2004?2018) at rates between 0.26?1.49°C per decade. A change point analysis suggests that a warming regime began in the surface waters in 2011 centered on Georges Bank and the Nantucket Shoals; in following years, most of the Northeast Shelf had experienced a shift in surface temperature. A similar analysis of bottom temperature suggests a warming regime began in 2008 in the eastern Gulf of Maine; in following years, change points in temperature occurred further to the west in the Gulf of Maine, finally reaching the Middle Atlantic Bight by 2010. The spatial pattern in bottom water warming is consistent with well-known oceanographic patterns that advect warming North Atlantic waters into the Gulf of Maine. The varying spatial and temporal progression of warming in the two layers suggests they were actuated by different sets of forcing factors. We then compared these trends and change points to responses of lower and higher trophic level organisms and identified a number of coincident shifts in distribution and biomass of key forage and fisheries species.}, + number = {n/a}, + journal = {Fisheries Oceanography}, + author = {Friedland, Kevin D. and Morse, Ryan E. and Manning, James P. and Melrose, Donald Christopher and Miles, Travis and Goode, Andrew G. and Brady, Damian C. and Kohut, Josh T. and Powell, Eric N.}, + month = jun, + year = {2020}, + keywords = {climate change, ecosystem, regime shift, temperature, resource species}, + annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd}, + file = {Friedland_etal-FO_2020 Trends and change point:C\:\\Users\\andrew.beet\\Zotero\\storage\\VZ2TCJK6\\Friedland_etal-FO_2020 Trends and change point.pdf:application/pdf}, +} + +@article{cohen_global_2018, + title = {A global synthesis of animal phenological responses to climate change}, + volume = {8}, + copyright = {2018 The Author(s)}, + issn = {1758-6798}, + url = {https://www.nature.com/articles/s41558-018-0067-3}, + doi = {10.1038/s41558-018-0067-3}, + abstract = {Shifts in phenology are already resulting in disruptions to the timing of migration and breeding, and asynchronies between interacting species1–5. Recent syntheses have concluded that trophic level1, latitude6and how phenological responses are measured7are key to determining the strength of phenological responses to climate change. However, researchers still lack a comprehensive framework that can predict responses to climate change globally and across diverse taxa. Here, we synthesize hundreds of published time series of animal phenology from across the planet to show that temperature primarily drives phenological responses at mid-latitudes, with precipitation becoming important at lower latitudes, probably reflecting factors that drive seasonality in each region. Phylogeny and body size are associated with the strength of phenological shifts, suggesting emerging asynchronies between interacting species that differ in body size, such as hosts and parasites and predators and prey. Finally, although there are many compelling biological explanations for spring phenological delays, some examples of delays are associated with short annual records that are prone to sampling error. Our findings arm biologists with predictions concerning which climatic variables and organismal traits drive phenological shifts.}, + language = {en}, + number = {3}, + urldate = {2024-03-08}, + journal = {Nature Climate Change}, + author = {Cohen, Jeremy M. and Lajeunesse, Marc J. and Rohr, Jason R.}, + month = mar, + year = {2018}, + note = {Publisher: Nature Publishing Group}, + keywords = {Animal migration, Phenology}, + pages = {224--228}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\4YQTZII8\\Cohen et al. - 2018 - A global synthesis of animal phenological response.pdf:application/pdf}, +} + +@article{pdf, + title = {Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the {United} {States}}, + volume = {733}, + issn = {1879-1026 (Electronic) 0048-9697 (Linking)}, + doi = {https://doi.org/10.1016/j.scitotenv.2020.137782}, + abstract = {Climate change is a pervasive and growing global threat to biodiversity and ecosystems. Here, we present the most up-to-date assessment of climate change impacts on biodiversity, ecosystems, and ecosystem services in the U.S. and implications for natural resource management. We draw from the 4th National Climate Assessment to summarize observed and projected changes to ecosystems and biodiversity, explore linkages to important ecosystem services, and discuss associated challenges and opportunities for natural resource management. We find that species are responding to climate change through changes in morphology and behavior, phenology, and geographic range shifts, and these changes are mediated by plastic and evolutionary responses. Responses by species and populations, combined with direct effects of climate change on ecosystems (including more extreme events), are resulting in widespread changes in productivity, species interactions, vulnerability to biological invasions, and other emergent properties. Collectively, these impacts alter the benefits and services that natural ecosystems can provide to society. Although not all impacts are negative, even positive changes can require costly societal adjustments. Natural resource managers need proactive, flexible adaptation strategies that consider historical and future outlooks to minimize costs over the long term. Many organizations are beginning to explore these approaches, but implementation is not yet prevalent or systematic across the nation.}, + journal = {Science of The Total Environment}, + author = {Weiskopf, Sarah R. and Rubenstein, Madeleine A. and Crozier, Lisa G. and Gaichas, Sarah and Griffis, Roger and Halofsky, Jessica E. and Hyde, Kimberly J. W. and Morelli, Toni Lyn and Morisette, Jeffrey T. and Muñoz, Roldan C. and Pershing, Andrew J. and Peterson, David L. and Poudel, Rajendra and Staudinger, Michelle D. and Sutton-Grier, Ariana E. and Thompson, Laura and Vose, James and Weltzin, Jake F. and Whyte, Kyle Powys}, + month = mar, + year = {2020}, + keywords = {Ecosystems, Ecosystem services, Conservation of Natural Resources, Biodiversity, United States, *Climate Change, *Ecosystem, competing financial interests or personal relationships that could have appeared, Global change, Natural resource management, Natural Resources, to influence the work reported in this paper.}, + pages = {137782}, + annote = {The following values have no corresponding Zotero field:edition: 2020/03/27accession-num: 32209235}, + file = {Weiskopfetal_etal-STE_2020 Climate change effe:C\:\\Users\\andrew.beet\\Zotero\\storage\\NYQPF8MN\\Weiskopfetal_etal-STE_2020 Climate change effe.pdf:application/pdf}, +} From c713223381c2ef6b06d1166f40f34c432726c6ee Mon Sep 17 00:00:00 2001 From: Brandon Beltz - NOAA Affiliate <136381970+BBeltz1@users.noreply.github.com> Date: Fri, 8 Mar 2024 15:04:35 -0500 Subject: [PATCH 5/8] update description of catalog added package name and authors to the description file of catalog. changed the ecodata dependency to the ecodata@5.0.1 release --- DESCRIPTION | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 38eb8d2a..ff66363d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,9 @@ -Package: placeholder +Package: catalog Type: Book -Title: Synthetic Indicator Cataloque +Title: Synthetic Indicator Catalogue Description: Catalogue containing indicator used in the State of the Ecosystem Report and Beyond... +Authors@R: c(person("Brandon", "Beltz", email = "brandon.beltz@noaa.gov", role = c("cre", "aut")), + person("Andy", "Beet", email = "andrew.beet@noaa.gov", role = "aut")) Version: 1.0.0 License: file LICENSE Depends: @@ -9,7 +11,7 @@ Depends: rmarkdown, tidyverse, DT, - ecodata + ecodata@5.0.1 Suggests: downlit Remotes: From de30befff2eed4cd93a22d486a8dae6d73e9e7e5 Mon Sep 17 00:00:00 2001 From: Sarah Gaichas Date: Mon, 11 Mar 2024 09:52:05 -0400 Subject: [PATCH 6/8] update catalog --- chapters/HMS_species_distribution.rmd | 171 +++++---- chapters/SAV.rmd | 180 ++++----- chapters/abc_acl.rmd | 222 ++++++------ chapters/aggregate_biomass.rmd | 222 ++++++------ chapters/aquaculture.rmd | 170 ++++----- chapters/atestimages.rmd | 79 ++++ chapters/bennet.rmd | 294 +++++++-------- chapters/bottom_temp.rmd | 182 +++++----- chapters/bottom_temp_comp.rmd | 246 +++++++------ chapters/bottom_temp_seasonal_gridded.rmd | 170 ++++----- chapters/calanus_variation.rmd | 194 +++++----- chapters/ch_bay_sal.rmd | 166 ++++----- chapters/ch_bay_temp.rmd | 166 ++++----- chapters/ches_bay_sst.rmd | 183 +++++----- chapters/ches_bay_synthesis.rmd | 148 ++++---- chapters/ches_bay_wq.rmd | 192 +++++----- chapters/chl_pp.rmd | 348 +++++++++--------- chapters/cold_pool.rmd | 247 +++++++------ chapters/comdat.rmd | 284 +++++++-------- chapters/commercial_div.rmd | 248 ++++++------- chapters/condition.rmd | 230 ++++++------ chapters/energy_density.rmd | 168 ++++----- chapters/engagement.rmd | 337 +++++++++-------- chapters/exp_n.rmd | 214 +++++------ chapters/forage_index.rmd | 234 ++++++------ chapters/gom_salmon.rmd | 171 +++++---- chapters/grayseal.rmd | 176 ++++----- chapters/gsi.rmd | 210 +++++------ chapters/habitat_diversity.rmd | 200 +++++----- chapters/habs.rmd | 232 ++++++------ chapters/harborporpoise.rmd | 176 ++++----- chapters/heatwave.rmd | 252 ++++++------- chapters/heatwave_year.rmd | 252 ++++++------- chapters/hms_cpue.rmd | 206 +++++------ chapters/hms_landings.rmd | 210 +++++------ chapters/hms_stock_status.rmd | 162 ++++----- chapters/long_term_sst.rmd | 162 ++++----- chapters/mab_inshore_survey.rmd | 180 ++++----- chapters/mass_inshore_survey.rmd | 180 ++++----- chapters/narw.rmd | 237 ++++++------ chapters/ne_inshore_survey.rmd | 180 ++++----- chapters/observation_synthesis.rmd | 195 +++++----- chapters/ocean_acidification.rmd | 216 +++++------ chapters/osw_survey_impact.rmd | 146 ++++---- chapters/persistent_hotspots.rmd | 140 +++---- chapters/phyto_size.rmd | 242 +++++++------ chapters/ppr.rmd | 321 ++++++++-------- chapters/productivity_anomaly.rmd | 246 +++++++------ chapters/rec_hms.rmd | 182 +++++----- chapters/recdat.rmd | 286 +++++++-------- chapters/seabird_ne.rmd | 234 ++++++------ chapters/seal_pups.rmd | 168 ++++----- chapters/seasonal_oisst_anom.rmd | 200 +++++----- chapters/seasonal_sst_anomaly_gridded.rmd | 148 ++++---- chapters/slopewater.rmd | 166 ++++----- chapters/spawn_timing.rmd | 422 +++++++++++----------- chapters/species_dist.rmd | 212 +++++------ chapters/species_groupings.rmd | 179 +++++---- chapters/stock_status.rmd | 211 ++++++----- chapters/thermal_habitat_area.rmd | 206 +++++------ chapters/thermal_habitat_persistence.rmd | 171 +++++---- chapters/timing_shifts.rmd | 150 ++++---- chapters/trans_dates.rmd | 206 +++++------ chapters/wbts_mesozooplankton.rmd | 158 ++++---- chapters/wcr.rmd | 166 ++++----- chapters/wind_dev_speed.rmd | 168 ++++----- chapters/wind_port.rmd | 184 +++++----- chapters/wind_revenue.rmd | 268 +++++++------- chapters/zoo_abundance_anom.rmd | 223 ++++++------ chapters/zoo_diversity.rmd | 192 +++++----- 70 files changed, 7272 insertions(+), 7215 deletions(-) create mode 100644 chapters/atestimages.rmd diff --git a/chapters/HMS_species_distribution.rmd b/chapters/HMS_species_distribution.rmd index 1841a343..d729f759 100644 --- a/chapters/HMS_species_distribution.rmd +++ b/chapters/HMS_species_distribution.rmd @@ -1,86 +1,85 @@ -# Cetacean Distribution Shifts {#HMS_species_distribution} - -**Description**: The data presented here are the locations of the center of core habitat for cetaceans by season as documented in 2010 versus 2017. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Sam Chavez, Elizabeth Josephson, Debra Palka - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Marine species are being affected by global climate changes, and in most cases the documented responses include distribution shifts from their historical habitat. In addition, human-caused drivers such as the noise and physical disturbances from oil and gas exploration, fishing, boat traffic and infrastructure such as offshore renewable energy developments, as well as other maritime activities could also result in shifts. [@chavez-rosales_detection_2022] used Northwest Atlantic cetacean location data collected in its changing environment to investigate if their habitats are changing, and if so, to what extent. - -A climate vulnerability assessment is published for Atlantic and Gulf of Mexico marine mammal populations [@lettrich_vulnerability_2023]. - -## Key Results and Visualizations -For seasons and cetacean species with the movements greater then 70 km, the season plots show the direction and magnitude of core habitat shifts. The locations of the tip and end of the arrow is the seasonal weighted centroid locations for 2010 (end of arrow) and 2017 (tip of arrow). The longer the arrow the more the seasonal shift in distribution. -For species that showed a clear displacement of the weighted centroid, the average magnitude of the shift was 178 km shifted towards the northeast and into deeper waters. Bottlenose dolphin habitat showed the most drastic shift for all seasons except during winter: spring= 294 km, summer=505 km, fall= 753 km and winter = 211. There was a clear tendency where the proportion of the estimated population in southern latitudes decreased, while north of 35° the proportion of the estimated population increased, especially during summer 2017. Other species that on average, showed a moderate-to-no spatial shift included humpback whale, minke whale, white-sided dolphin, Sowerby’s beaked whale, and long-finned pilot. - -```{r plot_HMS_species_distributionMAB} -# Plot indicator -ggplotObject <- ecodata::plot_HMS_species_distribution(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: All U.S. Atlantic waters north of Cape Hatteras, North Carolina. Thus including all EPU and beyond. - -Temporal scale: By each of the 4 seasons in 2010 and in 2017. - -**Synthesis Theme**: - -- [X] Regime Shifts -- [X] Ecosystem Reorganization - - -```{r autostats_HMS_species_distribution} -# Either from Contributor or ecodata -``` - -## Implications -Shifting species distributions alter both species interactions and fishery interactions. Those shifts affect the interaction of multiple system covariates and can result in ecosystem reorganization. In particular, shifting species distributions can alter expected management outcomes from spatial allocations and affect the efficacy of bycatch measures based on historical fish and protected species distributions. A UME for minke whales began in 2017 and is pending closure in [2024](https://www.fisheries.noaa.gov/national/marine-life-distress/active-and-closed-unusual-mortality-events). - -## Get the data - -**Point of contact**: [Sam Chavez (samuel.chavez@noaa.gov); Debra Palka (debra.palka@noaa.gov)](mailto:Sam Chavez (samuel.chavez@noaa.gov); Debra Palka (debra.palka@noaa.gov)){.email} - -**ecodata name**: `ecodata::HMS_species_distribution` - -**Variable definitions** - -1) Time=time period of centroid location. 2) species=cetacean species. 3) season. -4) wlat=latitude of centroid. 5) wlon=longitude of centroid. - -```{r vars_HMS_species_distribution} -# Pull all var names -vars <- ecodata::HMS_species_distribution |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Cetacean Distribution Shifts {#HMS_species_distribution} + +**Description**: The data presented here are the locations of the center of core habitat for cetaceans by season as documented in 2010 versus 2017. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Sam Chavez, Elizabeth Josephson, Debra Palka + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Marine species are being affected by global climate changes, and in most cases the documented responses include distribution shifts from their historical habitat. In addition, human-caused drivers such as the noise and physical disturbances from oil and gas exploration, fishing, boat traffic and infrastructure such as offshore renewable energy developments, as well as other maritime activities could also result in shifts. [@chavez-rosales_detection_2022] used Northwest Atlantic cetacean location data collected in its changing environment to investigate if their habitats are changing, and if so, to what extent. + +A climate vulnerability assessment is published for Atlantic and Gulf of Mexico marine mammal populations [@lettrich_vulnerability_2023]. + +## Key Results and Visualizations +For seasons and cetacean species with the movements greater then 70 km, the season plots show the direction and magnitude of core habitat shifts. The locations of the tip and end of the arrow is the seasonal weighted centroid locations for 2010 (end of arrow) and 2017 (tip of arrow). The longer the arrow the more the seasonal shift in distribution. +For species that showed a clear displacement of the weighted centroid, the average magnitude of the shift was 178 km shifted towards the northeast and into deeper waters. Bottlenose dolphin habitat showed the most drastic shift for all seasons except during winter: spring= 294 km, summer=505 km, fall= 753 km and winter = 211. There was a clear tendency where the proportion of the estimated population in southern latitudes decreased, while north of 35° the proportion of the estimated population increased, especially during summer 2017. Other species that on average, showed a moderate-to-no spatial shift included humpback whale, minke whale, white-sided dolphin, Sowerby’s beaked whale, and long-finned pilot. + +```{r plot_HMS_species_distributionMAB} +# Plot indicator +ggplotObject <- ecodata::plot_HMS_species_distribution(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: All U.S. Atlantic waters north of Cape Hatteras, North Carolina. Thus including all EPU and beyond. + +Temporal scale: By each of the 4 seasons in 2010 and in 2017. + +**Synthesis Theme**: + +- [X] Regime Shifts +- [X] Ecosystem Reorganization + + +```{r autostats_HMS_species_distribution} +# Either from Contributor or ecodata +``` + +## Implications +Shifting species distributions alter both species interactions and fishery interactions. Those shifts affect the interaction of multiple system covariates and can result in ecosystem reorganization. In particular, shifting species distributions can alter expected management outcomes from spatial allocations and affect the efficacy of bycatch measures based on historical fish and protected species distributions. A UME for minke whales began in 2017 and is pending closure in [2024](https://www.fisheries.noaa.gov/national/marine-life-distress/active-and-closed-unusual-mortality-events). + +## Get the data + +**Point of contact**: [Sam Chavez (samuel.chavez@noaa.gov); Debra Palka (debra.palka@noaa.gov)](mailto:Sam Chavez (samuel.chavez@noaa.gov); Debra Palka (debra.palka@noaa.gov)){.email} + +**ecodata name**: `ecodata::HMS_species_distribution` + +**Variable definitions** + +1) Time=time period of centroid location. 2) species=cetacean species. 3) season. 4) wlat=latitude of centroid. 5) wlon=longitude of centroid. + +```{r vars_HMS_species_distribution} +# Pull all var names +vars <- ecodata::HMS_species_distribution |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/SAV.rmd b/chapters/SAV.rmd index 88f9a014..2e3c3532 100644 --- a/chapters/SAV.rmd +++ b/chapters/SAV.rmd @@ -1,90 +1,90 @@ -# Submerged Aquatic Vegetation {#SAV} - -**Description**: The data provided here are the 1984-2022 area distribution and percent coverage of submerged aquatic vegetation in the Chesapeake Bay and its tributaries that area measured and calculated from photo-interpreted aerial imagery taken during surveys conducted in the growing season. - -**Indicator family**: - -- [X] Lower trophic levels - - -**Contributor(s)**: Christoper J. Patrick, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith - -**Affiliations**: VIMS - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Underwater grass beds are critical to the Chesapeake Bay ecosystem. They provide food and shelter to fish and wildlife, sequester carbon, add oxygen to the water, absorb nutrient pollution, reduce shoreline erosion and help suspended particles of sediment settle to the bottom. Because they are sensitive to pollution but quick to respond to improvements in water quality, underwater grass abundance is a good indicator of the Bay’s health. Before Europeans colonized the region, up to 600,000 acres of underwater grasses may have grown along the shorelines of the Bay and its tributaries. By the mid-1980s, nutrient and sediment pollution had weakened or eliminated many of these grass beds. While climate change, shoreline hardening and stressors that reduce water clarity will continue to impact our restoration success, many of these stressors can be managed with on-the-ground efforts to reduce pollution and research has shown that nutrient reductions made under the Chesapeake Bay Total Maximum Daily Load (Bay TMDL) have played a critical role in the overall underwater grass recovery documented since the Bay-wide aerial survey began in 1984. - -## Key Results and Visualizations -SAV increased in the Mesohaline and Polyhaline zones, where SAV continued to recover from recent declines in some areas. The Mesohaline zone showed a 28% increase from 2021 (2,768 hectares, 6,840 acres). The Polyhaline zone showed a 17% increase from 2021 (1,145 hectares, 2,828 acres). The Tidal Fresh zone stayed essentially the same with a small decrease (29 hectares, 73 acres) while the Oligohaline zone showed a 15% decrease (501 hectares, 1,239 acres). The increases in the Mesohaline and Polyhaline zone largely reflect recovery following the SAV crash in 2019. Those losses in 2019 were largely due to declines in widgeongrass which has expanded over the past decade due to increases in water quality but is sensitive to wet springs like the one experienced in 2019. The expansion in polyhaline zone is also attributable to a La Nina climate cycle which has resulted in cooler summers, benefiting eelgrass. The primary losses in the Oligohaline were concentrated in a small area, the Gunpowder River, the Middle River, and the adjacent mainstem. These declines may have been influenced by phytoplankton blooms observed in those segments in the spring and summer of 2022. - -### MAB - -```{r plot_SAVMAB} -# Plot indicator -ggplotObject <- ecodata::plot_SAV(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: The data covers the tidal Chesapeake Bay region. - -Temporal scale: 1984-2022, annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts - - -```{r autostats_SAV} -# Either from Contributor or ecodata -``` - -## Implications -Bay-wide: In 2022, 76,462 acres of SAV were mapped in the Chesapeake Bay. This is 41% of the Bay SAV goal. -• Tidal Fresh Salinity Zone: 19,185 acres in 2022 achieving 93% of the area’s 20,602-acre goal. -• Oligohaline Salinity Zone: 7,145 acres in 2022 achieving 69% of the area’s 10,334-acre goal. -• Mesohaline Salinity Zone: 30,932 acres in 2022 achieving 26% of the area’s 120,306-acre goal. -• Polyhaline Salinity Zone: 19,200 acres in 2022 achieving 57% of the area’s 33,647-acre goal. -The outlook toward achieving the outcome goal is uncertain. Gains from 2021 to 2022 are positive, indicating an on-course trajectory, but these gains don’t yet offset the recent major declines observed in 2019. Additional years of positive trajectory will help clarify whether this recent gain in 2022 is the start of a new positive trend toward higher levels of SAV across the Bay. - -## Get the data - -**Point of contact**: [Christoper J. Patrick (cpatrick@vims.edu), David J. Wilcox (dwilcox@vims.edu)](mailto:Christoper J. Patrick (cpatrick@vims.edu), David J. Wilcox (dwilcox@vims.edu)){.email} - -**ecodata name**: `ecodata::SAV` - -**Variable definitions** - -1) Name: Year; Definition: SAV growing season; year. 2) Name: Tidal Fresh Total; Definition: SAV area in the Tidal Fresh Zone; Units: acres. 3) Name: Oligohaline Total -; Definition: SAV area in the Oligohaline Zone; Units: acres. 4) Name: Mesohaline Total; Definition: SAV area in the Mesohaline Zone; Units: acres. 5) Name: Polyhaline Total; Definition: SAV area in the Polyhaline Zone; Units: acres. 5) Name: Baywide Total; Definition: Total SAV area in Chesapeake Bay; Units: acres. - -```{r vars_SAV} -# Pull all var names -vars <- ecodata::SAV |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Submerged Aquatic Vegetation {#SAV} + +**Description**: The data provided here are the 1984-2022 area distribution and percent coverage of submerged aquatic vegetation in the Chesapeake Bay and its tributaries that area measured and calculated from photo-interpreted aerial imagery taken during surveys conducted in the growing season. + +**Indicator family**: + +- [X] Lower trophic levels + + +**Contributor(s)**: Christoper J. Patrick, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith + +**Affiliations**: VIMS + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Underwater grass beds are critical to the Chesapeake Bay ecosystem. They provide food and shelter to fish and wildlife, sequester carbon, add oxygen to the water, absorb nutrient pollution, reduce shoreline erosion and help suspended particles of sediment settle to the bottom. Because they are sensitive to pollution but quick to respond to improvements in water quality, underwater grass abundance is a good indicator of the Bay’s health. Before Europeans colonized the region, up to 600,000 acres of underwater grasses may have grown along the shorelines of the Bay and its tributaries. By the mid-1980s, nutrient and sediment pollution had weakened or eliminated many of these grass beds. While climate change, shoreline hardening and stressors that reduce water clarity will continue to impact our restoration success, many of these stressors can be managed with on-the-ground efforts to reduce pollution and research has shown that nutrient reductions made under the Chesapeake Bay Total Maximum Daily Load (Bay TMDL) have played a critical role in the overall underwater grass recovery documented since the Bay-wide aerial survey began in 1984. + +## Key Results and Visualizations +SAV increased in the Mesohaline and Polyhaline zones, where SAV continued to recover from recent declines in some areas. The Mesohaline zone showed a 28% increase from 2021 (2,768 hectares, 6,840 acres). The Polyhaline zone showed a 17% increase from 2021 (1,145 hectares, 2,828 acres). The Tidal Fresh zone stayed essentially the same with a small decrease (29 hectares, 73 acres) while the Oligohaline zone showed a 15% decrease (501 hectares, 1,239 acres). The increases in the Mesohaline and Polyhaline zone largely reflect recovery following the SAV crash in 2019. Those losses in 2019 were largely due to declines in widgeongrass which has expanded over the past decade due to increases in water quality but is sensitive to wet springs like the one experienced in 2019. The expansion in polyhaline zone is also attributable to a La Nina climate cycle which has resulted in cooler summers, benefiting eelgrass. The primary losses in the Oligohaline were concentrated in a small area, the Gunpowder River, the Middle River, and the adjacent mainstem. These declines may have been influenced by phytoplankton blooms observed in those segments in the spring and summer of 2022. + +### MAB + +```{r plot_SAVMAB} +# Plot indicator +ggplotObject <- ecodata::plot_SAV(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: The data covers the tidal Chesapeake Bay region. + +Temporal scale: 1984-2022, annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts + + +```{r autostats_SAV} +# Either from Contributor or ecodata +``` + +## Implications +Bay-wide: In 2022, 76,462 acres of SAV were mapped in the Chesapeake Bay. This is 41% of the Bay SAV goal. +• Tidal Fresh Salinity Zone: 19,185 acres in 2022 achieving 93% of the area’s 20,602-acre goal. +• Oligohaline Salinity Zone: 7,145 acres in 2022 achieving 69% of the area’s 10,334-acre goal. +• Mesohaline Salinity Zone: 30,932 acres in 2022 achieving 26% of the area’s 120,306-acre goal. +• Polyhaline Salinity Zone: 19,200 acres in 2022 achieving 57% of the area’s 33,647-acre goal. +The outlook toward achieving the outcome goal is uncertain. Gains from 2021 to 2022 are positive, indicating an on-course trajectory, but these gains don’t yet offset the recent major declines observed in 2019. Additional years of positive trajectory will help clarify whether this recent gain in 2022 is the start of a new positive trend toward higher levels of SAV across the Bay. + +## Get the data + +**Point of contact**: [Christoper J. Patrick (cpatrick@vims.edu), David J. Wilcox (dwilcox@vims.edu)](mailto:Christoper J. Patrick (cpatrick@vims.edu), David J. Wilcox (dwilcox@vims.edu)){.email} + +**ecodata name**: `ecodata::SAV` + +**Variable definitions** + +1) Name: Year; Definition: SAV growing season; year. 2) Name: Tidal Fresh Total; Definition: SAV area in the Tidal Fresh Zone; Units: acres. 3) Name: Oligohaline Total +; Definition: SAV area in the Oligohaline Zone; Units: acres. 4) Name: Mesohaline Total; Definition: SAV area in the Mesohaline Zone; Units: acres. 5) Name: Polyhaline Total; Definition: SAV area in the Polyhaline Zone; Units: acres. 5) Name: Baywide Total; Definition: Total SAV area in Chesapeake Bay; Units: acres. + +```{r vars_SAV} +# Pull all var names +vars <- ecodata::SAV |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/abc_acl.rmd b/chapters/abc_acl.rmd index 770e2560..f2302f00 100644 --- a/chapters/abc_acl.rmd +++ b/chapters/abc_acl.rmd @@ -1,110 +1,112 @@ -# ABC or ACL for Managed Stocks {#abc_acl} - -**Description**: Mid-Atlantic Council catch limits (e.g., ABC or ACL) and associated total catch estimate by year for each species and sector (commercial or recreational, as appropriate). - -**Indicator family**: - -- [X] Social -- [X] Economic - - -**Contributor(s)**: Brandon Muffley, Jason, Didden, Julia Beaty, Jose Montanez, Karson Cisneros, Jessica Coakley, Hannah Hart, Kiley Dancy - -**Affiliations**: MAFMC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The ratio of total estimates catch (landings and dead discards) compared to the specified catch limit, either the Acceptable Biological Catch (ABC) or the Annual Catch Limit (ACL) by species and sector (e.g., commercial or recreational, if appropriate). This ratio can provide an understanding of management control to constrain total catch below the acceptable/specified limits through different management tools (e.g., trip limits, seasons, gear requirements etc.). Ratios above one can be an indicator of poor management control and/or uncertainties, or indicative that the catch limits in place are constraining on the fishery. Ratios below one could indicate greater management control and/or fewer uncertainties or that their may be other factors (e.g., markets, price, availability) may be limiting a sectors ability to reach its catch limits. - -## Key Results and Visualizations -Catch divided by ABC/ACL for MAFMC managed fisheries. Red line indicates the median ratio across all fisheries. A secondary figure is also produced that provides the sum of catch limits across all MAFMC managed commercial (C) and recreational (R) fisheries. - -### MidAtlantic - -```{r plot_abc_aclMidAtlanticStacked} -# Plot indicator -ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Stacked') -ggplotObject -``` - -```{r plot_abc_aclMidAtlanticCatch} -# Plot indicator -ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Catch') -ggplotObject -``` - -### NewEngland - -```{r plot_abc_aclNewEnglandStacked} -# Plot indicator -ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Stacked') -ggplotObject -``` - -```{r plot_abc_aclNewEnglandCatch} -# Plot indicator -ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Catch') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Cape Hatteras, NC north to Maine - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_abc_acl} -# Either from Contributor or ecodata -``` - -## Implications -Need to write later once updated data has been added to the data file. - -## Get the data - -**Point of contact**: [Brandon Muffley (bmuffley@mafmc.org)](mailto:Brandon Muffley (bmuffley@mafmc.org)){.email} - -**ecodata name**: `ecodata::abc_acl` - -**Variable definitions** - -Catch - definition: total landings and dead discards; units - metric tons (MT) -Acceptable biological catch (ABC) - the annual catch level recommended for a stock or stock complex by the Scientific and Statistical Committee that could be equal to or less than the Overfishing Limit (OFL); units - metric tons (MT) -Annual Catch Limit (ACL) - sector specific total catch limits which can be equal to or less than the ABC; units - metric tons (MT) - -```{r vars_abc_acl} -# Pull all var names -vars <- ecodata::abc_acl |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Other - - -**Indicator Category**: - -Management reports (e.g., specification packages, fishery information documents), stock assessment reports, or data pulls from CAMS - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# ABC or ACL for Managed Stocks {#abc_acl} + +**Description**: Mid-Atlantic Council catch limits (e.g., ABC or ACL) and associated total catch estimate by year for each species and sector (commercial or recreational, as appropriate). + +**Indicator family**: + +- [X] Social +- [X] Economic + + +**Contributor(s)**: Brandon Muffley, Jason, Didden, Julia Beaty, Jose Montanez, Karson Cisneros, Jessica Coakley, Hannah Hart, Kiley Dancy + +**Affiliations**: MAFMC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The ratio of total estimates catch (landings and dead discards) compared to the specified catch limit, either the Acceptable Biological Catch (ABC) or the Annual Catch Limit (ACL) by species and sector (e.g., commercial or recreational, if appropriate). This ratio can provide an understanding of management control to constrain total catch below the acceptable/specified limits through different management tools (e.g., trip limits, seasons, gear requirements etc.). Ratios above one can be an indicator of poor management control and/or uncertainties, or indicative that the catch limits in place are constraining on the fishery. Ratios below one could indicate greater management control and/or fewer uncertainties or that their may be other factors (e.g., markets, price, availability) may be limiting a sectors ability to reach its catch limits. + +## Key Results and Visualizations +Catch divided by ABC/ACL for MAFMC managed fisheries. Red line indicates the median ratio across all fisheries. A secondary figure is also produced that provides the sum of catch limits across all MAFMC managed commercial (C) and recreational (R) fisheries. + +### MidAtlantic + +```{r plot_abc_aclMidAtlanticStacked} +# Plot indicator +ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Stacked') +ggplotObject +``` + +```{r plot_abc_aclMidAtlanticCatch} +# Plot indicator +ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Catch') +ggplotObject +``` + +### NewEngland + +```{r plot_abc_aclNewEnglandStacked} +# Plot indicator +ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Stacked') +ggplotObject +``` + +```{r plot_abc_aclNewEnglandCatch} +# Plot indicator +ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Catch') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Cape Hatteras, NC north to Maine + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_abc_acl} +# Either from Contributor or ecodata +``` + +## Implications +Increasing/decreasing trends in the total ABC/ACL across all stocks can provide insights into potential changes to system productivity and fishing opportunities for Council managed species. While evaluating total estimated catch relative to the catch limit (ABC or ACL) can help provide insight into the degree of management control to constrain catch, limit overfishing, and identify potential underfishing situations (ie., not achieving the desired catch). + +Since 2012, the total catch limits in the Mid-Atlantic have fluctuated slightly without any trend and the 2022 catch limit was similar to those set over the last four years. There is more variability at the species and sector level, but this suggests that overall stock abundance, productivity, and fishing opportunities have remained relatively stable within the Mid-Atlantic. Similarly, total estimated catch relative to the total catch limits in the Mid-Atlantic has fluctuated relatively little and without trend and has been below the total catch limit every year from 2012-2022 with a median of about 70 percent. This suggests that current catch limits are generally not constraining for most stocks and that there is some level of management control to ensure total catch remains below the catch limits to limit the risks of overfishing. In general, those fisheries where total catch exceeded the associated catch limit are associated with the recreational sector, particularly scup and black sea bass that have high biomass and availability. + +## Get the data + +**Point of contact**: [Brandon Muffley (bmuffley@mafmc.org)](mailto:Brandon Muffley (bmuffley@mafmc.org)){.email} + +**ecodata name**: `ecodata::abc_acl` + +**Variable definitions** + +Catch - definition: total landings and dead discards; units - metric tons (MT) +Acceptable biological catch (ABC) - the annual catch level recommended for a stock or stock complex by the Scientific and Statistical Committee that could be equal to or less than the Overfishing Limit (OFL); units - metric tons (MT) +Annual Catch Limit (ACL) - sector specific total catch limits which can be equal to or less than the ABC; units - metric tons (MT) + +```{r vars_abc_acl} +# Pull all var names +vars <- ecodata::abc_acl |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Other + + +**Indicator Category**: + +Management reports (e.g., specification packages, fishery information documents), stock assessment reports, or data pulls from CAMS + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/aggregate_biomass.rmd b/chapters/aggregate_biomass.rmd index 4da76b6b..97fe696a 100644 --- a/chapters/aggregate_biomass.rmd +++ b/chapters/aggregate_biomass.rmd @@ -1,111 +1,111 @@ -# Aggregate Survey Biomass {#aggregate_biomass} - -**Description**: Aggregate biomass from Northeast Fisheries Science Center (NEFSC) bottom trawl survey. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Sean Lucey, Andrew Beet, and Sarah Gaichas. - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The NEFSC has been conducting bi-annual bottom trawl surveys along the Northeast US Continental Shelf for over 60 years. The survey is conducted in the spring and fall of each year. The fall survey began in 1964 and the spring in 1967. The survey is designed as a stratified random survey. We have calculated stratified means with sets of survey strata that closely align with our Ecosystem Production Unit boundaries. Species are aggregated into feeding guilds in order to gauge the relative stability and health of the system. - -## Key Results and Visualizations -Aggregate biomass levels have been relatively stable over time. - -### MAB - -```{r plot_aggregate_biomassMAB} -# Plot indicator -ggplotObject <- ecodata::plot_aggregate_biomass(report='MidAtlantic') -ggplotObject -``` - -### GB - -```{r plot_aggregate_biomassNEGB} -# Plot indicator -ggplotObject <- ecodata::plot_aggregate_biomass(report='NewEngland',EPU='GB') -ggplotObject -``` - -### GOM - -```{r plot_aggregate_biomassNEGOM} -# Plot indicator -ggplotObject <- ecodata::plot_aggregate_biomass(report='NewEngland',EPU='GOM') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: By EPU - -Temporal scale: Spring (March-May) and Fall (September-November) - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_aggregate_biomass} -# Either from Contributor or ecodata -``` - -## Implications -Aggregate biomass is a holistic indicator that reveals the underlying ecosystem that the fisheries operates within. While there has been evidence of overfishing of key commercial species, the overall ecosystem is relatively stable. - -## Get the data - -**Point of contact**: [Andrew Beet (Andrew.Beet@NOAA.gov)](mailto:Andrew Beet (Andrew.Beet@NOAA.gov)){.email} - -**ecodata name**: `ecodata::aggregate_biomass` - -**Variable definitions** - -1) Name: Guild Season Biomass Index; Description: Stratified mean biomass index of an aggregate group of species. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Biomass Index"; Units: kg tow^-1 -2) Name: Guild Season Biomass Index - inshore; Description: Stratified mean biomass index of an aggregate group of species using only strata designated as inshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Biomass Index - inshore"; Units: kg tow^-1 -3) Name: Guild Season Biomass Index - offshore; Description: Stratified mean biomass index of an aggregate group of species using only strata designated as offshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Biomass Index - inshore"; Units: kg tow^-1 -4) Name: Guild Management Body managed species - Season Biomass Index; Description: Stratified mean biomass index of an aggregate group of managed species. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Biomass Index"; Units: kg tow^-1 -5) Name: Guild Management Body managed species - Season Biomass Index - inshore; Description: Stratified mean biomass index of an aggregate group of managed species using only strata designated as inshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Biomass Index - inshore"; Units: kg tow^-1 -6) Name: Guild Management Body managed species - Season Biomass Index - offshore; Description: Stratified mean biomass index of an aggregate group of managed species using only strata designated as offshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Biomass Index - inshore"; Units: kg tow^-1 -7) Name: Guild Season Standard Error; Description: Variance associated with the stratified mean biomass index of an aggregate group of species. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Standard Error"; Units: kg tow^-1 -8) Name: Guild Season Biomass Index - inshore; Description: Variance associated with the stratified mean biomass index of an aggregate group of species using only strata designated as inshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Standard Error - inshore"; Units: kg tow^-1 -9) Name: Guild Season Biomass Index - offshore; Description: Variance associated with the stratified mean biomass index of an aggregate group of species using only strata designated as offshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Standard Error - inshore"; Units: kg tow^-1 -10) Name: Guild Management Body managed species - Season Biomass Index; Description: Variance associated with the stratified mean biomass index of an aggregate group of managed species. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Standard Error"; Units: kg tow^-1 -11) Name: Guild Management Body managed species - Season Biomass Index - inshore; Description: Variance associated with the stratified mean biomass index of an aggregate group of managed species using only strata designated as inshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Standard Error - inshore"; Units: kg tow^-1 -12) Name: Guild Management Body managed species - Season Biomass Index - offshore; Description: Variance associated with the stratified mean biomass index of an aggregate group of managed species using only strata designated as offshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Standard Error - inshore"; Units: kg tow^-1 - -```{r vars_aggregate_biomass} -# Pull all var names -vars <- ecodata::aggregate_biomass |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Aggregate Survey Biomass {#aggregate_biomass} + +**Description**: Aggregate biomass from Northeast Fisheries Science Center (NEFSC) bottom trawl survey. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Sean Lucey, Andrew Beet, and Sarah Gaichas. + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The NEFSC has been conducting bi-annual bottom trawl surveys along the Northeast US Continental Shelf for over 60 years. The survey is conducted in the spring and fall of each year. The fall survey began in 1964 and the spring in 1967. The survey is designed as a stratified random survey. We have calculated stratified means with sets of survey strata that closely align with our Ecosystem Production Unit boundaries. Species are aggregated into feeding guilds in order to gauge the relative stability and health of the system. + +## Key Results and Visualizations +Aggregate biomass levels have been relatively stable over time. + +### MAB + +```{r plot_aggregate_biomassMAB} +# Plot indicator +ggplotObject <- ecodata::plot_aggregate_biomass(report='MidAtlantic') +ggplotObject +``` + +### GB + +```{r plot_aggregate_biomassNEGB} +# Plot indicator +ggplotObject <- ecodata::plot_aggregate_biomass(report='NewEngland',EPU='GB') +ggplotObject +``` + +### GOM + +```{r plot_aggregate_biomassNEGOM} +# Plot indicator +ggplotObject <- ecodata::plot_aggregate_biomass(report='NewEngland',EPU='GOM') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: By EPU + +Temporal scale: Spring (March-May) and Fall (September-November) + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_aggregate_biomass} +# Either from Contributor or ecodata +``` + +## Implications +Aggregate biomass is a holistic indicator that reveals the underlying ecosystem that the fisheries operates within. While there has been evidence of overfishing of key commercial species, the overall ecosystem is relatively stable. + +## Get the data + +**Point of contact**: [Andrew Beet (Andrew.Beet@NOAA.gov)](mailto:Andrew Beet (Andrew.Beet@NOAA.gov)){.email} + +**ecodata name**: `ecodata::aggregate_biomass` + +**Variable definitions** + +1) Name: Guild Season Biomass Index; Description: Stratified mean biomass index of an aggregate group of species. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Biomass Index"; Units: kg tow^-1 +2) Name: Guild Season Biomass Index - inshore; Description: Stratified mean biomass index of an aggregate group of species using only strata designated as inshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Biomass Index - inshore"; Units: kg tow^-1 +3) Name: Guild Season Biomass Index - offshore; Description: Stratified mean biomass index of an aggregate group of species using only strata designated as offshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Biomass Index - inshore"; Units: kg tow^-1 +4) Name: Guild Management Body managed species - Season Biomass Index; Description: Stratified mean biomass index of an aggregate group of managed species. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Biomass Index"; Units: kg tow^-1 +5) Name: Guild Management Body managed species - Season Biomass Index - inshore; Description: Stratified mean biomass index of an aggregate group of managed species using only strata designated as inshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Biomass Index - inshore"; Units: kg tow^-1 +6) Name: Guild Management Body managed species - Season Biomass Index - offshore; Description: Stratified mean biomass index of an aggregate group of managed species using only strata designated as offshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Biomass Index - inshore"; Units: kg tow^-1 +7) Name: Guild Season Standard Error; Description: Variance associated with the stratified mean biomass index of an aggregate group of species. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Standard Error"; Units: kg tow^-1 +8) Name: Guild Season Biomass Index - inshore; Description: Variance associated with the stratified mean biomass index of an aggregate group of species using only strata designated as inshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Standard Error - inshore"; Units: kg tow^-1 +9) Name: Guild Season Biomass Index - offshore; Description: Variance associated with the stratified mean biomass index of an aggregate group of species using only strata designated as offshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. For example "Benthivore Spring Standard Error - inshore"; Units: kg tow^-1 +10) Name: Guild Management Body managed species - Season Biomass Index; Description: Variance associated with the stratified mean biomass index of an aggregate group of managed species. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Standard Error"; Units: kg tow^-1 +11) Name: Guild Management Body managed species - Season Biomass Index - inshore; Description: Variance associated with the stratified mean biomass index of an aggregate group of managed species using only strata designated as inshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Standard Error - inshore"; Units: kg tow^-1 +12) Name: Guild Management Body managed species - Season Biomass Index - offshore; Description: Variance associated with the stratified mean biomass index of an aggregate group of managed species using only strata designated as offshore. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Season is either Spring or Fall. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Spring Standard Error - inshore"; Units: kg tow^-1 + +```{r vars_aggregate_biomass} +# Pull all var names +vars <- ecodata::aggregate_biomass |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/aquaculture.rmd b/chapters/aquaculture.rmd index ff7a2731..e5d820de 100644 --- a/chapters/aquaculture.rmd +++ b/chapters/aquaculture.rmd @@ -1,85 +1,85 @@ -# Aquaculture Production {#aquaculture} - -**Description**: Oyster production: number of oysters harvested from aquaculture. - -**Indicator family**: - -- [X] Megafauna -- [X] Social -- [X] Economic - - -**Contributor(s)**: Christopher Schillaci, Maine DMR, NH DES, MA DMF, RI CRMC, MD DNR - -**Affiliations**: NOAA - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Aquaculture can produce seafood as well as other products. Here we summarize available data on aquaculture production from Northeast US states. - -## Key Results and Visualizations -Overall oyster production is increasing and production per area is also improving. Aquaculture reporting is state specific and because of this can be difficult to collate in a meaningful way. However, this is improving year to year as aquaculture increases across the coast. The data included here only holds oyster numbers at this time. - -```{r plot_aquacultureMAB} -# Plot indicator -ggplotObject <- ecodata::plot_aquaculture(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Mid Atlantic and New England - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_aquaculture} -# Either from Contributor or ecodata -``` - -## Implications -Aquaculture production contributes to overall seafood production in the Northeast US. This indicator provides only a portion of aquaculture production in the region, but increases in this portion are apparent. - -## Get the data - -**Point of contact**: [Chris Schillaci ](mailto:Chris Schillaci ){.email} - -**ecodata name**: `ecodata::aquaculture` - -**Variable definitions** - -Pieces: number of oysters produced (all regions) -Shellfish lease Acres: area used for shellfish production (New England states only), acres -Production/Acre: Pieces divided by Shellfish lease acres (New England states only) - -```{r vars_aquaculture} -# Pull all var names -vars <- ecodata::aquaculture |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Aquaculture Production {#aquaculture} + +**Description**: Oyster production: number of oysters harvested from aquaculture. + +**Indicator family**: + +- [X] Megafauna +- [X] Social +- [X] Economic + + +**Contributor(s)**: Christopher Schillaci, Maine DMR, NH DES, MA DMF, RI CRMC, MD DNR + +**Affiliations**: NOAA + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Aquaculture can produce seafood as well as other products. Here we summarize available data on aquaculture production from Northeast US states. + +## Key Results and Visualizations +Overall oyster production is increasing and production per area is also improving. Aquaculture reporting is state specific and because of this can be difficult to collate in a meaningful way. However, this is improving year to year as aquaculture increases across the coast. The data included here only holds oyster numbers at this time. + +```{r plot_aquacultureMAB} +# Plot indicator +ggplotObject <- ecodata::plot_aquaculture(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Mid Atlantic and New England + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_aquaculture} +# Either from Contributor or ecodata +``` + +## Implications +Aquaculture production contributes to overall seafood production in the Northeast US. This indicator provides only a portion of aquaculture production in the region, but increases in this portion are apparent. + +## Get the data + +**Point of contact**: [Chris Schillaci ](mailto:Chris Schillaci ){.email} + +**ecodata name**: `ecodata::aquaculture` + +**Variable definitions** + +Pieces: number of oysters produced (all regions) +Shellfish lease Acres: area used for shellfish production (New England states only), acres +Production/Acre: Pieces divided by Shellfish lease acres (New England states only) + +```{r vars_aquaculture} +# Pull all var names +vars <- ecodata::aquaculture |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/atestimages.rmd b/chapters/atestimages.rmd new file mode 100644 index 00000000..c76488c7 --- /dev/null +++ b/chapters/atestimages.rmd @@ -0,0 +1,79 @@ +# test {#atestimages} + +**Description**: data + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Ex: Brandon Beltz, Sarah Gaichas and Sean Lucey. + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +$R_t=j=p_{jt}y_{jt}$ + +## Key Results and Visualizations +testing images. Either option works. maybe straight markdown is cleaner since we get to see if the image appears in the issue + +NOTE: the path to the images is not intuitive. You need to specify the path with the `raw` "folder" in the url + +1. Using markdown: This works great. + +![Alexandrium cyst map](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Alexandrium_distribution_2024.png){width=100%} + +2. Using rmarkdown: you can specify output width relative to space available. the image isnt rendered in this issue. Note again the path to the image has `raw` in the url + +```{r ,fig.align='center',out.width="100%"} +knitr::include_graphics("https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Alexandrium_distribution_2024.png") +``` + + +## Indicator statistics +Spatial scale: _No response_ + +Temporal scale: _No response_ + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_atestimages} +# Either from Contributor or ecodata +``` + +## Implications +Proceed as though this were a short summary of a typical discussion section in a paper. + +## Get the data + +**Point of contact**: [andrew.beet@noaa.gov](mailto:andrew.beet@noaa.gov){.email} + +**ecodata name**: No dataset + +**Variable definitions** + +1) Name: piscivore_biomass; Definition: Biomass of piscivores; Units: kg tow^-1. 2) Name: forage_biomass; Definition: Biomass of forage fish; Units: kg tow^-1. + + +No Data + +**Indicator Category**: + +- [X] Extensive analysis, not yet published + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + diff --git a/chapters/bennet.rmd b/chapters/bennet.rmd index 94f17699..12e73a9f 100644 --- a/chapters/bennet.rmd +++ b/chapters/bennet.rmd @@ -1,147 +1,147 @@ -# Bennet Indicator {#bennet} - -**Description**: The data presented here are changes in revenue ($ real) split into a price indicator and a volume indicator. The sum of the price and the volume indicator is equal to the revenue change relative to a base year, which is 1982. - -**Indicator family**: - -- [X] Social -- [X] Economic - - -**Contributor(s)**: John Walden, Geret DePiper, Sean Lucey - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The Bennet (1920) indicator (BI) was first used to show how a change in social welfare could be decomposed into a sum of a price and quantity change indicator (@cross_value_2009). It is called an indicator because it is based on differences in value between time periods, rather than ratios, which are referred to as indices. The BI is the indicator equivalent of the more popular Fisher index (@balk_assumption-free_2010), and has been used to examine revenue changes in Swedish pharmacies, productivity change in U.S. railroads (@lim_profit_2009), and dividend changes in banking operations @grifell-tatje_decomposing_2004. An attractive feature of the BI is that the overall indicator is equal to the sum of its subcomponents @balk_assumption-free_2010. This allows us to examine whether increasing (decreasing) volumes, increasing (decreasing) prices, or some combination of the two is responsible for revenue change between time periods. The volume and price indicators can be further decomposed allowing us to examine the extent to which changing quantities or prices of each output is driving revenue change. - -Revenue in an EPU in a given year is the product of quantities landed and prices received from all species groups (or guilds) within the EPU. The change in revenue between any two time points can be decomposed into a volume indicator (VI) and a price indicator (PI). - -The overall BI is the sum of the VI and PI, and is equal to the overall revenue change. Since revenue change is being driven by changes in the individual prices and quantities landed of each species group within an EPU, changes at the EPU level can be examined separately by taking advantage of the additive property of the indicator. For example, if there are five different species groups, the sum of each group’s VI will equal the overall EPU VI, and likewise, the sum of the PI for each group will equal the overall PI for the EPU. - -## Key Results and Visualizations -Georges Bank revenue has exceeded 1982 levels in only two years (1990 and 2004), and this finding is due mainly to lower volumes (VI) prior to 2004, and lower prices (PI) after 2004. Breaking down the VI and PI further for Georges Bank shows that the negative VI is primarily due to changes in the Benthivore and Piscivore categories. Since 2010, there have been large positive gains in volumes for the Benthos category, leading to positive gains in the overall VI during some years. This is likely reflecting improvements in scallop harvests since the area rotation system has been implemented. The PI for Georges Bank shows a generally negative trend, particularly since 2004. Prices for the Benthivores contributed positively to the PI, but declines in the PI for the benthos category were the greatest contributor to the negative PI. All prices are adjusted for inflation, and positive changes means that prices are increasing faster than inflation, while negative prices means that they are not keeping pace with inflation. -Gulf of Maine revenue has generally exceeded 1982 levels except for very short time periods, the last being 2004 and 2006. Generally, revenue has been higher due to increasing prices (PI). Since 2010, both increasing volumes (VI) and prices (PI) have contributed to positive revenue change. The VI indicator showed a cyclical type of pattern between 1982 and 2022 with the VI being mostly influenced by the Benthivore and Benthos categories. After 2010, the Benthivore category was the biggest influence on the VI. This was likely due to increases in lobster landings. The PI was positive for the entire time period, and this was due to increase in the Benthivore category. The Piscivore and “Other” categories showed positive prices throughout the time-period, while the Benthos category was generally negative before turning positive after 2015. -In the Mid-Atlantic region revenue was positive compared to 1982 levels for all years in the series. This was due to increases in both volumes (VI) and Prices (PI). Prices were higher than 1982 levels with the exception of four years prior to 2000. Examination of the VI in more detail showed positive contributions to revenue gains from all categories peaking in 2003, followed by declines in subsequent years. The PI showed a positive trend for most of the years in the time series with the indicator increasing from 1999 through 2016, before declining. The PI gain after 1999 was mainly due to the Benthos category. - -### MidAtlantic - -```{r plot_bennetMidAtlanticguildMAB} -# Plot indicator -ggplotObject <- ecodata::plot_bennet(report= 'MidAtlantic', varName= 'guild' ,EPU= 'MAB') -ggplotObject -``` - -```{r plot_bennetMidAtlantictotalMAB} -# Plot indicator -ggplotObject <- ecodata::plot_bennet(report= 'MidAtlantic', varName= 'total' ,EPU= 'MAB') -ggplotObject -``` - -```{r plot_bennetMidAtlantictotal_guildMAB} -# Plot indicator -ggplotObject <- ecodata::plot_bennet(report= 'MidAtlantic', varName= 'total_guild' ,EPU= 'MAB') -ggplotObject -``` - -### NewEngland - -```{r plot_bennetNewEnglandguildGB} -# Plot indicator -ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'guild' ,EPU= 'GB') -ggplotObject -``` - -```{r plot_bennetNewEnglandguildGOM} -# Plot indicator -ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'guild' ,EPU= 'GOM') -ggplotObject -``` - -```{r plot_bennetNewEnglandtotalGB} -# Plot indicator -ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total' ,EPU= 'GB') -ggplotObject -``` - -```{r plot_bennetNewEnglandtotalGOM} -# Plot indicator -ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total' ,EPU= 'GOM') -ggplotObject -``` - -```{r plot_bennetNewEnglandtotal_guildGB} -# Plot indicator -ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total_guild' ,EPU= 'GB') -ggplotObject -``` - -```{r plot_bennetNewEnglandtotal_guildGOM} -# Plot indicator -ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total_guild' ,EPU= 'GOM') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: The BI, PI and VI are calculated for the GB, GOM and MAB EPU separately - -Temporal scale: The BI, PI and VI are all calculated on an Annual Basis. - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts -- [X] Ecosystem Reorganization - - -```{r autostats_bennet} -# Either from Contributor or ecodata -``` - -## Implications -It is troubling that the revenue from Georges Bank has only exceeded 1982 levels in two years of a 40 year time-series. In the first 20 years of the series, this was due to declining volumes, while in the later part of the series it was due to both declining volumes and prices compared to 1982. In terms of volumes, the positive gains in the Benthos category was offset by declines in the Benthivore category. This reflects a shift to an EPU whose value is being generated from shellfish rather than finfish. -Since 2007 increases in the Gulf of Maine VI for the Benthivore category has increased substantially, and has been the biggest contributor to the positive VI. Along with this gain, the PI for the Benthivore category has been positive for the entire time period. The increase in the Benthivore VI is thought to have been caused by increasing lobster harvests in the Gulf of Maine. Positive prices and volumes for this category contributed to the overall revenue gain seen, particularly after 2007. -For the Gulf of Maine and Georges Bank region, the decomposed VI shows that these two regions are becoming more reliant on shellfish for their landings, which likely increases risk for revenue losses if these volumes can’t be sustained. The overall BI for Gorges Bank was negative for all but two years in the time series, which is troubling because it shows that except for two years this particular EPU never achieved the same level of performance in terms of revenue as in 1982. -Although the Mid-Atlantic region showed declining revenue trends since 2016, revenue from harvested species is still greater than 1982 levels. The decline in revenue since 2016 is due to both declining prices and volumes. Declining volumes were due to the Benthos, Benthivore and “Other” category, while declining prices were due to the Benthos category. The declining Benthos category may be partially caused by decreases in surfclam and ocean quahogs in the southern part of their range as harvest have shifted northward. - -## Get the data - -**Point of contact**: [John Walden (john.walden@noaa.gov); Geret DePiper (geret.depiper@noaa.gov)](mailto:John Walden (john.walden@noaa.gov); Geret DePiper (geret.depiper@noaa.gov)){.email} - -**ecodata name**: `ecodata::bennet` - -**Variable definitions** - -1) Volume Indicator (VI): Change in Revenue compared to the base time period in deflated dollars attributed to changes in landings for harvested species (volumes). -2) Price Indicator (PI): Change in Revenue compared to the base time period in deflated dollars attributed to changes in prices for species harvested. -3) Bennet Indicator (BI): The Sum of a Volume Indicator (VI) and Price Indicator (PI) in inflation adjusted dollars, which is equal to the change in revenue. - -```{r vars_bennet} -# Pull all var names -vars <- ecodata::bennet |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Please email john.walden@noaa.gov for further information and queries of Bennet Indicator source data. - -**tech-doc link** - - +# Bennet Indicator {#bennet} + +**Description**: The data presented here are changes in revenue ($ real) split into a price indicator and a volume indicator. The sum of the price and the volume indicator is equal to the revenue change relative to a base year, which is 1982. + +**Indicator family**: + +- [X] Social +- [X] Economic + + +**Contributor(s)**: John Walden, Geret DePiper, Sean Lucey + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The Bennet (1920) indicator (BI) was first used to show how a change in social welfare could be decomposed into a sum of a price and quantity change indicator (@cross_value_2009). It is called an indicator because it is based on differences in value between time periods, rather than ratios, which are referred to as indices. The BI is the indicator equivalent of the more popular Fisher index (@balk_assumption-free_2010), and has been used to examine revenue changes in Swedish pharmacies, productivity change in U.S. railroads (@lim_profit_2009), and dividend changes in banking operations @grifell-tatje_decomposing_2004. An attractive feature of the BI is that the overall indicator is equal to the sum of its subcomponents @balk_assumption-free_2010. This allows us to examine whether increasing (decreasing) volumes, increasing (decreasing) prices, or some combination of the two is responsible for revenue change between time periods. The volume and price indicators can be further decomposed allowing us to examine the extent to which changing quantities or prices of each output is driving revenue change. + +Revenue in an EPU in a given year is the product of quantities landed and prices received from all species groups (or guilds) within the EPU. The change in revenue between any two time points can be decomposed into a volume indicator (VI) and a price indicator (PI). + +The overall BI is the sum of the VI and PI, and is equal to the overall revenue change. Since revenue change is being driven by changes in the individual prices and quantities landed of each species group within an EPU, changes at the EPU level can be examined separately by taking advantage of the additive property of the indicator. For example, if there are five different species groups, the sum of each group’s VI will equal the overall EPU VI, and likewise, the sum of the PI for each group will equal the overall PI for the EPU. + +## Key Results and Visualizations +Georges Bank revenue has exceeded 1982 levels in only two years (1990 and 2004), and this finding is due mainly to lower volumes (VI) prior to 2004, and lower prices (PI) after 2004. Breaking down the VI and PI further for Georges Bank shows that the negative VI is primarily due to changes in the Benthivore and Piscivore categories. Since 2010, there have been large positive gains in volumes for the Benthos category, leading to positive gains in the overall VI during some years. This is likely reflecting improvements in scallop harvests since the area rotation system has been implemented. The PI for Georges Bank shows a generally negative trend, particularly since 2004. Prices for the Benthivores contributed positively to the PI, but declines in the PI for the benthos category were the greatest contributor to the negative PI. All prices are adjusted for inflation, and positive changes means that prices are increasing faster than inflation, while negative prices means that they are not keeping pace with inflation. +Gulf of Maine revenue has generally exceeded 1982 levels except for very short time periods, the last being 2004 and 2006. Generally, revenue has been higher due to increasing prices (PI). Since 2010, both increasing volumes (VI) and prices (PI) have contributed to positive revenue change. The VI indicator showed a cyclical type of pattern between 1982 and 2022 with the VI being mostly influenced by the Benthivore and Benthos categories. After 2010, the Benthivore category was the biggest influence on the VI. This was likely due to increases in lobster landings. The PI was positive for the entire time period, and this was due to increase in the Benthivore category. The Piscivore and “Other” categories showed positive prices throughout the time-period, while the Benthos category was generally negative before turning positive after 2015. +In the Mid-Atlantic region revenue was positive compared to 1982 levels for all years in the series. This was due to increases in both volumes (VI) and Prices (PI). Prices were higher than 1982 levels with the exception of four years prior to 2000. Examination of the VI in more detail showed positive contributions to revenue gains from all categories peaking in 2003, followed by declines in subsequent years. The PI showed a positive trend for most of the years in the time series with the indicator increasing from 1999 through 2016, before declining. The PI gain after 1999 was mainly due to the Benthos category. + +### MidAtlantic + +```{r plot_bennetMidAtlanticguildMAB} +# Plot indicator +ggplotObject <- ecodata::plot_bennet(report= 'MidAtlantic', varName= 'guild' ,EPU= 'MAB') +ggplotObject +``` + +```{r plot_bennetMidAtlantictotalMAB} +# Plot indicator +ggplotObject <- ecodata::plot_bennet(report= 'MidAtlantic', varName= 'total' ,EPU= 'MAB') +ggplotObject +``` + +```{r plot_bennetMidAtlantictotal_guildMAB} +# Plot indicator +ggplotObject <- ecodata::plot_bennet(report= 'MidAtlantic', varName= 'total_guild' ,EPU= 'MAB') +ggplotObject +``` + +### NewEngland + +```{r plot_bennetNewEnglandguildGB} +# Plot indicator +ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'guild' ,EPU= 'GB') +ggplotObject +``` + +```{r plot_bennetNewEnglandguildGOM} +# Plot indicator +ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'guild' ,EPU= 'GOM') +ggplotObject +``` + +```{r plot_bennetNewEnglandtotalGB} +# Plot indicator +ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total' ,EPU= 'GB') +ggplotObject +``` + +```{r plot_bennetNewEnglandtotalGOM} +# Plot indicator +ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total' ,EPU= 'GOM') +ggplotObject +``` + +```{r plot_bennetNewEnglandtotal_guildGB} +# Plot indicator +ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total_guild' ,EPU= 'GB') +ggplotObject +``` + +```{r plot_bennetNewEnglandtotal_guildGOM} +# Plot indicator +ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total_guild' ,EPU= 'GOM') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: The BI, PI and VI are calculated for the GB, GOM and MAB EPU separately + +Temporal scale: The BI, PI and VI are all calculated on an Annual Basis. + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts +- [X] Ecosystem Reorganization + + +```{r autostats_bennet} +# Either from Contributor or ecodata +``` + +## Implications +It is troubling that the revenue from Georges Bank has only exceeded 1982 levels in two years of a 40 year time-series. In the first 20 years of the series, this was due to declining volumes, while in the later part of the series it was due to both declining volumes and prices compared to 1982. In terms of volumes, the positive gains in the Benthos category was offset by declines in the Benthivore category. This reflects a shift to an EPU whose value is being generated from shellfish rather than finfish. +Since 2007 increases in the Gulf of Maine VI for the Benthivore category has increased substantially, and has been the biggest contributor to the positive VI. Along with this gain, the PI for the Benthivore category has been positive for the entire time period. The increase in the Benthivore VI is thought to have been caused by increasing lobster harvests in the Gulf of Maine. Positive prices and volumes for this category contributed to the overall revenue gain seen, particularly after 2007. +For the Gulf of Maine and Georges Bank region, the decomposed VI shows that these two regions are becoming more reliant on shellfish for their landings, which likely increases risk for revenue losses if these volumes can’t be sustained. The overall BI for Gorges Bank was negative for all but two years in the time series, which is troubling because it shows that except for two years this particular EPU never achieved the same level of performance in terms of revenue as in 1982. +Although the Mid-Atlantic region showed declining revenue trends since 2016, revenue from harvested species is still greater than 1982 levels. The decline in revenue since 2016 is due to both declining prices and volumes. Declining volumes were due to the Benthos, Benthivore and “Other” category, while declining prices were due to the Benthos category. The declining Benthos category may be partially caused by decreases in surfclam and ocean quahogs in the southern part of their range as harvest have shifted northward. + +## Get the data + +**Point of contact**: [John Walden (john.walden@noaa.gov); Geret DePiper (geret.depiper@noaa.gov)](mailto:John Walden (john.walden@noaa.gov); Geret DePiper (geret.depiper@noaa.gov)){.email} + +**ecodata name**: `ecodata::bennet` + +**Variable definitions** + +1) Volume Indicator (VI): Change in Revenue compared to the base time period in deflated dollars attributed to changes in landings for harvested species (volumes). +2) Price Indicator (PI): Change in Revenue compared to the base time period in deflated dollars attributed to changes in prices for species harvested. +3) Bennet Indicator (BI): The Sum of a Volume Indicator (VI) and Price Indicator (PI) in inflation adjusted dollars, which is equal to the change in revenue. + +```{r vars_bennet} +# Pull all var names +vars <- ecodata::bennet |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Please email john.walden@noaa.gov for further information and queries of Bennet Indicator source data. + +**tech-doc link** + + diff --git a/chapters/bottom_temp.rmd b/chapters/bottom_temp.rmd index 5447647a..48ffa88a 100644 --- a/chapters/bottom_temp.rmd +++ b/chapters/bottom_temp.rmd @@ -1,91 +1,91 @@ -# Bottom Temperature - in situ {#bottom_temp} - -**Description**: The data presented here are time series of regional average bottom temperature anomalies from ship-based measurements made on the Northeast Continental Shelf. - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Paula Fratantoni; Chris Melrose; Tamara Holzwarth-Davis - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The bottom temperature index incorporates near-bottom temperature measurements collected on Northeast Fisheries Science Center (NEFSC) surveys between 1977-present. Early measurements were made using surface bucket samples, mechanical bathythermographs and expendable bathythermograph probes, but by 1991 the CTD – an acronym for conductivity temperature and depth – became standard equipment on all NEFSC surveys. Near-bottom refers to the deepest observation at each station that falls within 10 m of the reported water depth. Observations encompass the entire continental shelf area extending from Cape Hatteras, NC to Nova Scotia, Canada, inclusive of the Gulf of Maine and Georges Bank. - -## Key Results and Visualizations -_No response_ - -### MAB - -```{r plot_bottom_tempMAB} -# Plot indicator -ggplotObject <- ecodata::plot_bottom_temp(report='MidAtlantic') -ggplotObject -``` - -### NE - -```{r plot_bottom_tempNE} -# Plot indicator -ggplotObject <- ecodata::plot_bottom_temp(report='NewEngland') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: by EPU - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_bottom_temp} -# Either from Contributor or ecodata -``` - -## Implications -_No response_ - -## Get the data - -**Point of contact**: [Paula.Fratantoni@noaa.gov](mailto:Paula.Fratantoni@noaa.gov){.email} - -**ecodata name**: `ecodata::bottom_temp` - -**Variable definitions** - -Tbot_anom; Definition: Bottom temperature anomaly; Units: degree Celsius - -```{r vars_bottom_temp} -# Pull all var names -vars <- ecodata::bottom_temp |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Extensive analysis, not yet published - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Bottom Temperature - in situ {#bottom_temp} + +**Description**: The data presented here are time series of regional average bottom temperature anomalies from ship-based measurements made on the Northeast Continental Shelf. + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Paula Fratantoni; Chris Melrose; Tamara Holzwarth-Davis + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The bottom temperature index incorporates near-bottom temperature measurements collected on Northeast Fisheries Science Center (NEFSC) surveys between 1977-present. Early measurements were made using surface bucket samples, mechanical bathythermographs and expendable bathythermograph probes, but by 1991 the CTD – an acronym for conductivity temperature and depth – became standard equipment on all NEFSC surveys. Near-bottom refers to the deepest observation at each station that falls within 10 m of the reported water depth. Observations encompass the entire continental shelf area extending from Cape Hatteras, NC to Nova Scotia, Canada, inclusive of the Gulf of Maine and Georges Bank. + +## Key Results and Visualizations +_No response_ + +### MAB + +```{r plot_bottom_tempMAB} +# Plot indicator +ggplotObject <- ecodata::plot_bottom_temp(report='MidAtlantic') +ggplotObject +``` + +### NE + +```{r plot_bottom_tempNE} +# Plot indicator +ggplotObject <- ecodata::plot_bottom_temp(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: by EPU + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_bottom_temp} +# Either from Contributor or ecodata +``` + +## Implications +_No response_ + +## Get the data + +**Point of contact**: [Paula.Fratantoni@noaa.gov](mailto:Paula.Fratantoni@noaa.gov){.email} + +**ecodata name**: `ecodata::bottom_temp` + +**Variable definitions** + +Tbot_anom; Definition: Bottom temperature anomaly; Units: degree Celsius + +```{r vars_bottom_temp} +# Pull all var names +vars <- ecodata::bottom_temp |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Extensive analysis, not yet published + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/bottom_temp_comp.rmd b/chapters/bottom_temp_comp.rmd index 87b3cb2c..fe4daa2a 100644 --- a/chapters/bottom_temp_comp.rmd +++ b/chapters/bottom_temp_comp.rmd @@ -1,124 +1,122 @@ -# Bottom temperature - Seasonal Anomaly {#bottom_temp_comp} - -**Description**: The data are seasonal bottom temperature anomaly time series for each EPU - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Joseph Caracappa, Hubert du Pontavice, Vincent Saba, Zhuomin Chen - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The raw bottom temperature product is in a horizontal 1/12 degree grid between 1959 and 2023 and is made of daily bottom temperature estimates from: - -Bias-corrected ROMS-NWA (ROMScor) between 1959 and 1992 which was regridded in the same 1/12degree grid as GLORYS using bilinear interpolation; GLORYS12v1 in its original 1/12 degree grid between 1993 and 2023-08-29; and PSY in it's 1/12 degree grid from 2023-08-29 to 2023-12-31. - -Anomalies are calculated using the 1990-2020 reference period. - -## Key Results and Visualizations -Time series plots for seasonal bottom temperature anomaly for each EPU shows a long-term warming trend. PSY forecasted data also included for comparison and indicates varying skill in predicting regional bottom temperature. - -### MidAtlantic - -```{r plot_bottom_temp_compMidAtlanticseasonalMAB} -# Plot indicator -ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'MidAtlantic', varName= 'seasonal' ,EPU= 'MAB') -ggplotObject -``` - -```{r plot_bottom_temp_compMidAtlanticannualMAB} -# Plot indicator -ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'MidAtlantic', varName= 'annual' ,EPU= 'MAB') -ggplotObject -``` - -### NewEngland - -```{r plot_bottom_temp_compNewEnglandseasonalGB} -# Plot indicator -ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GB') -ggplotObject -``` - -```{r plot_bottom_temp_compNewEnglandseasonalGOM} -# Plot indicator -ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GOM') -ggplotObject -``` - -```{r plot_bottom_temp_compNewEnglandannualGB} -# Plot indicator -ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'annual' ,EPU= 'GB') -ggplotObject -``` - -```{r plot_bottom_temp_compNewEnglandannualGOM} -# Plot indicator -ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'annual' ,EPU= 'GOM') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: EPU - -Temporal scale: annual-seasonal - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_bottom_temp_comp} -# Either from Contributor or ecodata -``` - -## Implications -Bottom temperature is an important driver for benthic and demersal species growth, metabolism, and reproduction. Changes in seasonal bottom temperature have implications for species' phenology - -## Get the data - -**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} - -**ecodata name**: `ecodata::bottom_temp_comp` - -**Variable definitions** - -Season: 1 = winter (January – March), 2 = spring (April – June), 3 = summer (July – September), 4 = fall (October – December) -Subarea: EPU name -Source: ROMS (bias-corrected ROMS-NWA bottom temperature [@dupontavice_ocean_2022]), GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature), PSY (CMEM’s PSY global forecast bottom temperature) -bt_temp : mean bottom temperature for each year/season across entire EPU -ref_bt: bottom temperature climatology for season/EPU based on 1990-2020 - -```{r vars_bottom_temp_comp} -# Pull all var names -vars <- ecodata::bottom_temp_comp |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Bottom temperature - Seasonal Anomaly {#bottom_temp_comp} + +**Description**: The data are seasonal bottom temperature anomaly time series for each EPU + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Joseph Caracappa, Hubert du Pontavice, Vincent Saba, Zhuomin Chen + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The raw bottom temperature product is in a horizontal 1/12 degree grid between 1959 and 2023 and is made of daily bottom temperature estimates from: + +Bias-corrected ROMS-NWA (ROMScor) between 1959 and 1992 which was regridded in the same 1/12degree grid as GLORYS using bilinear interpolation; GLORYS12v1 in its original 1/12 degree grid between 1993 and 2023-08-29; and PSY in it's 1/12 degree grid from 2023-08-29 to 2023-12-31. + +Anomalies are calculated using the 1990-2020 reference period. + +## Key Results and Visualizations +Time series plots for seasonal bottom temperature anomaly for each EPU shows a long-term warming trend. PSY forecasted data also included for comparison and indicates varying skill in predicting regional bottom temperature. + +### MidAtlantic + +```{r plot_bottom_temp_compMidAtlanticseasonalMAB} +# Plot indicator +ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'MidAtlantic', varName= 'seasonal' ,EPU= 'MAB') +ggplotObject +``` + +```{r plot_bottom_temp_compMidAtlanticannualMAB} +# Plot indicator +ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'MidAtlantic', varName= 'annual' ,EPU= 'MAB') +ggplotObject +``` + +### NewEngland + +```{r plot_bottom_temp_compNewEnglandseasonalGB} +# Plot indicator +ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GB') +ggplotObject +``` + +```{r plot_bottom_temp_compNewEnglandseasonalGOM} +# Plot indicator +ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GOM') +ggplotObject +``` + +```{r plot_bottom_temp_compNewEnglandannualGB} +# Plot indicator +ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'annual' ,EPU= 'GB') +ggplotObject +``` + +```{r plot_bottom_temp_compNewEnglandannualGOM} +# Plot indicator +ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'annual' ,EPU= 'GOM') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: EPU + +Temporal scale: annual-seasonal + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_bottom_temp_comp} +# Either from Contributor or ecodata +``` + +## Implications +Bottom temperature is an important driver for benthic and demersal species growth, metabolism, and reproduction. Changes in seasonal bottom temperature have implications for species' phenology + +## Get the data + +**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} + +**ecodata name**: `ecodata::bottom_temp_comp` + +**Variable definitions** + +Season: 1 = winter (January – March), 2 = spring (April – June), 3 = summer (July – September), 4 = fall (October – December) Subarea: EPU name +Source: ROMS (bias-corrected ROMS-NWA bottom temperature [@dupontavice_ocean_2022]), GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature), PSY (CMEM’s PSY global forecast bottom temperature) +bt_temp : mean bottom temperature for each year/season across entire EPU ref_bt: bottom temperature climatology for season/EPU based on 1990-2020 + +```{r vars_bottom_temp_comp} +# Pull all var names +vars <- ecodata::bottom_temp_comp |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/bottom_temp_seasonal_gridded.rmd b/chapters/bottom_temp_seasonal_gridded.rmd index 074807aa..976a4d41 100644 --- a/chapters/bottom_temp_seasonal_gridded.rmd +++ b/chapters/bottom_temp_seasonal_gridded.rmd @@ -1,85 +1,85 @@ -# Bottom temperature - Seasonal Gridded {#bottom_temp_seasonal_gridded} - -**Description**: Seasonal mean bottom temperatures on the Northeast Continental Shelf between 1959 and 2023 in a 1/12° grid. - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Joseph Caracappa, Hubert du Pontavice, Vincent Saba, Zhuomin Chen - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The bottom temperature product is in a horizontal 1/12 degree grid between 1959 and 2022 and is made of daily bottom temperature estimates from: - -Bias-corrected ROMS-NWA between 1959 and 1992 which was regridded in the same 1/12degree grid as GLORYS using bilinear interpolation; Years 1993 through summer 2023 are from CMEMS GLORYS12V1 global reanalysis bottom temperature, and fall 2023 is from CMEMS PSY forecasting product. - -## Key Results and Visualizations -Maps of seasonal mean bottom temperature across NE shelf. - -```{r plot_bottom_temp_seasonal_griddedMAB} -# Plot indicator -ggplotObject <- ecodata::plot_bottom_temp_seasonal_gridded(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Whole shelf - -Temporal scale: Winter (Jan-Mar), Spring (April-June), Summer (July-Sept), Fall (Oct - Dec) from 1959-2023 - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_bottom_temp_seasonal_gridded} -# Either from Contributor or ecodata -``` - -## Implications -Bottom temperature is a key environmental parameter in defining the habitat and metabolic conditions of demersal and benthic species. Interannual and seasonal changes in bottom temperature can provide significant indicators of species productivity, spatial distributions, or mortality. Long-term trends in bottom temperature are indicators of regional implications of global climate change and may be used in evaluating climate risk for fisheries management. - -## Get the data - -**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} - -**ecodata name**: `ecodata::bottom_temp_seasonal_gridded` - -**Variable definitions** - -- Time: year - Lat: latitude - Lon: longitude - Variable: season - Value: bottom temperature (degrees Celcius) - -```{r vars_bottom_temp_seasonal_gridded} -# Pull all var names -vars <- ecodata::bottom_temp_seasonal_gridded |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Syntheses of published information -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Bottom temperature - Seasonal Gridded {#bottom_temp_seasonal_gridded} + +**Description**: Seasonal mean bottom temperatures on the Northeast Continental Shelf between 1959 and 2023 in a 1/12° grid. + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Joseph Caracappa, Hubert du Pontavice, Vincent Saba, Zhuomin Chen + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The bottom temperature product is in a horizontal 1/12 degree grid between 1959 and 2022 and is made of daily bottom temperature estimates from: + +Bias-corrected ROMS-NWA between 1959 and 1992 which was regridded in the same 1/12degree grid as GLORYS using bilinear interpolation; Years 1993 through summer 2023 are from CMEMS GLORYS12V1 global reanalysis bottom temperature, and fall 2023 is from CMEMS PSY forecasting product. + +## Key Results and Visualizations +Maps of seasonal mean bottom temperature across NE shelf. + +```{r plot_bottom_temp_seasonal_griddedMAB} +# Plot indicator +ggplotObject <- ecodata::plot_bottom_temp_seasonal_gridded(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Whole shelf + +Temporal scale: Winter (Jan-Mar), Spring (April-June), Summer (July-Sept), Fall (Oct - Dec) from 1959-2023 + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_bottom_temp_seasonal_gridded} +# Either from Contributor or ecodata +``` + +## Implications +Bottom temperature is a key environmental parameter in defining the habitat and metabolic conditions of demersal and benthic species. Interannual and seasonal changes in bottom temperature can provide significant indicators of species productivity, spatial distributions, or mortality. Long-term trends in bottom temperature are indicators of regional implications of global climate change and may be used in evaluating climate risk for fisheries management. + +## Get the data + +**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} + +**ecodata name**: `ecodata::bottom_temp_seasonal_gridded` + +**Variable definitions** + +- Time: year - Lat: latitude - Lon: longitude - Variable: season - Value: bottom temperature (degrees Celcius) + +```{r vars_bottom_temp_seasonal_gridded} +# Pull all var names +vars <- ecodata::bottom_temp_seasonal_gridded |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Syntheses of published information +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/calanus_variation.rmd b/chapters/calanus_variation.rmd index a3bb60db..bda36699 100644 --- a/chapters/calanus_variation.rmd +++ b/chapters/calanus_variation.rmd @@ -1,97 +1,97 @@ -# Seasonal Variation of Calanus finmarchicus {#calanus_variation} - -**Description**: Abundance of late copepodid stages of the planktonic copepod, Calanus finmarchicus, measured during seasonal surveys between 1977 and 2019. Data from NOAA EcoMon/MARMAP program - -**Indicator family**: - -- [X] Lower trophic levels - - -**Contributor(s)**: Rubao Ji, Jeffrey Runge, NOAA EcoMon Survey Team - -**Affiliations**: UMS - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Mesozooplankton (i.e. copepods), grazing on phytoplankton and smaller zooplankton, are the foundation of production for higher trophic levels. Early life stage copepods are the primary food source for fish larvae and variability in their abundance may contribute to fish recruitment success. Lipid-rich older copepods, most notably Calanus finmarchicus, are a primary food source for forage species (including herring, sand lance and krill) and the North Atlantic right whale. The seasonal abundance of C. finmarchicus (Fig. xx1) is an indicator of seasonality in mesozooplankton biomass. - -Older stages of mesozooplankton nourish and influence condition of forage fish such as Atlantic herring, sand lance and Atlantic mackerel in the GOM. In deeper waters of the GOM (>100m), the net-captured mesozooplankton biomass is dominated by the planktonic copepod, Calanus finmarchicus, a signature species of the subarctic ecosystem spanning the North Atlantic between the GOM in the south and west to the Norwegian and Barents Seas in the north and east. Supremely adapted to the seasonality of the subarctic North Atlantic, C. finmarchicus reproduces in late winter/spring when chlorophyll a concentrations are > 0.5 µg liter-1 and its early life stages develop and grow throughout the spring phytoplankton bloom into summer, at which point its last preadult stage (stage CV) descend to deeper water and undergoes a diapause during fall and early winter. The diapausing CV emerge from diapause in later winter, molt into adults, and the cycle starts over. Late winter-spring reproduction, especially influenced by the availability of phytoplankton, results in a peak in abundance of earlier life stages in early summer. The decline in abundance of the older stages later in the season is especially influenced by upstream supply of individuals into the western GOM and by the abundance of both visual (e.g. herring) and invertebrate, non-visual predators (e.g. jellyfish, krill and carnivorous copepods like Paraeuchaeta norvegica). A key feature of the Calanus life cycle is the accumulation of energy rich lipids, which the growing copepod acquires by grazing on lipid-manufacturing phytoplankton and then stores in an oil sac that attains its maximum volume, over half of its total body mass, during the preadult CV stage. The strong seasonality in Calanus abundance and in its drivers warrants consideration of seasonal indices (e.g. spring, summer, fall-winter) rather that an annual index, which would obscure interpretation of change in abundance patterns and linkage to higher trophic levels. - -Historically, the high abundance of C. finmarchicus in the GOM combined with the size of its older developmental stages, which are considerably larger than other planktonic copepod species, results in the dominance of this species in the biomass of net captured mesozooplankton in the GOM, such that the seasonal cycle of net-captured mesozooplankton biomass reflects the seasonal cycle of C. finmarchicus abundance. - - -This phenology indicator shows the change in abundance of the planktonic copepod, Calanus finmarchicus over a mean annual cycle in Wilkinson Basin, the primary overwintering habitat of this species in the western Gulf of Maine. The data are provided by the NOAA EcoMon/MARMAP survey, which has sampled stations along the Northeast U.S. Shelf, including the Gulf of Maine, seasonally (2-6 times per year) in nearly all years since 1977. The 333 µm mesh plankton nets used by the survey quantitatively capture only the late copepodid stages (C3-adult) of C. finmarchicus, but these stages nevertheless are representative of the seasonal variation in abundance of the population. This indicator serves as a baseline that can be used to interpret future changes in wGoM C. finmarchicus abundance. - -## Key Results and Visualizations -Seasonal abundance (number m-3) of C. finmarchicus late copepodid stages (mostly stages CIII-CVI) in Wilkinson Basin. X-axis represents time of year, from 1 January (yearday 0) to 31 December (yearday 365). Background gray circles show individual MARMAP/EcoMon abundance data points in Wilkinson Basin between 1977-2019. Solid black line shows the seasonal pattern in mean abundance from the MARMAP/EcoMon data; dotted lines show 2x (top) and ½ (bottom) of the mean abundance. Colored horizontal lines show conceptual model of seasonally variable predominant drivers. Predominant drivers in winter (Jan-Mar: days 1-100) suggested to be a combination of predation mortality and advective loss. - -The abundance of late stage Calanus finmarchicus in the western Gulf of Maine is seasonally variable. The highest abundances are observed in May-June, the result of reproduction, the magnitude of which depends on the timing of food availability to females (Stage CVI) in late-winter through spring. By late summer, most of the C. finmarchicus population is present as Stage CV, which overwinters at depth in a dormant state. The number of stage CV and hence the overall population abundance dwindles depending on net losses from advection and vertebrate and invertebrate predators. The abundance reaches its nadir in February-March, when the population is in stage CV or newly molted adult females and males. Note the difference between the late winter and late spring mean abundances is about three orders of magnitude. - - -## Indicator statistics -Spatial scale: Data from Wilkinson Basin, defined as the area under which bottom depth is >200m in the western Gulf of Maine. - -Temporal scale: NOAA EcoMon/MARMAP data between 1977 and 2019, collected during seasonal surveys in spring, summer, fall and winter, with particular emphasis on spring (April-May) and fall (September-November) time periods. - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts -- [X] Ecosystem Reorganization - - -```{r autostats_calanus_variation} -# Either from Contributor or ecodata -``` - -## Implications -This is the first of several indicators of trends in Gulf of Maine mesozooplankton abundance and diversity that the NERACOOS MBON (J. Runge/ C. Thompson/ L. Karp-Boss) and NE Shelf LTER (R. Ji) intend to submit to the NOAA SOE process over the next several years. The mean phenology of Calanus finmarchicus based on the NOAA EcoMon/MARMAP data is a starting point, as it provides a baseline from which future change in C. finmarchicus abundance can be interpreted. We have found that it is important to take into account the strong seasonality in abundance associated with the Calanus life cycle (winter spring reproduction, overwintering in the lipid rich late preadult stage in late summer through mid winter) in order to understand abundance trends. The drivers controlling Calanus abundance vary with season and may be synegistic or counteracting in their influence on Calanus abundance, such that an annual index of abundance may mask ecologically important trends. For example, observations from the NERACOOS fixed station in Wilkinson Basin (WBTS) indicate an early timing of food availability in late-winter matching emergence of females from dormancy and resulting in higher spring abundances since 2010. However, advective supply of older copepodid stages into Wilkinson Basin in summer is lower, by as much as 70%, since 2010, reflecting changes in external supply of Calanus into the Gulf of Maine (@record_rapid_2019; @meyer-gutbrod_ocean_2021) and perhaps also increased predation in the Maine Coastal Current, the proximal source of supply, in summer, associated with higher surface layer temperatures (@ji_drivers_2022; @pershing_decadal_2023). -Below are notes submitted to SOE members in November, 2023. The results and discussion are based on a final report submitted by J. Runge and coauthors in fulfillment of an award from BOEM supporting NERACOOS ISMN-MBON plankton observations at two fixed stations, the Coastal Maine Time Series (established 2008, located in mid-coast Maine at the western margin of the Maine Coastal Current) and the Wilkinson Basin Time Series (established 2005, located in the northwest corner of Wilkinson Basin) Stations. We are in the process of analyzing and archiving data for submission to primary journals in 2024, and expect these data to be vetted and available for the SOE process in 2025. - -NOTES: -A NERACOOS-ISMN Marine Biodiversity Observation Network report: Status of the zooplankton in the Gulf of Maine 2023 with focus on Calanus finmarchicus as a sentinel indicator - -Implications for interpretation of zooplankton data -o Copepods are the most abundant taxon in the mesozooplankton captured with 200-333µm mesh nets used in the AZMP, ISMN MBON and EcoMon surveys. -o In NW Atlantic, Calanus species dominate the mesozooplankton biomass in waters deeper than 70-100 m ([@casault_optical_2022]; Johnson et al. NW Atlantic Zooplankton Atlas, in prep.) -o The Calanus species found in the Gulf of Maine is Calanus finmarchicus. Apart from Paraeucheata norvegica, a very large carnivorous copepod, C. finmarchicus older stages are considerably larger than other copepods species found in the Gulf of Maine. -o C. finmarchicus is supremely adapted to the subarctic North Atlantic. It ingests lipids manufactured by primary producers (especially diatoms) and accumulates them in an oil sac that is used to overwinter in stage CV, the last preadult stage. Most of the zooplankton lipidscape in the Gulf of Maine is in the Calanus late stages, available May-Dec (approx.) -o In the decade since the shift in oceanographic conditions that occurred around 2010, C. finmarchicus abundance has declined to 30-40% of its 2005-2008 level in summer-fall in Wilkinson Basin, the center of C. finmarchicus abundance in the Gulf of Maine ([@runge_sustained_2023]). The primary driver of this decline is hypothesized to be a shift in supply of water into the Gulf of Maine starting around 2010, from relatively Calanus rich Scotian Shelf water to relatively Calanus poor Atlantic Temperate Water adjacent to the Gulf Stream. In addition the Calanus abundance on the Scotian Shelf has declined since 2010 [@casault_optical_2022]. The Calanus seed stock immigrating into the Gulf of Maine is amplified in the Maine Coastal Current, mitigating the reduction in supply, but there is nevertheless a reduction in abundance of the overwintering stock in Wilkinson Basin that is likely also negatively impacting by invertebrate and vertebrate predators. -o While summer-winter abundance of C. finmarchicus has declined since 2010, its abundance in spring has not declined ([@runge_sustained_2023]). Abundances were higher in the six years after 2010, before decreasing to pre-2010 levels in the early 2020s. The driver of spring abundance despite a lower overwintering stock is hypothesized to be increased food availability in late winter spring, driving higher reproductive rates generating the spring cohort. -o In the period between 2011-2017, after the 2010 oceanographic shift, abundances of many other copepod species have increased (Dullaert et al. in prep), including Centropages typicus, a fall dominant and Pseudocalanus spp., both of which are also prey for North Atlantic right whales in the western Gulf of Maine in spring, before the appearance of abundant lipid-rich late stage Calanus. The likely driver has been the increase in phytoplankton biomass (as measured by Chl. a concentration) in fall and winter and well as increased temperature driving higher population growth rates. -o In the period between 2011 and 2017, abundance number of zooplankton taxa other than copepoda also increased (@dullaert_response_2023), including Mollusca (pteropods), Ctenophora, Cheatognatha and siphonophores (from EcoMon and ISMN MBON data). The latter three taxa are predators on Calanus and other copepods. -o Despite increases in zooplankton other than Calanus, the total mesozooplankton biomass captured in ring net tows has declined, reflecting the predominance of C. finmarchicus. The decline in C. finmarchicus abundance and consequently zooplankton biomass represents lower availability of energy rich lipids to higher trophic levels, since there is no equivalent replacement to C. finmarchicus in the subarctic GoM food web ([@runge_sustained_2023]). -o Declines in forage fish (herring condition, sand lance recruitment and NARW foraging) would be expected to follow the declining trend in C. finmarchicus (e.g. @suca_sensitivity_2021) -o For these reasons, the seasonal indices in Calanus finmarchicus and other zooplankton abundance and biomass are important to track, as are the seasonal drivers: warming trend (surface and bottom waters), sources of external supply, predators and availability of phytoplankton food in late winter. Shifting abundances in zooplankton taxa have been observed in previous decades in the EcoMon survey (e.g. [@pershing_interdecadal_2005]; [@grieve_projecting_2017]). There has been no radical regime shift (e.g. a speculation that lipid-rich menhaden replace Calanus, with consequences for the structure of the higher trophic levels), but given the trend in increasing CO2 and ocean temperatures, close observation of change in the lower trophic levels in the GoM is warranted. - -## Get the data - -**Point of contact**: [Jeffrey Runge (jeffrey.runge@maine.edu)](mailto:Jeffrey Runge (jeffrey.runge@maine.edu)){.email} - -**ecodata name**: No dataset - -**Variable definitions** - -Calanus finmarchicus abundance from 333 um mesh bongo tows; Units: number m^-3 - - -No Data - -**Indicator Category**: - -- [X] Published Methods -- [X] Extensive analysis, not yet published -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - +# Seasonal Variation of Calanus finmarchicus {#calanus_variation} + +**Description**: Abundance of late copepodid stages of the planktonic copepod, Calanus finmarchicus, measured during seasonal surveys between 1977 and 2019. Data from NOAA EcoMon/MARMAP program + +**Indicator family**: + +- [X] Lower trophic levels + + +**Contributor(s)**: Rubao Ji, Jeffrey Runge, NOAA EcoMon Survey Team + +**Affiliations**: UMS + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Mesozooplankton (i.e. copepods), grazing on phytoplankton and smaller zooplankton, are the foundation of production for higher trophic levels. Early life stage copepods are the primary food source for fish larvae and variability in their abundance may contribute to fish recruitment success. Lipid-rich older copepods, most notably Calanus finmarchicus, are a primary food source for forage species (including herring, sand lance and krill) and the North Atlantic right whale. The seasonal abundance of C. finmarchicus (Fig. xx1) is an indicator of seasonality in mesozooplankton biomass. + +Older stages of mesozooplankton nourish and influence condition of forage fish such as Atlantic herring, sand lance and Atlantic mackerel in the GOM. In deeper waters of the GOM (>100m), the net-captured mesozooplankton biomass is dominated by the planktonic copepod, Calanus finmarchicus, a signature species of the subarctic ecosystem spanning the North Atlantic between the GOM in the south and west to the Norwegian and Barents Seas in the north and east. Supremely adapted to the seasonality of the subarctic North Atlantic, C. finmarchicus reproduces in late winter/spring when chlorophyll a concentrations are > 0.5 µg liter-1 and its early life stages develop and grow throughout the spring phytoplankton bloom into summer, at which point its last preadult stage (stage CV) descend to deeper water and undergoes a diapause during fall and early winter. The diapausing CV emerge from diapause in later winter, molt into adults, and the cycle starts over. Late winter-spring reproduction, especially influenced by the availability of phytoplankton, results in a peak in abundance of earlier life stages in early summer. The decline in abundance of the older stages later in the season is especially influenced by upstream supply of individuals into the western GOM and by the abundance of both visual (e.g. herring) and invertebrate, non-visual predators (e.g. jellyfish, krill and carnivorous copepods like Paraeuchaeta norvegica). A key feature of the Calanus life cycle is the accumulation of energy rich lipids, which the growing copepod acquires by grazing on lipid-manufacturing phytoplankton and then stores in an oil sac that attains its maximum volume, over half of its total body mass, during the preadult CV stage. The strong seasonality in Calanus abundance and in its drivers warrants consideration of seasonal indices (e.g. spring, summer, fall-winter) rather that an annual index, which would obscure interpretation of change in abundance patterns and linkage to higher trophic levels. + +Historically, the high abundance of C. finmarchicus in the GOM combined with the size of its older developmental stages, which are considerably larger than other planktonic copepod species, results in the dominance of this species in the biomass of net captured mesozooplankton in the GOM, such that the seasonal cycle of net-captured mesozooplankton biomass reflects the seasonal cycle of C. finmarchicus abundance. + + +This phenology indicator shows the change in abundance of the planktonic copepod, Calanus finmarchicus over a mean annual cycle in Wilkinson Basin, the primary overwintering habitat of this species in the western Gulf of Maine. The data are provided by the NOAA EcoMon/MARMAP survey, which has sampled stations along the Northeast U.S. Shelf, including the Gulf of Maine, seasonally (2-6 times per year) in nearly all years since 1977. The 333 µm mesh plankton nets used by the survey quantitatively capture only the late copepodid stages (C3-adult) of C. finmarchicus, but these stages nevertheless are representative of the seasonal variation in abundance of the population. This indicator serves as a baseline that can be used to interpret future changes in wGoM C. finmarchicus abundance. + +## Key Results and Visualizations +Seasonal abundance (number m-3) of C. finmarchicus late copepodid stages (mostly stages CIII-CVI) in Wilkinson Basin. X-axis represents time of year, from 1 January (yearday 0) to 31 December (yearday 365). Background gray circles show individual MARMAP/EcoMon abundance data points in Wilkinson Basin between 1977-2019. Solid black line shows the seasonal pattern in mean abundance from the MARMAP/EcoMon data; dotted lines show 2x (top) and ½ (bottom) of the mean abundance. Colored horizontal lines show conceptual model of seasonally variable predominant drivers. Predominant drivers in winter (Jan-Mar: days 1-100) suggested to be a combination of predation mortality and advective loss. + +The abundance of late stage Calanus finmarchicus in the western Gulf of Maine is seasonally variable. The highest abundances are observed in May-June, the result of reproduction, the magnitude of which depends on the timing of food availability to females (Stage CVI) in late-winter through spring. By late summer, most of the C. finmarchicus population is present as Stage CV, which overwinters at depth in a dormant state. The number of stage CV and hence the overall population abundance dwindles depending on net losses from advection and vertebrate and invertebrate predators. The abundance reaches its nadir in February-March, when the population is in stage CV or newly molted adult females and males. Note the difference between the late winter and late spring mean abundances is about three orders of magnitude. + + +## Indicator statistics +Spatial scale: Data from Wilkinson Basin, defined as the area under which bottom depth is >200m in the western Gulf of Maine. + +Temporal scale: NOAA EcoMon/MARMAP data between 1977 and 2019, collected during seasonal surveys in spring, summer, fall and winter, with particular emphasis on spring (April-May) and fall (September-November) time periods. + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts +- [X] Ecosystem Reorganization + + +```{r autostats_calanus_variation} +# Either from Contributor or ecodata +``` + +## Implications +This is the first of several indicators of trends in Gulf of Maine mesozooplankton abundance and diversity that the NERACOOS MBON (J. Runge/ C. Thompson/ L. Karp-Boss) and NE Shelf LTER (R. Ji) intend to submit to the NOAA SOE process over the next several years. The mean phenology of Calanus finmarchicus based on the NOAA EcoMon/MARMAP data is a starting point, as it provides a baseline from which future change in C. finmarchicus abundance can be interpreted. We have found that it is important to take into account the strong seasonality in abundance associated with the Calanus life cycle (winter spring reproduction, overwintering in the lipid rich late preadult stage in late summer through mid winter) in order to understand abundance trends. The drivers controlling Calanus abundance vary with season and may be synegistic or counteracting in their influence on Calanus abundance, such that an annual index of abundance may mask ecologically important trends. For example, observations from the NERACOOS fixed station in Wilkinson Basin (WBTS) indicate an early timing of food availability in late-winter matching emergence of females from dormancy and resulting in higher spring abundances since 2010. However, advective supply of older copepodid stages into Wilkinson Basin in summer is lower, by as much as 70%, since 2010, reflecting changes in external supply of Calanus into the Gulf of Maine (@record_rapid_2019; @meyer-gutbrod_ocean_2021) and perhaps also increased predation in the Maine Coastal Current, the proximal source of supply, in summer, associated with higher surface layer temperatures (@ji_drivers_2022; @pershing_decadal_2023). +Below are notes submitted to SOE members in November, 2023. The results and discussion are based on a final report submitted by J. Runge and coauthors in fulfillment of an award from BOEM supporting NERACOOS ISMN-MBON plankton observations at two fixed stations, the Coastal Maine Time Series (established 2008, located in mid-coast Maine at the western margin of the Maine Coastal Current) and the Wilkinson Basin Time Series (established 2005, located in the northwest corner of Wilkinson Basin) Stations. We are in the process of analyzing and archiving data for submission to primary journals in 2024, and expect these data to be vetted and available for the SOE process in 2025. + +NOTES: +A NERACOOS-ISMN Marine Biodiversity Observation Network report: Status of the zooplankton in the Gulf of Maine 2023 with focus on Calanus finmarchicus as a sentinel indicator + +Implications for interpretation of zooplankton data +o Copepods are the most abundant taxon in the mesozooplankton captured with 200-333µm mesh nets used in the AZMP, ISMN MBON and EcoMon surveys. +o In NW Atlantic, Calanus species dominate the mesozooplankton biomass in waters deeper than 70-100 m ([@casault_optical_2022]; Johnson et al. NW Atlantic Zooplankton Atlas, in prep.) +o The Calanus species found in the Gulf of Maine is Calanus finmarchicus. Apart from Paraeucheata norvegica, a very large carnivorous copepod, C. finmarchicus older stages are considerably larger than other copepods species found in the Gulf of Maine. +o C. finmarchicus is supremely adapted to the subarctic North Atlantic. It ingests lipids manufactured by primary producers (especially diatoms) and accumulates them in an oil sac that is used to overwinter in stage CV, the last preadult stage. Most of the zooplankton lipidscape in the Gulf of Maine is in the Calanus late stages, available May-Dec (approx.) +o In the decade since the shift in oceanographic conditions that occurred around 2010, C. finmarchicus abundance has declined to 30-40% of its 2005-2008 level in summer-fall in Wilkinson Basin, the center of C. finmarchicus abundance in the Gulf of Maine ([@runge_sustained_2023]). The primary driver of this decline is hypothesized to be a shift in supply of water into the Gulf of Maine starting around 2010, from relatively Calanus rich Scotian Shelf water to relatively Calanus poor Atlantic Temperate Water adjacent to the Gulf Stream. In addition the Calanus abundance on the Scotian Shelf has declined since 2010 [@casault_optical_2022]. The Calanus seed stock immigrating into the Gulf of Maine is amplified in the Maine Coastal Current, mitigating the reduction in supply, but there is nevertheless a reduction in abundance of the overwintering stock in Wilkinson Basin that is likely also negatively impacting by invertebrate and vertebrate predators. +o While summer-winter abundance of C. finmarchicus has declined since 2010, its abundance in spring has not declined ([@runge_sustained_2023]). Abundances were higher in the six years after 2010, before decreasing to pre-2010 levels in the early 2020s. The driver of spring abundance despite a lower overwintering stock is hypothesized to be increased food availability in late winter spring, driving higher reproductive rates generating the spring cohort. +o In the period between 2011-2017, after the 2010 oceanographic shift, abundances of many other copepod species have increased (Dullaert et al. in prep), including Centropages typicus, a fall dominant and Pseudocalanus spp., both of which are also prey for North Atlantic right whales in the western Gulf of Maine in spring, before the appearance of abundant lipid-rich late stage Calanus. The likely driver has been the increase in phytoplankton biomass (as measured by Chl. a concentration) in fall and winter and well as increased temperature driving higher population growth rates. +o In the period between 2011 and 2017, abundance number of zooplankton taxa other than copepoda also increased (@dullaert_response_2023), including Mollusca (pteropods), Ctenophora, Cheatognatha and siphonophores (from EcoMon and ISMN MBON data). The latter three taxa are predators on Calanus and other copepods. +o Despite increases in zooplankton other than Calanus, the total mesozooplankton biomass captured in ring net tows has declined, reflecting the predominance of C. finmarchicus. The decline in C. finmarchicus abundance and consequently zooplankton biomass represents lower availability of energy rich lipids to higher trophic levels, since there is no equivalent replacement to C. finmarchicus in the subarctic GoM food web ([@runge_sustained_2023]). +o Declines in forage fish (herring condition, sand lance recruitment and NARW foraging) would be expected to follow the declining trend in C. finmarchicus (e.g. @suca_sensitivity_2021) +o For these reasons, the seasonal indices in Calanus finmarchicus and other zooplankton abundance and biomass are important to track, as are the seasonal drivers: warming trend (surface and bottom waters), sources of external supply, predators and availability of phytoplankton food in late winter. Shifting abundances in zooplankton taxa have been observed in previous decades in the EcoMon survey (e.g. [@pershing_interdecadal_2005]; [@grieve_projecting_2017]). There has been no radical regime shift (e.g. a speculation that lipid-rich menhaden replace Calanus, with consequences for the structure of the higher trophic levels), but given the trend in increasing CO2 and ocean temperatures, close observation of change in the lower trophic levels in the GoM is warranted. + +## Get the data + +**Point of contact**: [Jeffrey Runge (jeffrey.runge@maine.edu)](mailto:Jeffrey Runge (jeffrey.runge@maine.edu)){.email} + +**ecodata name**: No dataset + +**Variable definitions** + +Calanus finmarchicus abundance from 333 um mesh bongo tows; Units: number m^-3 + + +No Data + +**Indicator Category**: + +- [X] Published Methods +- [X] Extensive analysis, not yet published +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + diff --git a/chapters/ch_bay_sal.rmd b/chapters/ch_bay_sal.rmd index e87302a0..83e92e40 100644 --- a/chapters/ch_bay_sal.rmd +++ b/chapters/ch_bay_sal.rmd @@ -1,83 +1,83 @@ -# Chesapeake Bay Salinity {#ch_bay_sal} - -**Description**: This data is collected from the CBIBS buoy system. - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Charles Pellerin - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The indicator is the Water Temperature and the Water Salinity. - -## Key Results and Visualizations -The key results are the state of the water temperature and the salinity - -### MAB - -```{r plot_ch_bay_salMAB} -# Plot indicator -ggplotObject <- ecodata::plot_ch_bay_sal(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Main stem of the Chesapeake Bay - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_ch_bay_sal} -# Either from Contributor or ecodata -``` - -## Implications -The changes in the temperature and salinity have implications in the habitat - -## Get the data - -**Point of contact**: [Charles Pellerin (charles.pellerin@noaa.gov)](mailto:Charles Pellerin (charles.pellerin@noaa.gov)){.email} - -**ecodata name**: `ecodata::ch_bay_sal` - -**Variable definitions** - -1) Salinity - -```{r vars_ch_bay_sal} -# Pull all var names -vars <- ecodata::ch_bay_sal |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Chesapeake Bay Salinity {#ch_bay_sal} + +**Description**: This data is collected from the CBIBS buoy system. + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Charles Pellerin + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The indicator is the Water Temperature and the Water Salinity. + +## Key Results and Visualizations +The key results are the state of the water temperature and the salinity + +### MAB + +```{r plot_ch_bay_salMAB} +# Plot indicator +ggplotObject <- ecodata::plot_ch_bay_sal(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Main stem of the Chesapeake Bay + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_ch_bay_sal} +# Either from Contributor or ecodata +``` + +## Implications +The changes in the temperature and salinity have implications in the habitat + +## Get the data + +**Point of contact**: [Charles Pellerin (charles.pellerin@noaa.gov)](mailto:Charles Pellerin (charles.pellerin@noaa.gov)){.email} + +**ecodata name**: `ecodata::ch_bay_sal` + +**Variable definitions** + +1) Salinity + +```{r vars_ch_bay_sal} +# Pull all var names +vars <- ecodata::ch_bay_sal |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/ch_bay_temp.rmd b/chapters/ch_bay_temp.rmd index a9973420..1a6211cc 100644 --- a/chapters/ch_bay_temp.rmd +++ b/chapters/ch_bay_temp.rmd @@ -1,83 +1,83 @@ -# Chesapeake Bay Temperature {#ch_bay_temp} - -**Description**: This data is collected from the CBIBS buoy system. - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Charles Pellerin - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The indicator is the Water Temperature and the Water Salinity. - -## Key Results and Visualizations -The key results are the state of the water temperature and the salinity - -### MAB - -```{r plot_ch_bay_tempMAB} -# Plot indicator -ggplotObject <- ecodata::plot_ch_bay_temp(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Main stem of the Chesapeake Bay - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_ch_bay_temp} -# Either from Contributor or ecodata -``` - -## Implications -The changes in the temperature and salinity have implications in the habitat - -## Get the data - -**Point of contact**: [Charles Pellerin (charles.pellerin@noaa.gov)](mailto:Charles Pellerin (charles.pellerin@noaa.gov)){.email} - -**ecodata name**: `ecodata::ch_bay_temp` - -**Variable definitions** - -1) Temperature; degree C - -```{r vars_ch_bay_temp} -# Pull all var names -vars <- ecodata::ch_bay_temp |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Chesapeake Bay Temperature {#ch_bay_temp} + +**Description**: This data is collected from the CBIBS buoy system. + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Charles Pellerin + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The indicator is the Water Temperature and the Water Salinity. + +## Key Results and Visualizations +The key results are the state of the water temperature and the salinity + +### MAB + +```{r plot_ch_bay_tempMAB} +# Plot indicator +ggplotObject <- ecodata::plot_ch_bay_temp(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Main stem of the Chesapeake Bay + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_ch_bay_temp} +# Either from Contributor or ecodata +``` + +## Implications +The changes in the temperature and salinity have implications in the habitat + +## Get the data + +**Point of contact**: [Charles Pellerin (charles.pellerin@noaa.gov)](mailto:Charles Pellerin (charles.pellerin@noaa.gov)){.email} + +**ecodata name**: `ecodata::ch_bay_temp` + +**Variable definitions** + +1) Temperature; degree C + +```{r vars_ch_bay_temp} +# Pull all var names +vars <- ecodata::ch_bay_temp |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/ches_bay_sst.rmd b/chapters/ches_bay_sst.rmd index 94887830..02a35455 100644 --- a/chapters/ches_bay_sst.rmd +++ b/chapters/ches_bay_sst.rmd @@ -1,92 +1,91 @@ -# Chesapeake Bay Seasonal Sea Surface Temperature Anomaly {#ches_bay_sst} - -**Description**: Chesapeake Bay Seasonal Sea Surface Temperature Anomaly - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Ron Vogel, Bruce Vogt - -**Affiliations**: ? - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Seasonal spatial anomaly maps represent the difference between a seasonal average sea surface temperature (SST) and a long-term average SST for that season. For the Chesapeake Bay, the long-term average is derived from the SST time series from 2007 to the year immediately prior to the current year (max(Year) - 1). This reference period serves as a benchmark for comparing current observations. Hence, the anomaly represents the degree to which the current seasonal average departs from historical average, either colder or warmer, indicating whether the current temperature conditions may be favorable or unfavorable for marine species. - -## Key Results and Visualizations -For 2023, winter SST’s show conditions to be roughly 1 degree Celsius warmer than the prior 16-year average winter (2007-2022) throughout Chesapeake Bay. Spring conditions are average. Summer conditions are roughly 0.5 degree Celsius colder than the average summer. Fall conditions are mixed spatially but do not exceed 0.5 degree Celsius colder or warmer. - -```{r plot_ches_bay_sstMAB} -# Plot indicator -ggplotObject <- ecodata::plot_ches_bay_sst(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Data from two satellite instruments, AVHRR at 1 km spatial resolution and VIIRS at 750 m spatial resolution, are co-gridded to an 830 m spatial grid. Overpasses from the two instruments for all current operational satellites are composited into a daily scene in order to maximize geographic coverage on a per-day basis, i.e. minimize data gaps from clouds. Seasonal averaging further increases geographic coverage. - -Temporal scale: Only nighttime satellite overpasses are used in the seasonal averages, i.e. the data do not represent daytime solar heating of the water surface. Seasons for Chesapeake Bay are Dec-Feb (winter), Mar-May (spring), Jun-Aug (summer), and Sep-Nov (fall). - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts -- [X] Ecosystem Reorganization - - -```{r autostats_ches_bay_sst} -# Either from Contributor or ecodata -``` - -## Implications -The warm water during the 2022-2023 winter were likely favorable to blue crabs (Callinectes sapidus) by reducing their overwintering mortality. - -The average temperatures of the spring season indicated favorable spawning, feeding, growth and recruitment conditions for key species such as striped bass (Morone saxatilis) and oysters (Crassostrea virginica). - -The summer’s cooler than average conditions were likely favorable for striped bass by reducing temperature-driven stratification of the water that reduces oxygen solubility. Without the high stress of a stratified low-oxygen environment and temperatures that exceed suitability thresholds, striped bass may have experienced “normal” habitat occupancy, growth potential, and mortality. Similarly, cooler summer temperatures may have benefited Eelgrass (Zostera marina) which provide nursery habitats for many estuarine species (e.g., blue crab) by preventing heat-related die-backs of this critical foraging and nursery ground for many species. - -In the fall season, there were warmer-than-average temperatures in the Western Shore tributaries, but cooler-than-average temperatures on the Eastern Shore. Warmer conditions likely developed due to low precipitation, which leads to lower freshwater flow into the Bay. Relatively calm conditions may have contributed as well by reducing mixing. Warmer conditions on the Western Shore may have delayed migration of some species to marine waters. - -## Get the data - -**Point of contact**: [Ron Vogel (ronald.vogel@noaa.gov)](mailto:Ron Vogel (ronald.vogel@noaa.gov)){.email} - -**ecodata name**: `ecodata::ches_bay_sst` - -**Variable definitions** - -1) sst: sea surface temperature 2023, Celsius -2) sst_climatol: sea surface temperature climatology 2007-2022, Celsius -3) sst_anomaly: sea surface temperature anomaly 2023 minus 2007-2022, Celsius - -```{r vars_ches_bay_sst} -# Pull all var names -vars <- ecodata::ches_bay_sst |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Database pull with analysis - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -For seasonal SST anomaly data files (including the SST long-term climatology), and other inquiries, please contact Ron Vogel at ronald.vogel@noaa.gov The time series of daily SST, seasonal average SST, and other time intervals for 2007 – present, are available to the public at: https://www.star.nesdis.noaa.gov/pub/socd1/ecn/data/avhrr-viirs/sst-ngt More information about this SST data set is available at: https://eastcoast.coastwatch.noaa.gov/cw_avhrr-viirs_sst.php - -**tech-doc link** - - +# Chesapeake Bay Seasonal Sea Surface Temperature Anomaly {#ches_bay_sst} + +**Description**: Chesapeake Bay Seasonal Sea Surface Temperature Anomaly + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Ron Vogel, Bruce Vogt + +**Affiliations**: ? + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Seasonal spatial anomaly maps represent the difference between a seasonal average sea surface temperature (SST) and a long-term average SST for that season. For the Chesapeake Bay, the long-term average is derived from the SST time series from 2007 to the year immediately prior to the current year (max(Year) - 1). This reference period serves as a benchmark for comparing current observations. Hence, the anomaly represents the degree to which the current seasonal average departs from historical average, either colder or warmer, indicating whether the current temperature conditions may be favorable or unfavorable for marine species. + +## Key Results and Visualizations +For 2023, winter SST’s show conditions to be roughly 1 degree Celsius warmer than the prior 16-year average winter (2007-2022) throughout Chesapeake Bay. Spring conditions are average. Summer conditions are roughly 0.5 degree Celsius colder than the average summer. Fall conditions are mixed spatially but do not exceed 0.5 degree Celsius colder or warmer. + +```{r plot_ches_bay_sstMAB} +# Plot indicator +ggplotObject <- ecodata::plot_ches_bay_sst(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Data from two satellite instruments, AVHRR at 1 km spatial resolution and VIIRS at 750 m spatial resolution, are co-gridded to an 830 m spatial grid. Overpasses from the two instruments for all current operational satellites are composited into a daily scene in order to maximize geographic coverage on a per-day basis, i.e. minimize data gaps from clouds. Seasonal averaging further increases geographic coverage. + +Temporal scale: Only nighttime satellite overpasses are used in the seasonal averages, i.e. the data do not represent daytime solar heating of the water surface. Seasons for Chesapeake Bay are Dec-Feb (winter), Mar-May (spring), Jun-Aug (summer), and Sep-Nov (fall). + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts +- [X] Ecosystem Reorganization + + +```{r autostats_ches_bay_sst} +# Either from Contributor or ecodata +``` + +## Implications +The warm water during the 2022-2023 winter were likely favorable to blue crabs (Callinectes sapidus) by reducing their overwintering mortality. + +The average temperatures of the spring season indicated favorable spawning, feeding, growth and recruitment conditions for key species such as striped bass (Morone saxatilis) and oysters (Crassostrea virginica). + +The summer’s cooler than average conditions were likely favorable for striped bass by reducing temperature-driven stratification of the water that reduces oxygen solubility. Without the high stress of a stratified low-oxygen environment and temperatures that exceed suitability thresholds, striped bass may have experienced “normal” habitat occupancy, growth potential, and mortality. Similarly, cooler summer temperatures may have benefited Eelgrass (Zostera marina) which provide nursery habitats for many estuarine species (e.g., blue crab) by preventing heat-related die-backs of this critical foraging and nursery ground for many species. + +In the fall season, there were warmer-than-average temperatures in the Western Shore tributaries, but cooler-than-average temperatures on the Eastern Shore. Warmer conditions likely developed due to low precipitation, which leads to lower freshwater flow into the Bay. Relatively calm conditions may have contributed as well by reducing mixing. Warmer conditions on the Western Shore may have delayed migration of some species to marine waters. + +## Get the data + +**Point of contact**: [Ron Vogel (ronald.vogel@noaa.gov)](mailto:Ron Vogel (ronald.vogel@noaa.gov)){.email} + +**ecodata name**: `ecodata::ches_bay_sst` + +**Variable definitions** + +1) sst: sea surface temperature 2023, Celsius 2) sst_climatol: sea surface temperature climatology 2007-2022, Celsius +3) sst_anomaly: sea surface temperature anomaly 2023 minus 2007-2022, Celsius + +```{r vars_ches_bay_sst} +# Pull all var names +vars <- ecodata::ches_bay_sst |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Database pull with analysis + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +For seasonal SST anomaly data files (including the SST long-term climatology), and other inquiries, please contact Ron Vogel at ronald.vogel@noaa.gov The time series of daily SST, seasonal average SST, and other time intervals for 2007 – present, are available to the public at: https://www.star.nesdis.noaa.gov/pub/socd1/ecn/data/avhrr-viirs/sst-ngt More information about this SST data set is available at: https://eastcoast.coastwatch.noaa.gov/cw_avhrr-viirs_sst.php + +**tech-doc link** + + diff --git a/chapters/ches_bay_synthesis.rmd b/chapters/ches_bay_synthesis.rmd index 3c3ea0b1..3d36acad 100644 --- a/chapters/ches_bay_synthesis.rmd +++ b/chapters/ches_bay_synthesis.rmd @@ -1,74 +1,74 @@ -# Chesapeake Bay 2023 Synthesis {#ches_bay_synthesis} - -**Description**: Synthesis of Chesapeake Bay 2023 habitat conditions with implications for managed species - -**Indicator family**: - -- [X] Habitat -- [X] Megafauna - - -**Contributor(s)**: Bruce Vogt - -**Affiliations**: NCBO - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Chesapeake Bay is the largest estuary in the US, providing important spawning, nursery, and feeding habitat for many managed species in the Northeast US. The combination of seasonal water temperature, precipitation, salinity, and oxygen conditions in Chesapeake Bay can represent favorable or unfavorable habitat for managed species. Each species responds differently, leading to changes in populations and communities within the Bay. - -## Key Results and Visualizations -Water column habitat conditions (temperature, salinity, and dissolved oxygen) in the Chesapeake Bay for summer and fall 2023 were generally favorable for striped bass, summer flounder, oysters, and blue crabs. However, warmer temperatures in the winter and spring may have contributed to low production of juvenile striped bass. - -Salinities throughout the year were higher than average, likely a result of dry conditions that persisted from winter through fall. Higher salinities increase the area of available habitat for species such as croaker, spot, menhaden, and red drum—all marine-oriented species that prefer higher salinities—while restricting habitat area for invasive blue catfish. Salinities over about 15ppt are not tolerable by most size classes of invasive blue catfish. This may have limited their ability to occupy larger parts of tributaries and the Bay which would lessen their predation pressure on many species. - -Higher salinities can also support improved oyster spawning success and spat set, but may increase disease prevalence. - -[Hypoxia levels](https://www.chesapeakebay.net/news/pressrelease/the-2023-chesapeake-bay-dead-zone-is-the-smallest-on-record) in 2023 were the lowest on record (1985-2022) according to independent reports from the states of Maryland and Virginia. Low hypoxia increases the area of suitable habitat for species such as striped bass and summer flounder. - - -## Indicator statistics -Spatial scale: Chesapeake Bay - -Temporal scale: Seasonal: Dec-Feb (winter), Mar-May (spring), Jun-Aug (summer), and Sep-Nov (fall) - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_ches_bay_synthesis} -# Either from Contributor or ecodata -``` - -## Implications -Overall, there may have been more suitable habitat available for multiple finfish and benthic species due to cooler summer water temperatures, higher salinity, and record-low hypoxia in Chesapeake Bay in 2023. Higher salinity, likely driven by low precipitation, also restricted the habitat area available for invasive blue catfish, possibly limiting their spread and impact as predators in 2023. - -## Get the data - -**Point of contact**: [Bruce Vogt, bruce.vogt@noaa.gov](mailto:Bruce Vogt, bruce.vogt@noaa.gov){.email} - -**ecodata name**: No dataset - -**Variable definitions** - -Synthesis of indicators Chesapeake Bay Salinity, Temperature, and Seasonal Sea Surface Temperature Anomaly - - -No Data - -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - +# Chesapeake Bay 2023 Synthesis {#ches_bay_synthesis} + +**Description**: Synthesis of Chesapeake Bay 2023 habitat conditions with implications for managed species + +**Indicator family**: + +- [X] Habitat +- [X] Megafauna + + +**Contributor(s)**: Bruce Vogt + +**Affiliations**: NCBO + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Chesapeake Bay is the largest estuary in the US, providing important spawning, nursery, and feeding habitat for many managed species in the Northeast US. The combination of seasonal water temperature, precipitation, salinity, and oxygen conditions in Chesapeake Bay can represent favorable or unfavorable habitat for managed species. Each species responds differently, leading to changes in populations and communities within the Bay. + +## Key Results and Visualizations +Water column habitat conditions (temperature, salinity, and dissolved oxygen) in the Chesapeake Bay for summer and fall 2023 were generally favorable for striped bass, summer flounder, oysters, and blue crabs. However, warmer temperatures in the winter and spring may have contributed to low production of juvenile striped bass. + +Salinities throughout the year were higher than average, likely a result of dry conditions that persisted from winter through fall. Higher salinities increase the area of available habitat for species such as croaker, spot, menhaden, and red drum—all marine-oriented species that prefer higher salinities—while restricting habitat area for invasive blue catfish. Salinities over about 15ppt are not tolerable by most size classes of invasive blue catfish. This may have limited their ability to occupy larger parts of tributaries and the Bay which would lessen their predation pressure on many species. + +Higher salinities can also support improved oyster spawning success and spat set, but may increase disease prevalence. + +[Hypoxia levels](https://www.chesapeakebay.net/news/pressrelease/the-2023-chesapeake-bay-dead-zone-is-the-smallest-on-record) in 2023 were the lowest on record (1985-2022) according to independent reports from the states of Maryland and Virginia. Low hypoxia increases the area of suitable habitat for species such as striped bass and summer flounder. + + +## Indicator statistics +Spatial scale: Chesapeake Bay + +Temporal scale: Seasonal: Dec-Feb (winter), Mar-May (spring), Jun-Aug (summer), and Sep-Nov (fall) + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_ches_bay_synthesis} +# Either from Contributor or ecodata +``` + +## Implications +Overall, there may have been more suitable habitat available for multiple finfish and benthic species due to cooler summer water temperatures, higher salinity, and record-low hypoxia in Chesapeake Bay in 2023. Higher salinity, likely driven by low precipitation, also restricted the habitat area available for invasive blue catfish, possibly limiting their spread and impact as predators in 2023. + +## Get the data + +**Point of contact**: [Bruce Vogt, bruce.vogt@noaa.gov](mailto:Bruce Vogt, bruce.vogt@noaa.gov){.email} + +**ecodata name**: No dataset + +**Variable definitions** + +Synthesis of indicators Chesapeake Bay Salinity, Temperature, and Seasonal Sea Surface Temperature Anomaly + + +No Data + +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + diff --git a/chapters/ches_bay_wq.rmd b/chapters/ches_bay_wq.rmd index 25096ff0..d7cc6dbb 100644 --- a/chapters/ches_bay_wq.rmd +++ b/chapters/ches_bay_wq.rmd @@ -1,96 +1,96 @@ -# Chesapeake Bay Water Quality Standards Attainment {#ches_bay_wq} - -**Description**: Chesapeake Bay Water Quality Attainment Indicator - -**Indicator family**: - -- [X] Lower trophic levels - - -**Contributor(s)**: Qian Zhang, Richard Tian, and Peter Tango - -**Affiliations**: ? - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -To protect the aquatic living resources of Chesapeake Bay, the Chesapeake Bay Program (CBP) partnership has developed a guidance framework of ambient water quality criteria with designated uses and assessment procedures for dissolved oxygen, chlorophyll-a, and water clarity/submerged aquatic vegetation (SAV) ([@us_epa_ambient_2003]). To achieve consistent assessment over time and between jurisdictions, a multimetric indicator was proposed by the CBP partnership to provide a means for tracking the progress in all 92 management segments of Chesapeake Bay ([@hernandez_cordero_development_2020]; [@us_epa_ambient_2017]). This indicator has been computed for each three-year assessment period since 1985-1987, providing an integrated measure of Chesapeake Bay’s water quality condition over the last three decades. - -The multimetric indicator required monitoring data on dissolved oxygen (DO) concentrations, chlorophyll-a concentrations, water clarity, SAV acreage, water temperature, and salinity. SAV acreage has been measured by the Virginia Institute of Marine Science in collaboration with the CBP, which is available via http://web.vims.edu/bio/sav/StateSegmentAreaTable.htm. Data for all other parameters were obtained from the CBP Water Quality Database (http://www.chesapeakebay.net/data/downloads/cbp_water_quality_database_1984_present). These data have been routinely reported to the CBP by the Maryland Department of Natural Resources, Virginia Department of Environmental Quality, Old Dominion University, Virginia Institute of Marine Science, and citizen/volunteer monitoring initiatives. - -Monitoring data of DO, chlorophyll-a, and water clarity/SAV were processed and compared with water quality criteria thresholds according to different designated uses (DUs). These DUs are migratory spawning and nursery (MSN), open water (OW), deep water (DW), deep channel (DC), and shallow water (SW), which reflect the seasonal nature of water column structure and the life history needs of living resources. Station-level DO and chlorophyll-a data were spatially interpolated in three dimensions. Salinity and water temperature data were used to compute the vertical density structure of the water column, which was translated into layers of different DUs. Criteria attainment was determined by comparing violation rates over a 3-year period to a reference cumulative frequency distribution that represents the extent of allowable violation. This approach was implemented using FORTRAN codes, which are provided as a zipped folder. For water clarity/SAV, the single best year in the 3-year assessment period was compared with the segment-specific acreage goal, the water clarity goal, or a combination of both. For more details, refer to the Methods section of Zhang [@zhang_chesapeake_2018]. - -The multimetric indicator quantifies the fraction of segment-DU-criterion combinations that meet all applicable season-specific thresholds for each 3-year assessment period from 1985-1987 to 2019-2021. For each 3-year assessment period, all applicable segment-DU-criterion combinations were evaluated in a binomial fashion and scored 1 for “in attainment” and 0 for “nonattainment”. The classified status of each segment-DU-criterion combination was weighted via segments’ surface area and summed to obtain the multimetric index score. This weighting scheme was adopted for two reasons: (1) segments vary in size over four orders of magnitude, and (2) surface area of each segment does not change with time or DUs, unlike seasonally variable habitat volume or bottom water area ([@us_epa_ambient_2017]). For more details, refer to the Methods section of Zhang [@zhang_chesapeake_2018]. - -## Key Results and Visualizations -The indicator provides an integrated measure of Chesapeake Bay’s water quality condition (Figure 1). In 2019-2021, 28.1% of all tidal water segment-DU-criterion combinations are estimated to have met or exceeded applicable water quality criteria thresholds. Overall, the indicator has a positive and statistically significant trend between 1985-1987 and 2019-2021, which shows that Chesapeake Bay is on a positive trajectory toward recovery. This pattern has been statistically linked to total nitrogen reduction, indicating responsiveness of attainment status to management actions implemented to reduce nutrients ([@zhang_chesapeake_2018]). - -### MAB - -```{r plot_ches_bay_wqMAB} -# Plot indicator -ggplotObject <- ecodata::plot_ches_bay_wq(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Chesapeake Bay - -Temporal scale: 3-year assessment period between 1985-1987 and 2019-2021. - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_ches_bay_wq} -# Either from Contributor or ecodata -``` - -## Implications -Patterns of attainment of individual designated uses are variable (Figure 2). According to Mann-Kendall trend analysis extended from Zhang [@zhang_chesapeake_2018], dissolved oxygen criterion attainment (migratory fish spawning & nursery) showed a statistically significant long-term decline. By contrast, dissolved oxygen criterion attainment (specifically deep water and deep channel) and water clarity/SAV criterion attainment showed statistically significant long-term improvements, which may be an indication of increasing resilience in the bay ecosystem since the Bay restoration began. - -## Get the data - -**Point of contact**: [Qian Zhang, qzhang@chesapeakebay.net](mailto:Qian Zhang, qzhang@chesapeakebay.net){.email} - -**ecodata name**: `ecodata::ches_bay_wq` - -**Variable definitions** - -Period: Assessment period Year 1: Starting year of the assessment period -Year 2: Ending year of the assessment period Total: The overall attainment indicator -MSN-DO: Estimated attainment of the dissolved oxygen criterion for the migratory spawning and nursery designated use -OW-DO: Estimated attainment of the dissolved oxygen criterion for the open water designated use -DW-DO: Estimated attainment of the dissolved oxygen criterion for the deep water designated use -DC-DO: Estimated attainment of the dissolved oxygen criterion for the deep channel designated use -OW-CHLA: Estimated attainment of the chlorophyll-a criterion -SW-Clarity/SAV: Estimated attainment of the bay grasses / water clarity criterion for the shallow water designated use - -```{r vars_ches_bay_wq} -# Pull all var names -vars <- ecodata::ches_bay_wq |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Chesapeake Bay Water Quality Standards Attainment {#ches_bay_wq} + +**Description**: Chesapeake Bay Water Quality Attainment Indicator + +**Indicator family**: + +- [X] Lower trophic levels + + +**Contributor(s)**: Qian Zhang, Richard Tian, and Peter Tango + +**Affiliations**: ? + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +To protect the aquatic living resources of Chesapeake Bay, the Chesapeake Bay Program (CBP) partnership has developed a guidance framework of ambient water quality criteria with designated uses and assessment procedures for dissolved oxygen, chlorophyll-a, and water clarity/submerged aquatic vegetation (SAV) ([@us_epa_ambient_2003]). To achieve consistent assessment over time and between jurisdictions, a multimetric indicator was proposed by the CBP partnership to provide a means for tracking the progress in all 92 management segments of Chesapeake Bay ([@hernandez_cordero_development_2020]; [@us_epa_ambient_2017]). This indicator has been computed for each three-year assessment period since 1985-1987, providing an integrated measure of Chesapeake Bay’s water quality condition over the last three decades. + +The multimetric indicator required monitoring data on dissolved oxygen (DO) concentrations, chlorophyll-a concentrations, water clarity, SAV acreage, water temperature, and salinity. SAV acreage has been measured by the Virginia Institute of Marine Science in collaboration with the CBP, which is available via http://web.vims.edu/bio/sav/StateSegmentAreaTable.htm. Data for all other parameters were obtained from the CBP Water Quality Database (http://www.chesapeakebay.net/data/downloads/cbp_water_quality_database_1984_present). These data have been routinely reported to the CBP by the Maryland Department of Natural Resources, Virginia Department of Environmental Quality, Old Dominion University, Virginia Institute of Marine Science, and citizen/volunteer monitoring initiatives. + +Monitoring data of DO, chlorophyll-a, and water clarity/SAV were processed and compared with water quality criteria thresholds according to different designated uses (DUs). These DUs are migratory spawning and nursery (MSN), open water (OW), deep water (DW), deep channel (DC), and shallow water (SW), which reflect the seasonal nature of water column structure and the life history needs of living resources. Station-level DO and chlorophyll-a data were spatially interpolated in three dimensions. Salinity and water temperature data were used to compute the vertical density structure of the water column, which was translated into layers of different DUs. Criteria attainment was determined by comparing violation rates over a 3-year period to a reference cumulative frequency distribution that represents the extent of allowable violation. This approach was implemented using FORTRAN codes, which are provided as a zipped folder. For water clarity/SAV, the single best year in the 3-year assessment period was compared with the segment-specific acreage goal, the water clarity goal, or a combination of both. For more details, refer to the Methods section of Zhang [@zhang_chesapeake_2018]. + +The multimetric indicator quantifies the fraction of segment-DU-criterion combinations that meet all applicable season-specific thresholds for each 3-year assessment period from 1985-1987 to 2019-2021. For each 3-year assessment period, all applicable segment-DU-criterion combinations were evaluated in a binomial fashion and scored 1 for “in attainment” and 0 for “nonattainment”. The classified status of each segment-DU-criterion combination was weighted via segments’ surface area and summed to obtain the multimetric index score. This weighting scheme was adopted for two reasons: (1) segments vary in size over four orders of magnitude, and (2) surface area of each segment does not change with time or DUs, unlike seasonally variable habitat volume or bottom water area ([@us_epa_ambient_2017]). For more details, refer to the Methods section of Zhang [@zhang_chesapeake_2018]. + +## Key Results and Visualizations +The indicator provides an integrated measure of Chesapeake Bay’s water quality condition (Figure 1). In 2019-2021, 28.1% of all tidal water segment-DU-criterion combinations are estimated to have met or exceeded applicable water quality criteria thresholds. Overall, the indicator has a positive and statistically significant trend between 1985-1987 and 2019-2021, which shows that Chesapeake Bay is on a positive trajectory toward recovery. This pattern has been statistically linked to total nitrogen reduction, indicating responsiveness of attainment status to management actions implemented to reduce nutrients ([@zhang_chesapeake_2018]). + +### MAB + +```{r plot_ches_bay_wqMAB} +# Plot indicator +ggplotObject <- ecodata::plot_ches_bay_wq(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Chesapeake Bay + +Temporal scale: 3-year assessment period between 1985-1987 and 2019-2021. + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_ches_bay_wq} +# Either from Contributor or ecodata +``` + +## Implications +Patterns of attainment of individual designated uses are variable (Figure 2). According to Mann-Kendall trend analysis extended from Zhang [@zhang_chesapeake_2018], dissolved oxygen criterion attainment (migratory fish spawning & nursery) showed a statistically significant long-term decline. By contrast, dissolved oxygen criterion attainment (specifically deep water and deep channel) and water clarity/SAV criterion attainment showed statistically significant long-term improvements, which may be an indication of increasing resilience in the bay ecosystem since the Bay restoration began. + +## Get the data + +**Point of contact**: [Qian Zhang, qzhang@chesapeakebay.net](mailto:Qian Zhang, qzhang@chesapeakebay.net){.email} + +**ecodata name**: `ecodata::ches_bay_wq` + +**Variable definitions** + +Period: Assessment period Year 1: Starting year of the assessment period Year 2: Ending year of the assessment period +Total: The overall attainment indicator +MSN-DO: Estimated attainment of the dissolved oxygen criterion for the migratory spawning and nursery designated use +OW-DO: Estimated attainment of the dissolved oxygen criterion for the open water designated use +DW-DO: Estimated attainment of the dissolved oxygen criterion for the deep water designated use +DC-DO: Estimated attainment of the dissolved oxygen criterion for the deep channel designated use +OW-CHLA: Estimated attainment of the chlorophyll-a criterion +SW-Clarity/SAV: Estimated attainment of the bay grasses / water clarity criterion for the shallow water designated use + +```{r vars_ches_bay_wq} +# Pull all var names +vars <- ecodata::ches_bay_wq |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/chl_pp.rmd b/chapters/chl_pp.rmd index 736be584..f0b5e982 100644 --- a/chapters/chl_pp.rmd +++ b/chapters/chl_pp.rmd @@ -1,174 +1,174 @@ -# Chlorophyll and Primary Production {#chl_pp} - -**Description**: Satellite derived phytoplankton data including chlorophyll concentration, phytoplankton size class, and primary production for the Northeast Continental Shelf and ecological production units. - -**Indicator family**: - -- [X] Lower trophic levels - - -**Contributor(s)**: Kimberly Hyde - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Phytoplankton are key biological regulators of the structure and function of most marine ecosystems. They are the foundation of the marine food web and are the primary food source for zooplankton and filter feeders such as shellfish. Numerous environmental and oceanographic factors interact to drive the abundance, composition, spatial distribution, seasonal timing and productivity of phytoplankton. Satellite derived measurements of chlorophyll, the dominant photosynthetic pigment in phytoplankton, are used to estimate total phytoplankton biomass. The size structure of the phytoplankton community influences important biogeochemical and ecological processes, including transfer of energy through the marine food web. Phytoplankton growth depends on the availability of carbon dioxide, sunlight and nutrients and their growth rates can be influenced by water temperature, water depth, wind, and grazing pressure. Primary productivity is a measure of the amount of carbon produced by phytoplankton. - -The unique physical characteristics of the Northeast U.S. continental shelf help make it among the most productive continental shelf systems in the world influenced by both bottom-up (e.g. nutrient concentrations, light availability, and mixing/stratification) and top-down (e.g. grazing) controls. Phytoplankton biomass, composition, and productivity all have high spatial, seasonal and interannual variability. The most pronounced spatial pattern is the decrease in phytoplankton biomass from the coast to the shelf break. Georges Bank and Nantucket Shoals are shallow regions that are well mixed by tides. This mixing supplies sufficient nutrients to support phytoplankton growth throughout the year. In other regions, blooms of large diatom species occur on a seasonal cycle when growing conditions are ideal. - -## Key Results and Visualizations -The seasonal cycles of phytoplankton size distribution are typically dominated by larger-celled microplankton during the winter-spring and fall bloom periods, while smaller-celled nanoplankton dominate during the warmer summer months. In 2023, MAB total chlorophyll was below average in early spring, near average through the summer and above average throughout the fall. A peak in primary production occurred in summer, followed by an above average productivity associated with the early fall bloom. Phytoplankton size class distributions were near average for most of the year, except during the early fall bloom. - -Total chlorophyll concentrations on Georges Bank were above average for most of the year. The early winter bloom was most likely associated with diatoms, however the above average chlorophyll, primary production and microplankton fraction from April through August can be attributed to the dinoflagellate _Tripos muelleri_. - -Total chlorophyll concentrations in the Gulf of Maine were above average for most of the year. The early winter bloom was most likely associated with diatoms, however the record high chlorophyll, primary production and microplankton fraction from April through August can be attributed to the dinoflagellate _Tripos muelleri_. - -There is high interannual variability of the seasonal phytoplankton cycle. At the monthly scale, MAB chlorophyll and primary production are increasing during January and there has been a decrease in September chlorophyll, likely due to extension of the [summer stratification](https://noaa-edab.github.io/catalog/transition-dates.html) and delayed fall turnover. Fall and winter chlorophyll and primary production are increasing on Georges Bank and Gulf of Maine. - -### MidAtlantic - -```{r plot_chl_ppMidAtlanticchlweekly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'chl' ,plottype= 'weekly') -ggplotObject -``` - -```{r plot_chl_ppMidAtlanticchlmonthly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'chl' ,plottype= 'monthly') -ggplotObject -``` - -```{r plot_chl_ppMidAtlanticchlanomaly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'chl' ,plottype= 'anomaly') -ggplotObject -``` - -```{r plot_chl_ppMidAtlanticppweekly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'pp' ,plottype= 'weekly') -ggplotObject -``` - -```{r plot_chl_ppMidAtlanticppmonthly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'pp' ,plottype= 'monthly') -ggplotObject -``` - -```{r plot_chl_ppMidAtlanticppanomaly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'pp' ,plottype= 'anomaly') -ggplotObject -``` - -### NewEngland - -```{r plot_chl_ppNewEnglandchlweekly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'chl' ,plottype= 'weekly') -ggplotObject -``` - -```{r plot_chl_ppNewEnglandchlmonthly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'chl' ,plottype= 'monthly') -ggplotObject -``` - -```{r plot_chl_ppNewEnglandchlanomaly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'chl' ,plottype= 'anomaly') -ggplotObject -``` - -```{r plot_chl_ppNewEnglandppweekly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'pp' ,plottype= 'weekly') -ggplotObject -``` - -```{r plot_chl_ppNewEnglandppmonthly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'pp' ,plottype= 'monthly') -ggplotObject -``` - -```{r plot_chl_ppNewEnglandppanomaly} -# Plot indicator -ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'pp' ,plottype= 'anomaly') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: By EPU and gridded for the entire shelf - -Temporal scale: Daily, weekly, monthly, annual, climatology (1998 to current year) - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_chl_pp} -# Either from Contributor or ecodata -``` - -## Implications -Phytoplankton abundance, productivity, diversity, cell size, phenology, and carbon fluxes are regulated by the local physical and chemical environment and grazing. Interannual and climatological changes in temperature, freshwater inputs (due to ice sheet melting and/or enhanced river discharge), wind direction, and wind speed can alter the circulation patterns, upwelling conditions, and nutrient fluxes, directly affecting the timing, location, species composition of phytoplankton blooms in the NES. As the NES responds to warming, changing phenologies, changing chemistry, and changes in circulation patterns, we must understand how varying biophysical interactions control phytoplankton and subsequently affect fisheries, their habitats and the people, businesses and communities that depend on them. - -## Get the data - -**Point of contact**: [kimberly.hyde@noaa.gov](mailto:kimberly.hyde@noaa.gov){.email} - -**ecodata name**: `ecodata::chl_pp` - -**Variable definitions** - -1) Chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters; mg m^-3 -2) Chlorophyll anomaly; Ratio of chlorophyll _a_ concentration to the long-term (1998 to present year) climatology; unitless (ratio) -3) Primary productivity; Daily net primary production of biomass expressed as carbon per unit volume in seawater per day; gCarbon m^-2 d^-1 -4) Primary productivity anomaly; Ratio of net primary production to the long-term (1998 to present year) climatology; unitless (ratio) -5) Microplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the microplankton (20-200 µm) fraction; mg m^-3 -6) Nanoplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the nanoplankton (2-20 µm) fraction; mg m^-3 -7) Picoplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the picoplankton (0.2-2 µm) fraction; mg m^-3 -8) Microplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from microplankton (20-200 µm); (percent) -9) Nanoplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from nanoplankton (2-20 µm); (percent) -10) Picoplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from picoplankton (0.2-2 µm); (percent) -11) Annual summed primary production - TBD - -```{r vars_chl_pp} -# Pull all var names -vars <- ecodata::chl_pp |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Other - - -**Indicator Category**: - -Publicly available satellite data that are processed and analyzed - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Chlorophyll and Primary Production {#chl_pp} + +**Description**: Satellite derived phytoplankton data including chlorophyll concentration, phytoplankton size class, and primary production for the Northeast Continental Shelf and ecological production units. + +**Indicator family**: + +- [X] Lower trophic levels + + +**Contributor(s)**: Kimberly Hyde + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Phytoplankton are key biological regulators of the structure and function of most marine ecosystems. They are the foundation of the marine food web and are the primary food source for zooplankton and filter feeders such as shellfish. Numerous environmental and oceanographic factors interact to drive the abundance, composition, spatial distribution, seasonal timing and productivity of phytoplankton. Satellite derived measurements of chlorophyll, the dominant photosynthetic pigment in phytoplankton, are used to estimate total phytoplankton biomass. The size structure of the phytoplankton community influences important biogeochemical and ecological processes, including transfer of energy through the marine food web. Phytoplankton growth depends on the availability of carbon dioxide, sunlight and nutrients and their growth rates can be influenced by water temperature, water depth, wind, and grazing pressure. Primary productivity is a measure of the amount of carbon produced by phytoplankton. + +The unique physical characteristics of the Northeast U.S. continental shelf help make it among the most productive continental shelf systems in the world influenced by both bottom-up (e.g. nutrient concentrations, light availability, and mixing/stratification) and top-down (e.g. grazing) controls. Phytoplankton biomass, composition, and productivity all have high spatial, seasonal and interannual variability. The most pronounced spatial pattern is the decrease in phytoplankton biomass from the coast to the shelf break. Georges Bank and Nantucket Shoals are shallow regions that are well mixed by tides. This mixing supplies sufficient nutrients to support phytoplankton growth throughout the year. In other regions, blooms of large diatom species occur on a seasonal cycle when growing conditions are ideal. + +## Key Results and Visualizations +The seasonal cycles of phytoplankton size distribution are typically dominated by larger-celled microplankton during the winter-spring and fall bloom periods, while smaller-celled nanoplankton dominate during the warmer summer months. In 2023, MAB total chlorophyll was below average in early spring, near average through the summer and above average throughout the fall. A peak in primary production occurred in summer, followed by an above average productivity associated with the early fall bloom. Phytoplankton size class distributions were near average for most of the year, except during the early fall bloom. + +Total chlorophyll concentrations on Georges Bank were above average for most of the year. The early winter bloom was most likely associated with diatoms, however the above average chlorophyll, primary production and microplankton fraction from April through August can be attributed to the dinoflagellate _Tripos muelleri_. + +Total chlorophyll concentrations in the Gulf of Maine were above average for most of the year. The early winter bloom was most likely associated with diatoms, however the record high chlorophyll, primary production and microplankton fraction from April through August can be attributed to the dinoflagellate _Tripos muelleri_. + +There is high interannual variability of the seasonal phytoplankton cycle. At the monthly scale, MAB chlorophyll and primary production are increasing during January and there has been a decrease in September chlorophyll, likely due to extension of the [summer stratification](https://noaa-edab.github.io/catalog/transition-dates.html) and delayed fall turnover. Fall and winter chlorophyll and primary production are increasing on Georges Bank and Gulf of Maine. + +### MidAtlantic + +```{r plot_chl_ppMidAtlanticchlweekly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'chl' ,plottype= 'weekly') +ggplotObject +``` + +```{r plot_chl_ppMidAtlanticchlmonthly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'chl' ,plottype= 'monthly') +ggplotObject +``` + +```{r plot_chl_ppMidAtlanticchlanomaly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'chl' ,plottype= 'anomaly') +ggplotObject +``` + +```{r plot_chl_ppMidAtlanticppweekly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'pp' ,plottype= 'weekly') +ggplotObject +``` + +```{r plot_chl_ppMidAtlanticppmonthly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'pp' ,plottype= 'monthly') +ggplotObject +``` + +```{r plot_chl_ppMidAtlanticppanomaly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'pp' ,plottype= 'anomaly') +ggplotObject +``` + +### NewEngland + +```{r plot_chl_ppNewEnglandchlweekly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'chl' ,plottype= 'weekly') +ggplotObject +``` + +```{r plot_chl_ppNewEnglandchlmonthly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'chl' ,plottype= 'monthly') +ggplotObject +``` + +```{r plot_chl_ppNewEnglandchlanomaly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'chl' ,plottype= 'anomaly') +ggplotObject +``` + +```{r plot_chl_ppNewEnglandppweekly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'pp' ,plottype= 'weekly') +ggplotObject +``` + +```{r plot_chl_ppNewEnglandppmonthly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'pp' ,plottype= 'monthly') +ggplotObject +``` + +```{r plot_chl_ppNewEnglandppanomaly} +# Plot indicator +ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'pp' ,plottype= 'anomaly') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: By EPU and gridded for the entire shelf + +Temporal scale: Daily, weekly, monthly, annual, climatology (1998 to current year) + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_chl_pp} +# Either from Contributor or ecodata +``` + +## Implications +Phytoplankton abundance, productivity, diversity, cell size, phenology, and carbon fluxes are regulated by the local physical and chemical environment and grazing. Interannual and climatological changes in temperature, freshwater inputs (due to ice sheet melting and/or enhanced river discharge), wind direction, and wind speed can alter the circulation patterns, upwelling conditions, and nutrient fluxes, directly affecting the timing, location, species composition of phytoplankton blooms in the NES. As the NES responds to warming, changing phenologies, changing chemistry, and changes in circulation patterns, we must understand how varying biophysical interactions control phytoplankton and subsequently affect fisheries, their habitats and the people, businesses and communities that depend on them. + +## Get the data + +**Point of contact**: [kimberly.hyde@noaa.gov](mailto:kimberly.hyde@noaa.gov){.email} + +**ecodata name**: `ecodata::chl_pp` + +**Variable definitions** + +1) Chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters; mg m^-3 +2) Chlorophyll anomaly; Ratio of chlorophyll _a_ concentration to the long-term (1998 to present year) climatology; unitless (ratio) +3) Primary productivity; Daily net primary production of biomass expressed as carbon per unit volume in seawater per day; gCarbon m^-2 d^-1 +4) Primary productivity anomaly; Ratio of net primary production to the long-term (1998 to present year) climatology; unitless (ratio) +5) Microplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the microplankton (20-200 µm) fraction; mg m^-3 +6) Nanoplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the nanoplankton (2-20 µm) fraction; mg m^-3 +7) Picoplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the picoplankton (0.2-2 µm) fraction; mg m^-3 +8) Microplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from microplankton (20-200 µm); (percent) +9) Nanoplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from nanoplankton (2-20 µm); (percent) +10) Picoplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from picoplankton (0.2-2 µm); (percent) +11) Annual summed primary production - TBD + +```{r vars_chl_pp} +# Pull all var names +vars <- ecodata::chl_pp |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Other + + +**Indicator Category**: + +Publicly available satellite data that are processed and analyzed + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/cold_pool.rmd b/chapters/cold_pool.rmd index 3e50c09b..6a512892 100644 --- a/chapters/cold_pool.rmd +++ b/chapters/cold_pool.rmd @@ -1,124 +1,123 @@ -# Cold Pool Index {#cold_pool} - -**Description**: Three annual cold pool indices (and standard error) for ss1959 through 2023 - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Joe Caracappa, Hubert du Pontavice, Vincent Saba, Zhuomin Chen - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The cold pool is seasonal feature in the Mid-Atlantic Bight that is defined by it's low temperature (< 10 deg C), relative freshness ( < 34 psu), and moderate depth (20 -200m). It is typically present between June and September. Cold pool dynamics can influence the recruitment and settlement of fish species. This indicator set shows the intensity, extent, and persistence of the cold pool each year based on gridded model and reanalysis products. - -Changes in ocean temperature and circulation alter habitat features such as the seasonal cold pool, a 20–60 m thick band of cold, relatively uniform near-bottom water that persists from spring to fall over the mid and outer shelf of the MAB and southern flank of Georges Bank [@lentz_seasonal_2017; @chen_seasonal_2018]. The cold pool plays an essential role in the structuring of the MAB ecosystem. It is a reservoir of nutrients that feeds phytoplankton productivity, is essential fish spawning and nursery habitat, and affects fish distribution and behavior [@lentz_seasonal_2017; @miles_offshore_2021]. The average temperature of the cold pool is getting warmer over time [@miller_state-space_2016; @dupontavice_ocean_2022], and the area is getting smaller [@friedland_middle_2022]. - -## Key Results and Visualizations -Time series plots of the three cold pool indices. Cold pool index shows the mean temperature within the cold pool where positive values indicate a warming cold pool. Cold pool extent shows the change in maximum area relative to the historical mean, where negative values indicate a shrinking cold pool. Cold pool persistence measures the duration of the cold pool relative to the historical mean. Negative values indicate a shorter duration. In general the cold pool has been getting warmer, has persisted for a shorter duration, and has covered a smaller footprint since the 1960s. - -### MidAtlantic - -```{r plot_cold_poolMidAtlanticcold_pool} -# Plot indicator -ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'cold_pool') -ggplotObject -``` - -```{r plot_cold_poolMidAtlanticpersistence} -# Plot indicator -ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'persistence') -ggplotObject -``` - -```{r plot_cold_poolMidAtlanticextent} -# Plot indicator -ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'extent') -ggplotObject -``` - -### NewEngland - -```{r plot_cold_poolNewEnglandcold_pool} -# Plot indicator -ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'cold_pool') -ggplotObject -``` - -```{r plot_cold_poolNewEnglandpersistence} -# Plot indicator -ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'persistence') -ggplotObject -``` - -```{r plot_cold_poolNewEnglandextent} -# Plot indicator -ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'extent') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: MAB - -Temporal scale: annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_cold_pool} -# Either from Contributor or ecodata -``` - -## Implications -Changes in cold pool indicators can be signs of changes in regional/seasonal oceanographic patterns. This may impact the recruitment and behavior of species dependent on the cold pool. - -Changes in the cold pool habitat can affect species distribution, recruitment, and migration timing for multiple federally managed species. Southern New England-Mid Atlantic yellowtail flounder recruitment and settlement are related to the strength of the cold pool [@miller_state-space_2016]. The settlement of pre-recruits during the cold pool event represents a bottleneck in yellowtail life history, during which a local and temporary increase in bottom temperature negatively impacts the survival of the settlers. Including the effect of cold pool variations on yellowtail recruitment reduced retrospective patterns and improved the skill of short-term forecasts in a stock assessment model [@dupontavice_ocean_2022; @miller_state-space_2016]. The cold pool also provides habitat for the ocean quahog [@powell_ocean_2020; @friedland_middle_2022]. Growth rates of ocean quahogs in the MAB (southern portion of their range) have increased over the last 200 years whereas little to no change has been documented in the northern portion of their range in southern New England, likely a response to a warming and shrinking cold pool [@pace_two-hundred_2018]. - -## Get the data - -**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} - -**ecodata name**: `ecodata::cold_pool` - -**Variable definitions** - -1) Source: ROMS (bias-corrected ROMS-NWA bottom temperature [@dupontavice_ocean_2022]), GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature), PSY (CMEM’s PSY global forecast bottom temperature) -2) year 3) cold_pool_index: measure of mean temperature within cold pool -4) se_cold_pool_index: standard error of cold_pool_index 5) persistence_index: measure of duration of cold pool -6) se_persistence_index: standard error of persistence_index 7) extent_index: measure of spatial extent of cold pool -8) se_extent_index: standard error of extent_index - -```{r vars_cold_pool} -# Pull all var names -vars <- ecodata::cold_pool |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Cold Pool Index {#cold_pool} + +**Description**: Three annual cold pool indices (and standard error) for ss1959 through 2023 + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Joe Caracappa, Hubert du Pontavice, Vincent Saba, Zhuomin Chen + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The cold pool is seasonal feature in the Mid-Atlantic Bight that is defined by it's low temperature (< 10 deg C), relative freshness ( < 34 psu), and moderate depth (20 -200m). It is typically present between June and September. Cold pool dynamics can influence the recruitment and settlement of fish species. This indicator set shows the intensity, extent, and persistence of the cold pool each year based on gridded model and reanalysis products. + +Changes in ocean temperature and circulation alter habitat features such as the seasonal cold pool, a 20–60 m thick band of cold, relatively uniform near-bottom water that persists from spring to fall over the mid and outer shelf of the MAB and southern flank of Georges Bank [@lentz_seasonal_2017; @chen_seasonal_2018]. The cold pool plays an essential role in the structuring of the MAB ecosystem. It is a reservoir of nutrients that feeds phytoplankton productivity, is essential fish spawning and nursery habitat, and affects fish distribution and behavior [@lentz_seasonal_2017; @miles_offshore_2021]. The average temperature of the cold pool is getting warmer over time [@miller_state-space_2016; @dupontavice_ocean_2022], and the area is getting smaller [@friedland_middle_2022]. + +## Key Results and Visualizations +Time series plots of the three cold pool indices. Cold pool index shows the mean temperature within the cold pool where positive values indicate a warming cold pool. Cold pool extent shows the change in maximum area relative to the historical mean, where negative values indicate a shrinking cold pool. Cold pool persistence measures the duration of the cold pool relative to the historical mean. Negative values indicate a shorter duration. In general the cold pool has been getting warmer, has persisted for a shorter duration, and has covered a smaller footprint since the 1960s. + +### MidAtlantic + +```{r plot_cold_poolMidAtlanticcold_pool} +# Plot indicator +ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'cold_pool') +ggplotObject +``` + +```{r plot_cold_poolMidAtlanticpersistence} +# Plot indicator +ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'persistence') +ggplotObject +``` + +```{r plot_cold_poolMidAtlanticextent} +# Plot indicator +ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'extent') +ggplotObject +``` + +### NewEngland + +```{r plot_cold_poolNewEnglandcold_pool} +# Plot indicator +ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'cold_pool') +ggplotObject +``` + +```{r plot_cold_poolNewEnglandpersistence} +# Plot indicator +ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'persistence') +ggplotObject +``` + +```{r plot_cold_poolNewEnglandextent} +# Plot indicator +ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'extent') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: MAB + +Temporal scale: annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_cold_pool} +# Either from Contributor or ecodata +``` + +## Implications +Changes in cold pool indicators can be signs of changes in regional/seasonal oceanographic patterns. This may impact the recruitment and behavior of species dependent on the cold pool. + +Changes in the cold pool habitat can affect species distribution, recruitment, and migration timing for multiple federally managed species. Southern New England-Mid Atlantic yellowtail flounder recruitment and settlement are related to the strength of the cold pool [@miller_state-space_2016]. The settlement of pre-recruits during the cold pool event represents a bottleneck in yellowtail life history, during which a local and temporary increase in bottom temperature negatively impacts the survival of the settlers. Including the effect of cold pool variations on yellowtail recruitment reduced retrospective patterns and improved the skill of short-term forecasts in a stock assessment model [@dupontavice_ocean_2022; @miller_state-space_2016]. The cold pool also provides habitat for the ocean quahog [@powell_ocean_2020; @friedland_middle_2022]. Growth rates of ocean quahogs in the MAB (southern portion of their range) have increased over the last 200 years whereas little to no change has been documented in the northern portion of their range in southern New England, likely a response to a warming and shrinking cold pool [@pace_two-hundred_2018]. + +## Get the data + +**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} + +**ecodata name**: `ecodata::cold_pool` + +**Variable definitions** + +1) Source: ROMS (bias-corrected ROMS-NWA bottom temperature [@dupontavice_ocean_2022]), GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature), PSY (CMEM’s PSY global forecast bottom temperature) +2) year 3) cold_pool_index: measure of mean temperature within cold pool 4) se_cold_pool_index: standard error of cold_pool_index +5) persistence_index: measure of duration of cold pool 6) se_persistence_index: standard error of persistence_index +7) extent_index: measure of spatial extent of cold pool 8) se_extent_index: standard error of extent_index + +```{r vars_cold_pool} +# Pull all var names +vars <- ecodata::cold_pool |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/comdat.rmd b/chapters/comdat.rmd index 0b7e58a3..967b868e 100644 --- a/chapters/comdat.rmd +++ b/chapters/comdat.rmd @@ -1,142 +1,142 @@ -# Commercial Landings and Revenue {#comdat} - -**Description**: Commercial landings and revenue from dealer reports - -**Indicator family**: - -- [X] Megafauna -- [X] Economic - - -**Contributor(s)**: Sean Lucey, Andrew Beet, and Sarah Gaichas. - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The Northeast US has a long, rich history of commercial fishing. Species of all different types are caught using a plethora of different fishing gear from North Carolina to Maine. This data set aggregates the value of those landings adjusted for inflation to the start of the report year. Revenue is calculated by total and managed by the Mid-Atlantic Fisheries Management Council and New England Fisheries Management Council. Revenue can be visualized by feeding guilds as well as by region. - -## Key Results and Visualizations -Landings have fluctuated over time in all regions. In the two northern regions, a majority of the catch is used for human consumption (seafood) while less so in the Mid-Atlantic. Commercial seafood landings have been declining in both the Mid-Atlantic and Gulf of Maine regions. - -Commercial revenue by managed species has generally been down. The exception is on Georges Bank were there is a cyclical nature to revenue arising from rotational scallop management. Total fisheries revenue in the Gulf of Maine has been increasing while total revenue has declined in the Mid-Atlantic. - -### MidAtlantic - -```{r plot_comdatMidAtlanticlandingstotal} -# Plot indicator -ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'total') -ggplotObject -``` - -```{r plot_comdatMidAtlanticlandingsguild} -# Plot indicator -ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'guild') -ggplotObject -``` - -```{r plot_comdatMidAtlanticrevenuetotal} -# Plot indicator -ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'total') -ggplotObject -``` - -```{r plot_comdatMidAtlanticrevenueguild} -# Plot indicator -ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'guild') -ggplotObject -``` - -### NewEngland - -```{r plot_comdatNewEnglandlandingstotal} -# Plot indicator -ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'total') -ggplotObject -``` - -```{r plot_comdatNewEnglandlandingsguild} -# Plot indicator -ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'guild') -ggplotObject -``` - -```{r plot_comdatNewEnglandrevenuetotal} -# Plot indicator -ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'total') -ggplotObject -``` - -```{r plot_comdatNewEnglandrevenueguild} -# Plot indicator -ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'guild') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: By EPU - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_comdat} -# Either from Contributor or ecodata -``` - -## Implications -Landings is a key indicator of the State of the Ecosystem report. It is the key driver in fisheries and is impacted by many of the other indicators presented throughout the report. Changes in landings can occur from a number of different factors including but not limited to the underlying ecosystem, changes in climate, fisheries economics, and management measures. - -Revenue is a key indicator of the State of the Ecosystem report. Revenue is largely driven by landings and fisheries economics. There are many factors that can effect revenue. Revenue is used as a proxy for profit but only tells one side of the story. Ultimately there is a need to get a better sense of fisheries costs to adequately address commercial profits. - -## Get the data - -**Point of contact**: [Andrew Beet (Andrew.Beet@NOAA.gov)](mailto:Andrew Beet (Andrew.Beet@NOAA.gov)){.email} - -**ecodata name**: `ecodata::comdat` - -**Variable definitions** - -*Landings* 1) Name: Landings; Definition: Total landings for a region; Units: metric tons. -2) Name: Seafood Landings; Definition: Total landings used for human consumption for a region; Units: metric tons. -3) Name: Guild Landings; Definition: Total landings for an aggregate group within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. For example "Benthivore Landings"; Units: metric tons -4) Name: Guild Landings - US only; Definition: Total landings from US flagged vessels for an aggregate group within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. For example "Benthivore Landings - US only"; Units: metric tons -5) Name: Guild Seafood Landings; Definition: Total landings for an aggregate group within a region used for human consumption. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. For example "Benthivore Seafood Landings"; Units: metric tons -6) Name: Guild Management Body managed species - Landings weight; Definition: Total landings for an aggregate group of managed species within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Landings weight"; Units: metric tons -7) Name: Guild Management Body managed species - Landings weight - US only; Definition: Total landings from US flagged vessels for an aggregate group of managed species within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Landings weight - US only"; Units: metric tons -8) Name: Guild Management Body managed species - Seafood Landings; Definition: Total landings for an aggregate group of managed species within a region used for human consumption. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Seafood Landings"; Units: metric tons - *Revenue* 1) Name: Revenue; Definition: Total revenue for a region; Units: US dollars. -2) Name: Guild Revenue; Definition: Total revenue for an aggregate group within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. For example "Benthivore Revenue"; Units: US dollars -3) Name: Guild Management Body managed species - Revenue; Definition: Total revenue for an aggregate group of managed species within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Revenue"; Units: US dollars - -```{r vars_comdat} -# Pull all var names -vars <- ecodata::comdat |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Please email Andrew.Beet@NOAA.gov for further questions. Access to data will require a non-disclosure agreement with NOAA. - -**tech-doc link** - - +# Commercial Landings and Revenue {#comdat} + +**Description**: Commercial landings and revenue from dealer reports + +**Indicator family**: + +- [X] Megafauna +- [X] Economic + + +**Contributor(s)**: Sean Lucey, Andrew Beet, and Sarah Gaichas. + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The Northeast US has a long, rich history of commercial fishing. Species of all different types are caught using a plethora of different fishing gear from North Carolina to Maine. This data set aggregates the value of those landings adjusted for inflation to the start of the report year. Revenue is calculated by total and managed by the Mid-Atlantic Fisheries Management Council and New England Fisheries Management Council. Revenue can be visualized by feeding guilds as well as by region. + +## Key Results and Visualizations +Landings have fluctuated over time in all regions. In the two northern regions, a majority of the catch is used for human consumption (seafood) while less so in the Mid-Atlantic. Commercial seafood landings have been declining in both the Mid-Atlantic and Gulf of Maine regions. + +Commercial revenue by managed species has generally been down. The exception is on Georges Bank were there is a cyclical nature to revenue arising from rotational scallop management. Total fisheries revenue in the Gulf of Maine has been increasing while total revenue has declined in the Mid-Atlantic. + +### MidAtlantic + +```{r plot_comdatMidAtlanticlandingstotal} +# Plot indicator +ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'total') +ggplotObject +``` + +```{r plot_comdatMidAtlanticlandingsguild} +# Plot indicator +ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'guild') +ggplotObject +``` + +```{r plot_comdatMidAtlanticrevenuetotal} +# Plot indicator +ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'total') +ggplotObject +``` + +```{r plot_comdatMidAtlanticrevenueguild} +# Plot indicator +ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'guild') +ggplotObject +``` + +### NewEngland + +```{r plot_comdatNewEnglandlandingstotal} +# Plot indicator +ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'total') +ggplotObject +``` + +```{r plot_comdatNewEnglandlandingsguild} +# Plot indicator +ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'guild') +ggplotObject +``` + +```{r plot_comdatNewEnglandrevenuetotal} +# Plot indicator +ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'total') +ggplotObject +``` + +```{r plot_comdatNewEnglandrevenueguild} +# Plot indicator +ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'guild') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: By EPU + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_comdat} +# Either from Contributor or ecodata +``` + +## Implications +Landings is a key indicator of the State of the Ecosystem report. It is the key driver in fisheries and is impacted by many of the other indicators presented throughout the report. Changes in landings can occur from a number of different factors including but not limited to the underlying ecosystem, changes in climate, fisheries economics, and management measures. + +Revenue is a key indicator of the State of the Ecosystem report. Revenue is largely driven by landings and fisheries economics. There are many factors that can effect revenue. Revenue is used as a proxy for profit but only tells one side of the story. Ultimately there is a need to get a better sense of fisheries costs to adequately address commercial profits. + +## Get the data + +**Point of contact**: [Andrew Beet (Andrew.Beet@NOAA.gov)](mailto:Andrew Beet (Andrew.Beet@NOAA.gov)){.email} + +**ecodata name**: `ecodata::comdat` + +**Variable definitions** + +*Landings* 1) Name: Landings; Definition: Total landings for a region; Units: metric tons. +2) Name: Seafood Landings; Definition: Total landings used for human consumption for a region; Units: metric tons. +3) Name: Guild Landings; Definition: Total landings for an aggregate group within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. For example "Benthivore Landings"; Units: metric tons +4) Name: Guild Landings - US only; Definition: Total landings from US flagged vessels for an aggregate group within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. For example "Benthivore Landings - US only"; Units: metric tons +5) Name: Guild Seafood Landings; Definition: Total landings for an aggregate group within a region used for human consumption. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. For example "Benthivore Seafood Landings"; Units: metric tons +6) Name: Guild Management Body managed species - Landings weight; Definition: Total landings for an aggregate group of managed species within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Landings weight"; Units: metric tons +7) Name: Guild Management Body managed species - Landings weight - US only; Definition: Total landings from US flagged vessels for an aggregate group of managed species within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Landings weight - US only"; Units: metric tons +8) Name: Guild Management Body managed species - Seafood Landings; Definition: Total landings for an aggregate group of managed species within a region used for human consumption. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Seafood Landings"; Units: metric tons + *Revenue* 1) Name: Revenue; Definition: Total revenue for a region; Units: US dollars. +2) Name: Guild Revenue; Definition: Total revenue for an aggregate group within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. For example "Benthivore Revenue"; Units: US dollars +3) Name: Guild Management Body managed species - Revenue; Definition: Total revenue for an aggregate group of managed species within a region. Guilds include Benthivore, Benthos, Other, Piscivore, and Planktivore. Management bodies include Mid-Atlantic Fisheries Management Council (MAFMC), New England Fisheries Management Council (NEFMC), jointly managed by MAFMC and NEFMC (JOINT), and species managed by other entities (Other). For example "Benthivore MAFMC managed species - Revenue"; Units: US dollars + +```{r vars_comdat} +# Pull all var names +vars <- ecodata::comdat |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Please email Andrew.Beet@NOAA.gov for further questions. Access to data will require a non-disclosure agreement with NOAA. + +**tech-doc link** + + diff --git a/chapters/commercial_div.rmd b/chapters/commercial_div.rmd index b3df79c4..bbddc21c 100644 --- a/chapters/commercial_div.rmd +++ b/chapters/commercial_div.rmd @@ -1,124 +1,124 @@ -# Commercial Catch and Fleet Diversity {#commercial_div} - -**Description**: Permit-level species diversity and Council-level fleet diversity. - -**Indicator family**: - -- [X] Social -- [X] Economic - - -**Contributor(s)**: Geret DePiper, Min-Yang Lee - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Diversity estimates have been developed to understand whether specialization, or alternatively stovepiping, is occurring in fisheries of the Northeastern Large Marine Ecosystem. We use the average effective Shannon indices for species revenue at the permit level, for all permits landing any amount of [NEFMC](https://www.nefmc.org/) or [MAFMC](http://www.mafmc.org/) Fishery Management Plan (FMP) species within a year (including both Monkfish and Spiny Dogfish). We also use the effective Shannon index of fleet revenue diversity and count of active fleets to assess the extent to which the distribution of fishing changes across fleet segments. - -## Key Results and Visualizations -Diversity estimates have been developed for species landed by commercial vessels with New England permits, and fleets landing managed species. Over the course of the last three years, there has been a steep decline in the effective number of species being landed in the commercial fleet, and current diversity is near a low since records began (Fig. 15). Commercial fishery fleet count has rebounded in recent years, although still at levels well below the historical average (Fig. 16). Here a fleet is defined as the combination of gear type (Scallop Dredge, Clam Dredge, Other Dredge, Gillnet, Hand Gear, Longline, Bottom Trawl, Midwater Trawl, Pot, or Purse Seine) and vessel length category (Less than 30 ft, 30 to 50 ft, 50 to 75 feet, 75 ft and above). - -Commercial fishery fleet count and fleet diversity have been stable over time in the MAB, with current values near the long-term average (Fig. 15). This indicates similar commercial fleet composition and species targeting opportunities over time. Commercial fisheries are relying on fewer species relative to the mid-90s, although current species revenue diversity has recovered somewhat in the last year (Fig. 16). - -### MidAtlantic - -```{r plot_commercial_divMidAtlanticFleetcount} -# Plot indicator -ggplotObject <- ecodata::plot_commercial_div(report= 'MidAtlantic', varName= 'Fleet count') -ggplotObject -``` - -```{r plot_commercial_divMidAtlanticFleetdiversityinrevenue} -# Plot indicator -ggplotObject <- ecodata::plot_commercial_div(report= 'MidAtlantic', varName= 'Fleet diversity in revenue') -ggplotObject -``` - -```{r plot_commercial_divMidAtlanticPermitrevenuespeciesdiversity} -# Plot indicator -ggplotObject <- ecodata::plot_commercial_div(report= 'MidAtlantic', varName= 'Permit revenue species diversity') -ggplotObject -``` - -### NewEngland - -```{r plot_commercial_divNewEnglandFleetcount} -# Plot indicator -ggplotObject <- ecodata::plot_commercial_div(report= 'NewEngland', varName= 'Fleet count') -ggplotObject -``` - -```{r plot_commercial_divNewEnglandFleetdiversityinrevenue} -# Plot indicator -ggplotObject <- ecodata::plot_commercial_div(report= 'NewEngland', varName= 'Fleet diversity in revenue') -ggplotObject -``` - -```{r plot_commercial_divNewEnglandPermitrevenuespeciesdiversity} -# Plot indicator -ggplotObject <- ecodata::plot_commercial_div(report= 'NewEngland', varName= 'Permit revenue species diversity') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: MA and NE - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts -- [X] Ecosystem Reorganization - - -```{r autostats_commercial_div} -# Either from Contributor or ecodata -``` - -## Implications -Fleet diversity indices can be used to evaluate stability objectives as well as risks to fishery resilience and to maintaining equity in access to fishery resources [@gaichas_implementing_2018]. In New England, the relatively low diversity estimates for the commercial fishery are likely driven by the continued reliance on a few species, sea scallops and lobster. This trend could diminish the capacity to respond to future fishing opportunities. - -In the Mid-Atlantic, stability in commercial fleet diversity metrics suggests stable capacity to respond to the current range of fishing opportunities. - -## Get the data - -**Point of contact**: [Geret DePiper (geret.depiper@noaa.gov)](mailto:Geret DePiper (geret.depiper@noaa.gov)){.email} - -**ecodata name**: `ecodata::commercial_div` - -**Variable definitions** - -1) Name: Permit revenue species diversity; Definition: Diversity of revenue across species averaged across permits; Units: effective Shannon. -2) Name: Fleet diversity in revenue; Definition: Diversity of revenue across fleet segments; Units: effective Shannon. -3) Name: Fleet count; Definition: Number of active fleets; Units: number of fleets. - -```{r vars_commercial_div} -# Pull all var names -vars <- ecodata::commercial_div |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Request data from GARFO - -**tech-doc link** - - +# Commercial Catch and Fleet Diversity {#commercial_div} + +**Description**: Permit-level species diversity and Council-level fleet diversity. + +**Indicator family**: + +- [X] Social +- [X] Economic + + +**Contributor(s)**: Geret DePiper, Min-Yang Lee + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Diversity estimates have been developed to understand whether specialization, or alternatively stovepiping, is occurring in fisheries of the Northeastern Large Marine Ecosystem. We use the average effective Shannon indices for species revenue at the permit level, for all permits landing any amount of [NEFMC](https://www.nefmc.org/) or [MAFMC](http://www.mafmc.org/) Fishery Management Plan (FMP) species within a year (including both Monkfish and Spiny Dogfish). We also use the effective Shannon index of fleet revenue diversity and count of active fleets to assess the extent to which the distribution of fishing changes across fleet segments. + +## Key Results and Visualizations +Diversity estimates have been developed for species landed by commercial vessels with New England permits, and fleets landing managed species. Over the course of the last three years, there has been a steep decline in the effective number of species being landed in the commercial fleet, and current diversity is near a low since records began (Fig. 15). Commercial fishery fleet count has rebounded in recent years, although still at levels well below the historical average (Fig. 16). Here a fleet is defined as the combination of gear type (Scallop Dredge, Clam Dredge, Other Dredge, Gillnet, Hand Gear, Longline, Bottom Trawl, Midwater Trawl, Pot, or Purse Seine) and vessel length category (Less than 30 ft, 30 to 50 ft, 50 to 75 feet, 75 ft and above). + +Commercial fishery fleet count and fleet diversity have been stable over time in the MAB, with current values near the long-term average (Fig. 15). This indicates similar commercial fleet composition and species targeting opportunities over time. Commercial fisheries are relying on fewer species relative to the mid-90s, although current species revenue diversity has recovered somewhat in the last year (Fig. 16). + +### MidAtlantic + +```{r plot_commercial_divMidAtlanticFleetcount} +# Plot indicator +ggplotObject <- ecodata::plot_commercial_div(report= 'MidAtlantic', varName= 'Fleet count') +ggplotObject +``` + +```{r plot_commercial_divMidAtlanticFleetdiversityinrevenue} +# Plot indicator +ggplotObject <- ecodata::plot_commercial_div(report= 'MidAtlantic', varName= 'Fleet diversity in revenue') +ggplotObject +``` + +```{r plot_commercial_divMidAtlanticPermitrevenuespeciesdiversity} +# Plot indicator +ggplotObject <- ecodata::plot_commercial_div(report= 'MidAtlantic', varName= 'Permit revenue species diversity') +ggplotObject +``` + +### NewEngland + +```{r plot_commercial_divNewEnglandFleetcount} +# Plot indicator +ggplotObject <- ecodata::plot_commercial_div(report= 'NewEngland', varName= 'Fleet count') +ggplotObject +``` + +```{r plot_commercial_divNewEnglandFleetdiversityinrevenue} +# Plot indicator +ggplotObject <- ecodata::plot_commercial_div(report= 'NewEngland', varName= 'Fleet diversity in revenue') +ggplotObject +``` + +```{r plot_commercial_divNewEnglandPermitrevenuespeciesdiversity} +# Plot indicator +ggplotObject <- ecodata::plot_commercial_div(report= 'NewEngland', varName= 'Permit revenue species diversity') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: MA and NE + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts +- [X] Ecosystem Reorganization + + +```{r autostats_commercial_div} +# Either from Contributor or ecodata +``` + +## Implications +Fleet diversity indices can be used to evaluate stability objectives as well as risks to fishery resilience and to maintaining equity in access to fishery resources [@gaichas_implementing_2018]. In New England, the relatively low diversity estimates for the commercial fishery are likely driven by the continued reliance on a few species, sea scallops and lobster. This trend could diminish the capacity to respond to future fishing opportunities. + +In the Mid-Atlantic, stability in commercial fleet diversity metrics suggests stable capacity to respond to the current range of fishing opportunities. + +## Get the data + +**Point of contact**: [Geret DePiper (geret.depiper@noaa.gov)](mailto:Geret DePiper (geret.depiper@noaa.gov)){.email} + +**ecodata name**: `ecodata::commercial_div` + +**Variable definitions** + +1) Name: Permit revenue species diversity; Definition: Diversity of revenue across species averaged across permits; Units: effective Shannon. +2) Name: Fleet diversity in revenue; Definition: Diversity of revenue across fleet segments; Units: effective Shannon. +3) Name: Fleet count; Definition: Number of active fleets; Units: number of fleets. + +```{r vars_commercial_div} +# Pull all var names +vars <- ecodata::commercial_div |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Request data from GARFO + +**tech-doc link** + + diff --git a/chapters/condition.rmd b/chapters/condition.rmd index 6451817a..ccb09e68 100644 --- a/chapters/condition.rmd +++ b/chapters/condition.rmd @@ -1,115 +1,115 @@ -# Relative condition {#condition} - -**Description**: NEFSC fall bottom trawl survey relative condition - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Laurel Smith - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The health and well being of individual fish can be related to body shape condition indices (i.e., weight at a given length) such as relative condition index, which is the ratio of observed weight to predicted weight based on length [@le_cren_length-weight_1951]. Heavier and fatter fish at a given length have higher relative condition which is expected to improve growth, reproductive output, and survival. - -Le Cren’s [@le_cren_length-weight_1951] relative condition (Kn) is used in this study: - -Kn = W/W’ - -Where W’ is the relative length-specific mean weight for the population in a given region. For this work, length-weight coefficients from Wigley et al. [@wigley_length-weight_2003] were used to calculate W’. Individual fish weights were total body weights from Northeast Fisheries Science Center (NEFSC) fall bottom trawl surveys. Most finfish species included in this study are spring or summer spawners, so the fall survey was chosen to reduce variability of gonad weights in the spring survey as the fish ramp up for spawning. - -## Key Results and Visualizations -MAB: Condition factor for fish species in the MAB based on fall NEFSC bottom trawl survey data. MAB data are missing for 2017 due to survey delays, and no survey was conducted in 2020. - -A pattern of generally good condition was observed across many MAB species prior to 2000, followed by a period of generally poor condition from 2001-2010, with a mix of good and poor condition from 2011-2019. Condition was again mixed in 2023, but a number of species improved in condition from the relatively low condition in 2021 - -GOM and GB: Condition factor for fish species in the GOM and on GB based on fall NEFSC bottom trawl survey data. No survey was conducted in 2020. - -A pattern of generally good condition was observed across many species in the GOM and GB regions prior to 2000, followed by a period of generally poor condition from 2001-2010, with a mix of good and poor condition from 2011-2019. Condition was again mixed in 2023, with many species in good condition on GB but a number of species with below avereage condition in the GOM - -Preliminary General Additive Models show that for many species, temperature and copepod size structure have the strongest associations with relative fish condition. Directional trends show that some species are improving in condition with increases in water temperature, likely as a result of increased metabolic rates with sufficient food availability. Whereas other species are declining in condition with increases in water temperature, likely as a result of food limitations with increased metabolic rates, or reaching thermal temperature maxims and having to move into less productive areas in search of suitable thermal habitat. These species may be more susceptible to climate change. Preliminary change point analyses show that the decline in relative condition in the 2000s that is seen across many species aligns with a period dominated by large bodied copepods. Perretti et al. [@perretti_regime_2017] found similar periods of copepod size structure changes, and found that periods dominated by large bodied copepods were associated with low recruitment in groundfish. Relative fish condition is likely the mechanism for these linkages, where poor body condition leads to low fish recruitment. - -### MAB - -```{r plot_conditionMAB} -# Plot indicator -ggplotObject <- ecodata::plot_condition(report='MidAtlantic') -ggplotObject -``` - -### GB - -```{r plot_conditionNEGB} -# Plot indicator -ggplotObject <- ecodata::plot_condition(report='NewEngland',EPU='GB') -ggplotObject -``` - -### GOM - -```{r plot_conditionNEGOM} -# Plot indicator -ggplotObject <- ecodata::plot_condition(report='NewEngland',EPU='GOM') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: The spatial scale for relative condition was on the EPU level. - -Temporal scale: The temporal scale for relative condition was the fall NEFSC bottom trawl survey (Sept.-Nov.). - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts - - -```{r autostats_condition} -# Either from Contributor or ecodata -``` - -## Implications -These changes in condition have direct implications for stock assessments, catch quotas and management, and may indirectly impact fish recruitment and mortality. - -## Get the data - -**Point of contact**: [Laurel.smith@noaa.gov](mailto:Laurel.smith@noaa.gov){.email} - -**ecodata name**: `ecodata::condition` - -**Variable definitions** - -Species: common name for fish species EPU: Ecological Production Unit YEAR: year of condition data -MeanCond: annual mean by EPU and species of relative condition (unitless) - -```{r vars_condition} -# Pull all var names -vars <- ecodata::condition |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Relative condition {#condition} + +**Description**: NEFSC fall bottom trawl survey relative condition + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Laurel Smith + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The health and well being of individual fish can be related to body shape condition indices (i.e., weight at a given length) such as relative condition index, which is the ratio of observed weight to predicted weight based on length [@le_cren_length-weight_1951]. Heavier and fatter fish at a given length have higher relative condition which is expected to improve growth, reproductive output, and survival. + +Le Cren’s [@le_cren_length-weight_1951] relative condition (Kn) is used in this study: + +Kn = W/W’ + +Where W’ is the relative length-specific mean weight for the population in a given region. For this work, length-weight coefficients from Wigley et al. [@wigley_length-weight_2003] were used to calculate W’. Individual fish weights were total body weights from Northeast Fisheries Science Center (NEFSC) fall bottom trawl surveys. Most finfish species included in this study are spring or summer spawners, so the fall survey was chosen to reduce variability of gonad weights in the spring survey as the fish ramp up for spawning. + +## Key Results and Visualizations +MAB: Condition factor for fish species in the MAB based on fall NEFSC bottom trawl survey data. MAB data are missing for 2017 due to survey delays, and no survey was conducted in 2020. + +A pattern of generally good condition was observed across many MAB species prior to 2000, followed by a period of generally poor condition from 2001-2010, with a mix of good and poor condition from 2011-2019. Condition was again mixed in 2023, but a number of species improved in condition from the relatively low condition in 2021 + +GOM and GB: Condition factor for fish species in the GOM and on GB based on fall NEFSC bottom trawl survey data. No survey was conducted in 2020. + +A pattern of generally good condition was observed across many species in the GOM and GB regions prior to 2000, followed by a period of generally poor condition from 2001-2010, with a mix of good and poor condition from 2011-2019. Condition was again mixed in 2023, with many species in good condition on GB but a number of species with below avereage condition in the GOM + +Preliminary General Additive Models show that for many species, temperature and copepod size structure have the strongest associations with relative fish condition. Directional trends show that some species are improving in condition with increases in water temperature, likely as a result of increased metabolic rates with sufficient food availability. Whereas other species are declining in condition with increases in water temperature, likely as a result of food limitations with increased metabolic rates, or reaching thermal temperature maxims and having to move into less productive areas in search of suitable thermal habitat. These species may be more susceptible to climate change. Preliminary change point analyses show that the decline in relative condition in the 2000s that is seen across many species aligns with a period dominated by large bodied copepods. Perretti et al. [@perretti_regime_2017] found similar periods of copepod size structure changes, and found that periods dominated by large bodied copepods were associated with low recruitment in groundfish. Relative fish condition is likely the mechanism for these linkages, where poor body condition leads to low fish recruitment. + +### MAB + +```{r plot_conditionMAB} +# Plot indicator +ggplotObject <- ecodata::plot_condition(report='MidAtlantic') +ggplotObject +``` + +### GB + +```{r plot_conditionNEGB} +# Plot indicator +ggplotObject <- ecodata::plot_condition(report='NewEngland',EPU='GB') +ggplotObject +``` + +### GOM + +```{r plot_conditionNEGOM} +# Plot indicator +ggplotObject <- ecodata::plot_condition(report='NewEngland',EPU='GOM') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: The spatial scale for relative condition was on the EPU level. + +Temporal scale: The temporal scale for relative condition was the fall NEFSC bottom trawl survey (Sept.-Nov.). + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts + + +```{r autostats_condition} +# Either from Contributor or ecodata +``` + +## Implications +These changes in condition have direct implications for stock assessments, catch quotas and management, and may indirectly impact fish recruitment and mortality. + +## Get the data + +**Point of contact**: [Laurel.smith@noaa.gov](mailto:Laurel.smith@noaa.gov){.email} + +**ecodata name**: `ecodata::condition` + +**Variable definitions** + +Species: common name for fish species EPU: Ecological Production Unit YEAR: year of condition data +MeanCond: annual mean by EPU and species of relative condition (unitless) + +```{r vars_condition} +# Pull all var names +vars <- ecodata::condition |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/energy_density.rmd b/chapters/energy_density.rmd index 82959e2b..da0cfb65 100644 --- a/chapters/energy_density.rmd +++ b/chapters/energy_density.rmd @@ -1,84 +1,84 @@ -# Forage Fish Energy Density {#energy_density} - -**Description**: Energy density of alewife, butterfish, sand lance, and Atlantic mackerel varies seasonally, with seasonal estimates both higher and lower than estimates from previous decades. The data presented are the seasonal (Spring and Fall) energy density (kJ/g) for eight important forage species; Alewife, Atlantic Herring, Silver Hake, Northern Sand Lance, Atlantic Mackerel, Butterfish, Northern Shortfin Squid, and Inshore Longfin Squid. Samples are obtained from the NEFSC seasonal bottom trawl surveys and processed in the lab to estimate energy content. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Mark Wuenschel - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The energy density of prey indicates the the amount of energy passing from lower trophic levels to higher predators. Changes in ecosystem productivity and/or bioenergetic demands (e.g. metabolic increases due to rising temperature) can impact energy density. Energy density of fishes can vary widely (several fold), particularly for some species that undergo seasonal cycles in energy allocation to reproduction, energy allocation to migration, or seasonal/ontogenetic shifts in energy storage. The value of forage species to higher trophic levels is a function of the their energy density. - -Forage energy density measurements from NEFSC trawl surveys 2017-2022 are building toward a time series to evaluate trends - -## Key Results and Visualizations -Variable plotted are the mean energy density (kJ/g) for eight species across seasons and years. The reference lines represent estimates from prior studies where available for comparison. The energy content of Atlantic herring from the NEFSC trawl surveys has increased to over 7 kJ/g wet weight in spring 2023, but is still well below that observed in the 1980s and 1990s (10.6-9.4 kJ/ g wet weight). Silver hake, longfin squid (Loligo in figure) and shortfin squid (Illex in figure) remain lower than previous estimates [@steimle_energy_1985; @lawson_important_1998]. Energy density of alewife, butterfish, sand lance, and Atlantic mackerel varies seasonally, with seasonal estimates both higher and lower than estimates from previous decades. - -```{r plot_energy_densityMAB} -# Plot indicator -ggplotObject <- ecodata::plot_energy_density(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Full shelf - -Temporal scale: Spring and Fall Bottom Trawl Survey - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Ecosystem Reorganization - - -```{r autostats_energy_density} -# Either from Contributor or ecodata -``` - -## Implications -The nutritional content of forage fish changes seasonally in response to ecosystem conditions, with apparent declines in energy density for Atlantic herring and *Illex* squid relative to the 1980s, but similar energy density for other forage species. - -## Get the data - -**Point of contact**: [Mark Wuenschel (mark.wuenschel@noaa.gov)](mailto:Mark Wuenschel (mark.wuenschel@noaa.gov)){.email} - -**ecodata name**: `ecodata::energy_density` - -**Variable definitions** - -Energy Density (kJ/g) for each species. - -```{r vars_energy_density} -# Pull all var names -vars <- ecodata::energy_density |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Extensive analysis, not yet published - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Email mark.wuenschel@noaa.gov for further information. Data tables are being created to make this readily available soon. - -**tech-doc link** - - +# Forage Fish Energy Density {#energy_density} + +**Description**: Energy density of alewife, butterfish, sand lance, and Atlantic mackerel varies seasonally, with seasonal estimates both higher and lower than estimates from previous decades. The data presented are the seasonal (Spring and Fall) energy density (kJ/g) for eight important forage species; Alewife, Atlantic Herring, Silver Hake, Northern Sand Lance, Atlantic Mackerel, Butterfish, Northern Shortfin Squid, and Inshore Longfin Squid. Samples are obtained from the NEFSC seasonal bottom trawl surveys and processed in the lab to estimate energy content. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Mark Wuenschel + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The energy density of prey indicates the the amount of energy passing from lower trophic levels to higher predators. Changes in ecosystem productivity and/or bioenergetic demands (e.g. metabolic increases due to rising temperature) can impact energy density. Energy density of fishes can vary widely (several fold), particularly for some species that undergo seasonal cycles in energy allocation to reproduction, energy allocation to migration, or seasonal/ontogenetic shifts in energy storage. The value of forage species to higher trophic levels is a function of the their energy density. + +Forage energy density measurements from NEFSC trawl surveys 2017-2022 are building toward a time series to evaluate trends + +## Key Results and Visualizations +Variable plotted are the mean energy density (kJ/g) for eight species across seasons and years. The reference lines represent estimates from prior studies where available for comparison. The energy content of Atlantic herring from the NEFSC trawl surveys has increased to over 7 kJ/g wet weight in spring 2023, but is still well below that observed in the 1980s and 1990s (10.6-9.4 kJ/ g wet weight). Silver hake, longfin squid (Loligo in figure) and shortfin squid (Illex in figure) remain lower than previous estimates [@steimle_energy_1985; @lawson_important_1998]. Energy density of alewife, butterfish, sand lance, and Atlantic mackerel varies seasonally, with seasonal estimates both higher and lower than estimates from previous decades. + +```{r plot_energy_densityMAB} +# Plot indicator +ggplotObject <- ecodata::plot_energy_density(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Full shelf + +Temporal scale: Spring and Fall Bottom Trawl Survey + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Ecosystem Reorganization + + +```{r autostats_energy_density} +# Either from Contributor or ecodata +``` + +## Implications +The nutritional content of forage fish changes seasonally in response to ecosystem conditions, with apparent declines in energy density for Atlantic herring and *Illex* squid relative to the 1980s, but similar energy density for other forage species. + +## Get the data + +**Point of contact**: [Mark Wuenschel (mark.wuenschel@noaa.gov)](mailto:Mark Wuenschel (mark.wuenschel@noaa.gov)){.email} + +**ecodata name**: `ecodata::energy_density` + +**Variable definitions** + +Energy Density (kJ/g) for each species. + +```{r vars_energy_density} +# Pull all var names +vars <- ecodata::energy_density |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Extensive analysis, not yet published + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Email mark.wuenschel@noaa.gov for further information. Data tables are being created to make this readily available soon. + +**tech-doc link** + + diff --git a/chapters/engagement.rmd b/chapters/engagement.rmd index a8056ed4..9bceec74 100644 --- a/chapters/engagement.rmd +++ b/chapters/engagement.rmd @@ -1,178 +1,175 @@ -# Engagement, Reliance, and Environmental Justice in Top Fishing Communities {#engagement} - -**Description**: The data presented here are 2021 environmental justice indicators in top commercial and top recreational communities in Mid-Atlantic and New England regions, respectively. - -**Indicator family**: - -- [X] Social - - -**Contributor(s)**: Lisa Colburn, Changhua Weng, Matt Cutler, Tanya Noteva - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -We report the top ten communities most engaged in, and/or reliant upon, commercial and recreational fisheries and the degree to which these communities may be vulnerable to environmental justice issues (i.e., Poverty, Population Composition, and Personal Disruption). To select and present these communities we developed indicators (or indices) that inform the importance of fishing and relative social conditions in each community. - -The engagement and reliance indices demonstrate the importance of commercial and recreational fishing to a given community relative to other coastal communities in a region. Similarly, the environmental justice indices characterize different facets and levels of social vulnerability in a given community relative to other coastal communities in a region. - -Environmental Justice is defined in Executive Order 12898 as federal actions intended to address disproportionately high and adverse human health and environmental effects of federal actions on minority and low-income populations. Three of the existing [NOAA Fisheries Community Social Vulnerability Indicators (CSVIs)](https://www.fisheries.noaa.gov/national/socioeconomics/social-indicators-coastal-communities), the Poverty Index, Population Composition Index, and Personal Disruption Index, can be used for mandated Environmental Justice analysis. - -Commercial fishing engagement measures the number of permits and dealers, and pounds and value landed in a community, while reliance expresses these numbers based on the level of fishing activity relative to the total population of a community. Recreational fishing engagement measures shore, private vessel, and for-hire fishing effort while reliance expresses these numbers based on fishing effort relative to the population of a community. - -In 2023, we reported the top ten most engaged, and top ten most reliant commercial and recreational fishing communities and their associated environmental justice vulnerability based on 2020 data. Here we apply the same selection standard for top ten fishing communities for both sectors using 2021 data, and again examine the environmental justice vulnerability in this updated set of communities. Changes in fishing activity between years changed community engagement and reliance rankings, and changes in vulnerability indicators changed environmental justice vulnerability scores. - -In the 2023 report, we presented environmental justice vulnerability as a dichotomous variable with categories for “medium-high to high” vulnerability and “all other communities.” For the 2024 report, we have broadened this rating to three categories, including now a middle classification to also highlight those communities with “medium” vulnerability. The increased level of detail in the environmental justice rating enables a more comprehensive and nuanced analysis with attention to those communities that fall in between the most and the least vulnerable, and which may see increased (or decreased) vulnerability in the future. - -## Key Results and Visualizations -#### Mid Atlantic - -##### Commercial - -Barnegat Light, NJ, and Reedville, VA are the only communities that scored high for both commercial engagement and reliance based on 2021 data. Cape May, NJ ranked high for both commercial engagement and commercial reliance based on 2020 data but decreased to medium-high for its commercial reliance in 2021. Hatteras and Hobucken, NC are no longer listed as top ten commercial fishing communities, replaced by Hampton, VA; Swan Quarter, NC; Bowers and Little Creek, DE. - -Communities that ranked medium-high or above for one or more of the environmental justice indicators are highlighted in bright orange, including Hampton Bays/Shinnecock, NY; Atlantic City, NJ; Swan Quarter and Columbia, NC; Bower and Little Creek, DE. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Point Pleasant Beach, NJ; Hampton, VA; Beaufort and Wilmington, NC. - -Detailed scores of the three environmental justice indicators for the same communities plotted in spider plots. Communities are plotted clockwise in a descending order of commercial engagement scores from high to low, with the most highly engaged community, Cape May, NJ, listed on the top. Among these communities, ranked medium-high or above for environmental justice vulnerability, Atlantic City, NJ scored high for all of the three environmental justice indicators. - -There is also variability in the specific issues facing communities with environmental justice concerns. Swan Quarter and Columbia, NC, and Little Creek, DE scored high for the personal disruption index and the poverty index. Higher scores in population composition indicate community vulnerability related to the presence of non-white, non-English speaking, and younger populations. Hobucken, NC scored high for the personal disruption index. Newport News, VA scored medium-high for the population composition index. Bowers, DE scored medium high for the poverty index. - -Among the communities ranked medium for environmental justice vulnerability, Hampton, VA scored medium for all three environmental justice indices. Beaufort, NC and Wilmington, NC scored medium for both personal disruption index and poverty index7. Point Pleasant Beach scored medium for the personal disruption index. - -![Mid Atlantic Commerical](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/EJ_Commercial_MAB_2024.PNG){width=100%} - - -##### Recreational - -Communities plotted in the upper right section scored high for both recreational engagement and reliance using 2021 data, including Babylon, Montauk, Orient and Point Lookout, NY; Atlantic Highlands, Point Pleasant Beach, Cape May and Barnegat Light, NJ; Ocean City, MD; Nags Head, Morehead City, Hatteras Township and North Topsail Beach, NC. Stevensville and Bivalve, MD; Manteo, Vandemere and Hobuken, NC are no longer listed as top ten recreational communities, replaced by Cape May and Barnegat Light, NJ; Orient, NY; Topsail Beach, Avon and Rodanthe, NC. - -Many MAB communities ranked high for both recreational engagement and reliance in 2021. Communities that ranked medium-high or above for one or more of the environmental justice indicators are highlighted in bright orange, including Ocean City, MD and Avon, NC. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Virginia Beach, VA; Point Pleasant Beach, NJ; Morehead City and Topsail Beach, NC. - -Detailed scores of the three environmental justice indicators for the top Mid Atlantic recreational communities are plotted clockwise in a descending order of recreational engagement scores from high to low, with the most highly engaged community, Nags Head, NC, listed on the top. The two communities, Ocean City, MD and Avon, NC, that ranked medium-high or above for environmental justice indicators, both scored medium high for personal disruption index. - -Among the communities ranked medium for environmental justice vulnerability, Morehead City, NC scored medium for the personal disruption index and the poverty index. Virginia Beach, VA scored medium for the population composition index. Point Pleasant Beach, NJ scored medium for personal disruption index. Topsail Beach, NC scored medium for the poverty index. - -![Mid Atlantic Recreational](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/EJ_Recreational_MAB_2024.PNG){width=100%} - - -##### Combined - -Both commercial and recreational fishing are important activities in Montauk, NY;, Cape May, Barnegat Light and Point Pleasant Beach, NJ; and Rodanthe, NC, meaning these communities may be impacted simultaneously by commercial and recreational regulatory changes. Among these communities, Montauk, NY; Cape May and Barnegat Light, NJ; Rodanthe, NC, scored lower than medium for all of the three environmental justice indicators, indicating that environmental justice may not be a major concern in these communities at the moment. Point Pleasant Beach, NJ scored medium for the personal disruption index, indicating that environmental justice may be a moderate concern in Point Pleasant Beach. - -#### New England - -##### Commercial - -Communities plotted in the upper right section of Fig. scored high for both commercial engagement and reliance using 2021 data, including Stonington and Beals, ME. - -Communities that ranked medium-high or above for one or more of the environmental justice indicators in 2021 are highlighted in bright orange, including New Bedford and Boston, MA; and Swans Island, ME. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Port Clyde-Tenants Harbor and Stonington, ME. Winter Harbor, ME is no longer listed as a top ten commercial fishing community due to decreased commercial fishing engagement/reliance, replaced by Swans Island, ME. - -Fig. shows the detailed scores of the three environmental justice indicators for the same communities plotted in Fig. Communities are plotted clockwise in a descending order of commercial engagement scores from high to low, with the most highly engaged community, New Bedford, MA, listed on the top. - -The specific issues facing communities with environmental justice concerns in New England vary widely. New Bedford, MA is the only community in New England that scored medium high for all of the three environmental justice indicators. Boston, MA scored medium high for the population composition index and the poverty index. Higher scores in population composition indicate community vulnerability related to the presence of non-white, non-English speaking, and younger populations. By contrast, Swans Island, ME scored medium high for the personal disruption index, but did not score highly in population composition. Swan’s Island has considerable unemployment concerns, but does not have the same demographic and age structure concerns as Boston or New Bedford. The two communities that ranked medium for environmental justice vulnerability overall, Port-Clyde-Tenants Harbor and Stonington, ME, both scored medium for the personal disruption index and poverty indices. - -![New England Commerical](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/EJ_Commercial_NE_2024.PNG){width=100%} - - -##### Recreational - -In New England Dennis and Bourne, MA scored high for both recreational engagement and reliance, whereas no communities did previously2019 (Fig..). Seabrook and Newington, NH; Sandwich and Yarmouth, MA; Groton and Clinton, CT have decreased in their recreational engagement/reliance and are no longer listed as top ten recreational communities, replaced by Barnstable Town , Plymouth, Falmouth and Chatham, MA; Sronington, CT; Tiverton and New Shoreham, RI. - -There are no communities ranked medium-high or above for environmental justice indicators. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Falmouth and Dennis, MA. - -Fig. shows the detailed scores of the three environmental justice indicators for the same communities plotted in Fig. Communities are plotted clockwise in a descending order of recreational engagement scores from high to low, with the most highly engaged community, Newburyport, MA, listed on the top. - -The two communities, Dennis and Falmouth, MA, that ranked medium for environmental justice indicators, both scored medium for the poverty index, meaning that environmental justice may be a moderate concern in these communities. - -![New England Recreational](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/EJ_Recreational_NE_2024.PNG){width=100%} - - -##### Combined - -Both commercial and recreational fishing are important activities in Narragansett/Point Judith, RI; Gloucester and Chatham, MA; and Newington, NH, meaning these communities may be impacted simultaneously by commercial and recreational regulatory changes. These three communities currently score low for all of the three environmental justice indicators, indicating that environmental justice may not be a major concern in these communities at the moment based on the indicators analyzed. - -### MidAtlantic - -```{r plot_engagementMidAtlanticCommercial} -# Plot indicator -ggplotObject <- ecodata::plot_engagement(report= 'MidAtlantic', varName= 'Commercial') -ggplotObject -``` - -```{r plot_engagementMidAtlanticRecreational} -# Plot indicator -ggplotObject <- ecodata::plot_engagement(report= 'MidAtlantic', varName= 'Recreational') -ggplotObject -``` - -### NewEngland - -```{r plot_engagementNewEnglandCommercial} -# Plot indicator -ggplotObject <- ecodata::plot_engagement(report= 'NewEngland', varName= 'Commercial') -ggplotObject -``` - -```{r plot_engagementNewEnglandRecreational} -# Plot indicator -ggplotObject <- ecodata::plot_engagement(report= 'NewEngland', varName= 'Recreational') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Communities located in marine coastal counties in the U.S. - -Temporal scale: Year of 2021 - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_engagement} -# Either from Contributor or ecodata -``` - -## Implications -These indicators provide a snapshot of the presence of environmental justice issues in the most highly engaged and most highly reliant commercial and recreational fishing communities in the Mid-Atlantic and New England. These communities may be especially vulnerable to changes in fishing patterns due to regulations and/or climate change. Some changes occurred among the top fishing communities due to shifts in fishing activities, both commercial and recreational in the Mid Atlantic, and recreational in New England. Many of these communities, especially top commercial fishing communities, demonstrated medium to high environmental justice vulnerability indicating that they may be at a disadvantage responding to change. - -It is also important to note that factor scores and their associated categorical rankings can be disproportionately influenced by certain variables within each index depending upon the circumstances underlying those data in each community. By extension, the overall environmental justice rating presented in the SOE Report may also be disproportionately influenced by particular environmental justice indices that comprise the overall rating. A community may have a medium-to-high score in Poverty or Personal Disruption, but a low score in Population Composition, suggesting that while such a community may not be as vulnerable in terms of racial, ethnic, and demographic representation, it still might face substantial socioeconomic challenges based on its level of poverty or unemployment. For example, in the 2023 SOE Report, Swan’s Island, ME, receives a “medium-high to high” environmental justice rating, and is therefore included alongside New Bedford and Boston, MA, as one of the three communities to receive such a rating in New England. Swan’s Island is a rural island town off the coast of Maine and is therefore very different from large cities, such as New Bedford and Boston, in terms of its social and economic structure. However, Swan’s Island scores medium-to-high in poverty and personal disruption, which indicates vulnerability related to high unemployment and low incomes and educational attainment among its residents. These are all important environmental justice concerns as well, especially as they relate to fishing-dependent communities in the New England region. - -## Get the data - -**Point of contact**: [Lisa Colburn (lisa.l.colburn@noaa.gov); Changhua Weng (changhua.weng@noaa.gov)](mailto:Lisa Colburn (lisa.l.colburn@noaa.gov); Changhua Weng (changhua.weng@noaa.gov)){.email} - -**ecodata name**: `ecodata::engagement` - -**Variable definitions** - -1) Name: Community Name: name of the community. 2) Commercial Engagement Index: commercial engagement factor score. -3) Commercial Reliance Index: commercial reliance factor score. -4) Recreational Engagement Score: recreational engagement factor score. -5) Recreational Reliance Index: recreational reliance factor score. -6) EJ Rating: environmental justice categorical rankings. -7) Personal Disruption Index: personal disruption factor score. -8) Population Composition Index: population composition factor score. 9) Poverty Index: poverty index factor score. -10) 1std: 1 standard deviation. 11) 0.5 std: 0.5 standard deviation. - -**Indicator Category**: +# Engagement, Reliance, and Environmental Justice in Top Fishing Communities {#engagement} + +**Description**: The data presented here are 2021 environmental justice indicators in top commercial and top recreational communities in Mid-Atlantic and New England regions, respectively. + +**Indicator family**: + +- [X] Social + + +**Contributor(s)**: Lisa Colburn, Changhua Weng, Matt Cutler, Tanya Noteva + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +We report the top ten communities most engaged in, and/or reliant upon, commercial and recreational fisheries and the degree to which these communities may be vulnerable to environmental justice issues (i.e., Poverty, Population Composition, and Personal Disruption). To select and present these communities we developed indicators (or indices) that inform the importance of fishing and relative social conditions in each community. -- [X] Database pull with analysis +The engagement and reliance indices demonstrate the importance of commercial and recreational fishing to a given community relative to other coastal communities in a region. Similarly, the environmental justice indices characterize different facets and levels of social vulnerability in a given community relative to other coastal communities in a region. +Environmental Justice is defined in Executive Order 12898 as federal actions intended to address disproportionately high and adverse human health and environmental effects of federal actions on minority and low-income populations. Three of the existing [NOAA Fisheries Community Social Vulnerability Indicators (CSVIs)](https://www.fisheries.noaa.gov/national/socioeconomics/social-indicators-coastal-communities), the Poverty Index, Population Composition Index, and Personal Disruption Index, can be used for mandated Environmental Justice analysis. -## Public Availability +Commercial fishing engagement measures the number of permits and dealers, and pounds and value landed in a community, while reliance expresses these numbers based on the level of fishing activity relative to the total population of a community. Recreational fishing engagement measures shore, private vessel, and for-hire fishing effort while reliance expresses these numbers based on fishing effort relative to the population of a community. -Source data are NOT publicly available. +In 2023, we reported the top ten most engaged, and top ten most reliant commercial and recreational fishing communities and their associated environmental justice vulnerability based on 2020 data. Here we apply the same selection standard for top ten fishing communities for both sectors using 2021 data, and again examine the environmental justice vulnerability in this updated set of communities. Changes in fishing activity between years changed community engagement and reliance rankings, and changes in vulnerability indicators changed environmental justice vulnerability scores. -## Accessibility and Constraints +In the 2023 report, we presented environmental justice vulnerability as a dichotomous variable with categories for “medium-high to high” vulnerability and “all other communities.” For the 2024 report, we have broadened this rating to three categories, including now a middle classification to also highlight those communities with “medium” vulnerability. The increased level of detail in the environmental justice rating enables a more comprehensive and nuanced analysis with attention to those communities that fall in between the most and the least vulnerable, and which may see increased (or decreased) vulnerability in the future. + +## Key Results and Visualizations +#### Mid Atlantic -Please email lisa.l.colburn@noaa.gov for further information and queries of fishing and environmental justice indicator source data. +##### Commercial -**tech-doc link** - +Barnegat Light, NJ, and Reedville, VA are the only communities that scored high for both commercial engagement and reliance based on 2021 data. Cape May, NJ ranked high for both commercial engagement and commercial reliance based on 2020 data but decreased to medium-high for its commercial reliance in 2021. Hatteras and Hobucken, NC are no longer listed as top ten commercial fishing communities, replaced by Hampton, VA; Swan Quarter, NC; Bowers and Little Creek, DE. +Communities that ranked medium-high or above for one or more of the environmental justice indicators are highlighted in bright orange, including Hampton Bays/Shinnecock, NY; Atlantic City, NJ; Swan Quarter and Columbia, NC; Bower and Little Creek, DE. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Point Pleasant Beach, NJ; Hampton, VA; Beaufort and Wilmington, NC. + +Detailed scores of the three environmental justice indicators for the same communities plotted in spider plots. Communities are plotted clockwise in a descending order of commercial engagement scores from high to low, with the most highly engaged community, Cape May, NJ, listed on the top. Among these communities, ranked medium-high or above for environmental justice vulnerability, Atlantic City, NJ scored high for all of the three environmental justice indicators. + +There is also variability in the specific issues facing communities with environmental justice concerns. Swan Quarter and Columbia, NC, and Little Creek, DE scored high for the personal disruption index and the poverty index. Higher scores in population composition indicate community vulnerability related to the presence of non-white, non-English speaking, and younger populations. Hobucken, NC scored high for the personal disruption index. Newport News, VA scored medium-high for the population composition index. Bowers, DE scored medium high for the poverty index. + +Among the communities ranked medium for environmental justice vulnerability, Hampton, VA scored medium for all three environmental justice indices. Beaufort, NC and Wilmington, NC scored medium for both personal disruption index and poverty index7. Point Pleasant Beach scored medium for the personal disruption index. + +![Mid Atlantic Commerical](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/EJ_Commercial_MAB_2024.PNG){width=100%} + + +##### Recreational + +Communities plotted in the upper right section scored high for both recreational engagement and reliance using 2021 data, including Babylon, Montauk, Orient and Point Lookout, NY; Atlantic Highlands, Point Pleasant Beach, Cape May and Barnegat Light, NJ; Ocean City, MD; Nags Head, Morehead City, Hatteras Township and North Topsail Beach, NC. Stevensville and Bivalve, MD; Manteo, Vandemere and Hobuken, NC are no longer listed as top ten recreational communities, replaced by Cape May and Barnegat Light, NJ; Orient, NY; Topsail Beach, Avon and Rodanthe, NC. + +Many MAB communities ranked high for both recreational engagement and reliance in 2021. Communities that ranked medium-high or above for one or more of the environmental justice indicators are highlighted in bright orange, including Ocean City, MD and Avon, NC. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Virginia Beach, VA; Point Pleasant Beach, NJ; Morehead City and Topsail Beach, NC. + +Detailed scores of the three environmental justice indicators for the top Mid Atlantic recreational communities are plotted clockwise in a descending order of recreational engagement scores from high to low, with the most highly engaged community, Nags Head, NC, listed on the top. The two communities, Ocean City, MD and Avon, NC, that ranked medium-high or above for environmental justice indicators, both scored medium high for personal disruption index. + +Among the communities ranked medium for environmental justice vulnerability, Morehead City, NC scored medium for the personal disruption index and the poverty index. Virginia Beach, VA scored medium for the population composition index. Point Pleasant Beach, NJ scored medium for personal disruption index. Topsail Beach, NC scored medium for the poverty index. + +![Mid Atlantic Recreational](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/EJ_Recreational_MAB_2024.PNG){width=100%} + + +##### Combined + +Both commercial and recreational fishing are important activities in Montauk, NY;, Cape May, Barnegat Light and Point Pleasant Beach, NJ; and Rodanthe, NC, meaning these communities may be impacted simultaneously by commercial and recreational regulatory changes. Among these communities, Montauk, NY; Cape May and Barnegat Light, NJ; Rodanthe, NC, scored lower than medium for all of the three environmental justice indicators, indicating that environmental justice may not be a major concern in these communities at the moment. Point Pleasant Beach, NJ scored medium for the personal disruption index, indicating that environmental justice may be a moderate concern in Point Pleasant Beach. + +#### New England + +##### Commercial + +Communities plotted in the upper right section of Fig. scored high for both commercial engagement and reliance using 2021 data, including Stonington and Beals, ME. + +Communities that ranked medium-high or above for one or more of the environmental justice indicators in 2021 are highlighted in bright orange, including New Bedford and Boston, MA; and Swans Island, ME. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Port Clyde-Tenants Harbor and Stonington, ME. Winter Harbor, ME is no longer listed as a top ten commercial fishing community due to decreased commercial fishing engagement/reliance, replaced by Swans Island, ME. + +Fig. shows the detailed scores of the three environmental justice indicators for the same communities plotted in Fig. Communities are plotted clockwise in a descending order of commercial engagement scores from high to low, with the most highly engaged community, New Bedford, MA, listed on the top. + +The specific issues facing communities with environmental justice concerns in New England vary widely. New Bedford, MA is the only community in New England that scored medium high for all of the three environmental justice indicators. Boston, MA scored medium high for the population composition index and the poverty index. Higher scores in population composition indicate community vulnerability related to the presence of non-white, non-English speaking, and younger populations. By contrast, Swans Island, ME scored medium high for the personal disruption index, but did not score highly in population composition. Swan’s Island has considerable unemployment concerns, but does not have the same demographic and age structure concerns as Boston or New Bedford. The two communities that ranked medium for environmental justice vulnerability overall, Port-Clyde-Tenants Harbor and Stonington, ME, both scored medium for the personal disruption index and poverty indices. + +![New England Commerical](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/EJ_Commercial_NE_2024.PNG){width=100%} + + +##### Recreational + +In New England Dennis and Bourne, MA scored high for both recreational engagement and reliance, whereas no communities did previously2019 (Fig..). Seabrook and Newington, NH; Sandwich and Yarmouth, MA; Groton and Clinton, CT have decreased in their recreational engagement/reliance and are no longer listed as top ten recreational communities, replaced by Barnstable Town , Plymouth, Falmouth and Chatham, MA; Sronington, CT; Tiverton and New Shoreham, RI. + +There are no communities ranked medium-high or above for environmental justice indicators. Communities that ranked medium for one or more of the environmental justice indicators are highlighted in purple, including Falmouth and Dennis, MA. + +Fig. shows the detailed scores of the three environmental justice indicators for the same communities plotted in Fig. Communities are plotted clockwise in a descending order of recreational engagement scores from high to low, with the most highly engaged community, Newburyport, MA, listed on the top. + +The two communities, Dennis and Falmouth, MA, that ranked medium for environmental justice indicators, both scored medium for the poverty index, meaning that environmental justice may be a moderate concern in these communities. + +![New England Recreational](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/EJ_Recreational_NE_2024.PNG){width=100%} + + +##### Combined + +Both commercial and recreational fishing are important activities in Narragansett/Point Judith, RI; Gloucester and Chatham, MA; and Newington, NH, meaning these communities may be impacted simultaneously by commercial and recreational regulatory changes. These three communities currently score low for all of the three environmental justice indicators, indicating that environmental justice may not be a major concern in these communities at the moment based on the indicators analyzed. + +### MidAtlantic + +```{r plot_engagementMidAtlanticCommercial} +# Plot indicator +ggplotObject <- ecodata::plot_engagement(report= 'MidAtlantic', varName= 'Commercial') +ggplotObject +``` + +```{r plot_engagementMidAtlanticRecreational} +# Plot indicator +ggplotObject <- ecodata::plot_engagement(report= 'MidAtlantic', varName= 'Recreational') +ggplotObject +``` + +### NewEngland + +```{r plot_engagementNewEnglandCommercial} +# Plot indicator +ggplotObject <- ecodata::plot_engagement(report= 'NewEngland', varName= 'Commercial') +ggplotObject +``` + +```{r plot_engagementNewEnglandRecreational} +# Plot indicator +ggplotObject <- ecodata::plot_engagement(report= 'NewEngland', varName= 'Recreational') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Communities located in marine coastal counties in the U.S. + +Temporal scale: Year of 2021 + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_engagement} +# Either from Contributor or ecodata +``` + +## Implications +These indicators provide a snapshot of the presence of environmental justice issues in the most highly engaged and most highly reliant commercial and recreational fishing communities in the Mid-Atlantic and New England. These communities may be especially vulnerable to changes in fishing patterns due to regulations and/or climate change. Some changes occurred among the top fishing communities due to shifts in fishing activities, both commercial and recreational in the Mid Atlantic, and recreational in New England. Many of these communities, especially top commercial fishing communities, demonstrated medium to high environmental justice vulnerability indicating that they may be at a disadvantage responding to change. + +It is also important to note that factor scores and their associated categorical rankings can be disproportionately influenced by certain variables within each index depending upon the circumstances underlying those data in each community. By extension, the overall environmental justice rating presented in the SOE Report may also be disproportionately influenced by particular environmental justice indices that comprise the overall rating. A community may have a medium-to-high score in Poverty or Personal Disruption, but a low score in Population Composition, suggesting that while such a community may not be as vulnerable in terms of racial, ethnic, and demographic representation, it still might face substantial socioeconomic challenges based on its level of poverty or unemployment. For example, in the 2023 SOE Report, Swan’s Island, ME, receives a “medium-high to high” environmental justice rating, and is therefore included alongside New Bedford and Boston, MA, as one of the three communities to receive such a rating in New England. Swan’s Island is a rural island town off the coast of Maine and is therefore very different from large cities, such as New Bedford and Boston, in terms of its social and economic structure. However, Swan’s Island scores medium-to-high in poverty and personal disruption, which indicates vulnerability related to high unemployment and low incomes and educational attainment among its residents. These are all important environmental justice concerns as well, especially as they relate to fishing-dependent communities in the New England region. + +## Get the data + +**Point of contact**: [Lisa Colburn (lisa.l.colburn@noaa.gov); Changhua Weng (changhua.weng@noaa.gov)](mailto:Lisa Colburn (lisa.l.colburn@noaa.gov); Changhua Weng (changhua.weng@noaa.gov)){.email} + +**ecodata name**: `ecodata::engagement` + +**Variable definitions** + +1) Name: Community Name: name of the community. 2) Commercial Engagement Index: commercial engagement factor score. +3) Commercial Reliance Index: commercial reliance factor score. 4) Recreational Engagement Score: recreational engagement factor score. +5) Recreational Reliance Index: recreational reliance factor score. 6) EJ Rating: environmental justice categorical rankings. +7) Personal Disruption Index: personal disruption factor score. 8) Population Composition Index: population composition factor score. +9) Poverty Index: poverty index factor score. 10) 1std: 1 standard deviation. 11) 0.5 std: 0.5 standard deviation. + +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Please email lisa.l.colburn@noaa.gov for further information and queries of fishing and environmental justice indicator source data. + +**tech-doc link** + + diff --git a/chapters/exp_n.rmd b/chapters/exp_n.rmd index 7c00323b..d86030c1 100644 --- a/chapters/exp_n.rmd +++ b/chapters/exp_n.rmd @@ -1,107 +1,107 @@ -# Expected Number of Species {#exp_n} - -**Description**: Diversity metric from the Northeast Fisheries Science Center (NEFSC) Bottom Trawl Surveys. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Sean Lucey, Andrew Beet, and Sarah Gaichas. - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Diversity estimates have been developed to understand whether the overall structure of the ecosystem has remained stable or is changing. There are a large number of diversity indices that can be used to measure diversity; for the purposes of the State of the Ecosystem report we report on the expected number of species in a sample size (E(Sn)). These “rarefied” samples allow for comparisons between sample sites with varying number of species present. The estimate of (E(Sn) is less biased than other diversity indices which usually increase with sample size. It also has a more meaningful biological interpretation than other indices. For example, if a predator eats 10 random individuals, -E(Sn) will predict the number of species consumed. - -## Key Results and Visualizations -Due to the shift to the NOAA vessel Henry B. Bigelow in 2009 and the inability to correct for species composition, the time series is broken into Albatross and Bigelow stanzas. The only apparent trend in the data is from the Albatross time period in the Gulf of Maine. - -### MidAtlantic - -```{r plot_exp_nMidAtlanticfall} -# Plot indicator -ggplotObject <- ecodata::plot_exp_n(report= 'MidAtlantic', varName= 'fall') -ggplotObject -``` - -```{r plot_exp_nMidAtlanticspring} -# Plot indicator -ggplotObject <- ecodata::plot_exp_n(report= 'MidAtlantic', varName= 'spring') -ggplotObject -``` - -### NewEngland - -```{r plot_exp_nNewEnglandfall} -# Plot indicator -ggplotObject <- ecodata::plot_exp_n(report= 'NewEngland', varName= 'fall') -ggplotObject -``` - -```{r plot_exp_nNewEnglandspring} -# Plot indicator -ggplotObject <- ecodata::plot_exp_n(report= 'NewEngland', varName= 'spring') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: By EPU - -Temporal scale: Spring (March-May) and Fall (September-November) - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts -- [X] Ecosystem Reorganization - - -```{r autostats_exp_n} -# Either from Contributor or ecodata -``` - -## Implications -Diversity is used as a proxy for stability. Changes in ecological diversity over time may indicate altered ecosystem structure with implications for fishery productivity and management [@friedland_changes_2020]. This indicator shows that the underlying ecosystem is relatively stable with the possibility that the Gulf of Maine is becoming more diverse. Increasing adult diversity in the Gulf of Maine suggests an increase in warm-water species and should be closely monitored. - -## Get the data - -**Point of contact**: [Andrew Beet (Andrew.Beet@NOAA.gov)](mailto:Andrew Beet (Andrew.Beet@NOAA.gov)){.email} - -**ecodata name**: `ecodata::exp_n` - -**Variable definitions** - -1) Name: Season Region Expected Number of Species - Vessel; Definition: The expected number of species per 1000 randomly sampled individuals from a region for a given season on either the Albatross or Bigelow. For example "FALL GB Expected Number of Species - Albatross"; Units: n species per 1000 ind. -2) Name: Season Region Expected Number of Species Standard Deviation - Vessel; Definition: The variance around the expected number of species per 1000 randomly sampled individuals from a region for a given season on either the Albatross or Bigelow. For example "FALL GB Expected Number of Species Standard Deviation - Albatross"; Units: n species per 1000 ind. - -```{r vars_exp_n} -# Pull all var names -vars <- ecodata::exp_n |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Expected Number of Species {#exp_n} + +**Description**: Diversity metric from the Northeast Fisheries Science Center (NEFSC) Bottom Trawl Surveys. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Sean Lucey, Andrew Beet, and Sarah Gaichas. + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Diversity estimates have been developed to understand whether the overall structure of the ecosystem has remained stable or is changing. There are a large number of diversity indices that can be used to measure diversity; for the purposes of the State of the Ecosystem report we report on the expected number of species in a sample size (E(Sn)). These “rarefied” samples allow for comparisons between sample sites with varying number of species present. The estimate of (E(Sn) is less biased than other diversity indices which usually increase with sample size. It also has a more meaningful biological interpretation than other indices. For example, if a predator eats 10 random individuals, +E(Sn) will predict the number of species consumed. + +## Key Results and Visualizations +Due to the shift to the NOAA vessel Henry B. Bigelow in 2009 and the inability to correct for species composition, the time series is broken into Albatross and Bigelow stanzas. The only apparent trend in the data is from the Albatross time period in the Gulf of Maine. + +### MidAtlantic + +```{r plot_exp_nMidAtlanticfall} +# Plot indicator +ggplotObject <- ecodata::plot_exp_n(report= 'MidAtlantic', varName= 'fall') +ggplotObject +``` + +```{r plot_exp_nMidAtlanticspring} +# Plot indicator +ggplotObject <- ecodata::plot_exp_n(report= 'MidAtlantic', varName= 'spring') +ggplotObject +``` + +### NewEngland + +```{r plot_exp_nNewEnglandfall} +# Plot indicator +ggplotObject <- ecodata::plot_exp_n(report= 'NewEngland', varName= 'fall') +ggplotObject +``` + +```{r plot_exp_nNewEnglandspring} +# Plot indicator +ggplotObject <- ecodata::plot_exp_n(report= 'NewEngland', varName= 'spring') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: By EPU + +Temporal scale: Spring (March-May) and Fall (September-November) + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts +- [X] Ecosystem Reorganization + + +```{r autostats_exp_n} +# Either from Contributor or ecodata +``` + +## Implications +Diversity is used as a proxy for stability. Changes in ecological diversity over time may indicate altered ecosystem structure with implications for fishery productivity and management [@friedland_changes_2020]. This indicator shows that the underlying ecosystem is relatively stable with the possibility that the Gulf of Maine is becoming more diverse. Increasing adult diversity in the Gulf of Maine suggests an increase in warm-water species and should be closely monitored. + +## Get the data + +**Point of contact**: [Andrew Beet (Andrew.Beet@NOAA.gov)](mailto:Andrew Beet (Andrew.Beet@NOAA.gov)){.email} + +**ecodata name**: `ecodata::exp_n` + +**Variable definitions** + +1) Name: Season Region Expected Number of Species - Vessel; Definition: The expected number of species per 1000 randomly sampled individuals from a region for a given season on either the Albatross or Bigelow. For example "FALL GB Expected Number of Species - Albatross"; Units: n species per 1000 ind. +2) Name: Season Region Expected Number of Species Standard Deviation - Vessel; Definition: The variance around the expected number of species per 1000 randomly sampled individuals from a region for a given season on either the Albatross or Bigelow. For example "FALL GB Expected Number of Species Standard Deviation - Albatross"; Units: n species per 1000 ind. + +```{r vars_exp_n} +# Pull all var names +vars <- ecodata::exp_n |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/forage_index.rmd b/chapters/forage_index.rmd index 444fdb5e..09d994dc 100644 --- a/chapters/forage_index.rmd +++ b/chapters/forage_index.rmd @@ -1,117 +1,117 @@ -# Forage Fish Index {#forage_index} - -**Description**: Aggregate forage fish biomass index from fish stomach contents - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Sarah Gaichas, James Gartland, Brian Smith, Anthony Wood, Elizabeth Ng, Michael Celestino, Katie Drew, Abigail Tyrell, and James Thorson - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The amount of forage fish available in the ecosystem combined with the energy content of the forage species determines the amount of energy potentially available to predators in the ecosystem. Changes in the forage base could pose a risk to managed and protected species production. This spatially-explicit forage index estimated the combined biomass of 21 forage species using stomach contents information from 22 predatory fish species collected on bottom trawl surveys. - -In addition to an index of forage abundance in each EPU, the coastwide center of gravity for the combined forage biomass was estimated. Consistent movement of the center of gravity towards the north or east indicates a distribution shift for combined forage fish. - -## Key Results and Visualizations -Variables plotted are Fall Forage Fish Biomass Estimate and Spring Forage Fish Biomass Estimate with error bands from Fall Forage Fish Biomass Estimate SE and Spring Forage Fish Biomass Estimate SE, respectively. Time series were extended back to 1982 and forward to 2022 in this update, but this did not change the portions of trends reported previously for 1985-2021. - -### MidAtlantic - -```{r plot_forage_indexMidAtlanticindex} -# Plot indicator -ggplotObject <- ecodata::plot_forage_index(report= 'MidAtlantic', varName= 'index') -ggplotObject -``` - -```{r plot_forage_indexMidAtlanticcog} -# Plot indicator -ggplotObject <- ecodata::plot_forage_index(report= 'MidAtlantic', varName= 'cog') -ggplotObject -``` - -### NewEngland - -```{r plot_forage_indexNewEnglandindex} -# Plot indicator -ggplotObject <- ecodata::plot_forage_index(report= 'NewEngland', varName= 'index') -ggplotObject -``` - -```{r plot_forage_indexNewEnglandcog} -# Plot indicator -ggplotObject <- ecodata::plot_forage_index(report= 'NewEngland', varName= 'cog') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: by EPU and full shelf - -Temporal scale: Spring (January-June), Fall (July-December) - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_forage_index} -# Either from Contributor or ecodata -``` - -## Implications -The resulting indices for the Mid-Atlantic show a long term decrease in fall and overall higher forage fish in fall relative to spring (Fig. \@ref(fig:MAforagebio)), with highest forage biomass during fall in the early-1980s. - -In New England, the forage index shows an overall higher forage fish biomass in fall relative to spring (Fig. \@ref(fig:NEforagebio)). There is a long-term increasing trend in the spring in GOM. - -Changes in the distribution of forage biomass also affects predator distribution. Since 1982, the fall center of gravity of forage fish has moved to the north and east. The spring forage center of gravity shows higher variability than fall, but no significant trend. - -## Get the data - -**Point of contact**: [Sarah Gaichas (Sarah.Gaichas@noaa.gov)](mailto:Sarah Gaichas (Sarah.Gaichas@noaa.gov)){.email} - -**ecodata name**: `ecodata::forage_index` - -**Variable definitions** - -Spring Forage Fish Biomass Estimate = aggregate forage fish biomass months 1-6, units relative grams per stomach -Spring Forage Fish Biomass Estimate SE = standard error of aggregate forage fish biomass months 1-6, units relative grams per stomach -Fall Forage Fish Biomass Estimate = aggregate forage fish biomass months 7-12, units relative grams per stomach -Fall Forage Fish Biomass Estimate SE = standard error of aggregate forage fish biomass months 7-12, units relative grams per stomach -Fall Eastward Forage Fish Center of Gravity = average eastward location of forage fish biomass months 7-12, units kilometers -Fall Eastward Forage Fish Center of Gravity SE = standard error of average eastward location of forage fish biomass months 7-12, units kilometers -Fall Northward Forage Fish Center of Gravity = Fall Northward Forage Fish Center of Gravity SE = -Spring Eastward Forage Fish Center of Gravity = Spring Eastward Forage Fish Center of Gravity SE = -Spring Northward Forage Fish Center of Gravity = Spring Northward Forage Fish Center of Gravity SE = - -```{r vars_forage_index} -# Pull all var names -vars <- ecodata::forage_index |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Forage Fish Index {#forage_index} + +**Description**: Aggregate forage fish biomass index from fish stomach contents + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Sarah Gaichas, James Gartland, Brian Smith, Anthony Wood, Elizabeth Ng, Michael Celestino, Katie Drew, Abigail Tyrell, and James Thorson + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The amount of forage fish available in the ecosystem combined with the energy content of the forage species determines the amount of energy potentially available to predators in the ecosystem. Changes in the forage base could pose a risk to managed and protected species production. This spatially-explicit forage index estimated the combined biomass of 21 forage species using stomach contents information from 22 predatory fish species collected on bottom trawl surveys. + +In addition to an index of forage abundance in each EPU, the coastwide center of gravity for the combined forage biomass was estimated. Consistent movement of the center of gravity towards the north or east indicates a distribution shift for combined forage fish. + +## Key Results and Visualizations +Variables plotted are Fall Forage Fish Biomass Estimate and Spring Forage Fish Biomass Estimate with error bands from Fall Forage Fish Biomass Estimate SE and Spring Forage Fish Biomass Estimate SE, respectively. Time series were extended back to 1982 and forward to 2022 in this update, but this did not change the portions of trends reported previously for 1985-2021. + +### MidAtlantic + +```{r plot_forage_indexMidAtlanticindex} +# Plot indicator +ggplotObject <- ecodata::plot_forage_index(report= 'MidAtlantic', varName= 'index') +ggplotObject +``` + +```{r plot_forage_indexMidAtlanticcog} +# Plot indicator +ggplotObject <- ecodata::plot_forage_index(report= 'MidAtlantic', varName= 'cog') +ggplotObject +``` + +### NewEngland + +```{r plot_forage_indexNewEnglandindex} +# Plot indicator +ggplotObject <- ecodata::plot_forage_index(report= 'NewEngland', varName= 'index') +ggplotObject +``` + +```{r plot_forage_indexNewEnglandcog} +# Plot indicator +ggplotObject <- ecodata::plot_forage_index(report= 'NewEngland', varName= 'cog') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: by EPU and full shelf + +Temporal scale: Spring (January-June), Fall (July-December) + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_forage_index} +# Either from Contributor or ecodata +``` + +## Implications +The resulting indices for the Mid-Atlantic show a long term decrease in fall and overall higher forage fish in fall relative to spring (Fig. \@ref(fig:MAforagebio)), with highest forage biomass during fall in the early-1980s. + +In New England, the forage index shows an overall higher forage fish biomass in fall relative to spring (Fig. \@ref(fig:NEforagebio)). There is a long-term increasing trend in the spring in GOM. + +Changes in the distribution of forage biomass also affects predator distribution. Since 1982, the fall center of gravity of forage fish has moved to the north and east. The spring forage center of gravity shows higher variability than fall, but no significant trend. + +## Get the data + +**Point of contact**: [Sarah Gaichas (Sarah.Gaichas@noaa.gov)](mailto:Sarah Gaichas (Sarah.Gaichas@noaa.gov)){.email} + +**ecodata name**: `ecodata::forage_index` + +**Variable definitions** + +Spring Forage Fish Biomass Estimate = aggregate forage fish biomass months 1-6, units relative grams per stomach +Spring Forage Fish Biomass Estimate SE = standard error of aggregate forage fish biomass months 1-6, units relative grams per stomach +Fall Forage Fish Biomass Estimate = aggregate forage fish biomass months 7-12, units relative grams per stomach +Fall Forage Fish Biomass Estimate SE = standard error of aggregate forage fish biomass months 7-12, units relative grams per stomach +Fall Eastward Forage Fish Center of Gravity = average eastward location of forage fish biomass months 7-12, units kilometers +Fall Eastward Forage Fish Center of Gravity SE = standard error of average eastward location of forage fish biomass months 7-12, units kilometers +Fall Northward Forage Fish Center of Gravity = Fall Northward Forage Fish Center of Gravity SE = Spring Eastward Forage Fish Center of Gravity = +Spring Eastward Forage Fish Center of Gravity SE = Spring Northward Forage Fish Center of Gravity = +Spring Northward Forage Fish Center of Gravity SE = + +```{r vars_forage_index} +# Pull all var names +vars <- ecodata::forage_index |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/gom_salmon.rmd b/chapters/gom_salmon.rmd index bf11b9a0..86274500 100644 --- a/chapters/gom_salmon.rmd +++ b/chapters/gom_salmon.rmd @@ -1,86 +1,85 @@ -# Gulf of Maine Atlantic salmon {#gom_salmon} - -**Description**: The data presented here are time series of documented Atlantic salmon returns to Gulf of Maine rivers since 1972 and return rates for two sea winter returns from hatchery smolt stockings. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Jon Kocik, Justin Stevens and Tim Sheehan - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -US Atlantic salmon historically ranged as far south as Long Island Sound but current populations are restricted to Maine. Populations south of Maine were extirpated in the 1800’s. The Gulf of Maine Atlantic Salmon Distinct Population Segment (GOM DPS) supported local commercial fisheries until a 1947 closure. Populations remained low (< 500) until the modern hatchery restoration programs started in the late 1960’s. This program led to relatively rapid population rebuilding (Figure ##). -GOM Atlantic salmon abundance is tracked through adult counts at traps in large rivers and redd surveys in smaller coastal drainages (USASAC 2023). These fish typically return to freshwater to spawn after two winters at sea (2SW) with higher return rates than those spending one winter at sea (1SW) or longer. Most 2SW spawners are female hatchery-origin fish, making their return rate a crucial measure of marine productivity. Return rates are calculated from known smolt stocking numbers and locations and counts of returning hatchery adults to the Penobscot River (@stevens_modeling_2019). Together, abundance and return rates allow monitoring of population status (Figure ##). - -## Key Results and Visualizations -A significant and persistent decrease in marine productivity of North American Atlantic salmon populations occurred around 1990, which impacted U.S. adult spawner abundance (Figure ##). The GOM DPS was listed as Endangered under the ESA in 2000. Primary threats are dams, marine survival and climate change. Decreased productivity was linked to a regime shift that resulted in a cascading effect of ecosystem conditions driven by large scale oceanic changes. -Atlantic salmon adult returns in 2022 were estimated at 1,520 with 85% originating from hatchery supplementation and 86% returning to the Penobscot River. Abundance remains critically low relative to recovery targets of 6,000 naturally-reared returns with only an estimated 218 natural returns. Return rate of Penobscot River hatchery origin 2SW salmon was 0.17%, over 2.5 times the rate for 2021 returns (Figure ##). While rates are comparable to the last decade they are significantly lower than in the past. - -```{r plot_gom_salmonMAB} -# Plot indicator -ggplotObject <- ecodata::plot_gom_salmon(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: EPU = Gulf of Maine - -Temporal scale: Annually 1972-2022 - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_gom_salmon} -# Either from Contributor or ecodata -``` - -## Implications -These large scale changes have impacted temperature, current patterns, and primary and secondary production dynamics throughout the Northwest Atlantic range of Atlantic salmon. Changes impacted the forage base overall and especially capelin where distribution, abundance, size and energy density changed rapidly. Although many ecosystem conditions in the Northwest Atlantic have reverted back to their pre-1990 conditions, a corresponding increase in U.S. Atlantic salmon marine productivity has not been noted. - -## Get the data - -**Point of contact**: [Jon Kocik (john.kocik@noaa.gov); Tim Sheehan (tim.sheehan@noaa.gov); Debra Palka (debra.palka@noaa.gov)](mailto:Jon Kocik (john.kocik@noaa.gov); Tim Sheehan (tim.sheehan@noaa.gov); Debra Palka (debra.palka@noaa.gov)){.email} - -**ecodata name**: `ecodata::gom_salmon` - -**Variable definitions** - -1) Return Year. -2) GoM Salmon Total = number of documented Atlantic salmon returns to Gulf of Maine rivers in number of animals -3) PSAR -2SW = return rates for 2 sea winter returns from hatchery smolt stockings in percentage. - -```{r vars_gom_salmon} -# Pull all var names -vars <- ecodata::gom_salmon |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Gulf of Maine Atlantic salmon {#gom_salmon} + +**Description**: The data presented here are time series of documented Atlantic salmon returns to Gulf of Maine rivers since 1972 and return rates for two sea winter returns from hatchery smolt stockings. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Jon Kocik, Justin Stevens and Tim Sheehan + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +US Atlantic salmon historically ranged as far south as Long Island Sound but current populations are restricted to Maine. Populations south of Maine were extirpated in the 1800’s. The Gulf of Maine Atlantic Salmon Distinct Population Segment (GOM DPS) supported local commercial fisheries until a 1947 closure. Populations remained low (< 500) until the modern hatchery restoration programs started in the late 1960’s. This program led to relatively rapid population rebuilding (Figure ##). +GOM Atlantic salmon abundance is tracked through adult counts at traps in large rivers and redd surveys in smaller coastal drainages (USASAC 2023). These fish typically return to freshwater to spawn after two winters at sea (2SW) with higher return rates than those spending one winter at sea (1SW) or longer. Most 2SW spawners are female hatchery-origin fish, making their return rate a crucial measure of marine productivity. Return rates are calculated from known smolt stocking numbers and locations and counts of returning hatchery adults to the Penobscot River (@stevens_modeling_2019). Together, abundance and return rates allow monitoring of population status (Figure ##). + +## Key Results and Visualizations +A significant and persistent decrease in marine productivity of North American Atlantic salmon populations occurred around 1990, which impacted U.S. adult spawner abundance (Figure ##). The GOM DPS was listed as Endangered under the ESA in 2000. Primary threats are dams, marine survival and climate change. Decreased productivity was linked to a regime shift that resulted in a cascading effect of ecosystem conditions driven by large scale oceanic changes. +Atlantic salmon adult returns in 2022 were estimated at 1,520 with 85% originating from hatchery supplementation and 86% returning to the Penobscot River. Abundance remains critically low relative to recovery targets of 6,000 naturally-reared returns with only an estimated 218 natural returns. Return rate of Penobscot River hatchery origin 2SW salmon was 0.17%, over 2.5 times the rate for 2021 returns (Figure ##). While rates are comparable to the last decade they are significantly lower than in the past. + +```{r plot_gom_salmonMAB} +# Plot indicator +ggplotObject <- ecodata::plot_gom_salmon(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: EPU = Gulf of Maine + +Temporal scale: Annually 1972-2022 + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_gom_salmon} +# Either from Contributor or ecodata +``` + +## Implications +These large scale changes have impacted temperature, current patterns, and primary and secondary production dynamics throughout the Northwest Atlantic range of Atlantic salmon. Changes impacted the forage base overall and especially capelin where distribution, abundance, size and energy density changed rapidly. Although many ecosystem conditions in the Northwest Atlantic have reverted back to their pre-1990 conditions, a corresponding increase in U.S. Atlantic salmon marine productivity has not been noted. + +## Get the data + +**Point of contact**: [Jon Kocik (john.kocik@noaa.gov); Tim Sheehan (tim.sheehan@noaa.gov); Debra Palka (debra.palka@noaa.gov)](mailto:Jon Kocik (john.kocik@noaa.gov); Tim Sheehan (tim.sheehan@noaa.gov); Debra Palka (debra.palka@noaa.gov)){.email} + +**ecodata name**: `ecodata::gom_salmon` + +**Variable definitions** + +1) Return Year. 2) GoM Salmon Total = number of documented Atlantic salmon returns to Gulf of Maine rivers in number of animals +3) PSAR -2SW = return rates for 2 sea winter returns from hatchery smolt stockings in percentage. + +```{r vars_gom_salmon} +# Pull all var names +vars <- ecodata::gom_salmon |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/grayseal.rmd b/chapters/grayseal.rmd index 7664886e..bd7dc2d9 100644 --- a/chapters/grayseal.rmd +++ b/chapters/grayseal.rmd @@ -1,88 +1,88 @@ -# Gray Seal Bycatch {#grayseal} - -**Description**: The data presented here are time series of the species specific estimates of bycatch from U.S. North Atlantic commercial fisheries. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Debra Palka, Kristen Procoda, Marjorie Lyssikatos, Kimberly Murray - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Protected species include marine mammals protected under the Marine Mammal Protection Act, endangered and threatened species protected under the Endangered Species Act, and migratory birds protected under the Migratory Bird Treaty Act. In the Northeast U.S., endangered/threatened species include Atlantic salmon, Atlantic and shortnose sturgeon, all sea turtle species, and five baleen whales. Northeast U.S. marine mammals that are protected under currently active Take Reduction Teams under the Marine Mammal Protection Act include harbor porpoises, bottlenose dolphins, pilot whales, North Atlantic right whales and humpback whales. Fishery management objectives for protected species generally focus on reducing threats and on habitat conservation/restoration. Protected species objectives include managing bycatch to remain below potential biological removal (PBR) thresholds, recovering endangered populations, and monitoring unusual mortality events (UMEs). Here we report on the status of these actions as well as indicating the potential for future interactions driven by observed and predicted ecosystem changes in the Northeast U.S. - -## Key Results and Visualizations -For each marine mammal species, variables plotted are the total estimated bycatch from all U.S. North Atlantic commercial fisheries by year (Fig. x). In 2022, a total of x marine mammals from 5 species was estimated to have been bycaught from 6 fisheries (including bottom gillnets, drift gillnets, bottom trawls, midwater trawls, pair trawls, and pelagic longline). Estimates of seabird bycatch from these fisheries are currently being updated and can be reported next year. -Marine mammal species specific bycatch estimates from 2022 are below current PBR thresholds, thus meeting current management objectives (Fig. x). However, historically, bycatch was above PBR for harbor porpoises and pilot whales. More recently bycatch of gray seals has increased since 1995 and on average leveled out after 2010; however, there is a large amount of inter-annual variability (Fig. x). - -```{r plot_graysealMAB} -# Plot indicator -ggplotObject <- ecodata::plot_grayseal(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: US waters from North Carolina to Canada from the U.S. coastline to the U.S. exclusive economic zone, 200 nautical miles offshore, thus, including all EPU, the full shelf and beyond. - -Temporal scale: Annual from 1990 to 2022. - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_grayseal} -# Either from Contributor or ecodata -``` - -## Implications -Bycatch reduction plans developed by Marine Mammal Protection Act’s Take Reduction Teams were needed to reduce the bycatch of harbor porpoises starting in 1997 (Fig. x) and reduce the bycatch of pilot whales starting in xxxx. The more recent reduction in harbor porpoise bycatch since 2010 is probably due not only to the Take Reduction Team’s bycatch mitigation plan but also related to a corresponding decrease in gillnet fishing effort and in seasonal shifts of harbor porpoises that appear to be related to climatic changes (see Fig xx). -The high level of variability of the annual gray seal estimates may be due in part by variable gillnet landings, limited observer coverage, especially since 2019 due to Covid-19, and the effects of the Northeast pinniped unusual mortality event for harbor and gray seals that was declared in 2022. The unusual mortalities were mostly located off the coast Maine and considered to be due to infectious diseases. - -## Get the data - -**Point of contact**: [Debra Palka (debra.palka@noaa.gov); Kristin Precoda (kristin.precoda@noaa.gov)](mailto:Debra Palka (debra.palka@noaa.gov); Kristin Precoda (kristin.precoda@noaa.gov)){.email} - -**ecodata name**: `ecodata::grayseal` - -**Variable definitions** - -1) pbr = Potential Biological Removal level. Unit = n (number of animals) -2) totalest1y = Total bycatch of 1 year annual estimate. Unit = n (number of animals) -3) totalest5y = Total bycatch of 5 year running average estimate. Unit = n (number of animals) -4) total5yLCI = Lower 95% confidence interval of totalest5y. Unit = n (number of animals) -5) total5yUCI= Upper 95% confidence interval of totalest5y. Unit = n (number of animals) -6) Ratio1ytoPBR = ratio of the total bycatch of 1 year annual estimate relative to the corresponding annual pbr. - -```{r vars_grayseal} -# Pull all var names -vars <- ecodata::grayseal |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Gray Seal Bycatch {#grayseal} + +**Description**: The data presented here are time series of the species specific estimates of bycatch from U.S. North Atlantic commercial fisheries. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Debra Palka, Kristen Procoda, Marjorie Lyssikatos, Kimberly Murray + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Protected species include marine mammals protected under the Marine Mammal Protection Act, endangered and threatened species protected under the Endangered Species Act, and migratory birds protected under the Migratory Bird Treaty Act. In the Northeast U.S., endangered/threatened species include Atlantic salmon, Atlantic and shortnose sturgeon, all sea turtle species, and five baleen whales. Northeast U.S. marine mammals that are protected under currently active Take Reduction Teams under the Marine Mammal Protection Act include harbor porpoises, bottlenose dolphins, pilot whales, North Atlantic right whales and humpback whales. Fishery management objectives for protected species generally focus on reducing threats and on habitat conservation/restoration. Protected species objectives include managing bycatch to remain below potential biological removal (PBR) thresholds, recovering endangered populations, and monitoring unusual mortality events (UMEs). Here we report on the status of these actions as well as indicating the potential for future interactions driven by observed and predicted ecosystem changes in the Northeast U.S. + +## Key Results and Visualizations +For each marine mammal species, variables plotted are the total estimated bycatch from all U.S. North Atlantic commercial fisheries by year (Fig. x). In 2022, a total of x marine mammals from 5 species was estimated to have been bycaught from 6 fisheries (including bottom gillnets, drift gillnets, bottom trawls, midwater trawls, pair trawls, and pelagic longline). Estimates of seabird bycatch from these fisheries are currently being updated and can be reported next year. +Marine mammal species specific bycatch estimates from 2022 are below current PBR thresholds, thus meeting current management objectives (Fig. x). However, historically, bycatch was above PBR for harbor porpoises and pilot whales. More recently bycatch of gray seals has increased since 1995 and on average leveled out after 2010; however, there is a large amount of inter-annual variability (Fig. x). + +```{r plot_graysealMAB} +# Plot indicator +ggplotObject <- ecodata::plot_grayseal(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: US waters from North Carolina to Canada from the U.S. coastline to the U.S. exclusive economic zone, 200 nautical miles offshore, thus, including all EPU, the full shelf and beyond. + +Temporal scale: Annual from 1990 to 2022. + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_grayseal} +# Either from Contributor or ecodata +``` + +## Implications +Bycatch reduction plans developed by Marine Mammal Protection Act’s Take Reduction Teams were needed to reduce the bycatch of harbor porpoises starting in 1997 (Fig. x) and reduce the bycatch of pilot whales starting in xxxx. The more recent reduction in harbor porpoise bycatch since 2010 is probably due not only to the Take Reduction Team’s bycatch mitigation plan but also related to a corresponding decrease in gillnet fishing effort and in seasonal shifts of harbor porpoises that appear to be related to climatic changes (see Fig xx). +The high level of variability of the annual gray seal estimates may be due in part by variable gillnet landings, limited observer coverage, especially since 2019 due to Covid-19, and the effects of the Northeast pinniped unusual mortality event for harbor and gray seals that was declared in 2022. The unusual mortalities were mostly located off the coast Maine and considered to be due to infectious diseases. + +## Get the data + +**Point of contact**: [Debra Palka (debra.palka@noaa.gov); Kristin Precoda (kristin.precoda@noaa.gov)](mailto:Debra Palka (debra.palka@noaa.gov); Kristin Precoda (kristin.precoda@noaa.gov)){.email} + +**ecodata name**: `ecodata::grayseal` + +**Variable definitions** + +1) pbr = Potential Biological Removal level. Unit = n (number of animals) +2) totalest1y = Total bycatch of 1 year annual estimate. Unit = n (number of animals) +3) totalest5y = Total bycatch of 5 year running average estimate. Unit = n (number of animals) +4) total5yLCI = Lower 95% confidence interval of totalest5y. Unit = n (number of animals) +5) total5yUCI= Upper 95% confidence interval of totalest5y. Unit = n (number of animals) +6) Ratio1ytoPBR = ratio of the total bycatch of 1 year annual estimate relative to the corresponding annual pbr. + +```{r vars_grayseal} +# Pull all var names +vars <- ecodata::grayseal |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/gsi.rmd b/chapters/gsi.rmd index 22db3f7d..300dcd82 100644 --- a/chapters/gsi.rmd +++ b/chapters/gsi.rmd @@ -1,105 +1,105 @@ -# Gulf Stream Index {#gsi} - -**Description**: The monthly Gulf Stream North Wall Index presented here are based on the gridded EN.4.2.2 analyses dataset from 1954 to 2022 (https://www.metoffice.gov.uk/hadobs/en4/), calculated following @joyce_relationship_2009. - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Zhuomin Chen - -**Affiliations**: ? - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The T200-based Gulf Stream Index is calculated as the standardized first principal component time series from the empirical orthogonal function (EOF) analysis of the 200 m temperature time series at the 20 base points (selected along the climatological 15°C isotherm at 200 m between 74° and 55°W) and it represents the meridional fluctuation of the Gulf Stream North Wall [@joyce_meridional_2019; @chi_distinction_2019]. - -## Key Results and Visualizations -The Gulf Stream Index suggest that recent years (2021-2022) the GS almost maintains its relative northward shift relative to the long-term mean. - -### MidAtlantic - -```{r plot_gsiMidAtlanticgsi} -# Plot indicator -ggplotObject <- ecodata::plot_gsi(report= 'MidAtlantic', varName= 'gsi') -ggplotObject -``` - -```{r plot_gsiMidAtlanticwestgsi} -# Plot indicator -ggplotObject <- ecodata::plot_gsi(report= 'MidAtlantic', varName= 'westgsi') -ggplotObject -``` - -### NewEngland - -```{r plot_gsiNewEnglandgsi} -# Plot indicator -ggplotObject <- ecodata::plot_gsi(report= 'NewEngland', varName= 'gsi') -ggplotObject -``` - -```{r plot_gsiNewEnglandwestgsi} -# Plot indicator -ggplotObject <- ecodata::plot_gsi(report= 'NewEngland', varName= 'westgsi') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Gulf Stream between 74°W and 55°W - -Temporal scale: Monthly from 1954 to 2022 - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_gsi} -# Either from Contributor or ecodata -``` - -## Implications -The Gulf Stream North Wall Index indicates the meridional shift of the Gulf Stream position on monthly timescale, which may affect the slope water properties intruding onto the continental shelf. The GSNW index is also suggested to be an good indicator for biomass distribution of multiple marine fishes (e.g., silver hake @nye_gulf_2011). As the Gulf Stream has become less stable and shifted northward in the last decade (Fig. ), warmer ocean temperatures have been observed on the northeast shelf @zhang_role_2007, and a higher proportion of Warm Slope Water has been present in the Gulf of Maine Northeast Channel @goddard_extreme_2015, and sea surface height along the U.S. east coast has increased. Since 2008, the Gulf Stream has moved closer to the Grand Banks, reducing the supply of cold, fresh, and oxygen-rich Labrador Current waters to the Northwest Atlantic Shelf @goncalves_neto_changes_2021 - -## Get the data - -**Point of contact**: [Zhuomin Chen (zhuomin.chen@uconn.edu)](mailto:Zhuomin Chen (zhuomin.chen@uconn.edu)){.email} - -**ecodata name**: `ecodata::gsi` - -**Variable definitions** - -Name: GSI; Definition: Gulf Stream North Wall Index (T200-based using EN4 datasets); Units: None. - -```{r vars_gsi} -# Pull all var names -vars <- ecodata::gsi |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Syntheses of published information -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Gulf Stream Index {#gsi} + +**Description**: The monthly Gulf Stream North Wall Index presented here are based on the gridded EN.4.2.2 analyses dataset from 1954 to 2022 (https://www.metoffice.gov.uk/hadobs/en4/), calculated following @joyce_relationship_2009. + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Zhuomin Chen + +**Affiliations**: ? + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The T200-based Gulf Stream Index is calculated as the standardized first principal component time series from the empirical orthogonal function (EOF) analysis of the 200 m temperature time series at the 20 base points (selected along the climatological 15°C isotherm at 200 m between 74° and 55°W) and it represents the meridional fluctuation of the Gulf Stream North Wall [@joyce_meridional_2019; @chi_distinction_2019]. + +## Key Results and Visualizations +The Gulf Stream Index suggest that recent years (2021-2022) the GS almost maintains its relative northward shift relative to the long-term mean. + +### MidAtlantic + +```{r plot_gsiMidAtlanticgsi} +# Plot indicator +ggplotObject <- ecodata::plot_gsi(report= 'MidAtlantic', varName= 'gsi') +ggplotObject +``` + +```{r plot_gsiMidAtlanticwestgsi} +# Plot indicator +ggplotObject <- ecodata::plot_gsi(report= 'MidAtlantic', varName= 'westgsi') +ggplotObject +``` + +### NewEngland + +```{r plot_gsiNewEnglandgsi} +# Plot indicator +ggplotObject <- ecodata::plot_gsi(report= 'NewEngland', varName= 'gsi') +ggplotObject +``` + +```{r plot_gsiNewEnglandwestgsi} +# Plot indicator +ggplotObject <- ecodata::plot_gsi(report= 'NewEngland', varName= 'westgsi') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Gulf Stream between 74°W and 55°W + +Temporal scale: Monthly from 1954 to 2022 + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_gsi} +# Either from Contributor or ecodata +``` + +## Implications +The Gulf Stream North Wall Index indicates the meridional shift of the Gulf Stream position on monthly timescale, which may affect the slope water properties intruding onto the continental shelf. The GSNW index is also suggested to be an good indicator for biomass distribution of multiple marine fishes (e.g., silver hake @nye_gulf_2011). As the Gulf Stream has become less stable and shifted northward in the last decade (Fig. ), warmer ocean temperatures have been observed on the northeast shelf @zhang_role_2007, and a higher proportion of Warm Slope Water has been present in the Gulf of Maine Northeast Channel @goddard_extreme_2015, and sea surface height along the U.S. east coast has increased. Since 2008, the Gulf Stream has moved closer to the Grand Banks, reducing the supply of cold, fresh, and oxygen-rich Labrador Current waters to the Northwest Atlantic Shelf @goncalves_neto_changes_2021 + +## Get the data + +**Point of contact**: [Zhuomin Chen (zhuomin.chen@uconn.edu)](mailto:Zhuomin Chen (zhuomin.chen@uconn.edu)){.email} + +**ecodata name**: `ecodata::gsi` + +**Variable definitions** + +Name: GSI; Definition: Gulf Stream North Wall Index (T200-based using EN4 datasets); Units: None. + +```{r vars_gsi} +# Pull all var names +vars <- ecodata::gsi |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Syntheses of published information +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/habitat_diversity.rmd b/chapters/habitat_diversity.rmd index 58136898..01d48f05 100644 --- a/chapters/habitat_diversity.rmd +++ b/chapters/habitat_diversity.rmd @@ -1,106 +1,94 @@ -# Species Richness {#habitat_diversity} - -**Description**: Abundance data were extracted from the NEFSC’s SVDBS database using Survdat for 55 fish species regularly sampled on spring and fall NEFSC bottom trawl surveys (see SOE Tech Doc for a list). Data were converted to presence/absence for species richness modeling. - -Species Richness was estimated using “joint” predictions of presence-absence in 100 randomly-drawn assemblages simulated by a joint species distribution model (part of the Northeast Regional Habitat Assessment), which was fitted to observations for 55 common species sampled by the NEFSC bottom trawl survey during the spring and fall of 2000-2019. The model controls for differences in capture efficiency across survey vessels, permitting predictions on a common scale (here calibrated to the Albatross IV). See SOE Tech Doc for details of the model and environmental covariates included. - -**Indicator family**: - -- [X] Habitat -- [X] Megafauna - - -**Contributor(s)**: Chris Haak, Laurel Smith and Tori Kentner - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Indices of species richness can indicate the health of the ecosystem as a metric of biodiversity. In this case, looking at a specified set of 55 species that are commonly caught in the NEFSC bottom trawl survey by year and EPU can indicate species distribution shifts as species richness declines in some areas and increases in others. - -## Key Results and Visualizations -Trends of declining richness are seen in the more southerly regions (i.e., the Mid-Atlantic Bight) and increasing richness in the more northerly regions (i.e., the Gulf of Maine). These patterns reflect the decreasing occurrence of cooler-water species in the south and the growing prevalence of warm-water species in the north, likely as a result of warming water temperatures. - -### MidAtlantic - -```{r plot_habitat_diversityMidAtlanticDiversity} -# Plot indicator -ggplotObject <- ecodata::plot_habitat_diversity(report= 'MidAtlantic', varName= 'Diversity') -ggplotObject -``` - -```{r plot_habitat_diversityMidAtlanticRichness} -# Plot indicator -ggplotObject <- ecodata::plot_habitat_diversity(report= 'MidAtlantic', varName= 'Richness') -ggplotObject -``` - -### NewEngland - -```{r plot_habitat_diversityNewEnglandDiversity} -# Plot indicator -ggplotObject <- ecodata::plot_habitat_diversity(report= 'NewEngland', varName= 'Diversity') -ggplotObject -``` - -```{r plot_habitat_diversityNewEnglandRichness} -# Plot indicator -ggplotObject <- ecodata::plot_habitat_diversity(report= 'NewEngland', varName= 'Richness') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Species richness by EPU - -Temporal scale: Spring (March-May) and fall (September-November) NEFSC bottom trawl surveys from 2000-2019 - -**Synthesis Theme**: - -- [X] Ecosystem Reorganization - - -```{r autostats_habitat_diversity} -# Either from Contributor or ecodata -``` - -## Implications -This species richness index provides a summary of how commonly caught fish in the NEFSC bottom trawl survey are changing over time, while controlling for differences in capture efficiency across survey vessels. The shifts of declining species richness in the Mid-Atlantic Bight indicate that fisheries in this region may need to shift away from reliance on these species that may be at the southern edge of their distributions, and may need to expand fisheries to more southerly species. The increase of species richness in the northerly regions such as the Gulf of Maine indicates that there is a likely an influx of southerly species, and management quotas may need to be adjusted between regions. These are likely direct implications that warming water temperatures have on fisheries management. - -## Get the data - -**Point of contact**: [Laurel Smith (Laurel.smith@noaa.gov)](mailto:Laurel Smith (Laurel.smith@noaa.gov)){.email} - -**ecodata name**: `ecodata::habitat_diversity` - -**Variable definitions** - -1) Name: Year, Definition: year of species richness data, 2) Name: Species Richness, Definition: Species richness - -```{r vars_habitat_diversity} -# Pull all var names -vars <- ecodata::habitat_diversity |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Species Richness {#habitat_diversity} + +**Description**: Abundance data were extracted from the NEFSC’s SVDBS database using Survdat for 55 fish species regularly sampled on spring and fall NEFSC bottom trawl surveys (see SOE Tech Doc for a list). Data were converted to presence/absence for species richness modeling. + +Species Richness was estimated using “joint” predictions of presence-absence in 100 randomly-drawn assemblages simulated by a joint species distribution model (part of the Northeast Regional Habitat Assessment), which was fitted to observations for 55 common species sampled by the NEFSC bottom trawl survey during the spring and fall of 2000-2019. The model controls for differences in capture efficiency across survey vessels, permitting predictions on a common scale (here calibrated to the Albatross IV). See SOE Tech Doc for details of the model and environmental covariates included. + +**Indicator family**: + +- [X] Habitat +- [X] Megafauna + + +**Contributor(s)**: Chris Haak, Laurel Smith and Tori Kentner + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Indices of species richness can indicate the health of the ecosystem as a metric of biodiversity. In this case, looking at a specified set of 55 species that are commonly caught in the NEFSC bottom trawl survey by year and EPU can indicate species distribution shifts as species richness declines in some areas and increases in others. + +## Key Results and Visualizations +Trends of declining richness are seen in the more southerly regions (i.e., the Mid-Atlantic Bight) and increasing richness in the more northerly regions (i.e., the Gulf of Maine). These patterns reflect the decreasing occurrence of cooler-water species in the south and the growing prevalence of warm-water species in the north, likely as a result of warming water temperatures. + +### MidAtlantic + +```{r plot_habitat_diversityMidAtlanticRichness} +# Plot indicator +ggplotObject <- ecodata::plot_habitat_diversity(report= 'MidAtlantic', varName= 'Richness') +ggplotObject +``` + +### NewEngland + +```{r plot_habitat_diversityNewEnglandRichness} +# Plot indicator +ggplotObject <- ecodata::plot_habitat_diversity(report= 'NewEngland', varName= 'Richness') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Species richness by EPU + +Temporal scale: Spring (March-May) and fall (September-November) NEFSC bottom trawl surveys from 2000-2019 + +**Synthesis Theme**: + +- [X] Ecosystem Reorganization + + +```{r autostats_habitat_diversity} +# Either from Contributor or ecodata +``` + +## Implications +This species richness index provides a summary of how commonly caught fish in the NEFSC bottom trawl survey are changing over time, while controlling for differences in capture efficiency across survey vessels. The shifts of declining species richness in the Mid-Atlantic Bight indicate that fisheries in this region may need to shift away from reliance on these species that may be at the southern edge of their distributions, and may need to expand fisheries to more southerly species. The increase of species richness in the northerly regions such as the Gulf of Maine indicates that there is a likely an influx of southerly species, and management quotas may need to be adjusted between regions. These are likely direct implications that warming water temperatures have on fisheries management. + +## Get the data + +**Point of contact**: [Laurel Smith (Laurel.smith@noaa.gov)](mailto:Laurel Smith (Laurel.smith@noaa.gov)){.email} + +**ecodata name**: `ecodata::habitat_diversity` + +**Variable definitions** + +1) Name: Year, Definition: year of species richness data, 2) Name: Species Richness, Definition: Species richness + +```{r vars_habitat_diversity} +# Pull all var names +vars <- ecodata::habitat_diversity |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/habs.rmd b/chapters/habs.rmd index 5a1b83c2..3f495f4c 100644 --- a/chapters/habs.rmd +++ b/chapters/habs.rmd @@ -1,116 +1,116 @@ -# Harmful Algal Blooms {#habs} - -**Description**: These data represent annual estimated abundance of Alexandrium catanella cysts in the Gulf of Maine and the presence of PSP toxins in blue mussels at coastal sites in the Gulf of Maine (MA, NH, ME), and shellfishery closures (MA). - -**Indicator family**: - -- [X] Lower trophic levels - - -**Contributor(s)**: Yizhen Li, NOAA/NOS NCCOS Stressor Detection and Impacts Division, HAB Forecasting Branch, Silver Spring MD - -**Affiliations**: ? - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Alexandrium cysts in sediments of the Gulf of Maine have been monitored through a cooperative effort of NOAA, WHOI, and other partners for over twenty years. Sampling methods are described in Anderson [@anderson_identification_2005]. In the annual survey cruises, samples are obtained with a Craib corer, and Alexandrium cysts are counted from the top 1- cm of sediment layer. Results are extrapolated to estimate overall cyst abundance in the eastern, western, and entire Gulf of Maine. - -These data represent the presence of PSP toxins in blue mussels (Mytilis edulis) sampled at coastal sites in Massachusetts (1972-2022), New Hampshire (2000-2022), and Maine (2005 to 2019). Results are summarized on an annual basis for each state. Variables include [@li_noaanos_nodate] total number of samples, number of samples above and below a designated threshold (44ug STX equivalent / 100g tissue), and percentage of samples above the threshold. Original data for Maine were collected by Maine Department of Marine Resources, which tests coastal shellfish areas for biotoxins weekly, annually beginning in March and going through October or later when necessary. Original data for New Hampshire were collected by NH Department of Environmental Services, and original data for Massachusetts were collected by Massachusetts Division of Marine Fisheries. - -## Key Results and Visualizations -Visualizations of the data include an annual time series plot of estimated cyst abundance, and a time series of maps depicting the spatial and temporal variability in the Gulf of Maine. Results are plotted as estimated total numbers of cells (10 to the 16th power) in Eastern Gulf of Maine (east of Penobscot Bay), Western Gulf of Maine (west of Penobscot Bay), Bay of Fundy (2003-2013 only), and entire Gulf of Maine. - -A line graph depicts the percentage of samples that exceed the designated threshold (44ug STX equivalent / 100g tissue), in the samples that had some level of STX detected. The percentages may be affected by protocols on where and when to sample – e.g. sampling may be more intense during bloom events, and protocols may differ between the three states (MA, NH, ME). -A three-tier column graph illustrates three different metrics of shellfish bed closures in Massachusetts waters due to PSP, 1990-2022. Annual metrics include number of sites closed, mean number of days closed per site, and total closed area (acres). Bloom events in the early 1990s, 2005, and 2009 are evident in the closure metrics. - -### MidAtlantic - -```{r plot_habsMidAtlanticAlexandrium} -# Plot indicator -ggplotObject <- ecodata::plot_habs(report= 'MidAtlantic', varName= 'Alexandrium') -ggplotObject -``` - -```{r plot_habsMidAtlanticPSP} -# Plot indicator -ggplotObject <- ecodata::plot_habs(report= 'MidAtlantic', varName= 'PSP') -ggplotObject -``` - -### NewEngland - -```{r plot_habsNewEnglandAlexandrium} -# Plot indicator -ggplotObject <- ecodata::plot_habs(report= 'NewEngland', varName= 'Alexandrium') -ggplotObject -``` - -```{r plot_habsNewEnglandPSP} -# Plot indicator -ggplotObject <- ecodata::plot_habs(report= 'NewEngland', varName= 'PSP') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Alexandrium results are reported for the entire Gulf of Maine, and also by sub-regions: Eastern Gulf of Maine (east of Penobscot Bay), Western Gulf of Maine (west of Penobscot Bay), and Bay of Fundy (2003-2013 only). PSP results are based on data from individual sites in the Gulf of Maine, aggregated by year and state (MA, NH, ME). - -Temporal scale: Alexandrium results are aggregated annually, and reported for 2003 to 2021. Cyst sampling is done in the late fall. PSP results are aggregated by year. - -**Synthesis Theme**: - -- [X] Regime Shifts - - -```{r autostats_habs} -# Either from Contributor or ecodata -``` - -## Implications -The annual fall surveys of Alexandrium cyst distribution and abundance in the Gulf of Maine provide data needed to predict bloom events in the following year. Other key variables in the predictive models include currents, nutrients, temperature, and salinity dynamics at multiple spatial and temporal scales (@anderson_identification_2005, @li_investigation_2009, @li_dynamics_2020, @mcgillicuddy_suppression_2011). After strong cyst deposition events in 2005 and 2009, the time series suggest lower overall cyst abundance through 2021. However, bloom events and shellfishery closures usually occur somewhere in any given year. Economic impacts to inshore shellfisheries (mussels, softshell clams, quahogs) can be substantial (@jin_value_2008). Impacts to offshore shellfisheries can occur as well, for example surf clam and ocean quahog on Georges Bank 1988-1990 (@anderson_bloom_1997). - -Alexandrium bloom events in the Gulf of Maine can result in Paralytic Shellfish Poisoning (PSP) toxins accumulating in shellfish and other species. Bloom events and shellfishery closures usually occur somewhere in any given year, but vary greatly between years and among local areas. The presence of PSP toxins in shellfish, and shellfishery closures have been used as metrics for assessing the severity of PSP outbreaks (@kleindinst_categorizing_2014). Economic impacts to inshore shellfisheries (mussels, softshell clams, quahogs) can be substantial (@@jin_value_2008). - -## Get the data - -**Point of contact**: [Moe Nelson, david.moe.nelson@noaa.gov](mailto:Moe Nelson, david.moe.nelson@noaa.gov){.email} - -**ecodata name**: `ecodata::habs` - -**Variable definitions** - -Alexandrium: 1) Year; Definition: calendar year; Units: yyyy. -2) Name: Var; Definition: Gulf of Maine region (West, East, Bay of Fundy, All); Units: categories. -3) Name: Value; Definition: Estimated cyst abundance; Units: numbers of cells * 10 to the 16th power) PSP: -1) Year; Definition: calendar year; Units: yyyy. 2) Name: State; Definition: MA, NH, or ME ; Units: categories. -3) Name: N_Rows; Definition: Number of sample events represented; Units: integer. -4) Name: PSP_Exceed_Threshold_Pct; Definition: Percentage of samples exceeding PSP threshold; Units: decimal number, 0-100. - -```{r vars_habs} -# Pull all var names -vars <- ecodata::habs |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Database pull with analysis - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Data were provided by consultation with Yizhen Li. Data are also used in operational HAB forecast models, freely available to the public. Data as aggregated are not publicly available. Data can be acquired upon request. - -**tech-doc link** - - +# Harmful Algal Blooms {#habs} + +**Description**: These data represent annual estimated abundance of Alexandrium catanella cysts in the Gulf of Maine and the presence of PSP toxins in blue mussels at coastal sites in the Gulf of Maine (MA, NH, ME), and shellfishery closures (MA). + +**Indicator family**: + +- [X] Lower trophic levels + + +**Contributor(s)**: Yizhen Li, NOAA/NOS NCCOS Stressor Detection and Impacts Division, HAB Forecasting Branch, Silver Spring MD + +**Affiliations**: ? + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Alexandrium cysts in sediments of the Gulf of Maine have been monitored through a cooperative effort of NOAA, WHOI, and other partners for over twenty years. Sampling methods are described in Anderson [@anderson_identification_2005]. In the annual survey cruises, samples are obtained with a Craib corer, and Alexandrium cysts are counted from the top 1- cm of sediment layer. Results are extrapolated to estimate overall cyst abundance in the eastern, western, and entire Gulf of Maine. + +These data represent the presence of PSP toxins in blue mussels (Mytilis edulis) sampled at coastal sites in Massachusetts (1972-2022), New Hampshire (2000-2022), and Maine (2005 to 2019). Results are summarized on an annual basis for each state. Variables include [@li_noaanos_nodate] total number of samples, number of samples above and below a designated threshold (44ug STX equivalent / 100g tissue), and percentage of samples above the threshold. Original data for Maine were collected by Maine Department of Marine Resources, which tests coastal shellfish areas for biotoxins weekly, annually beginning in March and going through October or later when necessary. Original data for New Hampshire were collected by NH Department of Environmental Services, and original data for Massachusetts were collected by Massachusetts Division of Marine Fisheries. + +## Key Results and Visualizations +Visualizations of the data include an annual time series plot of estimated cyst abundance, and a time series of maps depicting the spatial and temporal variability in the Gulf of Maine. Results are plotted as estimated total numbers of cells (10 to the 16th power) in Eastern Gulf of Maine (east of Penobscot Bay), Western Gulf of Maine (west of Penobscot Bay), Bay of Fundy (2003-2013 only), and entire Gulf of Maine. + +A line graph depicts the percentage of samples that exceed the designated threshold (44ug STX equivalent / 100g tissue), in the samples that had some level of STX detected. The percentages may be affected by protocols on where and when to sample – e.g. sampling may be more intense during bloom events, and protocols may differ between the three states (MA, NH, ME). +A three-tier column graph illustrates three different metrics of shellfish bed closures in Massachusetts waters due to PSP, 1990-2022. Annual metrics include number of sites closed, mean number of days closed per site, and total closed area (acres). Bloom events in the early 1990s, 2005, and 2009 are evident in the closure metrics. + +### MidAtlantic + +```{r plot_habsMidAtlanticAlexandrium} +# Plot indicator +ggplotObject <- ecodata::plot_habs(report= 'MidAtlantic', varName= 'Alexandrium') +ggplotObject +``` + +```{r plot_habsMidAtlanticPSP} +# Plot indicator +ggplotObject <- ecodata::plot_habs(report= 'MidAtlantic', varName= 'PSP') +ggplotObject +``` + +### NewEngland + +```{r plot_habsNewEnglandAlexandrium} +# Plot indicator +ggplotObject <- ecodata::plot_habs(report= 'NewEngland', varName= 'Alexandrium') +ggplotObject +``` + +```{r plot_habsNewEnglandPSP} +# Plot indicator +ggplotObject <- ecodata::plot_habs(report= 'NewEngland', varName= 'PSP') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Alexandrium results are reported for the entire Gulf of Maine, and also by sub-regions: Eastern Gulf of Maine (east of Penobscot Bay), Western Gulf of Maine (west of Penobscot Bay), and Bay of Fundy (2003-2013 only). PSP results are based on data from individual sites in the Gulf of Maine, aggregated by year and state (MA, NH, ME). + +Temporal scale: Alexandrium results are aggregated annually, and reported for 2003 to 2021. Cyst sampling is done in the late fall. PSP results are aggregated by year. + +**Synthesis Theme**: + +- [X] Regime Shifts + + +```{r autostats_habs} +# Either from Contributor or ecodata +``` + +## Implications +The annual fall surveys of Alexandrium cyst distribution and abundance in the Gulf of Maine provide data needed to predict bloom events in the following year. Other key variables in the predictive models include currents, nutrients, temperature, and salinity dynamics at multiple spatial and temporal scales (@anderson_identification_2005, @li_investigation_2009, @li_dynamics_2020, @mcgillicuddy_suppression_2011). After strong cyst deposition events in 2005 and 2009, the time series suggest lower overall cyst abundance through 2021. However, bloom events and shellfishery closures usually occur somewhere in any given year. Economic impacts to inshore shellfisheries (mussels, softshell clams, quahogs) can be substantial (@jin_value_2008). Impacts to offshore shellfisheries can occur as well, for example surf clam and ocean quahog on Georges Bank 1988-1990 (@anderson_bloom_1997). + +Alexandrium bloom events in the Gulf of Maine can result in Paralytic Shellfish Poisoning (PSP) toxins accumulating in shellfish and other species. Bloom events and shellfishery closures usually occur somewhere in any given year, but vary greatly between years and among local areas. The presence of PSP toxins in shellfish, and shellfishery closures have been used as metrics for assessing the severity of PSP outbreaks (@kleindinst_categorizing_2014). Economic impacts to inshore shellfisheries (mussels, softshell clams, quahogs) can be substantial (@@jin_value_2008). + +## Get the data + +**Point of contact**: [Moe Nelson, david.moe.nelson@noaa.gov](mailto:Moe Nelson, david.moe.nelson@noaa.gov){.email} + +**ecodata name**: `ecodata::habs` + +**Variable definitions** + +Alexandrium: 1) Year; Definition: calendar year; Units: yyyy. +2) Name: Var; Definition: Gulf of Maine region (West, East, Bay of Fundy, All); Units: categories. +3) Name: Value; Definition: Estimated cyst abundance; Units: numbers of cells * 10 to the 16th power) PSP: +1) Year; Definition: calendar year; Units: yyyy. 2) Name: State; Definition: MA, NH, or ME ; Units: categories. +3) Name: N_Rows; Definition: Number of sample events represented; Units: integer. +4) Name: PSP_Exceed_Threshold_Pct; Definition: Percentage of samples exceeding PSP threshold; Units: decimal number, 0-100. + +```{r vars_habs} +# Pull all var names +vars <- ecodata::habs |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Database pull with analysis + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Data were provided by consultation with Yizhen Li. Data are also used in operational HAB forecast models, freely available to the public. Data as aggregated are not publicly available. Data can be acquired upon request. + +**tech-doc link** + + diff --git a/chapters/harborporpoise.rmd b/chapters/harborporpoise.rmd index 5f912cbf..8db78b09 100644 --- a/chapters/harborporpoise.rmd +++ b/chapters/harborporpoise.rmd @@ -1,88 +1,88 @@ -# Harbor Porpoise Bycatch {#harborporpoise} - -**Description**: The data presented here are time series of the species specific estimates of bycatch from U.S. North Atlantic commercial fisheries. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Debra Palka, Kristen Procoda, Marjorie Lyssikatos, Kimberly Murray - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Protected species include marine mammals protected under the Marine Mammal Protection Act, endangered and threatened species protected under the Endangered Species Act, and migratory birds protected under the Migratory Bird Treaty Act. In the Northeast U.S., endangered/threatened species include Atlantic salmon, Atlantic and shortnose sturgeon, all sea turtle species, and five baleen whales. Northeast U.S. marine mammals that are protected under currently active Take Reduction Teams under the Marine Mammal Protection Act include harbor porpoises, bottlenose dolphins, pilot whales, North Atlantic right whales and humpback whales. Fishery management objectives for protected species generally focus on reducing threats and on habitat conservation/restoration. Protected species objectives include managing bycatch to remain below potential biological removal (PBR) thresholds, recovering endangered populations, and monitoring unusual mortality events (UMEs). Here we report on the status of these actions as well as indicating the potential for future interactions driven by observed and predicted ecosystem changes in the Northeast U.S. - -## Key Results and Visualizations -For each marine mammal species, variables plotted are the total estimated bycatch from all U.S. North Atlantic commercial fisheries by year (Fig. x). In 2022, a total of x marine mammals from 5 species was estimated to have been bycaught from 6 fisheries (including bottom gillnets, drift gillnets, bottom trawls, midwater trawls, pair trawls, and pelagic longline). Estimates of seabird bycatch from these fisheries are currently being updated and can be reported next year. -Marine mammal species specific bycatch estimates from 2022 are below current PBR thresholds, thus meeting current management objectives (Fig. x). However, historically, bycatch was above PBR for harbor porpoises and pilot whales. More recently bycatch of gray seals has increased since 1995 and on average leveled out after 2010; however, there is a large amount of inter-annual variability (Fig. x). - -```{r plot_harborporpoiseMAB} -# Plot indicator -ggplotObject <- ecodata::plot_harborporpoise(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: US waters from North Carolina to Canada from the U.S. coastline to the U.S. exclusive economic zone, 200 nautical miles offshore, thus, including all EPU, the full shelf and beyond. - -Temporal scale: Annual from 1990 to 2022. - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_harborporpoise} -# Either from Contributor or ecodata -``` - -## Implications -Bycatch reduction plans developed by Marine Mammal Protection Act’s Take Reduction Teams were needed to reduce the bycatch of harbor porpoises starting in 1997 (Fig. x) and reduce the bycatch of pilot whales starting in xxxx. The more recent reduction in harbor porpoise bycatch since 2010 is probably due not only to the Take Reduction Team’s bycatch mitigation plan but also related to a corresponding decrease in gillnet fishing effort and in seasonal shifts of harbor porpoises that appear to be related to climatic changes (see Fig xx). -The high level of variability of the annual gray seal estimates may be due in part by variable gillnet landings, limited observer coverage, especially since 2019 due to Covid-19, and the effects of the Northeast pinniped unusual mortality event for harbor and gray seals that was declared in 2022. The unusual mortalities were mostly located off the coast Maine and considered to be due to infectious diseases. - -## Get the data - -**Point of contact**: [Debra Palka (debra.palka@noaa.gov); Kristin Precoda (kristin.precoda@noaa.gov)](mailto:Debra Palka (debra.palka@noaa.gov); Kristin Precoda (kristin.precoda@noaa.gov)){.email} - -**ecodata name**: `ecodata::harborporpoise` - -**Variable definitions** - -1) pbr = Potential Biological Removal level. Unit = n (number of animals) -2) totalest1y = Total bycatch of 1 year annual estimate. Unit = n (number of animals) -3) totalest5y = Total bycatch of 5 year running average estimate. Unit = n (number of animals) -4) total5yLCI = Lower 95% confidence interval of totalest5y. Unit = n (number of animals) -5) total5yUCI= Upper 95% confidence interval of totalest5y. Unit = n (number of animals) -6) Ratio1ytoPBR = ratio of the total bycatch of 1 year annual estimate relative to the corresponding annual pbr. - -```{r vars_harborporpoise} -# Pull all var names -vars <- ecodata::harborporpoise |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Harbor Porpoise Bycatch {#harborporpoise} + +**Description**: The data presented here are time series of the species specific estimates of bycatch from U.S. North Atlantic commercial fisheries. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Debra Palka, Kristen Procoda, Marjorie Lyssikatos, Kimberly Murray + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Protected species include marine mammals protected under the Marine Mammal Protection Act, endangered and threatened species protected under the Endangered Species Act, and migratory birds protected under the Migratory Bird Treaty Act. In the Northeast U.S., endangered/threatened species include Atlantic salmon, Atlantic and shortnose sturgeon, all sea turtle species, and five baleen whales. Northeast U.S. marine mammals that are protected under currently active Take Reduction Teams under the Marine Mammal Protection Act include harbor porpoises, bottlenose dolphins, pilot whales, North Atlantic right whales and humpback whales. Fishery management objectives for protected species generally focus on reducing threats and on habitat conservation/restoration. Protected species objectives include managing bycatch to remain below potential biological removal (PBR) thresholds, recovering endangered populations, and monitoring unusual mortality events (UMEs). Here we report on the status of these actions as well as indicating the potential for future interactions driven by observed and predicted ecosystem changes in the Northeast U.S. + +## Key Results and Visualizations +For each marine mammal species, variables plotted are the total estimated bycatch from all U.S. North Atlantic commercial fisheries by year (Fig. x). In 2022, a total of x marine mammals from 5 species was estimated to have been bycaught from 6 fisheries (including bottom gillnets, drift gillnets, bottom trawls, midwater trawls, pair trawls, and pelagic longline). Estimates of seabird bycatch from these fisheries are currently being updated and can be reported next year. +Marine mammal species specific bycatch estimates from 2022 are below current PBR thresholds, thus meeting current management objectives (Fig. x). However, historically, bycatch was above PBR for harbor porpoises and pilot whales. More recently bycatch of gray seals has increased since 1995 and on average leveled out after 2010; however, there is a large amount of inter-annual variability (Fig. x). + +```{r plot_harborporpoiseMAB} +# Plot indicator +ggplotObject <- ecodata::plot_harborporpoise(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: US waters from North Carolina to Canada from the U.S. coastline to the U.S. exclusive economic zone, 200 nautical miles offshore, thus, including all EPU, the full shelf and beyond. + +Temporal scale: Annual from 1990 to 2022. + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_harborporpoise} +# Either from Contributor or ecodata +``` + +## Implications +Bycatch reduction plans developed by Marine Mammal Protection Act’s Take Reduction Teams were needed to reduce the bycatch of harbor porpoises starting in 1997 (Fig. x) and reduce the bycatch of pilot whales starting in xxxx. The more recent reduction in harbor porpoise bycatch since 2010 is probably due not only to the Take Reduction Team’s bycatch mitigation plan but also related to a corresponding decrease in gillnet fishing effort and in seasonal shifts of harbor porpoises that appear to be related to climatic changes (see Fig xx). +The high level of variability of the annual gray seal estimates may be due in part by variable gillnet landings, limited observer coverage, especially since 2019 due to Covid-19, and the effects of the Northeast pinniped unusual mortality event for harbor and gray seals that was declared in 2022. The unusual mortalities were mostly located off the coast Maine and considered to be due to infectious diseases. + +## Get the data + +**Point of contact**: [Debra Palka (debra.palka@noaa.gov); Kristin Precoda (kristin.precoda@noaa.gov)](mailto:Debra Palka (debra.palka@noaa.gov); Kristin Precoda (kristin.precoda@noaa.gov)){.email} + +**ecodata name**: `ecodata::harborporpoise` + +**Variable definitions** + +1) pbr = Potential Biological Removal level. Unit = n (number of animals) +2) totalest1y = Total bycatch of 1 year annual estimate. Unit = n (number of animals) +3) totalest5y = Total bycatch of 5 year running average estimate. Unit = n (number of animals) +4) total5yLCI = Lower 95% confidence interval of totalest5y. Unit = n (number of animals) +5) total5yUCI= Upper 95% confidence interval of totalest5y. Unit = n (number of animals) +6) Ratio1ytoPBR = ratio of the total bycatch of 1 year annual estimate relative to the corresponding annual pbr. + +```{r vars_harborporpoise} +# Pull all var names +vars <- ecodata::harborporpoise |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/heatwave.rmd b/chapters/heatwave.rmd index 8b95f286..0d980f89 100644 --- a/chapters/heatwave.rmd +++ b/chapters/heatwave.rmd @@ -1,126 +1,126 @@ -# Annual Heatwave Intensity {#heatwave} - -**Description**: Surface and bottom MHWs for 2023. - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Vincent Saba, Joseph Caracappa - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Marine heatwaves (MHWs) measure not just temperature, but how long the ecosystem is subjected to the high temperature. They are driven by both atmospheric and oceanographic factors and can have dramatic impacts on marine ecosystems. Marine heatwaves are measured in terms of intensity (water temperature) and duration (the cumulative number of degree days) using measurements of sea surface temperature (surface MHWs) or models of bottom temperature (bottom MHWs). Recent research (@jacox_thermal_2020 and @jacox_global_2022) have modified the original MHW methodology, @hobday_hierarchical_2016. The MHW indices for both surface and bottom use temperature time-series data that are detrended and the entire time-series are used as the climatology (e.g. 1982-2023 in this SOE). Surface MHW events are based on the criteria of a warming event that lasts for five or more days with sea surface temperatures above the 90th percentile of the historical daily climatology (1982-2023). Bottom MHW events are based on the criteria of a warming event that lasts for thirty or more days with bottom temperatures above the 90th percentile of the historical daily climatology (1982-2023). The longer time period criterion for bottom temperature is due to the longer persistence time of ocean bottom temperature anomalies in the U.S. northeast shelf (@chen_seasonal_2021). The new MHW indices can now discern extreme events that truly are “extreme” rather than occupying most of the year as was the case in the Gulf of Maine in 2021 (last year’s SOE). Because this approach moves from a fixed baseline to a shifting baseline by detrending ocean temperature data and using the entire time-series as a climatology, the global warming signal is removed and thus we are left with extremes in the variability of ocean temperature. To assess total heat stress on marine organisms, one can combine long-term ocean warming and MHWs. Therefore, the 2024 SOE also reports on long-term SST and bottom temperature time-series that clearly show the decadal ocean warming trend. - -## Key Results and Visualizations -Georges Bank - -Surface MHWs -In 2023, Georges Bank experienced three distinct surface MHWs beginning on January 26th (lasted 9 days), February 21st (lasted 15 days), and April 11th (lasted 9 days) respectively. In terms of intensity, these three surface MHWs did not rank in the top 10%. - -Bottom MHWs -In 2023, Georges Bank did not experience any bottom MHWs. The strongest bottom MHW on record in Georges Bank was in the fall of 2016. Of the identified 13 bottom MHWs on Georges Bank between 1982 and 2023, four of the top five events in terms of maximum intensity occurred in the last decade. - -Gulf of Maine - -Surface MHWs -In 2023, the Gulf of Maine experienced two distinct surface MHWs beginning on July 19th (lasted 11 days) and September 7th (lasted 9 days). In terms of intensity, these two surface MHWs did not rank in the top 10%. - -Bottom MHWs -In 2023, the Gulf of Maine did not experience any bottom MHWs. Of the identified 11 bottom MHWs in the Gulf of Maine between 1982 and 2023, the top four events in terms of maximum intensity occurred in the last decade. - -Middle Atlantic Bight - -Surface MHWs -In 2023, the Middle Atlantic Bight experienced two surface MHWs that began on July 8th (lasted 7 days) and September 7th (lasted 9 days). In terms of intensity, these two surface MHWs did not rank in the top 10%. - -Bottom MHWs -In 2023, the Middle Atlantic Bight did not experience any bottom MHWs. The strongest bottom MHW occurred in the fall of 1985. - -### MidAtlantic - -```{r plot_heatwaveMidAtlanticSurface} -# Plot indicator -ggplotObject <- ecodata::plot_heatwave(report= 'MidAtlantic', varName= 'Surface') -ggplotObject -``` - -```{r plot_heatwaveMidAtlanticBottom} -# Plot indicator -ggplotObject <- ecodata::plot_heatwave(report= 'MidAtlantic', varName= 'Bottom') -ggplotObject -``` - -### NewEngland - -```{r plot_heatwaveNewEnglandSurface} -# Plot indicator -ggplotObject <- ecodata::plot_heatwave(report= 'NewEngland', varName= 'Surface') -ggplotObject -``` - -```{r plot_heatwaveNewEnglandBottom} -# Plot indicator -ggplotObject <- ecodata::plot_heatwave(report= 'NewEngland', varName= 'Bottom') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: EPU - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_heatwave} -# Either from Contributor or ecodata -``` - -## Implications -Implications of MHWs on living marine resources in the U.S.NES are currently being investigated. The 2012 surface MHW events in the Gulf of Maine did have impacts on the lobster fishery in the summer and fall of 2012. - -## Get the data - -**Point of contact**: [Vincent Saba (vincent.saba@noaa.gov)](mailto:Vincent Saba (vincent.saba@noaa.gov)){.email} - -**ecodata name**: `ecodata::heatwave` - -**Variable definitions** - -1) MHW maximum Intensity (detrended) = degrees C 2) MHW duration = days. - -```{r vars_heatwave} -# Pull all var names -vars <- ecodata::heatwave |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Annual Heatwave Intensity {#heatwave} + +**Description**: Surface and bottom MHWs for 2023. + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Vincent Saba, Joseph Caracappa + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Marine heatwaves (MHWs) measure not just temperature, but how long the ecosystem is subjected to the high temperature. They are driven by both atmospheric and oceanographic factors and can have dramatic impacts on marine ecosystems. Marine heatwaves are measured in terms of intensity (water temperature) and duration (the cumulative number of degree days) using measurements of sea surface temperature (surface MHWs) or models of bottom temperature (bottom MHWs). Recent research (@jacox_thermal_2020 and @jacox_global_2022) have modified the original MHW methodology, @hobday_hierarchical_2016. The MHW indices for both surface and bottom use temperature time-series data that are detrended and the entire time-series are used as the climatology (e.g. 1982-2023 in this SOE). Surface MHW events are based on the criteria of a warming event that lasts for five or more days with sea surface temperatures above the 90th percentile of the historical daily climatology (1982-2023). Bottom MHW events are based on the criteria of a warming event that lasts for thirty or more days with bottom temperatures above the 90th percentile of the historical daily climatology (1982-2023). The longer time period criterion for bottom temperature is due to the longer persistence time of ocean bottom temperature anomalies in the U.S. northeast shelf (@chen_seasonal_2021). The new MHW indices can now discern extreme events that truly are “extreme” rather than occupying most of the year as was the case in the Gulf of Maine in 2021 (last year’s SOE). Because this approach moves from a fixed baseline to a shifting baseline by detrending ocean temperature data and using the entire time-series as a climatology, the global warming signal is removed and thus we are left with extremes in the variability of ocean temperature. To assess total heat stress on marine organisms, one can combine long-term ocean warming and MHWs. Therefore, the 2024 SOE also reports on long-term SST and bottom temperature time-series that clearly show the decadal ocean warming trend. + +## Key Results and Visualizations +Georges Bank + +Surface MHWs +In 2023, Georges Bank experienced three distinct surface MHWs beginning on January 26th (lasted 9 days), February 21st (lasted 15 days), and April 11th (lasted 9 days) respectively. In terms of intensity, these three surface MHWs did not rank in the top 10%. + +Bottom MHWs +In 2023, Georges Bank did not experience any bottom MHWs. The strongest bottom MHW on record in Georges Bank was in the fall of 2016. Of the identified 13 bottom MHWs on Georges Bank between 1982 and 2023, four of the top five events in terms of maximum intensity occurred in the last decade. + +Gulf of Maine + +Surface MHWs +In 2023, the Gulf of Maine experienced two distinct surface MHWs beginning on July 19th (lasted 11 days) and September 7th (lasted 9 days). In terms of intensity, these two surface MHWs did not rank in the top 10%. + +Bottom MHWs +In 2023, the Gulf of Maine did not experience any bottom MHWs. Of the identified 11 bottom MHWs in the Gulf of Maine between 1982 and 2023, the top four events in terms of maximum intensity occurred in the last decade. + +Middle Atlantic Bight + +Surface MHWs +In 2023, the Middle Atlantic Bight experienced two surface MHWs that began on July 8th (lasted 7 days) and September 7th (lasted 9 days). In terms of intensity, these two surface MHWs did not rank in the top 10%. + +Bottom MHWs +In 2023, the Middle Atlantic Bight did not experience any bottom MHWs. The strongest bottom MHW occurred in the fall of 1985. + +### MidAtlantic + +```{r plot_heatwaveMidAtlanticSurface} +# Plot indicator +ggplotObject <- ecodata::plot_heatwave(report= 'MidAtlantic', varName= 'Surface') +ggplotObject +``` + +```{r plot_heatwaveMidAtlanticBottom} +# Plot indicator +ggplotObject <- ecodata::plot_heatwave(report= 'MidAtlantic', varName= 'Bottom') +ggplotObject +``` + +### NewEngland + +```{r plot_heatwaveNewEnglandSurface} +# Plot indicator +ggplotObject <- ecodata::plot_heatwave(report= 'NewEngland', varName= 'Surface') +ggplotObject +``` + +```{r plot_heatwaveNewEnglandBottom} +# Plot indicator +ggplotObject <- ecodata::plot_heatwave(report= 'NewEngland', varName= 'Bottom') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: EPU + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_heatwave} +# Either from Contributor or ecodata +``` + +## Implications +Implications of MHWs on living marine resources in the U.S.NES are currently being investigated. The 2012 surface MHW events in the Gulf of Maine did have impacts on the lobster fishery in the summer and fall of 2012. + +## Get the data + +**Point of contact**: [Vincent Saba (vincent.saba@noaa.gov)](mailto:Vincent Saba (vincent.saba@noaa.gov)){.email} + +**ecodata name**: `ecodata::heatwave` + +**Variable definitions** + +1) MHW maximum Intensity (detrended) = degrees C 2) MHW duration = days. + +```{r vars_heatwave} +# Pull all var names +vars <- ecodata::heatwave |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/heatwave_year.rmd b/chapters/heatwave_year.rmd index c62c587d..b8c411e4 100644 --- a/chapters/heatwave_year.rmd +++ b/chapters/heatwave_year.rmd @@ -1,126 +1,126 @@ -# Marine Heatwave Events {#heatwave_year} - -**Description**: Surface and bottom MHWs for 2023. - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Vincent Saba, Joseph Caracappa - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Marine heatwaves (MHWs) measure not just temperature, but how long the ecosystem is subjected to the high temperature. They are driven by both atmospheric and oceanographic factors and can have dramatic impacts on marine ecosystems. Marine heatwaves are measured in terms of intensity (water temperature) and duration (the cumulative number of degree days) using measurements of sea surface temperature (surface MHWs) or models of bottom temperature (bottom MHWs). Recent research (@jacox_thermal_2020 and @jacox_global_2022) have modified the original MHW methodology, @hobday_hierarchical_2016. The MHW indices for both surface and bottom use temperature time-series data that are detrended and the entire time-series are used as the climatology (e.g. 1982-2023 in this SOE). Surface MHW events are based on the criteria of a warming event that lasts for five or more days with sea surface temperatures above the 90th percentile of the historical daily climatology (1982-2023). Bottom MHW events are based on the criteria of a warming event that lasts for thirty or more days with bottom temperatures above the 90th percentile of the historical daily climatology (1982-2023). The longer time period criterion for bottom temperature is due to the longer persistence time of ocean bottom temperature anomalies in the U.S. northeast shelf (@chen_seasonal_2021). The new MHW indices can now discern extreme events that truly are “extreme” rather than occupying most of the year as was the case in the Gulf of Maine in 2021 (last year’s SOE). Because this approach moves from a fixed baseline to a shifting baseline by detrending ocean temperature data and using the entire time-series as a climatology, the global warming signal is removed and thus we are left with extremes in the variability of ocean temperature. To assess total heat stress on marine organisms, one can combine long-term ocean warming and MHWs. Therefore, the 2024 SOE also reports on long-term SST and bottom temperature time-series that clearly show the decadal ocean warming trend. - -## Key Results and Visualizations -Georges Bank - -Surface MHWs -In 2023, Georges Bank experienced three distinct surface MHWs beginning on January 26th (lasted 9 days), February 21st (lasted 15 days), and April 11th (lasted 9 days) respectively. In terms of intensity, these three surface MHWs did not rank in the top 10%. - -Bottom MHWs -In 2023, Georges Bank did not experience any bottom MHWs. The strongest bottom MHW on record in Georges Bank was in the fall of 2016. Of the identified 13 bottom MHWs on Georges Bank between 1982 and 2023, four of the top five events in terms of maximum intensity occurred in the last decade. - -Gulf of Maine - -Surface MHWs -In 2023, the Gulf of Maine experienced two distinct surface MHWs beginning on July 19th (lasted 11 days) and September 7th (lasted 9 days). In terms of intensity, these two surface MHWs did not rank in the top 10%. - -Bottom MHWs -In 2023, the Gulf of Maine experienced a bottom MHW that started on Feb 16th, peaked on May 20th, and lasted 195 days until the end of the time series data on Aug 29th. Of the identified 12 bottom MHWs in the Gulf of Maine between 1982 and 2023, this bottom MHW ranked 2nd on record in terms of intensity. The top three bottom MHWs in the Gulf of Maine occurred over the last decade. - -Middle Atlantic Bight - -Surface MHWs -In 2023, the Middle Atlantic Bight experienced two surface MHWs that began on July 8th (lasted 7 days) and September 7th (lasted 9 days). In terms of intensity, these two surface MHWs did not rank in the top 10%. - -Bottom MHWs -In 2023, the Middle Atlantic Bight did not experience any bottom MHWs. The strongest bottom MHW occurred in the fall of 1985. - -### MidAtlantic - -```{r plot_heatwave_yearMidAtlanticSurface} -# Plot indicator -ggplotObject <- ecodata::plot_heatwave_year(report= 'MidAtlantic', varName= 'Surface') -ggplotObject -``` - -```{r plot_heatwave_yearMidAtlanticBottom} -# Plot indicator -ggplotObject <- ecodata::plot_heatwave_year(report= 'MidAtlantic', varName= 'Bottom') -ggplotObject -``` - -### NewEngland - -```{r plot_heatwave_yearNewEnglandSurface} -# Plot indicator -ggplotObject <- ecodata::plot_heatwave_year(report= 'NewEngland', varName= 'Surface') -ggplotObject -``` - -```{r plot_heatwave_yearNewEnglandBottom} -# Plot indicator -ggplotObject <- ecodata::plot_heatwave_year(report= 'NewEngland', varName= 'Bottom') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: EPU - -Temporal scale: Daily - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_heatwave_year} -# Either from Contributor or ecodata -``` - -## Implications -Implications of MHWs on living marine resources in the U.S.NES are currently being investigated. The 2012 surface MHW events in the Gulf of Maine did have impacts on the lobster fishery in the summer and fall of 2012. - -## Get the data - -**Point of contact**: [Vincent Saba (vincent.saba@noaa.gov)](mailto:Vincent Saba (vincent.saba@noaa.gov)){.email} - -**ecodata name**: `ecodata::heatwave_year` - -**Variable definitions** - -1) MHW maximum Intensity (detrended) = degrees C 2) MHW duration = days. - -```{r vars_heatwave_year} -# Pull all var names -vars <- ecodata::heatwave_year |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Marine Heatwave Events {#heatwave_year} + +**Description**: Surface and bottom MHWs for 2023. + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Vincent Saba, Joseph Caracappa + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Marine heatwaves (MHWs) measure not just temperature, but how long the ecosystem is subjected to the high temperature. They are driven by both atmospheric and oceanographic factors and can have dramatic impacts on marine ecosystems. Marine heatwaves are measured in terms of intensity (water temperature) and duration (the cumulative number of degree days) using measurements of sea surface temperature (surface MHWs) or models of bottom temperature (bottom MHWs). Recent research (@jacox_thermal_2020 and @jacox_global_2022) have modified the original MHW methodology, @hobday_hierarchical_2016. The MHW indices for both surface and bottom use temperature time-series data that are detrended and the entire time-series are used as the climatology (e.g. 1982-2023 in this SOE). Surface MHW events are based on the criteria of a warming event that lasts for five or more days with sea surface temperatures above the 90th percentile of the historical daily climatology (1982-2023). Bottom MHW events are based on the criteria of a warming event that lasts for thirty or more days with bottom temperatures above the 90th percentile of the historical daily climatology (1982-2023). The longer time period criterion for bottom temperature is due to the longer persistence time of ocean bottom temperature anomalies in the U.S. northeast shelf (@chen_seasonal_2021). The new MHW indices can now discern extreme events that truly are “extreme” rather than occupying most of the year as was the case in the Gulf of Maine in 2021 (last year’s SOE). Because this approach moves from a fixed baseline to a shifting baseline by detrending ocean temperature data and using the entire time-series as a climatology, the global warming signal is removed and thus we are left with extremes in the variability of ocean temperature. To assess total heat stress on marine organisms, one can combine long-term ocean warming and MHWs. Therefore, the 2024 SOE also reports on long-term SST and bottom temperature time-series that clearly show the decadal ocean warming trend. + +## Key Results and Visualizations +Georges Bank + +Surface MHWs +In 2023, Georges Bank experienced three distinct surface MHWs beginning on January 26th (lasted 9 days), February 21st (lasted 15 days), and April 11th (lasted 9 days) respectively. In terms of intensity, these three surface MHWs did not rank in the top 10%. + +Bottom MHWs +In 2023, Georges Bank did not experience any bottom MHWs. The strongest bottom MHW on record in Georges Bank was in the fall of 2016. Of the identified 13 bottom MHWs on Georges Bank between 1982 and 2023, four of the top five events in terms of maximum intensity occurred in the last decade. + +Gulf of Maine + +Surface MHWs +In 2023, the Gulf of Maine experienced two distinct surface MHWs beginning on July 19th (lasted 11 days) and September 7th (lasted 9 days). In terms of intensity, these two surface MHWs did not rank in the top 10%. + +Bottom MHWs +In 2023, the Gulf of Maine experienced a bottom MHW that started on Feb 16th, peaked on May 20th, and lasted 195 days until the end of the time series data on Aug 29th. Of the identified 12 bottom MHWs in the Gulf of Maine between 1982 and 2023, this bottom MHW ranked 2nd on record in terms of intensity. The top three bottom MHWs in the Gulf of Maine occurred over the last decade. + +Middle Atlantic Bight + +Surface MHWs +In 2023, the Middle Atlantic Bight experienced two surface MHWs that began on July 8th (lasted 7 days) and September 7th (lasted 9 days). In terms of intensity, these two surface MHWs did not rank in the top 10%. + +Bottom MHWs +In 2023, the Middle Atlantic Bight did not experience any bottom MHWs. The strongest bottom MHW occurred in the fall of 1985. + +### MidAtlantic + +```{r plot_heatwave_yearMidAtlanticSurface} +# Plot indicator +ggplotObject <- ecodata::plot_heatwave_year(report= 'MidAtlantic', varName= 'Surface') +ggplotObject +``` + +```{r plot_heatwave_yearMidAtlanticBottom} +# Plot indicator +ggplotObject <- ecodata::plot_heatwave_year(report= 'MidAtlantic', varName= 'Bottom') +ggplotObject +``` + +### NewEngland + +```{r plot_heatwave_yearNewEnglandSurface} +# Plot indicator +ggplotObject <- ecodata::plot_heatwave_year(report= 'NewEngland', varName= 'Surface') +ggplotObject +``` + +```{r plot_heatwave_yearNewEnglandBottom} +# Plot indicator +ggplotObject <- ecodata::plot_heatwave_year(report= 'NewEngland', varName= 'Bottom') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: EPU + +Temporal scale: Daily + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_heatwave_year} +# Either from Contributor or ecodata +``` + +## Implications +Implications of MHWs on living marine resources in the U.S.NES are currently being investigated. The 2012 surface MHW events in the Gulf of Maine did have impacts on the lobster fishery in the summer and fall of 2012. + +## Get the data + +**Point of contact**: [Vincent Saba (vincent.saba@noaa.gov)](mailto:Vincent Saba (vincent.saba@noaa.gov)){.email} + +**ecodata name**: `ecodata::heatwave_year` + +**Variable definitions** + +1) MHW maximum Intensity (detrended) = degrees C 2) MHW duration = days. + +```{r vars_heatwave_year} +# Pull all var names +vars <- ecodata::heatwave_year |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/hms_cpue.rmd b/chapters/hms_cpue.rmd index 9a2979a6..b589b2bf 100644 --- a/chapters/hms_cpue.rmd +++ b/chapters/hms_cpue.rmd @@ -1,103 +1,103 @@ -# Highly Migratory Species POP Catch Per Unit Effort {#hms_cpue} - -**Description**: CPUE from pelagic observer program (POP) observed hauls, presented as number of fish per haul, is provided for the northeast (i.e., the Northeast Coastal and Mid-Atlantic Bight fishing areas) by year/species from 1992-2022. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Jennifer Cudney, Tobey Curtis - -**Affiliations**: HMS - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The Pelagic Observer Program (POP) is operated out of the Southeast Fisheries Science Center, but provides observers in all regions (including the high seas) where U.S. flagged and HMS-permitted vessels fish under regulations for the HMS pelagic longline fishery. Data from the POP is collected during trips on pelagic longline vessels that are generally targeting swordfish, and yellowfin and bigeye tunas. Once a set is retrieved, information like the length, dressed weight, sex, and tag number of each individual fish is recorded. There have been times and areas where the agency has required 100-percent coverage over specific times or areas such as during bluefin tuna spawning time period in the Gulf of Mexico for a number of years and in the Mid-Atlantic Bight. Between 2017 and 2021, observer coverage for the entire pelagic longline fleet (i.e., from Maine to Texas, and the U.S. Caribbean and high seas) ranged from 9 to 13 percent of total overall reported sets. - -## Key Results and Visualizations -TBD; data may not be correctly displayed in graphs. - -### MidAtlantic - -```{r plot_hms_cpueMidAtlanticshark} -# Plot indicator -ggplotObject <- ecodata::plot_hms_cpue(report= 'MidAtlantic', varName= 'shark') -ggplotObject -``` - -```{r plot_hms_cpueMidAtlantictuna} -# Plot indicator -ggplotObject <- ecodata::plot_hms_cpue(report= 'MidAtlantic', varName= 'tuna') -ggplotObject -``` - -### NewEngland - -```{r plot_hms_cpueNewEnglandshark} -# Plot indicator -ggplotObject <- ecodata::plot_hms_cpue(report= 'NewEngland', varName= 'shark') -ggplotObject -``` - -```{r plot_hms_cpueNewEnglandtuna} -# Plot indicator -ggplotObject <- ecodata::plot_hms_cpue(report= 'NewEngland', varName= 'tuna') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Data are extracted from the MAB and NEC fishing areas (see "Fishing Areas" figure) - -Temporal scale: 1992-2022, Annual - -**Synthesis Theme**: - -- [X] Regime Shifts - - -```{r autostats_hms_cpue} -# Either from Contributor or ecodata -``` - -## Implications -Pelagic observer data summarizes catch per unit effort information for a subset of total pelagic longline effort in the U.S. EEZ, and should not be interpreted as total interaction information for the northeast region pelagic longline fleet. CPUE trends can be used to evaluate whether the number of interactions with longline vessels has changed through time. - -## Get the data - -**Point of contact**: [Jennifer Cudney (jennifer.cudney@noaa.gov)](mailto:Jennifer Cudney (jennifer.cudney@noaa.gov)){.email} - -**ecodata name**: `ecodata::hms_cpue` - -**Variable definitions** - -Year: year in which observations were made; Animal_Code: three letter abbreviation used by POP as a shorthand for species; Count: number of animals observed per year/species ; Haul_Num: number of observed hauls in the NE EEZ, in the two areas (NEC and MAB) within a given year (does not include damaged or incomplete sets). Num_per_haul: total # fish caught / total # hauls (for each species and year) - -```{r vars_hms_cpue} -# Pull all var names -vars <- ecodata::hms_cpue |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Pelagic observer data is considered confidential data, and must be screened to ensure that data meet requirements for "rule of three" at the set and vessel level before they can be distributed. Do not publish raw data. - -**tech-doc link** - - +# Highly Migratory Species POP Catch Per Unit Effort {#hms_cpue} + +**Description**: CPUE from pelagic observer program (POP) observed hauls, presented as number of fish per haul, is provided for the northeast (i.e., the Northeast Coastal and Mid-Atlantic Bight fishing areas) by year/species from 1992-2022. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Jennifer Cudney, Tobey Curtis + +**Affiliations**: HMS + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The Pelagic Observer Program (POP) is operated out of the Southeast Fisheries Science Center, but provides observers in all regions (including the high seas) where U.S. flagged and HMS-permitted vessels fish under regulations for the HMS pelagic longline fishery. Data from the POP is collected during trips on pelagic longline vessels that are generally targeting swordfish, and yellowfin and bigeye tunas. Once a set is retrieved, information like the length, dressed weight, sex, and tag number of each individual fish is recorded. There have been times and areas where the agency has required 100-percent coverage over specific times or areas such as during bluefin tuna spawning time period in the Gulf of Mexico for a number of years and in the Mid-Atlantic Bight. Between 2017 and 2021, observer coverage for the entire pelagic longline fleet (i.e., from Maine to Texas, and the U.S. Caribbean and high seas) ranged from 9 to 13 percent of total overall reported sets. + +## Key Results and Visualizations +TBD; data may not be correctly displayed in graphs. + +### MidAtlantic + +```{r plot_hms_cpueMidAtlanticshark} +# Plot indicator +ggplotObject <- ecodata::plot_hms_cpue(report= 'MidAtlantic', varName= 'shark') +ggplotObject +``` + +```{r plot_hms_cpueMidAtlantictuna} +# Plot indicator +ggplotObject <- ecodata::plot_hms_cpue(report= 'MidAtlantic', varName= 'tuna') +ggplotObject +``` + +### NewEngland + +```{r plot_hms_cpueNewEnglandshark} +# Plot indicator +ggplotObject <- ecodata::plot_hms_cpue(report= 'NewEngland', varName= 'shark') +ggplotObject +``` + +```{r plot_hms_cpueNewEnglandtuna} +# Plot indicator +ggplotObject <- ecodata::plot_hms_cpue(report= 'NewEngland', varName= 'tuna') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Data are extracted from the MAB and NEC fishing areas (see "Fishing Areas" figure) + +Temporal scale: 1992-2022, Annual + +**Synthesis Theme**: + +- [X] Regime Shifts + + +```{r autostats_hms_cpue} +# Either from Contributor or ecodata +``` + +## Implications +Pelagic observer data summarizes catch per unit effort information for a subset of total pelagic longline effort in the U.S. EEZ, and should not be interpreted as total interaction information for the northeast region pelagic longline fleet. CPUE trends can be used to evaluate whether the number of interactions with longline vessels has changed through time. + +## Get the data + +**Point of contact**: [Jennifer Cudney (jennifer.cudney@noaa.gov)](mailto:Jennifer Cudney (jennifer.cudney@noaa.gov)){.email} + +**ecodata name**: `ecodata::hms_cpue` + +**Variable definitions** + +Year: year in which observations were made; Animal_Code: three letter abbreviation used by POP as a shorthand for species; Count: number of animals observed per year/species ; Haul_Num: number of observed hauls in the NE EEZ, in the two areas (NEC and MAB) within a given year (does not include damaged or incomplete sets). Num_per_haul: total # fish caught / total # hauls (for each species and year) + +```{r vars_hms_cpue} +# Pull all var names +vars <- ecodata::hms_cpue |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Pelagic observer data is considered confidential data, and must be screened to ensure that data meet requirements for "rule of three" at the set and vessel level before they can be distributed. Do not publish raw data. + +**tech-doc link** + + diff --git a/chapters/hms_landings.rmd b/chapters/hms_landings.rmd index 2951c0bd..e9dbc4a3 100644 --- a/chapters/hms_landings.rmd +++ b/chapters/hms_landings.rmd @@ -1,105 +1,105 @@ -# Highly Migratory Species Landings {#hms_landings} - -**Description**: Aggregated Atlantic highly migratory species landings data prepared for the Fisheries of the United States (FUS) report, spanning 2015-2022. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Heather Baertlein, Jackie Wilson, George Silva, Jennifer Cudney - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -HMS landings and approximate value of landings are presented separately as an indicator, and are combined in several datasets presented in the SOE report (e.g., total commercial landings as discussed under “Seafood Production” and total commercial revenue as presented under “Commercial Profits”). - -## Key Results and Visualizations -TBD - recommend including a version of the plots or tables generated in the Technical Document / code that is linked. Pending finalization of the dataset for this year's report. - -### MidAtlantic - -```{r plot_hms_landingsMidAtlanticLandings} -# Plot indicator -ggplotObject <- ecodata::plot_hms_landings(report= 'MidAtlantic', varName= 'Landings') -ggplotObject -``` - -```{r plot_hms_landingsMidAtlanticRevenue} -# Plot indicator -ggplotObject <- ecodata::plot_hms_landings(report= 'MidAtlantic', varName= 'Revenue') -ggplotObject -``` - -### NewEngland - -```{r plot_hms_landingsNewEnglandLandings} -# Plot indicator -ggplotObject <- ecodata::plot_hms_landings(report= 'NewEngland', varName= 'Landings') -ggplotObject -``` - -```{r plot_hms_landingsNewEnglandRevenue} -# Plot indicator -ggplotObject <- ecodata::plot_hms_landings(report= 'NewEngland', varName= 'Revenue') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Regionally by EPU - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Regime Shifts - - -```{r autostats_hms_landings} -# Either from Contributor or ecodata -``` - -## Implications -TBD – pending finalization of dataset for 2024 report - -In 2021 the International Commission for the Conservation of Atlantic Tunas (ICCAT) finalized recommendations for a two-year retention ban for shortfin mako (ICCAT Rec. 21-09), which will also affect total overall landings of pelagic sharks in coming years. - -## Get the data - -**Point of contact**: [Jennifer Cudney (jennifer.cudney@noaa.gov)](mailto:Jennifer Cudney (jennifer.cudney@noaa.gov)){.email} - -**ecodata name**: `ecodata::hms_landings` - -**Variable definitions** - -Year: data are summarized/aggregated by year. EPU: region, Mid Atlantic Bight (MAB) or New England (NE). HMS_Group: Management group for HMS, as defined below in “Data Processing”. Var: description of data, whether total landings or revenue. Units: description of unit of measure for data. Value: Represents either metric tons of landings or dollar value of landings by year and region. - -```{r vars_hms_landings} -# Pull all var names -vars <- ecodata::hms_landings |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Data that has been properly screened to meet data standards and confidentiality are publicly available via the Fisheries of the United States (FUS) landings portal. Canadian landings information, which are included in this analysis, are not included in the FUS portal (https://www.fisheries.noaa.gov/national/sustainable-fisheries/commercial-fisheries-landings). Data should be aggregated to protect data confidentiality (raw data are lumped by year and species). Please email jennifer.cudney@noaa.gov for further information and queries of source data. - -**tech-doc link** - - +# Highly Migratory Species Landings {#hms_landings} + +**Description**: Aggregated Atlantic highly migratory species landings data prepared for the Fisheries of the United States (FUS) report, spanning 2015-2022. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Heather Baertlein, Jackie Wilson, George Silva, Jennifer Cudney + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +HMS landings and approximate value of landings are presented separately as an indicator, and are combined in several datasets presented in the SOE report (e.g., total commercial landings as discussed under “Seafood Production” and total commercial revenue as presented under “Commercial Profits”). + +## Key Results and Visualizations +TBD - recommend including a version of the plots or tables generated in the Technical Document / code that is linked. Pending finalization of the dataset for this year's report. + +### MidAtlantic + +```{r plot_hms_landingsMidAtlanticLandings} +# Plot indicator +ggplotObject <- ecodata::plot_hms_landings(report= 'MidAtlantic', varName= 'Landings') +ggplotObject +``` + +```{r plot_hms_landingsMidAtlanticRevenue} +# Plot indicator +ggplotObject <- ecodata::plot_hms_landings(report= 'MidAtlantic', varName= 'Revenue') +ggplotObject +``` + +### NewEngland + +```{r plot_hms_landingsNewEnglandLandings} +# Plot indicator +ggplotObject <- ecodata::plot_hms_landings(report= 'NewEngland', varName= 'Landings') +ggplotObject +``` + +```{r plot_hms_landingsNewEnglandRevenue} +# Plot indicator +ggplotObject <- ecodata::plot_hms_landings(report= 'NewEngland', varName= 'Revenue') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Regionally by EPU + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Regime Shifts + + +```{r autostats_hms_landings} +# Either from Contributor or ecodata +``` + +## Implications +TBD – pending finalization of dataset for 2024 report + +In 2021 the International Commission for the Conservation of Atlantic Tunas (ICCAT) finalized recommendations for a two-year retention ban for shortfin mako (ICCAT Rec. 21-09), which will also affect total overall landings of pelagic sharks in coming years. + +## Get the data + +**Point of contact**: [Jennifer Cudney (jennifer.cudney@noaa.gov)](mailto:Jennifer Cudney (jennifer.cudney@noaa.gov)){.email} + +**ecodata name**: `ecodata::hms_landings` + +**Variable definitions** + +Year: data are summarized/aggregated by year. EPU: region, Mid Atlantic Bight (MAB) or New England (NE). HMS_Group: Management group for HMS, as defined below in “Data Processing”. Var: description of data, whether total landings or revenue. Units: description of unit of measure for data. Value: Represents either metric tons of landings or dollar value of landings by year and region. + +```{r vars_hms_landings} +# Pull all var names +vars <- ecodata::hms_landings |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Data that has been properly screened to meet data standards and confidentiality are publicly available via the Fisheries of the United States (FUS) landings portal. Canadian landings information, which are included in this analysis, are not included in the FUS portal (https://www.fisheries.noaa.gov/national/sustainable-fisheries/commercial-fisheries-landings). Data should be aggregated to protect data confidentiality (raw data are lumped by year and species). Please email jennifer.cudney@noaa.gov for further information and queries of source data. + +**tech-doc link** + + diff --git a/chapters/hms_stock_status.rmd b/chapters/hms_stock_status.rmd index 8f1c0932..7f043bb0 100644 --- a/chapters/hms_stock_status.rmd +++ b/chapters/hms_stock_status.rmd @@ -1,81 +1,81 @@ -# Highly Migratory Species Stock Status {#hms_stock_status} - -**Description**: Summary of the most recent stock assessment results for each assessed Highly Migratory Species. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Jennifer Cudney - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -There is interest in the stock status of predators and commercially or recreationally valuable Highly Migratory Species. The latest Highly Migratory Species Stock Assessment and Fishery Evaluation (SAFE) report provides a summary of stock assessment information and current stock statuses of HMS under domestic and applicable international stock thresholds. Some stocks use proxies for biomass, such as spawning stock biomass (e.g., bluefin and northern albacore tunas) and spawning stock fecundity or number of fish (e.g., sharks). - -## Key Results and Visualizations -TBD – we have a meeting in mid-November to finalize decisions regarding this contribution. The most complete representation of these data can be found in our SAFE report (starting on pg 31, Table 2.1 – Table 2.4) - -```{r plot_hms_stock_statusMAB} -# Plot indicator -ggplotObject <- ecodata::plot_hms_stock_status(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: North Atlantic basin; full shelf - -Temporal scale: Annual summary of stock status - -**Synthesis Theme**: - -- [X] Regime Shifts - - -```{r autostats_hms_stock_status} -# Either from Contributor or ecodata -``` - -## Implications -TBD – our assessment team has asked that the Kobe plots be redone, so please do not include them here yet. - -## Get the data - -**Point of contact**: [Jennifer Cudney (jennifer.cudney@noaa.gov)](mailto:Jennifer Cudney (jennifer.cudney@noaa.gov)){.email} - -**ecodata name**: `ecodata::hms_stock_status` - -**Variable definitions** - -TBD – pending decisions from a mid-November meeting. - -```{r vars_hms_stock_status} -# Pull all var names -vars <- ecodata::hms_stock_status |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -All stock assessment results are publicly available (see Data Sources). Summarized Atlantic HMS data are available in the Atlantic HMS SAFE Reports. Species with a range of uncertainty estimates for F/Fmsy and B/Bmsy and assessments completed very recently may not be included in Stock Smart queries. - -**tech-doc link** - - +# Highly Migratory Species Stock Status {#hms_stock_status} + +**Description**: Summary of the most recent stock assessment results for each assessed Highly Migratory Species. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Jennifer Cudney + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +There is interest in the stock status of predators and commercially or recreationally valuable Highly Migratory Species. The latest Highly Migratory Species Stock Assessment and Fishery Evaluation (SAFE) report provides a summary of stock assessment information and current stock statuses of HMS under domestic and applicable international stock thresholds. Some stocks use proxies for biomass, such as spawning stock biomass (e.g., bluefin and northern albacore tunas) and spawning stock fecundity or number of fish (e.g., sharks). + +## Key Results and Visualizations +TBD – we have a meeting in mid-November to finalize decisions regarding this contribution. The most complete representation of these data can be found in our SAFE report (starting on pg 31, Table 2.1 – Table 2.4) + +```{r plot_hms_stock_statusMAB} +# Plot indicator +ggplotObject <- ecodata::plot_hms_stock_status(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: North Atlantic basin; full shelf + +Temporal scale: Annual summary of stock status + +**Synthesis Theme**: + +- [X] Regime Shifts + + +```{r autostats_hms_stock_status} +# Either from Contributor or ecodata +``` + +## Implications +TBD – our assessment team has asked that the Kobe plots be redone, so please do not include them here yet. + +## Get the data + +**Point of contact**: [Jennifer Cudney (jennifer.cudney@noaa.gov)](mailto:Jennifer Cudney (jennifer.cudney@noaa.gov)){.email} + +**ecodata name**: `ecodata::hms_stock_status` + +**Variable definitions** + +TBD – pending decisions from a mid-November meeting. + +```{r vars_hms_stock_status} +# Pull all var names +vars <- ecodata::hms_stock_status |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +All stock assessment results are publicly available (see Data Sources). Summarized Atlantic HMS data are available in the Atlantic HMS SAFE Reports. Species with a range of uncertainty estimates for F/Fmsy and B/Bmsy and assessments completed very recently may not be included in Stock Smart queries. + +**tech-doc link** + + diff --git a/chapters/long_term_sst.rmd b/chapters/long_term_sst.rmd index 7ea1ebe3..50acb74c 100644 --- a/chapters/long_term_sst.rmd +++ b/chapters/long_term_sst.rmd @@ -1,81 +1,81 @@ -# NE Shelf Annual Sea Surface Temperature (SST) {#long_term_sst} - -**Description**: Average annual sea-surface temperatures from the NOAA extended reconstructed sea surface temperature data set (ERSST V5) on the Northeast Continental Shelf. - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Kevin Friedland, Brandon Beltz - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The data presented here are average annual sea-surface temperatures from the NOAA extended reconstructed sea surface temperature data set (ERSST V5) on the Northeast Continental Shelf. - -## Key Results and Visualizations -Since the 1860’s, the Northeast US shelf sea surface temperature (SST) has exhibited an overall warming trend, with the past decade measuring well above the long term average. - -```{r plot_long_term_sstMAB} -# Plot indicator -ggplotObject <- ecodata::plot_long_term_sst(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Full shelf - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_long_term_sst} -# Either from Contributor or ecodata -``` - -## Implications -Long term SST demonstrates that the annual average sea surface temperatures observed during the most recent ten years are well above the historical average dating back to the 1860s. - -## Get the data - -**Point of contact**: [Brandon.Beltz@noaa.gov](mailto:Brandon.Beltz@noaa.gov){.email} - -**ecodata name**: `ecodata::long_term_sst` - -**Variable definitions** - -long-term sst in units of degreesC - -```{r vars_long_term_sst} -# Pull all var names -vars <- ecodata::long_term_sst |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# NE Shelf Annual Sea Surface Temperature (SST) {#long_term_sst} + +**Description**: Average annual sea-surface temperatures from the NOAA extended reconstructed sea surface temperature data set (ERSST V5) on the Northeast Continental Shelf. + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Kevin Friedland, Brandon Beltz + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The data presented here are average annual sea-surface temperatures from the NOAA extended reconstructed sea surface temperature data set (ERSST V5) on the Northeast Continental Shelf. + +## Key Results and Visualizations +Since the 1860’s, the Northeast US shelf sea surface temperature (SST) has exhibited an overall warming trend, with the past decade measuring well above the long term average. + +```{r plot_long_term_sstMAB} +# Plot indicator +ggplotObject <- ecodata::plot_long_term_sst(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Full shelf + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_long_term_sst} +# Either from Contributor or ecodata +``` + +## Implications +Long term SST demonstrates that the annual average sea surface temperatures observed during the most recent ten years are well above the historical average dating back to the 1860s. + +## Get the data + +**Point of contact**: [Brandon.Beltz@noaa.gov](mailto:Brandon.Beltz@noaa.gov){.email} + +**ecodata name**: `ecodata::long_term_sst` + +**Variable definitions** + +long-term sst in units of degreesC + +```{r vars_long_term_sst} +# Pull all var names +vars <- ecodata::long_term_sst |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/mab_inshore_survey.rmd b/chapters/mab_inshore_survey.rmd index bf00ede5..8434f68d 100644 --- a/chapters/mab_inshore_survey.rmd +++ b/chapters/mab_inshore_survey.rmd @@ -1,90 +1,90 @@ -# Inshore Survey (Mid Atlantic) {#mab_inshore_survey} - -**Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: - -- NEAMAP (mab_inshore_survey) from Cape Hatteras to RI - -- MA Inshore in MA state waters (mass_inshore_survey) - -- ME/NH Inshore in ME and NH state waters (ne_inshore_survey) - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: James Gartland, Matt Camisa, Rebecca Peters, Sean Lucey - -**Affiliations**: VIMS, Maine, NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Indicators from these inshore surveys are analagous to those produced by the NEFSC trawl survey in the `aggregate_biomass` indicator dataset - -## Key Results and Visualizations -Each survey shows trends by aggregate group. - -### MAB - -```{r plot_mab_inshore_surveyMAB} -# Plot indicator -ggplotObject <- ecodata::plot_mab_inshore_survey(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Nearshore regions of the MAB and GOM - -Temporal scale: Spring and Fall - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_mab_inshore_survey} -# Either from Contributor or ecodata -``` - -## Implications -Some trends match the NEFSC survey, some dont. This is driven by different species availability to surveys in time and space as well as the surveys sampling different habitats. Nearshore habitats are important to fish and fisheries so we monitor them as as well as trends from the larger EPUs. - -## Get the data - -**Point of contact**: [James Gartland (NEAMAP), jgartlan@vims.edu; Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov; Sean Lucey (MA Inshore Survey), sean.lucey@rws.com](mailto:James Gartland (NEAMAP), jgartlan@vims.edu; Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov; Sean Lucey (MA Inshore Survey), sean.lucey@rws.com){.email} - -**ecodata name**: `ecodata::mab_inshore_survey` - -**Variable definitions** - -See variable definitions for `aggregate_biomass` - -```{r vars_mab_inshore_survey} -# Pull all var names -vars <- ecodata::mab_inshore_survey |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information -- [X] Database pull - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Inshore Survey (Mid Atlantic) {#mab_inshore_survey} + +**Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: + +- NEAMAP (mab_inshore_survey) from Cape Hatteras to RI + +- MA Inshore in MA state waters (mass_inshore_survey) + +- ME/NH Inshore in ME and NH state waters (ne_inshore_survey) + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: James Gartland, Matt Camisa, Rebecca Peters, Sean Lucey + +**Affiliations**: VIMS, Maine, NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Indicators from these inshore surveys are analagous to those produced by the NEFSC trawl survey in the `aggregate_biomass` indicator dataset + +## Key Results and Visualizations +Each survey shows trends by aggregate group. + +### MAB + +```{r plot_mab_inshore_surveyMAB} +# Plot indicator +ggplotObject <- ecodata::plot_mab_inshore_survey(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Nearshore regions of the MAB and GOM + +Temporal scale: Spring and Fall + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_mab_inshore_survey} +# Either from Contributor or ecodata +``` + +## Implications +Some trends match the NEFSC survey, some dont. This is driven by different species availability to surveys in time and space as well as the surveys sampling different habitats. Nearshore habitats are important to fish and fisheries so we monitor them as as well as trends from the larger EPUs. + +## Get the data + +**Point of contact**: [James Gartland (NEAMAP), jgartlan@vims.edu; Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov; Sean Lucey (MA Inshore Survey), sean.lucey@rws.com](mailto:James Gartland (NEAMAP), jgartlan@vims.edu; Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov; Sean Lucey (MA Inshore Survey), sean.lucey@rws.com){.email} + +**ecodata name**: `ecodata::mab_inshore_survey` + +**Variable definitions** + +See variable definitions for `aggregate_biomass` + +```{r vars_mab_inshore_survey} +# Pull all var names +vars <- ecodata::mab_inshore_survey |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information +- [X] Database pull + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/mass_inshore_survey.rmd b/chapters/mass_inshore_survey.rmd index e5cdd184..4c715f96 100644 --- a/chapters/mass_inshore_survey.rmd +++ b/chapters/mass_inshore_survey.rmd @@ -1,90 +1,90 @@ -# Inshore Survey (Massachusetts) {#mass_inshore_survey} - -**Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: - -- NEAMAP (mab_inshore_survey) from Cape Hatteras to RI - -- MA Inshore in MA state waters (mass_inshore_survey) - -- ME/NH Inshore in ME and NH state waters (ne_inshore_survey) - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Sean Lucey - -**Affiliations**: RWS - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Indicators from these inshore surveys are analagous to those produced by the NEFSC trawl survey in the `aggregate_biomass` indicator dataset - -## Key Results and Visualizations -Each survey shows trends by aggregate group. - -### NE - -```{r plot_mass_inshore_surveyNE} -# Plot indicator -ggplotObject <- ecodata::plot_mass_inshore_survey(report='NewEngland') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Nearshore regions of the MAB and GOM - -Temporal scale: Spring and Fall - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_mass_inshore_survey} -# Either from Contributor or ecodata -``` - -## Implications -Some trends match the NEFSC survey, some dont. This is driven by different species availability to surveys in time and space as well as the surveys sampling different habitats. Nearshore habitats are important to fish and fisheries so we monitor them as as well as trends from the larger EPUs. - -## Get the data - -**Point of contact**: [Sean Lucey (MA Inshore Survey), sean.lucey@rws.com](mailto:Sean Lucey (MA Inshore Survey), sean.lucey@rws.com){.email} - -**ecodata name**: `ecodata::mass_inshore_survey` - -**Variable definitions** - -See variable definitions for `aggregate_biomass` - -```{r vars_mass_inshore_survey} -# Pull all var names -vars <- ecodata::mass_inshore_survey |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information -- [X] Database pull - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Inshore Survey (Massachusetts) {#mass_inshore_survey} + +**Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: + +- NEAMAP (mab_inshore_survey) from Cape Hatteras to RI + +- MA Inshore in MA state waters (mass_inshore_survey) + +- ME/NH Inshore in ME and NH state waters (ne_inshore_survey) + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Sean Lucey + +**Affiliations**: RWS + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Indicators from these inshore surveys are analagous to those produced by the NEFSC trawl survey in the `aggregate_biomass` indicator dataset + +## Key Results and Visualizations +Each survey shows trends by aggregate group. + +### NE + +```{r plot_mass_inshore_surveyNE} +# Plot indicator +ggplotObject <- ecodata::plot_mass_inshore_survey(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Nearshore regions of the MAB and GOM + +Temporal scale: Spring and Fall + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_mass_inshore_survey} +# Either from Contributor or ecodata +``` + +## Implications +Some trends match the NEFSC survey, some dont. This is driven by different species availability to surveys in time and space as well as the surveys sampling different habitats. Nearshore habitats are important to fish and fisheries so we monitor them as as well as trends from the larger EPUs. + +## Get the data + +**Point of contact**: [Sean Lucey (MA Inshore Survey), sean.lucey@rws.com](mailto:Sean Lucey (MA Inshore Survey), sean.lucey@rws.com){.email} + +**ecodata name**: `ecodata::mass_inshore_survey` + +**Variable definitions** + +See variable definitions for `aggregate_biomass` + +```{r vars_mass_inshore_survey} +# Pull all var names +vars <- ecodata::mass_inshore_survey |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information +- [X] Database pull + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/narw.rmd b/chapters/narw.rmd index 7b89df1d..2a87557f 100644 --- a/chapters/narw.rmd +++ b/chapters/narw.rmd @@ -1,120 +1,117 @@ -# Right Whale Abundance {#narw} - -**Description**: The data presented here are time series of the North Atlantic right whale population abundance estimates and calf abundance estimates. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Daniel Linden; Richard Pace; New England Aquarium - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Endangered North Atlantic right whales are approaching extinction. The latest population estimate for the beginning of 2022 indicates there 356 individuals remaining. The species has been experiencing an Unusual Mortality Event since 2017, which is ongoing. - -Primary threats to the species—and primary drivers of the Unusual Mortality Event—are entanglement in fishing gear and [vessel strikes](https://www.fisheries.noaa.gov/national/marine-life-distress/2017-2024-north-atlantic-right-whale-unusual-mortality-event) . Climate change is also affecting every aspect of their survival. It is changing their ocean habitat, their migratory patterns, the location and availability of their prey, and even their risk of becoming entangled in fishing gear or struck by vessels. - -## Key Results and Visualizations -The North Atlantic right whale population was on a recovery trajectory until 2010, but has since declined (Fig. x). -The most recent estimate of total population size in 2022 was 356 whales, with a 95% credible interval ranging from 346 to 363. The population continues to be in decline since 2011, though the short-term trend is equivocal due to the recent increase in survival. Reduced survival rates of adult females and diverging abundance trends between sexes have also been observed. - -North Atlantic right whale calf counts have generally declined after 2009 to the point of having zero new calves observed in 2018 (Fig. x). However, since 2019, we have seen more calf births each year with 15 births in 2022 and 12 in 2023. - -This year, the Unusual Mortality Event (UME) for North Atlantic right whales continued. Since 2017, the total UME right whale mortalities includes 36 dead stranded whales, 15 in the US and 21 in Canada. When alive but seriously injured whales (35) and sublethal injuries or ill whales (51) are taken into account, 122 individual whales are included in the UME. Recent research suggests that many mortalities go unobserved and the true number of mortalities are about three times the count of the observed mortalities [@pace_cryptic_2021]. The primary cause of death is “human interaction” from entanglements or vessel strikes. - -### MidAtlantic - -```{r plot_narwMidAtlanticadult} -# Plot indicator -ggplotObject <- ecodata::plot_narw(report= 'MidAtlantic', varName= 'adult') -ggplotObject -``` - -```{r plot_narwMidAtlanticcalf} -# Plot indicator -ggplotObject <- ecodata::plot_narw(report= 'MidAtlantic', varName= 'calf') -ggplotObject -``` - -### NewEngland - -```{r plot_narwNewEnglandadult} -# Plot indicator -ggplotObject <- ecodata::plot_narw(report= 'NewEngland', varName= 'adult') -ggplotObject -``` - -```{r plot_narwNewEnglandcalf} -# Plot indicator -ggplotObject <- ecodata::plot_narw(report= 'NewEngland', varName= 'calf') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Full shelf and farther offshore corresponding to all EPUs and beyond - -Temporal scale: Annual 1990 - 2022 - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts - - -```{r autostats_narw} -# Either from Contributor or ecodata -``` - -## Implications -Strong evidence exists to suggest that interactions between right whales and both the fixed gear fisheries in the U.S. and Canada and vessel strikes in the U.S. are contributing substantially to the decline of the species [@hayes_north_2018]. Further, right whale distribution has changed since 2010. New research suggests that recent climate driven changes in ocean circulation have resulted in right whale distribution changes driven by increased warm water influx through the Northeast Channel, which has reduced the primary right whale prey (Calanus finmarchicus) in the central and eastern portions of the Gulf of Maine [@hayes_north_2018; @record_rapid_2019; @sorochan_north_2019]. Additional potential stressors include offshore wind development, which overlaps with important habitat areas used year-round by right whales, including mother and calf migration corridors and foraging habitat [@quintana-rizzo_residency_2021; @schick_striking_2009]. This area is also a primary right whale winter foraging habitat. Additional information can be found in the offshore wind section. Turbine presence and extraction of energy from the system could alter local oceanography @christiansen_emergence_2022. Persistent foraging hotspots of right whales and seabirds overlap on Nantucket Shoals, where unique hydrography aggregates enhanced prey densities @white_spatial_2020 ; @sorochan_north_2019. - -The UMEs are under investigation and are likely the result of multiple drivers. For all large whale UMEs, human interaction appears to have contributed to increased mortalities, although investigations are not complete. - -## Get the data - -**Point of contact**: [Daniel Linden (daniel.linden@noaa.gov), Danielle Cholewiak (danielle.cholewiak@noaa.gov), Debra Palka (debra.palka@noaa.gov)](mailto:Daniel Linden (daniel.linden@noaa.gov), Danielle Cholewiak (danielle.cholewiak@noaa.gov), Debra Palka (debra.palka@noaa.gov)){.email} - -**ecodata name**: `ecodata::narw` - -**Variable definitions** - -"Palka_NARW_abundance_2023_10_02.csv 1) Year. -2) lower95 = lower 95% confidence interval value in number of animals. -3) Median=median estimate of right whale abundance in number of animals. -4) Upper95= upper 95% confidence interval value in number of animals. -5) Mean= mean estimate of right whale abundance in number of animals. -6) SD=standard deviation of estimate of right whale abundance in number of animals. -Palka_NARW_Calves_1980_2023.csv 1) Year. -2) Tot.Calves = total number of right whale calves born that year in number of animals. " - -```{r vars_narw} -# Pull all var names -vars <- ecodata::narw |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -Source data are available from the New England Aquarium upon request. Estimates and derived source data available in Linden 2023. - -**tech-doc link** - - +# Right Whale Abundance {#narw} + +**Description**: The data presented here are time series of the North Atlantic right whale population abundance estimates and calf abundance estimates. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Daniel Linden; Richard Pace; New England Aquarium + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Endangered North Atlantic right whales are approaching extinction. The latest population estimate for the beginning of 2022 indicates there 356 individuals remaining. The species has been experiencing an Unusual Mortality Event since 2017, which is ongoing. + +Primary threats to the species—and primary drivers of the Unusual Mortality Event—are entanglement in fishing gear and [vessel strikes](https://www.fisheries.noaa.gov/national/marine-life-distress/2017-2024-north-atlantic-right-whale-unusual-mortality-event) . Climate change is also affecting every aspect of their survival. It is changing their ocean habitat, their migratory patterns, the location and availability of their prey, and even their risk of becoming entangled in fishing gear or struck by vessels. + +## Key Results and Visualizations +The North Atlantic right whale population was on a recovery trajectory until 2010, but has since declined (Fig. x). +The most recent estimate of total population size in 2022 was 356 whales, with a 95% credible interval ranging from 346 to 363. The population continues to be in decline since 2011, though the short-term trend is equivocal due to the recent increase in survival. Reduced survival rates of adult females and diverging abundance trends between sexes have also been observed. + +North Atlantic right whale calf counts have generally declined after 2009 to the point of having zero new calves observed in 2018 (Fig. x). However, since 2019, we have seen more calf births each year with 15 births in 2022 and 12 in 2023. + +This year, the Unusual Mortality Event (UME) for North Atlantic right whales continued. Since 2017, the total UME right whale mortalities includes 36 dead stranded whales, 15 in the US and 21 in Canada. When alive but seriously injured whales (35) and sublethal injuries or ill whales (51) are taken into account, 122 individual whales are included in the UME. Recent research suggests that many mortalities go unobserved and the true number of mortalities are about three times the count of the observed mortalities [@pace_cryptic_2021]. The primary cause of death is “human interaction” from entanglements or vessel strikes. + +### MidAtlantic + +```{r plot_narwMidAtlanticadult} +# Plot indicator +ggplotObject <- ecodata::plot_narw(report= 'MidAtlantic', varName= 'adult') +ggplotObject +``` + +```{r plot_narwMidAtlanticcalf} +# Plot indicator +ggplotObject <- ecodata::plot_narw(report= 'MidAtlantic', varName= 'calf') +ggplotObject +``` + +### NewEngland + +```{r plot_narwNewEnglandadult} +# Plot indicator +ggplotObject <- ecodata::plot_narw(report= 'NewEngland', varName= 'adult') +ggplotObject +``` + +```{r plot_narwNewEnglandcalf} +# Plot indicator +ggplotObject <- ecodata::plot_narw(report= 'NewEngland', varName= 'calf') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Full shelf and farther offshore corresponding to all EPUs and beyond + +Temporal scale: Annual 1990 - 2022 + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts + + +```{r autostats_narw} +# Either from Contributor or ecodata +``` + +## Implications +Strong evidence exists to suggest that interactions between right whales and both the fixed gear fisheries in the U.S. and Canada and vessel strikes in the U.S. are contributing substantially to the decline of the species [@hayes_north_2018]. Further, right whale distribution has changed since 2010. New research suggests that recent climate driven changes in ocean circulation have resulted in right whale distribution changes driven by increased warm water influx through the Northeast Channel, which has reduced the primary right whale prey (Calanus finmarchicus) in the central and eastern portions of the Gulf of Maine [@hayes_north_2018; @record_rapid_2019; @sorochan_north_2019]. Additional potential stressors include offshore wind development, which overlaps with important habitat areas used year-round by right whales, including mother and calf migration corridors and foraging habitat [@quintana-rizzo_residency_2021; @schick_striking_2009]. This area is also a primary right whale winter foraging habitat. Additional information can be found in the offshore wind section. Turbine presence and extraction of energy from the system could alter local oceanography @christiansen_emergence_2022. Persistent foraging hotspots of right whales and seabirds overlap on Nantucket Shoals, where unique hydrography aggregates enhanced prey densities @white_spatial_2020 ; @sorochan_north_2019. + +The UMEs are under investigation and are likely the result of multiple drivers. For all large whale UMEs, human interaction appears to have contributed to increased mortalities, although investigations are not complete. + +## Get the data + +**Point of contact**: [Daniel Linden (daniel.linden@noaa.gov), Danielle Cholewiak (danielle.cholewiak@noaa.gov), Debra Palka (debra.palka@noaa.gov)](mailto:Daniel Linden (daniel.linden@noaa.gov), Danielle Cholewiak (danielle.cholewiak@noaa.gov), Debra Palka (debra.palka@noaa.gov)){.email} + +**ecodata name**: `ecodata::narw` + +**Variable definitions** + +"Palka_NARW_abundance_2023_10_02.csv 1) Year. 2) lower95 = lower 95% confidence interval value in number of animals. +3) Median=median estimate of right whale abundance in number of animals. 4) Upper95= upper 95% confidence interval value in number of animals. +5) Mean= mean estimate of right whale abundance in number of animals. +6) SD=standard deviation of estimate of right whale abundance in number of animals. Palka_NARW_Calves_1980_2023.csv 1) Year. +2) Tot.Calves = total number of right whale calves born that year in number of animals. " + +```{r vars_narw} +# Pull all var names +vars <- ecodata::narw |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +Source data are available from the New England Aquarium upon request. Estimates and derived source data available in Linden 2023. + +**tech-doc link** + + diff --git a/chapters/ne_inshore_survey.rmd b/chapters/ne_inshore_survey.rmd index 16359d22..f01b46bf 100644 --- a/chapters/ne_inshore_survey.rmd +++ b/chapters/ne_inshore_survey.rmd @@ -1,90 +1,90 @@ -# Inshore Survey (New England) {#ne_inshore_survey} - -**Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: - -- NEAMAP (mab_inshore_survey) from Cape Hatteras to RI - -- MA Inshore in MA state waters (mass_inshore_survey) - -- ME/NH Inshore in ME and NH state waters (ne_inshore_survey) - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Rebecca Peters - -**Affiliations**: Maine - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Indicators from these inshore surveys are analagous to those produced by the NEFSC trawl survey in the `aggregate_biomass` indicator dataset - -## Key Results and Visualizations -Each survey shows trends by aggregate group. - -### NE - -```{r plot_ne_inshore_surveyNE} -# Plot indicator -ggplotObject <- ecodata::plot_ne_inshore_survey(report='NewEngland') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Nearshore regions of the MAB and GOM - -Temporal scale: Spring and Fall - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_ne_inshore_survey} -# Either from Contributor or ecodata -``` - -## Implications -Some trends match the NEFSC survey, some dont. This is driven by different species availability to surveys in time and space as well as the surveys sampling different habitats. Nearshore habitats are important to fish and fisheries so we monitor them as as well as trends from the larger EPUs. - -## Get the data - -**Point of contact**: [Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov](mailto:Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov){.email} - -**ecodata name**: `ecodata::ne_inshore_survey` - -**Variable definitions** - -See variable definitions for `aggregate_biomass` - -```{r vars_ne_inshore_survey} -# Pull all var names -vars <- ecodata::ne_inshore_survey |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information -- [X] Database pull - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Inshore Survey (New England) {#ne_inshore_survey} + +**Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: + +- NEAMAP (mab_inshore_survey) from Cape Hatteras to RI + +- MA Inshore in MA state waters (mass_inshore_survey) + +- ME/NH Inshore in ME and NH state waters (ne_inshore_survey) + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Rebecca Peters + +**Affiliations**: Maine + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Indicators from these inshore surveys are analagous to those produced by the NEFSC trawl survey in the `aggregate_biomass` indicator dataset + +## Key Results and Visualizations +Each survey shows trends by aggregate group. + +### NE + +```{r plot_ne_inshore_surveyNE} +# Plot indicator +ggplotObject <- ecodata::plot_ne_inshore_survey(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Nearshore regions of the MAB and GOM + +Temporal scale: Spring and Fall + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_ne_inshore_survey} +# Either from Contributor or ecodata +``` + +## Implications +Some trends match the NEFSC survey, some dont. This is driven by different species availability to surveys in time and space as well as the surveys sampling different habitats. Nearshore habitats are important to fish and fisheries so we monitor them as as well as trends from the larger EPUs. + +## Get the data + +**Point of contact**: [Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov](mailto:Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov){.email} + +**ecodata name**: `ecodata::ne_inshore_survey` + +**Variable definitions** + +See variable definitions for `aggregate_biomass` + +```{r vars_ne_inshore_survey} +# Pull all var names +vars <- ecodata::ne_inshore_survey |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information +- [X] Database pull + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/observation_synthesis.rmd b/chapters/observation_synthesis.rmd index 1b0d084a..fc68cbac 100644 --- a/chapters/observation_synthesis.rmd +++ b/chapters/observation_synthesis.rmd @@ -1,97 +1,98 @@ -# 2023 Observation Synthesis {#observation_synthesis} - -**Description**: Synthesis of multiple anomalous and extreme conditions observed in 2023 that should be noted and considered in future analyses. - -**Indicator family**: - -- [X] Oceanographic -- [X] Habitat -- [X] Lower trophic levels - - -**Contributor(s)**: Kimberly Hyde, Sarah Gaichas, Joe Caracappa - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Multiple anomalous conditions and extreme events were observed in 2023 that could have brief local effects and/or widespread long-term ecosystem, fishery and management implications. This section intends to provide a record of these observations, the implications they may have for other ecosystem processes, and a reflection on how they fit into our understanding of the ecosystem. Many of these observations are being actively studied but should be noted and considered in future analyses and management decisions. - -## Key Results and Visualizations -Globally, 2023 was the warmest years on [record](https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature) - -#### Shelf-wide Phenomena - -The Gulf Stream was highly variable throughout the year, with northward shifts intermittently throughout the year and a more notable prolonged shift north along the continental shelf break in the southern Mid-Atlantic in the fall [Fig .]. This shift severely constricted the Slope Sea (the waters between the Gulf Stream and continental shelf) and inhibited formation of western warm core rings and limited warm core ring interactions at the continental shelf break. The position of Gulf Stream near the continental shelf break resulted in unusually warm and salty surface waters with strong northeastward currents in the southern Mid-Atlantic in October. The warm waters are a threat to temperature sensitive species, particularly those that are at the southern end of their range or are not mobile (e.g. scallops), while also providing suitable habitat for more southern species. - -![Gulf Stream October 2023](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/2023-GS-October-internalreview.png){width=100%} - - -While the total number of 2023 warm core rings (18) was below the decadal average (31), there were a few events worth noting. A large early season ring moved along the shelf break and created an anomalously large shelf streamer that pulled continental shelf water into the Slope Sea. Additionally, when warm core rings interact with the shelf break, they can create biological hotspots. Hotspots can aggregate multiple species in small areas, increasing bycatch risks and marine mammal shipstrike risks. In spring 2023, concentrations of North Atlantic right whales, humpback whales, basking sharks, and other large baleen whales were observed feeding near the edge of warm core rings that were adjacent to the continental shelf break. -Multiple fall 2023 tropical and coastal storms caused several flash flood events, above average coastal water levels, strong winds and high rainfall totals throughout the Northeast. These storms are potentially linked to the transition from the 2020-2022 La Niña conditions to strong El Niño conditions in late spring 2023. During El Niño winters, there is a noted increased frequency of East Coast storms. Storms increase the risk of coastal flooding and freshwater runoff into the coastal ocean, affect both commercial and recreational fishing, and can delay the spring transition from a well mixed water column to stratified. Increased freshwater flow decreases salinity in estuaries, reducing the amount of suitable estuarine habitat for juvenile marine fish species. In estuaries, hypoxia (low oxygen) also tends to be more severe in wet years, which negatively impacts habitat quality. The current El Niño is expected to gradually weaken and transition to neutral conditions in spring 2024. - -#### Regional/Coastal Phenomena - -There was a documented die-off of scallops in the Mid-Atlantic Elephant Trunk regions between the 2022 and 2023 surveys. In 2022, Elephant Trunk experienced stressful temperatures for scallops (17 - 19 ℃) for an average of 30 days, but ongoing research is being conducted to identify contributing factors. A fish and shellfish mortality event was observed in coastal New Jersey linked to hypoxia (low dissolved oxygen) and ocean acidification. - -![days exceeding scallop stress temperature 2022](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/bottom_temp_threshold_17_19_Elephant_Trunk.png){width=100%} - -Summer bottom temperatures in the Gulf of Maine were the warmest on record (since 1959) resulting in the second largest bottom marine heatwave. The heatwave started in February, peaked in May and likely continued beyond August (pending data update). 2023 bottom temperature exceeded the 15oC threshold for up to 59 days along the shelf break. Data are not available yet to determine the impact of the fall Gulf Stream position on the bottom temperatures in the Gulf of Maine. -A wide-spread, long-duration phytoplankton bloom of the dinoflagellate Tripos muelleri generated record high (since 1998) chlorophyll concentrations that were up to ten times greater than average. The bloom was observed throughout the Gulf of Maine from March to August and extended onto Georges Bank and the northern Mid-Atlantic Bight (Fig. ). The bloom severely reduced water clarity, impacting harpoon fishing and likely affecting visual predators. Blooms of large phytoplankton species such as diatoms (20-200 µm) are a primary source of energy to the system. However, despite the size of Tripos (100-200 µm), initial observations indicate that the bloom was not grazed, nor did it sink to the bottom. The specific drivers of the bloom and implications to the food web are still under investigation. - -![GOM bloom 2023](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/2023-GOMbloom-internalreview.png){width=100%} - -In Chesapeake Bay, hypoxia conditions were the lowest on record (since 1995), creating more suitable habitat for multiple fin fish and benthic species. Cooler Chesapeake Bay water temperatures paired with low hypoxia in the summer suggest conditions that season were favorable for striped bass. Cooler summer temperatures also support juvenile summer flounder growth. However, warmer winter and spring water temperatures in the Chesapeake Bay, along with other environmental factors (such as low flow), may have played a role in low production of juvenile striped bass in 2023. -Higher-than-average salinity across the Bay was likely driven by low precipitation and increased the area of available habitat for species such as croaker, spot, menhaden, and red drum, while restricting habitat area for invasive blue catfish. - - -## Indicator statistics -Spatial scale: NES - -Temporal scale: 2023 - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_observation_synthesis} -# Either from Contributor or ecodata -``` - -## Implications -TBD - -## Get the data - -**Point of contact**: [kimberly.hyde@noaa.gov](mailto:kimberly.hyde@noaa.gov){.email} - -**ecodata name**: No dataset - -**Variable definitions** - -NA - - -No Data - -**Indicator Category**: - -- [X] Other - - -**Indicator Category**: - -Synthesis of observations - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - +# 2023 Observation Synthesis {#observation_synthesis} + +**Description**: Synthesis of multiple anomalous and extreme conditions observed in 2023 that should be noted and considered in future analyses. + +**Indicator family**: + +- [X] Oceanographic +- [X] Habitat +- [X] Lower trophic levels + + +**Contributor(s)**: Kimberly Hyde, Sarah Gaichas, Joe Caracappa + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Multiple anomalous conditions and extreme events were observed in 2023 that could have brief local effects and/or widespread long-term ecosystem, fishery and management implications. This section intends to provide a record of these observations, the implications they may have for other ecosystem processes, and a reflection on how they fit into our understanding of the ecosystem. Many of these observations are being actively studied but should be noted and considered in future analyses and management decisions. + +## Key Results and Visualizations +Globally, 2023 was the warmest years on [record](https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature). North Atlantic sea surface temperatures were also the warmest on record, however Northeast U.S. shelf temperatures were more variable, with near record highs in winter and near average in other seasons. + +#### Shelf-wide Phenomena + +The Gulf Stream was highly variable throughout the year, with northward shifts intermittently throughout the year and a more notable prolonged shift north along the continental shelf break in the southern Mid-Atlantic in the fall [Fig .]. This shift severely constricted the Slope Sea, the waters between the Gulf Stream and continental shelf. This shift also inhibited formation of western warm core rings and limited warm core ring interactions at the continental shelf break. The position of Gulf Stream near the continental shelf break resulted in unusually warm and salty surface waters with strong northeastward currents in the southern Mid-Atlantic in October. The warm waters are a threat to temperature sensitive species, particularly those that are at the southern end of their range or are not mobile (e.g. scallops), while also providing suitable habitat for more southern species. + +![Gulf Stream October 2023](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/2023-GS-October-internalreview.png){width=100%} + + +While the total number of 2023 warm core rings (18) was below the decadal average (31), there were a few events worth noting. A large early season ring moved along the shelf break and created an anomalously large shelf streamer that pulled continental shelf water into the Slope Sea. Additionally, when warm core rings interact with the shelf break, they can create biological hotspots. Hotspots can aggregate multiple species in small areas, increasing bycatch risks and marine mammal shipstrike risks. In spring 2023, concentrations of North Atlantic right whales, humpback whales, basking sharks, and other large baleen whales were observed feeding near the edge of two warm core rings that were adjacent to the continental shelf break. + +Multiple fall 2023 tropical and coastal storms caused several flash flood events, above average coastal water levels, strong winds and high rainfall totals throughout the Northeast. These storms are potentially linked to the transition from the 2020-2022 La Niña conditions to strong El Niño conditions in late spring 2023. During El Niño winters, there is a noted increased frequency of East Coast storms. Storms increase the risk of coastal flooding and freshwater runoff into the coastal ocean, affect both commercial and recreational fishing, and can delay the spring transition from a well mixed water column to stratified. Increased freshwater flow decreases salinity in estuaries, reducing the amount of suitable estuarine habitat for juvenile marine fish species. In estuaries, hypoxia (low oxygen) also tends to be more severe in wet years, which negatively impacts habitat quality. The current El Niño is expected to gradually weaken and transition to neutral conditions in spring 2024. + +#### Regional/Coastal Phenomena + +There was a documented die-off of scallops in the Mid-Atlantic Elephant Trunk regions between the 2022 and 2023 surveys. In 2022, Elephant Trunk experienced stressful temperatures for scallops (17 - 19 ℃) for an average of 30 days, but ongoing research is being conducted to identify contributing factors. In summer 2023, a fish and shellfish mortality event was observed in coastal New Jersey linked to hypoxia (low dissolved oxygen) and [ocean acidification](https://noaa-edab.github.io/catalog/ocean_acidification.html). + +![days exceeding scallop stress temperature 2022](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/bottom_temp_threshold_17_19_Elephant_Trunk.png){width=100%} + +Summer bottom temperatures in the Gulf of Maine were the warmest on record (since 1959) resulting in the second largest bottom marine heatwave. The heatwave started in February, peaked in May and likely continued beyond August (pending data update). 2023 bottom temperature exceeded the 15oC threshold for up to 59 days along the shelf break. Data are not available yet to determine the primary source water into the Gulf of Maine nor, the impact of the fall Gulf Stream position on the bottom temperatures in the Gulf of Maine. + +A wide-spread, long-duration phytoplankton bloom of the dinoflagellate Tripos muelleri generated record high (since 1998) [chlorophyll concentrations](https://noaa-edab.github.io/catalog/chl_pp.html) that were up to ten times greater than average. The bloom was observed throughout the Gulf of Maine from March to August and extended onto Georges Bank and the northern Mid-Atlantic Bight (Fig. ). The bloom severely reduced water clarity, impacting harpoon fishing and likely affecting visual predators. Blooms of [large phytoplankton](https://noaa-edab.github.io/catalog/phyto_size.html) species such as diatoms (20-200 µm) are a primary source of energy to the system. However, despite the size of Tripos (100-200 µm), initial observations indicate that the bloom was not grazed, nor did it sink to the bottom. The specific drivers of the bloom and implications to the food web are still under investigation. + +![GOM bloom 2023](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/2023-GOMbloom-internalreview.png){width=100%} + +In Chesapeake Bay, hypoxia conditions were the lowest on record (since 1995), creating more suitable habitat for multiple fin fish and benthic species. Cooler Chesapeake Bay water temperatures paired with low hypoxia in the summer suggest conditions that season were favorable for striped bass. Cooler summer temperatures also support juvenile summer flounder growth. However, warmer winter and spring water temperatures in the Chesapeake Bay, along with other environmental factors (such as low flow), may have played a role in low production of juvenile striped bass in 2023. Higher-than-average salinity across the Bay was likely driven by low precipitation and increased the area of available habitat for species such as croaker, spot, menhaden, and red drum, while restricting habitat area for invasive blue catfish. + + +## Indicator statistics +Spatial scale: NES + +Temporal scale: 2023 + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_observation_synthesis} +# Either from Contributor or ecodata +``` + +## Implications +The implications of these events are still to be determined, but should be noted for future analyses. + +## Get the data + +**Point of contact**: [kimberly.hyde@noaa.gov](mailto:kimberly.hyde@noaa.gov){.email} + +**ecodata name**: No dataset + +**Variable definitions** + +NA + + +No Data + +**Indicator Category**: + +- [X] Other + + +**Indicator Category**: + +Synthesis of observations + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + diff --git a/chapters/ocean_acidification.rmd b/chapters/ocean_acidification.rmd index 3f15769a..5026fc31 100644 --- a/chapters/ocean_acidification.rmd +++ b/chapters/ocean_acidification.rmd @@ -1,123 +1,123 @@ -# Ocean Acidification and Other Stressors {#ocean_acidification} - -**Description**: Maps and variability of regional carbonate chemistry and other oceanographic properties - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Grace Saba, Lori Garzio - -**Affiliations**: Rutgers University - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Ocean acidification (OA) has caused measured declines in global ocean pH and is projected to continue declining by up to 0.30 pH units over the course of the 21st century if high carbon dioxide emissions continue (IPCC 2019). OA also changes the availability of minerals required by organisms to form calcified structures such as shells and other structures. Calcifying conditions in seawater can be determined by measuring aragonite saturation state (ΩArag or omega), the tendency of a common type of calcium carbonate, aragonite, to form or dissolve. When ΩArag is less than 1, shells and other calcium carbonate structures begin to dissolve. Typical surface ocean ΩArag is 2-4, but extremes can be <1 or >5. As the ocean absorbs carbon dioxide, both pH and ΩArag decrease and can cause organisms to respond with reduced survival, calcification rates, growth, and reproduction, as well as impaired development, and/or changes in energy allocation (reviewed in @kroeker_impacts_2013, @saba_recommended_2019). However, sensitivity levels vary, and some organisms exhibit negative responses to calcification and other processes when ΩArag is as low as 3. - -The U.S. Northeast Shelf (NES) is prone not only to global rates of ocean acidification, but also to coastal processes that can act to exacerbate acidification, including freshwater sources (primarily riverine), eutrophication and photosynthesis‐respiration cycles, coastal upwelling, and other influences (@goldsmith_scientific_2019, @wrightfairbanks_autonomous_2020, @xu_long-term_2020). Often times, other stressors such as ocean warming and/or low bottom water dissolved oxygen can co-occur. Dissolved oxygen concentrations at or below 5 mg/liter is considered problematic for marine life. Although concentrations between 3-5 mg/liter may not be low enough to directly cause death in many marine animals, research focused on marine species has identified other negative impacts such as reduced metabolism, feeding, growth, and reproduction at these levels. Lower hypoxic concentrations of dissolved oxygen (< 3 mg/liter) have been directly associated with mortalities in some organisms in other coastal regions around the world. - -Any one stressor may not itself be an issue due to the resiliency of many coastal species to fluctuating natural environmental conditions. However, when more than one stressor occurs simultaneously, an organism may become unable to fully withstand changes. The impacts of multiple stressors occurring simultaneously on organism health is much less well known. The co-occurrence of low dissolved oxygen and pH may exacerbate negative responses in organisms or increase their susceptibility to either or both oxygen and pH. - -The spatio-temporal variability of OA in the NES is still poorly described, and as of yet there are no analyses that have determined times or locations where multiple environmental stressors are co-located. The purpose of developing data products for OA and other stressors for the State of the Ecosystems reports is to determine locations and times where potential stressors overlap in space and time to assist in identifying potentially vulnerable species habitats. +# Ocean Acidification and Other Stressors {#ocean_acidification} + +**Description**: Maps and variability of regional carbonate chemistry and other oceanographic properties + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Grace Saba, Lori Garzio + +**Affiliations**: Rutgers University + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Ocean acidification (OA) has caused measured declines in global ocean pH and is projected to continue declining by up to 0.30 pH units over the course of the 21st century if high carbon dioxide emissions continue (IPCC 2019). OA also changes the availability of minerals required by organisms to form calcified structures such as shells and other structures. Calcifying conditions in seawater can be determined by measuring aragonite saturation state (ΩArag or omega), the tendency of a common type of calcium carbonate, aragonite, to form or dissolve. When ΩArag is less than 1, shells and other calcium carbonate structures begin to dissolve. Typical surface ocean ΩArag is 2-4, but extremes can be <1 or >5. As the ocean absorbs carbon dioxide, both pH and ΩArag decrease and can cause organisms to respond with reduced survival, calcification rates, growth, and reproduction, as well as impaired development, and/or changes in energy allocation (reviewed in @kroeker_impacts_2013, @saba_recommended_2019). However, sensitivity levels vary, and some organisms exhibit negative responses to calcification and other processes when ΩArag is as low as 3. -## Key Results and Visualizations -The products developed here include: plots of the seasonal progression (Spring-Fall 2023) of oceanographic properties (including temperature, chlorophyll, dissolved oxygen, pH, and aragonite saturation state) on the New Jersey coastal shelf; plots summarizing a multi-stressor event in the Mid-Atlantic during summer 2023; static and animated maps of summer-time bottom pH and aragonite saturation state on the U.S. Northeast Shelf (2007-2023); and maps of locations where species sensitivity levels for aragonite saturation state were reached in bottom water during the summer (2007-2023). - -Seasonal progression in the Mid-Atlantic (2023) - -With high temporal resolution (> seasonal) glider sampling off the coast of New Jersey in 2023, we observed the evolution of water column structure from spring stratification to fall breakdown, and the resulting changes in bottom water chemistry (see below section ‘Summer 2023 multi-stressor event in the Mid-Atlantic’). Event-based oceanographic changes were also observed due to upwelling, the passage of Hurricane Lee offshore of the sampling region around 09/16, and a strong hybrid storm (Nor’easter, and Tropical Storm Ophelia) that drove the eventual water column turnover in the fall (09/24-09/26). - - -Figure 1. Maps of four 2023 glider deployments on the coastal New Jersey shelf and the resulting vertical profiles of oceanographic parameters characterizing the evolution of temperature (in °C), chlorophyll, dissolved oxygen, pH, and aragonite saturation state (Omega) from Spring through Early Fall. Preliminary data presented here are the result of support provided by a grant from the New Jersey Research and Monitoring Initiative (RMI). - -![Figure 1](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure1-GraceSaba_2024.png){width=100%} - -Summer 2023 multi-stressor event in the Mid-Atlantic - -From August-September 2023, much of the bottom water sampled from Sandy Hook, New Jersey south to Tuckerton, New Jersey and from nearshore (15 meter water depth) to deeper depths (60 meter water depth), exhibited dissolved oxygen concentrations less than 5 mg/L and pH values less than 7.75. Coast-wide, hypoxic levels of dissolved oxygen (concentrations < 3 mg/L) were observed at shallower, more inshore locations. Some of these locations were shallow enough to be observed in glider ru40, but much of these hypoxic levels were observed in a shallow glider (ru28, with a 50 m depth capacity) deployed at the same time. In addition to low pH measured in bottom waters by glider ru39, which is indicative of ocean acidification, aragonite saturation state (Omega) was calculated to be < 1 in several locations. Normal, more optimal levels in seawater typically include dissolved oxygen concentrations > 7 mg/liter, pH of ~8.1, and aragonite saturation states > 3. - -During the time when low dissolved oxygen and pH/aragonite saturation state were observed, numerous mortalities of fish, lobsters and crabs within the sampling area were reported. The mortalities were observed in bottom waters primarily off the coast of Monmouth and Ocean Counties and included the Mud Hole, as far east as Lillian wreck, and southward in Sea Girt and Axel Carlson Reefs and the surrounding areas. Mortalities were reported for American lobsters, Jonah crab, Atlantic rock crab, spider crabs, black sea bass, and tautog not only in pots where trapped organisms would not have been able to escape poor conditions, but also on the open bottom. This observation suggests that if low dissolved oxygen and/or pH were indeed the culprit for these reported mortalities, the area may have been extensive enough that they could not escape in time. - -Healthy dissolved oxygen, pH, and aragonite saturation state levels were restored in bottom waters temporarily during the offshore passage of Hurricane Lee around 09/16, and for the remainder of the sampling after a strong hybrid storm that started on 09/24 and drove the eventual fall water column turnover on 09/26. - - -Figure 2. Left: Mission tracks of three gliders (named ru28, ru39, ru40) deployed off the coast of New Jersey in August and September. Gliders ru39 and ru40 were deployed as a pair along the same mission track. All gliders had sensors measuring temperature and salinity. Gliders ru28 and ru40 each had an additional sensor measuring dissolved oxygen (no pH or aragonite saturation state), and glider ru39 had an additional sensor measuring pH (no dissolved oxygen). Right: Locations of hypoxic levels of dissolved oxygen (magenta; < 3 mg/liter) and low aragonite saturation state (cyan; < 1) measured along the glider mission tracks and locations of reported fish, lobster, and/or crab mortalities (red X). Preliminary data presented here are the result of support provided by grants from the New Jersey Research and Monitoring Initiative (RMI) and New Jersey Department of Environmental Protection’s Bureau of Marine Water Monitoring. - -![Figure 2](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure2-GraceSaba_2024.png){width=100%} - -Figure 3. Complete cross-sections of dissolved oxygen concentrations (left top and bottom), pH (right top), and aragonite saturation state (omega, right bottom) measured along the associated mission tracks during the deployments of the three gliders (named ru28, ru39, ru40; see tracks in Figure 2) during August and September 2023. Dissolved oxygen concentrations between 3-5 mg/liters are expressed as orange & yellow, and hypoxic concentrations < 3 mg/liter are expressed as red. pH values < 7.75 and omega < 1 are highlighted in cyan. Preliminary data presented here are the result of support provided by grants from the New Jersey Research and Monitoring Initiative (RMI) and New Jersey Department of Environmental Protection’s Bureau of Marine Water Monitoring. - -![Figure 3](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure3-GraceSaba_2024.png){width=100%} - - -Summer-time bottom pH and aragonite saturation state on the U.S. Northeast Shelf - -Using available quality-controlled vessel- and glider-based datasets accompanying observations on the U.S. Northeast Shelf, maps of summer-time (June-August) bottom water pH and aragonite saturation state (omega) were developed for each year from 2007-2023 and combined to create animations of pH and omega over time in the region (pH: access [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/2024_submission/plots/summer_bottom_water_maps/pH/); omega: access [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/2024_submission/plots/summer_bottom_water_maps/omega/)). The animations depict high variability in time and space and increases in sampling frequency over time. Summer-time bottom pH and aragonite saturation state (2007-2023) ranged from 7.59-8.15 and 0.64-2.49, respectively. Spatially, the lowest bottom pH and aragonite saturation state have occurred primarily in the western Gulf of Maine, western Long Island Sound, nearshore to mid-shelf waters of the Mid-Atlantic Bight off the coast of New Jersey and New York, and in waters > 1000 meters. - - -Figure 4. Bottom summer-time (June-August) pH (left panel) and aragonite saturation state (right panel) on the U.S. Northeast Shelf from 2007-2023 plotted from available quality-controlled vessel- and glider-based datasets. - -![Figure 4](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure4-GraceSaba_2024.png){width=100%} - -Locations where species sensitivity levels for aragonite saturation state were reached - -Using the bottom water aragonite saturation state data, maps depicting locations where summer-time bottom aragonite saturation state reached lab-derived sensitivity levels of designated target species were developed. The target species selected for the Mid-Atlantic were Atlantic sea scallop (Placopecten magellanicus) and longfin squid (Doryteuthis pealeii), and the target species selected for the Gulf of Maine were Atlantic cod (Gadus morhua) and American lobster (Homarus americanus). - -Because there were no additional 2023 bottom water aragonite saturation state data available in the Gulf of Maine to update this same product from the previous year’s report, maps for Atlantic cod and American lobster are not included in this year’s catalog page. However, the maps for the individual years between 2007-2022 and the combined map for this same time period are available for these species [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/2024_submission/plots/summer_bottom_water_maps/omega_species_sensitivity/). Bottom water data collected during 2023 were incorporated to update this product for the Mid-Atlantic species, Atlantic sea scallop and longfin squid (available [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/2024_submission/plots/summer_bottom_water_maps/omega_species_sensitivity/) and summarized below). Aragonite saturation state was at or below the sensitivity levels for both Atlantic sea scallop (Placopecten magellanicus) and longfin squid (Doryteuthis pealeii) within their habitat depth ranges in Long Island Sound and the nearshore and mid-shelf regions of the New Jersey/New York shelf. The sensitivity levels of bottom aragonite saturation state occurred during August 2016, July 2018, August 2019, and July-August 2023 for both species, and additionally in August 2021 and August 2022 for the Atlantic sea scallop. The comparison between the 2007-2022 and 2007-2023 maps reveals that the lower aragonite saturation state conditions that occurred in the Mid-Atlantic coastal shelf during summer 2023 increased the spatial range of potentially unfavorable habitat for Atlantic sea scallops and longfin squid compared to the observed past years. - - -Figure 5. Locations where bottom aragonite saturation state (ΩArag; summer only: June-August) in the habitat depth range were at or below the laboratory-derived sensitivity level for Atlantic sea scallop (top panels) and longfin squid (bottom panels) for the time periods 2007-2022 (left panels) and 2007-2023 (right panels). The sensitivity value used for Atlantic sea scallop was ΩArag ≤ 1.1, based on reduced adult calcification rate observed at this level in @cameron_effects_2022. The sensitivity value used for longfin squid was ΩArag ≤ 0.96, based on embryo and paralarvae malformation, increased time to hatching and decreased hatching success, and changes to mantle length and statolith morphology observed at this level in @zakroff_dose-dependence_2019 and @zakroff_antagonistic_2020. Gray circles indicate locations where carbonate chemistry samples were collected, but bottom ΩArag values were higher than sensitivity values determined for that species and/or samples were collected outside of the species depth range. - -![Figure 5](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure5-GraceSaba_2024.png){width=100%} +The U.S. Northeast Shelf (NES) is prone not only to global rates of ocean acidification, but also to coastal processes that can act to exacerbate acidification, including freshwater sources (primarily riverine), eutrophication and photosynthesis‐respiration cycles, coastal upwelling, and other influences (@goldsmith_scientific_2019, @wrightfairbanks_autonomous_2020, @xu_long-term_2020). Often times, other stressors such as ocean warming and/or low bottom water dissolved oxygen can co-occur. Dissolved oxygen concentrations at or below 5 mg/liter is considered problematic for marine life. Although concentrations between 3-5 mg/liter may not be low enough to directly cause death in many marine animals, research focused on marine species has identified other negative impacts such as reduced metabolism, feeding, growth, and reproduction at these levels. Lower hypoxic concentrations of dissolved oxygen (< 3 mg/liter) have been directly associated with mortalities in some organisms in other coastal regions around the world. +Any one stressor may not itself be an issue due to the resiliency of many coastal species to fluctuating natural environmental conditions. However, when more than one stressor occurs simultaneously, an organism may become unable to fully withstand changes. The impacts of multiple stressors occurring simultaneously on organism health is much less well known. The co-occurrence of low dissolved oxygen and pH may exacerbate negative responses in organisms or increase their susceptibility to either or both oxygen and pH. -## Indicator statistics -Spatial scale: Seasonal progressions in the Mid-Atlantic (2023), Summer 2023 multi-stressor event in the Mid-Atlantic: New Jersey coastal shelf; Summertime bottom pH and aragonite saturation state on the U.S. Northeast Shelf: full U.S. Northeast Shelf; Locations where species sensitivity levels for aragonite saturation state were reached: Mid-Atlantic Bight +The spatio-temporal variability of OA in the NES is still poorly described, and as of yet there are no analyses that have determined times or locations where multiple environmental stressors are co-located. The purpose of developing data products for OA and other stressors for the State of the Ecosystems reports is to determine locations and times where potential stressors overlap in space and time to assist in identifying potentially vulnerable species habitats. + +## Key Results and Visualizations +The products developed here include: plots of the seasonal progression (Spring-Fall 2023) of oceanographic properties (including temperature, chlorophyll, dissolved oxygen, pH, and aragonite saturation state) on the New Jersey coastal shelf; plots summarizing a multi-stressor event in the Mid-Atlantic during summer 2023; static and animated maps of summer-time bottom pH and aragonite saturation state on the U.S. Northeast Shelf (2007-2023); and maps of locations where species sensitivity levels for aragonite saturation state were reached in bottom water during the summer (2007-2023). -Temporal scale: Seasonal progressions in the Mid-Atlantic (2023): Spring-Fall 2023; Summer 2023 multi-stressor event in the Mid-Atlantic: Summer (June-August) 2023; Summer-time bottom pH and aragonite saturation state on the U.S. Northeast Shelf: summer (June-August), 2007-2023; Locations where species sensitivity levels for aragonite saturation state were reached: summer (June-August), 2007-2023 +Seasonal progression in the Mid-Atlantic (2023) -**Synthesis Theme**: +With high temporal resolution (> seasonal) glider sampling off the coast of New Jersey in 2023, we observed the evolution of water column structure from spring stratification to fall breakdown, and the resulting changes in bottom water chemistry (see below section ‘Summer 2023 multi-stressor event in the Mid-Atlantic’). Event-based oceanographic changes were also observed due to upwelling, the passage of Hurricane Lee offshore of the sampling region around 09/16, and a strong hybrid storm (Nor’easter, and Tropical Storm Ophelia) that drove the eventual water column turnover in the fall (09/24-09/26). -- [X] Multiple System Drivers +Figure 1. Maps of four 2023 glider deployments on the coastal New Jersey shelf and the resulting vertical profiles of oceanographic parameters characterizing the evolution of temperature (in °C), chlorophyll, dissolved oxygen, pH, and aragonite saturation state (Omega) from Spring through Early Fall. Preliminary data presented here are the result of support provided by a grant from the New Jersey Research and Monitoring Initiative (RMI). -```{r autostats_ocean_acidification} -# Either from Contributor or ecodata -``` +![Figure 1](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure1-GraceSaba_2024.png){width=100%} -## Implications -While the sparsity of carbonate chemistry data at annual and seasonal scales to date limits our ability to determine exposure frequency and duration of unfavorable conditions to marine species, locations where recurring low levels of summer-time bottom water pH and aragonite saturation state can be identified from the available data. These areas include the western Gulf of Maine, western Long Island Sound, and nearshore to mid-shelf waters of the Mid-Atlantic Bight off the coast of New Jersey and New York. This information helps to identify potential vulnerable species habitats and can guide future targeted observations aimed at determining stressor exposure frequency and duration and the co-occurrence of additional environmental stressors. - -The seasonal level resolution of data collected in the Mid-Atlantic Bight in 2023 revealed the development of low bottom water dissolved oxygen, pH, and aragonite saturation state that co-occurred for over a month during summer through early fall. Mortalities of multiple crustacean and finfish species were reported during this multi-stressor event. The addition of the 2023 data to the bottom water products (2007-2023) highlighted that 2023 may have been an anomalous year that increased the spatial range of potentially unfavorable habitat for local species compared to past observed years. Events such as these that may prevent the ability to sustain normal populations of marine organisms are concerning, not only for the ocean ecosystem but also for the local economy and commercial and recreational fishing industries. Understanding the factors that cause these events will aid in projecting the severity and duration of these events under ongoing climate change and provide important support for guiding policy and management options and identifying priorities for science and monitoring. +Summer 2023 multi-stressor event in the Mid-Atlantic -## Get the data +From August-September 2023, much of the bottom water sampled from Sandy Hook, New Jersey south to Tuckerton, New Jersey and from nearshore (15 meter water depth) to deeper depths (60 meter water depth), exhibited dissolved oxygen concentrations less than 5 mg/L and pH values less than 7.75. Coast-wide, hypoxic levels of dissolved oxygen (concentrations < 3 mg/L) were observed at shallower, more inshore locations. Some of these locations were shallow enough to be observed in glider ru40, but much of these hypoxic levels were observed in a shallow glider (ru28, with a 50 m depth capacity) deployed at the same time. In addition to low pH measured in bottom waters by glider ru39, which is indicative of ocean acidification, aragonite saturation state (Omega) was calculated to be < 1 in several locations. Normal, more optimal levels in seawater typically include dissolved oxygen concentrations > 7 mg/liter, pH of ~8.1, and aragonite saturation states > 3. -**Point of contact**: [Grace Saba (saba@marine.rutgers.edu); Lori Garzio (lgarzio@marine.rutgers.edu)](mailto:Grace Saba (saba@marine.rutgers.edu); Lori Garzio (lgarzio@marine.rutgers.edu)){.email} +During the time when low dissolved oxygen and pH/aragonite saturation state were observed, numerous mortalities of fish, lobsters and crabs within the sampling area were reported. The mortalities were observed in bottom waters primarily off the coast of Monmouth and Ocean Counties and included the Mud Hole, as far east as Lillian wreck, and southward in Sea Girt and Axel Carlson Reefs and the surrounding areas. Mortalities were reported for American lobsters, Jonah crab, Atlantic rock crab, spider crabs, black sea bass, and tautog not only in pots where trapped organisms would not have been able to escape poor conditions, but also on the open bottom. This observation suggests that if low dissolved oxygen and/or pH were indeed the culprit for these reported mortalities, the area may have been extensive enough that they could not escape in time. -**ecodata name**: No dataset +Healthy dissolved oxygen, pH, and aragonite saturation state levels were restored in bottom waters temporarily during the offshore passage of Hurricane Lee around 09/16, and for the remainder of the sampling after a strong hybrid storm that started on 09/24 and drove the eventual fall water column turnover on 09/26. -**Variable definitions** -1) depth_interpolated meters 2) temperature degrees Celsius 3) chlorophyll_a µg L-1 -4) oxygen_concentration_shifted_mgL mg L-1 5) pH_shifted 6) aragonite_saturation_state - - -No Data - -**Indicator Category**: - -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ +Figure 2. Left: Mission tracks of three gliders (named ru28, ru39, ru40) deployed off the coast of New Jersey in August and September. Gliders ru39 and ru40 were deployed as a pair along the same mission track. All gliders had sensors measuring temperature and salinity. Gliders ru28 and ru40 each had an additional sensor measuring dissolved oxygen (no pH or aragonite saturation state), and glider ru39 had an additional sensor measuring pH (no dissolved oxygen). Right: Locations of hypoxic levels of dissolved oxygen (magenta; < 3 mg/liter) and low aragonite saturation state (cyan; < 1) measured along the glider mission tracks and locations of reported fish, lobster, and/or crab mortalities (red X). Preliminary data presented here are the result of support provided by grants from the New Jersey Research and Monitoring Initiative (RMI) and New Jersey Department of Environmental Protection’s Bureau of Marine Water Monitoring. +![Figure 2](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure2-GraceSaba_2024.png){width=100%} + +Figure 3. Complete cross-sections of dissolved oxygen concentrations (left top and bottom), pH (right top), and aragonite saturation state (omega, right bottom) measured along the associated mission tracks during the deployments of the three gliders (named ru28, ru39, ru40; see tracks in Figure 2) during August and September 2023. Dissolved oxygen concentrations between 3-5 mg/liters are expressed as orange & yellow, and hypoxic concentrations < 3 mg/liter are expressed as red. pH values < 7.75 and omega < 1 are highlighted in cyan. Preliminary data presented here are the result of support provided by grants from the New Jersey Research and Monitoring Initiative (RMI) and New Jersey Department of Environmental Protection’s Bureau of Marine Water Monitoring. + +![Figure 3](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure3-GraceSaba_2024.png){width=100%} + + +Summer-time bottom pH and aragonite saturation state on the U.S. Northeast Shelf + +Using available quality-controlled vessel- and glider-based datasets accompanying observations on the U.S. Northeast Shelf, maps of summer-time (June-August) bottom water pH and aragonite saturation state (omega) were developed for each year from 2007-2023 and combined to create animations of pH and omega over time in the region (pH: access [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/2024_submission/plots/summer_bottom_water_maps/pH/); omega: access [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/2024_submission/plots/summer_bottom_water_maps/omega/)). The animations depict high variability in time and space and increases in sampling frequency over time. Summer-time bottom pH and aragonite saturation state (2007-2023) ranged from 7.59-8.15 and 0.64-2.49, respectively. Spatially, the lowest bottom pH and aragonite saturation state have occurred primarily in the western Gulf of Maine, western Long Island Sound, nearshore to mid-shelf waters of the Mid-Atlantic Bight off the coast of New Jersey and New York, and in waters > 1000 meters. + + +Figure 4. Bottom summer-time (June-August) pH (left panel) and aragonite saturation state (right panel) on the U.S. Northeast Shelf from 2007-2023 plotted from available quality-controlled vessel- and glider-based datasets. + +![Figure 4](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure4-GraceSaba_2024.png){width=100%} + +Locations where species sensitivity levels for aragonite saturation state were reached + +Using the bottom water aragonite saturation state data, maps depicting locations where summer-time bottom aragonite saturation state reached lab-derived sensitivity levels of designated target species were developed. The target species selected for the Mid-Atlantic were Atlantic sea scallop (Placopecten magellanicus) and longfin squid (Doryteuthis pealeii), and the target species selected for the Gulf of Maine were Atlantic cod (Gadus morhua) and American lobster (Homarus americanus). + +Because there were no additional 2023 bottom water aragonite saturation state data available in the Gulf of Maine to update this same product from the previous year’s report, maps for Atlantic cod and American lobster are not included in this year’s catalog page. However, the maps for the individual years between 2007-2022 and the combined map for this same time period are available for these species [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/2024_submission/plots/summer_bottom_water_maps/omega_species_sensitivity/). Bottom water data collected during 2023 were incorporated to update this product for the Mid-Atlantic species, Atlantic sea scallop and longfin squid (available [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/2024_submission/plots/summer_bottom_water_maps/omega_species_sensitivity/) and summarized below). Aragonite saturation state was at or below the sensitivity levels for both Atlantic sea scallop (Placopecten magellanicus) and longfin squid (Doryteuthis pealeii) within their habitat depth ranges in Long Island Sound and the nearshore and mid-shelf regions of the New Jersey/New York shelf. The sensitivity levels of bottom aragonite saturation state occurred during August 2016, July 2018, August 2019, and July-August 2023 for both species, and additionally in August 2021 and August 2022 for the Atlantic sea scallop. The comparison between the 2007-2022 and 2007-2023 maps reveals that the lower aragonite saturation state conditions that occurred in the Mid-Atlantic coastal shelf during summer 2023 increased the spatial range of potentially unfavorable habitat for Atlantic sea scallops and longfin squid compared to the observed past years. + + +Figure 5. Locations where bottom aragonite saturation state (ΩArag; summer only: June-August) in the habitat depth range were at or below the laboratory-derived sensitivity level for Atlantic sea scallop (top panels) and longfin squid (bottom panels) for the time periods 2007-2022 (left panels) and 2007-2023 (right panels). The sensitivity value used for Atlantic sea scallop was ΩArag ≤ 1.1, based on reduced adult calcification rate observed at this level in @cameron_effects_2022. The sensitivity value used for longfin squid was ΩArag ≤ 0.96, based on embryo and paralarvae malformation, increased time to hatching and decreased hatching success, and changes to mantle length and statolith morphology observed at this level in @zakroff_dose-dependence_2019 and @zakroff_antagonistic_2020. Gray circles indicate locations where carbonate chemistry samples were collected, but bottom ΩArag values were higher than sensitivity values determined for that species and/or samples were collected outside of the species depth range. + +![Figure 5](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Figure5-GraceSaba_2024.png){width=100%} + + +## Indicator statistics +Spatial scale: Seasonal progressions in the Mid-Atlantic (2023), Summer 2023 multi-stressor event in the Mid-Atlantic: New Jersey coastal shelf; Summertime bottom pH and aragonite saturation state on the U.S. Northeast Shelf: full U.S. Northeast Shelf; Locations where species sensitivity levels for aragonite saturation state were reached: Mid-Atlantic Bight + +Temporal scale: Seasonal progressions in the Mid-Atlantic (2023): Spring-Fall 2023; Summer 2023 multi-stressor event in the Mid-Atlantic: Summer (June-August) 2023; Summer-time bottom pH and aragonite saturation state on the U.S. Northeast Shelf: summer (June-August), 2007-2023; Locations where species sensitivity levels for aragonite saturation state were reached: summer (June-August), 2007-2023 + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_ocean_acidification} +# Either from Contributor or ecodata +``` + +## Implications +While the sparsity of carbonate chemistry data at annual and seasonal scales to date limits our ability to determine exposure frequency and duration of unfavorable conditions to marine species, locations where recurring low levels of summer-time bottom water pH and aragonite saturation state can be identified from the available data. These areas include the western Gulf of Maine, western Long Island Sound, and nearshore to mid-shelf waters of the Mid-Atlantic Bight off the coast of New Jersey and New York. This information helps to identify potential vulnerable species habitats and can guide future targeted observations aimed at determining stressor exposure frequency and duration and the co-occurrence of additional environmental stressors. + +The seasonal level resolution of data collected in the Mid-Atlantic Bight in 2023 revealed the development of low bottom water dissolved oxygen, pH, and aragonite saturation state that co-occurred for over a month during summer through early fall. Mortalities of multiple crustacean and finfish species were reported during this multi-stressor event. The addition of the 2023 data to the bottom water products (2007-2023) highlighted that 2023 may have been an anomalous year that increased the spatial range of potentially unfavorable habitat for local species compared to past observed years. Events such as these that may prevent the ability to sustain normal populations of marine organisms are concerning, not only for the ocean ecosystem but also for the local economy and commercial and recreational fishing industries. Understanding the factors that cause these events will aid in projecting the severity and duration of these events under ongoing climate change and provide important support for guiding policy and management options and identifying priorities for science and monitoring. + +## Get the data + +**Point of contact**: [Grace Saba (saba@marine.rutgers.edu); Lori Garzio (lgarzio@marine.rutgers.edu)](mailto:Grace Saba (saba@marine.rutgers.edu); Lori Garzio (lgarzio@marine.rutgers.edu)){.email} + +**ecodata name**: No dataset + +**Variable definitions** + +1) depth_interpolated meters 2) temperature degrees Celsius 3) chlorophyll_a µg L-1 4) oxygen_concentration_shifted_mgL mg L-1 5) pH_shifted +6) aragonite_saturation_state + + +No Data + +**Indicator Category**: + +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + diff --git a/chapters/osw_survey_impact.rmd b/chapters/osw_survey_impact.rmd index c6cc76bd..2c1f2d99 100644 --- a/chapters/osw_survey_impact.rmd +++ b/chapters/osw_survey_impact.rmd @@ -1,73 +1,73 @@ -# Survey Impacts from Offshore Wind Development {#osw_survey_impact} - -**Description**: Offshore wind development is expected to have several impacts on federal and state surveys. - -**Indicator family**: - -- [X] Social - - -**Contributor(s)**: Douglas Christel - -**Affiliations**: GARFO - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Proceed as though this were a short summary of a typical introduction section in a paper. - -## Key Results and Visualizations -Proceed as though this were a short summary of a typical results section in a paper. - - -## Indicator statistics -Spatial scale: full shelf - -Temporal scale: Decadal - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_osw_survey_impact} -# Either from Contributor or ecodata -``` - -## Implications -Proposed wind development areas interact with the region’s federal scientific surveys @friedland_spatial_2023. Scientific surveys are impacted by offshore wind in four ways: - 1) Exclusion of NOAA Fisheries’ sampling platforms from the wind development area due to operational and safety limitations; - 2) Impacts on the random-stratified statistical design that is the basis for scientific assessments, advice, and analyses; -3) Alteration of benthic and pelagic habitats, and airspace in and around the wind energy development, requiring new designs and methods to sample new habitats; and, -4) Reduced sampling productivity through navigation impacts of wind energy infrastructure on aerial and vessel survey operations. - -Increased vessel transit between stations may decrease data collections that are already limited by annual days-at-sea day allocations. The total survey area overlap ranges from 1-70% for all Greater Atlantic federal surveys. The Gulf of Maine Cooperative Research Bottom Longline Survey (41%) and the Shrimp Survey (70%) have the largest percent overlap with the draft Gulf of Maine Wind Energy Areas. The remaining surveys range from 1-16% overlap. Individual survey strata have significant interaction with wind, including the sea scallop survey (up to 96% of individual strata) and the bottom trawl survey (BTS, up to 60% strata overlap). Additionally, up to 50% of the southern New England North Atlantic right whale survey’s area overlaps with proposed project areas and a region-wide survey mitigation program is underway @northeast_fisheries_science_center_us_fall_2022 - -## Get the data - -**Point of contact**: [douglas.christel@noaa.gov](mailto:douglas.christel@noaa.gov){.email} - -**ecodata name**: No dataset - -**Variable definitions** - -No Data - - -No Data - -**Indicator Category**: - -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - +# Survey Impacts from Offshore Wind Development {#osw_survey_impact} + +**Description**: Offshore wind development is expected to have several impacts on federal and state surveys. + +**Indicator family**: + +- [X] Social + + +**Contributor(s)**: Douglas Christel + +**Affiliations**: GARFO + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Proceed as though this were a short summary of a typical introduction section in a paper. + +## Key Results and Visualizations +Proceed as though this were a short summary of a typical results section in a paper. + + +## Indicator statistics +Spatial scale: full shelf + +Temporal scale: Decadal + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_osw_survey_impact} +# Either from Contributor or ecodata +``` + +## Implications +Proposed wind development areas interact with the region’s federal scientific surveys @friedland_spatial_2023. Scientific surveys are impacted by offshore wind in four ways: + 1) Exclusion of NOAA Fisheries’ sampling platforms from the wind development area due to operational and safety limitations; + 2) Impacts on the random-stratified statistical design that is the basis for scientific assessments, advice, and analyses; +3) Alteration of benthic and pelagic habitats, and airspace in and around the wind energy development, requiring new designs and methods to sample new habitats; and, +4) Reduced sampling productivity through navigation impacts of wind energy infrastructure on aerial and vessel survey operations. + +Increased vessel transit between stations may decrease data collections that are already limited by annual days-at-sea day allocations. The total survey area overlap ranges from 1-70% for all Greater Atlantic federal surveys. The Gulf of Maine Cooperative Research Bottom Longline Survey (41%) and the Shrimp Survey (70%) have the largest percent overlap with the draft Gulf of Maine Wind Energy Areas. The remaining surveys range from 1-16% overlap. Individual survey strata have significant interaction with wind, including the sea scallop survey (up to 96% of individual strata) and the bottom trawl survey (BTS, up to 60% strata overlap). Additionally, up to 50% of the southern New England North Atlantic right whale survey’s area overlaps with proposed project areas and a region-wide survey mitigation program is underway @northeast_fisheries_science_center_us_fall_2022 + +## Get the data + +**Point of contact**: [douglas.christel@noaa.gov](mailto:douglas.christel@noaa.gov){.email} + +**ecodata name**: No dataset + +**Variable definitions** + +No Data + + +No Data + +**Indicator Category**: + +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + diff --git a/chapters/persistent_hotspots.rmd b/chapters/persistent_hotspots.rmd index 1460b43f..0e63c53a 100644 --- a/chapters/persistent_hotspots.rmd +++ b/chapters/persistent_hotspots.rmd @@ -1,70 +1,70 @@ -# Persistent annual hotspots {#persistent_hotspots} - -**Description**: Integrated persistent annual hotspots derived from at-sea observations of seabirds, cetaceans and sea turtles collected on systematic ship and aerial surveys - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Timothy White, Jason Roberts - -**Affiliations**: BOEM - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Locations of high densities through time of seabirds, marine mammals, and sea turtles - -## Key Results and Visualizations -![North Atlantic Right Whale hotspots](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/NARW_hotspots_final_2024.jpg){width=100%} - -![Seabirds, Turtles, & marine mammals hotspots](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/SOE_2023_birds_mammals_turtles_2024.png){width=100%} - - -## Indicator statistics -Spatial scale: Atlantic EEZ - -Temporal scale: Annual, all seasons - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_persistent_hotspots} -# Either from Contributor or ecodata -``` - -## Implications -Persistent hotpots through time identify places where seabirds, marine mammals and sea turtles frequently aggregate to feed, and may indicate locations of seasonal and annual resilience. Individual hotspot richness maps represent annual persistent hotspots of 71 species and also common taxa challenging to identify to the species level on at-sea surveys but whose abundance and spatial patterns significantly contribute to richness and diversity on the Atlantic EEZ. The integrated maps represent very high densities and very high persistence; however, one or both parameters can be adjusted to identify other important locations, for example, to reveal areas of high density and moderate persistence. - -## Get the data - -**Point of contact**: [Timothy White (timothy.white@boem.gov)](mailto:Timothy White (timothy.white@boem.gov)){.email} - -**ecodata name**: No dataset - -**Variable definitions** - -Persistence probability. - - -No Data - -**Indicator Category**: - -- [X] Extensive analysis, not yet published -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - +# Persistent annual hotspots {#persistent_hotspots} + +**Description**: Integrated persistent annual hotspots derived from at-sea observations of seabirds, cetaceans and sea turtles collected on systematic ship and aerial surveys + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Timothy White, Jason Roberts + +**Affiliations**: BOEM + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Locations of high densities through time of seabirds, marine mammals, and sea turtles + +## Key Results and Visualizations +![North Atlantic Right Whale hotspots](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/NARW_hotspots_final_2024.jpg){width=100%} + +![Seabirds, Turtles, & marine mammals hotspots](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/SOE_2023_birds_mammals_turtles_2024.png){width=100%} + + +## Indicator statistics +Spatial scale: Atlantic EEZ + +Temporal scale: Annual, all seasons + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_persistent_hotspots} +# Either from Contributor or ecodata +``` + +## Implications +Persistent hotpots through time identify places where seabirds, marine mammals and sea turtles frequently aggregate to feed, and may indicate locations of seasonal and annual resilience. Individual hotspot richness maps represent annual persistent hotspots of 71 species and also common taxa challenging to identify to the species level on at-sea surveys but whose abundance and spatial patterns significantly contribute to richness and diversity on the Atlantic EEZ. The integrated maps represent very high densities and very high persistence; however, one or both parameters can be adjusted to identify other important locations, for example, to reveal areas of high density and moderate persistence. + +## Get the data + +**Point of contact**: [Timothy White (timothy.white@boem.gov)](mailto:Timothy White (timothy.white@boem.gov)){.email} + +**ecodata name**: No dataset + +**Variable definitions** + +Persistence probability. + + +No Data + +**Indicator Category**: + +- [X] Extensive analysis, not yet published +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + diff --git a/chapters/phyto_size.rmd b/chapters/phyto_size.rmd index 4ac108e1..956d7075 100644 --- a/chapters/phyto_size.rmd +++ b/chapters/phyto_size.rmd @@ -1,118 +1,124 @@ -# Phytoplankton Size Class {#phyto_size} - -**Description**: Satellite derived phytoplankton data including chlorophyll concentration, phytoplankton size class, and primary production for the Northeast Continental Shelf and ecological production units. - -(To be expanded) - -**Indicator family**: - -- [X] Lower trophic levels - - -**Contributor(s)**: Kimberly Hyde - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Phytoplankton are the foundation of the marine food web and are the primary food source for zooplankton and filter feeders such as shellfish. Numerous environmental and oceanographic factors interact to drive the abundance, composition, spatial distribution, and productivity of phytoplankton. Satellite derived measurements of chlorophyll, the dominant photosynthetic pigment in phytoplankton, are used to estimate phytoplankton biomass. Phytoplankton growth depends on the availability of carbon dioxide, sunlight and nutrients and their growth rates can be influenced by water temperature, water depth, wind, and grazing pressure. Primary productivity is a measure of the amount of carbon produced by phytoplankton. The seasonal cycle of phytoplankton size distribution are typically dominated by larger-celled microplankton during the winter-spring and fall bloom periods, while smaller-celled nanoplankton dominate during the warmer summer months. - -(To be expanded) - -## Key Results and Visualizations -(In development) - -### MAB - -```{r plot_phyto_sizeMAB} -# Plot indicator -ggplotObject <- ecodata::plot_phyto_size(report='MidAtlantic') -ggplotObject -``` - -### GB - -```{r plot_phyto_sizeNEGB} -# Plot indicator -ggplotObject <- ecodata::plot_phyto_size(report='NewEngland',EPU='GB') -ggplotObject -``` - -### GOM - -```{r plot_phyto_sizeNEGOM} -# Plot indicator -ggplotObject <- ecodata::plot_phyto_size(report='NewEngland',EPU='GOM') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: By EPU and gridded for the entire shelf - -Temporal scale: Daily, weekly, monthly, annual, climatology (1998 to current year) - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_phyto_size} -# Either from Contributor or ecodata -``` - -## Implications -(In development) - -## Get the data - -**Point of contact**: [kimberly.hyde@noaa.gov](mailto:kimberly.hyde@noaa.gov){.email} - -**ecodata name**: `ecodata::phyto_size` - -**Variable definitions** - -1) Chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters; mg m^-3 -2) Chlorophyll anomaly; Ratio of chlorophyll _a_ concentration to the long-term (1998 to present year) climatology; unitless (ratio) -3) Primary productivity; Daily net primary production of biomass expressed as carbon per unit volume in seawater per day; gCarbon m^-2 d^-1 -4) Primary productivity anomaly; Ratio of net primary production to the long-term (1998 to present year) climatology; unitless (ratio) -5) Microplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the microplankton (20-200 µm) fraction; mg m^-3 -6) Nanoplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the nanoplankton (2-20 µm) fraction; mg m^-3 -7) Picoplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the picoplankton (0.2-2 µm) fraction; mg m^-3 -8) Microplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from microplankton (20-200 µm); (percent) -9) Nanoplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from nanoplankton (2-20 µm); (percent) -10) Picoplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from picoplankton (0.2-2 µm); (percent) -11) Annual summed primary production - TBD - -```{r vars_phyto_size} -# Pull all var names -vars <- ecodata::phyto_size |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Other - - -**Indicator Category**: - -Publicly available satellite data that are processed and analyzed - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Phytoplankton Size Class {#phyto_size} + +**Description**: Satellite derived phytoplankton data including chlorophyll concentration, phytoplankton size class, and primary production for the Northeast Continental Shelf and ecological production units. + +(To be expanded) + +**Indicator family**: + +- [X] Lower trophic levels + + +**Contributor(s)**: Kimberly Hyde + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Phytoplankton are key biological regulators of the structure and function of most marine ecosystems. They are the foundation of the marine food web and are the primary food source for zooplankton and filter feeders such as shellfish. Numerous environmental and oceanographic factors interact to drive the abundance, composition, spatial distribution, seasonal timing and productivity of phytoplankton. Satellite derived measurements of chlorophyll, the dominant photosynthetic pigment in phytoplankton, are used to estimate total phytoplankton biomass. The size structure of the phytoplankton community influences important biogeochemical and ecological processes, including transfer of energy through the marine food web. Phytoplankton growth depends on the availability of carbon dioxide, sunlight and nutrients and their growth rates can be influenced by water temperature, water depth, wind, and grazing pressure. Primary productivity is a measure of the amount of carbon produced by phytoplankton. + +The unique physical characteristics of the Northeast U.S. continental shelf help make it among the most productive continental shelf systems in the world influenced by both bottom-up (e.g. nutrient concentrations, light availability, and mixing/stratification) and top-down (e.g. grazing) controls. Phytoplankton biomass, composition, and productivity all have high spatial, seasonal and interannual variability. The most pronounced spatial pattern is the decrease in phytoplankton biomass from the coast to the shelf break. Georges Bank and Nantucket Shoals are shallow regions that are well mixed by tides. This mixing supplies sufficient nutrients to support phytoplankton growth throughout the year. In other regions, blooms of large diatom species occur on a seasonal cycle when growing conditions are ideal. + +## Key Results and Visualizations +The seasonal cycles of phytoplankton size distribution are typically dominated by larger-celled microplankton during the winter-spring and fall bloom periods, while smaller-celled nanoplankton dominate during the warmer summer months. In 2023, MAB total chlorophyll was below average in early spring, near average through the summer and above average throughout the fall. A peak in primary production occurred in summer, followed by an above average productivity associated with the early fall bloom. Phytoplankton size class distributions were near average for most of the year, except during the early fall bloom. + +Total chlorophyll concentrations on Georges Bank were above average for most of the year. The early winter bloom was most likely associated with diatoms, however the above average chlorophyll, primary production and microplankton fraction from April through August can be attributed to the dinoflagellate Tripos muelleri. + +Total chlorophyll concentrations in the Gulf of Maine were above average for most of the year. The early winter bloom was most likely associated with diatoms, however the record high chlorophyll, primary production and microplankton fraction from April through August can be attributed to the dinoflagellate Tripos muelleri. + +There is high interannual variability of the seasonal phytoplankton cycle. At the monthly scale, MAB chlorophyll and primary production are increasing during January and there has been a decrease in September chlorophyll, likely due to extension of the [summer stratification](https://noaa-edab.github.io/catalog/transition-dates.html) and delayed fall turnover. Fall and winter chlorophyll and primary production are increasing on Georges Bank and Gulf of Maine. + +### MAB + +```{r plot_phyto_sizeMAB} +# Plot indicator +ggplotObject <- ecodata::plot_phyto_size(report='MidAtlantic') +ggplotObject +``` + +### GB + +```{r plot_phyto_sizeNEGB} +# Plot indicator +ggplotObject <- ecodata::plot_phyto_size(report='NewEngland',EPU='GB') +ggplotObject +``` + +### GOM + +```{r plot_phyto_sizeNEGOM} +# Plot indicator +ggplotObject <- ecodata::plot_phyto_size(report='NewEngland',EPU='GOM') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: By EPU and gridded for the entire shelf + +Temporal scale: Daily, weekly, monthly, annual, climatology (1998 to current year) + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_phyto_size} +# Either from Contributor or ecodata +``` + +## Implications +Phytoplankton abundance, productivity, diversity, cell size, phenology, and carbon fluxes are regulated by the local physical and chemical environment and grazing. Interannual and climatological changes in temperature, freshwater inputs (due to ice sheet melting and/or enhanced river discharge), wind direction, and wind speed can alter the circulation patterns, upwelling conditions, and nutrient fluxes, directly affecting the timing, location, species composition of phytoplankton blooms in the NES. As the NES responds to warming, changing phenologies, changing chemistry, and changes in circulation patterns, we must understand how varying biophysical interactions control phytoplankton and subsequently affect fisheries, their habitats and the people, businesses and communities that depend on them. + +## Get the data + +**Point of contact**: [kimberly.hyde@noaa.gov](mailto:kimberly.hyde@noaa.gov){.email} + +**ecodata name**: `ecodata::phyto_size` + +**Variable definitions** + +1) Chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters; mg m^-3 +2) Chlorophyll anomaly; Ratio of chlorophyll _a_ concentration to the long-term (1998 to present year) climatology; unitless (ratio) +3) Primary productivity; Daily net primary production of biomass expressed as carbon per unit volume in seawater per day; gCarbon m^-2 d^-1 +4) Primary productivity anomaly; Ratio of net primary production to the long-term (1998 to present year) climatology; unitless (ratio) +5) Microplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the microplankton (20-200 µm) fraction; mg m^-3 +6) Nanoplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the nanoplankton (2-20 µm) fraction; mg m^-3 +7) Picoplankton chlorophyll; Concentration of chlorophyll _a_ in the near surface (first euphotic depth) waters from the picoplankton (0.2-2 µm) fraction; mg m^-3 +8) Microplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from microplankton (20-200 µm); (percent) +9) Nanoplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from nanoplankton (2-20 µm); (percent) +10) Picoplankton fraction; The fraction of total chlorophyll concentration in the near surface (first euphotic depth) waters from picoplankton (0.2-2 µm); (percent) +11) Annual summed primary production - TBD + +```{r vars_phyto_size} +# Pull all var names +vars <- ecodata::phyto_size |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Other + + +**Indicator Category**: + +Publicly available satellite data that are processed and analyzed + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/ppr.rmd b/chapters/ppr.rmd index 56d03e89..d4faaa09 100644 --- a/chapters/ppr.rmd +++ b/chapters/ppr.rmd @@ -1,161 +1,160 @@ -# Ecosytem overfishing {#ppr} - -**Description**: Ecosystem overfishing is an ecological, and not legal, term that ultimately evaluates how much fish are caught in an ecosystem relative to how much can be produced. Several indices are used to evaluate ecosystem overfishing, the Ryther index, the Fogarty index, and primary production required. - -**Indicator family**: - -- [X] Megafauna -- [X] Economic - - -**Contributor(s)**: Andrew Beet - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The Ryther index is defined as total catch per unit area in the ecosystem [@link_global_2019]. The units are mt km^-2 year^-1. It measures the total removal of fish biomass by area in a Large Marine Ecosystem relative to how much that entire ecosystem can produce. In general terms, the lower the Ryther index, the less likely an ecosystem will be experiencing ecosystem overfishing. - -The Fogarty index is defined as ratio of total catches to total primary productivity in an ecosystem [@link_global_2019]. The units are parts per thousand. - -A modification of the indices are used. Total landings are used in lieu of total catch. Accountng for total IUU (Illegal, unreported, and unregulated fishing) data, at the spatial footprint required, is not currently available. This will have the effect of reducing the value of the index (compared to using total catch, including IUU). - -## Key Results and Visualizations -Compared to thresholds based on global estimates of primary production and catch, the Ryther index shows elevated levels of fishing in the Gulf of Maine and the Mid Atlantic but not at a point considered "extreme" [@link_global_2019]. When accounting for regional primary productivity the Fogarty index shows low levels of fishing relative to these global thresholds. - -Thresholds based on regional estimates of primary productivity, are not yet available. This work is ongoing - -### MidAtlantic - -```{r plot_pprMidAtlanticppglobal} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'pp' ,threshold= 'global') -ggplotObject -``` - -```{r plot_pprMidAtlanticppregional} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'pp' ,threshold= 'regional') -ggplotObject -``` - -```{r plot_pprMidAtlanticfogartyglobal} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'fogarty' ,threshold= 'global') -ggplotObject -``` - -```{r plot_pprMidAtlanticfogartyregional} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'fogarty' ,threshold= 'regional') -ggplotObject -``` - -```{r plot_pprMidAtlanticrytherglobal} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'ryther' ,threshold= 'global') -ggplotObject -``` - -```{r plot_pprMidAtlanticrytherregional} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'ryther' ,threshold= 'regional') -ggplotObject -``` - -### NewEngland - -```{r plot_pprNewEnglandppglobal} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'pp' ,threshold= 'global') -ggplotObject -``` - -```{r plot_pprNewEnglandppregional} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'pp' ,threshold= 'regional') -ggplotObject -``` - -```{r plot_pprNewEnglandfogartyglobal} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'fogarty' ,threshold= 'global') -ggplotObject -``` - -```{r plot_pprNewEnglandfogartyregional} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'fogarty' ,threshold= 'regional') -ggplotObject -``` - -```{r plot_pprNewEnglandrytherglobal} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'ryther' ,threshold= 'global') -ggplotObject -``` - -```{r plot_pprNewEnglandrytherregional} -# Plot indicator -ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'ryther' ,threshold= 'regional') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: EPU - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts -- [X] Ecosystem Reorganization - - -```{r autostats_ppr} -# Either from Contributor or ecodata -``` - -## Implications -There is insufficient evidence to determine whether ecosystem overfishing in occuring in our region. However the overall amount of ecosytem fishing has been declining over the past several years. - -## Get the data - -**Point of contact**: [andrew.beet@noaa.gov](mailto:andrew.beet@noaa.gov){.email} - -**ecodata name**: `ecodata::ppr` - -**Variable definitions** - -1. Ryther; The Ryther index; mt km^-2 y^-1 2. Fogarty; The Fogarty Index; Parts per thousand 0/00 -3. PP; Primary Production; mtC region^-1 year^-1 - -```{r vars_ppr} -# Pull all var names -vars <- ecodata::ppr |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Please email andrew.beet@noaa.gov for further information and queries of source data. - -**tech-doc link** - - +# Ecosytem overfishing {#ppr} + +**Description**: Ecosystem overfishing is an ecological, and not legal, term that ultimately evaluates how much fish are caught in an ecosystem relative to how much can be produced. Several indices are used to evaluate ecosystem overfishing, the Ryther index, the Fogarty index, and primary production required. + +**Indicator family**: + +- [X] Megafauna +- [X] Economic + + +**Contributor(s)**: Andrew Beet + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The Ryther index is defined as total catch per unit area in the ecosystem [@link_global_2019]. The units are mt km^-2 year^-1. It measures the total removal of fish biomass by area in a Large Marine Ecosystem relative to how much that entire ecosystem can produce. In general terms, the lower the Ryther index, the less likely an ecosystem will be experiencing ecosystem overfishing. + +The Fogarty index is defined as ratio of total catches to total primary productivity in an ecosystem [@link_global_2019]. The units are parts per thousand. + +A modification of the indices are used. Total landings are used in lieu of total catch. Accountng for total IUU (Illegal, unreported, and unregulated fishing) data, at the spatial footprint required, is not currently available. This will have the effect of reducing the value of the index (compared to using total catch, including IUU). + +## Key Results and Visualizations +Compared to thresholds based on global estimates of primary production and catch, the Ryther index shows elevated levels of fishing in the Gulf of Maine and the Mid Atlantic but not at a point considered "extreme" [@link_global_2019]. When accounting for regional primary productivity the Fogarty index shows low levels of fishing relative to these global thresholds. + +Thresholds based on regional estimates of primary productivity, are not yet available. This work is ongoing + +### MidAtlantic + +```{r plot_pprMidAtlanticppglobal} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'pp' ,threshold= 'global') +ggplotObject +``` + +```{r plot_pprMidAtlanticppregional} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'pp' ,threshold= 'regional') +ggplotObject +``` + +```{r plot_pprMidAtlanticfogartyglobal} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'fogarty' ,threshold= 'global') +ggplotObject +``` + +```{r plot_pprMidAtlanticfogartyregional} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'fogarty' ,threshold= 'regional') +ggplotObject +``` + +```{r plot_pprMidAtlanticrytherglobal} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'ryther' ,threshold= 'global') +ggplotObject +``` + +```{r plot_pprMidAtlanticrytherregional} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'ryther' ,threshold= 'regional') +ggplotObject +``` + +### NewEngland + +```{r plot_pprNewEnglandppglobal} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'pp' ,threshold= 'global') +ggplotObject +``` + +```{r plot_pprNewEnglandppregional} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'pp' ,threshold= 'regional') +ggplotObject +``` + +```{r plot_pprNewEnglandfogartyglobal} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'fogarty' ,threshold= 'global') +ggplotObject +``` + +```{r plot_pprNewEnglandfogartyregional} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'fogarty' ,threshold= 'regional') +ggplotObject +``` + +```{r plot_pprNewEnglandrytherglobal} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'ryther' ,threshold= 'global') +ggplotObject +``` + +```{r plot_pprNewEnglandrytherregional} +# Plot indicator +ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'ryther' ,threshold= 'regional') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: EPU + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts +- [X] Ecosystem Reorganization + + +```{r autostats_ppr} +# Either from Contributor or ecodata +``` + +## Implications +There is insufficient evidence to determine whether ecosystem overfishing in occuring in our region. However the overall amount of ecosytem fishing has been declining over the past several years. + +## Get the data + +**Point of contact**: [andrew.beet@noaa.gov](mailto:andrew.beet@noaa.gov){.email} + +**ecodata name**: `ecodata::ppr` + +**Variable definitions** + +1. Ryther; The Ryther index; mt km^-2 y^-1 2. Fogarty; The Fogarty Index; Parts per thousand 0/00 3. PP; Primary Production; mtC region^-1 year^-1 + +```{r vars_ppr} +# Pull all var names +vars <- ecodata::ppr |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Please email andrew.beet@noaa.gov for further information and queries of source data. + +**tech-doc link** + + diff --git a/chapters/productivity_anomaly.rmd b/chapters/productivity_anomaly.rmd index b3b56c2d..3003b7ba 100644 --- a/chapters/productivity_anomaly.rmd +++ b/chapters/productivity_anomaly.rmd @@ -1,124 +1,122 @@ -# Fish Productivity Indicators {#productivity_anomaly} - -**Description**: Amount of small fish produced per large fish biomass over time - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Sarah Gaichas, Andy Beet, Kimberly Bastille, Sean Lucey, and Charles Perretti. - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The amount of young fish produced by a population tells us both about the health of individual populations and about the productivity throughout the ecosystem when we look across multiple stocks. This has implications for both managing fisheries (lower productivity often leads to lower harvest) and for other components of the ecosystem. - -These indicators are based on the work of Perretti [@perretti_regime_2017]. We updated the data used in that analysis to describe patterns of aggregate fish productivity in the Mid-Atlantic, Georges Bank, and Gulf of Maine by evaluating changes in reproductive output relative to adult population size across multiple stocks. Both survey information and stock assessment information is used in separate indicators. - -## Key Results and Visualizations -The small fish per large fish anomaly indicator, derived from NEFSC bottom trawl survey data, shows that productivity has been declining in the Mid-Atlantic region since 2010. A similar analysis based on stock assessment model outputs (recruitment per spawning stock biomass anomaly) for stocks primarily inhabiting the Mid-Atlantic region also shows a decline in productivity. The indicators show great variability in the Gulf of Maine and Georges Bank, with both regions being below average for much of the past decade. - -### MidAtlantic - -```{r plot_productivity_anomalyMidAtlanticanomalyMAB} -# Plot indicator -ggplotObject <- ecodata::plot_productivity_anomaly(report= 'MidAtlantic', varName= 'anomaly' ,EPU= 'MAB') -ggplotObject -``` - -```{r plot_productivity_anomalyMidAtlanticassessmentMAB} -# Plot indicator -ggplotObject <- ecodata::plot_productivity_anomaly(report= 'MidAtlantic', varName= 'assessment' ,EPU= 'MAB') -ggplotObject -``` - -### NewEngland - -```{r plot_productivity_anomalyNewEnglandanomalyGB} -# Plot indicator -ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'anomaly' ,EPU= 'GB') -ggplotObject -``` - -```{r plot_productivity_anomalyNewEnglandanomalyGOM} -# Plot indicator -ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'anomaly' ,EPU= 'GOM') -ggplotObject -``` - -```{r plot_productivity_anomalyNewEnglandassessmentGB} -# Plot indicator -ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'assessment' ,EPU= 'GB') -ggplotObject -``` - -```{r plot_productivity_anomalyNewEnglandassessmentGOM} -# Plot indicator -ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'assessment' ,EPU= 'GOM') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: _No response_ - -Temporal scale: _No response_ - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_productivity_anomaly} -# Either from Contributor or ecodata -``` - -## Implications -The apparent decline in productivity across multiple managed species in the MAB, along with mixed fish conditions in 2022, also suggest changing ecosystem productivity at multiple levels. During the 1990s high relative abundance of smaller bodied copepods and a lower relative abundance of *Calanus finmarchicus* was associated with regime shifts to higher fish recruitment [@perretti_regime_2017]. The unprecedented climate signals along with the trends toward lower productivity across multiple managed species indicate a need to continually evaluate whether management reference points remain appropriate, and to evaluate if ecosystem regime shifts have occurred or reorganization is in progress. - -## Get the data - -**Point of contact**: [Sarah.Gaichas@noaa.gov](mailto:Sarah.Gaichas@noaa.gov){.email} - -**ecodata name**: `ecodata::productivity_anomaly` - -**Variable definitions** - -Variable names are organized using this format: [region] stock name - variable type and source -Variables ending with "_Survey" are survey derived recruits/spawner anomalies -Variables ending with "-Assessment" are assessment derived quantities Survey stock names are in ALL CAPS -NE LME prepended to a survey stock name means the anomalies are coastwide -Assessment stock names are in Sentence case -Units for survey variables are the Z score of (number of recruits in year+1/biomass of adults in year) -Units for plotted assessment variables are the Z score of (numbers of recruits per kg spawning biomass with recruits aligned to spawning biomass year using age at recruitment) -Other variables are available in the assessment derived dataset but are not plotted. To be added later. - -```{r vars_productivity_anomaly} -# Pull all var names -vars <- ecodata::productivity_anomaly |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Fish Productivity Indicators {#productivity_anomaly} + +**Description**: Amount of small fish produced per large fish biomass over time + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Sarah Gaichas, Andy Beet, Kimberly Bastille, Sean Lucey, and Charles Perretti. + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The amount of young fish produced by a population tells us both about the health of individual populations and about the productivity throughout the ecosystem when we look across multiple stocks. This has implications for both managing fisheries (lower productivity often leads to lower harvest) and for other components of the ecosystem. + +These indicators are based on the work of Perretti [@perretti_regime_2017]. We updated the data used in that analysis to describe patterns of aggregate fish productivity in the Mid-Atlantic, Georges Bank, and Gulf of Maine by evaluating changes in reproductive output relative to adult population size across multiple stocks. Both survey information and stock assessment information is used in separate indicators. + +## Key Results and Visualizations +The small fish per large fish anomaly indicator, derived from NEFSC bottom trawl survey data, shows that productivity has been declining in the Mid-Atlantic region since 2010. A similar analysis based on stock assessment model outputs (recruitment per spawning stock biomass anomaly) for stocks primarily inhabiting the Mid-Atlantic region also shows a decline in productivity. The indicators show great variability in the Gulf of Maine and Georges Bank, with both regions being below average for much of the past decade. + +### MidAtlantic + +```{r plot_productivity_anomalyMidAtlanticanomalyMAB} +# Plot indicator +ggplotObject <- ecodata::plot_productivity_anomaly(report= 'MidAtlantic', varName= 'anomaly' ,EPU= 'MAB') +ggplotObject +``` + +```{r plot_productivity_anomalyMidAtlanticassessmentMAB} +# Plot indicator +ggplotObject <- ecodata::plot_productivity_anomaly(report= 'MidAtlantic', varName= 'assessment' ,EPU= 'MAB') +ggplotObject +``` + +### NewEngland + +```{r plot_productivity_anomalyNewEnglandanomalyGB} +# Plot indicator +ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'anomaly' ,EPU= 'GB') +ggplotObject +``` + +```{r plot_productivity_anomalyNewEnglandanomalyGOM} +# Plot indicator +ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'anomaly' ,EPU= 'GOM') +ggplotObject +``` + +```{r plot_productivity_anomalyNewEnglandassessmentGB} +# Plot indicator +ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'assessment' ,EPU= 'GB') +ggplotObject +``` + +```{r plot_productivity_anomalyNewEnglandassessmentGOM} +# Plot indicator +ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'assessment' ,EPU= 'GOM') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: _No response_ + +Temporal scale: _No response_ + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_productivity_anomaly} +# Either from Contributor or ecodata +``` + +## Implications +The apparent decline in productivity across multiple managed species in the MAB, along with mixed fish conditions in 2022, also suggest changing ecosystem productivity at multiple levels. During the 1990s high relative abundance of smaller bodied copepods and a lower relative abundance of *Calanus finmarchicus* was associated with regime shifts to higher fish recruitment [@perretti_regime_2017]. The unprecedented climate signals along with the trends toward lower productivity across multiple managed species indicate a need to continually evaluate whether management reference points remain appropriate, and to evaluate if ecosystem regime shifts have occurred or reorganization is in progress. + +## Get the data + +**Point of contact**: [Sarah.Gaichas@noaa.gov](mailto:Sarah.Gaichas@noaa.gov){.email} + +**ecodata name**: `ecodata::productivity_anomaly` + +**Variable definitions** + +Variable names are organized using this format: [region] stock name - variable type and source +Variables ending with "_Survey" are survey derived recruits/spawner anomalies Variables ending with "-Assessment" are assessment derived quantities +Survey stock names are in ALL CAPS NE LME prepended to a survey stock name means the anomalies are coastwide +Assessment stock names are in Sentence case Units for survey variables are the Z score of (number of recruits in year+1/biomass of adults in year) +Units for plotted assessment variables are the Z score of (numbers of recruits per kg spawning biomass with recruits aligned to spawning biomass year using age at recruitment) +Other variables are available in the assessment derived dataset but are not plotted. To be added later. + +```{r vars_productivity_anomaly} +# Pull all var names +vars <- ecodata::productivity_anomaly |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/rec_hms.rmd b/chapters/rec_hms.rmd index a9a74ff3..56fb06e6 100644 --- a/chapters/rec_hms.rmd +++ b/chapters/rec_hms.rmd @@ -1,91 +1,91 @@ -# Recreational HMS {#rec_hms} - -**Description**: Recreational shark landings pulled from the Marine Recreational Information Program (MRIP). - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Brandon Beltz; Kim Bastille - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Recreational shark landings pulled from the MRIP database - -## Key Results and Visualizations -Recreational landings of sharks are plotted. Sharks are categorized as large coastal, pelagic, prohibited and small coastal. - -### MAB - -```{r plot_rec_hmsMAB} -# Plot indicator -ggplotObject <- ecodata::plot_rec_hms(report='MidAtlantic') -ggplotObject -``` - -### NE - -```{r plot_rec_hmsNE} -# Plot indicator -ggplotObject <- ecodata::plot_rec_hms(report='NewEngland') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: by EPU - -Temporal scale: annually from 1981 to 2022 - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_rec_hms} -# Either from Contributor or ecodata -``` - -## Implications -Sharks are landed recreationally in quantities that are relevant to fisheries management. These landings should be considered when assessing the populations of sharks. - -## Get the data - -**Point of contact**: [Brandon Beltz (brandon.beltz@noaa.gov)](mailto:Brandon Beltz (brandon.beltz@noaa.gov)){.email} - -**ecodata name**: `ecodata::rec_hms` - -**Variable definitions** - -See variables below - -```{r vars_rec_hms} -# Pull all var names -vars <- ecodata::rec_hms |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Recreational HMS {#rec_hms} + +**Description**: Recreational shark landings pulled from the Marine Recreational Information Program (MRIP). + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Brandon Beltz; Kim Bastille + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Recreational shark landings pulled from the MRIP database + +## Key Results and Visualizations +Recreational landings of sharks are plotted. Sharks are categorized as large coastal, pelagic, prohibited and small coastal. + +### MAB + +```{r plot_rec_hmsMAB} +# Plot indicator +ggplotObject <- ecodata::plot_rec_hms(report='MidAtlantic') +ggplotObject +``` + +### NE + +```{r plot_rec_hmsNE} +# Plot indicator +ggplotObject <- ecodata::plot_rec_hms(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: by EPU + +Temporal scale: annually from 1981 to 2022 + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_rec_hms} +# Either from Contributor or ecodata +``` + +## Implications +Sharks are landed recreationally in quantities that are relevant to fisheries management. These landings should be considered when assessing the populations of sharks. + +## Get the data + +**Point of contact**: [Brandon Beltz (brandon.beltz@noaa.gov)](mailto:Brandon Beltz (brandon.beltz@noaa.gov)){.email} + +**ecodata name**: `ecodata::rec_hms` + +**Variable definitions** + +See variables below + +```{r vars_rec_hms} +# Pull all var names +vars <- ecodata::rec_hms |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/recdat.rmd b/chapters/recdat.rmd index 9d939830..fb8a372d 100644 --- a/chapters/recdat.rmd +++ b/chapters/recdat.rmd @@ -1,143 +1,143 @@ -# Recreational Fishing Indicators {#recdat} - -**Description**: A variety of indicators derived from MRIP Recreational Fisheries Statistics, including total recreational catch, total angler trips by region, annual diversity of recreational fleet effort, and annual diversity of managed species. - -**Indicator family**: - -- [X] Social -- [X] Economic - - -**Contributor(s)**: Geret DePiper, Scott Steinback - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -We use total recreational harvest as an indicator of seafood production and total recreational trips and total recreational anglers as proxies for recreational value generated from the Mid-Atlantic and New England regions respectively. We estimate both recreational catch diversity in species managed by the Fisheries Management Councils; Mid-Atlantic (MAFMC), New England (NEFMC), South Atlantic (SAFMC) and Atlantic States (ASFMC), and fleet effort diversity using the effective Shannon index. - -## Key Results and Visualizations -Total recreational harvest (retained fish presumed to be eaten) is down in the MAB. Although harvest has increased from a historic low in 2018, it is still below the long term average. Overall, recreational harvest (harvested fish presumed to be eaten) have also declined in New England. Recreational harvest in 2022 is up somewhat from the historical low seen in 2020. - -Recreational effort (angler trips) in New England increased during 1980-2010, but has since declined to just around the long-term average. Recreational fleets are defined as private vessels, shore-based fishing, or party-charter vessels. Recreational fleet diversity, or the relative importance of each fleet type, has remained relatively stable over the latter half of the time series in New England. In the Mid-Atlantic, recreational effort (angler trips) in 2022 is above the long-term average. However, recreational fleet diversity has declined over the long term. - -In New England, recreational species catch diversity has been above the time series average since 2008 with a long-term positive trend. In the Mid-Atlantic, recreational species catch diversity has no long term trend so is considered stable, and has been at or above the long term average in 8 of the last 10 years. - -### MidAtlantic - -```{r plot_recdatMidAtlanticlandings} -# Plot indicator -ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'landings') -ggplotObject -``` - -```{r plot_recdatMidAtlanticeffortdiversity} -# Plot indicator -ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'effortdiversity') -ggplotObject -``` - -```{r plot_recdatMidAtlanticcatchdiversity} -# Plot indicator -ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'catchdiversity') -ggplotObject -``` - -```{r plot_recdatMidAtlanticeffort} -# Plot indicator -ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'effort') -ggplotObject -``` - -### NewEngland - -```{r plot_recdatNewEnglandlandings} -# Plot indicator -ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'landings') -ggplotObject -``` - -```{r plot_recdatNewEnglandeffortdiversity} -# Plot indicator -ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'effortdiversity') -ggplotObject -``` - -```{r plot_recdatNewEnglandcatchdiversity} -# Plot indicator -ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'catchdiversity') -ggplotObject -``` - -```{r plot_recdatNewEnglandeffort} -# Plot indicator -ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'effort') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: MA and NE - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts -- [X] Ecosystem Reorganization - - -```{r autostats_recdat} -# Either from Contributor or ecodata -``` - -## Implications -The decline in recreational seafood harvest in New England stems from multiple drivers. Changes in demographics and preferences over recreational activities likely play a role in non-HMS (Highly Migratory Species) declines in recreational harvest, with current harvests near the lowest in the time series. Drivers of the the decline in Mid-Atlantic recreational seafood harvest are unclear. NOAA Fisheries’ Marine Recreational Information Program survey methodology was updated in 2018, so it is unclear whether the record-low landings for species other than sharks in 2018 are driven by changes in fishing behavior or the change in the survey methodology. Nevertheless, the recreational harvest seems to be stabilizing at a lower level than historical estimates. - -Diversity indices can be used to evaluate stability objectives as well as risks to fishery resilience and to maintaining equity in access to fishery resources. In New England, the absence of a long term trend in recreational angler trips and fleet effort diversity suggests relative stability in the overall number of recreational opportunities in the region. While the overall number of recreational opportunities in the MAB is above the long-term average, the continuing decline in recreational fleet effort diversity suggests a potentially reduced range of recreational fishing options. The downward effort diversity trend is driven by party/charter contraction (currently below 2% of trips), and a shift toward shore-based angling, which currently makes up 59% of angler trips. Effort in private boats remains stable at around 40% of trips. Changes in recreational fleet diversity can be considered when managers seek options to maintain recreational opportunities. Shore anglers will have access to different species than vessel-based anglers, and when the same species is accessible both from shore and from a vessel, shore anglers typically have access to smaller individuals. Many states have developed shore-based regulations where the minimum size is lower than in other areas and sectors to maintain opportunities in the shore angling sector. - -The increase in recreational species catch diversity in New England is due to recent increases in ASMFC and MAFMC managed species within the region as well as decreased limits on more traditional regional species. Stability in Mid-Atlantic recreational species catch diversity has been maintained by a different set of species over time. A recent increase in Atlantic States Marine Fisheries Commission (ASMFC) and South Atlantic Fishery Management Council (SAFMC) managed species in recreational catch is helping to maintain diversity in the same range that MAFMC and New England Fishery Management Council (NEFMC) species supported in the 1990s. - -## Get the data - -**Point of contact**: [Geret DePiper (geret.depiper@noaa.gov)](mailto:Geret DePiper (geret.depiper@noaa.gov)){.email} - -**ecodata name**: `ecodata::recdat` - -**Variable definitions** - -Ex: 1) Name: piscivore_biomass; Definition: Biomass of piscivores; Units: kg tow^-1. 2) Name: forage_biomass; Definition: Biomass of forage fish; Units: kg tow^-1. - 1) Name: Recreational Seafood; Definition: Recreational Harvest; Units: lbs -2) Name: Recreational Effort; Definition: Recreational Trips; Units: Angler Trips -3) Name: Recreational Effort Diversity; Definition: Recreational fleet effort diversity across modes; Units: Effective Shannon Metric -4) Name: Recreational Catch Diversity; Definition: Recreational Diversity of Catch across managed species; Units: Effective Shannon Metric - -```{r vars_recdat} -# Pull all var names -vars <- ecodata::recdat |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Recreational Fishing Indicators {#recdat} + +**Description**: A variety of indicators derived from MRIP Recreational Fisheries Statistics, including total recreational catch, total angler trips by region, annual diversity of recreational fleet effort, and annual diversity of managed species. + +**Indicator family**: + +- [X] Social +- [X] Economic + + +**Contributor(s)**: Geret DePiper, Scott Steinback + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +We use total recreational harvest as an indicator of seafood production and total recreational trips and total recreational anglers as proxies for recreational value generated from the Mid-Atlantic and New England regions respectively. We estimate both recreational catch diversity in species managed by the Fisheries Management Councils; Mid-Atlantic (MAFMC), New England (NEFMC), South Atlantic (SAFMC) and Atlantic States (ASFMC), and fleet effort diversity using the effective Shannon index. + +## Key Results and Visualizations +Total recreational harvest (retained fish presumed to be eaten) is down in the MAB. Although harvest has increased from a historic low in 2018, it is still below the long term average. Overall, recreational harvest (harvested fish presumed to be eaten) have also declined in New England. Recreational harvest in 2022 is up somewhat from the historical low seen in 2020. + +Recreational effort (angler trips) in New England increased during 1980-2010, but has since declined to just around the long-term average. Recreational fleets are defined as private vessels, shore-based fishing, or party-charter vessels. Recreational fleet diversity, or the relative importance of each fleet type, has remained relatively stable over the latter half of the time series in New England. In the Mid-Atlantic, recreational effort (angler trips) in 2022 is above the long-term average. However, recreational fleet diversity has declined over the long term. + +In New England, recreational species catch diversity has been above the time series average since 2008 with a long-term positive trend. In the Mid-Atlantic, recreational species catch diversity has no long term trend so is considered stable, and has been at or above the long term average in 8 of the last 10 years. + +### MidAtlantic + +```{r plot_recdatMidAtlanticlandings} +# Plot indicator +ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'landings') +ggplotObject +``` + +```{r plot_recdatMidAtlanticeffortdiversity} +# Plot indicator +ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'effortdiversity') +ggplotObject +``` + +```{r plot_recdatMidAtlanticcatchdiversity} +# Plot indicator +ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'catchdiversity') +ggplotObject +``` + +```{r plot_recdatMidAtlanticeffort} +# Plot indicator +ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'effort') +ggplotObject +``` + +### NewEngland + +```{r plot_recdatNewEnglandlandings} +# Plot indicator +ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'landings') +ggplotObject +``` + +```{r plot_recdatNewEnglandeffortdiversity} +# Plot indicator +ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'effortdiversity') +ggplotObject +``` + +```{r plot_recdatNewEnglandcatchdiversity} +# Plot indicator +ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'catchdiversity') +ggplotObject +``` + +```{r plot_recdatNewEnglandeffort} +# Plot indicator +ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'effort') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: MA and NE + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts +- [X] Ecosystem Reorganization + + +```{r autostats_recdat} +# Either from Contributor or ecodata +``` + +## Implications +The decline in recreational seafood harvest in New England stems from multiple drivers. Changes in demographics and preferences over recreational activities likely play a role in non-HMS (Highly Migratory Species) declines in recreational harvest, with current harvests near the lowest in the time series. Drivers of the the decline in Mid-Atlantic recreational seafood harvest are unclear. NOAA Fisheries’ Marine Recreational Information Program survey methodology was updated in 2018, so it is unclear whether the record-low landings for species other than sharks in 2018 are driven by changes in fishing behavior or the change in the survey methodology. Nevertheless, the recreational harvest seems to be stabilizing at a lower level than historical estimates. + +Diversity indices can be used to evaluate stability objectives as well as risks to fishery resilience and to maintaining equity in access to fishery resources. In New England, the absence of a long term trend in recreational angler trips and fleet effort diversity suggests relative stability in the overall number of recreational opportunities in the region. While the overall number of recreational opportunities in the MAB is above the long-term average, the continuing decline in recreational fleet effort diversity suggests a potentially reduced range of recreational fishing options. The downward effort diversity trend is driven by party/charter contraction (currently below 2% of trips), and a shift toward shore-based angling, which currently makes up 59% of angler trips. Effort in private boats remains stable at around 40% of trips. Changes in recreational fleet diversity can be considered when managers seek options to maintain recreational opportunities. Shore anglers will have access to different species than vessel-based anglers, and when the same species is accessible both from shore and from a vessel, shore anglers typically have access to smaller individuals. Many states have developed shore-based regulations where the minimum size is lower than in other areas and sectors to maintain opportunities in the shore angling sector. + +The increase in recreational species catch diversity in New England is due to recent increases in ASMFC and MAFMC managed species within the region as well as decreased limits on more traditional regional species. Stability in Mid-Atlantic recreational species catch diversity has been maintained by a different set of species over time. A recent increase in Atlantic States Marine Fisheries Commission (ASMFC) and South Atlantic Fishery Management Council (SAFMC) managed species in recreational catch is helping to maintain diversity in the same range that MAFMC and New England Fishery Management Council (NEFMC) species supported in the 1990s. + +## Get the data + +**Point of contact**: [Geret DePiper (geret.depiper@noaa.gov)](mailto:Geret DePiper (geret.depiper@noaa.gov)){.email} + +**ecodata name**: `ecodata::recdat` + +**Variable definitions** + +Ex: 1) Name: piscivore_biomass; Definition: Biomass of piscivores; Units: kg tow^-1. 2) Name: forage_biomass; Definition: Biomass of forage fish; Units: kg tow^-1. + 1) Name: Recreational Seafood; Definition: Recreational Harvest; Units: lbs +2) Name: Recreational Effort; Definition: Recreational Trips; Units: Angler Trips +3) Name: Recreational Effort Diversity; Definition: Recreational fleet effort diversity across modes; Units: Effective Shannon Metric +4) Name: Recreational Catch Diversity; Definition: Recreational Diversity of Catch across managed species; Units: Effective Shannon Metric + +```{r vars_recdat} +# Pull all var names +vars <- ecodata::recdat |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/seabird_ne.rmd b/chapters/seabird_ne.rmd index 703be0bf..a86a303a 100644 --- a/chapters/seabird_ne.rmd +++ b/chapters/seabird_ne.rmd @@ -1,117 +1,117 @@ -# Seabird diet and productivity - New England {#seabird_ne} - -**Description**: Common tern annual diet and productivity at seven Gulf of Maine colonies managed by the National Audubon Society’s Seabird Restoration Program - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Don Lyons, Steve Kress, Paula Shannon, Sue Schubel - -**Affiliations**: National Audubon Society - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Seabird breeding colonies in the GOM are monitored and managed to promote recovery of formerly harvested species. Common terns are well-monitored and are considered good nearshore ecosystem indicators due to their wide distribution and generalist diet. Common terns breed on islands throughout the Gulf of Maine, feeding on a wide range of invertebrates and fish including Atlantic herring, juvenile (mainly white) hakes, and sand lance. As surface feeding birds, terns are sensitive to vertical distribution of prey as well as nearshore conditions in general, with a foraging distance of 10-20 km from a nesting colony. - -## Key Results and Visualizations -GOM common tern average productivity (fledglings per nest) across 7 colonies has varied over time. The pattern is similar to that observed for fish condition (high before 2000, lower 2001-2009, higher/variable since 2010. Productivity is affected by both food and predation mortality. While data on predation is lacking, productivity lows in 2004-2006 were associated with euphausiids, and the 2018 productivity low with butterfish in tern diets. The presence of butterfish in tern diets reflects the extension of this warm water species into GOM. Due to their thin, deep body form, butterfish are often difficult for small seabird chicks to ingest and swallow, causing chicks to starve and/or parent birds to increase foraging effort. 2020 was a challenging year for terns raising chicks. While diet composition was similar to the long term average, the quantity of food readily available was apparently less than normal, particularly around the time of chick hatching. This may have been confounded by cold, wet weather when chicks would normally be close to fledging in mid-to-late July. Anecdotal observations showed that feeding rates were low at both those times. - -### MidAtlantic - -```{r plot_seabird_neMidAtlanticdiversity} -# Plot indicator -ggplotObject <- ecodata::plot_seabird_ne(report= 'MidAtlantic', varName= 'diversity') -ggplotObject -``` - -```{r plot_seabird_neMidAtlanticproductivity} -# Plot indicator -ggplotObject <- ecodata::plot_seabird_ne(report= 'MidAtlantic', varName= 'productivity') -ggplotObject -``` - -```{r plot_seabird_neMidAtlanticprey} -# Plot indicator -ggplotObject <- ecodata::plot_seabird_ne(report= 'MidAtlantic', varName= 'prey') -ggplotObject -``` - -### NewEngland - -```{r plot_seabird_neNewEnglanddiversity} -# Plot indicator -ggplotObject <- ecodata::plot_seabird_ne(report= 'NewEngland', varName= 'diversity') -ggplotObject -``` - -```{r plot_seabird_neNewEnglandproductivity} -# Plot indicator -ggplotObject <- ecodata::plot_seabird_ne(report= 'NewEngland', varName= 'productivity') -ggplotObject -``` - -```{r plot_seabird_neNewEnglandprey} -# Plot indicator -ggplotObject <- ecodata::plot_seabird_ne(report= 'NewEngland', varName= 'prey') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Gulf of Maine coastal areas - -Temporal scale: Spring and summer seabird breeding season - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_seabird_ne} -# Either from Contributor or ecodata -``` - -## Implications -Declining productivity across multiple common tern colonies in the Gulf of Maine may indicate changes in the distribution, quality, and quantity of prey over time. - -## Get the data - -**Point of contact**: [Don Lyons, dlyons@audubon.org](mailto:Don Lyons, dlyons@audubon.org){.email} - -**ecodata name**: `ecodata::seabird_ne` - -**Variable definitions** - -Variable names include the code for the island, COTE (4 letter standard abbreviation for COmmon TErn), and either "Productivity" or a prey category. - -```{r vars_seabird_ne} -# Pull all var names -vars <- ecodata::seabird_ne |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Syntheses of published information -- [X] Database pull with analysis - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Please email dlyons@audubon.org for further information and queries on this indicator source data. - -**tech-doc link** - - +# Seabird diet and productivity - New England {#seabird_ne} + +**Description**: Common tern annual diet and productivity at seven Gulf of Maine colonies managed by the National Audubon Society’s Seabird Restoration Program + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Don Lyons, Steve Kress, Paula Shannon, Sue Schubel + +**Affiliations**: National Audubon Society + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Seabird breeding colonies in the GOM are monitored and managed to promote recovery of formerly harvested species. Common terns are well-monitored and are considered good nearshore ecosystem indicators due to their wide distribution and generalist diet. Common terns breed on islands throughout the Gulf of Maine, feeding on a wide range of invertebrates and fish including Atlantic herring, juvenile (mainly white) hakes, and sand lance. As surface feeding birds, terns are sensitive to vertical distribution of prey as well as nearshore conditions in general, with a foraging distance of 10-20 km from a nesting colony. + +## Key Results and Visualizations +GOM common tern average productivity (fledglings per nest) across 7 colonies has varied over time. The pattern is similar to that observed for fish condition (high before 2000, lower 2001-2009, higher/variable since 2010. Productivity is affected by both food and predation mortality. While data on predation is lacking, productivity lows in 2004-2006 were associated with euphausiids, and the 2018 productivity low with butterfish in tern diets. The presence of butterfish in tern diets reflects the extension of this warm water species into GOM. Due to their thin, deep body form, butterfish are often difficult for small seabird chicks to ingest and swallow, causing chicks to starve and/or parent birds to increase foraging effort. 2020 was a challenging year for terns raising chicks. While diet composition was similar to the long term average, the quantity of food readily available was apparently less than normal, particularly around the time of chick hatching. This may have been confounded by cold, wet weather when chicks would normally be close to fledging in mid-to-late July. Anecdotal observations showed that feeding rates were low at both those times. + +### MidAtlantic + +```{r plot_seabird_neMidAtlanticdiversity} +# Plot indicator +ggplotObject <- ecodata::plot_seabird_ne(report= 'MidAtlantic', varName= 'diversity') +ggplotObject +``` + +```{r plot_seabird_neMidAtlanticproductivity} +# Plot indicator +ggplotObject <- ecodata::plot_seabird_ne(report= 'MidAtlantic', varName= 'productivity') +ggplotObject +``` + +```{r plot_seabird_neMidAtlanticprey} +# Plot indicator +ggplotObject <- ecodata::plot_seabird_ne(report= 'MidAtlantic', varName= 'prey') +ggplotObject +``` + +### NewEngland + +```{r plot_seabird_neNewEnglanddiversity} +# Plot indicator +ggplotObject <- ecodata::plot_seabird_ne(report= 'NewEngland', varName= 'diversity') +ggplotObject +``` + +```{r plot_seabird_neNewEnglandproductivity} +# Plot indicator +ggplotObject <- ecodata::plot_seabird_ne(report= 'NewEngland', varName= 'productivity') +ggplotObject +``` + +```{r plot_seabird_neNewEnglandprey} +# Plot indicator +ggplotObject <- ecodata::plot_seabird_ne(report= 'NewEngland', varName= 'prey') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Gulf of Maine coastal areas + +Temporal scale: Spring and summer seabird breeding season + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_seabird_ne} +# Either from Contributor or ecodata +``` + +## Implications +Declining productivity across multiple common tern colonies in the Gulf of Maine may indicate changes in the distribution, quality, and quantity of prey over time. + +## Get the data + +**Point of contact**: [Don Lyons, dlyons@audubon.org](mailto:Don Lyons, dlyons@audubon.org){.email} + +**ecodata name**: `ecodata::seabird_ne` + +**Variable definitions** + +Variable names include the code for the island, COTE (4 letter standard abbreviation for COmmon TErn), and either "Productivity" or a prey category. + +```{r vars_seabird_ne} +# Pull all var names +vars <- ecodata::seabird_ne |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Syntheses of published information +- [X] Database pull with analysis + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Please email dlyons@audubon.org for further information and queries on this indicator source data. + +**tech-doc link** + + diff --git a/chapters/seal_pups.rmd b/chapters/seal_pups.rmd index 5757eab4..5178762c 100644 --- a/chapters/seal_pups.rmd +++ b/chapters/seal_pups.rmd @@ -1,84 +1,84 @@ -# Gray Seal Pups {#seal_pups} - -**Description**: The data presented here are counts of gray seal pups at 4 U.S. haulout sites from 1988 to 2021. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Stephanie Wood; Elizabeth Josephson - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Gray seals were extirpated from the northeast U.S. coast by the mid-20th century due to local and statewide bounty systems [@andrews_gray_1967; @lelli_seal_2009]. Since the late 1980s, ground and aerial surveys have documented the recovery and recolonization of pupping sites in northeast U.S. waters [@wood_gray_2022]. This recovery is due in large part to the protection provided by the Marine Mammal Protection Act (MMPA) of 1972. - -## Key Results and Visualizations -The increase in bycatch of gray seals (Fig. x) since 1995 corresponds to an increase in numbers of gray seals in U.S. waters, which has risen dramatically in the last three decades. Based on a survey conducted in 2021, the size of the gray seal population in the U.S. during the breeding season was approximately 28,000 animals, while in Canada the population was estimated to be roughly 425,000. The population in Canada is increasing at roughly 4% per year, and contributing to rates of increase in the U.S., where the number of pupping sites has increased from one in 1988 to nine in 2019. -Mean rates of increase in the number of pups born at various times since 1988 at four of the more data-rich pupping sites (Muskeget, Monomoy, Seal, and Green Islands) ranged from no change on Green Island to high rates of increase on the other three islands, with a maximum increase of 26.3% (95%CI: 21.6 - 31.4%; @wood_rates_2020 Fig. x). - -### NE - -```{r plot_seal_pupsNE} -# Plot indicator -ggplotObject <- ecodata::plot_seal_pups(report='NewEngland') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Haul out sites off New England (Maine to New York) that correspond roughly to EPU’s Gulf of Maine (GOM) and Mid-Atlantic Bight (MAB). - -Temporal scale: Annually 1988 to 1921. Survey is in January that corresponds to the gray seal pupping season. - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_seal_pups} -# Either from Contributor or ecodata -``` - -## Implications -These high rates of increase in gray seal pups born at US pupping sites provide further support for the hypothesis that seals from Canada are continually supplementing the breeding population in U.S. waters. - -## Get the data - -**Point of contact**: [Stephanie Wood (Stephanie.Wood@noaa.gov), Elizabeth Josephson (elizabeth.josephson@noaa.gov), Kristin Precoda (Kristin.Precoda@noaa.gov)](mailto:Stephanie Wood (Stephanie.Wood@noaa.gov), Elizabeth Josephson (elizabeth.josephson@noaa.gov), Kristin Precoda (Kristin.Precoda@noaa.gov)){.email} - -**ecodata name**: `ecodata::seal_pups` - -**Variable definitions** - -1) Name=Year. 2) Name=Count; Definition=count of gray seals on haulout site; unit=n=number. 3) Name=colony; Definition=name of haul site site. - -```{r vars_seal_pups} -# Pull all var names -vars <- ecodata::seal_pups |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Reach out to Stephanie Wood (stephanie.wood@noaa.gov) for data. - -**tech-doc link** - - +# Gray Seal Pups {#seal_pups} + +**Description**: The data presented here are counts of gray seal pups at 4 U.S. haulout sites from 1988 to 2021. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Stephanie Wood; Elizabeth Josephson + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Gray seals were extirpated from the northeast U.S. coast by the mid-20th century due to local and statewide bounty systems [@andrews_gray_1967; @lelli_seal_2009]. Since the late 1980s, ground and aerial surveys have documented the recovery and recolonization of pupping sites in northeast U.S. waters [@wood_gray_2022]. This recovery is due in large part to the protection provided by the Marine Mammal Protection Act (MMPA) of 1972. + +## Key Results and Visualizations +The increase in bycatch of gray seals (Fig. x) since 1995 corresponds to an increase in numbers of gray seals in U.S. waters, which has risen dramatically in the last three decades. Based on a survey conducted in 2021, the size of the gray seal population in the U.S. during the breeding season was approximately 28,000 animals, while in Canada the population was estimated to be roughly 425,000. The population in Canada is increasing at roughly 4% per year, and contributing to rates of increase in the U.S., where the number of pupping sites has increased from one in 1988 to nine in 2019. +Mean rates of increase in the number of pups born at various times since 1988 at four of the more data-rich pupping sites (Muskeget, Monomoy, Seal, and Green Islands) ranged from no change on Green Island to high rates of increase on the other three islands, with a maximum increase of 26.3% (95%CI: 21.6 - 31.4%; @wood_rates_2020 Fig. x). + +### NE + +```{r plot_seal_pupsNE} +# Plot indicator +ggplotObject <- ecodata::plot_seal_pups(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Haul out sites off New England (Maine to New York) that correspond roughly to EPU’s Gulf of Maine (GOM) and Mid-Atlantic Bight (MAB). + +Temporal scale: Annually 1988 to 1921. Survey is in January that corresponds to the gray seal pupping season. + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_seal_pups} +# Either from Contributor or ecodata +``` + +## Implications +These high rates of increase in gray seal pups born at US pupping sites provide further support for the hypothesis that seals from Canada are continually supplementing the breeding population in U.S. waters. + +## Get the data + +**Point of contact**: [Stephanie Wood (Stephanie.Wood@noaa.gov), Elizabeth Josephson (elizabeth.josephson@noaa.gov), Kristin Precoda (Kristin.Precoda@noaa.gov)](mailto:Stephanie Wood (Stephanie.Wood@noaa.gov), Elizabeth Josephson (elizabeth.josephson@noaa.gov), Kristin Precoda (Kristin.Precoda@noaa.gov)){.email} + +**ecodata name**: `ecodata::seal_pups` + +**Variable definitions** + +1) Name=Year. 2) Name=Count; Definition=count of gray seals on haulout site; unit=n=number. 3) Name=colony; Definition=name of haul site site. + +```{r vars_seal_pups} +# Pull all var names +vars <- ecodata::seal_pups |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Reach out to Stephanie Wood (stephanie.wood@noaa.gov) for data. + +**tech-doc link** + + diff --git a/chapters/seasonal_oisst_anom.rmd b/chapters/seasonal_oisst_anom.rmd index 29da4a45..28e74ae0 100644 --- a/chapters/seasonal_oisst_anom.rmd +++ b/chapters/seasonal_oisst_anom.rmd @@ -1,100 +1,100 @@ -# Sea-surface temperature anomaly {#seasonal_oisst_anom} - -**Description**: Seasonal sea surface temperature anomaly - -**Indicator family**: - -- [X] Oceanographic -- [X] Habitat - - -**Contributor(s)**: Brandon Beltz, Abigail Tyrell - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Sea surface temperature can be used as a proxy for overall thermal conditions in the system. Data for sea surface anomalies were derived from the National Oceanographic and Atmospheric Administration optimum interpolation sea surface temperature high resolution data set (NOAA OISST V2). Mean seasonal-annual SST was calculated for each EPU. To These data extend from 1981 to present. Anomalies are calculate by subtracting the long-term mean temperature is calculated from 1982-2010 for each season, from the seasonal-annual mean SST. - -## Key Results and Visualizations -Since 1982, SST has been increasing in all seasons in all three EPUs. 2023 was the warmest winter SST in the GOM and MAB on record. All record warmest seasonal SST years have occurred on or after 2012. 2023 also saw relatively cooler summer temperatures in GB and the GOM and fall temperatures in all regions. - -### MAB - -```{r plot_seasonal_oisst_anomMAB} -# Plot indicator -ggplotObject <- ecodata::plot_seasonal_oisst_anom(report='MidAtlantic') -ggplotObject -``` - -### GB - -```{r plot_seasonal_oisst_anomNEGB} -# Plot indicator -ggplotObject <- ecodata::plot_seasonal_oisst_anom(report='NewEngland',EPU='GB') -ggplotObject -``` - -### GOM - -```{r plot_seasonal_oisst_anomNEGOM} -# Plot indicator -ggplotObject <- ecodata::plot_seasonal_oisst_anom(report='NewEngland',EPU='GOM') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: EPU - -Temporal scale: Seasonal: Winter (January - March), Spring (April - June), Summer (July - September), Fall (October - December) - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_seasonal_oisst_anom} -# Either from Contributor or ecodata -``` - -## Implications -Sea surface temperature is an indicator of thermal habitat for pelagic species. Long-term warming trends suggest wide-spread environmental change in the system. Warming trends can have potential impacts on species spatial distributions, the seasonal timing of species life history events, and the overall productivity of the system. - -## Get the data - -**Point of contact**: [brandon.beltz@noaa.gov](mailto:brandon.beltz@noaa.gov){.email} - -**ecodata name**: `ecodata::seasonal_oisst_anom` - -**Variable definitions** - -Time: year, Var: season, Value: temperature anomaly (degrees Celcius), EPU - -```{r vars_seasonal_oisst_anom} -# Pull all var names -vars <- ecodata::seasonal_oisst_anom |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Sea-surface temperature anomaly {#seasonal_oisst_anom} + +**Description**: Seasonal sea surface temperature anomaly + +**Indicator family**: + +- [X] Oceanographic +- [X] Habitat + + +**Contributor(s)**: Brandon Beltz, Abigail Tyrell + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Sea surface temperature can be used as a proxy for overall thermal conditions in the system. Data for sea surface anomalies were derived from the National Oceanographic and Atmospheric Administration optimum interpolation sea surface temperature high resolution data set (NOAA OISST V2). Mean seasonal-annual SST was calculated for each EPU. To These data extend from 1981 to present. Anomalies are calculate by subtracting the long-term mean temperature is calculated from 1982-2010 for each season, from the seasonal-annual mean SST. + +## Key Results and Visualizations +Since 1982, SST has been increasing in all seasons in all three EPUs. 2023 was the warmest winter SST in the GOM and MAB on record. All record warmest seasonal SST years have occurred on or after 2012. 2023 also saw relatively cooler summer temperatures in GB and the GOM and fall temperatures in all regions. + +### MAB + +```{r plot_seasonal_oisst_anomMAB} +# Plot indicator +ggplotObject <- ecodata::plot_seasonal_oisst_anom(report='MidAtlantic') +ggplotObject +``` + +### GB + +```{r plot_seasonal_oisst_anomNEGB} +# Plot indicator +ggplotObject <- ecodata::plot_seasonal_oisst_anom(report='NewEngland',EPU='GB') +ggplotObject +``` + +### GOM + +```{r plot_seasonal_oisst_anomNEGOM} +# Plot indicator +ggplotObject <- ecodata::plot_seasonal_oisst_anom(report='NewEngland',EPU='GOM') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: EPU + +Temporal scale: Seasonal: Winter (January - March), Spring (April - June), Summer (July - September), Fall (October - December) + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_seasonal_oisst_anom} +# Either from Contributor or ecodata +``` + +## Implications +Sea surface temperature is an indicator of thermal habitat for pelagic species. Long-term warming trends suggest wide-spread environmental change in the system. Warming trends can have potential impacts on species spatial distributions, the seasonal timing of species life history events, and the overall productivity of the system. + +## Get the data + +**Point of contact**: [brandon.beltz@noaa.gov](mailto:brandon.beltz@noaa.gov){.email} + +**ecodata name**: `ecodata::seasonal_oisst_anom` + +**Variable definitions** + +Time: year, Var: season, Value: temperature anomaly (degrees Celcius), EPU + +```{r vars_seasonal_oisst_anom} +# Pull all var names +vars <- ecodata::seasonal_oisst_anom |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/seasonal_sst_anomaly_gridded.rmd b/chapters/seasonal_sst_anomaly_gridded.rmd index 9a5e80c5..0904fe53 100644 --- a/chapters/seasonal_sst_anomaly_gridded.rmd +++ b/chapters/seasonal_sst_anomaly_gridded.rmd @@ -1,74 +1,74 @@ -# Seasonal OISST Anomaly Map {#seasonal_sst_anomaly_gridded} - -**Description**: Mapped seasonal sea surface temperature anomaly - -**Indicator family**: - -- [X] Oceanographic -- [X] Habitat - - -**Contributor(s)**: Brandon Beltz, Abigail Tyrell - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Sea surface temperature can be used as a proxy for overall thermal conditions in the system. Data for sea surface anomalies were derived from the National Oceanographic and Atmospheric Administration optimum interpolation sea surface temperature high resolution data set (NOAA OISST V2). Mean seasonal-annual SST was calculated for each EPU. To These data extend from 1981 to present. Anomalies are calculate by subtracting the long-term mean temperature is calculated from 1982-2010 for each season, from the seasonal-annual mean SST. Gridded anomalies are presented on a map for each year. - -## Key Results and Visualizations -Despite record setting ocean temperatures in 2023, the Northeast US shelf had both warm and cool sea surface temperature anomalies in each season. - -```{r plot_seasonal_sst_anomaly_griddedMAB} -# Plot indicator -ggplotObject <- ecodata::plot_seasonal_sst_anomaly_gridded(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: EPUs on full shelf map - -Temporal scale: Seasonal: Winter (January - March), Spring (April - June), Summer (July - September), Fall (October - December) - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_seasonal_sst_anomaly_gridded} -# Either from Contributor or ecodata -``` - -## Implications -Sea surface temperature is an indicator of thermal habitat for pelagic species. Long-term warming trends suggest wide-spread environmental change in the system. Warming trends can have potential impacts on species spatial distributions, the seasonal timing of species life history events, and the overall productivity of the system. Maps show how temperature change has been distributed in each EPU each year. - -## Get the data - -**Point of contact**: [Brandon.Beltz@noaa.gov](mailto:Brandon.Beltz@noaa.gov){.email} - -**ecodata name**: `ecodata::seasonal_sst_anomaly_gridded` - -**Variable definitions** - -Ex: 1) Name: piscivore_biomass; Definition: Biomass of piscivores; Units: kg tow^-1. 2) Name: forage_biomass; Definition: Biomass of forage fish; Units: kg tow^-1. - -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Seasonal OISST Anomaly Map {#seasonal_sst_anomaly_gridded} + +**Description**: Mapped seasonal sea surface temperature anomaly + +**Indicator family**: + +- [X] Oceanographic +- [X] Habitat + + +**Contributor(s)**: Brandon Beltz, Abigail Tyrell + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Sea surface temperature can be used as a proxy for overall thermal conditions in the system. Data for sea surface anomalies were derived from the National Oceanographic and Atmospheric Administration optimum interpolation sea surface temperature high resolution data set (NOAA OISST V2). Mean seasonal-annual SST was calculated for each EPU. To These data extend from 1981 to present. Anomalies are calculate by subtracting the long-term mean temperature is calculated from 1982-2010 for each season, from the seasonal-annual mean SST. Gridded anomalies are presented on a map for each year. + +## Key Results and Visualizations +Despite record setting ocean temperatures in 2023, the Northeast US shelf had both warm and cool sea surface temperature anomalies in each season. + +```{r plot_seasonal_sst_anomaly_griddedMAB} +# Plot indicator +ggplotObject <- ecodata::plot_seasonal_sst_anomaly_gridded(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: EPUs on full shelf map + +Temporal scale: Seasonal: Winter (January - March), Spring (April - June), Summer (July - September), Fall (October - December) + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_seasonal_sst_anomaly_gridded} +# Either from Contributor or ecodata +``` + +## Implications +Sea surface temperature is an indicator of thermal habitat for pelagic species. Long-term warming trends suggest wide-spread environmental change in the system. Warming trends can have potential impacts on species spatial distributions, the seasonal timing of species life history events, and the overall productivity of the system. Maps show how temperature change has been distributed in each EPU each year. + +## Get the data + +**Point of contact**: [Brandon.Beltz@noaa.gov](mailto:Brandon.Beltz@noaa.gov){.email} + +**ecodata name**: `ecodata::seasonal_sst_anomaly_gridded` + +**Variable definitions** + +Ex: 1) Name: piscivore_biomass; Definition: Biomass of piscivores; Units: kg tow^-1. 2) Name: forage_biomass; Definition: Biomass of forage fish; Units: kg tow^-1. + +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/slopewater.rmd b/chapters/slopewater.rmd index f7ef256c..61553c9f 100644 --- a/chapters/slopewater.rmd +++ b/chapters/slopewater.rmd @@ -1,83 +1,83 @@ -# Slopewater Proportions {#slopewater} - -**Description**: This index gives the relative proportions of watermass type observed in the deep Northeast Channel (150-200 m water depth). - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Paula Fratantoni - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Temperature and salinity measurements are examined to assess the composition of the waters entering the Gulf of Maine through the deep Northeast Channel. The analysis closely follows the methodology described by @mountain_labrador_2012. This method assumes that the waters flowing into the Northeast Channel between 150 and 200 meters depth are composed of slope waters, originating offshore of the continental shelf, and shelf waters, originating on the continental shelf south of Nova Scotia. - -## Key Results and Visualizations -_No response_ - -### NE - -```{r plot_slopewaterNE} -# Plot indicator -ggplotObject <- ecodata::plot_slopewater(report='NewEngland') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Within the central Northeast Channel - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_slopewater} -# Either from Contributor or ecodata -``` - -## Implications -_No response_ - -## Get the data - -**Point of contact**: [Paula Fratantoni (paula.fratantoni@noaa.gov)](mailto:Paula Fratantoni (paula.fratantoni@noaa.gov)){.email} - -**ecodata name**: `ecodata::slopewater` - -**Variable definitions** - -Name: LSW; Definition: Percent total that is Labrador Slope Water; Units: percent; Name: WSW: Definition: Percent total that is Warm Slope Water; Units: percent - -```{r vars_slopewater} -# Pull all var names -vars <- ecodata::slopewater |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Extensive analysis, not yet published - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Slopewater Proportions {#slopewater} + +**Description**: This index gives the relative proportions of watermass type observed in the deep Northeast Channel (150-200 m water depth). + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Paula Fratantoni + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Temperature and salinity measurements are examined to assess the composition of the waters entering the Gulf of Maine through the deep Northeast Channel. The analysis closely follows the methodology described by @mountain_labrador_2012. This method assumes that the waters flowing into the Northeast Channel between 150 and 200 meters depth are composed of slope waters, originating offshore of the continental shelf, and shelf waters, originating on the continental shelf south of Nova Scotia. + +## Key Results and Visualizations +_No response_ + +### NE + +```{r plot_slopewaterNE} +# Plot indicator +ggplotObject <- ecodata::plot_slopewater(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Within the central Northeast Channel + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_slopewater} +# Either from Contributor or ecodata +``` + +## Implications +_No response_ + +## Get the data + +**Point of contact**: [Paula Fratantoni (paula.fratantoni@noaa.gov)](mailto:Paula Fratantoni (paula.fratantoni@noaa.gov)){.email} + +**ecodata name**: `ecodata::slopewater` + +**Variable definitions** + +Name: LSW; Definition: Percent total that is Labrador Slope Water; Units: percent; Name: WSW: Definition: Percent total that is Warm Slope Water; Units: percent + +```{r vars_slopewater} +# Pull all var names +vars <- ecodata::slopewater |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Extensive analysis, not yet published + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/spawn_timing.rmd b/chapters/spawn_timing.rmd index 0a8a778b..5c861960 100644 --- a/chapters/spawn_timing.rmd +++ b/chapters/spawn_timing.rmd @@ -1,212 +1,210 @@ -# Spawning Timing {#spawn_timing} - -**Description**: Maturity information for groundfish is used to evaluate changes in spawning seasonality. - -**Indicator family**: - -- [X] Megafauna - - -**Contributor(s)**: Mark Wuenschel - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Spawning phenology, the annual timing of reproduction, determines when spawning occurs and is therefore important to understanding individual fish energetics and condition, as well as the timing and location of release of eggs. Fish undergo seasonal (annual) patterns of weight gain and loss related to spawning. In some extreme cases, gonad weight can be > 30% body weight, therefore estimates of fish condition based on fish weight can be influenced by reproductive state. Thus, shifts in survey timing and/or spawning seasonality over time could produce changes in calculated weight based condition indices. - -Maturity data collected on NEFSC bottom trawl surveys were evaluated to determine spawning seasonality for haddock and yellowtail flounder, and to investigate if this may have changed over the available time series of data. - -These indicators were presented as working papers to the haddock and yellowtail flounder assessment working groups, and text from those working papers is reproduced here. - -## Key Results and Visualizations -**Georges Bank haddock** - -The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu [@brown-peterson_standardized_2011]), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals. The mean size of mature females sampled has declined over the time series. The time series indicates a greater proportion of post spawning fish collected at higher temperatures more recently. Although the dates sampled were similar early and late in the time series, the temperatures occurring on these dates was higher late in the time series. Spawning condition was related to bottom temperature, with samples from higher temperatures having a lower proportion of spawning active fish. Similarly, the proportion of spawning active fish declined with week of year sampled. Together, these declines in spawning activity as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. - -When summarized by decade, the most recent decade (2010s) sampled fish at slightly warmer temperatures and had a higher percentage of resting fish as compared to the 1970s to 2000s. Sampling has also occurred later into the spring in the recent decade (2010s) and has included fewer spawning active fish (indicated by developing and ripe fish) as compared to the 1970s to 2000s that rarely sampled beyond week 18. - -**Gulf of Maine haddock** - -The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu Brown-Peterson et al. 2010), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals, and the mean size of mature females sampled has declined over the time series. The time series indicates a greater proportion of post spawning fish collected at higher temperatures more recently. Although the dates sampled were similar early and late in the time series, the temperatures occurring on these dates was higher late in the time series. Spawning condition was related to bottom temperature, with samples from higher temperatures having a lower proportion of spawning active fish. Similarly, the proportion of spawning active fish declined with week of year sampled. Together, these declines in spawning activity as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. - -When summarized by decade, the most recent decade (2010s) sampled fish at slightly warmer temperatures and had a higher percentage of resting fish as compared to the 1970s to 2000s. Sampling has also occurred later into the spring in the recent decade (2010s) and has included fewer spawning active fish (indicated by developing and ripe fish) as compared to the 1970s to 2000s that rarely sampled beyond week 20. - -**CC GOM Yellowtail Flounder** - -The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu [@brown-peterson_standardized_2011]), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals. The mean size of mature females sampled slightly declined early the time series. The time series indicates a lower proportion of post spawning fish collected at higher temperatures more recently. Although the dates sampled were similar early and late in the time series, the temperatures occurring on these dates was higher late in the time series. Spawning condition was related to bottom temperature, with samples from higher temperatures having a lower proportion of prespawning fish. The proportion of spawning fish increased with week of year sampled. Together, these increases in prespawning and spawning fish as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. - -When summarized by decade, the most recent decades (2010s-2020s) sampled fish at slightly warmer temperatures and had a lower percentage of resting fish as compared to the 1970s to 2000s. Sampling has also occurred later into the spring in the recent decades (2010s-2020s) but has collected slightly higher proportions of prespawning and spawning fish combined 1970s to 2000s that had few observations beyond week 18. - -The multinomial model indicated significant effects of bottom temperature, week of year, and decade on the probability of individual females being in a prespawning, spawning, or postspawning condition. Pattern of increasing spawning and postspawning and decreasing prespawning with both week of year and temperate was evident. The pattern by decade indicated an increase in prespawning and decrease in postspawning groups after 2000; however, data is limited for the most recent decade. The increase in prespawning fish through time indicates sampling has shifted from the beginning or early portion of the spawning season up to the 1990s to occurring closer to the peak of the spawning season since 2000. - -**Georges Bank Yellowtail Flounder** - -The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu[@brown-peterson_standardized_2011]), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals . The mean size of mature females sampled varied over the time series. The time series indicates a similar proportion of post spawning fish collected at higher temperatures more recently. Although the dates sampled were similar, early and late in the time series, with a few exceptions, the temperatures occurring on these dates was higher late in the time series. Spawning condition was related to bottom temperature, with ripe fish declining at temperatures above 8 C, spent fish increasing with temperature, but little change in developing and resting fish over the range of temperatures. The proportion of developing fish decrease, while ripe, spent and resting fish increased with week of year sampled. Together, these increases in prespawning and spawning fish as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. - -When summarized by decade, for the most recent decades no clear patterns in the proportion of developing fish were evident, however more spent fish were encountered in the in the recent decades since 2000. Sampling has also occurred later into the spring in the recent decades (2010s-2020s) and has collected slightly higher proportions of spent and resting fish sampled at later weeks compared to the period from the 1970s to 2000s that had few observations beyond week 17. - -The multinomial model indicated significant effects of bottom temperature, week of year, and decade on the probability of individual females being in a prespawning, spawning, or postspawning condition. A pattern of increasing spawning and postspawning and decreasing prespawning with week of year was evident. Bottom temperature had the reverse effect, with increasing prespawing fish but decreasing spawning and spawning fish as temperature increased. The pattern by decade indicated relatively minor changes in the predicted probability of each spawning group over time, and sampling has consistently occurred close to the peak spawning period. - -**Southern New England Yellowtail Flounder** - -The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu [@brown-peterson_standardized_2011]), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals. The mean size of mature females sampled varied over the time series. The time series indicates a higher proportion of ripe fish collected early and late in the time series. The dates sampled have varied over time, with later sampling occurring 1977-1982, and more recently. The bottom temperatures showed a similar pattern for the recent period, however for the earlier period sampled late in the year, the water temperatures were much lower. Spawning condition was related to bottom temperature, with ripe fish increasing at temperatures above 5 °C, spent fish increasing with temperature, developing fish decreasing and resting fish increasing over this range. At lower temperatures, the patterns were reversed, but there were fewer observations. The proportion of developing fish decreased, while ripe, spent and resting fish increased with week of year sampled. Together, these increases in spawning and postspawning fish as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. - -When summarized by decade, the most recent decades indicate higher proportions of developing fish with subsequent declines in ripe fish compared to the earliest decade, however only a single mature female was sampled in the most recent decade. Sampling has also occurred during similar weeks (1970s-2010s) and has collected slightly higher proportions of spent and resting fish sampled in the 1990s at warmer temperatures. - -The multinomial model indicated significant effects of bottom temperature, week of year, and decade on the probability of individual females being in a prespawning, spawning, or postspawning condition. Pattern of increasing postspawning and decreasing spawning and prespawning with week of year was evident. The effect of bottom temperature differed, with increasing spawing and postspawning fish but decreasing prespawning fish as temperature increased. The pattern by decade indicated an increase in prespawning and decrease in spawning from the 1970s to 1990s, with little change since (ignoring the most recent decade, which is represented by a single fish). In the late 1970s and early 1980s SNE samples were collected later in the spring and contained large numbers of ripe fish, in the peak of spawning. From the mid-1980s to 2010 sampling occurred earlier and collected mostly prespawning fish. - -### MidAtlantic - -```{r plot_spawn_timingMidAtlanticResting} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Resting') -ggplotObject -``` - -```{r plot_spawn_timingMidAtlanticRipe} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Ripe') -ggplotObject -``` - -```{r plot_spawn_timingMidAtlanticSpent} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Spent') -ggplotObject -``` - -```{r plot_spawn_timingMidAtlanticDeveloping} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Developing') -ggplotObject -``` - -```{r plot_spawn_timingMidAtlanticMF} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'MF') -ggplotObject -``` - -```{r plot_spawn_timingMidAtlanticmeanTEMP} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'meanTEMP') -ggplotObject -``` - -```{r plot_spawn_timingMidAtlanticmeanJDAY} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'meanJDAY') -ggplotObject -``` - -### NewEngland - -```{r plot_spawn_timingNewEnglandResting} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Resting') -ggplotObject -``` - -```{r plot_spawn_timingNewEnglandRipe} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Ripe') -ggplotObject -``` - -```{r plot_spawn_timingNewEnglandSpent} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Spent') -ggplotObject -``` - -```{r plot_spawn_timingNewEnglandDeveloping} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Developing') -ggplotObject -``` - -```{r plot_spawn_timingNewEnglandMF} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'MF') -ggplotObject -``` - -```{r plot_spawn_timingNewEnglandmeanTEMP} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'meanTEMP') -ggplotObject -``` - -```{r plot_spawn_timingNewEnglandmeanJDAY} -# Plot indicator -ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'meanJDAY') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Stock-specific spatial scale - -Temporal scale: Spring and Fall - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_spawn_timing} -# Either from Contributor or ecodata -``` - -## Implications -Spawning timing is shifting earlier for multiple stocks, including haddock and yellowtail flounder. Spawning of both haddock stocks occurred earlier in the year (and ended earlier, as indicated by more resting (post-spawning) stage fish, in the 2010s as compared to earlier in the time series. Yellowtail flounder spawn late spring-summer, with timing varying by stock location. The northern (CC/GOM) stock shows earlier active spawning in recent years with a decline in pre-spawning resting females. Both GB and SNE stock are sampled during active spawning (low percent resting). The recent increase in resting females in the southern (SNE) stock also indicates a shift to earlier spawning (i.e. more post-spawn fish). Yellowtail flounder spawning is related to bottom temperature, week of year, and decade sampled for each of the three stocks. - -Haddock spawn during the late winter-spring months in the region, with the spring survey sampling fish in the latter portion of the spawning season (peak to end). There is evidence that spawning of both haddock stocks occurred earlier in the year (and ended earlier) in the recent decade (2010s) as compared to earlier in the time series. Recent spring surveys have sampled a greater proportion of post spawning fish. The post spawning haddock would be expected to have lower relative condition due to shedding of gametes, depletion of liver (used for oocyte development and maturation), and higher water content of muscle and liver tissue, as has been reported in cod ([@lambert_energetic_2000]) and flatfishes ([@wuenschel_reproductive_2019]). Some fishes also reduce or cease feeding during spawning, for behavioral or physical (gonad occupying most of the body cavity) reasons. The annual cycle of energy acquisition, storage, and depletion is lowest immediately following spawning in capital breeders such as haddock ([@mcbride_energy_2015]). Trippel and Neil [@trippel_maternal_2011] estimated female weight loss associated with spawning to be 24.1% in a laboratory study of haddock. Although the reduction in weight and condition due to spawning is temporary, comparisons of relative condition (weight at length) across years that sampled varying proportions of active and post spawning fish (whether due to spawning timing or survey timing) should be considered with caution. The results presented for mature females should be applicable to mature males as well, but immature fish of both sexes will not undergo weight loss due to spawning. Therefore comparison of condition in proximity to spawning season across years that contain varying proportions of immature and mature fish will also be problematic. Given the temporal separation from spawning, relative condition estimates derived from autumn surveys will be less influenced by alterations in timing of surveys or spawning season. - -Yellowtail Flounder spawn during the spring months in the region, with the spring survey sampling fish during the spawning season. There is evidence that spawning condition is related to the bottom temperature, week of year, and decade sampled for each of the three stocks and survey timing and environmental conditions (bottom temperature) have been variable over time. The analysis of macroscopic data presented here provides some evidence for a change in the spawning season of CC GOM and SNE stocks. Early in the time series, sampling captured the beginning/early portion of spawning season, while later in the time series sampling has captured more of the peak in spawning. However, some caveats to this general conclusion are warranted. First, since Yellowtail Flounder are batch spawners, individuals cycle from developing, to ripe, to running ripe, and back to developing for each of many batches over a >1 month period. As such, the developing class includes a mix of individuals that have not released any batches yet (true prespawners) and others who are ‘in between batches (i.e. partially spent, but before a next batch). Gonadal histology and/or GSI data could help to refine categorization of these stages, but such data does not exist for the long time series presented. Second, the analysis by decadal timeblocks presented is a rather coarse approach to investigating long term trends in spawning condition. More appropriate methods should be explored and evaluated in future studies. Nevertheless, even with significant inter-annual variability, the approach presented should detect directional change in spawning seasonality over the time series. Subtle shifts to earlier spawning, accompanied by more individuals sampled later in their annual reproductive cycle (e.g. closer to spent), were evident in CC GOM and SNE stocks. Even small changes in spawning seasonality and/or sampling timing can affect the total weight of individuals sampled given the gonad of Yellowtail Flounder females can reach 35 % of their body weight ([@wuenschel_measuring_2019]), with the ovary declining in weight over a period of months as batches of eggs are shed. Therefore, consideration of spawning condition when interpreting fish weight and relative condition during the spring season should be considered. Although sampling was not specifically designed to detect changes in spawning seasonality of Yellowtail Flounder, the present analysis provides a preliminary evaluation of spawning timing over a long time series (1971-2023). - -The post spawning Yellowtail Flounder would be expected to have lower relative condition due to shedding of gametes, depletion of liver (used for oocyte development and maturation), and higher water content of muscle and liver tissue, as has been reported in cod ([@lambert_energetic_2000]) and flatfishes ([@wuenschel_reproductive_2019]). Some fishes also reduce or cease feeding during spawning, for behavioral or physical (e.g. gonad occupying most of the body cavity) reasons. The annual cycle of energy acquisition, storage, and depletion is lowest immediately following spawning in capital breeders such as haddock ([@mcbride_energy_2015]). Manning and Crim [@manning_maternal_1998] estimated female weight loss associated with spawning to be 22-23% in a laboratory study of Yellowtail Flounder. Although the reduction in weight and condition due to spawning is temporary, comparisons of relative condition (weight at length) across years that sampled varying proportions of active and post spawning fish (whether due to spawning timing or survey timing) should be considered with caution. The results presented for mature females should be applicable to mature males as well, but immature fish of both sexes will not undergo weight loss due to spawning. Therefore, comparison of condition in proximity to spawning season across years that contain varying proportions of immature and mature fish will also be problematic. Given the temporal separation from spawning, relative condition estimates derived from autumn surveys will be less influenced by alterations in timing of surveys or spawning season. - -## Get the data - -**Point of contact**: [Mark Wuenschel mark.wuenschel@noaa.gov](mailto:Mark Wuenschel mark.wuenschel@noaa.gov){.email} - -**ecodata name**: `ecodata::spawn_timing` - -**Variable definitions** - -Variable names follow the convention "SEASON_Species_STOCK_Variable" where Variable and Units are: -meanTEMP: mean sampled bottom temperature in degrees C MF: number of mature females sampled -meanJDAY: mean julian day of year sampled Developing: percent of mature females at developing (pre-spawning) stage -Ripe: percent of mature females at ripe (spawning) stage -Spent: percent of mature females at spent (immediately post-spawning) stage -Resting: percent of mature females at resting (non-spawning) stage - -```{r vars_spawn_timing} -# Pull all var names -vars <- ecodata::spawn_timing |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Spawning Timing {#spawn_timing} + +**Description**: Maturity information for groundfish is used to evaluate changes in spawning seasonality. + +**Indicator family**: + +- [X] Megafauna + + +**Contributor(s)**: Mark Wuenschel + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Spawning phenology, the annual timing of reproduction, determines when spawning occurs and is therefore important to understanding individual fish energetics and condition, as well as the timing and location of release of eggs. Fish undergo seasonal (annual) patterns of weight gain and loss related to spawning. In some extreme cases, gonad weight can be > 30% body weight, therefore estimates of fish condition based on fish weight can be influenced by reproductive state. Thus, shifts in survey timing and/or spawning seasonality over time could produce changes in calculated weight based condition indices. + +Maturity data collected on NEFSC bottom trawl surveys were evaluated to determine spawning seasonality for haddock and yellowtail flounder, and to investigate if this may have changed over the available time series of data. + +These indicators were presented as working papers to the haddock and yellowtail flounder assessment working groups, and text from those working papers is reproduced here. + +## Key Results and Visualizations +**Georges Bank haddock** + +The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu [@brown-peterson_standardized_2011]), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals. The mean size of mature females sampled has declined over the time series. The time series indicates a greater proportion of post spawning fish collected at higher temperatures more recently. Although the dates sampled were similar early and late in the time series, the temperatures occurring on these dates was higher late in the time series. Spawning condition was related to bottom temperature, with samples from higher temperatures having a lower proportion of spawning active fish. Similarly, the proportion of spawning active fish declined with week of year sampled. Together, these declines in spawning activity as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. + +When summarized by decade, the most recent decade (2010s) sampled fish at slightly warmer temperatures and had a higher percentage of resting fish as compared to the 1970s to 2000s. Sampling has also occurred later into the spring in the recent decade (2010s) and has included fewer spawning active fish (indicated by developing and ripe fish) as compared to the 1970s to 2000s that rarely sampled beyond week 18. + +**Gulf of Maine haddock** + +The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu Brown-Peterson et al. 2010), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals, and the mean size of mature females sampled has declined over the time series. The time series indicates a greater proportion of post spawning fish collected at higher temperatures more recently. Although the dates sampled were similar early and late in the time series, the temperatures occurring on these dates was higher late in the time series. Spawning condition was related to bottom temperature, with samples from higher temperatures having a lower proportion of spawning active fish. Similarly, the proportion of spawning active fish declined with week of year sampled. Together, these declines in spawning activity as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. + +When summarized by decade, the most recent decade (2010s) sampled fish at slightly warmer temperatures and had a higher percentage of resting fish as compared to the 1970s to 2000s. Sampling has also occurred later into the spring in the recent decade (2010s) and has included fewer spawning active fish (indicated by developing and ripe fish) as compared to the 1970s to 2000s that rarely sampled beyond week 20. + +**CC GOM Yellowtail Flounder** + +The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu [@brown-peterson_standardized_2011]), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals. The mean size of mature females sampled slightly declined early the time series. The time series indicates a lower proportion of post spawning fish collected at higher temperatures more recently. Although the dates sampled were similar early and late in the time series, the temperatures occurring on these dates was higher late in the time series. Spawning condition was related to bottom temperature, with samples from higher temperatures having a lower proportion of prespawning fish. The proportion of spawning fish increased with week of year sampled. Together, these increases in prespawning and spawning fish as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. + +When summarized by decade, the most recent decades (2010s-2020s) sampled fish at slightly warmer temperatures and had a lower percentage of resting fish as compared to the 1970s to 2000s. Sampling has also occurred later into the spring in the recent decades (2010s-2020s) but has collected slightly higher proportions of prespawning and spawning fish combined 1970s to 2000s that had few observations beyond week 18. + +The multinomial model indicated significant effects of bottom temperature, week of year, and decade on the probability of individual females being in a prespawning, spawning, or postspawning condition. Pattern of increasing spawning and postspawning and decreasing prespawning with both week of year and temperate was evident. The pattern by decade indicated an increase in prespawning and decrease in postspawning groups after 2000; however, data is limited for the most recent decade. The increase in prespawning fish through time indicates sampling has shifted from the beginning or early portion of the spawning season up to the 1990s to occurring closer to the peak of the spawning season since 2000. + +**Georges Bank Yellowtail Flounder** + +The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu[@brown-peterson_standardized_2011]), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals . The mean size of mature females sampled varied over the time series. The time series indicates a similar proportion of post spawning fish collected at higher temperatures more recently. Although the dates sampled were similar, early and late in the time series, with a few exceptions, the temperatures occurring on these dates was higher late in the time series. Spawning condition was related to bottom temperature, with ripe fish declining at temperatures above 8 C, spent fish increasing with temperature, but little change in developing and resting fish over the range of temperatures. The proportion of developing fish decrease, while ripe, spent and resting fish increased with week of year sampled. Together, these increases in prespawning and spawning fish as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. + +When summarized by decade, for the most recent decades no clear patterns in the proportion of developing fish were evident, however more spent fish were encountered in the in the recent decades since 2000. Sampling has also occurred later into the spring in the recent decades (2010s-2020s) and has collected slightly higher proportions of spent and resting fish sampled at later weeks compared to the period from the 1970s to 2000s that had few observations beyond week 17. + +The multinomial model indicated significant effects of bottom temperature, week of year, and decade on the probability of individual females being in a prespawning, spawning, or postspawning condition. A pattern of increasing spawning and postspawning and decreasing prespawning with week of year was evident. Bottom temperature had the reverse effect, with increasing prespawing fish but decreasing spawning and spawning fish as temperature increased. The pattern by decade indicated relatively minor changes in the predicted probability of each spawning group over time, and sampling has consistently occurred close to the peak spawning period. + +**Southern New England Yellowtail Flounder** + +The spring survey (SBTS) generally occurs during the spawning season, capturing a mix of developing (spawning capable phase, sensu [@brown-peterson_standardized_2011]), ripe (spawning active phase), spent (regressing phase), and resting (regenerating phase) individuals. The mean size of mature females sampled varied over the time series. The time series indicates a higher proportion of ripe fish collected early and late in the time series. The dates sampled have varied over time, with later sampling occurring 1977-1982, and more recently. The bottom temperatures showed a similar pattern for the recent period, however for the earlier period sampled late in the year, the water temperatures were much lower. Spawning condition was related to bottom temperature, with ripe fish increasing at temperatures above 5 °C, spent fish increasing with temperature, developing fish decreasing and resting fish increasing over this range. At lower temperatures, the patterns were reversed, but there were fewer observations. The proportion of developing fish decreased, while ripe, spent and resting fish increased with week of year sampled. Together, these increases in spawning and postspawning fish as temperature and week of year sampled increases, indicate that the spring survey captures the peak to end of annual spawning depending on the year. + +When summarized by decade, the most recent decades indicate higher proportions of developing fish with subsequent declines in ripe fish compared to the earliest decade, however only a single mature female was sampled in the most recent decade. Sampling has also occurred during similar weeks (1970s-2010s) and has collected slightly higher proportions of spent and resting fish sampled in the 1990s at warmer temperatures. + +The multinomial model indicated significant effects of bottom temperature, week of year, and decade on the probability of individual females being in a prespawning, spawning, or postspawning condition. Pattern of increasing postspawning and decreasing spawning and prespawning with week of year was evident. The effect of bottom temperature differed, with increasing spawing and postspawning fish but decreasing prespawning fish as temperature increased. The pattern by decade indicated an increase in prespawning and decrease in spawning from the 1970s to 1990s, with little change since (ignoring the most recent decade, which is represented by a single fish). In the late 1970s and early 1980s SNE samples were collected later in the spring and contained large numbers of ripe fish, in the peak of spawning. From the mid-1980s to 2010 sampling occurred earlier and collected mostly prespawning fish. + +### MidAtlantic + +```{r plot_spawn_timingMidAtlanticResting} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Resting') +ggplotObject +``` + +```{r plot_spawn_timingMidAtlanticRipe} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Ripe') +ggplotObject +``` + +```{r plot_spawn_timingMidAtlanticSpent} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Spent') +ggplotObject +``` + +```{r plot_spawn_timingMidAtlanticDeveloping} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Developing') +ggplotObject +``` + +```{r plot_spawn_timingMidAtlanticMF} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'MF') +ggplotObject +``` + +```{r plot_spawn_timingMidAtlanticmeanTEMP} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'meanTEMP') +ggplotObject +``` + +```{r plot_spawn_timingMidAtlanticmeanJDAY} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'meanJDAY') +ggplotObject +``` + +### NewEngland + +```{r plot_spawn_timingNewEnglandResting} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Resting') +ggplotObject +``` + +```{r plot_spawn_timingNewEnglandRipe} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Ripe') +ggplotObject +``` + +```{r plot_spawn_timingNewEnglandSpent} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Spent') +ggplotObject +``` + +```{r plot_spawn_timingNewEnglandDeveloping} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Developing') +ggplotObject +``` + +```{r plot_spawn_timingNewEnglandMF} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'MF') +ggplotObject +``` + +```{r plot_spawn_timingNewEnglandmeanTEMP} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'meanTEMP') +ggplotObject +``` + +```{r plot_spawn_timingNewEnglandmeanJDAY} +# Plot indicator +ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'meanJDAY') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Stock-specific spatial scale + +Temporal scale: Spring and Fall + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_spawn_timing} +# Either from Contributor or ecodata +``` + +## Implications +Spawning timing is shifting earlier for multiple stocks, including haddock and yellowtail flounder. Spawning of both haddock stocks occurred earlier in the year (and ended earlier, as indicated by more resting (post-spawning) stage fish, in the 2010s as compared to earlier in the time series. Yellowtail flounder spawn late spring-summer, with timing varying by stock location. The northern (CC/GOM) stock shows earlier active spawning in recent years with a decline in pre-spawning resting females. Both GB and SNE stock are sampled during active spawning (low percent resting). The recent increase in resting females in the southern (SNE) stock also indicates a shift to earlier spawning (i.e. more post-spawn fish). Yellowtail flounder spawning is related to bottom temperature, week of year, and decade sampled for each of the three stocks. + +Haddock spawn during the late winter-spring months in the region, with the spring survey sampling fish in the latter portion of the spawning season (peak to end). There is evidence that spawning of both haddock stocks occurred earlier in the year (and ended earlier) in the recent decade (2010s) as compared to earlier in the time series. Recent spring surveys have sampled a greater proportion of post spawning fish. The post spawning haddock would be expected to have lower relative condition due to shedding of gametes, depletion of liver (used for oocyte development and maturation), and higher water content of muscle and liver tissue, as has been reported in cod ([@lambert_energetic_2000]) and flatfishes ([@wuenschel_reproductive_2019]). Some fishes also reduce or cease feeding during spawning, for behavioral or physical (gonad occupying most of the body cavity) reasons. The annual cycle of energy acquisition, storage, and depletion is lowest immediately following spawning in capital breeders such as haddock ([@mcbride_energy_2015]). Trippel and Neil [@trippel_maternal_2011] estimated female weight loss associated with spawning to be 24.1% in a laboratory study of haddock. Although the reduction in weight and condition due to spawning is temporary, comparisons of relative condition (weight at length) across years that sampled varying proportions of active and post spawning fish (whether due to spawning timing or survey timing) should be considered with caution. The results presented for mature females should be applicable to mature males as well, but immature fish of both sexes will not undergo weight loss due to spawning. Therefore comparison of condition in proximity to spawning season across years that contain varying proportions of immature and mature fish will also be problematic. Given the temporal separation from spawning, relative condition estimates derived from autumn surveys will be less influenced by alterations in timing of surveys or spawning season. + +Yellowtail Flounder spawn during the spring months in the region, with the spring survey sampling fish during the spawning season. There is evidence that spawning condition is related to the bottom temperature, week of year, and decade sampled for each of the three stocks and survey timing and environmental conditions (bottom temperature) have been variable over time. The analysis of macroscopic data presented here provides some evidence for a change in the spawning season of CC GOM and SNE stocks. Early in the time series, sampling captured the beginning/early portion of spawning season, while later in the time series sampling has captured more of the peak in spawning. However, some caveats to this general conclusion are warranted. First, since Yellowtail Flounder are batch spawners, individuals cycle from developing, to ripe, to running ripe, and back to developing for each of many batches over a >1 month period. As such, the developing class includes a mix of individuals that have not released any batches yet (true prespawners) and others who are ‘in between batches (i.e. partially spent, but before a next batch). Gonadal histology and/or GSI data could help to refine categorization of these stages, but such data does not exist for the long time series presented. Second, the analysis by decadal timeblocks presented is a rather coarse approach to investigating long term trends in spawning condition. More appropriate methods should be explored and evaluated in future studies. Nevertheless, even with significant inter-annual variability, the approach presented should detect directional change in spawning seasonality over the time series. Subtle shifts to earlier spawning, accompanied by more individuals sampled later in their annual reproductive cycle (e.g. closer to spent), were evident in CC GOM and SNE stocks. Even small changes in spawning seasonality and/or sampling timing can affect the total weight of individuals sampled given the gonad of Yellowtail Flounder females can reach 35 % of their body weight ([@wuenschel_measuring_2019]), with the ovary declining in weight over a period of months as batches of eggs are shed. Therefore, consideration of spawning condition when interpreting fish weight and relative condition during the spring season should be considered. Although sampling was not specifically designed to detect changes in spawning seasonality of Yellowtail Flounder, the present analysis provides a preliminary evaluation of spawning timing over a long time series (1971-2023). + +The post spawning Yellowtail Flounder would be expected to have lower relative condition due to shedding of gametes, depletion of liver (used for oocyte development and maturation), and higher water content of muscle and liver tissue, as has been reported in cod ([@lambert_energetic_2000]) and flatfishes ([@wuenschel_reproductive_2019]). Some fishes also reduce or cease feeding during spawning, for behavioral or physical (e.g. gonad occupying most of the body cavity) reasons. The annual cycle of energy acquisition, storage, and depletion is lowest immediately following spawning in capital breeders such as haddock ([@mcbride_energy_2015]). Manning and Crim [@manning_maternal_1998] estimated female weight loss associated with spawning to be 22-23% in a laboratory study of Yellowtail Flounder. Although the reduction in weight and condition due to spawning is temporary, comparisons of relative condition (weight at length) across years that sampled varying proportions of active and post spawning fish (whether due to spawning timing or survey timing) should be considered with caution. The results presented for mature females should be applicable to mature males as well, but immature fish of both sexes will not undergo weight loss due to spawning. Therefore, comparison of condition in proximity to spawning season across years that contain varying proportions of immature and mature fish will also be problematic. Given the temporal separation from spawning, relative condition estimates derived from autumn surveys will be less influenced by alterations in timing of surveys or spawning season. + +## Get the data + +**Point of contact**: [Mark Wuenschel mark.wuenschel@noaa.gov](mailto:Mark Wuenschel mark.wuenschel@noaa.gov){.email} + +**ecodata name**: `ecodata::spawn_timing` + +**Variable definitions** + +Variable names follow the convention "SEASON_Species_STOCK_Variable" where Variable and Units are: +meanTEMP: mean sampled bottom temperature in degrees C MF: number of mature females sampled meanJDAY: mean julian day of year sampled +Developing: percent of mature females at developing (pre-spawning) stage Ripe: percent of mature females at ripe (spawning) stage +Spent: percent of mature females at spent (immediately post-spawning) stage Resting: percent of mature females at resting (non-spawning) stage + +```{r vars_spawn_timing} +# Pull all var names +vars <- ecodata::spawn_timing |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/species_dist.rmd b/chapters/species_dist.rmd index 241c8825..2c9afc51 100644 --- a/chapters/species_dist.rmd +++ b/chapters/species_dist.rmd @@ -1,106 +1,106 @@ -# Species Distribution Indicators {#species_dist} - -**Description**: Species mean depth, along-shelf distance, and distance to coastline - -**Indicator family**: - -- [X] Habitat -- [X] Megafauna - - -**Contributor(s)**: Kevin Friedland, Brandon Beltz - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Distribution shifts for a suite of 48 commercially or ecologically important fish species were evaluated using center of gravity metrics based on NEFSC bottom trawl survey data. - -Along-shelf distance is a metric for quantifying the distribution of a species through time along the axis of the US Northeast Continental Shelf, which extends northeastward from the Outer Banks of North Carolina. Once mean distance is found, depth of occurrence and distance to coastline can be calculated for each species’ positional center. - -## Key Results and Visualizations -The center of distribution for a suite of 48 commercially or ecologically important fish species along the entire Northeast Shelf continues to show movement towards the northeast and generally into deeper water. - -### MidAtlantic - -```{r plot_species_distMidAtlanticalong} -# Plot indicator -ggplotObject <- ecodata::plot_species_dist(report= 'MidAtlantic', varName= 'along') -ggplotObject -``` - -```{r plot_species_distMidAtlanticdepth} -# Plot indicator -ggplotObject <- ecodata::plot_species_dist(report= 'MidAtlantic', varName= 'depth') -ggplotObject -``` - -### NewEngland - -```{r plot_species_distNewEnglandalong} -# Plot indicator -ggplotObject <- ecodata::plot_species_dist(report= 'NewEngland', varName= 'along') -ggplotObject -``` - -```{r plot_species_distNewEnglanddepth} -# Plot indicator -ggplotObject <- ecodata::plot_species_dist(report= 'NewEngland', varName= 'depth') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Shelfwide - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_species_dist} -# Either from Contributor or ecodata -``` - -## Implications -Temperature change is a major driver of changing fish distributions [@friedland_event_2019]. - -## Get the data - -**Point of contact**: [Kevin Friedland, kevin.friedland@noaa.gov](mailto:Kevin Friedland, kevin.friedland@noaa.gov){.email} - -**ecodata name**: `ecodata::species_dist` - -**Variable definitions** - -"along-shelf distance" "depth" "distance to coast" "Latitude" "Longitude" - -```{r vars_species_dist} -# Pull all var names -vars <- ecodata::species_dist |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Contact Kevin Friedland, kevin.friedland@noaa.gov for data access - -**tech-doc link** - - +# Species Distribution Indicators {#species_dist} + +**Description**: Species mean depth, along-shelf distance, and distance to coastline + +**Indicator family**: + +- [X] Habitat +- [X] Megafauna + + +**Contributor(s)**: Kevin Friedland, Brandon Beltz + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Distribution shifts for a suite of 48 commercially or ecologically important fish species were evaluated using center of gravity metrics based on NEFSC bottom trawl survey data. + +Along-shelf distance is a metric for quantifying the distribution of a species through time along the axis of the US Northeast Continental Shelf, which extends northeastward from the Outer Banks of North Carolina. Once mean distance is found, depth of occurrence and distance to coastline can be calculated for each species’ positional center. + +## Key Results and Visualizations +The center of distribution for a suite of 48 commercially or ecologically important fish species along the entire Northeast Shelf continues to show movement towards the northeast and generally into deeper water. + +### MidAtlantic + +```{r plot_species_distMidAtlanticalong} +# Plot indicator +ggplotObject <- ecodata::plot_species_dist(report= 'MidAtlantic', varName= 'along') +ggplotObject +``` + +```{r plot_species_distMidAtlanticdepth} +# Plot indicator +ggplotObject <- ecodata::plot_species_dist(report= 'MidAtlantic', varName= 'depth') +ggplotObject +``` + +### NewEngland + +```{r plot_species_distNewEnglandalong} +# Plot indicator +ggplotObject <- ecodata::plot_species_dist(report= 'NewEngland', varName= 'along') +ggplotObject +``` + +```{r plot_species_distNewEnglanddepth} +# Plot indicator +ggplotObject <- ecodata::plot_species_dist(report= 'NewEngland', varName= 'depth') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Shelfwide + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_species_dist} +# Either from Contributor or ecodata +``` + +## Implications +Temperature change is a major driver of changing fish distributions [@friedland_event_2019]. + +## Get the data + +**Point of contact**: [Kevin Friedland, kevin.friedland@noaa.gov](mailto:Kevin Friedland, kevin.friedland@noaa.gov){.email} + +**ecodata name**: `ecodata::species_dist` + +**Variable definitions** + +"along-shelf distance" "depth" "distance to coast" "Latitude" "Longitude" + +```{r vars_species_dist} +# Pull all var names +vars <- ecodata::species_dist |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Contact Kevin Friedland, kevin.friedland@noaa.gov for data access + +**tech-doc link** + + diff --git a/chapters/species_groupings.rmd b/chapters/species_groupings.rmd index 50b5823b..44400b56 100644 --- a/chapters/species_groupings.rmd +++ b/chapters/species_groupings.rmd @@ -1,90 +1,89 @@ -# Feeding guilds by management bodies {#species_groupings} - -**Description**: Classification of species guild membership by management bodies. - -**Indicator family**: - -- [X] Megafauna -- [X] Economic - - -**Contributor(s)**: Sarah Gaichas and Sean Lucey. - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Feeding guilds are groups of species that feed similarly. At the ecosystem level, the food web is likely stable if overall biomass of feeding groups is stable over time, even if populations of species within the groups may be changing. - -We defined feeding guilds for fish and invertebrates captured by bottom trawl surveys using diet similarity, either from diet analysis or from literature [@garrison_dietary_2000; @smith_trophic_2010], and see [NEFSC food habits online](https://fwdp.shinyapps.io/tm2020/) - -## Key Results and Visualizations -Each management body (MAFMC, NEFSC, State etc) has its managed species associated with different feeding guilds. This data set shows which managed species for each management body are in which guilds. - -### MidAtlantic - -```{r plot_species_groupingsMidAtlantic2024} -# Plot indicator -ggplotObject <- ecodata::plot_species_groupings(report= 'MidAtlantic', varName= '2024') -ggplotObject -``` - -### NewEngland - -```{r plot_species_groupingsNewEngland2024} -# Plot indicator -ggplotObject <- ecodata::plot_species_groupings(report= 'NewEngland', varName= '2024') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Coastwide - -Temporal scale: N/A - -**Synthesis Theme**: - - - - -```{r autostats_species_groupings} -# Either from Contributor or ecodata -``` - -## Implications -We changed species groupings in response to comments over the years. The table shows feeding guilds used in the 2024 report. - -## Get the data - -**Point of contact**: [Sarah.Gaichas@noaa.gov](mailto:Sarah.Gaichas@noaa.gov){.email} - -**ecodata name**: `ecodata::species_groupings` - -**Variable definitions** - -1. SOE.24 = Feeding guild definitions for State of the Ecosystem report (2024) -2. MAFMC = Mid Atlantic Fishery Management Council - list of managed species by feeding guild -3. NEFMC = New England Fishery Management Council - list of managed species by feeding guild -4. Joint = Jointly managed species by feeding guild -5. State or Other = Species managed by other bodies listed by feeding guild - -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Feeding guilds by management bodies {#species_groupings} + +**Description**: Classification of species guild membership by management bodies. + +**Indicator family**: + +- [X] Megafauna +- [X] Economic + + +**Contributor(s)**: Sarah Gaichas and Sean Lucey. + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Feeding guilds are groups of species that feed similarly. At the ecosystem level, the food web is likely stable if overall biomass of feeding groups is stable over time, even if populations of species within the groups may be changing. + +We defined feeding guilds for fish and invertebrates captured by bottom trawl surveys using diet similarity, either from diet analysis or from literature [@garrison_dietary_2000; @smith_trophic_2010], and see [NEFSC food habits online](https://fwdp.shinyapps.io/tm2020/) + +## Key Results and Visualizations +Each management body (MAFMC, NEFSC, State etc) has its managed species associated with different feeding guilds. This data set shows which managed species for each management body are in which guilds. + +### MidAtlantic + +```{r plot_species_groupingsMidAtlantic2024} +# Plot indicator +ggplotObject <- ecodata::plot_species_groupings(report= 'MidAtlantic', varName= '2024') +ggplotObject +``` + +### NewEngland + +```{r plot_species_groupingsNewEngland2024} +# Plot indicator +ggplotObject <- ecodata::plot_species_groupings(report= 'NewEngland', varName= '2024') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Coastwide + +Temporal scale: N/A + +**Synthesis Theme**: + + + + +```{r autostats_species_groupings} +# Either from Contributor or ecodata +``` + +## Implications +We changed species groupings in response to comments over the years. The table shows feeding guilds used in the 2024 report. + +## Get the data + +**Point of contact**: [Sarah.Gaichas@noaa.gov](mailto:Sarah.Gaichas@noaa.gov){.email} + +**ecodata name**: `ecodata::species_groupings` + +**Variable definitions** + +1. SOE.24 = Feeding guild definitions for State of the Ecosystem report (2024) +2. MAFMC = Mid Atlantic Fishery Management Council - list of managed species by feeding guild +3. NEFMC = New England Fishery Management Council - list of managed species by feeding guild 4. Joint = Jointly managed species by feeding guild +5. State or Other = Species managed by other bodies listed by feeding guild + +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/stock_status.rmd b/chapters/stock_status.rmd index 54029ca8..36ee3289 100644 --- a/chapters/stock_status.rmd +++ b/chapters/stock_status.rmd @@ -1,106 +1,105 @@ -# Stock Status {#stock_status} - -**Description**: Summary of the most recent stock status results for each assessed species - -**Indicator family**: - -- [X] Social -- [X] Economic - - -**Contributor(s)**: Sarah Gaichas, Andy Beet, Jeff Vieser, Chris Legault - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Stock assessments are conducted regularly for fisheries managed by the New England and Mid-Atlantic Fishery Management Councils to support decisions on catch limits and to determine whether conservation objectives for each stock are being met. Single species management objectives include -1. Maintaining biomass above minimum thresholds, and -2. Maintaining fishing mortality below overfishing limits. - -This indicator summarizes the most recent stock status, defined as stock assessment estimated current biomass (B) relative to the biomass threshold (1/2 BMSY) and biomass target (BMSY), and current fishing mortality (F) relative to the fishing mortality limit (FMSY). - -## Key Results and Visualizations -Single species management objectives (1. maintaining biomass above minimum thresholds and 2. maintaining fishing mortality below overfishing limits) are being met for all but two MAFMC managed species, though the status of six stocks is unknown. Single species management objectives are not being met for some NEFMC managed species. Nine stocks are currently estimated to be below BMSY, while status relative to BMSY could not be assessed for 13 additional stocks. - -### MAB - -```{r plot_stock_statusMAB} -# Plot indicator -ggplotObject <- ecodata::plot_stock_status(report='MidAtlantic') -ggplotObject -``` - -### NE - -```{r plot_stock_statusNE} -# Plot indicator -ggplotObject <- ecodata::plot_stock_status(report='NewEngland') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Stock-specific spatial scale, reported by Council - -Temporal scale: Annual update for the aggregate; individual stock status 1-5 years depending on assessment cycle - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_stock_status} -# Either from Contributor or ecodata -``` - -## Implications -Stock status affects catch limits established by the Council, which in turn may affect landings trends. - -In the Mid Atlantic, stock status is above the minimum threshold for all but one stock, and aggregate biomass trends appear stable, so the decline in commercial seafood landings is most likely driven by market dynamics affecting the landings of surfclams and ocean quahogs, as landings have been below quotas for these species. The long term decline in total planktivore landings is largely driven by Atlantic menhaden fishery dynamics, including a consolidation of processors leading to reduced fishing capacity between the 1990s and mid-2000s. - -In New England, stock status and associated management constraints are likely contributing to decreased landings. With the poor or unknown stock status of many managed species, the decline in commercial seafood landings in the Gulf of Maine most likely reflects lower catch quotas implemented to rebuild overfished stocks, as well as market dynamics. - -## Get the data - -**Point of contact**: [Sarah.Gaichas@noaa.gov](mailto:Sarah.Gaichas@noaa.gov){.email} - -**ecodata name**: `ecodata::stock_status` - -**Variable definitions** - -Variables in `ecodata::stock_status` **Stock:** Name of stock -**Last assessment:** Most recent assessment year for stock status -**Council:** "MAFMC" (Mid Atlantic Fishery Management Council), "NEFMC" (New England Fishery Management Council), or "Both" - **Code:** Short stock name for plotting corresponding to **Stock** -**Var:** "F.Fmsy" (Current year fishing mortality F relative to FMSY) "B.Bmsy" (Current year biomass B relative to BMSY) - **Value:** Decimal value of Var **Units:** Unitless - -```{r vars_stock_status} -# Pull all var names -vars <- ecodata::stock_status |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Stock Status {#stock_status} + +**Description**: Summary of the most recent stock status results for each assessed species + +**Indicator family**: + +- [X] Social +- [X] Economic + + +**Contributor(s)**: Sarah Gaichas, Andy Beet, Jeff Vieser, Chris Legault + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Stock assessments are conducted regularly for fisheries managed by the New England and Mid-Atlantic Fishery Management Councils to support decisions on catch limits and to determine whether conservation objectives for each stock are being met. Single species management objectives include +1. Maintaining biomass above minimum thresholds, and +2. Maintaining fishing mortality below overfishing limits. + +This indicator summarizes the most recent stock status, defined as stock assessment estimated current biomass (B) relative to the biomass threshold (1/2 BMSY) and biomass target (BMSY), and current fishing mortality (F) relative to the fishing mortality limit (FMSY). + +## Key Results and Visualizations +Single species management objectives (1. maintaining biomass above minimum thresholds and 2. maintaining fishing mortality below overfishing limits) are being met for all but two MAFMC managed species, though the status of six stocks is unknown. Single species management objectives are not being met for some NEFMC managed species. Nine stocks are currently estimated to be below BMSY, while status relative to BMSY could not be assessed for 13 additional stocks. + +### MAB + +```{r plot_stock_statusMAB} +# Plot indicator +ggplotObject <- ecodata::plot_stock_status(report='MidAtlantic') +ggplotObject +``` + +### NE + +```{r plot_stock_statusNE} +# Plot indicator +ggplotObject <- ecodata::plot_stock_status(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Stock-specific spatial scale, reported by Council + +Temporal scale: Annual update for the aggregate; individual stock status 1-5 years depending on assessment cycle + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_stock_status} +# Either from Contributor or ecodata +``` + +## Implications +Stock status affects catch limits established by the Council, which in turn may affect landings trends. + +In the Mid Atlantic, stock status is above the minimum threshold for all but one stock, and aggregate biomass trends appear stable, so the decline in commercial seafood landings is most likely driven by market dynamics affecting the landings of surfclams and ocean quahogs, as landings have been below quotas for these species. The long term decline in total planktivore landings is largely driven by Atlantic menhaden fishery dynamics, including a consolidation of processors leading to reduced fishing capacity between the 1990s and mid-2000s. + +In New England, stock status and associated management constraints are likely contributing to decreased landings. With the poor or unknown stock status of many managed species, the decline in commercial seafood landings in the Gulf of Maine most likely reflects lower catch quotas implemented to rebuild overfished stocks, as well as market dynamics. + +## Get the data + +**Point of contact**: [Sarah.Gaichas@noaa.gov](mailto:Sarah.Gaichas@noaa.gov){.email} + +**ecodata name**: `ecodata::stock_status` + +**Variable definitions** + +Variables in `ecodata::stock_status` **Stock:** Name of stock **Last assessment:** Most recent assessment year for stock status +**Council:** "MAFMC" (Mid Atlantic Fishery Management Council), "NEFMC" (New England Fishery Management Council), or "Both" +**Code:** Short stock name for plotting corresponding to **Stock** +**Var:** "F.Fmsy" (Current year fishing mortality F relative to FMSY) "B.Bmsy" (Current year biomass B relative to BMSY) +**Value:** Decimal value of Var **Units:** Unitless + +```{r vars_stock_status} +# Pull all var names +vars <- ecodata::stock_status |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/thermal_habitat_area.rmd b/chapters/thermal_habitat_area.rmd index 9f807c08..381e4607 100644 --- a/chapters/thermal_habitat_area.rmd +++ b/chapters/thermal_habitat_area.rmd @@ -1,103 +1,103 @@ -# Thermal Habitat Area {#thermal_habitat_area} - -**Description**: Calculates the proportion of each EPU that exceeds temperature thresholds as a daily time series from 1993 – 2023 - -**Indicator family**: - -- [X] Oceanographic -- [X] Habitat - - -**Contributor(s)**: Joe Caracappa - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -See "Thermal Habitat Persistence" for cell-based calculations. Many deep water benthic and demersal exhibit thermal preferences for metabolic, reproductive, and growth processes. Temperatures above these thermal presences may impair these processes. Two temperature thresholds (15 and 24 degrees C) were chosen based on cutoff points where demersal species are less commonly found. Thermal habitat area is calculated by identifying 1/12 degree cells within a given EPU that are greater than or equal to the temperature threshold then taking the sum of all cell areas. Data from 1993-01-01 to 2023-08-29 were obtained from the CMEMS' GLORYS12V1 global reanalysis product, and from 2023-08-30 to 2023-12-31 data were obtained from the CMEMS' PSY global forecasting product. - -## Key Results and Visualizations -For each EPU, bottom temperature data were separate by depth bin (0-25m; 25m-100m; and >100m), as well as temperature threshold (>15C , >24C), and the proportion of each EPU exceeding these thresholds was calculated on a daily basis from 1993 to 2023. Overlayed time series of each year's proportional area shows that recent years have resulted in a increasing proportion of the MAB and GB mid-depths have experienced high temperatures. The 24C threshold is rarely seen in GB and GOM. - -### MAB - -```{r plot_thermal_habitat_areaMAB} -# Plot indicator -ggplotObject <- ecodata::plot_thermal_habitat_area(report='MidAtlantic') -ggplotObject -``` - -### GB - -```{r plot_thermal_habitat_areaNEGB} -# Plot indicator -ggplotObject <- ecodata::plot_thermal_habitat_area(report='NewEngland',EPU='GB') -ggplotObject -``` - -### GOM - -```{r plot_thermal_habitat_areaNEGOM} -# Plot indicator -ggplotObject <- ecodata::plot_thermal_habitat_area(report='NewEngland',EPU='GOM') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: EPU - -Temporal scale: daily - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_thermal_habitat_area} -# Either from Contributor or ecodata -``` - -## Implications -If a large proportion of species current habitat become thermally inhospitable, it can influence species productivity and in extreme cases cause mortality. - -## Get the data - -**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} - -**ecodata name**: `ecodata::thermal_habitat_area` - -**Variable definitions** - -Source: GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature) and PSY (CMEM’s PSY global forecast bottom temperature) -min.depth: minimum of depth band max.depth: maximum of depth band -temp.threshold: cutoff temperature for thermal area calculations (all areas greater than or equal to this temperature) -area: area exceeding temperature threshold (m2) area.prop: proportion of EPU area exceeding temperature threshold - -```{r vars_thermal_habitat_area} -# Pull all var names -vars <- ecodata::thermal_habitat_area |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Extensive analysis, not yet published - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Thermal Habitat Area {#thermal_habitat_area} + +**Description**: Calculates the proportion of each EPU that exceeds temperature thresholds as a daily time series from 1993 – 2023 + +**Indicator family**: + +- [X] Oceanographic +- [X] Habitat + + +**Contributor(s)**: Joe Caracappa + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +See "Thermal Habitat Persistence" for cell-based calculations. Many deep water benthic and demersal exhibit thermal preferences for metabolic, reproductive, and growth processes. Temperatures above these thermal presences may impair these processes. Two temperature thresholds (15 and 24 degrees C) were chosen based on cutoff points where demersal species are less commonly found. Thermal habitat area is calculated by identifying 1/12 degree cells within a given EPU that are greater than or equal to the temperature threshold then taking the sum of all cell areas. Data from 1993-01-01 to 2023-08-29 were obtained from the CMEMS' GLORYS12V1 global reanalysis product, and from 2023-08-30 to 2023-12-31 data were obtained from the CMEMS' PSY global forecasting product. + +## Key Results and Visualizations +For each EPU, bottom temperature data were separate by depth bin (0-25m; 25m-100m; and >100m), as well as temperature threshold (>15C , >24C), and the proportion of each EPU exceeding these thresholds was calculated on a daily basis from 1993 to 2023. Overlayed time series of each year's proportional area shows that recent years have resulted in a increasing proportion of the MAB and GB mid-depths have experienced high temperatures. The 24C threshold is rarely seen in GB and GOM. + +### MAB + +```{r plot_thermal_habitat_areaMAB} +# Plot indicator +ggplotObject <- ecodata::plot_thermal_habitat_area(report='MidAtlantic') +ggplotObject +``` + +### GB + +```{r plot_thermal_habitat_areaNEGB} +# Plot indicator +ggplotObject <- ecodata::plot_thermal_habitat_area(report='NewEngland',EPU='GB') +ggplotObject +``` + +### GOM + +```{r plot_thermal_habitat_areaNEGOM} +# Plot indicator +ggplotObject <- ecodata::plot_thermal_habitat_area(report='NewEngland',EPU='GOM') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: EPU + +Temporal scale: daily + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_thermal_habitat_area} +# Either from Contributor or ecodata +``` + +## Implications +If a large proportion of species current habitat become thermally inhospitable, it can influence species productivity and in extreme cases cause mortality. + +## Get the data + +**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} + +**ecodata name**: `ecodata::thermal_habitat_area` + +**Variable definitions** + +Source: GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature) and PSY (CMEM’s PSY global forecast bottom temperature) +min.depth: minimum of depth band max.depth: maximum of depth band +temp.threshold: cutoff temperature for thermal area calculations (all areas greater than or equal to this temperature) +area: area exceeding temperature threshold (m2) area.prop: proportion of EPU area exceeding temperature threshold + +```{r vars_thermal_habitat_area} +# Pull all var names +vars <- ecodata::thermal_habitat_area |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Extensive analysis, not yet published + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/thermal_habitat_persistence.rmd b/chapters/thermal_habitat_persistence.rmd index 96bbc5c9..93156ef3 100644 --- a/chapters/thermal_habitat_persistence.rmd +++ b/chapters/thermal_habitat_persistence.rmd @@ -1,86 +1,85 @@ -# Thermal Habitat Persistence {#thermal_habitat_persistence} - -**Description**: The number of days per year per 1/12 degree cell that exceeds a temperature threshold. - -**Indicator family**: - -- [X] Oceanographic -- [X] Habitat - - -**Contributor(s)**: Joe Caracappa - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Many deep water benthic and demersal exhibit thermal preferences for metabolic, reproductive, and growth processes. Temperatures above these thermal presences may impair these processes. Data originate from GLORYS12V1 global reanalysis for 1993-01-01 to 2023-08-29, and from PSY forecasts from 2021-01-01 to 2023-12-31. Cells are mapped to 3 depth bins: 0-25m, 25-100m, and >100m. Two temperature thresholds are used representing a temperature where moderate (15C) and extreme (24C) thermal stresses are likely to occur across several species. GLORYS and PSY 1/12 degree grid is mapped to EPU_NOESTUARIES shape files by the center point of each grid cell. - -## Key Results and Visualizations -Maps of full NE shelf with cells shading gradient showing number of days exceeding temperature thresholds. For 15C threshold, much of the southern MAB experienced >100 days, as well as along the shelf break and around Nantucket Shoals. In 2023, only the southern and/or shallow portion of the MAB exceeded the 24C threshold. - -```{r plot_thermal_habitat_persistenceMAB} -# Plot indicator -ggplotObject <- ecodata::plot_thermal_habitat_persistence(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: full shelf - -Temporal scale: annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_thermal_habitat_persistence} -# Either from Contributor or ecodata -``` - -## Implications -Using a high resolution bottom temperature product allows for localized heat stress within EPUs that may be masked when looking at mean EPU bottom temperature. This can be important to sessile species who aren't able to move out of warm water conditions. - -## Get the data - -**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} - -**ecodata name**: `ecodata::thermal_habitat_persistence` - -**Variable definitions** - -Source: GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature) and PSY (CMEM’s PSY global forecast bottom temperature) -min.depth: minimum of depth band max.depth: maximum of depth band -temp.threshold: cutoff temperature for thermal area calculations (all areas greater than or equal to this temperature) -longitude: longitude of cell center point latitude: latitude of cell center point -Ndays: number of days exceeding temp.threshold - -```{r vars_thermal_habitat_persistence} -# Pull all var names -vars <- ecodata::thermal_habitat_persistence |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Extensive analysis, not yet published - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Thermal Habitat Persistence {#thermal_habitat_persistence} + +**Description**: The number of days per year per 1/12 degree cell that exceeds a temperature threshold. + +**Indicator family**: + +- [X] Oceanographic +- [X] Habitat + + +**Contributor(s)**: Joe Caracappa + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Many deep water benthic and demersal exhibit thermal preferences for metabolic, reproductive, and growth processes. Temperatures above these thermal presences may impair these processes. Data originate from GLORYS12V1 global reanalysis for 1993-01-01 to 2023-08-29, and from PSY forecasts from 2021-01-01 to 2023-12-31. Cells are mapped to 3 depth bins: 0-25m, 25-100m, and >100m. Two temperature thresholds are used representing a temperature where moderate (15C) and extreme (24C) thermal stresses are likely to occur across several species. GLORYS and PSY 1/12 degree grid is mapped to EPU_NOESTUARIES shape files by the center point of each grid cell. + +## Key Results and Visualizations +Maps of full NE shelf with cells shading gradient showing number of days exceeding temperature thresholds. For 15C threshold, much of the southern MAB experienced >100 days, as well as along the shelf break and around Nantucket Shoals. In 2023, only the southern and/or shallow portion of the MAB exceeded the 24C threshold. + +```{r plot_thermal_habitat_persistenceMAB} +# Plot indicator +ggplotObject <- ecodata::plot_thermal_habitat_persistence(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: full shelf + +Temporal scale: annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_thermal_habitat_persistence} +# Either from Contributor or ecodata +``` + +## Implications +Using a high resolution bottom temperature product allows for localized heat stress within EPUs that may be masked when looking at mean EPU bottom temperature. This can be important to sessile species who aren't able to move out of warm water conditions. + +## Get the data + +**Point of contact**: [joseph.caracappa@noaa.gov](mailto:joseph.caracappa@noaa.gov){.email} + +**ecodata name**: `ecodata::thermal_habitat_persistence` + +**Variable definitions** + +Source: GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature) and PSY (CMEM’s PSY global forecast bottom temperature) +min.depth: minimum of depth band max.depth: maximum of depth band +temp.threshold: cutoff temperature for thermal area calculations (all areas greater than or equal to this temperature) +longitude: longitude of cell center point latitude: latitude of cell center point Ndays: number of days exceeding temp.threshold + +```{r vars_thermal_habitat_persistence} +# Pull all var names +vars <- ecodata::thermal_habitat_persistence |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Extensive analysis, not yet published + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/timing_shifts.rmd b/chapters/timing_shifts.rmd index 85341857..8b0d9201 100644 --- a/chapters/timing_shifts.rmd +++ b/chapters/timing_shifts.rmd @@ -1,74 +1,76 @@ -# Timing shifts: Risks to Seasonal Management {#timing_shifts} - -**Description**: Shifts in the timing of life-cycle events are a risk to meeting seasonal and temporal management objectives. - -**Indicator family**: - -- [X] Oceanographic -- [X] Habitat -- [X] Lower trophic levels -- [X] Megafauna - - -**Contributor(s)**: Kimberly Hyde, Sarah Gaichas, Joe Carracappa - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Changes in phenology, the seasonal timing of recurring life-cycle events, are a primary indicator of species responses to climate change [@staudinger_its_2019]. Observed phenological changes in the Northeast Shelf include spawning (REFS), migration [@crear_climate-influenced_2023], prey availability, and ... The phenological responses are often species-specific and vary depending on the primary environmental driver [@staudinger_its_2019]. - -## Key Results and Visualizations -Migration timing of some tuna and large whale species has also changed. For example, tuna were caught in recreational fisheries 50 days earlier in the year in 2019 compared to 2002. [@crear_climate-influenced_2023] - -In Cape Cod Bay, peak spring habitat use by right and humpback whales has shifted 18-19 days later over time. [@pendleton_decadal-scale_2022] - - -## Indicator statistics -Spatial scale: NES - -Temporal scale: Seasonal - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_timing_shifts} -# Either from Contributor or ecodata -``` - -## Implications -In progress - -Additionally, prolonged fall temperatures have been linked to the increased number of cold-stunned Kemp’s ridley sea turtles found in Cape Cod Bay [@griffin_warming_2019] - -## Get the data - -**Point of contact**: [nefsc.soe.leads@noaa.gov](mailto:nefsc.soe.leads@noaa.gov){.email} - -**ecodata name**: No dataset - -**Variable definitions** - -NA - - -No Data - -**Indicator Category**: - -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - +# Timing shifts: Risks to Seasonal Management {#timing_shifts} + +**Description**: Shifts in the timing of life-cycle events are a risk to meeting seasonal and temporal management objectives. + +**Indicator family**: + +- [X] Oceanographic +- [X] Habitat +- [X] Lower trophic levels +- [X] Megafauna + + +**Contributor(s)**: Kimberly Hyde, Sarah Gaichas, Joe Carracappa + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Changes in phenology, the seasonal timing of recurring life-cycle events, are a primary indicator of species responses to climate change @staudinger_its_2019. Observed phenological changes in the Northeast Shelf include shifts in [spawning](https://noaa-edab.github.io/catalog/spawn_timing.html), migration @crear_climate-influenced_2023, prey availability, and seasonal phytoplankton bloom timing. Changes in the timing of physical drivers such as the onset of stratification and fall turnover timing directly and indirectly affect life-cycle events. The phenological responses are often species-specific and vary depending on the primary environmental driver @staudinger_its_2019. + +## Key Results and Visualizations +Migration timing of some tuna and large whale species has also changed. For example, tuna were caught in recreational fisheries 50 days earlier in the year in 2019 compared to 2002. [@crear_climate-influenced_2023] + +In Cape Cod Bay, peak spring habitat use by right and humpback whales has shifted 18-19 days later over time. [@pendleton_decadal-scale_2022] + +Prolonged fall temperatures have been linked to the increased number of cold-stunned Kemp’s ridley sea turtles found in Cape Cod Bay [@griffin_warming_2019] + + +## Indicator statistics +Spatial scale: NES + +Temporal scale: Seasonal + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_timing_shifts} +# Either from Contributor or ecodata +``` + +## Implications +Changes in phenology are key indicators of the effects of climate change on ecosystems and well documented in terrestrial ecosystems @cohen_global_2018. Trends in phenology are often not homogenous due to high variability in climate drivers and phenological responses @okeefe_forming_2013. Phenological changes are less well documented in marine ecosystems, but there are clear, documented shifts in the timing of seasonal marine abiotic factors including earlier [transitions](https://noaa-edab.github.io/catalog/trans_dates.html) from winter to spring temperatures in the Northeast Continental Shelf @friedland_changes_2020; @thomas_seasonal_2017. Lower trophic levels, phytoplankton and zooplankton, are able to quickly adapt to abiotic changes, which can lead to a mismatch with consumers and alter the food web structure. Differential shifts in phenology can drive population declines through increased predation or competition and/or declines in reproductive success @weiskopf_climate_2020 + +From a management perspective, changes in species-specific phenology can alterfishery interactions and bycatch, as well as reduce the effectiveness of time/area closures to protect sensitive seasonal processes such as spawning. Highly migratory species are susceptible to incidental catch in a large number of fisheries using a variety of fishing gears @okeefe_forming_2013, and changes in migration timing may increase these unintended interactions if seasonal measures do not adjust to these changes. + +## Get the data + +**Point of contact**: [nefsc.soe.leads@noaa.gov](mailto:nefsc.soe.leads@noaa.gov){.email} + +**ecodata name**: No dataset + +**Variable definitions** + +NA + + +No Data + +**Indicator Category**: + +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + diff --git a/chapters/trans_dates.rmd b/chapters/trans_dates.rmd index c67e6bf7..f90c9100 100644 --- a/chapters/trans_dates.rmd +++ b/chapters/trans_dates.rmd @@ -1,103 +1,103 @@ -# Transition Dates {#trans_dates} - -**Description**: The date that cool winter conditions transition to warm stratified summer conditions. - -**Indicator family**: - -- [X] Oceanographic - - -**Contributor(s)**: Kevin Friedland - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Transition dates are defned as the day of the year when surface temperatures changeover from cool to warm conditions in the spring and back to cool conditions in the fall. - -## Key Results and Visualizations -Ocean summer length in Mid-Atlantic: the annual total number of days between the spring thermal transition date and the fall thermal transition date. The transition dates are defined as the day of the year when surface temperatures changeover from cool to warm conditions in the spring and back to cool conditions in the fall. - -### MidAtlantic - -```{r plot_trans_datesMidAtlantictiming} -# Plot indicator -ggplotObject <- ecodata::plot_trans_dates(report= 'MidAtlantic', varName= 'timing') -ggplotObject -``` - -```{r plot_trans_datesMidAtlanticlength} -# Plot indicator -ggplotObject <- ecodata::plot_trans_dates(report= 'MidAtlantic', varName= 'length') -ggplotObject -``` - -### NewEngland - -```{r plot_trans_datesNewEnglandtiming} -# Plot indicator -ggplotObject <- ecodata::plot_trans_dates(report= 'NewEngland', varName= 'timing') -ggplotObject -``` - -```{r plot_trans_datesNewEnglandlength} -# Plot indicator -ggplotObject <- ecodata::plot_trans_dates(report= 'NewEngland', varName= 'length') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: by EPU - -Temporal scale: Annual time series (1982 to 2023) - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_trans_dates} -# Either from Contributor or ecodata -``` - -## Implications -Prolonged fall temperatures have been linked to the increased number of cold-stunned Kemp’s ridley sea turtles found in Cape Cod Bay @griffin_warming_2019 - -## Get the data - -**Point of contact**: [kevin.friedland@noaa.gov](mailto:kevin.friedland@noaa.gov){.email} - -**ecodata name**: `ecodata::trans_dates` - -**Variable definitions** - -1. falltrans 2. sprtrans 3. maxday - -```{r vars_trans_dates} -# Pull all var names -vars <- ecodata::trans_dates |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Transition Dates {#trans_dates} + +**Description**: The date that cool winter conditions transition to warm stratified summer conditions. + +**Indicator family**: + +- [X] Oceanographic + + +**Contributor(s)**: Kevin Friedland + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Transition dates are defned as the day of the year when surface temperatures changeover from cool to warm conditions in the spring and back to cool conditions in the fall. + +## Key Results and Visualizations +Ocean summer length in Mid-Atlantic: the annual total number of days between the spring thermal transition date and the fall thermal transition date. The transition dates are defined as the day of the year when surface temperatures changeover from cool to warm conditions in the spring and back to cool conditions in the fall. + +### MidAtlantic + +```{r plot_trans_datesMidAtlantictiming} +# Plot indicator +ggplotObject <- ecodata::plot_trans_dates(report= 'MidAtlantic', varName= 'timing') +ggplotObject +``` + +```{r plot_trans_datesMidAtlanticlength} +# Plot indicator +ggplotObject <- ecodata::plot_trans_dates(report= 'MidAtlantic', varName= 'length') +ggplotObject +``` + +### NewEngland + +```{r plot_trans_datesNewEnglandtiming} +# Plot indicator +ggplotObject <- ecodata::plot_trans_dates(report= 'NewEngland', varName= 'timing') +ggplotObject +``` + +```{r plot_trans_datesNewEnglandlength} +# Plot indicator +ggplotObject <- ecodata::plot_trans_dates(report= 'NewEngland', varName= 'length') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: by EPU + +Temporal scale: Annual time series (1982 to 2023) + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_trans_dates} +# Either from Contributor or ecodata +``` + +## Implications +Prolonged fall temperatures have been linked to the increased number of cold-stunned Kemp’s ridley sea turtles found in Cape Cod Bay @griffin_warming_2019 + +## Get the data + +**Point of contact**: [kevin.friedland@noaa.gov](mailto:kevin.friedland@noaa.gov){.email} + +**ecodata name**: `ecodata::trans_dates` + +**Variable definitions** + +1. falltrans 2. sprtrans 3. maxday + +```{r vars_trans_dates} +# Pull all var names +vars <- ecodata::trans_dates |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/wbts_mesozooplankton.rmd b/chapters/wbts_mesozooplankton.rmd index 2fa0bb4f..bfb83188 100644 --- a/chapters/wbts_mesozooplankton.rmd +++ b/chapters/wbts_mesozooplankton.rmd @@ -1,79 +1,79 @@ -# Mesozooplankton Biomass at Wilkinson Basin {#wbts_mesozooplankton} - -**Description**: Mesozooplankton biomass at the Wilkinson Basin Time Series Station (WBTS): 2005-2022 - -**Indicator family**: - -- [X] Lower trophic levels - - -**Contributor(s)**: Jeffrey Runge, Emma Dullaert, Cameron Thompson, Rebecca Jones - -**Affiliations**: UMS - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The Wilkinson Basin Time Series Station (WBTS: 257 m depth), located in the northwest corner of Wilkinson Basin, was established in December, 2004. For about fifteen years it was maintained by PIs at the University of New Hampshire and University of Maine, funded through various short term research projects with several funding gaps in coverage. In 2019, with funding from BOEM and NOPP, the time series was integrated into the Gulf of Maine Marine Biodiversity Observation Network (GoM MBON), administered by NERACOOS. The WBTS station was favored because of the existing time series data, the proximity to coastal ports allowing single-day missions to collect samples and its strategic importance representing the deep western GoM overwintering habitat for the planktonic copepod, Calanus finmarchicus, a key sentinel variable in the GoM pelagic food web. - -Data collected at the WBTS station include CTD- rosette measurements of salinity, temperature and chlorophyll a concentration, microscopic enumeration of phytoplankton species, bacterial and microplankton measurements using flow cytometer, eDNA measurements, measurement of total mesozooplankton biomass and microscopic enumeration of zooplankton species collected with a 0.75 m, 200µm ring net towed from near bottom to the surface. Only the mesozooplankton biomass data are reported for the 2024 SOE; a fuller reporting of the time series data awaits further vetting and publication of the data in the primary literature. - -## Key Results and Visualizations -Planktonic copepods typically constituted the great majority of catch of the vertically integrated ring net tow. Larger microzooplankton, like euphausids and jellyfish, are underrepresented. Chaetognaths, round tentaculate ctenophores, notably Pleurobrachia, and salps were captured, although the latter tend to degrade in formaldehyde over time and are likely underrepresented. Given these limitations, the mesozooplankton dry mass data allow comparison of biomass across pelagic ecosystems where similar measurements have been taken. Notably, at WBTS, the copepodid stages of Calanus finmarchicus typically make up 50% or more of the total mesozooplankton biomass in spring through fall. - -Following the seasonal life cycle of C. finmarchicus, mesozooplankton biomass is lowest in late winter and highest in summer. Biomass levels of 10-20 g m-2 observed in 2005-2008 in summer and winter were among the highest observed across the subarctic North Atlantic Ocean, including the Gulf of St. Lawrence (@de_lafontaine_pelagic_1991; @sorochan_north_2019), Scotian Shelf (@casault_optical_2022) and the Norwegian and Barents Seas (@melle_north_2014; @skjoldal_size-fractioned_2022). The mesozooplankton biomass collected at WBTS in late summer (Aug-Oct) and winter (Nov-Mar) has since declined significantly, by about 50%, between the start of the time series in 2005-2008 and 2021-2022 (see figure). The summer and winter biomass levels reflect the predominance of the larger, lipid rich late stage C. finmarchicus (CIV-CVI) as compared to spring, which is dominated by younger C. finmarchicus stages CI-CIV. - -The interannual pattern by season of zooplankton dry weight at the WBTS stations reflect a decline in abundance of C. finmarchicus, which because of its large size relative to other planktonic copepods, brings the recent biomass levels down despite increases in other, smaller planktonic copepod species, like species of Centropages, Pseudocalanus, Metridia and Oithona @runge_sustained_2023. - - -## Indicator statistics -Spatial scale: Fixed time series station representing Wilkinson Basin in the western Gulf of Maine - -Temporal scale: Monthly samples (with data gaps) between 2005-2022 - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts -- [X] Ecosystem Reorganization - - -```{r autostats_wbts_mesozooplankton} -# Either from Contributor or ecodata -``` - -## Implications -A prominent result of the WBTS time series is confirmation of substantial reduction mesozooplankton biomass, reflecting a decline in abundance of the energy-rich stages of the planktonic copepod, Calanus finmarchicus, in the Gulf of Maine. Mean surface layer temperatures in the Gulf of Maine were at a record high in 2021, and the recent data indicate a shift in planktonic biodiversity away from the Calanus-dominated subarctic food web that has been the historic foundation for Gulf of Maine ecosystem services @runge_sustained_2023. - -The primary drivers of C. finmarchicus abundance in the Gulf of Maine, analyzed by @ji_drivers_2022, are local production in spring and early summer, external supply from the surface layer Nova Scotia Current and deep water through the Northeast Channel, and predation from both visual (e.g. herring, sandlance) and non-visual (e.g. euphausids, chaetognaths, jellyfish and other invertebrates) predators. The WBTS time series data here indicate a shifting phenology toward higher phytoplankton biomass available in fall, winter and early spring, supporting local production in spring and early summer not only for C. finmarchicus, but also for other, smaller mesozooplankton species (e.g. copepods in the genera Oithona, Pseudocalanus, Centropages and Metridia). This shifting phenology, allowing local late winter-early spring copepod reproduction, is consistent with observations of sustained C. finmarchicus abundance in spring. However, a combination of summer-fall predation and reduced external supply, associated with a shift in transport away from cold, Calanus-rich subarctic water to warm, Calanus-poor Atlantic slope water is likely responsible for the substantial reduction in the energy-rich stock of C. finmarchicus in the western Gulf of Maine in summer and fall, the period during which forage fish (e.g. herring and sand lance) as well as North Atlantic right whales, accumulate lipids to sustain their growth and reproduction. - -A value of the NERACOOS time series is the timely provision of plankton indicators at seasonal scales. For example, data from summer 2022 indicate a rebound in Calanus abundance (@runge_sustained_2023) which has implications for the present condition of North Atlantic right whale foraging habitat as well as for forage fish recruitment and condition in the Gulf of Maine. Further monitoring and analysis of hydrographic and other data is needed to understand whether 2022 conditions represent a longer term shift or whether external supply continues to constrain C. finmarchicus abundance in the Gulf of Maine. While time series samples will indicate the abundance trends of some invertebrate predators (e.g. carnivorous copepods and chaetognaths), supplemental data are needed to understand trends in euphausids, jellyfish, and visual predators. - -## Get the data - -**Point of contact**: [Jeffrey Runge (jeffrey.runge@maine.edu](mailto:Jeffrey Runge (jeffrey.runge@maine.edu){.email} - -**ecodata name**: No dataset - -**Variable definitions** - -Name: Mesozooplankton biomass, Definition: biomass of plankton collected with a 200 um vertically integrated ring net tow; Units: g m^2 - - -No Data - -**Indicator Category**: - -- [X] Extensive analysis, not yet published - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -While some of the data (e.g. the biomass data) are publicly available, other accompanying data, eg. abundance of Calanus finmarchicus and other zooplankton and chlorophyll a time series are pending submission in 2024 for publication in the primary literature - +# Mesozooplankton Biomass at Wilkinson Basin {#wbts_mesozooplankton} + +**Description**: Mesozooplankton biomass at the Wilkinson Basin Time Series Station (WBTS): 2005-2022 + +**Indicator family**: + +- [X] Lower trophic levels + + +**Contributor(s)**: Jeffrey Runge, Emma Dullaert, Cameron Thompson, Rebecca Jones + +**Affiliations**: UMS + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The Wilkinson Basin Time Series Station (WBTS: 257 m depth), located in the northwest corner of Wilkinson Basin, was established in December, 2004. For about fifteen years it was maintained by PIs at the University of New Hampshire and University of Maine, funded through various short term research projects with several funding gaps in coverage. In 2019, with funding from BOEM and NOPP, the time series was integrated into the Gulf of Maine Marine Biodiversity Observation Network (GoM MBON), administered by NERACOOS. The WBTS station was favored because of the existing time series data, the proximity to coastal ports allowing single-day missions to collect samples and its strategic importance representing the deep western GoM overwintering habitat for the planktonic copepod, Calanus finmarchicus, a key sentinel variable in the GoM pelagic food web. + +Data collected at the WBTS station include CTD- rosette measurements of salinity, temperature and chlorophyll a concentration, microscopic enumeration of phytoplankton species, bacterial and microplankton measurements using flow cytometer, eDNA measurements, measurement of total mesozooplankton biomass and microscopic enumeration of zooplankton species collected with a 0.75 m, 200µm ring net towed from near bottom to the surface. Only the mesozooplankton biomass data are reported for the 2024 SOE; a fuller reporting of the time series data awaits further vetting and publication of the data in the primary literature. + +## Key Results and Visualizations +Planktonic copepods typically constituted the great majority of catch of the vertically integrated ring net tow. Larger microzooplankton, like euphausids and jellyfish, are underrepresented. Chaetognaths, round tentaculate ctenophores, notably Pleurobrachia, and salps were captured, although the latter tend to degrade in formaldehyde over time and are likely underrepresented. Given these limitations, the mesozooplankton dry mass data allow comparison of biomass across pelagic ecosystems where similar measurements have been taken. Notably, at WBTS, the copepodid stages of Calanus finmarchicus typically make up 50% or more of the total mesozooplankton biomass in spring through fall. + +Following the seasonal life cycle of C. finmarchicus, mesozooplankton biomass is lowest in late winter and highest in summer. Biomass levels of 10-20 g m-2 observed in 2005-2008 in summer and winter were among the highest observed across the subarctic North Atlantic Ocean, including the Gulf of St. Lawrence (@de_lafontaine_pelagic_1991; @sorochan_north_2019), Scotian Shelf (@casault_optical_2022) and the Norwegian and Barents Seas (@melle_north_2014; @skjoldal_size-fractioned_2022). The mesozooplankton biomass collected at WBTS in late summer (Aug-Oct) and winter (Nov-Mar) has since declined significantly, by about 50%, between the start of the time series in 2005-2008 and 2021-2022 (see figure). The summer and winter biomass levels reflect the predominance of the larger, lipid rich late stage C. finmarchicus (CIV-CVI) as compared to spring, which is dominated by younger C. finmarchicus stages CI-CIV. + +The interannual pattern by season of zooplankton dry weight at the WBTS stations reflect a decline in abundance of C. finmarchicus, which because of its large size relative to other planktonic copepods, brings the recent biomass levels down despite increases in other, smaller planktonic copepod species, like species of Centropages, Pseudocalanus, Metridia and Oithona @runge_sustained_2023. + + +## Indicator statistics +Spatial scale: Fixed time series station representing Wilkinson Basin in the western Gulf of Maine + +Temporal scale: Monthly samples (with data gaps) between 2005-2022 + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts +- [X] Ecosystem Reorganization + + +```{r autostats_wbts_mesozooplankton} +# Either from Contributor or ecodata +``` + +## Implications +A prominent result of the WBTS time series is confirmation of substantial reduction mesozooplankton biomass, reflecting a decline in abundance of the energy-rich stages of the planktonic copepod, Calanus finmarchicus, in the Gulf of Maine. Mean surface layer temperatures in the Gulf of Maine were at a record high in 2021, and the recent data indicate a shift in planktonic biodiversity away from the Calanus-dominated subarctic food web that has been the historic foundation for Gulf of Maine ecosystem services @runge_sustained_2023. + +The primary drivers of C. finmarchicus abundance in the Gulf of Maine, analyzed by @ji_drivers_2022, are local production in spring and early summer, external supply from the surface layer Nova Scotia Current and deep water through the Northeast Channel, and predation from both visual (e.g. herring, sandlance) and non-visual (e.g. euphausids, chaetognaths, jellyfish and other invertebrates) predators. The WBTS time series data here indicate a shifting phenology toward higher phytoplankton biomass available in fall, winter and early spring, supporting local production in spring and early summer not only for C. finmarchicus, but also for other, smaller mesozooplankton species (e.g. copepods in the genera Oithona, Pseudocalanus, Centropages and Metridia). This shifting phenology, allowing local late winter-early spring copepod reproduction, is consistent with observations of sustained C. finmarchicus abundance in spring. However, a combination of summer-fall predation and reduced external supply, associated with a shift in transport away from cold, Calanus-rich subarctic water to warm, Calanus-poor Atlantic slope water is likely responsible for the substantial reduction in the energy-rich stock of C. finmarchicus in the western Gulf of Maine in summer and fall, the period during which forage fish (e.g. herring and sand lance) as well as North Atlantic right whales, accumulate lipids to sustain their growth and reproduction. + +A value of the NERACOOS time series is the timely provision of plankton indicators at seasonal scales. For example, data from summer 2022 indicate a rebound in Calanus abundance (@runge_sustained_2023) which has implications for the present condition of North Atlantic right whale foraging habitat as well as for forage fish recruitment and condition in the Gulf of Maine. Further monitoring and analysis of hydrographic and other data is needed to understand whether 2022 conditions represent a longer term shift or whether external supply continues to constrain C. finmarchicus abundance in the Gulf of Maine. While time series samples will indicate the abundance trends of some invertebrate predators (e.g. carnivorous copepods and chaetognaths), supplemental data are needed to understand trends in euphausids, jellyfish, and visual predators. + +## Get the data + +**Point of contact**: [Jeffrey Runge (jeffrey.runge@maine.edu](mailto:Jeffrey Runge (jeffrey.runge@maine.edu){.email} + +**ecodata name**: No dataset + +**Variable definitions** + +Name: Mesozooplankton biomass, Definition: biomass of plankton collected with a 200 um vertically integrated ring net tow; Units: g m^2 + + +No Data + +**Indicator Category**: + +- [X] Extensive analysis, not yet published + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +While some of the data (e.g. the biomass data) are publicly available, other accompanying data, eg. abundance of Calanus finmarchicus and other zooplankton and chlorophyll a time series are pending submission in 2024 for publication in the primary literature + diff --git a/chapters/wcr.rmd b/chapters/wcr.rmd index d3867f21..742aed03 100644 --- a/chapters/wcr.rmd +++ b/chapters/wcr.rmd @@ -1,83 +1,83 @@ -# Warm Core Rings {#wcr} - -**Description**: Number of warm core rings produced annually by the Gulf Stream off the Northeast US - -**Indicator family**: - -- [X] Oceanographic -- [X] Habitat - - -**Contributor(s)**: Avijit Gangopadhyay - -**Affiliations**: UMass - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Warm core rings are eddies formed from Gulf Stream meanders that transport warm Gulf Stream water into the cooler waters of the slope sea just off the Northeast US continental shelf. These rings transport both warm water and associated plankton and fish from the Gulf Stream towards the shelf, and may form important habitat for oceanic fishery species such as Illex squid. The indicator presented here extends published work [@gangopadhyay_observed_2019]; with updated counts of warm core rings. - -## Key Results and Visualizations -Prior to 2000, an average of 18 warm core rings were formed by the Gulf Stream off the Northeast US shelf. From 2000-2017, an average of 33 warm core rings were formed. Annual numbers of warm core rings have been updated using the same methods for each year since 2017, but the regime shift analysis has not been updated. - -```{r plot_wcrMAB} -# Plot indicator -ggplotObject <- ecodata::plot_wcr(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Full shelf - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers -- [X] Regime Shifts - - -```{r autostats_wcr} -# Either from Contributor or ecodata -``` - -## Implications -The increased instability of the Gulf Stream position and warming of the Slope Sea may be connected to the regime shift increase in the number of warm core rings formed annually in the Northwest Atlantic [@gangopadhyay_observed_2019; @gangopadhyay_census_2020]. When warm core rings and eddies interact with the continental slope they can transport warm, salty water to the continental shelf [@chen_mesoscale_2022], which can alter the habitat and disrupt seasonal movements of fish [@gawarkiewicz_changing_2018]. Transport of offshore water onto the shelf is happening more frequently [@gawarkiewicz_changing_2018; @gawarkiewicz_increasing_nodate], and can contribute to marine heatwaves in the Mid-Atlantic Bight [@gawarkiewicz_characteristics_2019; @chen_mesoscale_2022] as well as the movement of shelf-break species inshore [@gawarkiewicz_changing_2018; @potter_horizontal_2011; @worm_predator_2003]. - -## Get the data - -**Point of contact**: [Avijit Gangopadhyay avijit.gangopadhyay@umassd.edu](mailto:Avijit Gangopadhyay avijit.gangopadhyay@umassd.edu){.email} - -**ecodata name**: `ecodata::wcr` - -**Variable definitions** - -Warm Core Rings: number - -```{r vars_wcr} -# Pull all var names -vars <- ecodata::wcr |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Please contact Kimberly.Hyde@noaa.gov - -**tech-doc link** - - +# Warm Core Rings {#wcr} + +**Description**: Number of warm core rings produced annually by the Gulf Stream off the Northeast US + +**Indicator family**: + +- [X] Oceanographic +- [X] Habitat + + +**Contributor(s)**: Avijit Gangopadhyay + +**Affiliations**: UMass + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Warm core rings are eddies formed from Gulf Stream meanders that transport warm Gulf Stream water into the cooler waters of the slope sea just off the Northeast US continental shelf. These rings transport both warm water and associated plankton and fish from the Gulf Stream towards the shelf, and may form important habitat for oceanic fishery species such as Illex squid. The indicator presented here extends published work [@gangopadhyay_observed_2019]; with updated counts of warm core rings. + +## Key Results and Visualizations +Prior to 2000, an average of 18 warm core rings were formed by the Gulf Stream off the Northeast US shelf. From 2000-2017, an average of 33 warm core rings were formed. Annual numbers of warm core rings have been updated using the same methods for each year since 2017, but the regime shift analysis has not been updated. + +```{r plot_wcrMAB} +# Plot indicator +ggplotObject <- ecodata::plot_wcr(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Full shelf + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers +- [X] Regime Shifts + + +```{r autostats_wcr} +# Either from Contributor or ecodata +``` + +## Implications +The increased instability of the Gulf Stream position and warming of the Slope Sea may be connected to the regime shift increase in the number of warm core rings formed annually in the Northwest Atlantic [@gangopadhyay_observed_2019; @gangopadhyay_census_2020]. When warm core rings and eddies interact with the continental slope they can transport warm, salty water to the continental shelf [@chen_mesoscale_2022], which can alter the habitat and disrupt seasonal movements of fish [@gawarkiewicz_changing_2018]. Transport of offshore water onto the shelf is happening more frequently [@gawarkiewicz_changing_2018; @gawarkiewicz_increasing_nodate], and can contribute to marine heatwaves in the Mid-Atlantic Bight [@gawarkiewicz_characteristics_2019; @chen_mesoscale_2022] as well as the movement of shelf-break species inshore [@gawarkiewicz_changing_2018; @potter_horizontal_2011; @worm_predator_2003]. + +## Get the data + +**Point of contact**: [Avijit Gangopadhyay avijit.gangopadhyay@umassd.edu](mailto:Avijit Gangopadhyay avijit.gangopadhyay@umassd.edu){.email} + +**ecodata name**: `ecodata::wcr` + +**Variable definitions** + +Warm Core Rings: number + +```{r vars_wcr} +# Pull all var names +vars <- ecodata::wcr |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Please contact Kimberly.Hyde@noaa.gov + +**tech-doc link** + + diff --git a/chapters/wind_dev_speed.rmd b/chapters/wind_dev_speed.rmd index da782ad9..f9249d2d 100644 --- a/chapters/wind_dev_speed.rmd +++ b/chapters/wind_dev_speed.rmd @@ -1,84 +1,84 @@ -# Speed and Scale of Offshore Wind Development in the Northeast {#wind_dev_speed} - -**Description**: The footprint and timeline of offshore wind development in the Northeast by 2030 - -**Indicator family**: - -- [X] Social -- [X] Economic - - -**Contributor(s)**: Angela Silva - -**Affiliations**: ? - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -The data presented here is a timeline of proposed construction in the Northeast of offshore wind development projects to 2030. The lease area color corresponds to the year of proposed development. Project componenet data (e.g., number of foundations, cable miles, GW, and acreage for each project are described in the table. Areas currently under planning for additional lease areas are outlined in red and totals reflected in the bottom of the table. This information is up to date as of December 2023 and project statistics come from Appendix E3 of the Revolution Wind Final Environmental Impact Statement Table E-1. This indicator does not reflect potential changes to schedules from recently terminated Power Purchase Agreements for some projects (Ocean Wind 1 and 2, Empire Wind 2, and Skipjack Wind). - -## Key Results and Visualizations -The colored chart in Fig. also presents the offshore wind development timeline in the Greater Atlantic region with the estimated year that foundations would be constructed (matches the color of the wind areas). These timelines and data estimates are expected to shift, but represent the most recent information available as of July 2023. - -![Speed of offshore wind development](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Cumulative_Timeline_FullRegion_SoE2024_v2_2024.png){width=100%} - -```{r plot_wind_dev_speedMAB} -# Plot indicator -ggplotObject <- ecodata::plot_wind_dev_speed(report='MidAtlantic') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: NA - -Temporal scale: NA - -**Synthesis Theme**: - - - - -```{r autostats_wind_dev_speed} -# Either from Contributor or ecodata -``` - -## Implications -NA - -## Get the data - -**Point of contact**: [Angela Silva (angela.silva@noaa.gov)](mailto:Angela Silva (angela.silva@noaa.gov)){.email} - -**ecodata name**: `ecodata::wind_dev_speed` - -**Variable definitions** - -NA - -```{r vars_wind_dev_speed} -# Pull all var names -vars <- ecodata::wind_dev_speed |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Speed and Scale of Offshore Wind Development in the Northeast {#wind_dev_speed} + +**Description**: The footprint and timeline of offshore wind development in the Northeast by 2030 + +**Indicator family**: + +- [X] Social +- [X] Economic + + +**Contributor(s)**: Angela Silva + +**Affiliations**: ? + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +The data presented here is a timeline of proposed construction in the Northeast of offshore wind development projects to 2030. The lease area color corresponds to the year of proposed development. Project componenet data (e.g., number of foundations, cable miles, GW, and acreage for each project are described in the table. Areas currently under planning for additional lease areas are outlined in red and totals reflected in the bottom of the table. This information is up to date as of December 2023 and project statistics come from Appendix E3 of the Revolution Wind Final Environmental Impact Statement Table E-1. This indicator does not reflect potential changes to schedules from recently terminated Power Purchase Agreements for some projects (Ocean Wind 1 and 2, Empire Wind 2, and Skipjack Wind). + +## Key Results and Visualizations +The colored chart in Fig. also presents the offshore wind development timeline in the Greater Atlantic region with the estimated year that foundations would be constructed (matches the color of the wind areas). These timelines and data estimates are expected to shift, but represent the most recent information available as of July 2023. + +![Speed of offshore wind development](https://github.com/NOAA-EDAB/ecodata/raw/master/workshop/images/Cumulative_Timeline_FullRegion_SoE2024_v2_2024.png){width=100%} + +```{r plot_wind_dev_speedMAB} +# Plot indicator +ggplotObject <- ecodata::plot_wind_dev_speed(report='MidAtlantic') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: NA + +Temporal scale: NA + +**Synthesis Theme**: + + + + +```{r autostats_wind_dev_speed} +# Either from Contributor or ecodata +``` + +## Implications +NA + +## Get the data + +**Point of contact**: [Angela Silva (angela.silva@noaa.gov)](mailto:Angela Silva (angela.silva@noaa.gov)){.email} + +**ecodata name**: `ecodata::wind_dev_speed` + +**Variable definitions** + +NA + +```{r vars_wind_dev_speed} +# Pull all var names +vars <- ecodata::wind_dev_speed |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/wind_port.rmd b/chapters/wind_port.rmd index f47dbb11..c29abcb3 100644 --- a/chapters/wind_port.rmd +++ b/chapters/wind_port.rmd @@ -1,92 +1,92 @@ -# Community Port Landings and Revenue from Wind Energy Areas (WEAs) {#wind_port} - -**Description**: NA - -**Indicator family**: - -- [X] Social -- [X] Economic - - -**Contributor(s)**: Angela Silva, Doug Christel - -**Affiliations**: ? - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -NA - -## Key Results and Visualizations -This figure links historic port revenue (2008-2022) from within all wind energy areas (including all lease areas, the Central Atlantic proposed lease areas A-2, B-1 and C-1, GOM Draft Wind Energy Area and secondary areas), as a proportion of a port’s total fisheries revenue based on vessel trip reports as described in the revenue and landings of species in the wind indicator above. The range (minimum and maximum) of total percent fisheries revenue from within wind energy areas is presented in the graph and ports are sorted from greatest to least fisheries revenue from within wind areas. Those communities that score Med-High or higher in at least one of the vulnerability indicators that address environmental justice concerns (i.e., Poverty, Population Composition, Personal Disruption; see indicator definitions) are noted with a triangle. Gentrification pressure is also highlighted here, with those communities that score Med-High or higher in one or more gentrification pressure indicators (i.e., Housing Disruption, Retiree Migration, Urban Sprawl) represented with a circle. - -### MAB - -```{r plot_wind_portMAB} -# Plot indicator -ggplotObject <- ecodata::plot_wind_port(report='MidAtlantic') -ggplotObject -``` - -### NE - -```{r plot_wind_portNE} -# Plot indicator -ggplotObject <- ecodata::plot_wind_port(report='NewEngland') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Full Shelf, broken down into Mid-Atlantic and New England communities - -Temporal scale: 2008-2022 - -**Synthesis Theme**: - - - - -```{r autostats_wind_port} -# Either from Contributor or ecodata -``` - -## Implications -BOEM reports that cumulative offshore wind development (if all proposed projects are developed) could have moderate impacts on low-income members of environmental justice communities who work in the commercial fishing and for-hire fishing industry due to disruptions to fish populations, restrictions on navigation and increased vessel traffic as well as existing vulnerabilities of low-income workers to economic impacts @boem_vineyard_2020. impacts of offshore wind development may unevenly affect individual operators, with [permit-based revenue]([https://www.greateratlantic.fisheries.noaa.gov/ro/fso/reports/WIND/WIND_AREA_REPORTS/com/OCS_A_0486_Revolution_Wind_com.html#Percentage_of_Revenue_by_Permit](https://www.google.com/url?q=https://www.greateratlantic.fisheries.noaa.gov/ro/fso/reports/WIND/WIND_AREA_REPORTS/com/OCS_A_0486_Revolution_Wind_com.html%23Percentage_of_Revenue_by_Permit&sa=D&source=docs&ust=1707341641564496&usg=AOvVaw2ws51j_uyqjKtgIRFCpijd)) being much higher than the port-based mean for some permit holders - -## Get the data - -**Point of contact**: [Angela Silva (angela.silva@noaa.gov)](mailto:Angela Silva (angela.silva@noaa.gov)){.email} - -**ecodata name**: `ecodata::wind_port` - -**Variable definitions** - -NA - -```{r vars_wind_port} -# Pull all var names -vars <- ecodata::wind_port |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Database pull with analysis - - -## Public Availability - -Source data are publicly available. - -## Accessibility and Constraints - -_No response_ - -**tech-doc link** - - +# Community Port Landings and Revenue from Wind Energy Areas (WEAs) {#wind_port} + +**Description**: NA + +**Indicator family**: + +- [X] Social +- [X] Economic + + +**Contributor(s)**: Angela Silva, Doug Christel + +**Affiliations**: ? + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +NA + +## Key Results and Visualizations +This figure links historic port revenue (2008-2022) from within all wind energy areas (including all lease areas, the Central Atlantic proposed lease areas A-2, B-1 and C-1, GOM Draft Wind Energy Area and secondary areas), as a proportion of a port’s total fisheries revenue based on vessel trip reports as described in the revenue and landings of species in the wind indicator above. The range (minimum and maximum) of total percent fisheries revenue from within wind energy areas is presented in the graph and ports are sorted from greatest to least fisheries revenue from within wind areas. Those communities that score Med-High or higher in at least one of the vulnerability indicators that address environmental justice concerns (i.e., Poverty, Population Composition, Personal Disruption; see indicator definitions) are noted with a triangle. Gentrification pressure is also highlighted here, with those communities that score Med-High or higher in one or more gentrification pressure indicators (i.e., Housing Disruption, Retiree Migration, Urban Sprawl) represented with a circle. + +### MAB + +```{r plot_wind_portMAB} +# Plot indicator +ggplotObject <- ecodata::plot_wind_port(report='MidAtlantic') +ggplotObject +``` + +### NE + +```{r plot_wind_portNE} +# Plot indicator +ggplotObject <- ecodata::plot_wind_port(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Full Shelf, broken down into Mid-Atlantic and New England communities + +Temporal scale: 2008-2022 + +**Synthesis Theme**: + + + + +```{r autostats_wind_port} +# Either from Contributor or ecodata +``` + +## Implications +BOEM reports that cumulative offshore wind development (if all proposed projects are developed) could have moderate impacts on low-income members of environmental justice communities who work in the commercial fishing and for-hire fishing industry due to disruptions to fish populations, restrictions on navigation and increased vessel traffic as well as existing vulnerabilities of low-income workers to economic impacts @boem_vineyard_2020. impacts of offshore wind development may unevenly affect individual operators, with [permit-based revenue]([https://www.greateratlantic.fisheries.noaa.gov/ro/fso/reports/WIND/WIND_AREA_REPORTS/com/OCS_A_0486_Revolution_Wind_com.html#Percentage_of_Revenue_by_Permit](https://www.google.com/url?q=https://www.greateratlantic.fisheries.noaa.gov/ro/fso/reports/WIND/WIND_AREA_REPORTS/com/OCS_A_0486_Revolution_Wind_com.html%23Percentage_of_Revenue_by_Permit&sa=D&source=docs&ust=1707341641564496&usg=AOvVaw2ws51j_uyqjKtgIRFCpijd)) being much higher than the port-based mean for some permit holders + +## Get the data + +**Point of contact**: [Angela Silva (angela.silva@noaa.gov)](mailto:Angela Silva (angela.silva@noaa.gov)){.email} + +**ecodata name**: `ecodata::wind_port` + +**Variable definitions** + +NA + +```{r vars_wind_port} +# Pull all var names +vars <- ecodata::wind_port |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Database pull with analysis + + +## Public Availability + +Source data are publicly available. + +## Accessibility and Constraints + +_No response_ + +**tech-doc link** + + diff --git a/chapters/wind_revenue.rmd b/chapters/wind_revenue.rmd index b3a46860..5a1b98f4 100644 --- a/chapters/wind_revenue.rmd +++ b/chapters/wind_revenue.rmd @@ -1,134 +1,134 @@ -# Fishery Impacts from Offshore Wind Development {#wind_revenue} - -**Description**: The data presented here include landings and revenue of managed species within existing offshore wind lease areas, Central Atlantic Bight final wind energy areas, and the Gulf of Maine draft wind energy area. - -**Indicator family**: - -- [X] Social -- [X] Economic - - -**Contributor(s)**: Benjamin Galuardi, Geret DePiper, Dennis Corvi, Douglas Christel - -**Affiliations**: ? - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Estimates of landings and associated revenue of managed species within existing and proposed offshore wind lease areas provide an estimate of the potential socioeconomic impacts to fishery participants and fishing communities from regional offshore development projects. The presence of offshore wind project infrastructure could result in fishing effort displacement outside of lease areas, which could affect the scale, composition, and location of fishery landings and revenues and interactions with protected species. These data present absolute landings/revenue within lease areas, but also relative proportions of regional landings/revenue within lease areas to illustrate the scale of potential impacts to specific fisheries. - -## Key Results and Visualizations -Figures include annual landings and revenue for each managed species caught within existing and proposed offshore wind lease areas. Tables include average annual landings and revenue for the top ten species caught within existing and proposed offshore wind lease areas and the maximum proportion of total annual regional landings and revenues for all managed species. - -### MidAtlantic - -```{r plot_wind_revenueMidAtlanticlandingfacets} -# Plot indicator -ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'landing' ,plottype= 'facets') -ggplotObject -``` - -```{r plot_wind_revenueMidAtlanticlandingnofacets} -# Plot indicator -ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'landing' ,plottype= 'nofacets') -ggplotObject -``` - -```{r plot_wind_revenueMidAtlanticvaluefacets} -# Plot indicator -ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'value' ,plottype= 'facets') -ggplotObject -``` - -```{r plot_wind_revenueMidAtlanticvaluenofacets} -# Plot indicator -ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'value' ,plottype= 'nofacets') -ggplotObject -``` - -### NewEngland - -```{r plot_wind_revenueNewEnglandlandingfacets} -# Plot indicator -ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'landing' ,plottype= 'facets') -ggplotObject -``` - -```{r plot_wind_revenueNewEnglandlandingnofacets} -# Plot indicator -ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'landing' ,plottype= 'nofacets') -ggplotObject -``` - -```{r plot_wind_revenueNewEnglandvaluefacets} -# Plot indicator -ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'value' ,plottype= 'facets') -ggplotObject -``` - -```{r plot_wind_revenueNewEnglandvaluenofacets} -# Plot indicator -ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'value' ,plottype= 'nofacets') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: Offshore wind lease areas throughout the Exclusive Economic Zone from Maine through North Carolina - -Temporal scale: Annual from 2008-2022 - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_wind_revenue} -# Either from Contributor or ecodata -``` - -## Implications -Plots of annual landings and revenue within existing and proposed offshore wind lease areas show a general decline since 2008 for managed species with periodic spikes in both metrics. This suggests lower reliance on offshore wind lease areas for most fisheries, although some are more reliant upon these areas at large, particularly the longfin squid, surfclam, scallop, herring and skate fisheries. The Gulf of Maine draft wind energy area overlaps with a substantial portion of historic groundfish fishery landings/revenues, particularly for species such as redfish, pollock, white hake, and American plaice. - -## Get the data - -**Point of contact**: [Douglas Christel (douglas.christel@noaa.gov)](mailto:Douglas Christel (douglas.christel@noaa.gov)){.email} - -**ecodata name**: `ecodata::wind_revenue` - -**Variable definitions** - -1) Managed species; Definition: Fishery species managed by the New England and Mid-Atlantic Fishery Management Councils or the Atlantic States Marine Fisheries Commission -2) Year; Definition: Calendar year in which fishery landings occurred -3) Landings; Definition: Weight of each species landed, as reported in dealer reports; Units: pounds -4) Revenue; Definition: Total amount paid to the vessel for each species landed; Units: 2022 dollars -5) Percent GARFO Landings; Definition: Proportion of landings of each managed species from within existing/proposed offshore wind lease areas relative to the total landings of that species reported annually to the Greater Atlantic Regional Fisheries Office; Units: Percentage -6) Percent GARFO Revenue; Definition: Proportion of revenue of each managed species from within existing/proposed offshore wind lease areas relative to the total revenue of that species reported annually to the Greater Atlantic Regional Fisheries Office; Units: Percentage - -```{r vars_wind_revenue} -# Pull all var names -vars <- ecodata::wind_revenue |> - dplyr::select(Var) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Syntheses of published information -- [X] Database pull with analysis - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Please email douglas.christel@noaa.gov for further information or request supporting data from nmfs.gar.data.requests@noaa.gov. - -**tech-doc link** - - +# Fishery Impacts from Offshore Wind Development {#wind_revenue} + +**Description**: The data presented here include landings and revenue of managed species within existing offshore wind lease areas, Central Atlantic Bight final wind energy areas, and the Gulf of Maine draft wind energy area. + +**Indicator family**: + +- [X] Social +- [X] Economic + + +**Contributor(s)**: Benjamin Galuardi, Geret DePiper, Dennis Corvi, Douglas Christel + +**Affiliations**: ? + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Estimates of landings and associated revenue of managed species within existing and proposed offshore wind lease areas provide an estimate of the potential socioeconomic impacts to fishery participants and fishing communities from regional offshore development projects. The presence of offshore wind project infrastructure could result in fishing effort displacement outside of lease areas, which could affect the scale, composition, and location of fishery landings and revenues and interactions with protected species. These data present absolute landings/revenue within lease areas, but also relative proportions of regional landings/revenue within lease areas to illustrate the scale of potential impacts to specific fisheries. + +## Key Results and Visualizations +Figures include annual landings and revenue for each managed species caught within existing and proposed offshore wind lease areas. Tables include average annual landings and revenue for the top ten species caught within existing and proposed offshore wind lease areas and the maximum proportion of total annual regional landings and revenues for all managed species. + +### MidAtlantic + +```{r plot_wind_revenueMidAtlanticlandingfacets} +# Plot indicator +ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'landing' ,plottype= 'facets') +ggplotObject +``` + +```{r plot_wind_revenueMidAtlanticlandingnofacets} +# Plot indicator +ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'landing' ,plottype= 'nofacets') +ggplotObject +``` + +```{r plot_wind_revenueMidAtlanticvaluefacets} +# Plot indicator +ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'value' ,plottype= 'facets') +ggplotObject +``` + +```{r plot_wind_revenueMidAtlanticvaluenofacets} +# Plot indicator +ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'value' ,plottype= 'nofacets') +ggplotObject +``` + +### NewEngland + +```{r plot_wind_revenueNewEnglandlandingfacets} +# Plot indicator +ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'landing' ,plottype= 'facets') +ggplotObject +``` + +```{r plot_wind_revenueNewEnglandlandingnofacets} +# Plot indicator +ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'landing' ,plottype= 'nofacets') +ggplotObject +``` + +```{r plot_wind_revenueNewEnglandvaluefacets} +# Plot indicator +ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'value' ,plottype= 'facets') +ggplotObject +``` + +```{r plot_wind_revenueNewEnglandvaluenofacets} +# Plot indicator +ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'value' ,plottype= 'nofacets') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: Offshore wind lease areas throughout the Exclusive Economic Zone from Maine through North Carolina + +Temporal scale: Annual from 2008-2022 + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_wind_revenue} +# Either from Contributor or ecodata +``` + +## Implications +Plots of annual landings and revenue within existing and proposed offshore wind lease areas show a general decline since 2008 for managed species with periodic spikes in both metrics. This suggests lower reliance on offshore wind lease areas for most fisheries, although some are more reliant upon these areas at large, particularly the longfin squid, surfclam, scallop, herring and skate fisheries. The Gulf of Maine draft wind energy area overlaps with a substantial portion of historic groundfish fishery landings/revenues, particularly for species such as redfish, pollock, white hake, and American plaice. + +## Get the data + +**Point of contact**: [Douglas Christel (douglas.christel@noaa.gov)](mailto:Douglas Christel (douglas.christel@noaa.gov)){.email} + +**ecodata name**: `ecodata::wind_revenue` + +**Variable definitions** + +1) Managed species; Definition: Fishery species managed by the New England and Mid-Atlantic Fishery Management Councils or the Atlantic States Marine Fisheries Commission +2) Year; Definition: Calendar year in which fishery landings occurred +3) Landings; Definition: Weight of each species landed, as reported in dealer reports; Units: pounds +4) Revenue; Definition: Total amount paid to the vessel for each species landed; Units: 2022 dollars +5) Percent GARFO Landings; Definition: Proportion of landings of each managed species from within existing/proposed offshore wind lease areas relative to the total landings of that species reported annually to the Greater Atlantic Regional Fisheries Office; Units: Percentage +6) Percent GARFO Revenue; Definition: Proportion of revenue of each managed species from within existing/proposed offshore wind lease areas relative to the total revenue of that species reported annually to the Greater Atlantic Regional Fisheries Office; Units: Percentage + +```{r vars_wind_revenue} +# Pull all var names +vars <- ecodata::wind_revenue |> + dplyr::select(Var) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Syntheses of published information +- [X] Database pull with analysis + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Please email douglas.christel@noaa.gov for further information or request supporting data from nmfs.gar.data.requests@noaa.gov. + +**tech-doc link** + + diff --git a/chapters/zoo_abundance_anom.rmd b/chapters/zoo_abundance_anom.rmd index 04858f06..6831b02d 100644 --- a/chapters/zoo_abundance_anom.rmd +++ b/chapters/zoo_abundance_anom.rmd @@ -1,112 +1,111 @@ -# Zooplankton Abundance Anomalies {#zoo_abundance_anom} - -**Description**: Abundance anomalies for 20 zooplankton taxa - -**Indicator family**: - -- [X] Lower trophic levels - - -**Contributor(s)**: Ryan Morse, Kevin Friedland, Harvey Walsh, Mike Jones - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Zooplankton represent a critical trophic link from primary producers to fish in marine ecosystems. - -## Key Results and Visualizations -Abundance anomalies of small and large copepods have varied over time by EPU. -Small bodied copepods and cnidarians show increasing trends in all EPUs. -Large bodied copepods and euphausiids show no significant trend in any EPU. - -### MidAtlantic - -```{r plot_zoo_abundance_anomMidAtlanticcopepod} -# Plot indicator -ggplotObject <- ecodata::plot_zoo_abundance_anom(report= 'MidAtlantic', varName= 'copepod') -ggplotObject -``` - -```{r plot_zoo_abundance_anomMidAtlanticeuphausid} -# Plot indicator -ggplotObject <- ecodata::plot_zoo_abundance_anom(report= 'MidAtlantic', varName= 'euphausid') -ggplotObject -``` - -### NewEngland - -```{r plot_zoo_abundance_anomNewEnglandcopepod} -# Plot indicator -ggplotObject <- ecodata::plot_zoo_abundance_anom(report= 'NewEngland', varName= 'copepod') -ggplotObject -``` - -```{r plot_zoo_abundance_anomNewEnglandeuphausid} -# Plot indicator -ggplotObject <- ecodata::plot_zoo_abundance_anom(report= 'NewEngland', varName= 'euphausid') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: by EPU - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_zoo_abundance_anom} -# Either from Contributor or ecodata -``` - -## Implications -Check these. If they are correct, we could be seeing the less energy dense zooplankton becoming more abundant in each system. - -## Get the data - -**Point of contact**: [Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov](mailto:Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov){.email} - -**ecodata name**: `ecodata::zoo_abundance_anom` - -**Variable definitions** - -All are unitless anomalies from the 1977-2020 mean abundance for each taxon. -Variables are taxa names: (to be described by contributors) "Calfin" "LgCopepods" "SmCopepods" -"Cyclopoida" "Diplostraca" "Ostracoda" "Cirripedia" "Euphausiacea" "Gammaridea" -"Hyperiidea" "Mysidacea" "Decapoda" "Polychaeta" "Echinodermata" "Mollusca" -"Pteropod" "Chaetognatha" "Cnidaria" "Tunicate" "Protozoa" - -```{r vars_zoo_abundance_anom} -# Pull all var names -vars <- ecodata::zoo_abundance_anom |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Extensive analysis, not yet published -- [X] Syntheses of published information -- [X] Database pull with analysis - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Request from Harvey Walsh, harvey.walsh@noaa.gov - -**tech-doc link** - - +# Zooplankton Abundance Anomalies {#zoo_abundance_anom} + +**Description**: Abundance anomalies for 20 zooplankton taxa + +**Indicator family**: + +- [X] Lower trophic levels + + +**Contributor(s)**: Ryan Morse, Kevin Friedland, Harvey Walsh, Mike Jones + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Zooplankton represent a critical trophic link from primary producers to fish in marine ecosystems. + +## Key Results and Visualizations +Abundance anomalies of small and large copepods have varied over time by EPU. +Small bodied copepods and cnidarians show increasing trends in all EPUs. +Large bodied copepods and euphausiids show no significant trend in any EPU. + +### MidAtlantic + +```{r plot_zoo_abundance_anomMidAtlanticcopepod} +# Plot indicator +ggplotObject <- ecodata::plot_zoo_abundance_anom(report= 'MidAtlantic', varName= 'copepod') +ggplotObject +``` + +```{r plot_zoo_abundance_anomMidAtlanticeuphausid} +# Plot indicator +ggplotObject <- ecodata::plot_zoo_abundance_anom(report= 'MidAtlantic', varName= 'euphausid') +ggplotObject +``` + +### NewEngland + +```{r plot_zoo_abundance_anomNewEnglandcopepod} +# Plot indicator +ggplotObject <- ecodata::plot_zoo_abundance_anom(report= 'NewEngland', varName= 'copepod') +ggplotObject +``` + +```{r plot_zoo_abundance_anomNewEnglandeuphausid} +# Plot indicator +ggplotObject <- ecodata::plot_zoo_abundance_anom(report= 'NewEngland', varName= 'euphausid') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: by EPU + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_zoo_abundance_anom} +# Either from Contributor or ecodata +``` + +## Implications +Check these. If they are correct, we could be seeing the less energy dense zooplankton becoming more abundant in each system. + +## Get the data + +**Point of contact**: [Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov](mailto:Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov){.email} + +**ecodata name**: `ecodata::zoo_abundance_anom` + +**Variable definitions** + +All are unitless anomalies from the 1977-2020 mean abundance for each taxon. Variables are taxa names: (to be described by contributors) +"Calfin" "LgCopepods" "SmCopepods" "Cyclopoida" "Diplostraca" "Ostracoda" "Cirripedia" "Euphausiacea" +"Gammaridea" "Hyperiidea" "Mysidacea" "Decapoda" "Polychaeta" "Echinodermata" "Mollusca" "Pteropod" +"Chaetognatha" "Cnidaria" "Tunicate" "Protozoa" + +```{r vars_zoo_abundance_anom} +# Pull all var names +vars <- ecodata::zoo_abundance_anom |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Extensive analysis, not yet published +- [X] Syntheses of published information +- [X] Database pull with analysis + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Request from Harvey Walsh, harvey.walsh@noaa.gov + +**tech-doc link** + + diff --git a/chapters/zoo_diversity.rmd b/chapters/zoo_diversity.rmd index 53208e9a..414d68e3 100644 --- a/chapters/zoo_diversity.rmd +++ b/chapters/zoo_diversity.rmd @@ -1,96 +1,96 @@ -# Zooplankton Diversity {#zoo_diversity} - -**Description**: Effective Shannon diversity calculated using 42 zooplankton taxa collected from EcoMon cruises - -**Indicator family**: - -- [X] Lower trophic levels - - -**Contributor(s)**: Ryan Morse, Kevin Friedland, Harvey Walsh, Mike Jones - -**Affiliations**: NEFSC - -```{r echo=FALSE} -knitr::opts_chunk$set(echo = F) -library(ecodata) -``` -## Introduction to Indicator -Zooplankton represent a critical trophic link from primary producers to fish in marine ecosystems. Trends in zooplankton community diversity may indicate changes in trophic stability over time. - -## Key Results and Visualizations -Zooplankton diversity is increasing in the Mid-Atlantic and on Georges Bank, but shows no trend in the Gulf of Maine. There is no vessel correction for this metric, so indices collected aboard the research vessel Albatross IV (up to 2008) and the research vessel Henry B. Bigelow (2009 - Present) are calculated separately (Fig. ). - -### MAB - -```{r plot_zoo_diversityMAB} -# Plot indicator -ggplotObject <- ecodata::plot_zoo_diversity(report='MidAtlantic') -ggplotObject -``` - -### NE - -```{r plot_zoo_diversityNE} -# Plot indicator -ggplotObject <- ecodata::plot_zoo_diversity(report='NewEngland') -ggplotObject -``` - - -## Indicator statistics -Spatial scale: by EPU - -Temporal scale: Annual - -**Synthesis Theme**: - -- [X] Multiple System Drivers - - -```{r autostats_zoo_diversity} -# Either from Contributor or ecodata -``` - -## Implications -Zooplankton community diversity varies with changes in dominance of taxa. Increasing zooplankton diversity in the Mid-Atlantic is due to increases in abundance of several taxa and stable or declining dominance of an important copepod species. This suggests a shift in the zooplankton community that warrants continued monitoring to determine if managed species are affected. - -While still showing an overall increasing trend, the GB zooplankton community declined in diversity in 2021 due to the increase in abundance of the copepod *Centropages typicus* and salps. The GOM zooplankton community is usually dominated by *Calanus finmarchicus*, however their abundance decreased in 2021. This decrease plus an increase in abundance of other copepods (*C. typicus, Metridia lucens, Oithona spp.*), siphonophores, and pteropods resulted in high zooplankton diversity index in 2021. - -## Get the data - -**Point of contact**: [Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov](mailto:Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov){.email} - -**ecodata name**: `ecodata::zoo_diversity` - -**Variable definitions** - -Zoo_Shannon-Wiener_Diversity_index, unitless - -```{r vars_zoo_diversity} -# Pull all var names -vars <- ecodata::zoo_diversity |> - dplyr::select(Var, Units) |> - dplyr::distinct() - -DT::datatable(vars) -``` -**Indicator Category**: - -- [X] Published Methods -- [X] Extensive analysis, not yet published -- [X] Syntheses of published information -- [X] Database pull with analysis - - -## Public Availability - -Source data are NOT publicly available. - -## Accessibility and Constraints - -Request from Harvey Walsh, harvey.walsh@noaa.gov - -**tech-doc link** - - +# Zooplankton Diversity {#zoo_diversity} + +**Description**: Effective Shannon diversity calculated using 42 zooplankton taxa collected from EcoMon cruises + +**Indicator family**: + +- [X] Lower trophic levels + + +**Contributor(s)**: Ryan Morse, Kevin Friedland, Harvey Walsh, Mike Jones + +**Affiliations**: NEFSC + +```{r echo=FALSE} +knitr::opts_chunk$set(echo = F) +library(ecodata) +``` +## Introduction to Indicator +Zooplankton represent a critical trophic link from primary producers to fish in marine ecosystems. Trends in zooplankton community diversity may indicate changes in trophic stability over time. + +## Key Results and Visualizations +Zooplankton diversity is increasing in the Mid-Atlantic and on Georges Bank, but shows no trend in the Gulf of Maine. There is no vessel correction for this metric, so indices collected aboard the research vessel Albatross IV (up to 2008) and the research vessel Henry B. Bigelow (2009 - Present) are calculated separately (Fig. ). + +### MAB + +```{r plot_zoo_diversityMAB} +# Plot indicator +ggplotObject <- ecodata::plot_zoo_diversity(report='MidAtlantic') +ggplotObject +``` + +### NE + +```{r plot_zoo_diversityNE} +# Plot indicator +ggplotObject <- ecodata::plot_zoo_diversity(report='NewEngland') +ggplotObject +``` + + +## Indicator statistics +Spatial scale: by EPU + +Temporal scale: Annual + +**Synthesis Theme**: + +- [X] Multiple System Drivers + + +```{r autostats_zoo_diversity} +# Either from Contributor or ecodata +``` + +## Implications +Zooplankton community diversity varies with changes in dominance of taxa. Increasing zooplankton diversity in the Mid-Atlantic is due to increases in abundance of several taxa and stable or declining dominance of an important copepod species. This suggests a shift in the zooplankton community that warrants continued monitoring to determine if managed species are affected. + +While still showing an overall increasing trend, the GB zooplankton community declined in diversity in 2021 due to the increase in abundance of the copepod *Centropages typicus* and salps. The GOM zooplankton community is usually dominated by *Calanus finmarchicus*, however their abundance decreased in 2021. This decrease plus an increase in abundance of other copepods (*C. typicus, Metridia lucens, Oithona spp.*), siphonophores, and pteropods resulted in high zooplankton diversity index in 2021. + +## Get the data + +**Point of contact**: [Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov](mailto:Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov){.email} + +**ecodata name**: `ecodata::zoo_diversity` + +**Variable definitions** + +Zoo_Shannon-Wiener_Diversity_index, unitless + +```{r vars_zoo_diversity} +# Pull all var names +vars <- ecodata::zoo_diversity |> + dplyr::select(Var, Units) |> + dplyr::distinct() + +DT::datatable(vars) +``` +**Indicator Category**: + +- [X] Published Methods +- [X] Extensive analysis, not yet published +- [X] Syntheses of published information +- [X] Database pull with analysis + + +## Public Availability + +Source data are NOT publicly available. + +## Accessibility and Constraints + +Request from Harvey Walsh, harvey.walsh@noaa.gov + +**tech-doc link** + + From e067431561dd4b563d9157fd88e56475a1b21a40 Mon Sep 17 00:00:00 2001 From: andybeet <22455149+andybeet@users.noreply.github.com> Date: Mon, 11 Mar 2024 09:57:19 -0400 Subject: [PATCH 7/8] edit bibligraphy --- bibliography/StateOftheEcosystem.bib | 62 +++++++++++----------------- 1 file changed, 25 insertions(+), 37 deletions(-) diff --git a/bibliography/StateOftheEcosystem.bib b/bibliography/StateOftheEcosystem.bib index 28da2284..b5958788 100644 --- a/bibliography/StateOftheEcosystem.bib +++ b/bibliography/StateOftheEcosystem.bib @@ -3311,36 +3311,6 @@ @article{okeefe_forming_2013 pages = {434--444}, } -@article{okeefe_forming_2013-1, - title = {Forming a {Partnership} to {Avoid} {Bycatch}}, - volume = {38}, - issn = {0363-2415}, - url = {https://doi.org/10.1080/03632415.2013.838122}, - doi = {10.1080/03632415.2013.838122}, - abstract = {Bycatch of Yellowtail Flounder in the U.S. Sea Scallop Fishery is a constraint to achieving optimum yield of scallops. Between 2000 and 2009, in-season bycatch closures of prime scallop grounds resulted in economic losses over US{\textbackslash}100 million. To address this constraint, we collaborated with the scallop fishing industry to implement a bycatch avoidance program in the Nantucket Lightship harvest area in 2010. Vessels shared near real-time location information about bycatch amounts during fishing activities. We compiled the information, identified bycatch hotspots, and provided daily advisories to vessels on the fishing grounds. Catch per tow of Yellowtail and fishing effort in high bycatch regions significantly declined after the fleet received the advisories. The fleet harvested the target scallop allocation worth US{\textbackslash}40 million while catching only 32\% of the Yellowtail bycatch limit. This program continues as a collaborative, iterative approach to bycatch reduction that balances fleet objectives with conservation constraints.}, - number = {10}, - urldate = {2024-03-08}, - journal = {Fisheries}, - author = {O'Keefe, Catherine E. and DeCelles, Gregory R.}, - month = nov, - year = {2013}, - note = {Publisher: Taylor \& Francis -\_eprint: https://doi.org/10.1080/03632415.2013.838122}, - pages = {434--444}, -} - -@article{pdf, - title = {Seasonal trends and phenology shifts in sea surface temperature on the {North} {American} northeastern continental shelf}, - volume = {5}, - doi = {10.1525/journal.elementa.240}, - abstract = {The northeastern North American continental shelf from Cape Hatteras to the Scotian Shelf is a region of globally extreme positive trends in sea surface temperature (SST). Here, a 33-year (1982–2014) time series of daily satellite SST data was used to quantify and map spatial patterns in SST trends and phenology over this shelf. Strongest trends are over the Scotian Shelf ({\textgreater}0.6°C decade–1) and Gulf of Maine ({\textgreater}0.4°C decade–1) with weaker trends over the inner Mid-Atlantic Bight ({\textasciitilde}0.3°C decade–1). Winter (January–April) trends are relatively weak, and even negative in some areas; early summer (May–June) trends are positive everywhere, and later summer (July–September) trends are strongest ({\textasciitilde}1.0°C decade–1). These seasonal differences shift the phenology of many metrics of the SST cycle. The yearday on which specific temperature thresholds (8° and 12°C) are reached in spring trends earlier, most strongly over the Scotian Shelf and Gulf of Maine ({\textasciitilde} –0.5 days year–1). Three metrics defining the warmest summer period show significant trends towards earlier summer starts, later summer ends and longer summer duration over the entire study region. Trends in start and end dates are strongest ({\textasciitilde}1 day year–1) over the Gulf of Maine and Scotian Shelf. Trends in increased summer duration are {\textgreater}2.0 days year–1 in parts of the Gulf of Maine. Regression analyses show that phenology trends have regionally varying links to the North Atlantic Oscillation, to local spring and summer atmospheric pressure and air temperature and to Gulf Stream position. For effective monitoring and management of dynamically heterogeneous shelf regions, the results highlight the need to quantify spatial and seasonal differences in SST trends as well as trends in SST phenology, each of which likely has implications for the ecological functioning of the shelf}, - journal = {Elementa: Science of the Anthropocene}, - author = {Thomas, A. C. and Pershing, A. J. and Friedland, K. D. and Nye, J. A. and Mills, K. E. and Alexander, M. A. and Record, N. R. and Weatherbee, R. A. and Henderson, M. E.}, - year = {2017}, - pages = {48--65}, - file = {Thomas_etal-Elem_2017 Seasonal trends and phen:C\:\\Users\\andrew.beet\\Zotero\\storage\\RC8HLQVZ\\Thomas_etal-Elem_2017 Seasonal trends and phen.pdf:application/pdf}, -} - @article{pdf, title = {Trends and change points in surface and bottom thermal environments of the {US} {Northeast} {Continental} {Shelf} {Ecosystem}}, volume = {n/a}, @@ -3378,18 +3348,36 @@ @article{cohen_global_2018 file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\4YQTZII8\\Cohen et al. - 2018 - A global synthesis of animal phenological response.pdf:application/pdf}, } -@article{pdf, +@article{thomas_seasonal_2017, + title = {Seasonal trends and phenology shifts in sea surface temperature on the {North} {American} northeastern continental shelf}, + volume = {5}, + issn = {2325-1026}, + url = {https://doi.org/10.1525/elementa.240}, + doi = {10.1525/elementa.240}, + abstract = {The northeastern North American continental shelf from Cape Hatteras to the Scotian Shelf is a region of globally extreme positive trends in sea surface temperature (SST). Here, a 33-year (1982–2014) time series of daily satellite SST data was used to quantify and map spatial patterns in SST trends and phenology over this shelf. Strongest trends are over the Scotian Shelf (\>0.6°C decade–1) and Gulf of Maine (\>0.4°C decade–1) with weaker trends over the inner Mid-Atlantic Bight ({\textasciitilde}0.3°C decade–1). Winter (January–April) trends are relatively weak, and even negative in some areas; early summer (May–June) trends are positive everywhere, and later summer (July–September) trends are strongest ({\textasciitilde}1.0°C decade–1). These seasonal differences shift the phenology of many metrics of the SST cycle. The yearday on which specific temperature thresholds (8° and 12°C) are reached in spring trends earlier, most strongly over the Scotian Shelf and Gulf of Maine ({\textasciitilde} –0.5 days year–1). Three metrics defining the warmest summer period show significant trends towards earlier summer starts, later summer ends and longer summer duration over the entire study region. Trends in start and end dates are strongest ({\textasciitilde}1 day year–1) over the Gulf of Maine and Scotian Shelf. Trends in increased summer duration are \>2.0 days year–1 in parts of the Gulf of Maine. Regression analyses show that phenology trends have regionally varying links to the North Atlantic Oscillation, to local spring and summer atmospheric pressure and air temperature and to Gulf Stream position. For effective monitoring and management of dynamically heterogeneous shelf regions, the results highlight the need to quantify spatial and seasonal differences in SST trends as well as trends in SST phenology, each of which likely has implications for the ecological functioning of the shelf.}, + urldate = {2024-03-08}, + journal = {Elementa: Science of the Anthropocene}, + author = {Thomas, Andrew C. and Pershing, Andrew J. and Friedland, Kevin D. and Nye, Janet A. and Mills, Katherine E. and Alexander, Michael A. and Record, Nicholas R. and Weatherbee, Ryan and Henderson, M. Elisabeth}, + editor = {Deming, Jody W. and Drinkwater, Ken}, + month = aug, + year = {2017}, + pages = {48}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\SGJABFQW\\Thomas et al. - 2017 - Seasonal trends and phenology shifts in sea surfac.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\JRMK8DUH\\Seasonal-trends-and-phenology-shifts-in-sea.html:text/html}, +} + +@article{weiskopf_climate_2020, title = {Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the {United} {States}}, volume = {733}, - issn = {1879-1026 (Electronic) 0048-9697 (Linking)}, - doi = {https://doi.org/10.1016/j.scitotenv.2020.137782}, + issn = {0048-9697}, + url = {https://www.sciencedirect.com/science/article/pii/S0048969720312948}, + doi = {10.1016/j.scitotenv.2020.137782}, abstract = {Climate change is a pervasive and growing global threat to biodiversity and ecosystems. Here, we present the most up-to-date assessment of climate change impacts on biodiversity, ecosystems, and ecosystem services in the U.S. and implications for natural resource management. We draw from the 4th National Climate Assessment to summarize observed and projected changes to ecosystems and biodiversity, explore linkages to important ecosystem services, and discuss associated challenges and opportunities for natural resource management. We find that species are responding to climate change through changes in morphology and behavior, phenology, and geographic range shifts, and these changes are mediated by plastic and evolutionary responses. Responses by species and populations, combined with direct effects of climate change on ecosystems (including more extreme events), are resulting in widespread changes in productivity, species interactions, vulnerability to biological invasions, and other emergent properties. Collectively, these impacts alter the benefits and services that natural ecosystems can provide to society. Although not all impacts are negative, even positive changes can require costly societal adjustments. Natural resource managers need proactive, flexible adaptation strategies that consider historical and future outlooks to minimize costs over the long term. Many organizations are beginning to explore these approaches, but implementation is not yet prevalent or systematic across the nation.}, + urldate = {2024-03-08}, journal = {Science of The Total Environment}, author = {Weiskopf, Sarah R. and Rubenstein, Madeleine A. and Crozier, Lisa G. and Gaichas, Sarah and Griffis, Roger and Halofsky, Jessica E. and Hyde, Kimberly J. W. and Morelli, Toni Lyn and Morisette, Jeffrey T. and Muñoz, Roldan C. and Pershing, Andrew J. and Peterson, David L. and Poudel, Rajendra and Staudinger, Michelle D. and Sutton-Grier, Ariana E. and Thompson, Laura and Vose, James and Weltzin, Jake F. and Whyte, Kyle Powys}, - month = mar, + month = sep, year = {2020}, - keywords = {Ecosystems, Ecosystem services, Conservation of Natural Resources, Biodiversity, United States, *Climate Change, *Ecosystem, competing financial interests or personal relationships that could have appeared, Global change, Natural resource management, Natural Resources, to influence the work reported in this paper.}, + keywords = {Biodiversity, Ecosystem services, Ecosystems, Global change, Natural resource management}, pages = {137782}, - annote = {The following values have no corresponding Zotero field:edition: 2020/03/27accession-num: 32209235}, - file = {Weiskopfetal_etal-STE_2020 Climate change effe:C\:\\Users\\andrew.beet\\Zotero\\storage\\NYQPF8MN\\Weiskopfetal_etal-STE_2020 Climate change effe.pdf:application/pdf}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\DYF6TMSH\\S0048969720312948.html:text/html}, } From 5b093fc4a2d8cd671116273225aabc8ba25ad9ee Mon Sep 17 00:00:00 2001 From: Brandon Beltz - NOAA Affiliate <136381970+BBeltz1@users.noreply.github.com> Date: Mon, 11 Mar 2024 10:03:11 -0400 Subject: [PATCH 8/8] Update DESCRIPTION with correct ecodata version changed remote from ecodata@dev to ecodata@5.0.1 --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index ff66363d..bcfed7a5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,9 +11,9 @@ Depends: rmarkdown, tidyverse, DT, - ecodata@5.0.1 + ecodata Suggests: downlit Remotes: - NOAA-EDAB/ecodata@dev + NOAA-EDAB/ecodata@5.0.1