update catalog

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 ([email protected]); Debra Palka ([email protected])](mailto:Sam Chavez ([email protected]); Debra Palka ([email protected])){.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**
-<https://noaa-edab.github.io/tech-doc/HMS_species_distribution.html>
-
+# 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 ([email protected]); Debra Palka ([email protected])](mailto:Sam Chavez ([email protected]); Debra Palka ([email protected])){.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**
+<https://noaa-edab.github.io/tech-doc/HMS_species_distribution.html>
+

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 ([email protected]), David J. Wilcox ([email protected])](mailto:Christoper J. Patrick ([email protected]), David J. Wilcox ([email protected])){.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**
-<https://noaa-edab.github.io/tech-doc/SAV.html>
-
+# 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 ([email protected]), David J. Wilcox ([email protected])](mailto:Christoper J. Patrick ([email protected]), David J. Wilcox ([email protected])){.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**
+<https://noaa-edab.github.io/tech-doc/SAV.html>
+