diff --git a/.github/workflows/bookdown.yaml b/.github/workflows/bookdown.yaml index 56723e67..b3e959c1 100644 --- a/.github/workflows/bookdown.yaml +++ b/.github/workflows/bookdown.yaml @@ -4,6 +4,7 @@ on: push: branches: - master + - operation_phoenix pull_request: branches: - master diff --git a/.gitignore b/.gitignore index b9822f7d..ba6d3be3 100644 --- a/.gitignore +++ b/.gitignore @@ -5,4 +5,5 @@ # local files/folders _book -_bookdown_files \ No newline at end of file +_bookdown_files +docs \ No newline at end of file diff --git a/DESCRIPTION b/DESCRIPTION index 44323cf9..38eb8d2a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -13,5 +13,5 @@ Depends: Suggests: downlit Remotes: - NOAA-EDAB/ecodata + NOAA-EDAB/ecodata@dev diff --git a/R/find_dataset_submissions.R b/R/find_dataset_submissions.R index 69d73306..e80a67f8 100644 --- a/R/find_dataset_submissions.R +++ b/R/find_dataset_submissions.R @@ -4,6 +4,8 @@ #' #' @return character vector +source(here::here("R/parse_issue.r")) + find_dataset_submissions <- function(){ issueData <- readRDS(here::here("data-raw/submissionIssueNumbers.rds")) diff --git a/R/find_rmds_not_in_book.r b/R/find_rmds_not_in_book.r index c4802f08..eabb24b8 100644 --- a/R/find_rmds_not_in_book.r +++ b/R/find_rmds_not_in_book.r @@ -23,7 +23,7 @@ find_rmds_not_in_book <- function(){ # find the rmdfiles not referenced in the yml missingrmds <- setdiff(rmdfiles,rmdrefs) message("The following rmds are not found in the _bookdown.yml") - print(missingrmds) + #print(missingrmds) return(missingrmds) diff --git a/R/make_rmd.R b/R/make_rmd.R index 3b12d3ed..b8471472 100644 --- a/R/make_rmd.R +++ b/R/make_rmd.R @@ -21,6 +21,7 @@ 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$dataname),append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space cat(paste0("**Description**: ",listobject$description),append=T,fill=T,file=con) @@ -59,7 +60,8 @@ make_rmd <- function(listobject){ at <- attributes(eval(parse(text=paste0("ecodata::plot_",listobject$indicatorname)))) functionArgs <- names(formals(eval(parse(text=paste0("ecodata::plot_",listobject$indicatorname))))) - if (length(functionArgs) == 2) { + + if (length(functionArgs) == 2 | ((length(functionArgs) == 3) & (any(c("year","scale") %in% functionArgs)))) { # this is standard shadedRegion and report # check to see how many EPUs are listed in data object and/or they are "All" (shelfwide) indicatorData <- eval(parse(text=paste0("ecodata::",listobject$indicatorname))) @@ -69,7 +71,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,"MAB}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report='MidAtlantic')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space } else { @@ -81,7 +83,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,"MAB}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report='MidAtlantic')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space } @@ -92,7 +94,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,"NE}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report='NewEngland')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space } @@ -105,7 +107,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,"MAB}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report='MidAtlantic')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space @@ -115,7 +117,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,"NEGB}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report='NewEngland',EPU='GB')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space @@ -125,7 +127,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,"NEGOM}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report='NewEngland',EPU='GOM')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space @@ -143,7 +145,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,arep,avarws,"}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report= '",arep,"', varName= '",avar,"')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space } @@ -162,7 +164,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,arep,avarws,"}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report= '",arep,"', plottype= '",avar,"')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space @@ -195,7 +197,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,arep,avarws,aval,"}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report= '",arep,"', varName= '",avar,"' ,",newV,"= '",aval,"')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space @@ -216,7 +218,7 @@ make_rmd <- function(listobject){ cat(paste0("```{r plot_",listobject$indicatorname,arep,avarws,atype,"}"),append=T,fill=T,file=con) cat("# Plot indicator",append=T,fill=T,file=con) cat(paste0("ggplotObject <- ecodata::plot_",listobject$indicatorname,"(report= '",arep,"', varName= '",avar,"', plottype = '",atype,"')"),append=T,fill=T,file=con) - cat("print(ggplotObject)",append=T,fill=T,file=con) + cat("ggplotObject",append=T,fill=T,file=con) cat("```",append=T,fill=T,file=con) cat("",append=T,fill=T,file=con) # add space @@ -326,15 +328,19 @@ make_rmd <- function(listobject){ cat("",append=T,fill=T,file=con) # add space - cat("**tech-doc link**",append=T,fill=T,file=con) - - cat(paste0(""),append=T,fill=T,file=con) - cat("",append=T,fill=T,file=con) # add space - - - cat("## References",append=T,fill=T,file=con) - cat("",append=T,fill=T,file=con) # add space + # write catalog link if ecodata data is present + if (exists(paste0(listobject$indicatorname))) { + cat("**tech-doc link**",append=T,fill=T,file=con) + + cat(paste0(""),append=T,fill=T,file=con) + cat("",append=T,fill=T,file=con) # add space + + } else { + # No link since this is a synthesis type of page + } + # References are generated automatically + #close the connection close(con) diff --git a/R/parse_issue.R b/R/parse_issue.R index e5d126ed..eb99e86c 100644 --- a/R/parse_issue.R +++ b/R/parse_issue.R @@ -18,22 +18,24 @@ parse_issue <- function(issueData,issueNum){ body <- issueData$issues[id,]$body } - headings <- unlist(stringr::str_extract_all(body,"###\\s+[a-zA-Z (.)\"\\?,]+")) + headings <- unlist(stringr::str_extract_all(body,"\n###\\s+[a-zA-Z (.)\"\\?,]+")) + for (ahead in headings) { modifiedHead <- gsub("\\","",ahead,fixed=T) modifiedHead <- gsub("(","\\(",modifiedHead,fixed=T) modifiedHead <- gsub(")","\\)",modifiedHead,fixed=T) modifiedHead <- gsub("?","\\?",modifiedHead,fixed=T) byhead <- unlist(strsplit(body,modifiedHead))[2] - res <- unlist(strsplit(byhead,"###"))[1] + res <- unlist(strsplit(byhead,"\n### "))[1] # remove beginning and trailing \n (line feed) and \r (carriage return) modifiedRes <- trimws(res) # modifiedRes <- sub("\\n+$","",modifiedRes) # modifiedRes <- sub("^\\r\\n+","",modifiedRes) # modifiedRes <- sub("\\r\\n+$","",modifiedRes) + ahead <- trimws(ahead) objectList[ahead] <- modifiedRes } - + return(objectList) } \ No newline at end of file diff --git a/README.md b/README.md index 05db9562..4efb3cc1 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,8 @@ ---- -editor_options: - markdown: - wrap: 72 ---- # [Synthetic Indicator Catalog](https://noaa-edab.github.io/catalog/) -![gitleaks](https://github.com/NOAA-EDAB/catalog/workflows/gitleaks/badge.svg) +[![build-book](https://github.com/NOAA-EDAB/catalog/actions/workflows/bookdown.yaml/badge.svg)](https://github.com/NOAA-EDAB/catalog/actions/workflows/bookdown.yaml) +[![gitleaks](https://github.com/NOAA-EDAB/catalog/actions/workflows/secretScan.yml/badge.svg)](https://github.com/NOAA-EDAB/catalog/actions/workflows/secretScan.yml) Catalog of indicators used in the State of the Ecosystem reports and other ecosystem assessment products. diff --git a/_bookdown.yml b/_bookdown.yml index f20a9fb0..ff49595e 100644 --- a/_bookdown.yml +++ b/_bookdown.yml @@ -1,51 +1,59 @@ book_filename: "catalog" rmd_files: - "index.Rmd" + #- "chapters/introduction.rmd" #################### LOWER TROPHIC ################## - "chapters/sectionHeaders/ltl.rmd" - - "chapters/ches_bay_wq.rmd" - - "chapters/chl_pp.rmd" + - "chapters/ches_bay_wq.rmd" + - "chapters/chl_pp.rmd" + - "chapters/phyto_size.rmd" #- "chapters/annual_chl_pp.rmd" - - "chapters/habs.rmd" - - "chapters/SAV.rmd" - - "chapters/calanus_variation.rmd" - - "chapters/wbts_mesozooplankton.rmd" - - -#################### MEGAFAUNA ################## - - "chapters/sectionHeaders/megafauna.rmd" - - "chapters/aggregate_biomass.rmd" - - "chapters/mab_inshore_survey.rmd" - - "chapters/condition.rmd" - - "chapters/energy_density.rmd" + - "chapters/habs.rmd" + - "chapters/SAV.rmd" + - "chapters/calanus_variation.rmd" + - "chapters/wbts_mesozooplankton.rmd" + - "chapters/zoo_abundance_anom.rmd" + - "chapters/zoo_diversity.rmd" +# +# +# #################### MEGAFAUNA ################## + - "chapters/sectionHeaders/megafauna.rmd" + - "chapters/aggregate_biomass.rmd" + - "chapters/mab_inshore_survey.rmd" + - "chapters/mass_inshore_survey.rmd" + - "chapters/ne_inshore_survey.rmd" + - "chapters/osw_survey_impact.rmd" + - "chapters/condition.rmd" + - "chapters/habitat_diversity.rmd" + - "chapters/species_dist.rmd" + - "chapters/energy_density.rmd" - "chapters/spawn_timing.rmd" - - "chapters/exp_n.rmd" - - "chapters/forage_index.rmd" - - "chapters/harborporpoise.rmd" - - "chapters/hms_cpue.rmd" - - "chapters/hms_landings.rmd" - - "chapters/hms_stock_status.rmd" - - "chapters/HMS_species_distribution.rmd" - - "chapters/narw.rmd" - - "chapters/productivity_anomaly.rmd" - - "chapters/seal_pups.rmd" - - "chapters/gom_salmon.rmd" - - "chapters/grayseal.rmd" - - "chapters/seabird_ne.rmd" - - "chapters/species_groupings.rmd" - - -#################### OCEANOGRAPHIC ################## + - "chapters/exp_n.rmd" + - "chapters/forage_index.rmd" + - "chapters/harborporpoise.rmd" + - "chapters/hms_cpue.rmd" + - "chapters/hms_landings.rmd" + - "chapters/hms_stock_status.rmd" + - "chapters/HMS_species_distribution.rmd" + - "chapters/narw.rmd" + - "chapters/productivity_anomaly.rmd" + - "chapters/seal_pups.rmd" + - "chapters/gom_salmon.rmd" + - "chapters/grayseal.rmd" + - "chapters/seabird_ne.rmd" + - "chapters/species_groupings.rmd" +# +# +# #################### OCEANOGRAPHIC ################## - "chapters/sectionHeaders/oceanographic.rmd" - - "chapters/bottom_temp.rmd" + - "chapters/bottom_temp.rmd" - "chapters/bottom_temp_comp.rmd" - #- "chapters/bottom_temp_glorys.rmd" - - "chapters/bottom_temp_seasonal_gridded.rmd" + - "chapters/bottom_temp_seasonal_gridded.rmd" - "chapters/seasonal_sst_anomaly_gridded.rmd" - "chapters/seasonal_oisst_anom.rmd" - - "chapters/trans_dates.rmd" + - "chapters/trans_dates.rmd" - "chapters/long_term_sst.rmd" - "chapters/cold_pool.rmd" - "chapters/gsi.rmd" @@ -54,18 +62,20 @@ rmd_files: - "chapters/wcr.rmd" - "chapters/thermal_habitat_area.rmd" - "chapters/thermal_habitat_persistence.rmd" - - "chapters/ch_bay_sal.rmd" - - "chapters/ch_bay_temp.rmd" - - "chapters/ches_bay_sst.rmd" - - "chapters/ches_bay_synthesis.rmd" - - "chapters/heatwave.rmd" - - "chapters/heatwave_year.rmd" + - "chapters/ch_bay_sal.rmd" + - "chapters/ch_bay_temp.rmd" + - "chapters/ches_bay_sst.rmd" + - "chapters/ches_bay_synthesis.rmd" + - "chapters/heatwave.rmd" + - "chapters/heatwave_year.rmd" + - "chapters/timing_shifts.rmd" + - "chapters/observation_synthesis.rmd" - "chapters/ocean_acidification.rmd" - - -######################## ECONOMIC ################## +# +# +# ######################## ECONOMIC ################## - "chapters/sectionHeaders/economic.rmd" - - "chapters/comdat.rmd" + - "chapters/comdat.rmd" - "chapters/commercial_div.rmd" - "chapters/ppr.rmd" - "chapters/recdat.rmd" diff --git a/_output.yml b/_output.yml index 3271d3fd..7aaf9f21 100644 --- a/_output.yml +++ b/_output.yml @@ -1,4 +1,5 @@ bookdown::bs4_book: + split_bib: true css: style.css theme: primary: "#4D6F8D" diff --git a/bibliography/StateOftheEcosystem.bib b/bibliography/StateOftheEcosystem.bib new file mode 100644 index 00000000..e37fdb67 --- /dev/null +++ b/bibliography/StateOftheEcosystem.bib @@ -0,0 +1,3277 @@ + +@article{gangopadhyay_observed_2019, + title = {An {Observed} {Regime} {Shift} in the {Formation} of {Warm} {Core} {Rings} from the {Gulf} {Stream}}, + volume = {9}, + copyright = {2019 The Author(s)}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-019-48661-9}, + doi = {10.1038/s41598-019-48661-9}, + abstract = {We present observational evidence that a significant regime change occurred around the year 2000 in the formation of Warm Core Rings (WCRs) from the Gulf Stream (GS) between 75° and 55°W. The dataset for this study is a set of synoptic oceanographic charts available over the thirty-eight-year period of 1980–2017. The upward regime change shows an increase to 33 WCRs per year during 2000–2017 from an average of 18 WCRs during 1980 to 1999. A seasonal analysis confirms May-June-July as the peak time for WCR births in agreement with earlier studies. The westernmost region (75°-70°W) is least ring-productive, while the region from 65°W to 60°W is most productive. This regime shift around 2000 is detected in WCR formation for all of the four 5-degree wide sub-regions and the whole region (75°-55°W). This might be related to a reduction of the deformation radius for ring formation, allowing unstable meanders to shed more frequent rings in recent years. A number of possible factors resulting in such a regime shift related to the possible changes in reduced gravity, instability, transport of the GS, large-scale changes in the wind system and atmospheric fluxes are outlined, which suggest new research directions. The increase in WCRs has likely had an impact on the marine ecosystem since 2000, a topic worthy for future studies.}, + language = {en}, + number = {1}, + urldate = {2020-02-10}, + journal = {Scientific Reports}, + author = {Gangopadhyay, Avijit and Gawarkiewicz, Glen and Silva, E. Nishchitha S. and Monim, M. and Clark, Jenifer}, + month = aug, + year = {2019}, + pages = {1--9}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\YN8GHAWK\\Gangopadhyay et al. - 2019 - An Observed Regime Shift in the Formation of Warm .pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\JHFINVB3\\s41598-019-48661-9.html:text/html}, +} + +@article{mills_fisheries_2013, + title = {Fisheries {Management} in a {Changing} {Climate}: {Lessons} {From} the 2012 {Ocean} {Heat} {Wave} in the {Northwest} {Atlantic}}, + volume = {26}, + issn = {10428275}, + shorttitle = {Fisheries {Management} in a {Changing} {Climate}}, + url = {https://tos.org/oceanography/article/fisheries-management-in-a-changing-climate-lessonsfrom-the-2012-ocean-heat-}, + doi = {10.5670/oceanog.2013.27}, + number = {2}, + urldate = {2020-02-10}, + journal = {Oceanography}, + author = {Mills, Katherine and Pershing, Andrew and Brown, Curtis and Chen, Yong and Chiang, Fu-Sung and Holland, Daniel and Lehuta, Sigrid and Nye, Janet and Sun, Jenny and Thomas, Andrew and Wahle, Richard}, + year = {2013}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\DY4W3JYE\\Mills et al. - 2013 - Fisheries Management in a Changing Climate Lesson.pdf:application/pdf}, +} + +@article{lawson_important_1998, + title = {Important prey species of marine vertebrate predators in the northwest {Atlantic}: proximate composition and energy density}, + volume = {164}, + issn = {0171-8630}, + shorttitle = {Important prey species of marine vertebrate predators in the northwest {Atlantic}}, + url = {https://www.jstor.org/stable/24825521}, + abstract = {Prey energy density values are crucial inputs to bioenergetic consumption models. Vertebrate predators in the northwest Atlantic consume a variety of prey species, but the proximate composition (PC; proportions of lipid, protein, ash and water) and energy density (ED; kJ g–1) of prey, and their variability, are known poorly. In this study, key prey species from Newfoundland and Labrador were studied: Atlantic cod Gadus morhua, American plaice Hippoglossoides platessoides, sand lance Ammodytes dubius, Arctic cod Boreogadus saida, northern shrimp Pandalus borealis, redfish Sebastes spp., Greenland halibut Reinhardtius hippoglossoides, squid Illex illecebrosus and Gonatus fabricii, capelin Mallotus villosus, Atlantic herring Clupea harengus and daubed shanny Lumpenus maculatus. PC and ED varied greatly among species and were influenced by size, season, geography and year. Herring, capelin and G. fabricii had the highest ED, whereas Atlantic cod, plaice, sand lance and shrimp had the lowest. Halibut and I. illecebrosus increased in ED with size. EDs of capelin and redfish varied seasonally; that of plaice and sand lance did not. Herring and halibut had higher ED in the early 1990s than in recent years. Such variation in prey ED has important implications for digestive efficiency, foraging energetics, and dietary preferences of vertebrate predators.}, + urldate = {2020-02-13}, + journal = {Marine Ecology Progress Series}, + author = {Lawson, John W. and Magalhães, Alexandra M. and Miller, Edward H.}, + year = {1998}, + pages = {13--20}, +} + +@article{hobday_hierarchical_2016, + title = {A hierarchical approach to defining marine heatwaves}, + volume = {141}, + issn = {0079-6611}, + url = {http://www.sciencedirect.com/science/article/pii/S0079661116000057}, + doi = {10.1016/j.pocean.2015.12.014}, + abstract = {Marine heatwaves (MHWs) have been observed around the world and are expected to increase in intensity and frequency under anthropogenic climate change. A variety of impacts have been associated with these anomalous events, including shifts in species ranges, local extinctions and economic impacts on seafood industries through declines in important fishery species and impacts on aquaculture. Extreme temperatures are increasingly seen as important influences on biological systems, yet a consistent definition of MHWs does not exist. A clear definition will facilitate retrospective comparisons between MHWs, enabling the synthesis and a mechanistic understanding of the role of MHWs in marine ecosystems. Building on research into atmospheric heatwaves, we propose both a general and specific definition for MHWs, based on a hierarchy of metrics that allow for different data sets to be used in identifying MHWs. We generally define a MHW as a prolonged discrete anomalously warm water event that can be described by its duration, intensity, rate of evolution, and spatial extent. Specifically, we consider an anomalously warm event to be a MHW if it lasts for five or more days, with temperatures warmer than the 90th percentile based on a 30-year historical baseline period. This structure provides flexibility with regard to the description of MHWs and transparency in communicating MHWs to a general audience. The use of these metrics is illustrated for three 21st century MHWs; the northern Mediterranean event in 2003, the Western Australia ‘Ningaloo Niño’ in 2011, and the northwest Atlantic event in 2012. We recommend a specific quantitative definition for MHWs to facilitate global comparisons and to advance our understanding of these phenomena.}, + language = {en}, + urldate = {2020-02-10}, + journal = {Progress in Oceanography}, + author = {Hobday, Alistair J. and Alexander, Lisa V. and Perkins, Sarah E. and Smale, Dan A. and Straub, Sandra C. and Oliver, Eric C. J. and Benthuysen, Jessica A. and Burrows, Michael T. and Donat, Markus G. and Feng, Ming and Holbrook, Neil J. and Moore, Pippa J. and Scannell, Hillary A. and Sen Gupta, Alex and Wernberg, Thomas}, + month = feb, + year = {2016}, + pages = {227--238}, + file = {ScienceDirect Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\EAJ6P3XR\\Hobday et al. - 2016 - A hierarchical approach to defining marine heatwav.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\B4WY7WYM\\S0079661116000057.html:text/html}, +} + +@article{steimle_energy_1985, + title = {Energy {Equivalents} of {Marine} {Organisms} from the {Continental} {Shelf} of the {Temperate} {Northwest} {Atlantic}}, + volume = {6}, + doi = {10.2960/J.v6.a11}, + abstract = {The availability of appropriate energy equivalents is one of the needs in species or community bioenergetic models. Energy­ equivalent data for 151 common invertebrate and vertebrate species on the continental shelf of the temperate Northwest Atlantic from Nova Scotia to North Carolina are presented and compared with the more limited results of other studies. Energy equivalents were found to be variable among major taxonomic ortrophic groups and general trends were not evident. A review of equivalents from some recent ecosystem models for the Northwest Atlnatic indicates that the models can be substantially improved.}, + journal = {Journal of Northwest Atlantic Fishery Science}, + author = {Steimle, Frank and Terranova, Russell}, + month = dec, + year = {1985}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\C3HQBRTS\\Jr and Terranova - 1985 - Energy Equivalents of Marine Organisms from the Co.pdf:application/pdf}, +} + +@article{tam_comparing_2017, + title = {Comparing {Apples} to {Oranges}: {Common} {Trends} and {Thresholds} in {Anthropogenic} and {Environmental} {Pressures} across {Multiple} {Marine} {Ecosystems}}, + volume = {4}, + issn = {2296-7745}, + shorttitle = {Comparing {Apples} to {Oranges}}, + url = {https://www.frontiersin.org/articles/10.3389/fmars.2017.00282/full}, + doi = {10.3389/fmars.2017.00282}, + abstract = {Ecosystem-based management (EBM) in marine ecosystems considers impacts caused by complex interactions between environmental and anthropogenic pressures (i.e. oceanographic, climatic, socio-economic) and marine communities. EBM depends, in part, on ecological indicators that facilitate understanding of inherent properties and the dynamics of pressures within marine communities. Thresholds of ecological indicators delineate ecosystem status because they represent points at which a small increase in one or many pressure variables results in an abrupt change of ecosystem responses. The difficulty in developing appropriate thresholds and reference points for EBM lies in the multidimensionality of both the ecosystem responses and the pressures impacting the ecosystem. Here, we develop thresholds using gradient forest for a suite of ecological indicators in response to multiple pressures that convey ecosystem status for large marine ecosystems from the US Pacific, Atlantic, sub-Arctic, and Gulf of Mexico. We detected these thresholds of ecological indicators based on multiple pressures. Commercial fisheries landings above approximately 2-4.5 t km-2 and fisheries exploitation above 20-40\% of the total estimated biomass (of invertebrates and fish) of the ecosystem resulted in a change in the direction of ecosystem structure and functioning in the ecosystems examined. Our comparative findings reveal common trends in ecosystem thresholds along pressure gradients and also indicate that thresholds of ecological indicators are useful tools for comparing the impacts of environmental and anthropogenic pressures across multiple ecosystems. These critical points can be used to inform the development of EBM decision criteria.}, + language = {English}, + urldate = {2020-03-09}, + journal = {Frontiers in Marine Science}, + author = {Tam, Jamie C. and Link, Jason S. and Large, Scott I. and Andrews, Kelly and Friedland, Kevin D. and Gove, Jamison and Hazen, Elliott and Holsman, Kirstin and Karnauskas, Mandy and Samhouri, Jameal F. and Shuford, Rebecca and Tomilieri, Nick and Zador, Stephani}, + year = {2017}, + note = {Publisher: Frontiers}, + keywords = {anthropogenic and environmental pressures, comparative analysis, Ecological Indicators, ecosystem-based management, Gradient forest, thresholds-based reference points}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\RTFYBX68\\Tam et al. - 2017 - Comparing Apples to Oranges Common Trends and Thr.pdf:application/pdf}, +} + +@article{thorson_guidance_2019, + title = {Guidance for decisions using the {Vector} {Autoregressive} {Spatio}-{Temporal} ({VAST}) package in stock, ecosystem, habitat and climate assessments}, + volume = {210}, + issn = {0165-7836}, + url = {http://www.sciencedirect.com/science/article/pii/S0165783618302820}, + doi = {10.1016/j.fishres.2018.10.013}, + abstract = {Fisheries scientists provide stock, ecosystem, habitat, and climate assessments to support interdisplinary fisheries management in the US and worldwide. These assessment activities have evolved different models, using different review standards, and are communicated using different vocabulary. Recent research shows that spatio-temporal models can estimate population density for multiple locations, times, and species, and that this is a “common currency” for addressing core goals in stock, ecosystem, habitat, and climate assessments. I therefore review the history and “design principles” for one spatio-temporal modelling package, the Vector Autoregressive Spatio-Temporal (VAST) package. I then provide guidance on fifteen major decisions that must be made by users of VAST, including: whether to use a univariate or multivariate model; when to include spatial and/or spatio-temporal variation; how many factors to use within a multivariate model; whether to include density or catchability covariates; and when to include a temporal correlation on model components. I finally demonstrate these decisions using three case studies. The first develops indices of abundance, distribution shift, and range expansion for arrowtooth flounder (Atheresthes stomias) in the Eastern Bering Sea, showing the range expansion for this species. The second involves “species ordination” of eight groundfishes in the Gulf of Alaska bottom trawl survey, which highlights the different spatial distribution of flathead sole (Hippoglossoides elassodon) relative to sablefish (Anoplopoma fimbria) and dover sole (Microstomus pacificus). The third involves a short-term forecast of the proportion of coastwide abundance for five groundfishes within three spatial strata in the US West Coast groundfish bottom trawl survey, and predicts large interannual variability (and high uncertainty) in the distribution of lingcod (Ophiodon elongatus). I conclude by recommending further research exploring the benefits and limitations of a “common currency” approach to stock, ecosystem, habitat, and climate assessments, and discuss extending this approach to optimal survey design and economic assessments.}, + language = {en}, + urldate = {2020-02-24}, + journal = {Fisheries Research}, + author = {Thorson, James T.}, + month = feb, + year = {2019}, + keywords = {Climate vulnerability analysis, Distribution shift, Habitat assessment, Index standardization, Integrated ecosystem assessment, Spatio-temporal model, Stock assessment, VAST}, + pages = {143--161}, + file = {ScienceDirect Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\W7IBXWVK\\Thorson - 2019 - Guidance for decisions using the Vector Autoregres.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\V3KPTUYK\\S0165783618302820.html:text/html}, +} + +@article{hardison_simulation_2019, + title = {A simulation study of trend detection methods for integrated ecosystem assessment}, + volume = {76}, + issn = {1054-3139}, + url = {https://academic.oup.com/icesjms/article/76/7/2060/5512306}, + doi = {10.1093/icesjms/fsz097}, + abstract = {Abstract. The identification of trends in ecosystem indicators has become a core component of ecosystem approaches to resource management, although oftentimes}, + language = {en}, + number = {7}, + urldate = {2020-02-26}, + journal = {ICES Journal of Marine Science}, + author = {Hardison, Sean and Perretti, Charles T. and DePiper, Geret S. and Beet, Andrew}, + month = dec, + year = {2019}, + pages = {2060--2069}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\TCF5T9KA\\Hardison et al. - 2019 - A simulation study of trend detection methods for .pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\77FEQEX7\\5512306.html:text/html}, +} + +@article{ekstrom_vulnerability_2015, + title = {Vulnerability and adaptation of {US} shellfisheries to ocean acidification}, + volume = {5}, + copyright = {2015 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.}, + issn = {1758-6798}, + url = {https://www.nature.com/articles/nclimate2508}, + doi = {10.1038/nclimate2508}, + abstract = {Understanding the vulnerability of different US coastal communities to the likely harmful effects of ocean acidification on shellfisheries should inform the development of effective adaptation measures.}, + language = {en}, + number = {3}, + urldate = {2020-03-06}, + journal = {Nature Climate Change}, + author = {Ekstrom, Julia A. and Suatoni, Lisa and Cooley, Sarah R. and Pendleton, Linwood H. and Waldbusser, George G. and Cinner, Josh E. and Ritter, Jessica and Langdon, Chris and van Hooidonk, Ruben and Gledhill, Dwight and Wellman, Katharine and Beck, Michael W. and Brander, Luke M. and Rittschof, Dan and Doherty, Carolyn and Edwards, Peter E. T. and Portela, Rosimeiry}, + month = mar, + year = {2015}, + note = {Number: 3 +Publisher: Nature Publishing Group}, + pages = {207--214}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\XJCI7DCX\\nclimate2508.html:text/html}, +} + +@article{large_defining_2013, + title = {Defining trends and thresholds in responses of ecological indicators to fishing and environmental pressures}, + volume = {70}, + issn = {1054-3139, 1095-9289}, + url = {http://icesjms.oxfordjournals.org/content/70/4/755}, + doi = {10.1093/icesjms/fst067}, + abstract = {Large, S. I., Fay, G., Friedland, K. D., and Link, J. S. 2013. Defining trends and thresholds in responses of ecological indicators to fishing and environmental pressures. – ICES Journal of Marine Science, 70: 755–767. Both fishing and environmental forces can influence the structure of marine ecosystems. To further understand marine ecosystems and to implement ecosystem-based fisheries management (EBFM), an evaluation of ecosystem indicators is warranted. In this context, it is particularly important to identify thresholds where fishing and environmental pressures significantly influence ecological indicators. We empirically determined numerical values of environmental forces and fishing pressure that significantly altered the response of ecological indicators for the Northeast Shelf Large Marine Ecosystem. Generalized additive models predicted a non-linear relationship for each pressure–response pairing. With this smoother, 95\% confidence intervals (CI) for estimated first and second derivatives for each relationship were determined via parametric bootstrap. A significant trend or threshold was noted when the CI for the first or second derivative was greater or less than zero, delineating the level at which pressure variables influence the rate and direction of ecosystem indicator responses. We identify reference levels where environmental forces and fishing pressure result in ecosystem change by collectively examining the responses of multiple ecological indicators. Individual indicators showed unique responses to pressures, however, similar values for the pressures were associated with significant changes for multiple indicators. These reference levels establish a foundation for implementation of EBFM.}, + language = {en}, + number = {4}, + urldate = {2016-10-14}, + journal = {ICES Journal of Marine Science: Journal du Conseil}, + author = {Large, Scott I. and Fay, Gavin and Friedland, Kevin D. and Link, Jason S.}, + month = jul, + year = {2013}, + keywords = {decision criteria, Ecosystem-based management, generalized additive model, overfishing, reference points}, + pages = {755--767}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\8JFINTNX\\Large et al. - 2013 - Defining trends and thresholds in responses of eco.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\HHWWVVBA\\Large et al. - 2013 - Defining trends and thresholds in responses of eco.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\XDCNX8QE\\755.html:text/html;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\IHJNGJTU\\727721.html:text/html}, +} + +@article{miller_state-space_2016, + title = {A state-space approach to incorporating environmental effects on recruitment in an age-structured assessment model with an application to southern {New} {England} yellowtail flounder}, + volume = {73}, + issn = {0706-652X}, + url = {https://www.nrcresearchpress.com/doi/10.1139/cjfas-2015-0339}, + doi = {10.1139/cjfas-2015-0339}, + abstract = {The state-space model framework provides a natural, probabilistic approach to stock assessment by modeling the stochastic nature of population survival and recruitment separately from sampling uncertainty inherent in observations on the population. We propose a state-space assessment model that is expanded to simultaneously treat environmental covariates as stochastic processes and estimate their effects on recruitment. We apply the model to southern New England yellowtail flounder (Limanda ferruginea) using data from the most recent benchmark assessment to evaluate evidence for effects of the mid-Atlantic cold pool and spawning stock biomass on recruitment. Based on Akaike’s information criterion, both the cold pool and spawning stock biomass were important predictors of recruitment and led to annual variation in estimated biomass reference points and associated yield. We also demonstrate the effect of the stochasticity of the mid-Atlantic cold pool on short-term forecasts of the stock size, biomass reference point, and stock status.}, + number = {8}, + urldate = {2020-02-10}, + journal = {Canadian Journal of Fisheries and Aquatic Sciences}, + author = {Miller, Timothy J. and Hare, Jonathan A. and Alade, Larry A.}, + month = feb, + year = {2016}, + pages = {1261--1270}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\22GJMCCS\\Miller et al. - 2016 - A state-space approach to incorporating environmen.pdf:application/pdf;NRC Research Press Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\GXGW3JES\\cjfas-2015-0339.html:text/html;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\LXNLXZIV\\cjfas-2015-0339.html:text/html}, +} + +@article{chen_seasonal_2018, + title = {Seasonal {Variability} of the {Cold} {Pool} {Over} the {Mid}-{Atlantic} {Bight} {Continental} {Shelf}}, + volume = {123}, + issn = {21699275}, + url = {http://doi.wiley.com/10.1029/2018JC014148}, + doi = {10.1029/2018JC014148}, + abstract = {The Mid-Atlantic Bight (MAB) Cold Pool is a distinctive cold (lower than 10 ∘C) and relatively fresh (lower than 34 practical salinity unit) water mass. It is located over the middle and outer shelf of the MAB, below the seasonal thermocline, and is attached to the bottom. Following this definition, we put forward a method that includes three criteria to capture and quantify Cold Pool characteristics, based on a 50-year (1958–2007) high-resolution regional ocean model hindcast. The seasonal climatology of the Cold Pool and its properties are investigated during its onset-peak-decline cycle. Three stages of the Cold Pool event are defined according to its evolution and characteristics. The Cold Pool cores travel along the 60-m isobath starting south of the New England shelf to the Hudson Shelf Valley at a speed of 2–3 cm/s. Furthermore, the northern extent of the Cold Pool retreats about 2.6 times faster than the southern extent during the summer progression. The heat balance of near-bottom waters over the MAB and Georges Bank is computed and it is found that the heat advection, rather than vertical diffusion, dominates the resulting spatial patterns of warming. Possible origins of the Cold Pool are investigated by performing a lead-lag correlation analysis. Results suggest that the Cold Pool originates not only from local remnants of winter water near the Nantucket Shoals, but has an upstream source traveling in the spring time from the southwestern flank of the Georges Bank along the 80-m isobath.}, + language = {en}, + number = {11}, + urldate = {2020-05-18}, + journal = {Journal of Geophysical Research: Oceans}, + author = {Chen, Zhuomin and Curchitser, Enrique and Chant, Robert and Kang, Dujuan}, + month = nov, + year = {2018}, + pages = {8203--8226}, + file = {Chen et al. - 2018 - Seasonal Variability of the Cold Pool Over the Mid.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\YT6J2CZS\\Chen et al. - 2018 - Seasonal Variability of the Cold Pool Over the Mid.pdf:application/pdf}, +} + +@article{pdf, + title = {Optimization and assessment of phytoplankton size class algorithms for ocean color data on the {Northeast} {U}.{S}. continental shelf}, + volume = {267}, + issn = {0034-4257}, + doi = {https://doi.org/10.1016/j.rse.2021.112729}, + abstract = {The size structure of phytoplankton communities influences important ecological and biogeochemical processes, including the transfer of energy through marine food webs. A variety of algorithms have been developed to estimate phytoplankton size classes (PSCs) from satellite ocean color data. However, many of these algorithms were developed for application to the global ocean, and their performance in more productive, optically complex coastal and continental shelf regions warrants evaluation. In this study, several existing PSC models were applied in the Northeast U.S. continental shelf (NES) region and compared with in situ PSC estimates derived from a local HPLC pigment data set. The effect of regional re-parameterization and incorporation of sea surface temperature (SST) into existing abundance-based model frameworks was investigated and model performance was assessed using an independent data set. Abundance-based model re-parameterization alone did not result in significant improvement in model performance compared with other models. However, the inclusion of SST led to a consistent reduction in model error for all size classes. Of two absorption-based algorithms tested, the best performing approach displayed similar performance metrics to the regional SST-dependent abundance-based model. The SST-dependent model and the absorption-based method were applied to monthly composites of the NES region for April and September 2019 and qualitatively compared. The results highlight the benefit of considering SST in abundance-based models and the applicability of absorption-based PSC methods in optically complex regions.}, + journal = {Remote Sensing of Environment}, + author = {Turner, Kyle J. and Mouw, Colleen B. and Hyde, Kimberly J. W. and Morse, Ryan and Ciochetto, Audrey B.}, + month = dec, + year = {2021}, + pages = {112729}, + file = {Turner_etal-RSE_2021 Optimization and assessme:C\:\\Users\\andrew.beet\\Zotero\\storage\\F6TA9NTB\\Turner_etal-RSE_2021 Optimization and assessme.pdf:application/pdf}, +} + +@article{pendleton_decadal-scale_2022, + title = {Decadal-scale phenology and seasonal climate drivers of migratory baleen whales in a rapidly warming marine ecosystem}, + volume = {28}, + copyright = {© 2022 The Authors. Global Change Biology published by John Wiley \& Sons Ltd.}, + issn = {1365-2486}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16225}, + doi = {10.1111/gcb.16225}, + abstract = {Species' response to rapid climate change can be measured through shifts in timing of recurring biological events, known as phenology. The Gulf of Maine is one of the most rapidly warming regions of the ocean, and thus an ideal system to study phenological and biological responses to climate change. A better understanding of climate-induced changes in phenology is needed to effectively and adaptively manage human-wildlife conflicts. Using data from a 20+ year marine mammal observation program, we tested the hypothesis that the phenology of large whale habitat use in Cape Cod Bay has changed and is related to regional-scale shifts in the thermal onset of spring. We used a multi-season occupancy model to measure phenological shifts and evaluate trends in the date of peak habitat use for North Atlantic right (Eubalaena glacialis), humpback (Megaptera novaeangliae), and fin (Balaenoptera physalus) whales. The date of peak habitat use shifted by +18.1 days (0.90 days/year) for right whales and +19.1 days (0.96 days/year) for humpback whales. We then evaluated interannual variability in peak habitat use relative to thermal spring transition dates (STD), and hypothesized that right whales, as planktivorous specialist feeders, would exhibit a stronger response to thermal phenology than fin and humpback whales, which are more generalist piscivorous feeders. There was a significant negative effect of western region STD on right whale habitat use, and a significant positive effect of eastern region STD on fin whale habitat use indicating differential responses to spatial seasonal conditions. Protections for threatened and endangered whales have been designed to align with expected phenology of habitat use. Our results show that whales are becoming mismatched with static seasonal management measures through shifts in their timing of habitat use, and they suggest that effective management strategies may need to alter protections as species adapt to climate change.}, + language = {en}, + number = {16}, + urldate = {2024-02-02}, + journal = {Global Change Biology}, + author = {Pendleton, Daniel E. and Tingley, Morgan W. and Ganley, Laura C. and Friedland, Kevin D. and Mayo, Charles and Brown, Moira W. and McKenna, Brigid E. and Jordaan, Adrian and Staudinger, Michelle D.}, + year = {2022}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16225}, + keywords = {climate change, endangered species, fin whale, Gulf of Maine, humpback whale, North Atlantic right whale, ocean warming, phenology}, + pages = {4989--5005}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\U95ZX4Z7\\Pendleton et al. - 2022 - Decadal-scale phenology and seasonal climate drive.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\LTR2A577\\gcb.html:text/html}, +} + +@article{northeast_fisheries_science_center_us_state_2022, + title = {State of the {Ecosystem} 2022: {Mid}-{Atlantic}}, + shorttitle = {State of the {Ecosystem} 2022}, + url = {https://repository.library.noaa.gov/view/noaa/38949}, + doi = {10.25923/5S5Y-0H81}, + urldate = {2023-03-02}, + author = {Northeast Fisheries Science Center (U.S.)}, + year = {2022}, + note = {Publisher: Northeast Fisheries Science Center (U.S.)}, +} + +@article{hernandez_seasonal_2019, + title = {Seasonal variability and individual consistency in gray seal ({Halichoerus} grypus) isotopic niches}, + volume = {97}, + issn = {0008-4301}, + url = {https://cdnsciencepub.com/doi/10.1139/cjz-2019-0032}, + doi = {10.1139/cjz-2019-0032}, + abstract = {Although it is often assumed that individuals in generalist populations are equivalent, recent research indicates that individual dietary specialization can be common in marine predators. Gray seals (Halichoerus grypus (Fabricius, 1791)) were considered locally extinct in United States waters by 1958 but have since recolonized the region. Although considered generalists, less is known about gray seal foraging ecology in the United States. To address this, we used carbon and nitrogen stable isotope analyses to investigate the foraging niches of adult gray seals in Massachusetts, USA. We examined skin, fur, and blood components to investigate seasonal variability and individual consistency in foraging niches, and serially sampled vibrissae to quantify the degree of individual foraging specialization in this population. Our results suggest that seals shift from coastal foraging habitats before molt to offshore habitats after molt, with a coincident shift from higher to lower trophic-level prey. Adult gray seals also exhibited individual consistency in foraging niches independent of population-level shifts and reflect a generalist population composed of individual foraging specialists. These findings serve as a baseline for subsequent research on gray seals in United States waters that could help to determine the mechanisms which promote individual specialization in this population.}, + number = {11}, + urldate = {2023-02-10}, + journal = {Canadian Journal of Zoology}, + author = {Hernandez, K.M. and Bogomolni, A.L. and Moxley, J.H. and Waring, G.T. and DiGiovanni, R.A. and Hammill, M.O. and Johnston, D.W. and Sette, L. and Polito, M.J.}, + month = nov, + year = {2019}, + note = {Publisher: NRC Research Press}, + pages = {1071--1077}, +} + +@article{northeast_fisheries_science_center_us_state_2022-1, + title = {State of the {Ecosystem} 2022: {New} {England}}, + shorttitle = {State of the {Ecosystem} 2022}, + url = {https://repository.library.noaa.gov/view/noaa/38948}, + doi = {10.25923/YPV2-MW79}, + urldate = {2023-03-02}, + author = {Northeast Fisheries Science Center (U.S.)}, + year = {2022}, + note = {Publisher: Northeast Fisheries Science Center (U.S.)}, +} + +@article{fabrizio_characterization_2022, + title = {Characterization of {Nursery} {Habitats} used by {Black} {Sea} {Bass} and {Summer} {Flounder} in {Chesapeake} {Bay} and the {Coastal} {Lagoons}}, + url = {https://scholarworks.wm.edu/reports/2837}, + doi = {doi: 10.25773/PJCC-RG41}, + journal = {Reports}, + author = {Fabrizio, Mary and Tuckey, Troy and Smith, Shannon and Ross, Paige and Snyder, Richard and Wang, Harry and Bever, Aaron}, + month = jan, + year = {2022}, + file = {"Characterization of Nursery Habitats used by Black Sea Bass and Summer" by Mary C. Fabrizio, Troy D. Tuckey et al.:C\:\\Users\\andrew.beet\\Zotero\\storage\\Z39AE9UV\\2837.html:text/html}, +} + +@article{mccosker_metabarcoding_2020, + title = {Metabarcoding {Fecal} {DNA} {Reveals} {Extent} of {Halichoerus} grypus ({Gray} {Seal}) {Foraging} on {Invertebrates} and {Incidence} of {Parasite} {Exposure}}, + volume = {27}, + issn = {1092-6194, 1938-5307}, + url = {https://bioone.org/journals/northeastern-naturalist/volume-27/issue-4/045.027.0409/Metabarcoding-Fecal-DNA-Reveals-Extent-of-Halichoerus-grypus-Gray-Seal/10.1656/045.027.0409.full}, + doi = {10.1656/045.027.0409}, + abstract = {Halichoerus grypus (Gray Seal) is a top predator in New England waters, yet little data exists on seals foraging on invertebrate prey. We combined analysis of hard parts of scat samples (n = 98) from 2 breeding sites in Massachusetts with metabarcoding of fecal DNA. Invertebrates were detected in 4 samples via analysis of hard parts (4.1\%) but in 35 samples (35.7\%) using a metabarcoding approach. Metabarcoding also detected parasite DNA in 82.7\% of scat samples. Nematode DNA (78.6\%) was more prevalent than trematode (28.6\%) or cestode (22.5\%) DNA. Prey species, biomass, and diversity within each sample were not affected by seal sex, parasite exposure, or type of parasitic exposure. Previous diet studies may have underestimated the diversity and abundance of invertebrates in the diet of Gray Seals.}, + number = {4}, + urldate = {2023-02-10}, + journal = {Northeastern Naturalist}, + author = {McCosker, Christina and Flanders, Kelly and Ono, Kathryn and Dufault, Michelle and Mellone, Dominique and Olson, Zachary}, + month = nov, + year = {2020}, + note = {Publisher: Eagle Hill Institute}, + pages = {681--700}, +} + +@article{ono_detecting_2019, + title = {Detecting spiny dogfish in grey seal diets}, + volume = {11}, + issn = {1877-7260}, + url = {https://doi.org/10.1007/s12686-018-1044-x}, + doi = {10.1007/s12686-018-1044-x}, + abstract = {Grey seal (Halichoerus grypus) populations declined dramatically in U.S. waters during the nineteenth and twentieth centuries, but have rebounded in recent decades as a result of legal protection. In the Gulf of Maine, increasing grey seal populations have the potential to impact commercially important groundfish species such as cod and haddock, but the potential also exists for more complex ecological interactions to occur given the recent increase of spiny dogfish (Squalus acanthias), another potential groundfish predator. Specifically, seal predation on dogfish could free groundfish species from additional predation pressure. Little quantitative data exists on intraguild predation rates by grey seals on spiny dogfish, in part because of the limitations of current methods of food habits analysis for grey seals. We report the development, validation, and field testing of a non-invasive genetic method for the detection of spiny dogfish in grey seal diets using DNA collected from seal feces. Our method amplified spiny dogfish DNA from 10 of 10 positive control scats from a captive seal with a known diet, and amplified spiny dogfish DNA from 0 of 10 negative control scats. We field tested the method on 20 scats from wild seals and detected spiny dogfish in two of the samples. All positive amplifications were sequenced to verify the species identify, and all were spiny dogfish. Our method has the potential to provide valuable information about changing predator prey dynamics in the Gulf of Maine and elsewhere.}, + language = {en}, + number = {4}, + urldate = {2023-02-10}, + journal = {Conservation Genetics Resources}, + author = {Ono, Kathryn A. and Steinbeiser, Cathleen M. and Coco, Austin B. and Sheehan, Matthew J. and Beck, Abigail J. and Dufault, Michelle N. and Gilbert, Katelyn M. and Antonez, Jessica J. and Olson, Zachary H.}, + month = dec, + year = {2019}, + pages = {481--485}, +} + +@article{flanders_utilizing_2020, + title = {Utilizing next-generation sequencing to identify prey {DNA} in western {North} {Atlantic} grey seal {Halichoerus} grypus diet}, + volume = {655}, + issn = {0171-8630, 1616-1599}, + url = {https://www.int-res.com/abstracts/meps/v655/p227-240/}, + doi = {10.3354/meps13520}, + abstract = {Increasing grey seal Halichoerus grypus abundance in coastal New England is leading to social, political, economic, and ecological controversies. Central to these issues is the foraging ecology and diet composition of the seals. We studied grey seal feeding habits through next-generation sequencing of prey DNA using 16S amplicons from seal scat (n = 74) collected from a breeding colony on Monomoy Island in Massachusetts, USA, and report frequency of occurrence and relative read abundance. We also assigned seal sex to scat samples using a revised PCR assay. In contrast to current understanding of grey seal diet from hard parts and fatty acid analysis, we found no significant difference between male and female diet measured by alpha and beta diversity. Overall, we detected 24 prey groups, 18 of which resolved to species. Sand lance Ammodytes spp. were the most frequently consumed prey group, with a frequency of occurrence (FO) of 97.3\%, consistent with previous studies, but Atlantic menhaden Brevoortia tyrannus, the second most frequently consumed species (FO = 60.8\%), has not previously been documented in US grey seal diet. Our results suggest that a metabarcoding approach to seal food habits can yield important new ecological insights, but that traditional hard parts analysis does not underestimate consumption of Atlantic cod Gadus morhua (FO = 6.7\%, Gadidae spp.) and salmon Salmo salar (FO = 0\%), 2 particularly valuable species of concern.}, + language = {en}, + urldate = {2023-02-10}, + journal = {Marine Ecology Progress Series}, + author = {Flanders, Kelly R. and Olson, Zachary H. and Ono, Kathryn A.}, + month = nov, + year = {2020}, + keywords = {Diet, Foraging ecology, Grey seal, Halichoerus grypus, Molecular scatology, Next-generation sequencing, Prey DNA}, + pages = {227--240}, +} + +@article{saba_recommended_2019, + title = {Recommended priorities for research on ecological impacts of ocean and coastal acidification in the {U}.{S}. {Mid}-{Atlantic}}, + volume = {225}, + issn = {0272-7714}, + url = {https://www.sciencedirect.com/science/article/pii/S0272771418308710}, + doi = {10.1016/j.ecss.2019.04.022}, + abstract = {The estuaries and continental shelf system of the United States Mid-Atlantic are subject to ocean acidification driven by atmospheric CO2, and coastal acidification caused by nearshore and land-sea interactions that include biological, chemical, and physical processes. These processes include freshwater and nutrient input from rivers and groundwater; tidally-driven outwelling of nutrients, inorganic carbon, alkalinity; high productivity and respiration; and hypoxia. Hence, these complex dynamic systems exhibit substantial daily, seasonal, and interannual variability that is not well captured by current acidification research on Mid-Atlantic organisms and ecosystems. We present recommendations for research priorities that target better understanding of the ecological impacts of acidification in the U. S. Mid-Atlantic region. Suggested priorities are: 1) Determining the impact of multiple stressors on our resource species as well as the magnitude of acidification; 2) Filling information gaps on major taxa and regionally important species in different life stages to improve understanding of their response to variable temporal scales and sources of acidification; 3) Improving experimental approaches to incorporate realistic environmental variability and gradients, include interactions with other environmental stressors, increase transferability to other systems or organisms, and evaluate community and ecosystem response; 4) Determining the capacity of important species to acclimate or adapt to changing ocean conditions; 5) Considering multi-disciplinary, ecosystem-level research that examines acidification impacts on biodiversity and biotic interactions; and 6) Connecting potential acidification-induced ecological impacts to ecosystem services and the economy. These recommendations, while developed for the Mid-Atlantic, can be applicable to other regions will help align research towards knowledge of potential larger-scale ecological and economic impacts.}, + language = {en}, + urldate = {2023-02-10}, + journal = {Estuarine, Coastal and Shelf Science}, + author = {Saba, Grace K. and Goldsmith, Kaitlin A. and Cooley, Sarah R. and Grosse, Daniel and Meseck, Shannon L. and Miller, A. Whitman and Phelan, Beth and Poach, Matthew and Rheault, Robert and St.Laurent, Kari and Testa, Jeremy M. and Weis, Judith S. and Zimmerman, Richard}, + month = sep, + year = {2019}, + pages = {106188}, + file = {ScienceDirect Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\CH36QAJB\\Saba et al. - 2019 - Recommended priorities for research on ecological .pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\SAP7FLGR\\S0272771418308710.html:text/html}, +} + +@article{zakroff_antagonistic_2020, + title = {Antagonistic {Interactions} and {Clutch}-{Dependent} {Sensitivity} {Induce} {Variable} {Responses} to {Ocean} {Acidification} and {Warming} in {Squid} ({Doryteuthis} pealeii) {Embryos} and {Paralarvae}}, + volume = {11}, + issn = {1664-042X}, + url = {https://www.frontiersin.org/articles/10.3389/fphys.2020.00501}, + abstract = {Ocean acidification (OA) and warming seas are significant concerns for coastal systems and species. The Atlantic longfin squid, Doryteuthis pealeii, a core component of the Northwest Atlantic trophic web, has demonstrated impacts, such as reduced growth and delayed development, under high chronic exposure to acidification (2200 ppm), but the combined effects of OA and warming have not been explored in this species. In this study, D. pealeii egg capsules were reared under a combination of several acidification levels (400, 2200, and 3500 ppm) and temperatures (20 and 27°C). Hatchlings were measured for a range of metrics [dorsal mantle length (DML), yolk sac volume (YV), malformation, and hatching success] in three trials over the 2016 breeding season (May – October). Although notable resistance to stressors was seen, highlighting variability within and between clutches, reduced DML and malformation of the embryos occurred at the highest OA exposure. Surprisingly, increased temperatures did not appear to exacerbate OA impacts, although responses were variable. Time to hatching, which increased with acidification, decreased much more drastically under warming and, further, decreased or removed delays caused by acidification. Hatching success, while variable by clutch, showed consistent patterns of greater late stage loss of embryos under acidification and greater early stage loss under warming, highlighting the potential difference in timing between these stressors for this system, i.e., that acidification stress builds up and causes impacts over time within the egg capsule as the embryos grow and respire. High OA-exposed hatchlings from the warmer conditions often showed reduced impacts compared to those reared in ambient temperatures. This may be due to the increased developmental rate and subsequently reduced OA exposure time of embryos in the higher temperature treatment. These results indicate a substantive potential plasticity to multiple stressors during the embryonic development of this species of squid, but do not predict how this species would fare under these future ocean scenarios.}, + urldate = {2023-02-09}, + journal = {Frontiers in Physiology}, + author = {Zakroff, Casey J. and Mooney, T. Aran}, + year = {2020}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\IIG335QG\\Zakroff and Mooney - 2020 - Antagonistic Interactions and Clutch-Dependent Sen.pdf:application/pdf}, +} + +@article{chavez-rosales_detection_2022, + title = {Detection of {Habitat} {Shifts} of {Cetacean} {Species}: {A} {Comparison} {Between} 2010 and 2017 {Habitat} {Suitability} {Conditions} in the {Northwest} {Atlantic} {Ocean}}, + volume = {9}, + issn = {2296-7745}, + shorttitle = {Detection of {Habitat} {Shifts} of {Cetacean} {Species}}, + url = {https://www.frontiersin.org/articles/10.3389/fmars.2022.877580}, + abstract = {The simultaneous effects of human activities in the ocean and climate change have already produced a series of responses from the marine ecosystems. With the potential increment of future human activities, such as offshore renewable energy developments, proactive management is required. To facilitate effective management and conservation actions, it is imperative to identify species potentially at risk and their critical habitats. Here we examine 16 cetacean species habitat suitability in the western North Atlantic Ocean using generalized additive models developed from data collected by NOAA- Northeast and Southeast Fisheries Science Centers from 2010 to 2017. The models were based on observed species distribution as a function of 21 environmental covariates and compare species-specific core habitats between 2010 and 2017. We identified seasonal differences in patterns of habitat change across guilds and an average northward shift of 178 km across the study area. The effects of these shifts are still unknown, but for already stressed species, the contraction or displacement of their historical habitat could worsen their population status. Therefore, the imminent development of offshore regions, in addition to the effects of climate change emphasize the need of adaptively managing ecosystems on the face of multiple challenges.}, + urldate = {2023-02-09}, + journal = {Frontiers in Marine Science}, + author = {Chavez-Rosales, Samuel and Josephson, Elizabeth and Palka, Debra and Garrison, Lance}, + year = {2022}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\CVGTYCW6\\Chavez-Rosales et al. - 2022 - Detection of Habitat Shifts of Cetacean Species A.pdf:application/pdf}, +} + +@article{zakroff_dose-dependence_2019, + title = {Dose-dependence and small-scale variability in responses to ocean acidification during squid, {Doryteuthis} pealeii, development}, + volume = {166}, + issn = {1432-1793}, + url = {https://doi.org/10.1007/s00227-019-3510-8}, + doi = {10.1007/s00227-019-3510-8}, + abstract = {Coastal squids lay their eggs on the benthos, leaving them to develop in a dynamic system that is undergoing rapid acidification due to human influence. Prior studies have broadly investigated the impacts of ocean acidification on embryonic squid, but have not addressed the thresholds at which these responses occur or their potential variability. We raised squid, Doryteuthis pealeii (captured in Vineyard Sound, Massachusetts, USA: 41°23.370'N 70°46.418′W), eggs in three trials across the breeding season (May–September, 2013) in a total of six chronic pCO2 exposures (400, 550, 850, 1300, 1900, and 2200 ppm). Hatchlings were counted and subsampled for mantle length, yolk volume, hatching time, hatching success, and statolith morphology. New methods for analysis of statolith shape, rugosity, and surface degradation were developed and are presented (with code). Responses to acidification (e.g., reduced mantle lengths, delayed hatching, and smaller, more degraded statoliths) were evident at {\textasciitilde} 1300 ppm CO2. However, patterns of physiological response and energy management, based on comparisons of yolk consumption and growth, varied among trials. Interactions between pCO2 and hatching day indicated a potential influence of exposure time on responses, while interactions with culture vessel highlighted the substantive natural variability within a clutch of eggs. While this study is consistent with, and expands upon, previous findings of sensitivity of the early life stages to acidification, it also highlights the plasticity and potential for resilience in this population of squid.}, + language = {en}, + number = {5}, + urldate = {2023-02-09}, + journal = {Marine Biology}, + author = {Zakroff, Casey and Mooney, T. Aran and Berumen, Michael L.}, + month = apr, + year = {2019}, + pages = {62}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\GR29VF7U\\Zakroff et al. - 2019 - Dose-dependence and small-scale variability in res.pdf:application/pdf}, +} + +@article{cameron_effects_2022, + title = {Effects of elevated {pCO2} and temperature on the calcification rate, survival, extrapallial fluid chemistry, and respiration of the {Atlantic} {Sea} scallop {Placopecten} magellanicus}, + volume = {67}, + issn = {1939-5590}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lno.12153}, + doi = {10.1002/lno.12153}, + abstract = {Anthropogenic CO2-emission is causing ocean warming and acidification. Understanding how basic physiological processes of marine organisms respond to these stressors is important for predicting their responses to future global change. We examined the effects of elevated pCO2 and temperature (pCO2 = 344–2199 ppm; temperature = 6°C, 9°C, and 12°C) on the calcification rate, extrapallial fluid (EPF) carbonate chemistry, respiration, and survivorship of Atlantic sea scallops (Placopecten magellanicus) in a fully crossed 143-d experiment. Rates of calcification and respiration were inhibited by elevated pCO2, and mortality occurred when elevated pCO2 was accompanied by high-temperature stress. Declines in growth and survivorship were likely caused by external shell dissolution, thermal stress, and hypercapnic reduction of metabolism under elevated pCO2. Concentrations of dissolved inorganic carbon (DIC) and total alkalinity in the EPF increased above seawater concentrations in response to elevated pCO2. EPF pH declined, but did not decline as much as seawater pH, indicating that scallops regulate EPF pH to support calcification. The combination of EPF pH regulation and DIC elevation yielded an increase in EPF [CO32−] under elevated pCO2 treatments. The combination of low respiration rates, high EPF [CO32−], and low calcification rates under elevated pCO2 suggests that the impaired calcification arises more from hypercapnic inhibition of metabolic activity and external shell dissolution than from chemically unfavorable conditions in the EPF. These results demonstrate the importance of EPF chemistry for bivalve biomineralization, but show that regulation efforts are insufficient to fully offset the deleterious effects of elevated pCO2 on scallop performance.}, + language = {en}, + number = {8}, + urldate = {2023-02-09}, + journal = {Limnology and Oceanography}, + author = {Cameron, Louise P. and Grabowski, Jonathan H. and Ries, Justin B.}, + year = {2022}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/lno.12153}, + pages = {1670--1686}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\YYPRLBMB\\Cameron et al. - 2022 - Effects of elevated pCO2 and temperature on the ca.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\R9LYV4DJ\\lno.html:text/html}, +} + +@article{kroeker_impacts_2013, + title = {Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming}, + volume = {19}, + issn = {1365-2486}, + shorttitle = {Impacts of ocean acidification on marine organisms}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.12179}, + doi = {10.1111/gcb.12179}, + abstract = {Ocean acidification represents a threat to marine species worldwide, and forecasting the ecological impacts of acidification is a high priority for science, management, and policy. As research on the topic expands at an exponential rate, a comprehensive understanding of the variability in organisms' responses and corresponding levels of certainty is necessary to forecast the ecological effects. Here, we perform the most comprehensive meta-analysis to date by synthesizing the results of 228 studies examining biological responses to ocean acidification. The results reveal decreased survival, calcification, growth, development and abundance in response to acidification when the broad range of marine organisms is pooled together. However, the magnitude of these responses varies among taxonomic groups, suggesting there is some predictable trait-based variation in sensitivity, despite the investigation of approximately 100 new species in recent research. The results also reveal an enhanced sensitivity of mollusk larvae, but suggest that an enhanced sensitivity of early life history stages is not universal across all taxonomic groups. In addition, the variability in species' responses is enhanced when they are exposed to acidification in multi-species assemblages, suggesting that it is important to consider indirect effects and exercise caution when forecasting abundance patterns from single-species laboratory experiments. Furthermore, the results suggest that other factors, such as nutritional status or source population, could cause substantial variation in organisms' responses. Last, the results highlight a trend towards enhanced sensitivity to acidification when taxa are concurrently exposed to elevated seawater temperature.}, + language = {en}, + number = {6}, + urldate = {2023-02-09}, + journal = {Global Change Biology}, + author = {Kroeker, Kristy J. and Kordas, Rebecca L. and Crim, Ryan and Hendriks, Iris E. and Ramajo, Laura and Singh, Gerald S. and Duarte, Carlos M. and Gattuso, Jean-Pierre}, + year = {2013}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.12179}, + keywords = {climate change, calcification, carbonate chemistry, cumulative effects, pH}, + pages = {1884--1896}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\DYRRBCYG\\Kroeker et al. - 2013 - Impacts of ocean acidification on marine organisms.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\999I9Z9S\\Kroeker et al. - 2013 - Impacts of ocean acidification on marine organisms.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\CX69QCAH\\gcb.html:text/html}, +} + +@incollection{intergovernmental_panel_on_climate_change_ipcc_technical_2022, + address = {Cambridge}, + title = {Technical {Summary}}, + isbn = {978-1-00-915796-4}, + url = {https://www.cambridge.org/core/books/ocean-and-cryosphere-in-a-changing-climate/technical-summary/3428BF3C6ADE0C52755DBC3AA5CFE48B}, + urldate = {2023-02-09}, + booktitle = {The {Ocean} and {Cryosphere} in a {Changing} {Climate}: {Special} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, + publisher = {Cambridge University Press}, + editor = {{Intergovernmental Panel on Climate Change (IPCC)}}, + year = {2022}, + doi = {10.1017/9781009157964.002}, + pages = {39--70}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\HHCSDHSB\\Intergovernmental Panel on Climate Change (IPCC) - 2022 - Technical Summary.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\GRTJWG2H\\3428BF3C6ADE0C52755DBC3AA5CFE48B.html:text/html}, +} + +@article{cheng_another_2023, + title = {Another {Year} of {Record} {Heat} for the {Oceans}}, + issn = {1861-9533}, + url = {https://doi.org/10.1007/s00376-023-2385-2}, + doi = {10.1007/s00376-023-2385-2}, + abstract = {Changes in ocean heat content (OHC), salinity, and stratification provide critical indicators for changes in Earth’s energy and water cycles. These cycles have been profoundly altered due to the emission of greenhouse gasses and other anthropogenic substances by human activities, driving pervasive changes in Earth’s climate system. In 2022, the world’s oceans, as given by OHC, were again the hottest in the historical record and exceeded the previous 2021 record maximum. According to IAP/CAS data, the 0–2000 m OHC in 2022 exceeded that of 2021 by 10.9 ± 8.3 ZJ (1 Zetta Joules = 1021 Joules); and according to NCEI/NOAA data, by 9.1 ± 8.7 ZJ. Among seven regions, four basins (the North Pacific, North Atlantic, the Mediterranean Sea, and southern oceans) recorded their highest OHC since the 1950s. The salinity-contrast index, a quantification of the “salty gets saltier—fresh gets fresher” pattern, also reached its highest level on record in 2022, implying continued amplification of the global hydrological cycle. Regional OHC and salinity changes in 2022 were dominated by a strong La Niña event. Global upper-ocean stratification continued its increasing trend and was among the top seven in 2022.}, + language = {en}, + urldate = {2023-02-09}, + journal = {Advances in Atmospheric Sciences}, + author = {Cheng, Lijing and Abraham, John and Trenberth, Kevin E. and Fasullo, John and Boyer, Tim and Mann, Michael E. and Zhu, Jiang and Wang, Fan and Locarnini, Ricardo and Li, Yuanlong and Zhang, Bin and Yu, Fujiang and Wan, Liying and Chen, Xingrong and Feng, Licheng and Song, Xiangzhou and Liu, Yulong and Reseghetti, Franco and Simoncelli, Simona and Gouretski, Viktor and Chen, Gengxin and Mishonov, Alexey and Reagan, Jim and Li, Guancheng}, + month = jan, + year = {2023}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\Q3V76Y9J\\Cheng et al. - 2023 - Another Year of Record Heat for the Oceans.pdf:application/pdf}, +} + +@article{northeast_fisheries_science_center_us_noaa_2022, + series = {{NOAA} technical memorandum {NMFS}-{NE} ; 292}, + title = {{NOAA} {Fisheries} and {BOEM} {Federal} {Survey} {Mitigation} {Implementation} {Strategy} - {Northeast} {U}.{S}. {Region}}, + url = {https://repository.library.noaa.gov/view/noaa/47925}, + doi = {10.25923/jqse-x746}, + abstract = {This Federal Survey Mitigation Strategy (hereafter Strategy) is intended to guide the development and implementation of a program to mitigate impacts of wind energy development on fisheries surveys over the expected full duration (30+ years) of wind energy development in the Northeast U.S. (Mitigation Program). The Mitigation Program will include survey-specific mitigation plans for each impacted survey, including both vessel and aerial surveys (Survey Specific Mitigation Plans). This strategy is specific to the Northeast U.S. Region (Maine to North Carolina) and generally applicable to other regions of the country.}, + author = {Hare, J.A. and Blythe, B.J. and Ford, K.H. and Godfrey-McKee, S. and Hooker, B.R. and Jensen, B.M. and Lipsky, A. and Nachman, C. and Pfeiffer, L. and Rasser, M. and Renshaw, K.}, + editor = {{Northeast Fisheries Science Center (U.S.)}}, + year = {2022}, + keywords = {Effect of wind power plants on, Fish surveys}, + annote = {SeriesInformation +NOAA technical memorandum NMFS-NE ; 292}, + annote = {SeriesInformation +NOAA technical memorandum NMFS-NE ; 292}, +} + +@article{pousse_dynamic_2022, + title = {Dynamic energy budget modeling of {Atlantic} surfclam, {Spisula} solidissima, under future ocean acidification and warming}, + volume = {177}, + issn = {0141-1136}, + url = {https://www.sciencedirect.com/science/article/pii/S0141113622000472}, + doi = {10.1016/j.marenvres.2022.105602}, + abstract = {A dynamic energy budget (DEB) model integrating pCO2 was used to describe ocean acidification (OA) effects on Atlantic surfclam, Spisula solidissima, bioenergetics. Effects of elevated pCO2 on ingestion and somatic maintenance costs were simulated, validated, and adapted in the DEB model based upon growth and biological rates acquired during a 12-week laboratory experiment. Temperature and pCO2 were projected for the next 100 years following the intergovernmental panel on climate change representative concentration pathways scenarios (2.6, 6.0, and 8.5) and used as forcing variables to project surfclam growth and reproduction. End-of-century water warming and acidification conditions resulted in simulated faster growth for young surfclams and more energy allocated to reproduction until the beginning of the 22nd century when a reduction in maximum shell length and energy allocated to reproduction was observed for the RCP 8.5 scenario.}, + language = {en}, + urldate = {2023-02-07}, + journal = {Marine Environmental Research}, + author = {Pousse, Émilien and Munroe, Daphne and Hart, Deborah and Hennen, Daniel and Cameron, Louise P. and Rheuban, Jennie E. and Wang, Zhaohui Aleck and Wikfors, Gary H. and Meseck, Shannon L.}, + month = may, + year = {2022}, + keywords = {Climate change, Atlantic surfclam, Dynamic energy budget, Ocean acidification}, + pages = {105602}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\82SNY4Q3\\S0141113622000472.html:text/html}, +} + +@article{jacox_global_2022, + title = {Global seasonal forecasts of marine heatwaves}, + volume = {604}, + copyright = {2022 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply}, + issn = {1476-4687}, + url = {https://www.nature.com/articles/s41586-022-04573-9}, + doi = {10.1038/s41586-022-04573-9}, + abstract = {Marine heatwaves (MHWs)—periods of exceptionally warm ocean temperature lasting weeks to years—are now widely recognized for their capacity to disrupt marine ecosystems1–3. The substantial ecological and socioeconomic impacts of these extreme events present significant challenges to marine resource managers4–7, who would benefit from forewarning of MHWs to facilitate proactive decision-making8–11. However, despite extensive research into the physical drivers of MHWs11,12, there has been no comprehensive global assessment of our ability to predict these events. Here we use a large multimodel ensemble of global climate forecasts13,14 to develop and assess MHW forecasts that cover the world’s oceans with lead times of up to a year. Using 30 years of retrospective forecasts, we show that the onset, intensity and duration of MHWs are often predictable, with skilful forecasts possible from 1 to 12 months in advance depending on region, season and the state of large-scale climate modes, such as the El Niño/Southern Oscillation. We discuss considerations for setting decision thresholds based on the probability that a MHW will occur, empowering stakeholders to take appropriate actions based on their risk profile. These results highlight the potential for operational MHW forecasts, analogous to forecasts of extreme weather phenomena, to promote climate resilience in global marine ecosystems.}, + language = {en}, + number = {7906}, + urldate = {2023-02-07}, + journal = {Nature}, + author = {Jacox, Michael G. and Alexander, Michael A. and Amaya, Dillon and Becker, Emily and Bograd, Steven J. and Brodie, Stephanie and Hazen, Elliott L. and Pozo Buil, Mercedes and Tommasi, Desiree}, + month = apr, + year = {2022}, + note = {Number: 7906 +Publisher: Nature Publishing Group}, + keywords = {Natural hazards, Physical oceanography}, + pages = {486--490}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\UUUPVNQR\\Jacox et al. - 2022 - Global seasonal forecasts of marine heatwaves.pdf:application/pdf}, +} + +@article{jacox_thermal_2020, + title = {Thermal displacement by marine heatwaves}, + volume = {584}, + copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited}, + issn = {1476-4687}, + url = {https://www.nature.com/articles/s41586-020-2534-z}, + doi = {10.1038/s41586-020-2534-z}, + abstract = {Marine heatwaves (MHWs)—discrete but prolonged periods of anomalously warm ocean temperatures—can drastically alter ocean ecosystems, with profound ecological and socioeconomic impacts1–8. Considerable effort has been directed at understanding the patterns, drivers and trends of MHWs globally9–11. Typically, MHWs are characterized on the basis of their intensity and persistence at a given location—an approach that is particularly relevant for corals and other sessile organisms that must endure increased temperatures. However, many ecologically and commercially important marine species respond to environmental disruptions by relocating to favourable habitats, and dramatic range shifts of mobile marine species are among the conspicuous impacts of MHWs1,4,12,13. Whereas spatial temperature shifts have been studied extensively in the context of long-term warming trends14–18, they are unaccounted for in existing global MHW analyses. Here we introduce thermal displacement as a metric that characterizes MHWs by the spatial shifts of surface temperature contours, instead of by local temperature anomalies, and use an observation-based global sea surface temperature dataset to calculate thermal displacements for all MHWs from 1982 to 2019. We show that thermal displacements during MHWs vary from tens to thousands of kilometres across the world’s oceans and do not correlate spatially with MHW intensity. Furthermore, short-term thermal displacements during MHWs are of comparable magnitude to century-scale shifts inferred from warming trends18, although their global spatial patterns are very different. These results expand our understanding of MHWs and their potential impacts on marine species, revealing which regions are most susceptible to thermal displacement, and how such shifts may change under projected ocean warming. The findings also highlight the need for marine resource management to account for MHW-driven spatial shifts, which are of comparable scale to those associated with long-term climate change and are already happening.}, + language = {en}, + number = {7819}, + urldate = {2023-02-07}, + journal = {Nature}, + author = {Jacox, Michael G. and Alexander, Michael A. and Bograd, Steven J. and Scott, James D.}, + month = aug, + year = {2020}, + note = {Number: 7819 +Publisher: Nature Publishing Group}, + keywords = {Natural hazards, Physical oceanography, Environmental health}, + pages = {82--86}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\ZCEBPKE5\\Jacox et al. - 2020 - Thermal displacement by marine heatwaves.pdf:application/pdf}, +} + +@article{jiang_climatological_2015, + title = {Climatological distribution of aragonite saturation state in the global oceans}, + volume = {29}, + issn = {1944-9224}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/2015GB005198}, + doi = {10.1002/2015GB005198}, + abstract = {Aragonite saturation state (Ωarag) in surface and subsurface waters of the global oceans was calculated from up-to-date (through the year of 2012) ocean station dissolved inorganic carbon (DIC) and total alkalinity (TA) data. Surface Ωarag in the open ocean was always supersaturated (Ω {\textgreater} 1), ranging between 1.1 and 4.2. It was above 2.0 (2.0–4.2) between 40°N and 40°S but decreased toward higher latitude to below 1.5 in polar areas. The influences of water temperature on the TA/DIC ratio, combined with the temperature effects on inorganic carbon equilibrium and apparent solubility product (K′sp), explain the latitudinal differences in surface Ωarag. Vertically, Ωarag was highest in the surface mixed layer. Higher hydrostatic pressure, lower water temperature, and more CO2 buildup from biological activity in the absence of air-sea gas exchange helped maintain lower Ωarag in the deep ocean. Below the thermocline, aerobic decomposition of organic matter along the pathway of global thermohaline circulation played an important role in controlling Ωarag distributions. Seasonally, surface Ωarag above 30° latitudes was about 0.06 to 0.55 higher during warmer months than during colder months in the open-ocean waters of both hemispheres. Decadal changes of Ωarag in the Atlantic and Pacific Oceans showed that Ωarag in waters shallower than 100 m depth decreased by 0.10 ± 0.09 (−0.40 ± 0.37\% yr−1) on average from the decade spanning 1989–1998 to the decade spanning 1998–2010.}, + language = {en}, + number = {10}, + urldate = {2023-02-06}, + journal = {Global Biogeochemical Cycles}, + author = {Jiang, Li-Qing and Feely, Richard A. and Carter, Brendan R. and Greeley, Dana J. and Gledhill, Dwight K. and Arzayus, Krisa M.}, + year = {2015}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/2015GB005198}, + keywords = {pH, aragonite saturation state, climatology, global oceans, ocean acidification, omega}, + pages = {1656--1673}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\4YWKM4FH\\Jiang et al. - 2015 - Climatological distribution of aragonite saturatio.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\ANLLFDHC\\Jiang et al. - 2015 - Climatological distribution of aragonite saturatio.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\UHSW7LKY\\2015GB005198.html:text/html}, +} + +@article{frick_spatiotemporal_2022, + title = {Spatiotemporal {Variation} in {Distribution}, {Size}, and {Relative} {Abundance} within a {Salish} {Sea} {Nearshore} {Forage} {Fish} {Community}}, + volume = {14}, + issn = {1942-5120}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mcf2.10202}, + doi = {10.1002/mcf2.10202}, + abstract = {Forage fish are schooling species commonly occurring in both offshore pelagic and nearshore coastal habitats. Beyond use by some species for spawning, the dynamics of nearshore habitat use are not well understood. The objective of our study was to evaluate the spring–summer dynamics of forage fish occurrence in nearshore habitats of the Strait of Juan de Fuca, Washington. We suspected that habitat changes resulting from removal of two large dams on the Elwha River (2009–2011) may have altered fish presence and abundance. Monthly beach seine sampling in four regions along 40 km of shoreline was conducted from April to September between 2006 and 2019. We caught nearly 600,000 fish, comprising 82 different species. Nine species of forage fish accounted for 81.7\% of all fishes caught; most were classified as postlarvae and juveniles based on size. There were spatial differences in the forage fish assemblage between two of our sites but no discernable year effects and no obvious impact of dam removal on forage community composition. Three species represented 78.8\% of the catch: Pacific Herring Clupea pallasii, Pacific Sand Lance Ammodytes hexapterus, and Surf Smelt Hypomesus pretiosus. We used a Bayesian generalized linear mixed model to evaluate spatial and temporal variability in the probability of occurrence of these species. Each species exhibited a unique pattern of intra-annual, interannual, and regional fluctuations. Pacific Herring occurrence progressively increased monthly, Pacific Sand Lance occurrence decreased, and Surf Smelt probability of occurrence peaked in June. Temporal variations in distribution and abundance of these species are likely driven by life history differences and biological requirements. We speculate that specific characteristics of each region, including proximity to spawning areas, spawn timing, extant current patterns, and ecosystem processes, drove variations in distribution between species.}, + language = {en}, + number = {2}, + urldate = {2023-01-30}, + journal = {Marine and Coastal Fisheries}, + author = {Frick, Kinsey E. and Kagley, Anna N. and Fresh, Kurt L. and Samhouri, Jameal F. and Ward, Larry S. and Stapleton, Justin T. and Shelton, Andrew O.}, + year = {2022}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mcf2.10202}, + pages = {e10202}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\DVX64IVC\\Frick et al. - 2022 - Spatiotemporal Variation in Distribution, Size, an.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\G39AQAUN\\mcf2.html:text/html}, +} + +@article{schonfeld_spatial_2022, + title = {Spatial differences in estuarine utilization by seasonally resident species in {Mid}-{Atlantic} {Bight}, {USA}}, + volume = {31}, + issn = {1365-2419}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/fog.12611}, + doi = {10.1111/fog.12611}, + abstract = {Climate-driven distributional shifts have been well-documented for fisheries resources along the East Coast of the United States, yet little attention has been given to adjacent estuarine systems. The Chesapeake Bay is the largest estuary in the continental United States and serves as important habitat for a diversity of fishes and invertebrates, many of which are seasonal residents. Survey data indicate that relative abundance of finfish in Chesapeake Bay has diminished substantially, while coastwide stock status has remained unchanged. In response to warming, seasonal estuarine residents may remain in coastal waters or inhabit a northerly estuary, but the extent to which changing environmental conditions may drive exchange between the coastal ocean and estuarine systems remains unresolved. This study analyzed data collected from 2008 to 2019 by three fisheries-independent trawl surveys to explore temporal patterns and associated environmental drivers of the estuarine–coastal ocean exchange in the Mid-Atlantic for eight economically and ecologically important species. Relative habitat utilization of Chesapeake Bay declined for most species, while utilization patterns for Delaware Bay were largely constant or increasing over time. Broad-scale, multispecies analyses of relative habitat utilization time series revealed that the North Atlantic Oscillation (NAO) was an important driver of Chesapeake Bay exchange, but that average Apr/May coastal ocean bottom temperature was significant for Delaware Bay. Collectively, the results demonstrate that several Mid-Atlantic species have altered their estuarine habitat use over time, climate drivers associated with estuarine–coastal ocean exchange operate on different time scales, and that the impacts of warming within the Mid-Atlantic vary spatially.}, + language = {en}, + number = {6}, + urldate = {2022-11-02}, + journal = {Fisheries Oceanography}, + author = {Schonfeld, Adena J. and Gartland, James and Latour, Robert J.}, + year = {2022}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/fog.12611}, + keywords = {climate change, Chesapeake Bay, dynamic factor analysis, ecosystem exchange, quantitative fisheries ecology}, + pages = {615--628}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\GSNGARVS\\Schonfeld et al. - 2022 - Spatial differences in estuarine utilization by se.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\8L95MKRE\\Schonfeld et al. - 2022 - Spatial differences in estuarine utilization by se.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\22R23CIQ\\Schonfeld et al. - 2022 - Spatial differences in estuarine utilization by se.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\B7GJJU76\\fog.html:text/html}, +} + +@article{depiper_learning_2021, + title = {Learning by doing: collaborative conceptual modelling as a path forward in ecosystem-based management}, + volume = {78}, + issn = {1054-3139}, + shorttitle = {Learning by doing}, + url = {https://doi.org/10.1093/icesjms/fsab054}, + doi = {10.1093/icesjms/fsab054}, + abstract = {Managers, stakeholders, and scientists recognize the need for collaborative, transparent, integrated approaches to complex resource management issues, and frameworks to address these complex issues are developing. Through the course of 2019, the Mid-Atlantic Fishery Management Council developed a conceptual model of ecosystem linkages and risks for summer flounder, a species of recreational and commercial fisheries importance. The proximal aim of the model was to develop a list of integrated management questions that could be refined and addressed through a future quantitative management strategy evaluation. As such, this conceptual model served as a scoping tool. However, the true value of the conceptual model lays elsewhere: familiarizing resource managers historically focused on single-species management with the potential utility of an ecosystem approach to management. This paper details the goals and development of the conceptual model and situates this process in the broader context of best practices for collaborative open science and scientific reproducibility. Further, it highlights a successful path by which the shift towards ecosystem-based management can be actuated.}, + number = {4}, + urldate = {2022-08-08}, + journal = {ICES Journal of Marine Science}, + author = {DePiper, Geret and Gaichas, Sarah and Muffley, Brandon and Ardini, Greg and Brust, Jeffrey and Coakley, Jessica and Dancy, Kiley and Elliott, G Warren and Leaning, Dustin C and Lipton, Douglas and McNamee, Jason and Perretti, Charles and Rootes-Murdy, Kirby and Wilberg, Michael J}, + month = aug, + year = {2021}, + pages = {1217--1228}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\C3RGFFYZ\\DePiper et al. - 2021 - Learning by doing collaborative conceptual modell.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\3MH8VAJU\\6207633.html:text/html}, +} + +@article{caracappa_northeast_2022, + title = {A northeast {United} {States} {Atlantis} marine ecosystem model with ocean reanalysis and ocean color forcing}, + volume = {471}, + issn = {0304-3800}, + url = {https://www.sciencedirect.com/science/article/pii/S030438002200148X}, + doi = {10.1016/j.ecolmodel.2022.110038}, + abstract = {The northeast United States Atlantis model (NEUSv2) is an end-to-end ecosystem model that can simulate biogeochemical, ecological, fishery, management, and socio-economic processes within marine ecosystems. As a major update to the original model, NEUSv2 includes changes to the model's functional group definitions and forcing data. NEUSv2 is the first Atlantis model to use a satellite-ocean-color-derived phytoplankton size class model that was tuned specifically for the region to force marine primary production. Additionally, physical ocean variables (currents, temperature, and salinity) were updated using a high-resolution global ocean reanalysis. Despite its coarse resolution, NEUSv2 was capable of reproducing the broad spatial patterns seen in the physical and biological forcing sources, with the exception of some circulation features. NEUSv2 produced plausible zooplankton and planktivore biomass, a stable lower trophic food web, and recent trends in zooplankton biomass. NEUSv2 meets calibration criteria for the persistence and long-term stability of functional group biomass. Given the success of this new Atlantis forcing approach, we detail the observations and challenges regarding spatial scale-related processes, data assimilation, and biological calibration. We also discuss possible tradeoffs with model scope, calibration, and the availability of feedback mechanisms. This NEUSv2 hindcast is well suited for exploring ecosystem-level sensitivity to lower trophic processes and for testing alternative biogeochemical forcing. Further developments will improve model performance for higher trophic levels.}, + language = {en}, + urldate = {2022-08-08}, + journal = {Ecological Modelling}, + author = {Caracappa, Joseph C. and Beet, Andrew and Gaichas, Sarah and Gamble, Robert J. and Hyde, Kimberly J. W. and Large, Scott I. and Morse, Ryan E. and Stock, Charles A. and Saba, Vincent S.}, + month = sep, + year = {2022}, + keywords = {Atlantis, biogeochemical forcing, end-to-end models, global reanalysis, lower trophic levels, marine ecosystem models, ocean color, primary production}, + pages = {110038}, + file = {ScienceDirect Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\DILX5W83\\Caracappa et al. - 2022 - A northeast United States Atlantis marine ecosyste.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\S672JAVG\\S030438002200148X.html:text/html}, +} + +@article{christiansen_emergence_2022, + title = {Emergence of {Large}-{Scale} {Hydrodynamic} {Structures} {Due} to {Atmospheric} {Offshore} {Wind} {Farm} {Wakes}}, + volume = {9}, + issn = {2296-7745}, + url = {https://www.frontiersin.org/article/10.3389/fmars.2022.818501}, + abstract = {The potential impact of offshore wind farms through decreasing sea surface wind speed on the shear forcing and its consequences for the ocean dynamics are investigated. Based on the unstructured-grid model SCHISM, we present a new cross-scale hydrodynamic model setup for the southern North Sea, which enables high-resolution analysis of offshore wind farms in the marine environment. We introduce an observational-based empirical approach to parameterize the atmospheric wakes in a hydrodynamic model and simulate the seasonal cycle of the summer stratification in consideration of the recent state of wind farm development in the southern North Sea. The simulations show the emergence of large-scale attenuation in the wind forcing and associated alterations in the local hydro- and thermodynamics. The wake effects lead to unanticipated spatial variability in the mean horizontal currents and to the formation of large-scale dipoles in the sea surface elevation. Induced changes in the vertical and lateral flow are sufficiently strong to influence the residual currents and entail alterations of the temperature and salinity distribution in areas of wind farm operation. Ultimately, the dipole-related processes affect the stratification development in the southern North Sea and indicate potential impact on marine ecosystem processes. In the German Bight, in particular, we observe large-scale structural change in stratification strength, which eventually enhances the stratification during the decline of the summer stratification toward autumn.}, + urldate = {2022-03-09}, + journal = {Frontiers in Marine Science}, + author = {Christiansen, Nils and Daewel, Ute and Djath, Bughsin and Schrum, Corinna}, + year = {2022}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\TEC2AI85\\Christiansen et al. - 2022 - Emergence of Large-Scale Hydrodynamic Structures D.pdf:application/pdf}, +} + +@article{quintana-rizzo_residency_2021, + title = {Residency, demographics, and movement patterns of {North} {Atlantic} right whales {Eubalaena} glacialis in an offshore wind energy development area in southern {New} {England}, {USA}}, + volume = {45}, + issn = {1863-5407, 1613-4796}, + url = {https://www.int-res.com/abstracts/esr/v45/p251-268/}, + doi = {10.3354/esr01137}, + abstract = {Offshore wind energy development is growing quickly around the world. In southern New England, USA, one of the largest commercial offshore wind energy farms in the USA will be established in the waters off Massachusetts and Rhode Island, an area used by the Critically Endangered North Atlantic right whale Eubalaena glacialis. Prior to 2011, little was known about the use of this area by right whales. We examined aerial survey data collected between 2011-2015 and 2017-2019 to quantify right whale distribution, residency, demography, and movements in the region. Right whale occurrence increased during the study period. Since 2017, whales have been sighted in the area nearly every month, with peak sighting rates between late winter and spring. Model outputs suggest that 23\% of the species’ population is present from December through May, and the mean residence time has tripled to an average of 13 d during these months. Age and sex ratios of the individuals present in the area are similar to those of the species as a whole, with adult males the most common demographic group. Movement models showed that southern New England is an important destination for right whales, including conceptive and reproductive females, and qualitative observations included animals feeding and socializing. Implementing mitigation procedures in coordination with these findings will be crucial in lessening the potential impacts on right whales from construction noise, increased vessel traffic, and habitat disruption in this region.}, + language = {en}, + urldate = {2022-03-09}, + journal = {Endangered Species Research}, + author = {Quintana-Rizzo, E. and Leiter, S. and Cole, T. V. N. and Hagbloom, M. N. and Knowlton, A. R. and Nagelkirk, P. and O’Brien, O. and Khan, C. B. and Henry, A. G. and Duley, P. A. and Crowe, L. M. and Mayo, C. A. and Kraus, S. D.}, + month = jul, + year = {2021}, + keywords = {Aerial surveys, Alternative energy, Migratory species}, + pages = {251--268}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\LJSI7QIZ\\Quintana-Rizzo et al. - 2021 - Residency, demographics, and movement patterns of .pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\E7DCYRH5\\p251-268.html:text/html}, +} + +@article{du_pontavice_incorporating_nodate, + title = {Incorporating environmental effects from ocean models improves a marine fish stock assessment}, + journal = {ICES Journal of Marine Science}, + author = {du Pontavice, H. and Miller, Timothy J. and Stock, Brian C. and Chen, Zhuomin and Saba, Vincent S.}, +} + +@article{gawarkiewicz_increasing_nodate, + title = {Increasing {Frequency} of {Mid}-depth {Salinity} {Maximum} {Intrusions} in the {Middle} {Atlantic} {Bight}}, + journal = {Journal of Geophysical Research: Oceans}, + author = {Gawarkiewicz, Glen and Fratantoni, Paula and Bahr, Frank and Ellertson, Aubrey}, +} + +@article{farr_assessment_2021, + title = {An assessment of marine, estuarine, and riverine habitat vulnerability to climate change in the {Northeast} {U}.{S}.}, + volume = {16}, + issn = {1932-6203}, + url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260654}, + doi = {10.1371/journal.pone.0260654}, + abstract = {Climate change is impacting the function and distribution of habitats used by marine, coastal, and diadromous species. These impacts often exacerbate the anthropogenic stressors that habitats face, particularly in the coastal environment. We conducted a climate vulnerability assessment of 52 marine, estuarine, and riverine habitats in the Northeast U.S. to develop an ecosystem-scale understanding of the impact of climate change on these habitats. The trait-based assessment considers the overall vulnerability of a habitat to climate change to be a function of two main components, sensitivity and exposure, and relies on a process of expert elicitation. The climate vulnerability ranks ranged from low to very high, with living habitats identified as the most vulnerable. Over half of the habitats examined in this study are expected to be impacted negatively by climate change, while four habitats are expected to have positive effects. Coastal habitats were also identified as highly vulnerable, in part due to the influence of non-climate anthropogenic stressors. The results of this assessment provide regional managers and scientists with a tool to inform habitat conservation, restoration, and research priorities, fisheries and protected species management, and coastal and ocean planning.}, + language = {en}, + number = {12}, + urldate = {2022-03-02}, + journal = {PLOS ONE}, + author = {Farr, Emily R. and Johnson, Michael R. and Nelson, Mark W. and Hare, Jonathan A. and Morrison, Wendy E. and Lettrich, Matthew D. and Vogt, Bruce and Meaney, Christopher and Howson, Ursula A. and Auster, Peter J. and Borsuk, Frank A. and Brady, Damian C. and Cashman, Matthew J. and Colarusso, Phil and Grabowski, Jonathan H. and Hawkes, James P. and Mercaldo-Allen, Renee and Packer, David B. and Stevenson, David K.}, + month = dec, + year = {2021}, + note = {Publisher: Public Library of Science}, + keywords = {Marine fish, Reefs, Water columns, Climate change, Anthropogenic climate change, Habitats, Surface water, Wetlands}, + pages = {e0260654}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\6RBHCZHP\\Farr et al. - 2021 - An assessment of marine, estuarine, and riverine h.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\XIDW7DS9\\article.html:text/html}, +} + +@article{schick_striking_2009, + title = {Striking the right balance in right whale conservation}, + volume = {66}, + issn = {0706-652X}, + url = {https://cdnsciencepub.com/doi/10.1139/F09-115}, + doi = {10.1139/F09-115}, + number = {9}, + urldate = {2022-03-09}, + journal = {Canadian Journal of Fisheries and Aquatic Sciences}, + author = {Schick, Robert S. and Halpin, Patrick N. and Read, Andrew J. and Slay, Christopher K. and Kraus, Scott D. and Mate, Bruce R. and Baumgartner, Mark F. and Roberts, Jason J. and Best, Benjamin D. and Good, Caroline P. and Loarie, Scott R. and Clark, James S.}, + month = sep, + year = {2009}, + note = {Publisher: NRC Research Press}, + pages = {1399--1403}, + file = {Submitted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\HND4AUNI\\Schick et al. - 2009 - Striking the right balance in right whale conserva.pdf:application/pdf}, +} + +@book{boem_vineyard_2020, + title = {Vineyard {Wind} 1 {Offshore} {Wind} {Energy} {Project} {Supplement} to the {Draft} {Environmental} {Impact} {Statement}. {OCS} {EIS}/{EA}, {BOEM} 2020-025}, + url = {https://www.boem.gov/sites/default/files/documents/renewable-energy/Vineyard-Wind-1-Supplement-to-EIS.pdf}, + urldate = {2022-02-11}, + author = {BOEM}, + year = {2020}, + file = {Vineyard-Wind-1-Supplement-to-EIS.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\YKRBWL2B\\Vineyard-Wind-1-Supplement-to-EIS.pdf:application/pdf}, +} + +@article{perretti_regime_2017, + title = {Regime shifts in fish recruitment on the {Northeast} {US} {Continental} {Shelf}}, + volume = {574}, + issn = {0171-8630, 1616-1599}, + url = {http://www.int-res.com/abstracts/meps/v574/p1-11/}, + doi = {10.3354/meps12183}, + abstract = {There is accumulating evidence for decadal-scale regime shifts at the base of the food web on the Northeast US Continental Shelf. However, less evidence exists for regime shifts in fish recruitment success, particularly for synchronized regimes across multiple species. Here, we analyzed stock assessment output and survey data to test for regimes in the recruitment success of 18 commercially important marine species over a 29 yr timespan. We then tested whether recruitment regimes coincide with regimes in the zooplankton community, and whether the abundance of large spawners could explain the patterns in recruitment. We found evidence for 3 decadal-scale recruitment regimes: low recruitment success in the 1980s, high success in the 1990s, and a return to low recruitment success in the 2000s. This general pattern was found in all datasets of recruitment. The abundance of large spawners did not appear to be a broad-scale driver of recruitment success; however, recruitment regimes coincided with regimes in copepod abundance and size structure.}, + language = {en}, + urldate = {2022-02-10}, + journal = {Marine Ecology Progress Series}, + author = {Perretti, Ct and Fogarty, Mj and Friedland, Kd and Hare, Ja and Lucey, Sm and McBride, Rs and Miller, Tj and Morse, Re and O’Brien, L and Pereira, Jj and Smith, La and Wuenschel, Mj}, + month = jul, + year = {2017}, + pages = {1--11}, + file = {Perretti et al. - 2017 - Regime shifts in fish recruitment on the Northeast.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\NRAGBLGA\\Perretti et al. - 2017 - Regime shifts in fish recruitment on the Northeast.pdf:application/pdf;Perretti et al. - 2017 - Regime shifts in fish recruitment on the Northeast.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\ZXKKMXZZ\\Perretti et al. - 2017 - Regime shifts in fish recruitment on the Northeast.pdf:application/pdf}, +} + +@article{johnson_savory_2015, + title = {The {Savory} {Swimmer} {Swims} {North}: {A} {Northern} {Range} {Extension} of the {Blue} {Crab} {Callinectes} {Sapidus}?}, + volume = {35}, + issn = {0278-0372}, + shorttitle = {The {Savory} {Swimmer} {Swims} {North}}, + url = {https://doi.org/10.1163/1937240X-00002293}, + doi = {10.1163/1937240X-00002293}, + abstract = {Worldwide, climate-change is shifting species distributions poleward. Here I present recent (2012-2014) observations of the blue crab, Callinectes sapidusRathbun, 1896, in the Gulf of Maine (GoM), north of its historical range of Cape Cod, Massachusetts. To test the hypothesis of a climate-driven range expansion, I examined near-surface ocean temperatures. On average, ocean temperatures in the GoM in summer 2012 and 2013 were up to \$1.3{\textasciicircum}{\textbackslash}circ\{{\textbackslash}rm\{C\}\}\$ higher than the average of the previous decade, suggesting that warmer waters may have promoted the recruitment of C. sapidus to the GoM. Previous ephemeral populations of C. sapidus in the Gulf of Maine have been reported since the 1860s. Recent observations and continued warming in the northwest Atlantic may signal a permanent poleward expansion of C. sapidus into the GoM. If so, then a key goal for ecologists and managers will be to understand the effect of C. sapidus on GoM food-webs and fisheries.}, + number = {1}, + urldate = {2022-02-10}, + journal = {Journal of Crustacean Biology}, + author = {Johnson, David Samuel}, + month = jan, + year = {2015}, + pages = {105--110}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\XHPF9TU5\\Johnson - 2015 - The Savory Swimmer Swims North A Northern Range E.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\6A9YDFM4\\2547817.html:text/html}, +} + +@article{deibel_predictability_2009, + title = {Predictability of patches of neritic salps and doliolids ({Tunicata}, {Thaliacea})}, + volume = {31}, + issn = {0142-7873, 1464-3774}, + url = {https://academic.oup.com/plankt/article-lookup/doi/10.1093/plankt/fbp091}, + doi = {10.1093/plankt/fbp091}, + language = {en}, + number = {12}, + urldate = {2022-02-10}, + journal = {Journal of Plankton Research}, + author = {Deibel, D. and Paffenhofer, G.-A.}, + month = dec, + year = {2009}, + pages = {1571--1579}, + file = {Deibel and Paffenhofer - 2009 - Predictability of patches of neritic salps and dol.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\5DQSAXWX\\Deibel and Paffenhofer - 2009 - Predictability of patches of neritic salps and dol.pdf:application/pdf}, +} + +@article{powell_ocean_2020, + title = {Ocean quahogs ({Arctica} islandica) and {Atlantic} surfclams ({Spisula} solidissima) on the {Mid}-{Atlantic} {Bight} continental shelf and {Georges} {Bank}: {The} death assemblage as a recorder of climate change and the reorganization of the continental shelf benthos}, + volume = {537}, + issn = {0031-0182}, + shorttitle = {Ocean quahogs ({Arctica} islandica) and {Atlantic} surfclams ({Spisula} solidissima) on the {Mid}-{Atlantic} {Bight} continental shelf and {Georges} {Bank}}, + url = {https://www.sciencedirect.com/science/article/pii/S0031018218309118}, + doi = {10.1016/j.palaeo.2019.05.027}, + abstract = {The degree to which evidence of range shifts in shelf biomass dominants, Atlantic surfclams and ocean quahogs, as a function of climate change is examined by comparing the dissimilarity in distribution of the living community and the record of habitation ensconced in the death assemblage. Comparison of the distribution of live animals and dead shells reveals for both species that live animals are rarely found where dead shells were not collected, but dead shells were collected at a substantively larger number of sites than where live animals were found. The geographic footprint resolved by the dead shells was much broader than that of live animals. Extensive spatially-coherent regions exist inshore and offshore of the present-day habitat of occupation where evidence of previous occupation exists. Sites where ocean quahog shells were found without live ocean quahogs included a portion of the continental shelf inshore of the present-day habitable range, an area only habitable under colder climatic conditions than observed today. Surfclam shells were found offshore of the present-day habitable region, in deeper water: additional warming of bottom waters offshore would be necessary for surfclams to occupy the region now occupied solely by dead shells. The death assemblage shows that these biomass dominants have repositioned themselves across the continental shelf during the past and this repositioning certainly is driven by changes in bottom water temperature. Moreover, the distribution of surfclam shells indicates that the death assemblage may provide a possible window into the future, as movement of the present-day population is towards these deeper-water previously-occupied regions. Overall, the death assemblage may provide an important source of information on range shifts sparing the need to carry out extensive and frequent benthic surveys and may permit reconstruction of ongoing shifts in community composition as a product of climate change when adequate survey data are not available.}, + language = {en}, + urldate = {2022-02-10}, + journal = {Palaeogeography, Palaeoclimatology, Palaeoecology}, + author = {Powell, Eric N. and Ewing, Anja M. and Kuykendall, Kelsey M.}, + month = jan, + year = {2020}, + keywords = {Conservation paleobiology, Geographic distribution, Live-dead comparison, Range shift, Spatial time averaging, Species interaction}, + pages = {109205}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\FVKEAPR5\\S0031018218309118.html:text/html}, +} + +@article{pace_two-hundred_2018, + title = {Two-hundred year record of increasing growth rates for ocean quahogs ({Arctica} islandica) from the northwestern {Atlantic} {Ocean}}, + volume = {503}, + issn = {0022-0981}, + url = {https://www.sciencedirect.com/science/article/pii/S0022098117303568}, + doi = {10.1016/j.jembe.2018.01.010}, + abstract = {Ocean quahogs [Arctica islandica (Linnaeus, 1769)] are the longest-lived, non-colonial animal known today, with a maximum life span exceeding 500 years. Ocean quahogs are a commercially important bivalve, inhabiting the continental shelf of the North Atlantic basin. We examined growth rates of ocean quahogs that were fully recruited to the commercial fishery ({\textgreater}80-mm shell length) from four sites covering the range of the stock along the east coast of the U.S. through analysis of annual growth lines in the hinge plate. Both geographic and temporal differences (on a scale of decadal or longer) in growth rates exist throughout the range of the stock. The age at which animals reached 60, 80, and 90 mm decreased significantly, and average growth rates to 60, 80, and 90 mm increased significantly with birth year at a New Jersey and a Long Island site, both located in the southwestern portion of the stock, since the late 1700s/early 1800s, likely in response to increasing bottom water temperatures. That is, growth rates vary temporally with birth date at the southwestern sites, with younger animals growing at a much faster rate in recent decades than those born many decades previously, whereas at the northern sites off southern New England and on Georges Bank, changes in growth rates through time are limited to older adult animals or absent altogether. Thus, at the southern portion of the range, variation in growth rate over time exists in all phases of ocean quahog life, whereas on Georges Bank, little evidence exists for any differential in growth rate over the last {\textasciitilde}200 years. The fact that ocean quahogs record the rise in ocean temperatures after the Little Ice Age in the Mid-Atlantic Bight southeast of southern New England, yet demonstrate little evidence of such a rise in the southern New England and Georges Bank region, would suggest a differential response of ocean circulation and its control of bottom water temperature between the northern and southern portions of the Mid-Atlantic Bight over the last 200+ years.}, + language = {en}, + urldate = {2022-02-10}, + journal = {Journal of Experimental Marine Biology and Ecology}, + author = {Pace, Sara M. and Powell, Eric N. and Mann, Roger}, + month = jun, + year = {2018}, + keywords = {Climate change, Growth, Little Ice Age, Ocean quahog}, + pages = {8--22}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\IKCT8QWJ\\S0022098117303568.html:text/html}, +} + +@article{worm_predator_2003, + title = {Predator diversity hotspots in the blue ocean}, + volume = {100}, + copyright = {Copyright © 2003, The National Academy of Sciences}, + issn = {0027-8424, 1091-6490}, + url = {https://www.pnas.org/content/100/17/9884}, + doi = {10.1073/pnas.1333941100}, + abstract = {Concentrations of biodiversity, or hotspots, represent conservation priorities in terrestrial ecosystems but remain largely unexplored in marine habitats. In the open ocean, many large predators such as tunas, sharks, billfishes, and sea turtles are of current conservation concern because of their vulnerability to overfishing and ecosystem role. Here we use scientific-observer records from pelagic longline fisheries in the Atlantic and Pacific Oceans to show that oceanic predators concentrate in distinct diversity hotspots. Predator diversity consistently peaks at intermediate latitudes (20–30° N and S), where tropical and temperate species ranges overlap. Individual hotspots are found close to prominent habitat features such as reefs, shelf breaks, or seamounts and often coincide with zooplankton and coral reef hotspots. Closed-area models in the northwest Atlantic predict that protection of hotspots outperforms other area closures in safeguarding threatened pelagic predators from ecological extinction. We conclude that the seemingly monotonous landscape of the open ocean shows rich structure in species diversity and that these features should be used to focus future conservation efforts.}, + language = {en}, + number = {17}, + urldate = {2022-02-10}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Worm, Boris and Lotze, Heike K. and Myers, Ransom A.}, + month = aug, + year = {2003}, + pmid = {12907699}, + note = {Publisher: National Academy of Sciences +Section: Biological Sciences}, + pages = {9884--9888}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\RYABUMGF\\Worm et al. - 2003 - Predator diversity hotspots in the blue ocean.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\UM6QF23W\\9884.html:text/html}, +} + +@article{madin_periodic_2006, + title = {Periodic swarms of the salp {Salpa} aspera in the {Slope} {Water} off the {NE} {United} {States}: {Biovolume}, vertical migration, grazing, and vertical flux}, + volume = {53}, + issn = {0967-0637}, + shorttitle = {Periodic swarms of the salp {Salpa} aspera in the {Slope} {Water} off the {NE} {United} {States}}, + url = {https://www.sciencedirect.com/science/article/pii/S0967063706000173}, + doi = {10.1016/j.dsr.2005.12.018}, + abstract = {Sampling during four summers over a twenty-seven year period has documented dense populations of Salpa aspera in the Slope Water south of New England, northeastern United States. The salps demonstrated a strong pattern of diel vertical migration, moving to depth (mostly 600–800m) during the day and aggregating in the epipelagic ({\textless}100m) at night. Filtration rates determined from both gut pigment analysis and direct feeding experiments indicated that both the aggregate and solitary stages filtered water at rates ranging from 0.5 to 6lh-1ml-1 biovolume. Maximum displacement volumes of salps measured were 5.7lm-2 in 1986 and 1.6lm-2 in 1993. Depending on the year, the sampled salp populations were calculated to clear between 8 and 74\% of the upper 50m during each 8h night. Total fecal output for the same populations was estimated to be between 5 and 91mgCm-2night-1. These results, and other observations, suggest this region is a salp “hot spot”, with swarms of S. aspera developing seasonally on a frequent basis.}, + language = {en}, + number = {5}, + urldate = {2022-02-10}, + journal = {Deep Sea Research Part I: Oceanographic Research Papers}, + author = {Madin, L. P. and Kremer, P. and Wiebe, P. H. and Purcell, J. E. and Horgan, E. H. and Nemazie, D. A.}, + month = may, + year = {2006}, + keywords = {Zooplankton, Atlantic Slope Water, Grazing, Salps, Vertical flux, Vertical migration}, + pages = {804--819}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\76VQA2RT\\S0967063706000173.html:text/html}, +} + +@article{potter_horizontal_2011, + title = {Horizontal movement of ocean sunfish, {Mola} mola, in the northwest {Atlantic}}, + volume = {158}, + issn = {1432-1793}, + url = {https://doi.org/10.1007/s00227-010-1578-2}, + doi = {10.1007/s00227-010-1578-2}, + abstract = {Data were retrieved from 25 ocean sunfish (Mola mola) that were tagged with pop-up satellite archival tags in the southern Gulf of Maine (n = 6), off Nantucket Island (n = 17), and off the coast of Georgia (n = 2) between September 2005 and March 2008. Tags remained attached from 7 to 242 days, with a mean attachment period of (X ± SD) 107.2 ± 80.6 days. Ocean sunfish tagged in the Gulf of Maine and southern New England left those areas in the late summer and early autumn and moved south along the continental shelf break. Fish traveled as far south as the Bahamas and the Gulf of Mexico. By moving south, sunfish experienced similar mean sea surface temperatures throughout the tagging period. The maximum straight-line distance traveled by a tagged Mola mola was 2,520 km in 130 days. Two tagged ocean sunfish entered the Gulf of Mexico, one in the December and one in July. Movements were associated with frontal features created by the Gulf Stream and fish moved farther offshore in 2007 when the Gulf Stream was deflected from the shelf break.}, + language = {en}, + number = {3}, + urldate = {2022-02-10}, + journal = {Marine Biology}, + author = {Potter, Inga F. and Galuardi, Benjamin and Howell, W. Huntting}, + month = mar, + year = {2011}, + pages = {531--540}, + file = {Springer Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\YUG6HGHA\\Potter et al. - 2011 - Horizontal movement of ocean sunfish, Mola mola, i.pdf:application/pdf}, +} + +@article{caesar_current_2021, + title = {Current {Atlantic} {Meridional} {Overturning} {Circulation} weakest in last millennium}, + volume = {14}, + copyright = {2021 The Author(s), under exclusive licence to Springer Nature Limited}, + issn = {1752-0908}, + url = {https://www.nature.com/articles/s41561-021-00699-z}, + doi = {10.1038/s41561-021-00699-z}, + abstract = {The Atlantic Meridional Overturning Circulation (AMOC)—one of Earth’s major ocean circulation systems—redistributes heat on our planet and has a major impact on climate. Here, we compare a variety of published proxy records to reconstruct the evolution of the AMOC since about ad 400. A fairly consistent picture of the AMOC emerges: after a long and relatively stable period, there was an initial weakening starting in the nineteenth century, followed by a second, more rapid, decline in the mid-twentieth century, leading to the weakest state of the AMOC occurring in recent decades.}, + language = {en}, + number = {3}, + urldate = {2022-02-10}, + journal = {Nature Geoscience}, + author = {Caesar, L. and McCarthy, G. D. and Thornalley, D. J. R. and Cahill, N. and Rahmstorf, S.}, + month = mar, + year = {2021}, + note = {Number: 3 +Publisher: Nature Publishing Group}, + keywords = {Climate change, Physical oceanography, Palaeoclimate}, + pages = {118--120}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\DPJZKDCQ\\s41561-021-00699-z.html:text/html;Submitted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\NNTXSEI2\\Caesar et al. - 2021 - Current Atlantic Meridional Overturning Circulatio.pdf:application/pdf}, +} + +@article{caesar_observed_2018, + title = {Observed fingerprint of a weakening {Atlantic} {Ocean} overturning circulation}, + volume = {556}, + copyright = {2018 Macmillan Publishers Ltd., part of Springer Nature}, + issn = {1476-4687}, + url = {https://www.nature.com/articles/s41586-018-0006-5}, + doi = {10.1038/s41586-018-0006-5}, + abstract = {The Atlantic meridional overturning circulation (AMOC)—a system of ocean currents in the North Atlantic—has a major impact on climate, yet its evolution during the industrial era is poorly known owing to a lack of direct current measurements. Here we provide evidence for a weakening of the AMOC by about 3 ± 1 sverdrups (around 15 per cent) since the mid-twentieth century. This weakening is revealed by a characteristic spatial and seasonal sea-surface temperature ‘fingerprint’—consisting of a pattern of cooling in the subpolar Atlantic Ocean and warming in the Gulf Stream region—and is calibrated through an ensemble of model simulations from the CMIP5 project. We find this fingerprint both in a high-resolution climate model in response to increasing atmospheric carbon dioxide concentrations, and in the temperature trends observed since the late nineteenth century. The pattern can be explained by a slowdown in the AMOC and reduced northward heat transport, as well as an associated northward shift of the Gulf Stream. Comparisons with recent direct measurements from the RAPID project and several other studies provide a consistent depiction of record-low AMOC values in recent years.}, + language = {en}, + number = {7700}, + urldate = {2022-02-10}, + journal = {Nature}, + author = {Caesar, L. and Rahmstorf, S. and Robinson, A. and Feulner, G. and Saba, V.}, + month = apr, + year = {2018}, + note = {Number: 7700 +Publisher: Nature Publishing Group}, + keywords = {Physical oceanography, Climate and Earth system modelling, Ocean sciences}, + pages = {191--196}, + file = {Accepted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\UEBMA38Z\\Caesar et al. - 2018 - Observed fingerprint of a weakening Atlantic Ocean.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\STMDY3GI\\s41586-018-0006-5.html:text/html}, +} + +@article{miles_offshore_2021, + title = {Offshore {Wind} {Energy} and the {Mid}-{Atlantic} {Cold} {Pool}: {A} {Review} of {Potential} {Interactions}}, + volume = {55}, + shorttitle = {Offshore {Wind} {Energy} and the {Mid}-{Atlantic} {Cold} {Pool}}, + doi = {10.4031/MTSJ.55.4.8}, + abstract = {Abstract +The U.S. East Coast has 1.7 million acres of federal bottom under lease for the development of wind energy installations, with plans for more than 1,500 foundations to be placed. The scale of these wind farms has the potential to alter the unique and delicate oceanographic +conditions along the expansive Atlantic continental shelf, a region characterized by a strong seasonal thermocline that overlies cold bottom water, known as the “Cold Pool.” Strong seasonal stratification traps cold (typically less than 10°C) water above the ocean bottom sustaining +a boreal fauna that represents vast fisheries, including the most lucrative shellfish fisheries in the United States. This paper reviews the existing literature and research pertaining to the ways in which offshore wind farms may alter processes that establish, maintain, and degrade stratification +associated with the Cold Pool through vertical mixing in this seasonally dynamic system. Changes in stratification could have important consequences in Cold Pool setup and degradation, processes fundamental to high fishery productivity of the region. The potential for these multiple wind energy +arrays to alter oceanographic processes and the biological systems that rely on them is possible; however, a great deal of uncertainty remains about the nature and scale of these interactions. Research should be prioritized that identifies stratification thresholds of influence, below which +turbines and wind farm arrays may alter oceanographic processes. These should be examined within context of spatial and seasonal dynamics of the Cold Pool and offshore wind lease areas to identify potential areas of further study.}, + number = {4}, + journal = {Marine Technology Society Journal}, + author = {Miles, Travis and Murphy, Sarah and Kohut, Josh and Borsetti, Sarah and Munroe, Daphne}, + month = jul, + year = {2021}, + keywords = {Mid-Atlantic Cold Pool, ocean interactions, offshore wind}, + pages = {72--87}, +} + +@article{gawarkiewicz_characteristics_2019, + title = {Characteristics of an {Advective} {Marine} {Heatwave} in the {Middle} {Atlantic} {Bight} in {Early} 2017}, + volume = {6}, + issn = {2296-7745}, + url = {https://www.frontiersin.org/article/10.3389/fmars.2019.00712}, + abstract = {There has been wide interest in Marine Heatwaves and their ecological consequences in recent years. Most analyses have focused on remotely sensed sea surface temperature data due to the temporal and spatial coverage it provides in order to establish the presence and duration of Heatwaves. Using hydrographic data from a variety of sources, we show that an advective Marine Heatwave was initiated by an event in late December of 2016 south of New England, with temperature anomalies measuring up to 6°C and salinity anomalies exceeding 1 PSU. Similar features were observed off of New Jersey in February 2017, and are associated with the Shelfbreak Front migrating from its normal position to mid-shelf or further onshore. Shelf water of 34 PSU was observed just north of Cape Hatteras at the 30 m isobath and across the continental shelf in late April 2017. These observations reveal that the 2017 Marine Heatwave was associated with a strong positive salinity anomaly, that its total duration was approximately 4 months, and its advective path extended roughly 850 km along the length of the continental shelf in the Middle Atlantic Bight. The southward advective velocity implied by the arrival north of Cape Hatteras is consistent with previous estimates of alongshelf velocity for the region. The origin of this Marine Heatwave is likely related to cross-shelf advection driven by the presence of a Warm Core Ring adjacent to the shelfbreak south of New England.}, + urldate = {2022-02-10}, + journal = {Frontiers in Marine Science}, + author = {Gawarkiewicz, Glen and Chen, Ke and Forsyth, Jacob and Bahr, Frank and Mercer, Anna M. and Ellertson, Aubrey and Fratantoni, Paula and Seim, Harvey and Haines, Sara and Han, Lu}, + year = {2019}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\9VF6DG7F\\Gawarkiewicz et al. - 2019 - Characteristics of an Advective Marine Heatwave in.pdf:application/pdf}, +} + +@article{chen_mesoscale_2022, + title = {Mesoscale and {Submesoscale} {Shelf}-{Ocean} {Exchanges} {Initialize} an {Advective} {Marine} {Heatwave}}, + volume = {127}, + url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021JC017927}, + doi = {https://doi.org/10.1029/2021JC017927}, + abstract = {Abstract Observations and high-resolution numerical modeling are used to investigate the dynamical processes related to the initiation of an advective Marine Heatwave in the Middle Atlantic Bight of the Northwest Atlantic continental shelf. Both the observations and the model identify two significant cross-shelf intrusions in November 2016 and January 2017, with the latter inducing large-magnitude water mass anomalies across the shelf. Model prognostic fields reveal the importance of the combination of cyclonic eddies or ringlets and upwelling-favorable winds in producing the large-distance cross-shelf penetration and temperature/salinity anomalies. The cyclonic eddies in close proximity to the shelfbreak set up local along-isobath pressure gradients and provide favorable conditions for the intensification of the shelfbreak front, both processes driving cross-isobath intrusions of warm, salty offshore water onto the outer continental shelf. Subsequently, strong and persistent upwelling-favorable winds drive a rapid, bottom intensified cross-shelf penetration in January 2017 composed of the anomalous water mass off the shelfbreak. The along-shelf settings including realistic representation of bathymetric features are essential in the characteristics of the cross-shelf penetration. The results highlight the importance of smaller scale cyclonic eddies and the intricacy of the interplay between multiple processes to drive significant cross-shelf events.}, + number = {1}, + journal = {Journal of Geophysical Research: Oceans}, + author = {Chen, Ke and Gawarkiewicz, Glen and Yang, Jiayan}, + year = {2022}, + note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2021JC017927}, + keywords = {cross-shelf exchange, drivers of Marine heatwave, pressure gradient setup, shelfbreak front and frontogenesis, warm core rings and cyclonic eddies, wind-driven upwelling and bottom intrusion}, + pages = {e2021JC017927}, + annote = {e2021JC017927 2021JC017927}, +} + +@article{friedland_middle_2022, + title = {The {Middle} {Atlantic} {Bight} {Cold} {Pool} is warming and shrinking: {Indices} from in situ autumn seafloor temperatures}, + volume = {31}, + issn = {1365-2419}, + shorttitle = {The {Middle} {Atlantic} {Bight} {Cold} {Pool} is warming and shrinking}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/fog.12573}, + doi = {10.1111/fog.12573}, + abstract = {The Cold Pool feature of the Middle Atlantic Bight (MAB) is a body of cold bottom water that develops in the spring and persists through the summer-autumn months. It is maintained by northerly currents and can be traced back to Arctic water masses. The Cold Pool provides habitat for many boreal species at latitudes far south of their normal range and plays an important role in the population dynamics of lower and upper trophic level organisms. Here, we describe changes in the extent and thermal properties of the Cold Pool using both observations and models. Two indices are developed based on a gridded, interpolated bottom temperature dataset; the first is a mean temperature indicator, and the second is a spatial extent indicator. The temperature indicator showed a significant increasing trend over the study period 1968–2019 and a single change point in 2008. Similarly, the area indicator declined significantly, also displaying a change point in 2008. Cold Pool maximum temperature and minimum size were observed in 2017, which is also known as a heatwave year in the MAB. The indices presented here support the view of a rapidly warming Cold Pool that is being limited in its spatial extent. Changes in Cold Pool hydrography will likely affect boreal species distributions and total ecosystem productivity.}, + language = {en}, + number = {2}, + urldate = {2022-02-10}, + journal = {Fisheries Oceanography}, + author = {Friedland, Kevin D. and Miles, Travis and Goode, Andrew G. and Powell, Eric N. and Brady, Damian C.}, + year = {2022}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/fog.12573}, + keywords = {climate change, Cold Pool, ecosystem, regime shift, temperature}, + pages = {217--223}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\JG9MK62Q\\fog.html:text/html}, +} + +@article{gangopadhyay_census_2020, + title = {A {Census} of the {Warm}-{Core} {Rings} of the {Gulf} {Stream}: 1980–2017}, + volume = {125}, + issn = {2169-9291}, + shorttitle = {A {Census} of the {Warm}-{Core} {Rings} of the {Gulf} {Stream}}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JC016033}, + doi = {10.1029/2019JC016033}, + abstract = {A census of Gulf Stream (GS) warm-core rings (WCRs) is presented based on 38 years (1980–2017) of data. The census documents formation and demise times and locations, and formation size for all 961 WCRs formed in the study period that live for a week or more. A clear regime shift was observed around the Year 2000 and was reported by a subset of authors (Gangopadhyay et al., 2019, https://doi.org/10.1038/s41598-019-48661-9). The WCR formation over the whole region (75–55°W) increased from an average of 18 per year during Regime 1 (1980–1999) to 33 per year during Regime 2 (2000–2017). For geographic analysis formation locations were grouped in four 5° zones between 75°W and 55°W. Seasonally, WCR formations show a significant summer maxima and winter minima, a pattern that is consistent through all zones and both temporal regimes. The lifespan and size distribution show progressively more rings with higher longevity and greater size when formed to the east of 70°W. The average lifespan of the WCRs in all four zones decreased by 20–40\% depending on zones and/or seasons from Regime 1 to Regime 2, while the size distribution remained unchanged across regimes. The ring footprint index, a first-order signature of impact of the WCRs on the slope, increased significantly (26–90\%) for all zones from Regime 1 to Regime 2, with the highest percent increase in Zone 2 (70–65°W). This observational study establishes critical statistical and dynamical benchmarks for validating numerical models and highlights the need for further dynamical understanding of the GS-ring formation processes.}, + language = {en}, + number = {8}, + urldate = {2022-02-10}, + journal = {Journal of Geophysical Research: Oceans}, + author = {Gangopadhyay, Avijit and Gawarkiewicz, Glen and Silva, E. Nishchitha S. and Silver, Adrienne M. and Monim, M. and Clark, Jenifer}, + year = {2020}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019JC016033}, + keywords = {Gulf Stream, Lifespan of rings, Regime-Shift, Ring Census, size of rings, Warm Core Rings}, + pages = {e2019JC016033}, + annote = {e2019JC016033 2019JC016033}, + file = {Accepted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\4GAVAJBF\\Gangopadhyay et al. - 2020 - A Census of the Warm-Core Rings of the Gulf Stream.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\8UCSFM2W\\2019JC016033.html:text/html}, +} + +@article{silver_forecasting_2021, + title = {Forecasting the {Gulf} {Stream} {Path} {Using} {Buoyancy} and {Wind} {Forcing} {Over} the {North} {Atlantic}}, + volume = {126}, + issn = {2169-9291}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021JC017614}, + doi = {10.1029/2021JC017614}, + abstract = {Fluctuations in the path of the Gulf Stream (GS) have been previously studied by primarily connecting to either the wind-driven subtropical gyre circulation or buoyancy forcing via the subpolar gyre. Here we present a statistical model for 1 year predictions of the GS path (represented by the GS northern wall—GSNW) between W and W incorporating both mechanisms in a combined framework. An existing model with multiple parameters including the previous year's GSNW index, center location, and amplitude of the Icelandic Low and the Southern Oscillation Index was augmented with basin-wide Ekman drift over the Azores High. The addition of the wind is supported by a validation of the simpler two-layer Parsons-Veronis model of GS separation over the last 40 years. A multivariate analysis was carried out to compare 1-year-in-advance forecast correlations from four different models. The optimal predictors of the best performing model include: (a) the GSNW index from the previous year, (b) gyre-scale integrated Ekman Drift over the past 2 years, and (c) longitude of the Icelandic Low center lagged by 3 years. The forecast correlation over the 27 years (1994–2020) is 0.65, an improvement from the previous multi-parameter model's forecast correlation of 0.52. The improvement is attributed to the addition of the wind-drift component. The sensitivity of forecasting the GS path after extreme atmospheric years is quantified. Results indicate the possibility of better understanding and enhanced predictability of the dominant wind-driven variability of the Atlantic Meridional Overturning Circulation and of fisheries management models that use the GS path as a metric.}, + language = {en}, + number = {8}, + urldate = {2022-02-10}, + journal = {Journal of Geophysical Research: Oceans}, + author = {Silver, Adrienne and Gangopadhyay, Avijit and Gawarkiewicz, Glen and Taylor, Arnold and Sanchez-Franks, Alejandra}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021JC017614}, + keywords = {Gulf Stream, AMOC, Azores high, forecasting, Icelandic low, North Atlantic}, + pages = {e2021JC017614}, + annote = {e2021JC017614 2021JC017614}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\E6A2PALX\\Silver et al. - 2021 - Forecasting the Gulf Stream Path Using Buoyancy an.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\RTB5CUQN\\2021JC017614.html:text/html}, +} + +@article{silver_interannual_2021, + title = {Interannual and seasonal asymmetries in {Gulf} {Stream} {Ring} {Formations} from 1980 to 2019}, + volume = {11}, + copyright = {2021 The Author(s)}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-021-81827-y}, + doi = {10.1038/s41598-021-81827-y}, + abstract = {As the Gulf Stream separates from the coast, it sheds both Warm and Cold Core Rings between \$\$75{\textasciicircum}{\textbackslash}circ\$\$and \$\$55{\textasciicircum}{\textbackslash}circ {\textbackslash},{\textbackslash}hbox \{W\}\$\$. We present evidence that this ring formation behavior has been asymmetric over both interannual and seasonal time-scales. After a previously reported regime-shift in 2000, 15 more Warm Core Rings have been forming yearly compared to 1980–1999. In contrast, there have been no changes in the annual formation rate of the Cold Core Rings. This increase in Warm Core Ring production leads to an excess heat transfer of 0.10 PW to the Slope Sea, amounting to 7.7–12.4\% of the total Gulf Stream heat transport, or 5.4–7.3\% of the global oceanic heat budget at \$\$30{\textasciicircum}{\textbackslash}circ {\textbackslash},{\textbackslash}hbox \{N\}\$\$. Seasonally, more Cold Core Rings are produced in the winter and spring and more Warm Core Rings are produced in the summer and fall leading to more summertime heat transfer to the north of the Stream. The seasonal cycle of relative ring formation numbers is strongly correlated (r = 0.82) with that of the difference in upper layer temperatures between the Sargasso and Slope seas. This quantification motivates future efforts to understand the recent increasing influence of the Gulf Stream on the circulation and ecosystem in the western North Atlantic.}, + language = {en}, + number = {1}, + urldate = {2022-02-10}, + journal = {Scientific Reports}, + author = {Silver, Adrienne and Gangopadhyay, Avijit and Gawarkiewicz, Glen and Silva, E. Nishchitha S. and Clark, Jenifer}, + month = jan, + year = {2021}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Physical oceanography, Attribution}, + pages = {2207}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\6VUHKBAB\\Silver et al. - 2021 - Interannual and seasonal asymmetries in Gulf Strea.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\QDX62JJL\\s41598-021-81827-y.html:text/html}, +} + +@article{goncalves_neto_changes_2021, + title = {Changes in the {Gulf} {Stream} preceded rapid warming of the {Northwest} {Atlantic} {Shelf}}, + volume = {2}, + copyright = {2021 The Author(s)}, + issn = {2662-4435}, + url = {https://www.nature.com/articles/s43247-021-00143-5}, + doi = {10.1038/s43247-021-00143-5}, + abstract = {The Northwest Atlantic Shelf provides ecological and economic benefits along the heavily populated North American coastline and beyond. In 2009-2010, abrupt warming prompted an ecosystem shift with consequences for fisheries, yet the cause of this event is unclear. Here we use satellite altimetry and in situ measurements to show that, in 2008, the Gulf Stream migrated closer to the Tail of the Grand Banks, a shift that has persisted ever since. This change reduced the westward connectivity of the Labrador Current that otherwise supplies cold, fresh, oxygen-rich waters to the shelf. Within one year after the appearance of anomalously warm and saline water at the Tail of Grand Banks, subsurface warming progressed south-westwards. Historical observations suggest a similar sequence of events may have occurred in the 1970s. Therefore, monitoring water properties at the Tail of Grand Banks may offer predictability for shelf properties and ecosystem perturbations with substantial lead time.}, + language = {en}, + number = {1}, + urldate = {2022-02-10}, + journal = {Communications Earth \& Environment}, + author = {Gonçalves Neto, Afonso and Langan, Joseph A. and Palter, Jaime B.}, + month = apr, + year = {2021}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Environmental impact, Climate-change impacts, Physical oceanography}, + pages = {1--10}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\TREA4KKH\\Gonçalves Neto et al. - 2021 - Changes in the Gulf Stream preceded rapid warming .pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\U6672DEQ\\s43247-021-00143-5.html:text/html}, +} + +@incollection{thunberg_northeast_2021, + title = {Northeast {Region} {Fisheries} {Impacts} from {COVID}-19}, + url = {https://spo.nmfs.noaa.gov/sites/default/files/TM221.pdf}, + language = {en}, + booktitle = {U.{S}. {Seafood} {Industry} and {For}-{Hire} {Sector} {Impacts} from {COVID}-19: 2020 in {Perspective}. {NOAA} {Tech}. {Memo}. {NMFS}-{SPO}-221}, + author = {Thunberg, E.M.}, + year = {2021}, + pages = {53--64}, + file = {U.S. Seafood Industry and For-Hire Sector Impacts .pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\BYJBLBUB\\U.S. Seafood Industry and For-Hire Sector Impacts .pdf:application/pdf}, +} + +@article{chen_seasonal_2021, + title = {Seasonal {Prediction} of {Bottom} {Temperature} on the {Northeast} {U}.{S}. {Continental} {Shelf}}, + volume = {126}, + issn = {2169-9291}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2021JC017187}, + doi = {10.1029/2021JC017187}, + abstract = {The Northeast U.S. shelf (NES) is an oceanographically dynamic marine ecosystem and supports some of the most valuable demersal fisheries in the world. A reliable prediction of NES environmental variables, particularly ocean bottom temperature, could lead to a significant improvement in demersal fisheries management. However, the current generation of climate model-based seasonal-to-interannual predictions exhibits limited prediction skill in this continental shelf environment. Here, we have developed a hierarchy of statistical seasonal predictions for NES bottom temperatures using an eddy-resolving ocean reanalysis data set. A simple, damped local persistence prediction model produces significant skill for lead times up to ∼5 months in the Mid-Atlantic Bight and up to ∼10 months in the Gulf of Maine, although the prediction skill varies notably by season. Considering temperature from a nearby or upstream (i.e., more poleward) region as an additional predictor generally improves prediction skill, presumably as a result of advective processes. Large-scale atmospheric and oceanic indices, such as Gulf Stream path indices (GSIs) and the North Atlantic Oscillation Index, are also tested as predictors for NES bottom temperatures. Only the GSI constructed from temperature observed at 200 m depth significantly improves the prediction skill relative to local persistence. However, the prediction skill from this GSI is not larger than that gained using models incorporating nearby or upstream shelf/slope temperatures. Based on these results, a simplified statistical model has been developed, which can be tailored to fisheries management for the NES.}, + language = {en}, + number = {5}, + urldate = {2021-10-27}, + journal = {Journal of Geophysical Research: Oceans}, + author = {Chen, Zhuomin and Kwon, Young-Oh and Chen, Ke and Fratantoni, Paula and Gawarkiewicz, Glen and Joyce, Terrence M. and Miller, Timothy J. and Nye, Janet A. and Saba, Vincent S. and Stock, Brian C.}, + year = {2021}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021JC017187}, + keywords = {bottom temperature, Northeast U.S. shelf, seasonal prediction}, + pages = {e2021JC017187}, + annote = {e2021JC017187 2021JC017187}, + annote = {e2021JC017187 2021JC017187}, + annote = {e2021JC017187 2021JC017187}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\CZWV2IDZ\\Chen et al. - 2021 - Seasonal Prediction of Bottom Temperature on the N.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\DRQ2UP9S\\Chen et al. - 2021 - Seasonal Prediction of Bottom Temperature on the N.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\M8PC2CQ2\\2021JC017187.html:text/html}, +} + +@article{lucey_conducting_2020, + title = {Conducting reproducible ecosystem modeling using the open source mass balance model {Rpath}}, + volume = {427}, + issn = {0304-3800}, + url = {https://www.sciencedirect.com/science/article/pii/S0304380020301290}, + doi = {10.1016/j.ecolmodel.2020.109057}, + abstract = {Ecosystem models are important tools for conducting ecosystem-based management. A particularly useful method of characterizing the flow of energy through an ecosystem and the subsequent direct and indirect implications of management actions is mass balance modeling. Here we outline the equations as utilized in Rpath, an R implementation of the mass balance algorithms popularized by Ecopath with Ecosim that are designed to work with fisheries data sources. We believe that common practices in R will aid in the reproducibility of conducting analysis using a mass balance model as all of the code is contained within a single script file. This includes the built-in statistical and graphical functions of R. In addition to added reproducibility, R is a coding language with which ecologists are familiar. This familiarity offers greater flexibility for practitioners to tailor the model to their needs. We have made the code available on an open software development platform which should aid in continuous community development of the tool.}, + language = {en}, + urldate = {2021-04-16}, + journal = {Ecological Modelling}, + author = {Lucey, Sean M. and Gaichas, Sarah K. and Aydin, Kerim Y.}, + month = jul, + year = {2020}, + keywords = {Ecopath, Ecosim, Mass balance, R, Reproducibility, Rpath}, + pages = {109057}, + file = {ScienceDirect Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\GC7K2S7N\\Lucey et al. - 2020 - Conducting reproducible ecosystem modeling using t.pdf:application/pdf;ScienceDirect Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\X3HXPY7R\\Lucey et al. - 2020 - Conducting reproducible ecosystem modeling using t.pdf:application/pdf;ScienceDirect Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\TH28V9A5\\Lucey et al. - 2020 - Conducting reproducible ecosystem modeling using t.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\UFVKGJYL\\S0304380020301290.html:text/html;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\4H8EQEJR\\S0304380020301290.html:text/html}, +} + +@article{bastille_improving_2021, + title = {Improving the {IEA} {Approach} {Using} {Principles} of {Open} {Data} {Science}}, + volume = {49}, + issn = {0892-0753}, + url = {https://doi.org/10.1080/08920753.2021.1846155}, + doi = {10.1080/08920753.2021.1846155}, + abstract = {Integrated ecosystem assessments (IEAs) compile and use indicators, risk assessments, and other analyses to address regional policy needs at varying spatial scales. Although approaches to implementing IEAs are context-specific, challenges in data acquisition, management, processing, analysis, and communication are universal. By embracing open science, in which scientific data, methods, and products are made publicly accessible, along with the ever-expanding tools facilitating open science, IEA practitioners will be better equipped to address these challenges. Here, we provide a snapshot of the state of open science practices in IEAs on-going across the United States. We show that open science has improved the flexibility, reproducibility, and efficiency of the scientific workflows within the IEA framework. Although the initial time investment necessary for developing open science workflows may appear daunting, we show that the subsequent returns provided by the efficient and transparent development of IEA products are worth the effort. By improving the implementation of IEAs, open science tools and principals have the potential to further Ecosystem Based Management (EBM) worldwide.}, + number = {1}, + urldate = {2021-04-16}, + journal = {Coastal Management}, + author = {Bastille, Kimberly and Hardison, Sean and deWitt, Lynn and Brown, Jennifer and Samhouri, Jameal and Gaichas, Sarah and Lucey, Sean and Kearney, Kelly and Best, Ben and Cross, Scott and Large, Scott and Spooner, Ellen}, + month = jan, + year = {2021}, + note = {Publisher: Taylor \& Francis +\_eprint: https://doi.org/10.1080/08920753.2021.1846155}, + keywords = {IEA framwork, open science, reproducilibity}, + pages = {72--89}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\SQ23VV28\\Bastille et al. - 2020 - Improving the IEA Approach Using Principles of Ope.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\2TRPDNGD\\Bastille et al. - 2021 - Improving the IEA Approach Using Principles of Ope.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\HAF7YFL7\\08920753.2021.html:text/html;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\LUTHUBQD\\08920753.2021.html:text/html}, +} + +@article{muffley_there_2021, + title = {There {Is} no {I} in {EAFM} {Adapting} {Integrated} {Ecosystem} {Assessment} for {Mid}-{Atlantic} {Fisheries} {Management}}, + volume = {49}, + issn = {0892-0753}, + url = {https://doi.org/10.1080/08920753.2021.1846156}, + doi = {10.1080/08920753.2021.1846156}, + abstract = {Resource managers worldwide are being asked to consider the ecosystem while making management decisions. Integrated Ecosystem Assessment (IEA) provides a flexible framework for addressing ecosystem considerations in decision making. The US Mid-Atlantic Fishery Management Council (Council) adapted the IEA approach and implemented a structured decision framework to address species, fleet, habitat, and climate interactions as part of their Ecosystem Approach to Fisheries Management (EAFM) in 2016. The Council’s EAFM decision framework first uses risk assessment to prioritize fishery-ecosystem interactions for consideration. The Council’s 2017 EAFM risk assessment identified a range of ecological, social, and management objectives or risk elements. Development of a conceptual model to identify key environmental, ecological, social, economic, and management linkages for a high-priority fishery is the second step in the framework. The Council identified summer flounder (Paralichthys dentatus) as a high-risk fishery and finalized an EAFM conceptual model that considers high-risk factors and ecosystem elements in 2019. The Council used the conceptual model to identify three priority summer flounder management questions (recreational data uncertainty, recreational discards, and distribution shifts) to be considered for quantitative management strategy evaluation, the third step in the EAFM framework and set to begin in 2020. Finally, as strategies are implemented, outcomes are monitored and the process is adjusted, and/or other priorities identified in the risk assessment can be addressed. The Council’s rapid progress in implementing EAFM resulted from an extensive, positive, and collaborative process between managers, stakeholders, and scientists. Collaboration helps build trust and buy-in from all participants and is essential to IEA and to the success of EAFM.}, + number = {1}, + urldate = {2021-04-16}, + journal = {Coastal Management}, + author = {Muffley, Brandon and Gaichas, Sarah and DePiper, Geret and Seagraves, Richard and Lucey, Sean}, + month = jan, + year = {2021}, + note = {Publisher: Taylor \& Francis +\_eprint: https://doi.org/10.1080/08920753.2021.1846156}, + keywords = {conceptual modeling, economic indicators, ecosystem approach, ecosystem indicators, fisheries, integrated ecosystem assessment, management objectives, risk assessment}, + pages = {90--106}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\XLDZAXTT\\Muffley et al. - 2020 - There Is no I in EAFM Adapting Integrated Ecosyste.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\42WLVQP7\\Muffley et al. - 2021 - There Is no I in EAFM Adapting Integrated Ecosyste.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\6W6VBQXV\\Muffley et al. - 2021 - There Is no I in EAFM Adapting Integrated Ecosyste.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\XZP62S8C\\08920753.2021.html:text/html;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\G82GWCW3\\08920753.2021.html:text/html;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\JXQAMQBG\\08920753.2021.html:text/html;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\5A25PBJX\\08920753.2021.html:text/html}, +} + +@article{depiper_operationalizing_2017, + title = {Operationalizing integrated ecosystem assessments within a multidisciplinary team: lessons learned from a worked example}, + volume = {74}, + copyright = {All rights reserved}, + issn = {1054-3139}, + shorttitle = {Operationalizing integrated ecosystem assessments within a multidisciplinary team}, + url = {https://academic.oup.com/icesjms/article/74/8/2076/3094701}, + doi = {10.1093/icesjms/fsx038}, + abstract = {Between 2014 and 2016, an interdisciplinary team of researchers including physical oceanographers, biologists, economists and anthropologists developed a working example of an Integrated Ecosystem Assessment (IEA) for three ecologically distinct regions of the Northwest Atlantic; Georges Bank, the Gulf of Maine and the Grand Banks, as part of the International Council for the Exploration of the Sea (ICES) Working Group on the Northwest Atlantic Regional Sea (WGNARS). In this paper, we review the transdisciplinary and collaborative process by which the IEA was developed, with a particular focus on the decision points arising from the IEA construct itself. The aim is to identify key issues faced in developing any IEA, practical decisions made to address these issues within the working group and lessons learned from the process.}, + language = {en}, + number = {8}, + urldate = {2018-03-09}, + journal = {ICES Journal of Marine Science}, + author = {DePiper, Geret S. and Gaichas, Sarah K. and Lucey, Sean M. and Pinto da Silva, Patricia and Anderson, M. Robin and Breeze, Heather and Bundy, Alida and Clay, Patricia M. and Fay, Gavin and Gamble, Robert J. and Gregory, Robert S. and Fratantoni, Paula S. and Johnson, Catherine L. and Koen-Alonso, Mariano and Kleisner, Kristin M. and Olson, Julia and Perretti, Charles T. and Pepin, Pierre and Phelan, Fred and Saba, Vincent S. and Smith, Laurel A. and Tam, Jamie C. and Templeman, Nadine D. and Wildermuth, Robert P.}, + month = oct, + year = {2017}, + pages = {2076--2086}, + annote = {Publisher: Oxford Academic}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\J8TP6MIQ\\DePiper et al. - 2017 - Operationalizing integrated ecosystem assessments .pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\XQHQHIGZ\\3094701.html:text/html}, +} + +@article{gaichas_implementing_2018, + title = {Implementing {Ecosystem} {Approaches} to {Fishery} {Management}: {Risk} {Assessment} in the {US} {Mid}-{Atlantic}}, + volume = {5}, + copyright = {All rights reserved}, + issn = {2296-7745}, + shorttitle = {Implementing {Ecosystem} {Approaches} to {Fishery} {Management}}, + url = {https://www.frontiersin.org/articles/10.3389/fmars.2018.00442/abstract}, + doi = {10.3389/fmars.2018.00442}, + abstract = {Fishery managers worldwide are evaluating methods for incorporating climate, habitat, ecological, social, and economic factors into current operations in order to implement Ecosystem Approaches to Fishery Management (EAFM). While this can seem overwhelming, it is possible to take practical steps towards EAFM implementation that make use of existing information and provide managers with valuable strategic advice. Here, we describe the process used by the U.S. Mid-Atlantic Fishery Management Council (Council) to develop an ecosystem-level risk assessment, the initial step proposed in their recently adopted EAFM guidance document. The Council first defined five types of Risk Elements (ecological, economic, social, food production, management) and identified which management objectives aligned with each element. Based on an existing ecosystem status report for the region and other existing sources (including expert opinion), potential ecological, social, economic, and management indicators were identified for each risk element. Finally, low, low-moderate, moderate-high, and high risk criteria were defined for each indicator, and the indicator data were used to score each risk element using the criteria. The ultimate outcome is a ranked risk assessment in order to focus on the highest risk issues for further evaluation and mitigation. The risk assessment highlights certain species and certain management issues as posing higher cumulative risks to meeting Council management objectives when considering a broad range of ecological, social, and economic factors. Tabular color coded summaries of risk assessment results will be used by the Council to prioritize further EAFM analyses as well as research plans over the coming five years. As ecosystem reporting and operational EAFM continue to evolve in future years, the Council foresees integrating these efforts so that ecosystem indicators are refined to meet the needs of fishery managers in identifying and managing risks to achieving ecological, social, and economic fishery objectives. Overall, ecosystem indicator-based risk assessment is a method that can be adapted to a wide range of resource management systems and available information, and therefore represent a promising way forward in the implementation of EAFM.}, + language = {English}, + urldate = {2018-11-20}, + journal = {Frontiers in Marine Science}, + author = {Gaichas, Sarah Kathleen and DePiper, Geret Sean and Seagraves, Richard John and Muffley, Brandon W. and Sabo, Mary and Colburn, Lisa L. and Loftus, Andrew L.}, + year = {2018}, + keywords = {Ecosystem indicators, Fisheries, Economic indicators, Ecosystem Approach, Integrated Ecosystem Assessment, Management objectives, natural resource management, Risk Assessment}, + annote = {Publisher: Frontiers}, +} + +@article{lentz_seasonal_2017, + title = {Seasonal warming of the {Middle} {Atlantic} {Bight} {Cold} {Pool}}, + volume = {122}, + copyright = {© 2017. American Geophysical Union. All Rights Reserved.}, + issn = {2169-9291}, + url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2016JC012201}, + doi = {10.1002/2016JC012201}, + abstract = {The Cold Pool is a 20–60 m thick band of cold, near-bottom water that persists from spring to fall over the midshelf and outer shelf of the Middle Atlantic Bight (MAB) and Southern Flank of Georges Bank. The Cold Pool is remnant winter water bounded above by the seasonal thermocline and offshore by warmer slope water. Historical temperature profiles are used to characterize the average annual evolution and spatial structure of the Cold Pool. The Cold Pool gradually warms from spring to summer at a rate of order 1°C month−1. The warming rate is faster in shallower water where the Cold Pool is thinner, consistent with a vertical turbulent heat flux from the thermocline to the Cold Pool. The Cold Pool warming rate also varies along the shelf; it is larger over Georges Bank and smaller in the southern MAB. The mean turbulent diffusivities at the top of the Cold Pool, estimated from the spring to summer mean heat balance, are an order of magnitude larger over Georges Bank than in the southern MAB, consistent with much stronger tidal mixing over Georges Bank than in the southern MAB. The stronger tidal mixing causes the Cold Pool to warm more rapidly over Georges Bank and the eastern New England shelf than in the New York Bight or southern MAB. Consequently, the coldest Cold Pool water is located in the New York Bight from late spring to summer.}, + language = {en}, + number = {2}, + urldate = {2020-05-18}, + journal = {Journal of Geophysical Research: Oceans}, + author = {Lentz, S. J.}, + year = {2017}, + note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JC012201}, + keywords = {Cold Pool, continental shelf circulation, Middle Atlantic Bight, seasonal cycle}, + pages = {941--954}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\A3J8YNHQ\\Lentz - 2017 - Seasonal warming of the Middle Atlantic Bight Cold.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\MWQDFXEZ\\Lentz - 2017 - Seasonal warming of the Middle Atlantic Bight Cold.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\CE787RMC\\2016JC012201.html:text/html;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\DUQUAJFH\\2016JC012201.html:text/html;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\KFR9M4QU\\2016JC012201.html:text/html}, +} + +@article{lucey_evaluating_2021, + title = {Evaluating fishery management strategies using an ecosystem model as an operating model}, + volume = {234}, + copyright = {All rights reserved}, + issn = {0165-7836}, + url = {http://www.sciencedirect.com/science/article/pii/S0165783620302976}, + doi = {10.1016/j.fishres.2020.105780}, + abstract = {Management Strategy Evaluation (MSE) is an effective tool to gauge the relative performance of fishery management options. For the most part, MSEs have been applied to single-species management procedures. However, to be more inclusive of all the biological and technical interactions occurring within a system, ecosystem-based strategies are emerging. In order to test the feasibility of these strategies, a full ecosystem model should be used as an operating model. Mass balance food web models include many features that managers are interested in and therefore can be useful as an operating model. Until recently, full feedback interactions between a management strategy and a mass balance operating model were impractical. However, with the development of Rpath, users now have the ability to fully customize their mass balance models. We developed new functionality for the Rpath modelling framework that allows it to be used as a flexible operating model. Using an example Georges Bank model, we demonstrate how Rpath can now pause the simulation, evaluate an external model, and use the results to modify the parameters of the operating model. This new flexibility will allow users to test a variety of management strategies or couple to other models making Rpath a valuable tool for conducting MSEs.}, + language = {en}, + urldate = {2020-12-09}, + journal = {Fisheries Research}, + author = {Lucey, Sean M. and Aydin, Kerim Y. and Gaichas, Sarah K. and Cadrin, Steven X. and Fay, Gavin and Fogarty, Michael J. and Punt, André}, + month = feb, + year = {2021}, + pages = {105780}, + file = {ScienceDirect Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\AVNLHXS4\\Lucey et al. - 2021 - Evaluating fishery management strategies using an .pdf:application/pdf;ScienceDirect Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\WHZPQ5QN\\Lucey et al. - 2021 - Evaluating fishery management strategies using an .pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\H45YFIUT\\S0165783620302976.html:text/html;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\NC5YVEXC\\S0165783620302976.html:text/html}, +} + +@article{pace_cryptic_2021, + title = {Cryptic mortality of {North} {Atlantic} right whales}, + volume = {n/a}, + copyright = {© 2021 The Authors. Conservation Science and Practice published by Wiley Periodicals LLC. on behalf of Society for Conservation Biology}, + issn = {2578-4854}, + url = {https://conbio.onlinelibrary.wiley.com/doi/abs/10.1111/csp2.346}, + doi = {https://doi.org/10.1111/csp2.346}, + abstract = {Evaluations of the conservation status of the endangered North Atlantic right whale as well as many other wildlife species often rely extensively on counts and cause-of-death determinations of carcasses found accidentally or during dedicated surveys. Even when survey effort dedicated to a population is extensive, many deaths may go unseen. We used an abundance estimation model to derive estimates of cryptic mortality for North Atlantic right whales and found that observed carcasses accounted for only 36\% of all estimated death during 1990–2017. We found strong evidence that total mortality varied over time, and that observed carcass counts were poor predictors of estimated annual numbers of whales dying. Importantly, there were substantial differences between fractions of deaths determined to be entanglement related during necropsy (49\%) and the fraction of cryptic deaths suffering serious injuries related to entanglement (87\%). Although we concluded that a single year's observations produced poor estimates of carcass detection rates due to the volatility of ratios of small counts, ratio estimates of data pooled over periods of consistent survey may offer better information on detection rates. Additionally, it appears unwise to consider cause of death determinations from detected carcasses as representative of cause-specific mortality rates in right whales given the large number of seriously injured whales from entanglement that are likely part of the unseen mortality.}, + language = {en}, + number = {n/a}, + urldate = {2021-02-11}, + journal = {Conservation Science and Practice}, + author = {Pace, Richard M. and Williams, Rob and Kraus, Scott D. and Knowlton, Amy R. and Pettis, Heather M.}, + year = {2021}, + note = {\_eprint: https://conbio.onlinelibrary.wiley.com/doi/pdf/10.1111/csp2.346}, + keywords = {carcass detection, cryptic mortality, detection bias, right whale, total mortality}, + pages = {e346}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\RY2UKF8B\\Pace et al. - Cryptic mortality of North Atlantic right whales.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\4ZMDM74U\\csp2.html:text/html}, +} + +@article{wrightfairbanks_autonomous_2020, + title = {Autonomous {Observation} of {Seasonal} {Carbonate} {Chemistry} {Dynamics} in the {Mid}-{Atlantic} {Bight}}, + volume = {125}, + copyright = {©2020. The Authors.}, + issn = {2169-9291}, + url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020JC016505}, + doi = {https://doi.org/10.1029/2020JC016505}, + abstract = {Ocean acidification alters the oceanic carbonate system, increasing potential for ecological, economic, and cultural losses. Historically, productive coastal oceans lack vertically resolved high-resolution carbonate system measurements on time scales relevant to organism ecology and life history. The recent development of a deep ion-sensitive field-effect transistor (ISFET)-based pH sensor system integrated into a Slocum glider has provided a platform for achieving high-resolution carbonate system profiles. From May 2018 to November 2019, seasonal deployments of the pH glider were conducted in the central Mid-Atlantic Bight. Simultaneous measurements from the glider's pH and salinity sensors enabled the derivation of total alkalinity and calculation of other carbonate system parameters including aragonite saturation state. Carbonate system parameters were then mapped against other variables, such as temperature, dissolved oxygen, and chlorophyll, over space and time. The seasonal dynamics of carbonate chemistry presented here provide a baseline to begin identifying drivers of acidification in this vital economic zone.}, + language = {en}, + number = {11}, + urldate = {2021-02-26}, + journal = {Journal of Geophysical Research: Oceans}, + author = {Wright‐Fairbanks, Elizabeth K. and Miles, Travis N. and Cai, Wei-Jun and Chen, Baoshan and Saba, Grace K.}, + year = {2020}, + note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020JC016505}, + keywords = {ocean acidification, autonomous underwater vehicle, carbonate system, glider, Mid-Atlantic Bight}, + pages = {e2020JC016505}, + annote = {e2020JC016505 2020JC016505}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\UKLG3CRR\\Wright‐Fairbanks et al. - 2020 - Autonomous Observation of Seasonal Carbonate Chemi.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\V2JQ8US9\\2020JC016505.html:text/html}, +} + +@article{link_global_2019, + title = {Global ecosystem overfishing: {Clear} delineation within real limits to production}, + volume = {5}, + copyright = {Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.}, + issn = {2375-2548}, + shorttitle = {Global ecosystem overfishing}, + url = {https://advances.sciencemag.org/content/5/6/eaav0474}, + doi = {10.1126/sciadv.aav0474}, + abstract = {The well-documented value of marine fisheries is threatened by overfishing. Management typically focuses on target populations but lacks effective tools to document or restrain overexploitation of marine ecosystems. Here, we present three indices and accompanying thresholds to detect and delineate ecosystem overfishing (EOF): the Fogarty, Friedland, and Ryther indices. These are based on widely available and readily interpreted catch and satellite data that link fisheries landings to primary production using known limits of trophic transfer efficiency. We propose theoretically and empirically based thresholds for each of those indices; with these criteria, several ecosystems are fished sustainably, but nearly 40 to 50\% of tropical and temperate ecosystems exceed even extreme thresholds. Applying these criteria to global fisheries data results in strong evidence for two specific instances of EOF, increases in both pressure on tropical fish and a climate-mediated polar shift. Here, we show that these two patterns represent evidence for global EOF. +Newly derived thresholds show that about half the world’s marine ecosystems are experiencing “ecosystem” overfishing. +Newly derived thresholds show that about half the world’s marine ecosystems are experiencing “ecosystem” overfishing.}, + language = {en}, + number = {6}, + urldate = {2021-01-29}, + journal = {Science Advances}, + author = {Link, Jason S. and Watson, Reg A.}, + month = jun, + year = {2019}, + note = {Publisher: American Association for the Advancement of Science +Section: Research Article}, + pages = {eaav0474}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\YG42T8TT\\Link and Watson - 2019 - Global ecosystem overfishing Clear delineation wi.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\VFFABX4W\\eaav0474.html:text/html}, +} + +@article{le_cren_length-weight_1951, + title = {The {Length}-{Weight} {Relationship} and {Seasonal} {Cycle} in {Gonad} {Weight} and {Condition} in the {Perch} ({Perca} fluviatilis)}, + volume = {20}, + issn = {0021-8790}, + url = {https://www.jstor.org/stable/1540}, + doi = {10.2307/1540}, + abstract = {1. The methods of analysing length-weight data from fish are reviewed. Emphasis is laid on empirical formulae of the type W = aLn, and the limitations of the conventional condition factor. 2. The length-weight relationship of perch of all sizes was determined from a series of regressions of log weight on log length, and an analysis of covariance. Relative condition factors were calculated for individual fish from smoothed mean weights obtained from the regression lines. 3. In length-weight relationship it was found that the perch could be divided into a series of six groups corresponding with age, sex and maturity. Each group was generally homogeneous within itself throughout the seasons, but usually differed significantly from the other groups. Relative condition was found to vary significantly with the season. 4. At any one season the gonad weight is a constant percentage of the body weight for fish of all sizes. The seasonal changes in gonad weight are described and differ somewhat for the two sexes. Stomach contents weigh up to 2\% of the body weight in summer. 5. There is a regular seasonal cycle in condition which is at its maximum in September and minimum in early spring. The different seasonal changes in condition between mature and immature fish can largely be accounted for by the cycle in gonad weight of the former.}, + number = {2}, + urldate = {2021-02-26}, + journal = {Journal of Animal Ecology}, + author = {Le Cren, E. D.}, + year = {1951}, + note = {Publisher: [Wiley, British Ecological Society]}, + pages = {201--219}, + file = {Submitted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\ZMLBKARD\\Le Cren - 1951 - The Length-Weight Relationship and Seasonal Cycle .pdf:application/pdf}, +} + +@book{boem_bureau_2021, + title = {Bureau of {Ocean} {Energy} {Management} ({BOEM}). {South} {Fork} {Wind} {Farm} and {South} {Fork} {Export} {Cable} {Project} {Draft} {Environmental} {Impact} {Statement}. {OCS} {EIS}/{EA} {BOEM} 2020-057.}, + url = {https://www.boem.gov/sites/default/files/documents/renewable-energy/SFWF-DEIS_0.pdf}, + author = {BOEM}, + year = {2021}, +} + +@article{bauer_temperature-_2010, + title = {Temperature-, {Salinity}-, and {Size}-{Dependent} {Winter} {Mortality} of {Juvenile} {Blue} {Crabs} ( {Callinectes} sapidus )}, + volume = {33}, + issn = {1559-2723}, + url = {https://www.jstor.org/stable/40663676}, + abstract = {At midlatitudes within its range, overwintering mortality may play an important role in regulating blue crab (Callinectes sapidus) populations. We conducted a 121-day experiment to estimate winter mortality that used realistic temperature acclimation periods prior to and light levels during experiments. A 2×2×2 factorial experimental design was used to test for the effects of temperature (3°C, 5°C), salinity (10, 25), and sediment (sediment, no sediment) on the survival of juvenile crabs (14-68-mm carapace width) of both hatchery and wild origin. The presence of sediment did not significantly alter crab survival, while hatchery-raised crabs experienced significantly lower survivorship than wild-caught crabs. An accelerated failure time model fit to the survival data indicated that time to death increased significantly with increasing temperature, salinity, and crab size. These results suggest that winter survival varies with winter severity, is spatially dynamic, and that small juveniles are more at risk of dying over the winter than larger juveniles.}, + number = {3}, + urldate = {2021-02-26}, + journal = {Estuaries and Coasts}, + author = {Bauer, Laurie J. and Miller, Thomas J.}, + year = {2010}, + note = {Publisher: Coastal and Estuarine Research Federation}, + pages = {668--677}, +} + +@inproceedings{hines_predicting_2011, + title = {Predicting {Effects} of {Climate} {Change} on {Blue} {Crabs} in {Chesapeake} {Bay}}, + isbn = {978-1-56612-154-5}, + url = {http://www.alaskaseagrant.org/bookstore/pubs/AK-SG-10-01.html}, + doi = {10.4027/bmecpcc.2010.22}, + language = {en}, + urldate = {2021-02-26}, + booktitle = {Biology and {Management} of {Exploited} {Crab} {Populations} under {Climate} {Change}}, + publisher = {Alaska Sea Grant, University of Alaska Fairbanks}, + author = {Hines, A.H. and Johnson, E.G. and Darnell, M.Z. and Rittschof, D. and Miller, T.J. and Bauer, L.J. and Rodgers, P. and Aguilar, R.}, + month = apr, + year = {2011}, + pages = {109--127}, + file = {Hines et al. - 2011 - Predicting Effects of Climate Change on Blue Crabs.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\2ANKMV3U\\Hines et al. - 2011 - Predicting Effects of Climate Change on Blue Crabs.pdf:application/pdf}, +} + +@article{kimmel_relationship_2014, + title = {The {Relationship} between {Interannual} {Climate} {Variability} and {Juvenile} {Eastern} {Oyster} {Abundance} at a {Regional} {Scale} in {Chesapeake} {Bay}}, + volume = {34}, + issn = {0275-5947}, + url = {https://doi.org/10.1080/02755947.2013.830999}, + doi = {10.1080/02755947.2013.830999}, + abstract = {The eastern oyster, Crassostrea virginica, is an important ecological and commercial species along the Eastern Seaboard of North America, although its abundance in Chesapeake Bay (Maryland and Virginia) has declined precipitously since the mid-1800s. Management activities, including efforts to enhance larval settlement, to rebuild the Chesapeake Bay eastern oyster population have met with limited success. Since 1939, juvenile oyster abundance has been measured at oyster bars in the northern portion of Chesapeake Bay. Prior analyses of those data indicate that juvenile oyster abundance is influenced by environmental conditions, which are related to regional climate. We quantified a regional scale (1,000 km2) climate signal using a synoptic climatology approach by identifying predominant weather patterns. We related these weather patterns to Susquehanna River freshwater discharge, a major influencing factor on Chesapeake Bay salinity, and the Palmer Hydrological Drought Index (PHDI), an indicator of the overall wetness or dryness of a given region. We developed path analysis models that linked weather patterns to Susquehanna River discharge and thence to either the PHDI or ambient salinity and water temperature and finally to juvenile oyster abundance in one of four preidentified spatial groupings. The path models indicated that weather patterns that produce wet conditions, leading to a higher PHDI and lower salinity, resulted in reduced juvenile oyster abundance; weather patterns that produce dry conditions that decreased the PHDI and increased salinity resulted in higher juvenile oyster abundance. Our results indicate that regions of higher juvenile oyster abundance may be predicted several months in advance using the path model. These predictions can be used to increase the effectiveness of oyster restoration efforts by indicating where hard substrate added to oyster bars in the four regions is likely to result in greater than average numbers of juvenile oysters.Received January 16, 2013; accepted July 24, 2013Published online December 3, 2013}, + number = {1}, + urldate = {2021-02-26}, + journal = {North American Journal of Fisheries Management}, + author = {Kimmel, David G. and Tarnowski, Mitchell and Newell, Roger I. E.}, + month = jan, + year = {2014}, + note = {Publisher: Taylor \& Francis +\_eprint: https://doi.org/10.1080/02755947.2013.830999}, + pages = {1--15}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\ZJFCEYCC\\02755947.2013.html:text/html}, +} + +@article{martino_recruitment_2010, + title = {Recruitment of striped bass in {Chesapeake} {Bay}:: spatial and temporal environmental variability and availability of zooplankton prey}, + volume = {409}, + issn = {0171-8630}, + shorttitle = {Recruitment of striped bass in {Chesapeake} {Bay}}, + url = {https://www.jstor.org/stable/24873989}, + abstract = {ABSTRACT: Causes of recruitment variability in young-of-the-year (YOY) striped bass Morone saxatilis from Chesapeake Bay were investigated based on (1) surveys from 2001 to 2003 to document spatio-temporal variability in abundance of larval striped bass, zooplankton prey, and feeding success of larvae; (2) a synthetic analysis (1996, 1998, 1999, 2001 to 2003) to describe how environmental factors and prey affect recruitment success; and (3) a 10 yr analysis (1993 to 2002) of inter-annual differences in spatial and temporal patterns of copepods and cladocera eaten by striped bass larvae. Striped bass YOY recruitment levels varied {\textgreater}11-fold in the 6 years examined. In those years, mean daily freshwater flows from the Susquehanna River to the bay in March and April varied {\textgreater}2-fold and controlled distribution and apparent survival of striped bass larvae. Strong recruitments of YOY striped bass were associated with matches in space and time of larval striped bass and high concentrations of zooplankton prey, especially the copepod Eurytemora affinis and cladoceran Bosmina longirostris. The strongest year classes (1996, 2003) were produced in years of high freshwater flow, characterized by a high abundance of feeding-stage larvae and a spatio-temporal match between peak abundance of larvae and zooplankton prey. Enhanced feeding opportunities were most pronounced in high freshwater-flow years (1996, 1998, 2003), when larvae and zooplankton prey were strongly associated with, and apparently retained near, the estuarine turbidity maximum. First-feeding larvae fed more successfully in a high-flow year (2003; prey incidence 91\%) than in a drier year (2001; prey incidence 35\%). A regression model that may have forecasting potential was developed to describe recruitment of YOY striped bass for the years from 1985 to 2006. The model includes spring freshwater flow and air temperatures to predict age-0 striped bass recruitment strength (R² = 0.65). Flow and temperature control environmental and hydrographic conditions that strongly influence spatio-temporal overlap of larval striped bass and zooplankton. The model provided accurate recruitment forecasts for 2007 and 2009, but was less successful in 2008, a year of exceptionally low recruitment.}, + urldate = {2021-02-26}, + journal = {Marine Ecology Progress Series}, + author = {Martino, E. J. and Houde, E. D.}, + year = {2010}, + note = {Publisher: Inter-Research Science Center}, + pages = {213--228}, +} + +@article{moore_impacts_2014, + title = {Impacts of {Varying} {Estuarine} {Temperature} and {Light} {Conditions} on {Zostera} marina ({Eelgrass}) and its {Interactions} {With} {Ruppia} maritima ({Widgeongrass})}, + volume = {37}, + issn = {1559-2723}, + url = {https://www.jstor.org/stable/44851580}, + abstract = {Seagrass populations have been declining globally, with changes attributed to anthropogenic stresses and, more recently, negative effects of global climate change. We examined the distribution of Zostera marina (eelgrass) dominated beds in the York River, Chesapeake Bay, VA over an 8-year time period. Using a temperature-dependent light model, declines in upriver areas were associated with higher light attenuation, resulting in lower light availability relative to compensating light requirements of Z. marina compared with downriver areas. An inverse relationship was observed between SAV growth and temperature with a change between net bed cover increases and decreases for the period of 2004-2011 observed at approximately 23 °C. Z. marina-dominated beds in the lower river have been recovering from a die-off event in 2005 and experienced another near complete decline in 2010, losing an average of 97 \% of coverage of Z. marina from June to October. These 2010 declines were attributed to an early summer heat event in which daily mean water temperatures increased from 25 to 30 °C over a 2-week time period, considerably higher than previous years when complete die-offs were not observed. Z. marina recovery from this event was minimal, while Ruppia maritima (widgeongrass) expanded its abundance. Water temperatures are projected to continue to increase in the Chesapeake Bay and elsewhere. These results suggest that short-term exposures to rapidly increasing temperatures by 4—5 °C above normal during summer months can result in widespread diebacks that may lead to Z. marina extirpation from historically vegetated areas, with the potential replacement by other species.}, + urldate = {2021-02-26}, + journal = {Estuaries and Coasts}, + author = {Moore, Kenneth A. and Shields, Erin C. and Parrish, David B.}, + year = {2014}, + note = {Publisher: [Coastal and Estuarine Research Federation, Springer]}, + pages = {S20--S30}, +} + +@article{north_linking_2003, + title = {Linking {ETM} physics, zooplankton prey, and fish early-life histories to striped bass {Morone} saxatilis and white perch {M}. americana recruitment}, + volume = {260}, + issn = {0171-8630, 1616-1599}, + url = {http://www.int-res.com/abstracts/meps/v260/p219-236/}, + doi = {10.3354/meps260219}, + abstract = {Transport to and retention of early-life history stages within the estuarine turbidity maximum (ETM) region may be an important component of recruitment success of the anadromous striped bass Morone saxatilis and the white perch M. americana. This hypothesis was evaluated with spatially resolved surveys of the physical and biological characteristics of the upper Chesapeake Bay estuary during 5 cruises, 3 in May 1998 and 2 in May 1999. Gradients in depth-specific patterns of larval abundance and zooplankton prey were evaluated with respect to salinity, temperature and total suspended solids (TSS). Although physical conditions in the upper estuary differed between cruises and years, the spatial patterns in distribution of larvae and potential prey were consistent and tracked changes in salt front and ETM locations. A statistical analysis suggested that location relative to the ETM explained a significant amount of variability in concentrations of eggs, yolk-sac larvae, and larval prey, and that prey concentrations accounted for a significant amount of variability in concentrations of feeding larvae. Transport to the ETM region probably occurs during the egg (striped bass) and yolk-sac larva (white perch) stages and results in retention of early-stage feeding larvae in a zone of high prey concentrations. Physical conditions in the ETM region differed markedly between a low freshwater-flow year (1999) and a high flow year (1998), and abundances of striped bass and white perch post-yolk-sac larvae were significantly lower in 1999. Recruitment variability was poorly correlated with spawning stock biomass, but spawner-recruitment models that incorporated spring freshwater discharge explained an additional 41\% (striped bass) and 30\% (white perch) of the variability in recruitment. Annual changes in freshwater flow could control larval survival and recruitment by modifying the physical and biological characteristics of the ETM region.}, + language = {en}, + urldate = {2021-02-26}, + journal = {Marine Ecology Progress Series}, + author = {North, Ew and Houde, Ed}, + year = {2003}, + pages = {219--236}, + file = {North and Houde - 2003 - Linking ETM physics, zooplankton prey, and fish ea.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\SKWLG5HP\\North and Houde - 2003 - Linking ETM physics, zooplankton prey, and fish ea.pdf:application/pdf}, +} + +@article{millette_water_2020, + title = {Water temperature during winter may control striped bass recruitment during spring by affecting the development time of copepod nauplii}, + volume = {77}, + issn = {1054-3139}, + url = {https://doi.org/10.1093/icesjms/fsz203}, + doi = {10.1093/icesjms/fsz203}, + abstract = {Striped bass (Morone saxatilis) are anadromous fish that support an important fishery along the east coast of North America. In Chesapeake Bay, strong juvenile recruitment of striped bass can occur when larvae overlap with high concentrations of their zooplankton prey, but the mechanisms fostering the temporal overlap are unknown. Here, the influence of winter temperature on the peak abundances of a key prey, Eurytemora carolleeae, was estimated with a temperature-dependent developmental model. The role of these peaks in regulating striped bass recruitment was explored in three nursery areas: upper Chesapeake Bay, Choptank River, and Patuxent River. Model results indicated that cold winters delay the timing and increase the size of peak E. carolleeae spring abundance. When the model output was used in regression relationships with striped bass juvenile recruitment and freshwater discharge, the regression models explained up to 78\% of annual recruitment variability. Results suggests that cold, wet winters could increase the chance of a match between striped bass larvae and high concentrations of their prey. This mechanistic link between winter temperatures and striped bass production, acting through prey dynamics, could further understanding of fish recruitment variability and indicates that warmer winters could negatively affect some striped bass populations.}, + number = {1}, + urldate = {2021-02-26}, + journal = {ICES Journal of Marine Science}, + author = {Millette, N C and Pierson, J J and North, E W}, + month = jan, + year = {2020}, + pages = {300--314}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\G2LJDACB\\Millette et al. - 2020 - Water temperature during winter may control stripe.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\RB7SALYL\\5638886.html:text/html}, +} + +@article{rome_linking_2005, + series = {Proceedings of the 2003 {Blue} {Crab} {Symposium}}, + title = {Linking temperature and salinity tolerance to winter mortality of {Chesapeake} {Bay} blue crabs ({Callinectes} sapidus)}, + volume = {319}, + issn = {0022-0981}, + url = {https://www.sciencedirect.com/science/article/pii/S0022098105000249}, + doi = {10.1016/j.jembe.2004.06.014}, + abstract = {Blue crabs (Callinectes sapidus) form one of the most important and largest commercial fisheries in Chesapeake Bay. Blue crabs have evolutionary origins in the tropics, although they currently inhabit temperature estuaries that exhibit major fluctuations in diurnal, monthly, and seasonal environmental conditions. Therefore, harsh winter conditions in Chesapeake Bay are a potentially important source of blue crab stock loss. However, this variable has been largely unexamined. To assess the effects of variation in winter environmental conditions on blue crab survival, we measured winter morality of crabs in the field and conducted laboratory experiments to test the interactive effects of low temperature, salinity, and blue crab life stage. Field studies indicated that blue crabs suffered relatively low winter morality rates (≤3\%) during five out of eight winters, when bottom water temperature in February was at or above the 8-year average (3.4 °C). However, in years when bottom water temperature fell below the February average, annual mortality rates rose to 6.0–14.5\%. Mortality rates were highest in the coldest regions of Chesapeake Bay, and larger crabs and female crabs were most vulnerable to these stressful conditions. Similarly, in the laboratory, mortality was highest in the lowest temperature (1 °C) and salinity (8 ppt) treatments. Mature females were more sensitive to winter conditions than juvenile crabs. Of the juvenile life stages, recruits ({\textless}15 mm carapace width) were least tolerant to winter conditions. These results indicate that temperature, salinity, and blue crab life stage are important variables in predicting survivorship over winter months. Because winter mortality may be a significant source of stock loss for blue crabs, especially during severe winters and in low salinity areas of Chesapeake Bay, these predictions can be used to improve management estimates of stock size prior to each summer fishing season.}, + language = {en}, + number = {1}, + urldate = {2021-02-26}, + journal = {Journal of Experimental Marine Biology and Ecology}, + author = {Rome, M. S. and Young-Williams, A. C. and Davis, G. R. and Hines, A. H.}, + month = jun, + year = {2005}, + keywords = {Blue crab, Salinity tolerance, Temperature tolerance, Winter mortality}, + pages = {129--145}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\C5XNRUSR\\S0022098105000249.html:text/html}, +} + +@article{rybicki_light_2002, + title = {Light and {Temperature} {Effects} on the {Growth} of {Wild} {Celery} and {Hydrilla}}, + abstract = {Wild celery (Vallisneria americana L.) has coexisted with the dominant species hydrilla (Hydrilla verticillata (L.f.) Royle) since the resurgence of submersed aquatic vegetation in the tidal Potomac River in 1983. In 1989, particularly turbid, cool, and cloudy spring conditions were associated with a substantial decrease in hydrilla coverage. We measured growth and elongation potential of wild celery and hydrilla propagules under various temperature and irradiance conditions to compare these two species and in part explain the stable persistence of wild celery and the variability in hydrilla coverage. A plant growth experiment was conducted to simulate actual temperatures in the Potomac River during spring of 1986 (plant coverage increased) and 1989 (plant coverage decreased). In the 1989 temperature treatment, final heights of hydrilla and wild celery were unaffected by a 6-C decrease in temperature 2 weeks following tuber germination. Heights of wild celery, however, were more than twice that of hydrilla, and elongation rates of wild celery were greater than those of hydrilla when temperatures reached 17 to 22C. Laboratory studies conducted in complete darkness showed that wild celery tubers germinate at 13C, whereas hydrilla tubers germinate at 15C, and that wild celery elongated to heights twice those of hydrilla. Heights were positively correlated to tuber length. If irradiance is diminished at incipience, differences in tuber reserves and elongation potential may be sufficient to ensure that wild celery can survive when hydrilla is not successful.}, + language = {en}, + journal = {J. Aquat. Plant Manage.}, + author = {Rybicki, Nancy B and Carter, Virginia}, + year = {2002}, + pages = {8}, + file = {Rybicki and Carter - Light and Temperature Effects on the Growth of Wil.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\GANUDBQD\\Rybicki and Carter - Light and Temperature Effects on the Growth of Wil.pdf:application/pdf}, +} + +@article{wood_rates_2020, + title = {Rates of increase in gray seal ({Halichoerus} grypus atlantica) pupping at recolonized sites in the {United} {States}, 1988–2019}, + volume = {101}, + issn = {0022-2372, 1545-1542}, + url = {https://academic.oup.com/jmammal/article/101/1/121/5675096}, + doi = {10.1093/jmammal/gyz184}, + abstract = {Abstract + Gray seals were historically distributed along the northeastern coast of the United States, but bounties and lack of protection reduced numbers and they were rarely observed for most of the 20th century. Once protections were enacted, the population started to rebound. Here, we describe the recolonization and recovery of gray seals in the United States, focusing on the re-establishment of pupping sites. We fit individual generalized linear models to various time series (1988–2019) to estimate rates of increase in observed pup counts at four of the more data-rich sites. Annual rate of increase at individual sites ranged from −0.2\% (95\% CI: −2.3–1.9\%) to 26.3\% (95\% CI: 21.6–31.4\%). The increase in sites and number of pups born in the United States is driven by population growth and immigration from Canadian colonies and is part of a larger recovery of the Northwest Atlantic population. Wildlife protection, a healthy source population, habitat availability, and species traits that allow for dispersal and high productivity were all important factors in this recovery.}, + language = {en}, + number = {1}, + urldate = {2021-03-01}, + journal = {Journal of Mammalogy}, + author = {Wood, Stephanie A and Murray, Kimberly T and Josephson, Elizabeth and Gilbert, James}, + editor = {Swanson, Bradley}, + month = feb, + year = {2020}, + pages = {121--128}, + file = {Wood et al. - 2020 - Rates of increase in gray seal (Halichoerus grypus.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\L2KSKFJ9\\Wood et al. - 2020 - Rates of increase in gray seal (Halichoerus grypus.pdf:application/pdf}, +} + +@article{pousse_energetic_2020, + title = {Energetic response of {Atlantic} surfclam {Spisula} solidissima to ocean acidification}, + volume = {161}, + issn = {0025-326X}, + url = {https://www.sciencedirect.com/science/article/pii/S0025326X20308584}, + doi = {10.1016/j.marpolbul.2020.111740}, + abstract = {In this study, we assessed the Atlantic surfclam (Spisula solidissima) energy budget under different ocean acidification conditions (OA). During 12 weeks, 126 individuals were maintained at three different ρCO2 concentrations. Every two weeks, individuals were sampled for physiological measurements and scope for growth (SFG). In the high ρCO2 treatment, clearance rate decreased and excretion rate increased relative to the low ρCO2 treatment, resulting in reduced SFG. Moreover, oxygen:nitrogen (O:N) excretion ratio dropped, suggesting that a switch in metabolic strategy occurred. The medium ρCO2 treatment had no significant effects upon SFG; however, metabolic loss increased, suggesting a rise in energy expenditure. In addition, a significant increase in food selection efficiency was observed in the medium treatment, which could be a compensatory reaction to the metabolic over-costs. Results showed that surfclams are particularly sensitive to OA; however, the different compensatory mechanisms observed indicate that they are capable of some temporary resilience.}, + language = {en}, + urldate = {2021-03-01}, + journal = {Marine Pollution Bulletin}, + author = {Pousse, Emilien and Poach, Matthew E. and Redman, Dylan H. and Sennefelder, George and White, Lauren E. and Lindsay, Jessica M. and Munroe, Daphne and Hart, Deborah and Hennen, Daniel and Dixon, Mark S. and Li, Yaqin and Wikfors, Gary H. and Meseck, Shannon L.}, + month = dec, + year = {2020}, + keywords = {Ocean acidification, Atlantic surfclams, Bioenergetics, Bivalves, Scope for growth}, + pages = {111740}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\IEWPC7FQ\\S0025326X20308584.html:text/html}, +} + +@article{sorochan_north_2019, + title = {North {Atlantic} right whale ({Eubalaena} glacialis) and its food: ({II}) interannual variations in biomass of {Calanus} spp. on western {North} {Atlantic} shelves}, + volume = {41}, + issn = {0142-7873}, + shorttitle = {North {Atlantic} right whale ({Eubalaena} glacialis) and its food}, + url = {https://doi.org/10.1093/plankt/fbz044}, + doi = {10.1093/plankt/fbz044}, + abstract = {The North Atlantic right whale (NARW), Eubalaena glacialis, feeds on zooplankton, particularly copepods of the genus Calanus. We quantified interannual variation in anomalies of abundance and biomass of Calanus spp. and near-surface and near-bottom ocean temperature and salinity from 19 subregions spanning the Gulf of Maine–Georges Bank (GoM–GBK), Scotian Shelf (SS), Gulf of St. Lawrence (GSL) and Newfoundland and Labrador Shelves. We analyzed time series from 1977 to 2016 in GoM–GBK, 1982 to 2016 in southwest GSL and 1999 to 2016 in remaining areas. Calanus finmarchicus dominated abundance and biomass, except in the GSL where Calanus hyperboreus was abundant. The biomass of Calanus spp. declined in many subregions over years 1999–2016 and was negatively correlated with sea surface temperature in GoM–GBK and on the SS. We detected ``regime shifts” to lower biomass of Calanus spp. in the GoM–GBK in 2010 and on the SS in 2011. In the GoM–GBK, shifts to lower biomass of C. finmarchicus coincided with shifts to warmer ocean temperature and with published reports of changes in spatial distribution and reduced calving rate of NARW. We hypothesize that warming has negatively impacted population levels of Calanus spp. near their southern range limit, reducing the availability of prey to NARW.}, + number = {5}, + urldate = {2021-03-01}, + journal = {Journal of Plankton Research}, + author = {Sorochan, Kevin A and Plourde, StÉphane and Morse, Ryan and Pepin, Pierre and Runge, Jeffrey and Thompson, Cameron and Johnson, Catherine L}, + month = sep, + year = {2019}, + pages = {687--708}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\MMBNNE9F\\Sorochan et al. - 2019 - North Atlantic right whale (Eubalaena glacialis) a.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\C3TQ2WUJ\\5583901.html:text/html}, +} + +@article{record_rapid_2019, + title = {Rapid {Climate}-{Driven} {Circulation} {Changes} {Threaten} {Conservation} of {Endangered} {North} {Atlantic} {Right} {Whales}}, + volume = {32}, + issn = {10428275}, + url = {https://tos.org/oceanography/article/rapid-climate-driven-circulation-changes-threaten-conservation-of-endangere}, + doi = {10.5670/oceanog.2019.201}, + abstract = {As climate trends accelerate, ecosystems will be pushed rapidly into new states, reducing the potential efficacy of conservation strategies based on historical patterns. In the Gulf of Maine, climate-driven changes have restructured the ecosystem rapidly over the past decade. Changes in the Atlantic meridional overturning circulation have altered deepwater dynamics, driving warming rates twice as high as the fastest surface rates. This has had implications for the copepod Calanus finmarchicus, a critical food supply for the endangered North Atlantic right whale (Eubalaena glacialis). The oceanographic changes have driven a deviation in the seasonal foraging patterns of E. glacialis upon which conservation strategies depend, making the whales more vulnerable to ship strikes and gear entanglements. The effects of rapid climate-driven changes on a species at risk undermine current management approaches.}, + language = {en}, + number = {2}, + urldate = {2021-03-01}, + journal = {Oceanography}, + author = {Record, Nicholas and Runge, Jeffrey and Pendleton, Daniel and Balch, William and Davies, Kimberley and Pershing, Andrew and Johnson, Catherine and Stamieszkin, Karen and Ji, Rubao and Feng, Zhixuan and Kraus, Scott and Kenney, Robert and Hudak, Christy and Mayo, Charles and Chen, Changsheng and Salisbury, Joseph and Thompson, Cameron}, + month = jun, + year = {2019}, + file = {Bigelow Laboratory for Ocean Sciences et al. - 2019 - Rapid Climate-Driven Circulation Changes Threaten .pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\XZ9NLG7Z\\Bigelow Laboratory for Ocean Sciences et al. - 2019 - Rapid Climate-Driven Circulation Changes Threaten .pdf:application/pdf;JSTOR Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\EJGWY8WD\\Record et al. - 2019 - Rapid Climate-Driven Circulation Changes Threaten .pdf:application/pdf}, +} + +@book{hayes_north_2018, + title = {North {Atlantic} {Right} {Whales}-{Evaluating} {Their} {Recovery} {Challenges} in 2018. {NOAA} {Tech} {Memo} {NMFS} {NEFSC} 247.}, + author = {Hayes, Sean and Gardner, S and Garrison, Lance P. and Henry, A and Leandro, L}, + year = {2018}, +} + +@article{thomsen_moderate_2010, + title = {Moderate seawater acidification does not elicit long-term metabolic depression in the blue mussel {Mytilus} edulis}, + volume = {157}, + issn = {1432-1793}, + url = {https://doi.org/10.1007/s00227-010-1527-0}, + doi = {10.1007/s00227-010-1527-0}, + abstract = {Marine organisms are exposed to increasingly acidic oceans, as a result of equilibration of surface ocean water with rising atmospheric CO2 concentrations. In this study, we examined the physiological response of Mytilus edulis from the Baltic Sea, grown for 2 months at 4 seawater pCO2 levels (39, 113, 243 and 405 Pa/385, 1,120, 2,400 and 4,000 μatm). Shell and somatic growth, calcification, oxygen consumption and \$\$ \{{\textbackslash}text\{NH\}\}\_\{4\}{\textasciicircum}\{ + \} \$\$excretion rates were measured in order to test the hypothesis whether exposure to elevated seawater pCO2 is causally related to metabolic depression. During the experimental period, mussel shell mass and shell-free dry mass (SFDM) increased at least by a factor of two and three, respectively. However, shell length and shell mass growth decreased linearly with increasing pCO2 by 6–20 and 10–34\%, while SFDM growth was not significantly affected by hypercapnia. We observed a parabolic change in routine metabolic rates with increasing pCO2 and the highest rates (+60\%) at 243 Pa. \$\$ \{{\textbackslash}text\{NH\}\}\_\{4\}{\textasciicircum}\{ + \} \$\$excretion rose linearly with increasing pCO2. Decreased O:N ratios at the highest seawater pCO2 indicate enhanced protein metabolism which may contribute to intracellular pH regulation. We suggest that reduced shell growth under severe acidification is not caused by (global) metabolic depression but is potentially due to synergistic effects of increased cellular energy demand and nitrogen loss.}, + language = {en}, + number = {12}, + urldate = {2021-03-01}, + journal = {Marine Biology}, + author = {Thomsen, Jörn and Melzner, Frank}, + month = dec, + year = {2010}, + pages = {2667--2676}, +} + +@book{johnson_vulnerability_nodate, + title = {A {Vulnerability} {Assessment} of {Habitats} to {Climate} {Change} in the {Northeast} {U}.{S}.}, + author = {Johnson, M and Farr, E and Nelson, M and et al.}, +} + +@article{lannig_impact_2010, + title = {Impact of {Ocean} {Acidification} on {Energy} {Metabolism} of {Oyster}, {Crassostrea} gigas—{Changes} in {Metabolic} {Pathways} and {Thermal} {Response}}, + volume = {8}, + copyright = {http://creativecommons.org/licenses/by/3.0/}, + url = {https://www.mdpi.com/1660-3397/8/8/2318}, + doi = {10.3390/md8082318}, + abstract = {Climate change with increasing temperature and ocean acidification (OA) poses risks for marine ecosystems. According to Pörtner and Farrell [1], synergistic effects of elevated temperature and CO2-induced OA on energy metabolism will narrow the thermal tolerance window of marine ectothermal animals. To test this hypothesis, we investigated the effect of an acute temperature rise on energy metabolism of the oyster, Crassostrea gigas chronically exposed to elevated CO2 levels (partial pressure of CO2 in the seawater {\textasciitilde}0.15 kPa, seawater pH {\textasciitilde} 7.7). Within one month of incubation at elevated PCO2 and 15 °C hemolymph pH fell (pHe = 7.1 ± 0.2 (CO2-group) vs. 7.6 ± 0.1 (control)) and PeCO2 values in hemolymph increased (0.5 ± 0.2 kPa (CO2-group) vs. 0.2 ± 0.04 kPa (control)). Slightly but significantly elevated bicarbonate concentrations in the hemolymph of CO2-incubated oysters ([HCO-3]e = 1.8 ± 0.3 mM (CO2-group) vs. 1.3 ± 0.1 mM (control)) indicate only minimal regulation of extracellular acid-base status. At the acclimation temperature of 15 °C the OA-induced decrease in pHe did not lead to metabolic depression in oysters as standard metabolism rates (SMR) of CO2-exposed oysters were similar to controls. Upon acute warming SMR rose in both groups, but displayed a stronger increase in the CO2-incubated group. Investigation in isolated gill cells revealed a similar temperature-dependence of respiration between groups. Furthermore, the fraction of cellular energy demand for ion regulation via Na+/K+-ATPase was not affected by chronic hypercapnia or temperature. Metabolic profiling using 1H-NMR spectroscopy revealed substantial changes in some tissues following OA exposure at 15 °C. In mantle tissue alanine and ATP levels decreased significantly whereas an increase in succinate levels was observed in gill tissue. These findings suggest shifts in metabolic pathways following OA-exposure. Our study confirms that OA affects energy metabolism in oysters and suggests that climate change may affect populations of sessile coastal invertebrates such as mollusks.}, + language = {en}, + number = {8}, + urldate = {2021-03-01}, + journal = {Marine Drugs}, + author = {Lannig, Gisela and Eilers, Silke and Pörtner, Hans O. and Sokolova, Inna M. and Bock, Christian}, + month = aug, + year = {2010}, + note = {Number: 8 +Publisher: Molecular Diversity Preservation International}, + keywords = {$^{\textrm{1}}$H-NMR spectroscopy, acid-base status, acute warming, long-term hypercapnia, metabolic profiling, Na$^{\textrm{+}}$/K$^{\textrm{+}}$-ATPase}, + pages = {2318--2339}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\TZ6C8NJD\\Lannig et al. - 2010 - Impact of Ocean Acidification on Energy Metabolism.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\MHF9CVXI\\htm.html:text/html}, +} + +@article{kritzer_importance_2016, + title = {The {Importance} of {Benthic} {Habitats} for {Coastal} {Fisheries}}, + volume = {66}, + issn = {0006-3568}, + url = {https://doi.org/10.1093/biosci/biw014}, + doi = {10.1093/biosci/biw014}, + abstract = {Ecosystem-based management requires greater attention to habitat conservation. We evaluate the importance of benthic habitats as space for shelter, feeding, and breeding by coastal fishes and motile invertebrates in four biogeographic regions of the eastern United States. The importance of different habitats changed with latitude. Soft sediments and riverine systems scored higher in northern regions, and marshes and coral reefs scored higher in the south. The importance of soft sediments is notable because environmental assessments often assume their ecological value is lower. Submerged aquatic vegetation was a key nursery habitat coast-wide. An important consideration is that anthropogenic impacts have altered habitat availability and use, and climate change is causing ongoing disturbance. Furthermore, distinct habitats should not be managed in isolation but rather as interconnected mosaics. Our approach, combined with information on status, vulnerability, and other ecological functions, can be used to evaluate trade-offs and develop habitat-management strategies.}, + number = {4}, + urldate = {2021-03-01}, + journal = {BioScience}, + author = {Kritzer, Jacob P. and DeLucia, Mari-Beth and Greene, Emily and Shumway, Caroly and Topolski, Marek F. and Thomas-Blate, Jessie and Chiarella, Louis A. and Davy, Kay B. and Smith, Kent}, + month = apr, + year = {2016}, + pages = {274--284}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\DTYZTVY8\\Kritzer et al. - 2016 - The Importance of Benthic Habitats for Coastal Fis.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\W5FNNCQT\\2464081.html:text/html}, +} + +@book{drohan_essential_2007, + title = {Essential fish habitat source document. {Black} sea bass, {Centropristis} striata, life history and habitat characteristics. {NOAA} {Technical} {Memorandum} {NMFS}-{NE}-200}, + url = {https://repository.library.noaa.gov/view/noaa/4038}, + urldate = {2021-03-02}, + author = {Drohan, Amy F and Manderson, John P and Packer, David B}, + year = {2007}, + file = {Essential fish habitat source document. Black sea bass, Centropristis striata, life history and habitat characteristics:C\:\\Users\\andrew.beet\\Zotero\\storage\\K5FTJ597\\4038.html:text/html}, +} + +@article{samhouri_defining_2017, + title = {Defining ecosystem thresholds for human activities and environmental pressures in the {California} {Current}}, + volume = {8}, + issn = {2150-8925}, + url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecs2.1860}, + doi = {10.1002/ecs2.1860}, + abstract = {The oceans are changing more rapidly than ever before. Unprecedented climatic variability is interacting with unmistakable long-term trends, all against a backdrop of intensifying human activities. What remains unclear, however, is how to evaluate whether conditions have changed sufficiently to provoke major responses of species, habitats, and communities. We developed a framework based on multimodel inference to define ecosystem-based thresholds for human and environmental pressures in the California Current marine ecosystem. To demonstrate how to apply the framework, we explored two decades of data using gradient forest and generalized additive model analyses, screening for nonlinearities and potential threshold responses of ecosystem states (n = 9) across environmental (n = 6) and human (n = 10) pressures. These analyses identified the existence of threshold responses of five ecosystem states to four environmental and two human pressures. Both methods agreed on threshold relationships in two cases: (1) the winter copepod anomaly and habitat modification, and (2) sea lion pup production and the summer mode of the Pacific Decadal Oscillation (PDO). Considered collectively, however, these alternative analytical approaches imply that as many as five of the nine ecosystem states may exhibit threshold changes in response to negative PDO values in the summer (copepods, scavengers, groundfish, and marine mammals). This result is consistent with the idea that the influence of the PDO extends across multiple trophic levels, but extends current knowledge by defining the nonlinear nature of these responses. This research provides a new way to interpret changes in the intensities of human and environmental pressures as they relate to the ecological integrity of the California Current ecosystem. These insights can be used to make more informed assessments of when and under what conditions intervention, preparation, and mitigation may enhance progress toward ecosystem-based management goals.}, + language = {en}, + number = {6}, + urldate = {2018-12-10}, + journal = {Ecosphere}, + author = {Samhouri, Jameal F. and Andrews, Kelly S. and Fay, Gavin and Harvey, Chris J. and Hazen, Elliott L. and Hennessey, Shannon M. and Holsman, Kirstin and Hunsicker, Mary E. and Large, Scott I. and Marshall, Kristin N. and Stier, Adrian C. and Tam, Jamie C. and Zador, Stephani G.}, + month = jun, + year = {2017}, + keywords = {ecosystem-based management, reference points, ecosystem indicator, ecosystem threshold, nonlinear}, + pages = {e01860}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\83CJQHRK\\Samhouri et al. - 2017 - Defining ecosystem thresholds for human activities.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\PBVDAQ3P\\ecs2.html:text/html}, +} + +@article{goddard_extreme_2015, + title = {An extreme event of sea-level rise along the {Northeast} coast of {North} {America} in 2009–2010}, + volume = {6}, + issn = {2041-1723}, + url = {http://www.nature.com/articles/ncomms7346}, + doi = {10.1038/ncomms7346}, + language = {en}, + number = {1}, + urldate = {2019-02-14}, + journal = {Nature Communications}, + author = {Goddard, Paul B. and Yin, Jianjun and Griffies, Stephen M. and Zhang, Shaoqing}, + month = dec, + year = {2015}, + file = {Goddard et al. - 2015 - An extreme event of sea-level rise along the North.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\G73TKV2E\\Goddard et al. - 2015 - An extreme event of sea-level rise along the North.pdf:application/pdf}, +} + +@article{zhang_role_2007, + title = {The {Role} of {Bottom} {Vortex} {Stretching} on the {Path} of the {North} {Atlantic} {Western} {Boundary} {Current} and on the {Northern} {Recirculation} {Gyre}}, + volume = {37}, + issn = {0022-3670}, + url = {https://journals.ametsoc.org/doi/10.1175/JPO3102.1}, + doi = {10.1175/JPO3102.1}, + abstract = {The mechanisms affecting the path of the depth-integrated North Atlantic western boundary current and the formation of the northern recirculation gyre are investigated using a hierarchy of models, namely, a robust diagnostic model, a prognostic model using a global 1° ocean general circulation model coupled to a two-dimensional atmospheric energy balance model with a hydrological cycle, a simple numerical barotropic model, and an analytic model. The results herein suggest that the path of this boundary current and the formation of the northern recirculation gyre are sensitive to both the magnitude of lateral viscosity and the strength of the deep western boundary current (DWBC). In particular, it is shown that bottom vortex stretching induced by a downslope DWBC near the south of the Grand Banks leads to the formation of a cyclonic northern recirculation gyre and keeps the path of the depth-integrated western boundary current downstream of Cape Hatteras separated from the North American coast. Both south of the Grand Banks and at the crossover region of the DWBC and Gulf Stream, the downslope DWBC induces strong bottom downwelling over the steep continental slope, and the magnitude of the bottom downwelling is locally stronger than surface Ekman pumping velocity, providing strong positive vorticity through bottom vortex-stretching effects. The bottom vortex-stretching effect is also present in an extensive area in the North Atlantic, and the contribution to the North Atlantic subpolar and subtropical gyres is on the same order as the local surface wind stress curl. Analytic solutions show that the bottom vortex stretching is important near the western boundary only when the continental slope is wider than the Munk frictional layer scale.}, + number = {8}, + urldate = {2019-02-14}, + journal = {Journal of Physical Oceanography}, + author = {Zhang, Rong and Vallis, Geoffrey K.}, + month = aug, + year = {2007}, + pages = {2053--2080}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\F4H2XV23\\Zhang and Vallis - 2007 - The Role of Bottom Vortex Stretching on the Path o.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\LY6VRJAF\\JPO3102.html:text/html}, +} + +@article{greene_remote_2013, + title = {Remote climate forcing of decadal-scale regime shifts in {Northwest} {Atlantic} shelf ecosystems}, + volume = {58}, + issn = {1939-5590}, + url = {https://aslopubs.onlinelibrary.wiley.com/doi/abs/10.4319/lo.2013.58.3.0803}, + doi = {10.4319/lo.2013.58.3.0803}, + abstract = {Decadal-scale regime shifts in Northwest Atlantic shelf ecosystems can be remotely forced by climate-associated atmosphere-ocean interactions in the North Atlantic and Arctic Ocean Basins. This remote climate forcing is mediated primarily by basin- and hemispheric-scale changes in ocean circulation. We review and synthesize results from process-oriented field studies and retrospective analyses of time-series data to document the linkages between climate, ocean circulation, and ecosystem dynamics. Bottom-up forcing associated with climate plays a prominent role in the dynamics of these ecosystems, comparable in importance to that of top-down forcing associated with commercial fishing. A broad perspective, one encompassing the effects of basin- and hemispheric-scale climate processes on marine ecosystems, will be critical to the sustainable management of marine living resources in the Northwest Atlantic.}, + language = {en}, + number = {3}, + urldate = {2019-02-14}, + journal = {Limnology and Oceanography}, + author = {Greene, Charles H. and Meyer-Gutbrod, Erin and Monger, Bruce C. and McGarry, Louise P. and Pershing, Andrew J. and Belkin, Igor M. and Fratantoni, Paula S. and Mountain, David G. and Pickart, Robert S. and Proshutinsky, Andrey and Ji, Rubao and Bisagni, James J. and Hakkinen, Sirpa M. A. and Haidvogel, Dale B. and Wang, Jia and Head, Erica and Smith, Peter and Reid, Philip C. and Conversi, Alessandra}, + year = {2013}, + pages = {803--816}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\V9LSLIS2\\Greene et al. - 2013 - Remote climate forcing of decadal-scale regime shi.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\3UFWZQV3\\lo.2013.58.3.html:text/html}, +} + +@article{bi_decadal_2014, + title = {Decadal {Changes} in {Zooplankton} of the {Northeast} {U}.{S}. {Continental} {Shelf}}, + volume = {9}, + issn = {1932-6203}, + url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087720}, + doi = {10.1371/journal.pone.0087720}, + abstract = {The abundance of the subarctic copepod, Calanus finmarchicus, and temperate, shelf copepod, Centropages typicus, was estimated from samples collected bi-monthly over the Northeast U.S. continental shelf (NEUS) from 1977–2010. Latitudinal variation in long term trends and seasonal patterns for the two copepod species were examined for four sub-regions: the Gulf of Maine (GOM), Georges Bank (GB), Southern New England (SNE), and Mid-Atlantic Bight (MAB). Results suggested that there was significant difference in long term variation between northern region (GOM and GB), and the MAB for both species. C. finmarchicus generally peaked in May – June throughout the entire study region and Cen. typicus had a more complex seasonal pattern. Time series analysis revealed that the peak time for Cen. typicus switched from November – December to January - March after 1985 in the MAB. The long term abundance of C. finmarchicus showed more fluctuation in the MAB than the GOM and GB, whereas the long term abundance of Cen. typicus was more variable in the GB than other sub-regions. Alongshore transport was significantly correlated with the abundance of C. finmarchicus, i.e., more water from north, higher abundance for C. finmarchicus. The abundance of Cen. typicus showed positive relationship with the Gulf Stream north wall index (GSNWI) in the GOM and GB, but the GSNWI only explained 12–15\% of variation in Cen. typicus abundance. In general, the alongshore current was negatively correlated with the GSNWI, suggesting that Cen. typicus is more abundant when advection from the north is less. However, the relationship between Cen. typicus and alongshore transport was not significant. The present study highlights the importance of spatial scales in the study of marine populations: observed long term changes in the northern region were different from the south for both species.}, + language = {en}, + number = {1}, + urldate = {2019-02-14}, + journal = {PLOS ONE}, + author = {Bi, Hongsheng and Ji, Rubao and Liu, Hui and Jo, Young-Heon and Hare, Jonathan A.}, + month = jan, + year = {2014}, + keywords = {Ecosystems, Zooplankton, Maine, Sea water, Copepods, Seasonal variations, Seasons, Time series analysis}, + pages = {e87720}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\JKT8A44M\\Bi et al. - 2014 - Decadal Changes in Zooplankton of the Northeast U..pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\SY2YSGT4\\article.html:text/html}, +} + +@article{pace_statespace_2017, + title = {State–space mark–recapture estimates reveal a recent decline in abundance of {North} {Atlantic} right whales}, + volume = {7}, + issn = {2045-7758}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.3406}, + doi = {10.1002/ece3.3406}, + abstract = {North Atlantic right whales (Eubalaena glacialis Müller 1776) present an interesting problem for abundance and trend estimation in marine wildlife conservation. They are long lived, individually identifiable, highly mobile, and one of the rarest of cetaceans. Individuals are annually resighted at different rates, primarily due to varying stay durations among several principal habitats within a large geographic range. To date, characterizations of abundance have been produced that use simple accounting procedures with differing assumptions about mortality. To better characterize changing abundance of North Atlantic right whales between 1990 and 2015, we adapted a state–space formulation with Jolly-Seber assumptions about population entry (birth and immigration) to individual resighting histories and fit it using empirical Bayes methodology. This hierarchical model included accommodation for the effect of the substantial individual capture heterogeneity. Estimates from this approach were only slightly higher than published accounting procedures, except for the most recent years (when recapture rates had declined substantially). North Atlantic right whales' abundance increased at about 2.8\% per annum from median point estimates of 270 individuals in 1990 to 483 in 2010, and then declined to 2015, when the final estimate was 458 individuals (95\% credible intervals 444–471). The probability that the population's trajectory post-2010 was a decline was estimated at 99.99\%. Of special concern was the finding that reduced survival rates of adult females relative to adult males have produced diverging abundance trends between sexes. Despite constraints in recent years, both biological (whales' distribution changing) and logistical (fewer resources available to collect individual photo-identifications), it is still possible to detect this relatively recent, small change in the population's trajectory. This is thanks to the massive dataset of individual North Atlantic right whale identifications accrued over the past three decades. Photo-identification data provide biological information that allows more informed inference on the status of this species.}, + language = {en}, + number = {21}, + urldate = {2019-02-14}, + journal = {Ecology and Evolution}, + author = {Pace, Richard M. and Corkeron, Peter J. and Kraus, Scott D.}, + year = {2017}, + keywords = {Bayesian mark–recapture, Eubalaena glacialis, open population abundance, recovery, survival}, + pages = {8730--8741}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\2C2WU392\\Pace et al. - 2017 - State–space mark–recapture estimates reveal a rece.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\X3AR35R8\\ece3.html:text/html}, +} + +@article{zhang_chesapeake_2018, + title = {Chesapeake {Bay}'s water quality condition has been recovering: {Insights} from a multimetric indicator assessment of thirty years of tidal monitoring data}, + volume = {637-638}, + issn = {0048-9697}, + shorttitle = {Chesapeake {Bay}'s water quality condition has been recovering}, + url = {http://www.sciencedirect.com/science/article/pii/S0048969718316577}, + doi = {10.1016/j.scitotenv.2018.05.025}, + abstract = {To protect the aquatic living resources of Chesapeake Bay, the Chesapeake Bay Program partnership has developed guidance for state water quality standards, which include ambient water quality criteria to protect designated uses (DUs), and associated assessment procedures for dissolved oxygen (DO), water clarity/underwater bay grasses, and chlorophyll-a. For measuring progress toward meeting the respective states' water quality standards, a multimetric attainment indicator approach was developed to estimate combined standards attainment. We applied this approach to three decades of monitoring data of DO, water clarity/underwater bay grasses, and chlorophyll-a data on annually updated moving 3-year periods to track the progress in all 92 management segments of tidal waters in Chesapeake Bay. In 2014–2016, 40\% of tidal water segment-DU-criterion combinations in the Bay (n = 291) are estimated to meet thresholds for attainment of their water quality criteria. This index score marks the best 3-year status in the entire record. Since 1985–1987, the indicator has followed a nonlinear trajectory, consistent with impacts from extreme weather events and subsequent recoveries. Over the period of record (1985–2016), the indicator exhibited a positive and statistically significant trend (p {\textless} 0.05), indicating that the Bay has been recovering since 1985. Patterns of attainment of individual DUs are variable, but improvements in open water DO, deep channel DO, and water clarity/submerged aquatic vegetation have combined to drive the improvement in the Baywide indicator in 2014–2016 relative to its long-term median. Finally, the improvement in estimated Baywide attainment was statistically linked to the decline of total nitrogen, indicating responsiveness of attainment status to the reduction of nutrient load through various management actions since at least the 1980s.}, + urldate = {2019-02-14}, + journal = {Science of The Total Environment}, + author = {Zhang, Qian and Murphy, Rebecca R. and Tian, Richard and Forsyth, Melinda K. and Trentacoste, Emily M. and Keisman, Jennifer and Tango, Peter J.}, + month = oct, + year = {2018}, + keywords = {Chesapeake Bay, Chlorophyll-a, Dissolved oxygen, Monitoring and assessment, Trends, Water quality standards}, + pages = {1617--1625}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\Z6LATKQH\\S0048969718316577.html:text/html}, +} + +@article{morse_distinct_2017, + title = {Distinct zooplankton regime shift patterns across ecoregions of the {U}.{S}. {Northeast} continental shelf {Large} {Marine} {Ecosystem}}, + volume = {165}, + issn = {09247963}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0924796316303098}, + doi = {10.1016/j.jmarsys.2016.09.011}, + abstract = {We investigated regime shifts in seasonal zooplankton communities of the Northeast continental shelf Large Marine Ecosystem (NES) and its subcomponent ecoregions over a multi-decadal period (1977–2013). Our cross ecoregion analysis shows that regime shifts in different ecoregions often exhibited very distinct characteristics, emphasizing more granular fluctuations in NES plankton communities relative to previous work. Shifts early in the time series generally reflected an increase in abundance levels. The response of zooplankton abundance within fall communities was more similar among ecoregions than for spring communities. The Gulf of Maine exhibited highly distinct patterns from other ecoregions, with regime shifts identified in the early 1980s, early 2000s, and mid-2000s for spring communities. Regime shifts were identified in the early to mid-1990s for the NES, Georges Bank, and the Mid-Atlantic Bight ecoregions, while the fall communities experienced shifts in the early 1990s and late 1980s for the NES and Georges Bank, but in the late 1990s in the Mid-Atlantic Bight. A constrained correspondence analysis of zooplankton community against local and basin-scale climatological indices suggests that water temperature, stratification, and the Atlantic multidecadal oscillation (AMO) were the predominant factors in driving the zooplankton community composition.}, + language = {en}, + urldate = {2019-02-14}, + journal = {Journal of Marine Systems}, + author = {Morse, R.E. and Friedland, K.D. and Tommasi, D. and Stock, C. and Nye, J.}, + month = jan, + year = {2017}, + keywords = {Zooplankton, Seasonal variations, Climate, Community composition, Long-term changes, Marine ecology}, + pages = {77--91}, + file = {Morse et al. - 2017 - Distinct zooplankton regime shift patterns across .pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\4529XXYW\\Morse et al. - 2017 - Distinct zooplankton regime shift patterns across .pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\3SREYWET\\S0924796316303098.html:text/html}, +} + +@article{large_quantifying_2015, + title = {Quantifying {Patterns} of {Change} in {Marine} {Ecosystem} {Response} to {Multiple} {Pressures}: e0119922}, + volume = {10}, + copyright = {© 2015 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Citation: Large SI, Fay G, Friedland KD, Link JS (2015) Quantifying Patterns of Change in Marine Ecosystem Response to Multiple Pressures. PLoS ONE 10(3): e0119922. doi:10.1371/journal.pone.0119922}, + shorttitle = {Quantifying {Patterns} of {Change} in {Marine} {Ecosystem} {Response} to {Multiple} {Pressures}}, + url = {http://search.proquest.com/docview/1664222461/abstract/120C1207C54548BEPQ/1}, + doi = {http://dx.doi.org/10.1371/journal.pone.0119922}, + abstract = {The ability to understand and ultimately predict ecosystem response to multiple pressures is paramount to successfully implement ecosystem-based management. Thresholds shifts and nonlinear patterns in ecosystem responses can be used to determine reference points that identify levels of a pressure that may drastically alter ecosystem status, which can inform management action. However, quantifying ecosystem reference points has proven elusive due in large part to the multi-dimensional nature of both ecosystem pressures and ecosystem responses. We used ecological indicators, synthetic measures of ecosystem status and functioning, to enumerate important ecosystem attributes and to reduce the complexity of the Northeast Shelf Large Marine Ecosystem (NES LME). Random forests were used to quantify the importance of four environmental and four anthropogenic pressure variables to the value of ecological indicators, and to quantify shifts in aggregate ecological indicator response along pressure gradients. Anthropogenic pressure variables were critical defining features and were able to predict an average of 8-13\% (up to 25-66\% for individual ecological indicators) of the variation in ecological indicator values, whereas environmental pressures were able to predict an average of 1-5 \% (up to 9-26\% for individual ecological indicators) of ecological indicator variation. Each pressure variable predicted a different suite of ecological indicator's variation and the shapes of ecological indicator responses along pressure gradients were generally nonlinear. Threshold shifts in ecosystem response to exploitation, the most important pressure variable, occurred when commercial landings were 20 and 60\% of total surveyed biomass. Although present, threshold shifts in ecosystem response to environmental pressures were much less important, which suggests that anthropogenic pressures have significantly altered the ecosystem structure and functioning of the NES LME. Gradient response curves provide ecologically informed transformations of pressure variables to explain patterns of ecosystem structure and functioning. By concurrently identifying thresholds for a suite of ecological indicator responses to multiple pressures, we demonstrate that ecosystem reference points can be evaluated and used to support ecosystem-based management.}, + language = {English}, + number = {3}, + urldate = {2016-10-14}, + journal = {PLoS One}, + author = {Large, Scott I. and Fay, Gavin and Friedland, Kevin D. and Link, Jason S.}, + month = mar, + year = {2015}, + keywords = {Ecosystems, Fisheries management, Decision making, fish, Forests, Medical Sciences, Sciences: Comprehensive Works, Variables}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\FCU7JZHL\\Large et al. - 2015 - Quantifying Patterns of Change in Marine Ecosystem.pdf:application/pdf}, +} + +@article{hare_vulnerability_2016, + title = {A {Vulnerability} {Assessment} of {Fish} and {Invertebrates} to {Climate} {Change} on the {Northeast} {U}.{S}. {Continental} {Shelf}}, + volume = {11}, + issn = {1932-6203}, + url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0146756}, + doi = {10.1371/journal.pone.0146756}, + abstract = {Climate change and decadal variability are impacting marine fish and invertebrate species worldwide and these impacts will continue for the foreseeable future. Quantitative approaches have been developed to examine climate impacts on productivity, abundance, and distribution of various marine fish and invertebrate species. However, it is difficult to apply these approaches to large numbers of species owing to the lack of mechanistic understanding sufficient for quantitative analyses, as well as the lack of scientific infrastructure to support these more detailed studies. Vulnerability assessments provide a framework for evaluating climate impacts over a broad range of species with existing information. These methods combine the exposure of a species to a stressor (climate change and decadal variability) and the sensitivity of species to the stressor. These two components are then combined to estimate an overall vulnerability. Quantitative data are used when available, but qualitative information and expert opinion are used when quantitative data is lacking. Here we conduct a climate vulnerability assessment on 82 fish and invertebrate species in the Northeast U.S. Shelf including exploited, forage, and protected species. We define climate vulnerability as the extent to which abundance or productivity of a species in the region could be impacted by climate change and decadal variability. We find that the overall climate vulnerability is high to very high for approximately half the species assessed; diadromous and benthic invertebrate species exhibit the greatest vulnerability. In addition, the majority of species included in the assessment have a high potential for a change in distribution in response to projected changes in climate. Negative effects of climate change are expected for approximately half of the species assessed, but some species are expected to be positively affected (e.g., increase in productivity or move into the region). These results will inform research and management activities related to understanding and adapting marine fisheries management and conservation to climate change and decadal variability.}, + number = {2}, + urldate = {2016-03-01}, + journal = {PLOS ONE}, + author = {Hare, Jonathan A. and Morrison, Wendy E. and Nelson, Mark W. and Stachura, Megan M. and Teeters, Eric J. and Griffis, Roger B. and Alexander, Michael A. and Scott, James D. and Alade, Larry and Bell, Richard J. and Chute, Antonie S. and Curti, Kiersten L. and Curtis, Tobey H. and Kircheis, Daniel and Kocik, John F. and Lucey, Sean M. and McCandless, Camilla T. and Milke, Lisa M. and Richardson, David E. and Robillard, Eric and Walsh, Harvey J. and McManus, M. Conor and Marancik, Katrin E. and Griswold, Carolyn A.}, + month = feb, + year = {2016}, + keywords = {Marine fish, Fisheries, climate change, Habitats, Marine ecology, Invertebrates, ocean temperature, Oceans}, + pages = {e0146756}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\PWLSV283\\Hare et al. - 2016 - A Vulnerability Assessment of Fish and Invertebrat.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\IUN3JGZ7\\article.html:text/html}, +} + +@article{smith_consumption_2015, + title = {Consumption by marine mammals on the {Northeast} {U}.{S}. continental shelf}, + volume = {25}, + issn = {1939-5582}, + url = {http://onlinelibrary.wiley.com/doi/10.1890/13-1656.1/abstract}, + doi = {10.1890/13-1656.1}, + abstract = {The economic and ecological impacts of fish consumption by marine mammals, the associated interactions with commercial fish stocks, and the forage demands of these marine mammal populations are largely unknown. Consumption estimates are often either data deficient or not fully evaluated in a rigorous, quantitative manner. Although consumption estimates exist for the Northeast United States (NEUS) Large Marine Ecosystem, there is considerable uncertainty in those estimates. We examined consumption estimates for 12 marine mammal species inhabiting the regional ecosystem. We used sensitivity analyses to examine metabolically driven daily individual consumption rates, resulting in a suite of feasible parameter-pair ranges for each of three taxonomic groups: mysticetes, odontocetes, and pinnipeds. We expanded daily individual consumption to annual consumption based on abundance estimates of marine mammals found on the NEUS continental shelf coupled with estimates of annual residence time for each species. To examine consumptive removals for specific prey, diet compositions were summarized into major prey categories, and predatory removals by marine mammal species as well as for total marine mammal consumption were estimated for each prey taxa. Bounds on consumption estimates for each marine mammal species were determined using Monte Carlo resampling simulations. Our results suggest that consumption for these 12 marine mammal species combined may be similar in magnitude to commercial fishery landings for small pelagic and groundfish prey groups. Consumption by marine mammals warrants consideration both as a source of mortality in assessments of prey stocks, and to determine marine mammal forage demands in ecosystem assessment models. The approach that we present represents a rigorous, quantitative method to scope the bounds of the biomass that marine mammals are expected to consume, and is appropriate for use in other ecosystems where the interaction between marine mammals and commercial fisheries is thought to be prominent.}, + language = {en}, + number = {2}, + urldate = {2016-04-26}, + journal = {Ecological Applications}, + author = {Smith, Laurel A. and Link, Jason S. and Cadrin, Steven X. and Palka, Debra L.}, + month = mar, + year = {2015}, + keywords = {commercial fisheries, Northeast U.S. shelf, Atlantic Ocean, competition, conservation, consumption estimates, ecosystem modeling, energetics, marine mammal diets, uncertainty estimation}, + pages = {373--389}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\B4PVYBPE\\abstract.html:text/html}, +} + +@article{zador_ecosystem_2016, + title = {Ecosystem considerations in {Alaska}: the value of qualitative assessments}, + copyright = {All rights reserved}, + issn = {1054-3139, 1095-9289}, + shorttitle = {Ecosystem considerations in {Alaska}}, + url = {http://icesjms.oxfordjournals.org/content/early/2016/09/12/icesjms.fsw144}, + doi = {10.1093/icesjms/fsw144}, + abstract = {The application of ecosystem considerations, and in particular ecosystem report cards, in federal groundfish fisheries management in Alaska can be described as an ecosystem approach to fisheries management (EAFM). Ecosystem information is provided to managers to establish an ecosystem context within which deliberations of fisheries quota occur. Our goal is to make the case for the need for qualitative ecosystem assessments in EAFM, specifically that qualitative synthesis has advantages worthy to keep a permanent place at the fisheries management table. These advantages include flexibility and speed in responding to and synthesizing new information from a variety of sources. First, we use the development of indicator-based ecosystem report cards as an example of adapting ecosystem information to management needs. Second, we review lessons learned and provide suggestions for best practices for applying EAFM to large and diverse fisheries in multiple marine ecosystems. Adapting ecosystem indicator information to better suit the needs of fisheries managers resulted in succinct report cards that summarize ecosystem trends, complementing more detailed ecosystem information to provide context for EAFM. There were several lessons learned in the process of developing the ecosystem report cards. The selection of indicators for each region was influenced by geography, the extent of scientific knowledge/data, and the particular expertise of the selection teams. Optimizing the opportunity to qualitatively incorporate ecosystem information into management decisions requires a good understanding of the management system in question. We found that frequent dialogue with managers and other stakeholders leads to adaptive products. We believe that there will always be a need for qualitative ecosystem assessment because it allows for rapid incorporation of new ideas and data and unexpected events. As we build modelling and predictive capacity, we will still need qualitative synthesis to capture events outside the bounds of current models and to detect impacts of the unexpected.}, + language = {en}, + urldate = {2016-09-24}, + journal = {ICES Journal of Marine Science: Journal du Conseil}, + author = {Zador, Stephani G. and Holsman, Kirstin K. and Aydin, Kerim Y. and Gaichas, Sarah K.}, + month = aug, + year = {2016}, + keywords = {Ecosystem-based fisheries management, Alaska, ecosystem approach to fisheries, ecosystem assessment, ecosystem report cards.}, + pages = {fsw144}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\J8TGK3YC\\Zador et al. - 2016 - Ecosystem considerations in Alaska the value of q.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\T3INLMFK\\icesjms.html:text/html}, +} + +@article{gawarkiewicz_changing_2018, + title = {The {Changing} {Nature} of {Shelf}-{Break} {Exchange} {Revealed} by the {OOI} {Pioneer} {Array}}, + volume = {31}, + issn = {10428275}, + url = {https://tos.org/oceanography/article/the-changing-nature-of-shelf-break-exchange-revealed-by-the-ooi-pioneer-arr}, + doi = {10.5670/oceanog.2018.110}, + number = {1}, + urldate = {2020-02-10}, + journal = {Oceanography}, + author = {Gawarkiewicz, Glen and Todd, Robert and Zhang, Weifeng and Partida, Jacob and Gangopadhyay, Avijit and Monim, Mahmud-Ul-Hasan and Fratantoni, Paula and Malek Mercer, Anna and Dent, Margaret}, + month = mar, + year = {2018}, + pages = {60--70}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\XJCTJY7Z\\Gawarkiewicz et al. - 2018 - The Changing Nature of Shelf-Break Exchange Reveal.pdf:application/pdf}, +} + +@article{friedland_changes_2020, + title = {Changes in higher trophic level productivity, diversity and niche space in a rapidly warming continental shelf ecosystem}, + volume = {704}, + issn = {0048-9697}, + url = {http://www.sciencedirect.com/science/article/pii/S0048969719352623}, + doi = {10.1016/j.scitotenv.2019.135270}, + abstract = {There is long-standing ecological and socioeconomic interest in what controls the diversity and productivity of ecosystems. That focus has intensified with shifting environmental conditions associated with accelerating climate change. The U.S. Northeast Shelf (NES) is a well-studied continental shelf marine ecosystem that is among the more rapidly warming marine systems worldwide. Furthermore, many constituent species have experienced significant distributional shifts. However, the system response of the NES to climate change goes beyond simple shifts in species distribution. The fish and macroinvertebrate communities of the NES have increased in species diversity and overall productivity in recent decades, despite no significant decline in fishing pressure. Species distribution models constructed using random forest classification and regression trees were fit for the dominant species in the system. Over time, the areal distribution of occupancy habitat has increased for approximately 80\% of the modeled taxa, suggesting most species have significantly increased their range and niche space. These niche spaces were analyzed to determine the area of niche overlap between species pairs. For the vast majority of species pairs, interaction has increased over time suggesting greater niche overlap and the increased probability for more intense species interactions, such as between competitors or predators and prey. Furthermore, the species taxonomic composition and size structure indicate a potential tropicalization of the fish community. The system and community changes are consistent with the view that the NES may be transitioning from a cold temperate or boreal ecoregion to one more consistent with the composition of a warm temperate or Carolinian system.}, + language = {en}, + urldate = {2020-02-07}, + journal = {Science of The Total Environment}, + author = {Friedland, Kevin D. and Langan, Joseph A. and Large, Scott I. and Selden, Rebecca L. and Link, Jason S. and Watson, Reg A. and Collie, Jeremy S.}, + month = feb, + year = {2020}, + keywords = {Biodiversity, Habitat, Niche overlap, Species distribution model, Species interactions}, + pages = {135270}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\LU6932BM\\S0048969719352623.html:text/html}, +} + +@article{andres_recent_2016, + title = {On the recent destabilization of the {Gulf} {Stream} path downstream of {Cape} {Hatteras}}, + volume = {43}, + copyright = {©2016. American Geophysical Union. All Rights Reserved.}, + issn = {1944-8007}, + url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2016GL069966}, + doi = {10.1002/2016GL069966}, + abstract = {Mapped satellite altimetry reveals interannual variability in the position of initiation of Gulf Stream meanders downstream of Cape Hatteras. The longitude where the Gulf Stream begins meandering varies by 1500 km. There has been a general trend for the destabilization point to shift west, and 5 of the last 6 years had a Gulf Stream destabilization point upstream of the New England Seamounts. Independent in situ data suggest that this shift has increased both upper-ocean/deep-ocean interaction events at Line W and open-ocean/shelf interactions across the Middle Atlantic Bight (MAB) shelf break. Mooring data and along-track altimetry indicate a recent increase in the number of deep cyclones that stir Deep Western Boundary Current waters from the MAB slope into the deep interior. Temperature profiles from the Oleander Program suggest that recent enhanced warming of the MAB shelf may be related to shifts in the Gulf Stream's destabilization point.}, + language = {en}, + number = {18}, + urldate = {2020-02-10}, + journal = {Geophysical Research Letters}, + author = {Andres, M.}, + year = {2016}, + keywords = {Gulf Stream, Middle Atlantic Bight, altimetry, Cape Hatteras, Deep Western Boundary Current, Line W}, + pages = {9836--9842}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\CF2LBGAU\\Andres - 2016 - On the recent destabilization of the Gulf Stream p.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\8TKG8B66\\2016GL069966.html:text/html}, +} + +@article{lettrich_vulnerability_2023, + title = {Vulnerability to climate change of {United} {States} marine mammal stocks in the western {North} {Atlantic}, {Gulf} of {Mexico}, and {Caribbean}}, + volume = {18}, + issn = {1932-6203}, + url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0290643}, + doi = {10.1371/journal.pone.0290643}, + abstract = {Climate change and climate variability are affecting marine mammal species and these impacts are projected to continue in the coming decades. Vulnerability assessments provide a framework for evaluating climate impacts over a broad range of species using currently available information. We conducted a trait-based climate vulnerability assessment using expert elicitation for 108 marine mammal stocks and stock groups in the western North Atlantic, Gulf of Mexico, and Caribbean Sea. Our approach combined the exposure (projected change in environmental conditions) and sensitivity (ability to tolerate and adapt to changing conditions) of marine mammal stocks to estimate vulnerability to climate change, and categorize stocks with a vulnerability index. The climate vulnerability score was very high for 44\% (n = 47) of these stocks, high for 29\% (n = 31), moderate for 20\% (n = 22), and low for 7\% (n = 8). The majority of stocks (n = 78; 72\%) scored very high exposure, whereas 24\% (n = 26) scored high, and 4\% (n = 4) scored moderate. The sensitivity score was very high for 33\% (n = 36) of these stocks, high for 18\% (n = 19), moderate for 34\% (n = 37), and low for 15\% (n = 16). Vulnerability results were summarized for stocks in five taxonomic groups: pinnipeds (n = 4; 25\% high, 75\% moderate), mysticetes (n = 7; 29\% very high, 57\% high, 14\% moderate), ziphiids (n = 8; 13\% very high, 50\% high, 38\% moderate), delphinids (n = 84; 52\% very high, 23\% high, 15\% moderate, 10\% low), and other odontocetes (n = 5; 60\% high, 40\% moderate). Factors including temperature, ocean pH, and dissolved oxygen were the primary drivers of high climate exposure, with effects mediated through prey and habitat parameters. We quantified sources of uncertainty by bootstrapping vulnerability scores, conducting leave-one-out analyses of individual attributes and individual scorers, and through scoring data quality for each attribute. These results provide information for researchers, managers, and the public on marine mammal responses to climate change to enhance the development of more effective marine mammal management, restoration, and conservation activities that address current and future environmental variation and biological responses due to climate change.}, + language = {en}, + number = {9}, + urldate = {2024-02-05}, + journal = {PLOS ONE}, + author = {Lettrich, Matthew D. and Asaro, Michael J. and Borggaard, Diane L. and Dick, Dorothy M. and Griffis, Roger B. and Litz, Jenny A. and Orphanides, Christopher D. and Palka, Debra L. and Soldevilla, Melissa S. and Balmer, Brian and Chavez, Samuel and Cholewiak, Danielle and Claridge, Diane and Ewing, Ruth Y. and Fazioli, Kristi L. and Fertl, Dagmar and Fougeres, Erin M. and Gannon, Damon and Garrison, Lance and Gilbert, James and Gorgone, Annie and Hohn, Aleta and Horstman, Stacey and Josephson, Beth and Kenney, Robert D. and Kiszka, Jeremy J. and Maze-Foley, Katherine and McFee, Wayne and Mullin, Keith D. and Murray, Kimberly and Pendleton, Daniel E. and Robbins, Jooke and Roberts, Jason J. and Ferrer, Grisel Rodriguez- and Ronje, Errol I. and Rosel, Patricia E. and Speakman, Todd and Stanistreet, Joy E. and Stevens, Tara and Stolen, Megan and Moore, Reny Tyson and Vollmer, Nicole L. and Wells, Randall and Whitehead, Heidi R. and Whitt, Amy}, + month = sep, + year = {2023}, + note = {Publisher: Public Library of Science}, + keywords = {Climate change, Animal migration, Ocean temperature, Anthropogenic climate change, Caribbean, Dolphins, Marine mammals, Sea level rise}, + pages = {e0290643}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\J7RIM3FD\\Lettrich et al. - 2023 - Vulnerability to climate change of United States m.pdf:application/pdf}, +} + +@article{staudinger_its_2019, + title = {It’s about time: {A} synthesis of changing phenology in the {Gulf} of {Maine} ecosystem}, + volume = {28}, + copyright = {© 2019 The Authors. Fisheries Oceanography Published by John Wiley \& Sons Ltd}, + issn = {1365-2419}, + shorttitle = {It’s about time}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/fog.12429}, + doi = {10.1111/fog.12429}, + abstract = {The timing of recurring biological and seasonal environmental events is changing on a global scale relative to temperature and other climate drivers. This study considers the Gulf of Maine ecosystem, a region of high social and ecological importance in the Northwest Atlantic Ocean and synthesizes current knowledge of (a) key seasonal processes, patterns, and events; (b) direct evidence for shifts in timing; (c) implications of phenological responses for linked ecological-human systems; and (d) potential phenology-focused adaptation strategies and actions. Twenty studies demonstrated shifts in timing of regional marine organisms and seasonal environmental events. The most common response was earlier timing, observed in spring onset, spring and winter hydrology, zooplankton abundance, occurrence of several larval fishes, and diadromous fish migrations. Later timing was documented for fall onset, reproduction and fledging in Atlantic puffins, spring and fall phytoplankton blooms, and occurrence of additional larval fishes. Changes in event duration generally increased and were detected in zooplankton peak abundance, early life history periods of macro-invertebrates, and lobster fishery landings. Reduced duration was observed in winter–spring ice-affected stream flows. Two studies projected phenological changes, both finding diapause duration would decrease in zooplankton under future climate scenarios. Phenological responses were species-specific and varied depending on the environmental driver, spatial, and temporal scales evaluated. Overall, a wide range of baseline phenology and relevant modeling studies exist, yet surprisingly few document long-term shifts. Results reveal a need for increased emphasis on phenological shifts in the Gulf of Maine and identify opportunities for future research and consideration of phenological changes in adaptation efforts.}, + language = {en}, + number = {5}, + urldate = {2024-02-05}, + journal = {Fisheries Oceanography}, + author = {Staudinger, Michelle D. and Mills, Katherine E. and Stamieszkin, Karen and Record, Nicholas R. and Hudak, Christine A. and Allyn, Andrew and Diamond, Antony and Friedland, Kevin D. and Golet, Walt and Henderson, Meghan Elisabeth and Hernandez, Christina M. and Huntington, Thomas G. and Ji, Rubao and Johnson, Catherine L. and Johnson, David Samuel and Jordaan, Adrian and Kocik, John and Li, Yun and Liebman, Matthew and Nichols, Owen C. and Pendleton, Daniel and Richards, R. Anne and Robben, Thomas and Thomas, Andrew C. and Walsh, Harvey J. and Yakola, Keenan}, + year = {2019}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/fog.12429}, + keywords = {Gulf of Maine, phenology, fish, coastal, life cycle, marine, marine invertebrates, marine mammals, migration, phytoplankton, seabirds, seasonal, timing, zooplankton}, + pages = {532--566}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\X26EBHB6\\Staudinger et al. - 2019 - It’s about time A synthesis of changing phenology.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\NSLLYQJE\\fog.html:text/html}, +} + +@article{crear_climate-influenced_2023, + title = {Climate-influenced shifts in a highly migratory species recreational fishery}, + volume = {32}, + copyright = {© 2023 John Wiley \& Sons Ltd.}, + issn = {1365-2419}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/fog.12632}, + doi = {10.1111/fog.12632}, + abstract = {The distribution of marine species is changing as a direct result of climate change. Large pelagic highly migratory species (HMS), like tunas, billfishes, and sharks, are particularly sensitive to environmental change due to their migratory nature and use of large-scale ocean features. These temporal and spatial shifts are likely reflected in the Atlantic HMS recreational fishery and could have downstream effects on coastal communities. We utilized dockside intercept data from the Large Pelagics Survey (LPS) from 2002 to 2019, which conducts sampling from June to October and spans Maine to Virginia, USA. We assessed how species catch composition has changed and developed spatiotemporal models to understand latitudinal and temporal shifts in the recreational catch of 12 HMS groups. Latitudinal shifts were significantly affected by Northeast Shelf SST for 11 of the 12 HMS groups and ranged from 3 ± 1 km°C−1 for the large bluefin tuna to 40 ± 1 km°C−1 for the blue shark. In addition, the estimated day of the year when the first 25\% of bigeye tuna intercepts occurred, happened over 50 days earlier in 2019 compared to 2002, suggesting the initial catch is happening earlier in recent years. These results suggest that changes in species distribution and phenology are affecting where and when HMS recreational catch occurs. Understanding these shifts would allow managers to be more responsive and flexible and also help communities prepare for changes, whether it would be a switch to a new species or shifts in the fishing season.}, + language = {en}, + number = {4}, + urldate = {2024-02-06}, + journal = {Fisheries Oceanography}, + author = {Crear, Daniel P. and Curtis, Tobey H. and Hutt, Cliff P. and Lee, Yong-Woo}, + year = {2023}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/fog.12632}, + keywords = {fisheries management, climate change, billfishes, Large Pelagics Survey, pelagic sharks, spatiotemporal models, tunas}, + pages = {327--340}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\9IPF4TBA\\fog.html:text/html;Submitted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\487LDGYG\\Crear et al. - 2023 - Climate-influenced shifts in a highly migratory sp.pdf:application/pdf}, +} + +@article{grieve_projecting_2017, + title = {Projecting the effects of climate change on {Calanus} finmarchicus distribution within the {U}.{S}. {Northeast} {Continental} {Shelf}}, + volume = {7}, + copyright = {2017 The Author(s)}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-017-06524-1}, + doi = {10.1038/s41598-017-06524-1}, + abstract = {Calanus finmarchicus is vital to pelagic ecosystems in the North Atlantic Ocean. Previous studies suggest the species is vulnerable to the effects of global warming, particularly on the Northeast U.S. Shelf, which is in the southern portion of its range. In this study, we evaluate an ensemble of six different downscaled climate models and a high-resolution global climate model, and create a generalized additive model (GAM) to examine how future changes in temperature and salinity could affect the distribution and density of C. finmarchicus. By 2081–2100, we project average C. finmarchicus density will decrease by as much as 50\% under a high greenhouse gas emissions scenario. These decreases are particularly pronounced in the spring and summer in the Gulf of Maine and Georges Bank. When compared to a high-resolution global climate model, the ensemble showed a more uniform change throughout the Northeast U.S. Shelf, while the high-resolution model showed larger decreases in the Northeast Channel, Shelf Break, and Central Gulf of Maine. C. finmarchicus is an important link between primary production and higher trophic levels, and the decrease projected here could be detrimental to the North Atlantic Right Whale and a host of important fishery species.}, + language = {en}, + number = {1}, + urldate = {2024-02-06}, + journal = {Scientific Reports}, + author = {Grieve, Brian D. and Hare, Jon A. and Saba, Vincent S.}, + month = jul, + year = {2017}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Marine biology, Ocean sciences, Biooceanography, Climate-change ecology}, + pages = {6264}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\LAL7J7VB\\Grieve et al. - 2017 - Projecting the effects of climate change on Calanu.pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\ZD5G4FW4\\Grieve et al. - 2017 - Projecting the effects of climate change on Calanu.pdf:application/pdf}, +} + +@article{stevens_modeling_2019, + title = {Modeling the impacts of dams and stocking practices on an endangered {Atlantic} salmon ({Salmo} salar) population in the {Penobscot} {River}, {Maine}, {USA}}, + volume = {76}, + issn = {0706-652X}, + url = {https://cdnsciencepub.com/doi/abs/10.1139/cjfas-2018-0225}, + doi = {10.1139/cjfas-2018-0225}, + abstract = {Dams challenge Atlantic salmon (Salmo salar) conservation, while hatcheries are a common but poorly evaluated recovery tool. We developed a spatially explicit smolt survival model for the Penobscot River, Maine, USA, population. By partitioning survival through dams (with flow dependency), free-flowing river reaches, and the estuary (with dam dependency), the model quantified how these factors influenced the number of fish entering the ocean. Given historical impounded conditions, 74\%–22\% of hatchery smolts released entered the ocean annually from 1970 to 2012. Of 19.7 million smolts stocked, 7.7 million entered the ocean (39\%). Survival was most variable at dams (range 95\% to 63\%), followed by in-river (range 98\% to 70\%) and estuary (range 88\% to 82\%). Overall, lower-river stocking sites resulted in significantly higher numbers at ocean entry because of fewer dam encounters and shorter migrations. Higher flows also resulted in reduced losses. By reconstructing these freshwater and estuary dynamics, the model provides a more accurate estimate of ocean recruitment annually and can be used for scenario planning of future stocking locations relative to predicted flows while being adaptable to new survival rates.}, + number = {10}, + urldate = {2024-02-07}, + journal = {Canadian Journal of Fisheries and Aquatic Sciences}, + author = {Stevens, Justin R. and Kocik, John F. and Sheehan, Timothy F.}, + month = oct, + year = {2019}, + note = {Publisher: NRC Research Press}, + pages = {1795--1807}, +} + +@article{mountain_volume_1991, + title = {The volume of {Shelf} {Water} in the {Middle} {Atlantic} {Bight}: seasonal and interannual variability, 1977–1987}, + volume = {11}, + issn = {0278-4343}, + shorttitle = {The volume of {Shelf} {Water} in the {Middle} {Atlantic} {Bight}}, + url = {https://www.sciencedirect.com/science/article/pii/027843439190068H}, + doi = {10.1016/0278-4343(91)90068-H}, + abstract = {The volume of Shelf Water (salinity {\textless}34psu) is determined for three regions in the Middle Atlantic Bight (MAB) and for Georges Bank from a combination of hydrographic data and shelf/slope front positions derived from satellite imagery. The hydrographic data were obtained on 49 surveys of the shelf between 1977 and 1987. In each region the shelf water volume exhibits a significant annual cycle with a range of 50–80\% of the mean volume. The annual volume cycle appears to be advected southwestward from Georges Bank through the MAB with the mean circulation. Large interannual changes in volume are observed which have a range comparable to the mean volume of Shelf Water in the MAB. Both the seasonal and interannual changes in volume appear to be related to changes in the inflow to the Gulf of Maine through Northeast Channel and from the Scotian Shelf.}, + number = {3}, + urldate = {2024-02-07}, + journal = {Continental Shelf Research}, + author = {Mountain, David G.}, + month = mar, + year = {1991}, + pages = {251--267}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\FWDR7722\\027843439190068H.html:text/html}, +} + +@article{mountain_labrador_2012, + title = {Labrador slope water entering the {Gulf} of {Maine}—response to the {North} {Atlantic} {Oscillation}}, + volume = {47}, + issn = {0278-4343}, + url = {https://www.sciencedirect.com/science/article/pii/S0278434312001938}, + doi = {10.1016/j.csr.2012.07.008}, + abstract = {The percent of Labrador Slope Water (\%LSW) in the waters entering the Gulf of Maine at depth (150–200m) through the Northeast Channel is estimated from temperature/salinity measurements for the period 1964–2008. Comparison of the annual average \%LSW with the North Atlantic Oscillation (NAO) lagged two years shows that both series have very similar, but opposite, long term trends. High \%LSW in the 1960s decreased to low values in the 1990s as the NAO increased from low to high values over the same period. A regression of the residuals of the two series from their long term trends also is significant at the 5\% level, indicating that the \%LSW at the Northeast Channel has a persistent relationship on a year-to-year basis with the NAO over the full range of variation in the NAO. The response of the \%LSW to the NAO, however, appeared substantially reduced after about 1990. Changes in the potential nutrient flux into the Gulf of Maine/Georges Bank system associated with changes in the \%LSW cannot account for changes in chlorophyll a concentrations estimated for three multi-year stanzas during the sampling period.}, + urldate = {2024-02-07}, + journal = {Continental Shelf Research}, + author = {Mountain, David G.}, + month = sep, + year = {2012}, + keywords = {Gulf of Maine, Labrador slope water, North Atlantic Oscillation}, + pages = {150--155}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\79KAYHBV\\S0278434312001938.html:text/html}, +} + +@article{benjamin_visualizing_2018, + title = {Visualizing {Fishing} {Data} as {Rasters}}, + url = {https://repository.library.noaa.gov/view/noaa/23030}, + doi = {10.25923/C14D-G218}, + urldate = {2024-02-07}, + author = {Benjamin, Sharon and Lee, Min-Yang A. and DePiper, Geret Sean}, + year = {2018}, + note = {Publisher: Northeast Fisheries Science Center (U.S.)}, + annote = {SeriesInformation +Northeast Fisheries Science Center Reference Document ; 18-12}, +} + +@article{northeast_fisheries_science_center_us_fall_2022, + title = {Fall {Management} {Track} {Assessments} 2020}, + url = {https://repository.library.noaa.gov/view/noaa/39404}, + doi = {10.25923/8N72-Q136}, + urldate = {2024-02-07}, + author = {{Northeast Fisheries Science Center (U.S.)}}, + year = {2022}, + note = {Publisher: Northeast Fisheries Science Center (U.S.)}, + annote = {SeriesInformation +Northeast Fisheries Science Center reference document ; 22-08}, +} + +@misc{palka_atlantic_2021, + title = {Atlantic {Marine} {Assessment} {Program} for {Protected} {Species}: {FY15}-{FY19}}, + url = {https://repository.library.noaa.gov/view/noaa/47287}, + urldate = {2024-02-07}, + author = {Palka, D and Aichinger Dias,, L and Broughton, E and Chavez-Rosales, S and Cholewiak, D and Davis, G and DeAngelis, A and Garrison, L and Haas, H and Hatch, J and Hyde, K and Jech, M and Josephson, E and Mueller-Brennan, L and Orphanides, C and Pegg,, N and Sasso, C and Sigourney, D and Soldevilla, M and Walsh, H}, + year = {2021}, + file = {Atlantic Marine Assessment Program for Protected Species\: FY15-FY19:C\:\\Users\\andrew.beet\\Zotero\\storage\\N27ZDQJ7\\47287.html:text/html}, +} + +@article{petrenko_evaluation_2014, + title = {Evaluation and selection of {SST} regression algorithms for {JPSS} {VIIRS}}, + volume = {119}, + copyright = {©2014. American Geophysical Union. All Rights Reserved.}, + issn = {2169-8996}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/2013JD020637}, + doi = {10.1002/2013JD020637}, + abstract = {AbstractTwo global level 2 sea surface temperature (SST) products are generated at NOAA from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data records (L1) with two independent processing systems, the Joint Polar Satellite System (JPSS) Interface Data Processing Segment (IDPS) and the NOAA heritage Advanced Clear-Sky Processor for Oceans (ACSPO). The two systems use different SST retrieval and cloud masking algorithms. Validation against in situ and L4 analyses has shown suboptimal performance of the IDPS product. In this context, existing operational and proposed SST algorithms have been evaluated for their potential implementation in IDPS. This paper documents the evaluation methodology and results. The performance of SST retrievals is characterized with bias and standard deviation with respect to in situ SSTs and sensitivity to true SST. Given three retrieval metrics, all being variable in space and with observational conditions, an additional integral metric is needed to evaluate the overall performance of SST algorithms. Therefore, we introduce the Quality Retrieval Domain (QRD) as a part of the global ocean, where the retrieval characteristics meet predefined specifications. Based on the QRDs analyses for all tested algorithms over a representative range of specifications for accuracy, precision, and sensitivity, we have selected the algorithms developed at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF) for implementation in IDPS and ACSPO. Testing the OSI-SAF algorithms with ACSPO and IDPS products shows the improved consistency between VIIRS SST and Reynolds L4 daily analysis. Further improvement of the IDPS SST product requires adjustment of the VIIRS cloud and ice masks.}, + language = {en}, + number = {8}, + urldate = {2024-02-07}, + journal = {Journal of Geophysical Research: Atmospheres}, + author = {Petrenko, Boris and Ignatov, Alexander and Kihai, Yury and Stroup, John and Dash, Prasanjit}, + year = {2014}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/2013JD020637}, + keywords = {accuracy, algorithm, precision, regression, sea surface temperature, sensitivity}, + pages = {4580--4599}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\CM93WUCS\\Petrenko et al. - 2014 - Evaluation and selection of SST regression algorit.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\B4UKL23F\\2013JD020637.html:text/html}, +} + +@article{petrenko_sensor-specific_2016, + title = {Sensor-{Specific} {Error} {Statistics} for {SST} in the {Advanced} {Clear}-{Sky} {Processor} for {Oceans}}, + volume = {33}, + issn = {0739-0572, 1520-0426}, + url = {https://journals.ametsoc.org/view/journals/atot/33/2/jtech-d-15-0166_1.xml}, + doi = {10.1175/JTECH-D-15-0166.1}, + abstract = {Abstract The formulation of the sensor-specific error statistics (SSES) has been redesigned in the latest implementation of the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) to enable efficient use of SSES for assimilation of the ACSPO baseline regression SST (BSST) into level 4 (L4) analyses. The SSES algorithm employs segmentation of the SST domain in the space of regressors and derives the segmentation parameter from the statistics of regressors within the global dataset of matchups. For each segment, local regression coefficients and standard deviations (SDs) of BSST minus in situ SST are calculated from the corresponding subset of matchups. The local regression coefficients are used to generate an auxiliary product—piecewise regression (PWR) SST—and SSES biases are estimated as differences between BSST and PWR SST. Correction of SSES biases, which transforms BSST back into PWR SST, reduces the effects of residual cloud; variations in view zenith angle; and, during the daytime, diurnal surface warming. This results in significant reduction in the global SD of fitting in situ SST, making it comparable with SD for the Canadian Meteorological Centre (CMC) L4 SST. Unlike the foundation CMC SST (which is consistent with in situ SST at night but biased cold during the daytime), the PWR SST is consistent with in situ data during both day and night and thus may be viewed as an estimate of “depth” in situ SST. The PWR SST is expected to be a useful input into L4 SST analyses, especially for foundation SST products, such as the CMC L4.}, + language = {EN}, + number = {2}, + urldate = {2024-02-07}, + journal = {Journal of Atmospheric and Oceanic Technology}, + author = {Petrenko, B. and Ignatov, A. and Kihai, Y. and Dash, P.}, + month = feb, + year = {2016}, + note = {Publisher: American Meteorological Society +Section: Journal of Atmospheric and Oceanic Technology}, + pages = {345--359}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\XAEUF4JQ\\Petrenko et al. - 2016 - Sensor-Specific Error Statistics for SST in the Ad.pdf:application/pdf}, +} + +@article{ji_drivers_2022, + title = {Drivers of variability of {Calanus} finmarchicus in the {Gulf} of {Maine}: roles of internal production and external exchange}, + volume = {79}, + issn = {1054-3139}, + shorttitle = {Drivers of variability of {Calanus} finmarchicus in the {Gulf} of {Maine}}, + url = {https://doi.org/10.1093/icesjms/fsab147}, + doi = {10.1093/icesjms/fsab147}, + abstract = {The lipid-rich calanoid copepod, Calanus finmarchicus, plays a critical role in the Gulf of Maine pelagic food web. Despite numerous studies over the last several decades, a clear picture of variability patterns and links with key environmental drivers remains elusive. This study applies model-based scaling and sensitivity analyses to a regional plankton dataset collected over the last four decades (1977–2017). The focus is to describe the gulf-wide spatio-temporal patterns across three major basins, and to assess the relative roles of internal population dynamics and external exchanges. For the spring stock, there is strong synchrony of interannual variability among three basins. This variability is largely driven by internal population dynamics rather than external exchanges, and the internal population dynamics are more sensitive to the change of top-down mortality regime than the bottom-up forcings. For the fall stock, the synchrony among basins weakens, and the variability is influenced by both internal mortality and external dilution loss. There appears to be no direct connection between the spring stock with either the preceding or subsequent fall stock, suggesting seasonal or sub-seasonal scales of population variability and associated drivers. The results highlight seasonally varying drivers responsible for population variability, including previously less recognized top-down control.}, + number = {3}, + urldate = {2024-02-07}, + journal = {ICES Journal of Marine Science}, + author = {Ji, Rubao and Runge, Jeffrey A and Davis, Cabell S and Wiebe, Peter H}, + month = apr, + year = {2022}, + pages = {775--784}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\M6HYRJ4U\\Ji et al. - 2022 - Drivers of variability of Calanus finmarchicus in .pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\ZTTCEYGY\\Ji et al. - 2022 - Drivers of variability of Calanus finmarchicus in .pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\8GF2LJ5T\\Ji et al. - 2022 - Drivers of variability of Calanus finmarchicus in .pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\FMJMVE6E\\6350837.html:text/html}, +} + +@article{melle_north_2014, + series = {North {Atlantic} {Ecosystems}, the role of climate and anthropogenic forcing on their structure and function}, + title = {The {North} {Atlantic} {Ocean} as habitat for {Calanus} finmarchicus: {Environmental} factors and life history traits}, + volume = {129}, + issn = {0079-6611}, + shorttitle = {The {North} {Atlantic} {Ocean} as habitat for {Calanus} finmarchicus}, + url = {https://www.sciencedirect.com/science/article/pii/S0079661114000743}, + doi = {10.1016/j.pocean.2014.04.026}, + abstract = {Here we present a new, pan-Atlantic compilation and analysis of data on Calanus finmarchicus abundance, demography, dormancy, egg production and mortality in relation to basin-scale patterns of temperature, phytoplankton biomass, circulation and other environmental characteristics in the context of understanding factors determining the distribution and abundance of C. finmarchicus across its North Atlantic habitat. A number of themes emerge: (1) the south-to-north transport of plankton in the northeast Atlantic contrasts with north-to-south transport in the western North Atlantic, which has implications for understanding population responses of C. finmarchicus to climate forcing, (2) recruitment to the youngest copepodite stages occurs during or just after the phytoplankton bloom in the east whereas it occurs after the bloom at many western sites, with up to 3.5months difference in recruitment timing, (3) the deep basin and gyre of the southern Norwegian Sea is the centre of production and overwintering of C. finmarchicus, upon which the surrounding waters depend, whereas, in the Labrador/Irminger Seas production mainly occurs along the margins, such that the deep basins serve as collection areas and refugia for the overwintering populations, rather than as centres of production, (4) the western North Atlantic marginal seas have an important role in sustaining high C. finmarchicus abundance on the nearby coastal shelves, (5) differences in mean temperature and chlorophyll concentration between the western and eastern North Atlantic are reflected in regional differences in female body size and egg production, (6) regional differences in functional responses of egg production rate may reflect genetic differences between western and eastern populations, (7) dormancy duration is generally shorter in the deep waters adjacent to the lower latitude western North Atlantic shelves than in the east, (8) there are differences in stage-specific daily mortality rates between eastern and western shelves and basins, but the survival trajectories for cohort development from CI to CV are similar, and (9) early life stage survival is much lower in regions where C. finmarchicus is found with its congeners, C. glacialis and/or C. hyperboreus. This compilation and analysis provides new knowledge for evaluation and parameterisation of population models of C. finmarchicus and their responses to climate change in the North Atlantic. The strengths and weaknesses of modeling approaches, including a statistical approach based on ecological niche theory and a dynamical approach based on knowledge of spatial population dynamics and life history, are discussed, as well as needs for further research.}, + urldate = {2024-02-07}, + journal = {Progress in Oceanography}, + author = {Melle, Webjørn and Runge, Jeffrey and Head, Erica and Plourde, Stéphane and Castellani, Claudia and Licandro, Priscilla and Pierson, James and Jonasdottir, Sigrun and Johnson, Catherine and Broms, Cecilie and Debes, Høgni and Falkenhaug, Tone and Gaard, Eilif and Gislason, Astthor and Heath, Michael and Niehoff, Barbara and Nielsen, Torkel Gissel and Pepin, Pierre and Stenevik, Erling Kaare and Chust, Guillem}, + month = dec, + year = {2014}, + pages = {244--284}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\U7UFWQF6\\Melle et al. - 2014 - The North Atlantic Ocean as habitat for Calanus fi.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\BI4NGCXD\\S0079661114000743.html:text/html}, +} + +@article{skjoldal_size-fractioned_2022, + title = {Size-fractioned zooplankton biomass in the {Barents} {Sea}: {Spatial} patterns and temporal variations during three decades of warming and strong fluctuations of the capelin stock (1989–2020)}, + volume = {206}, + issn = {0079-6611}, + shorttitle = {Size-fractioned zooplankton biomass in the {Barents} {Sea}}, + url = {https://www.sciencedirect.com/science/article/pii/S0079661122001112}, + doi = {10.1016/j.pocean.2022.102852}, + abstract = {Zooplankton biomass has been monitored on joint Norwegian-Russian surveys in late summer and autumn since the 1980s. We report here on zooplankton biomass in three size fractions ({\textless}1, 1–2, and {\textgreater} 2 mm in screen mesh opening) obtained with WP-2 plankton net (180 μm mesh size) hauled vertically over the water column from near bottom to the surface for the period 1989–2020. The number of samples (stations) collected each year has been about 100–200, with a total number of 4543 stations for the whole data set. The size composition of zooplankton reflected by the three fractions has shown remarkable stability, with about 50\% of biomass contained in the medium fraction (made up largely of Calanus species), about 1/3 in the small fraction (36\%), and 16\% in the large fraction. The depth integrated biomass was generally larger in basins compared to shallower bank areas. The temporal (interannual) pattern of change was characterized by a marked peak in biomass in 1994 and 1995 with values up to {\textgreater}20 g dry weight (dw) m−2, driven to large extent by the small size fraction. Subsequently the biomass decreased to lower values but with a divergence of relatively high values (10–15 g dw m−2) in the inflow area of Atlantic water in southwest, and low values (2–6 g dw m−2) in the central area. The difference is interpreted to reflect an increase in a second summer generation of Calanus finmarchicus in the Atlantic water and a decrease of C. glacialis in the central area. The zooplankton biomass fluctuated inversely with the biomass of the Barents Sea capelin (Mallotus villosus) stock, reflecting a top-down predation effect. However, biomass was also negatively correlated with temperature of the Atlantic water, suggesting an additional and confounding effect of climate variability and change. The decrease in biomass of the central area used as a forage area by capelin, was associated with a shift to dominance by the small size fraction. This is likely an effect of predation and could be associated with a lower trophic conversion efficiency from phytoplankton to planktivorous fish and higher trophic levels by smaller zooplankton (smaller copepods such as Pseudocalanus and others) compared to the larger Calanus species.}, + urldate = {2024-02-07}, + journal = {Progress in Oceanography}, + author = {Skjoldal, Hein Rune and Eriksen, Elena and Gjøsæter, Harald}, + month = aug, + year = {2022}, + keywords = {Climate, Fish predation, Top-down, Trait-based}, + pages = {102852}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\3AK2DKRE\\Skjoldal et al. - 2022 - Size-fractioned zooplankton biomass in the Barents.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\F2VZ6U8Z\\S0079661122001112.html:text/html}, +} + +@article{friedland_event_2019, + title = {Event scale and persistent drivers of fish and macroinvertebrate distributions on the {Northeast} {US} {Shelf}}, + volume = {76}, + issn = {1054-3139}, + url = {https://doi.org/10.1093/icesjms/fsy167}, + doi = {10.1093/icesjms/fsy167}, + abstract = {The Northeast US Continental Shelf Large Marine Ecosystem is experiencing warming at a rate exceeding that of many other large marine ecosystems and has undergone significant climate-related changes. We examined the effect of thermal events and shifting patterns of primary and secondary productivity on the distribution of fish and macroinvertebrate species during the period 1968–2016. Though subject to inter-annual change, the along-shelf centre of gravity of chlorophyll concentration lacked a trend. Similarly, zooplankton bio-volume and total abundance along-shelf distance were also found to be without trend. However, the trend in the centre of gravity of copepod taxa diverged from the trends in bio-volume and non-copepod zooplankton abundance, suggesting most of these taxa had shifted in distribution to the northeast. The centres of gravity of fish and macroinvertebrate species have trended significantly to the northeast, suggesting copepods may play a key role in the distribution of higher trophic levels. Analysis of thermal events suggest that abrupt change in temperature can actuate persistent change in the distribution of fish and macroinvertebrate species. In aggregate, these broad trophic level patterns imply that distributional changes affecting upper trophic levels were dominated by thermal mechanisms, whereas lower trophic productivity, although subject to the same thermal conditions, exhibited less of a response. We hypothesize this lack of distributional response at lower trophic levels is due to their higher rates of production and turnover, and hence reflect a capacity to better integrate seasonal thermal changes. Furthermore, distributional changes of upper trophic levels may also be significantly impacted by feeding interactions at specific life history stages, where temperature affects both predator and prey.}, + number = {5}, + urldate = {2024-02-07}, + journal = {ICES Journal of Marine Science}, + author = {Friedland, Kevin D and McManus, M Conor and Morse, Ryan E and Link, Jason S}, + month = sep, + year = {2019}, + pages = {1316--1334}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\SNWW4K7P\\Friedland et al. - 2019 - Event scale and persistent drivers of fish and mac.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\HH8P2JLP\\5197071.html:text/html}, +} + +@article{dupontavice_ocean_2022, + title = {Ocean model-based covariates improve a marine fish stock assessment when observations are limited}, + volume = {79}, + issn = {1054-3139, 1095-9289}, + url = {https://academic.oup.com/icesjms/article/79/4/1259/6567589}, + doi = {10.1093/icesjms/fsac050}, + abstract = {The productivity of many fish populations is influenced by the environment, but developing environment-linked stock assessments remain challenging and current management of most commercial species assumes that stock productivity is time-invariant. In the Northeast United States, previous studies suggest that the recruitment of Southern New England-Mid Atlantic yellowtail flounder is closely related to the strength of the Cold Pool, a seasonally formed cold water mass on the continental shelf. Here, we developed three new indices that enhance the characterization of Cold Pool interannual variations using bottom temperature from a regional hindcast ocean model and a global ocean data assimilated hindcast. We associated these new indices to yellowtail flounder recruitment in a state–space, age-structured stock assessment framework using the Woods Hole Assessment Model. We demonstrate that incorporating Cold Pool effects on yellowtail flounder recruitment reduces the retrospective patterns and may improve the predictive skill of recruitment and, to a lesser extent, spawning stock biomass. We also show that the performance of the assessment models that incorporated ocean model-based indices is improved compared to the model using only the observation-based index. Instead of relying on limited subsurface observations, using validated ocean model products as environmental covariates in stock assessments may both improve predictions and facilitate operationalization.}, + language = {en}, + number = {4}, + urldate = {2024-02-07}, + journal = {ICES Journal of Marine Science}, + author = {du Pontavice, Hubert and Miller, Timothy J and Stock, Brian C and Chen, Zhuomin and Saba, Vincent S}, + editor = {Hidalgo, Manuel}, + month = may, + year = {2022}, + pages = {1259--1273}, + file = {du Pontavice et al. - 2022 - Ocean model-based covariates improve a marine fish.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\NEB85ACU\\du Pontavice et al. - 2022 - Ocean model-based covariates improve a marine fish.pdf:application/pdf}, +} + +@article{griffin_warming_2019, + title = {Warming seas increase cold-stunning events for {Kemp}’s ridley sea turtles in the northwest {Atlantic}}, + volume = {14}, + issn = {1932-6203}, + url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0211503}, + doi = {10.1371/journal.pone.0211503}, + abstract = {Since the 1970s, the magnitude of turtle cold-stun strandings have increased dramatically within the northwestern Atlantic. Here, we examine oceanic, atmospheric, and biological factors that may affect the increasing trend of cold-stunned Kemp’s ridleys in Cape Cod Bay, Massachusetts, United States of America. Using machine learning and Bayesian inference modeling techniques, we demonstrate higher cold-stunning years occur when the Gulf of Maine has warmer sea surface temperatures in late October through early November. Surprisingly, hatchling numbers in Mexico, a proxy for population abundance, was not identified as an important factor. Further, using our Bayesian count model and forecasted sea surface temperature projections, we predict more than 2,300 Kemp’s ridley turtles may cold-stun annually by 2031 as sea surface temperatures continue to increase within the Gulf of Maine. We suggest warmer sea surface temperatures may have modified the northerly distribution of Kemp’s ridleys and act as an ecological bridge between the Gulf Stream and nearshore waters. While cold-stunning may currently account for a minor proportion of juvenile mortality, we recommend continuing efforts to rehabilitate cold-stunned individuals to maintain population resiliency for this critically endangered species in the face of a changing climate and continuing anthropogenic threats.}, + language = {en}, + number = {1}, + urldate = {2024-02-07}, + journal = {PLOS ONE}, + author = {Griffin, Lucas P. and Griffin, Curtice R. and Finn, John T. and Prescott, Robert L. and Faherty, Mark and Still, Brett M. and Danylchuk, Andy J.}, + month = jan, + year = {2019}, + note = {Publisher: Public Library of Science}, + keywords = {Gulf of Mexico, Animal migration, Beaches, Maine, Ocean temperature, Sea water, Surface temperature, Turtles}, + pages = {e0211503}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\VXN9D7IX\\Griffin et al. - 2019 - Warming seas increase cold-stunning events for Kem.pdf:application/pdf}, +} + +@article{burnett_finfish_1989, + title = {Finfish {Maturity} {Sampling} and {Classification} {Schemes} {Used} {During} {Northeast} {Fisheries} {Center} {Bottom} {Trawl} {Surveys}, 1963-1989}, + journal = {NOAA Technical Memorandum.}, + author = {Burnett, J and O'Brien, L and Mayo, R. K. and Darde, J. A. and Bohan, M}, + year = {1989}, +} + +@article{wigley_length-weight_2003, + title = {Length-weight relationships for 74 fish species collected during {NEFSC} research vessel bottom trawl surveys, 1992-99}, + url = {https://repository.library.noaa.gov/view/noaa/3346}, + author = {Wigley, SE and McBride, NJ and McHugh, NJ}, + year = {2003}, +} + +@article{meyer-gutbrod_ocean_2021, + title = {Ocean {Regime} {Shift} {Is} {Driving} {Collapse} of the {North} {Atlantic} {Right} {Whale} {Population}}, + volume = {34}, + issn = {1042-8275}, + url = {https://www.jstor.org/stable/27051387}, + abstract = {Ocean warming linked to anthropogenic climate change is impacting the ecology of marine species around the world. In 2010, the Gulf of Maine and Scotian Shelf regions of the Northwest Atlantic underwent an unprecedented regime shift. Forced by climate-driven changes in the Gulf Stream, warm slope waters entered the region and created a less favorable foraging environment for the endangered North Atlantic right whale population. By mid-decade, right whales had shifted their late spring/summer foraging grounds from the Gulf of Maine and the western Scotian Shelf to the Gulf of St. Lawrence. The population also began exhibiting unusually high mortality in 2017. Here, we report that climate-driven changes in ocean circulation have altered the foraging environment and habitat use of right whales, reducing the population’s calving rate and exposing it to greater mortality risks from ship strikes and fishing gear entanglement. The case of the North Atlantic right whale provides a cautionary tale for the management of protected species in a changing ocean.}, + number = {3}, + urldate = {2024-02-08}, + journal = {Oceanography}, + author = {Meyer-Gutbrod, Erin L. and Greene, Charles H. and Davies, Kimberley T.A. and Johns, David G.}, + year = {2021}, + note = {Publisher: Oceanography Society}, + pages = {22--31}, + file = {JSTOR Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\8WH9XZD3\\Meyer-Gutbrod et al. - 2021 - Ocean Regime Shift Is Driving Collapse of the Nort.pdf:application/pdf}, +} + +@phdthesis{dullaert_response_2023, + title = {The {Response} of the {Zooplankton} {Community} in the {Western} {Gulf} of {Maine} to a {Shift} in {Oceanographic} {Conditions}: 2005-2017 - {ProQuest}}, + shorttitle = {The {Response} of the {Zooplankton} {Community} in the {Western} {Gulf} of {Maine} to a {Shift} in {Oceanographic} {Conditions}}, + url = {https://www.proquest.com/openview/94bd0ad57c25a1623c75df0c0d4f5d82/1?pq-origsite=gscholar&cbl=18750&diss=y}, + abstract = {Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.}, + language = {en}, + urldate = {2024-02-08}, + author = {Dullaert, Emma Cecile}, + year = {2023}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\7TTA5E25\\1.html:text/html}, +} + +@article{pershing_decadal_2023, + title = {Decadal comparisons identify the drivers of persistent changes in the zooplankton community structure in the northwest {Atlantic}}, + issn = {1054-3139}, + url = {https://doi.org/10.1093/icesjms/fsad198}, + doi = {10.1093/icesjms/fsad198}, + abstract = {Plankton community structure changes seasonally in response to the annual cycles of stratification, temperature, and primary productivity. These communities also change from year-to-year, in some cases exhibiting persistent regime shifts. How changes in physical conditions structure the plankton community and why conditions persist is a fundamental question in oceanography. Continuous plankton recorders have been used to sample the plankton community across the Gulf of Maine since 1961. Historically, this community has had a classic subarctic structure dominated by Calanus finmarchicus. However, during the 1990s, C. finmarchicus became less prominent, and a more diverse community of smaller copepods emerged. This shift was related to an influx of cold, low-salinity water. We show that a similar community shift occurred around 2012. We use high-resolution hydrographic data to link the shift to an influx of saltier water and warmer conditions. By comparing the 1990s with the recent decade, we develop a synthesis for how physical changes lead to community shifts. Our synthesis suggests that the link between the 1990s and 2010s is enhanced water column stratification. We further propose that ecological interactions link declines in C. finmarchicus with the emergence of the more diverse community, drawing parallels with classic food web ecology.}, + urldate = {2024-02-08}, + journal = {ICES Journal of Marine Science}, + author = {Pershing, Andrew J and Kemberling, Adam}, + month = dec, + year = {2023}, + pages = {fsad198}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\XKH4EDIQ\\Pershing and Kemberling - 2023 - Decadal comparisons identify the drivers of persis.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\VBMNFDQ2\\7475010.html:text/html}, +} + +@misc{noauthor_sensitivity_nodate, + title = {Sensitivity of sand lance to shifting prey and hydrography indicates forthcoming change to the northeast {US} shelf forage fish complex {\textbar} {ICES} {Journal} of {Marine} {Science} {\textbar} {Oxford} {Academic}}, + url = {https://academic.oup.com/icesjms/article/78/3/1023/6120244}, + urldate = {2024-02-08}, + file = {Sensitivity of sand lance to shifting prey and hydrography indicates forthcoming change to the northeast US shelf forage fish complex | ICES Journal of Marine Science | Oxford Academic:C\:\\Users\\andrew.beet\\Zotero\\storage\\LHUVY5EX\\6120244.html:text/html}, +} + +@article{white_spatial_2020, + title = {Spatial ecology of long-tailed ducks and white-winged scoters wintering on {Nantucket} {Shoals}}, + volume = {11}, + copyright = {© 2020 The Authors.}, + issn = {2150-8925}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ecs2.3002}, + doi = {10.1002/ecs2.3002}, + abstract = {We examine the long-term co-occurrence of long-tailed ducks (Clangula hyemalis) and white-winged scoters (Melanitta fusca) wintering at the Nantucket Shoals off Massachusetts, USA, and ask: (1) What oceanographic features attract these aggregations? (2) How are distributions of prey and sea ducks related to one another? and (3) What is the explanation for the spatial association between these two species? A winter concentration of long-tailed ducks on the order of 3 × 105 birds have been present near Nantucket Island and the Nantucket Shoals since the 1970s, and there is evidence of some concentration there since the late 1800s. Despite the difference in diet, the two duck species overlap in spatial distribution in relation to a prominent tidal front on the west side of Nantucket Shoals where nested swarms of pelagic amphipods and massive clam beds occur in persistent broadscale concentrations. Gammarid amphipods were collected seasonally by bongo tows over 39 yr (1977–2016), and Atlantic surf clams and ocean quahogs were collected by hydraulic dredge over a 34-yr timescale (1982–2016). Long-tailed ducks and white-winged scoters were counted during systematic low-level aerial surveys. Both long-tailed ducks and white-winged scoters associated with amphipod and clam aggregations at similar scales, and tracked amphipod and clam concentrations at larger spatial scales, up to 30 km. These multi-scalar nested associations suggest that the sea ducks foraged within a hierarchical patch system structured by the tidal regime, and concentrated in areas of spatially anchored recurring prey, with highest densities in southwest areas. Enhanced concentrations of prey within a relatively narrow zone on Nantucket Shoals provide a predictable food resource to wintering sea ducks whose populations are in decline. As supported by our generalized additive models, we propose that the positive spatial associations among these species are at least partly explained by local enhancement or facilitation, whereby each species of duck derives foraging benefit from noting the location of aggregations of the other. This interaction will be important to the designation of marine protected areas.}, + language = {en}, + number = {1}, + urldate = {2024-02-08}, + journal = {Ecosphere}, + author = {White, Timothy P. and Veit, Richard R.}, + year = {2020}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.3002}, + keywords = {Atlantic Ocean, aerial surveys, ecotone, feeding ecology, local enhancement, long-tailed duck, Nantucket Shoals, sea duck, spatial anchor, spatial synchrony, white-winged scoter}, + pages = {e03002}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\LV4G6RYG\\White and Veit - 2020 - Spatial ecology of long-tailed ducks and white-win.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\G7NMQQJE\\ecs2.html:text/html}, +} + +@article{friedland_spatial_2023, + title = {The spatial correlation between trawl surveys and planned wind energy infrastructure on the {US} {Northeast} {Continental} {Shelf}}, + issn = {1054-3139}, + url = {https://doi.org/10.1093/icesjms/fsad167}, + doi = {10.1093/icesjms/fsad167}, + abstract = {The development of wind energy on the US Northeast Continental Shelf (NES) may preclude resource-monitoring programmes from continuing in their original study designs. We considered the spatial requirements of energy developers and the spatial autocorrelation of resident species as a means to inform survey mitigation. The spatial requirements of the NES wind industry were considered in respect to the delineation of settled lease areas. We suggest access metrics ranging from 8 to 21 km representing the interquartile range of chord distances across the settled lease areas. Using survey data and concentrating on commercial species with stock assessment requirements, we computed indices of spatial autocorrelation. Tests of spatial autocorrelation using Moran’s I and join counts statistics were significant (p \< 0.05) for most species, suggesting a high level of spatial correlation in their distributions. To characterize a scalar extent of spatial correlation, variograms were fit to estimate the physical range of correlated catches. These data suggest most species were spatially correlated well beyond the distance metrics derived from the lease area shapes. Sampling by a range of gears has the potential of producing spatially accurate depictions of species distributions and abundance despite the restrictions wind lease areas may place on sampling designs.}, + urldate = {2024-02-08}, + journal = {ICES Journal of Marine Science}, + author = {Friedland, K D and Boucher, J M and Jones, A W and Methratta, E T and Morse, R E and Foley, C and Rago, P J}, + month = nov, + year = {2023}, + pages = {fsad167}, + file = {Friedland et al. - 2023 - The spatial correlation between trawl surveys and .pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\838HM8RW\\Friedland et al. - 2023 - The spatial correlation between trawl surveys and .pdf:application/pdf;Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\XWCCSMF7\\Friedland et al. - 2023 - The spatial correlation between trawl surveys and .pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\5A6ZY3WU\\7337020.html:text/html}, +} + +@misc{casault_optical_2022, + title = {Optical, chemical, and biological oceanographic conditions on the {Scotian} {Shelf} and in the eastern {Gulf} of {Maine} during 2020 / {B}. {Casault}, {C}. {Johnson}, {E}. {Devred}, {E}. {Head}, {L}. {Beazley}, and {J}. {Spry}.: {Fs70}-5/2022-{018E}-{PDF} - {Government} of {Canada} {Publications} - {Canada}.ca}, + shorttitle = {Optical, chemical, and biological oceanographic conditions on the {Scotian} {Shelf} and in the eastern {Gulf} of {Maine} during 2020 / {B}. {Casault}, {C}. {Johnson}, {E}. {Devred}, {E}. {Head}, {L}. {Beazley}, and {J}. {Spry}.}, + url = {https://publications.gc.ca/site/eng/9.909043/publication.html}, + abstract = {Publication information / bibliographic Record.}, + language = {eng}, + urldate = {2024-02-08}, + author = {Casault, B and Johnson, C and Devred, E and Head, E and Beazley, L and Spry, J}, + month = jul, + year = {2022}, + note = {Last Modified: 2013-04-03}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\E8JS4SVF\\publication.html:text/html}, +} + +@article{pershing_interdecadal_2005, + title = {Interdecadal variability in the {Gulf} of {Maine} zooplankton community, with potential impacts on fish recruitment}, + volume = {62}, + issn = {1054-3139}, + url = {https://doi.org/10.1016/j.icesjms.2005.04.025}, + doi = {10.1016/j.icesjms.2005.04.025}, + abstract = {We used principal component analysis (PCA) to explore interannual changes in a time-series lasting more than 40 years of zooplankton abundance from NOAA's Continuous Plankton Recorder (CPR) survey. This analysis identified a complex of taxa, including Centropages typicus, Oithona spp., Pseudocalanus spp., and Metridia lucens that followed a common pattern of interdecadal variability characterized by a dramatic increase in these taxa around 1990, followed by a rapid decline in 2002. All of these taxa showed a large proportional increase in winter abundance between the 1980s and 1990s. These changes could be driven by increased primary productivity during winter, caused by a large-scale freshening of the Northwest Atlantic Shelf. In addition to the “community shift” mode, the analysis found a strong mode of interannual variability attributed to previously described changes in the abundance of late-stage Calanus finmarchicus. To explore the impact of these modes on higher trophic levels, we correlated the zooplankton modes with recruitment time-series from 12 fish stocks from the Gulf of Maine region. Several significant correlations were found, suggesting that the changes in the zooplankton modes may reflect broad changes in the Gulf of Maine ecosystem.}, + number = {7}, + urldate = {2024-02-08}, + journal = {ICES Journal of Marine Science}, + author = {Pershing, Andrew J. and Greene, Charles H. and Jossi, Jack W. and O'Brien, Loretta and Brodziak, Jon K.T. and Bailey, Barbara A.}, + month = jan, + year = {2005}, + pages = {1511--1523}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\7YXWS5R5\\Pershing et al. - 2005 - Interdecadal variability in the Gulf of Maine zoop.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\KQTELFTV\\661385.html:text/html}, +} + +@article{hernandez_cordero_development_2020, + title = {Development of a multimetric water quality {Indicator} for tracking progress towards the achievement of {Chesapeake} {Bay} water quality standards}, + volume = {192}, + issn = {1573-2959}, + doi = {10.1007/s10661-019-7969-z}, + abstract = {A multimetric water quality standards (WQS) attainment indicator (MMI) was created to assess the habitat quality of the Chesapeake Bay and its tidal tributaries. The indicator uses metrics of dissolved oxygen concentrations, water clarity assessments, underwater bay grass acreages, and season-specific chlorophyll a distributions. This suite of metrics is recognized as symptomatic of eutrophication and responsive to nutrient and sediment management actions. Habitat criteria for these metrics were established by the US Environmental Protection Agency (USEPA) to protect the survival, growth, and reproduction of tidal bay living resources. The criteria were adopted into state WQS used to define outcome targets of the regulatory 2010 Chesapeake Bay Total Maximum Daily Load (TMDL). Direct accounting of monitoring results compared with state standards would be the most direct measure of water quality status related to achieving habitat health goal conditions under the TMDL. However, the Chesapeake Bay Program long-term water quality monitoring program that supports Clean Water Act 303d water quality impairment assessments does not provide sufficient measurement resolution across all temporal scales represented in these TMDL-related WQS. Recognizing data and analysis gaps, we developed the indicator to provide estimates of WQS attainment for the Chesapeake Bay. The structure of the indicator uses metrics that have been measured consistently with the historical Chesapeake Bay tidal water quality data collections since 1985. The ability to compute scores on contemporary and historical data will provide for bay-wide and spatially explicit long-term trend evaluations of habitat quality.}, + language = {eng}, + number = {2}, + journal = {Environmental Monitoring and Assessment}, + author = {Hernandez Cordero, Ana L. and Tango, Peter J. and Batiuk, Richard A.}, + month = jan, + year = {2020}, + pmid = {31907685}, + keywords = {Chesapeake Bay, Water quality standards, Bays, Chlorophyll A, Ecosystem, Environmental Monitoring, Eutrophication, Indicator, Multimetric, Oxygen, TMDL, United States, United States Environmental Protection Agency, Water Quality}, + pages = {94}, +} + +@misc{us_epa_ambient_2003, + title = {Ambient water quality criteria for dissolved oxygen, water clarity and chlorophyll-a for the {Chesapeake} {Bay} and its tidal tributaries.}, + publisher = {USEPA Region III Chesapeake Bay Program Office, Annapolis, Maryland,}, + author = {US EPA}, + year = {2003}, +} + +@misc{us_epa_ambient_2017, + title = {Ambient water quality criteria for dissolved oxygen, water clarity and chlorophyll-a for the {Chesapeake} {Bay} and its tidal tributaries: 2017 addendum}, + publisher = {USEPA Region III Chesapeake Bay Program Office, Annapolis, Maryland,}, + author = {US EPA}, + year = {2017}, +} + +@misc{runge_sustained_2023, + title = {Sustained monitoring of zooplankton populations at the {Coastal} {Maine} {Time} {Series} ({CMTS}) and {Wilkinson} {Basin} {Time} {Series} ({WBTS}) stations in the western {Gulf} of {Maine}: {Results} from 2005-2022}, + publisher = {Sterling (VA): U.S. Department of the Interior, Bureau of Ocean Energy Management}, + author = {Runge, J and Karp-Boss, L and Dullaert, E and Ji, R and Motyka, J and Young-Morse, R and Pugh, D and Shellito, S and Vandemark, D}, + year = {2023}, +} + +@misc{runge_fixed_nodate, + title = {Fixed time series station observations of zooplankton responses to changing oceanographic conditions in the western {Gulf} of {Maine}: the {Calanus} {Index}}, + author = {Runge, J and Dullaert, E and Shellito, S and Karp-Boss, L and Honda, I and Ji, R and Motyka, J and Pugh, D and Thompson, C and Young-Morse, R and Vandemark, D}, +} + +@article{brown-peterson_standardized_2011, + title = {A {Standardized} {Terminology} for {Describing} {Reproductive} {Development} in {Fishes}}, + volume = {3}, + issn = {null}, + url = {https://doi.org/10.1080/19425120.2011.555724}, + doi = {10.1080/19425120.2011.555724}, + abstract = {As the number of fish reproduction studies has proliferated, so has the number of gonadal classification schemes and terms. This has made it difficult for both scientists and resource managers to communicate and for comparisons to be made among studies. We propose the adoption of a simple, universal terminology for the phases in the reproductive cycle, which can be applied to all male and female elasmobranch and teleost fishes. These phases were chosen because they define key milestones in the reproductive cycle; the phases include immature, developing, spawning capable, regressing, and regenerating. Although the temporal sequence of events during gamete development in each phase may vary among species, each phase has specific histological and physiological markers and is conceptually universal. The immature phase can occur only once. The developing phase signals entry into the gonadotropin-dependent stage of oogenesis and spermatogenesis and ultimately results in gonadal growth. The spawning capable phase includes (1) those fish with gamete development that is sufficiently advanced to allow for spawning within the current reproductive cycle and (2) batch-spawning females that show signs of previous spawns (i.e., postovulatory follicle complex) and that are also capable of additional spawns during the current cycle. Within the spawning capable phase, an actively spawning subphase is defined that corresponds to hydration and ovulation in females and spermiation in males. The regressing phase indicates completion of the reproductive cycle and, for many fish, completion of the spawning season. Fish in the regenerating phase are sexually mature but reproductively inactive. Species-specific histological criteria or classes can be incorporated within each of the universal phases, allowing for more specific divisions (subphases) while preserving the overall reproductive terminology for comparative purposes. This terminology can easily be modified for fishes with alternate reproductive strategies, such as hermaphrodites (addition of a transition phase) and livebearers (addition of a gestation phase).}, + number = {1}, + urldate = {2024-02-09}, + journal = {Marine and Coastal Fisheries}, + author = {Brown-Peterson, Nancy J. and Wyanski, David M. and Saborido-Rey, Fran and Macewicz, Beverly J. and Lowerre-Barbieri, Susan K.}, + month = jan, + year = {2011}, + note = {Publisher: Taylor \& Francis +\_eprint: https://doi.org/10.1080/19425120.2011.555724}, + pages = {52--70}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\YARN2WRN\\Brown-Peterson et al. - 2011 - A Standardized Terminology for Describing Reproduc.pdf:application/pdf}, +} + +@article{suca_sensitivity_2021, + title = {Sensitivity of sand lance to shifting prey and hydrography indicates forthcoming change to the northeast {US} shelf forage fish complex}, + volume = {78}, + issn = {1054-3139}, + url = {https://doi.org/10.1093/icesjms/fsaa251}, + doi = {10.1093/icesjms/fsaa251}, + abstract = {Northern sand lance (Ammodytes dubius) and Atlantic herring (Clupea harengus) represent the dominant lipid-rich forage fish species throughout the Northeast US shelf and are critical prey for numerous top predators. However, unlike Atlantic herring, there is little research on sand lance or information about drivers of their abundance. We use intra-annual measurements of sand lance diet, growth, and condition to explain annual variability in sand lance abundance on the Northeast US Shelf. Our observations indicate that northern sand lance feed, grow, and accumulate lipids in the late winter through summer, predominantly consuming the copepod Calanus finmarchicus. Sand lance then cease feeding, utilize lipids, and begin gonad development in the fall. We show that the abundance of C. finmarchicus influences sand lance parental condition and recruitment. Atlantic herring can mute this effect through intra-guild predation. Hydrography further impacts sand lance abundance as increases in warm slope water decrease overwinter survival of reproductive adults. The predicted changes to these drivers indicate that sand lance will no longer be able to fill the role of lipid-rich forage during times of low Atlantic herring abundance—changing the Northeast US shelf forage fish complex by the end of the century.}, + number = {3}, + urldate = {2024-02-09}, + journal = {ICES Journal of Marine Science}, + author = {Suca, Justin J and Wiley, David N and Silva, Tammy L and Robuck, Anna R and Richardson, David E and Glancy, Sarah G and Clancey, Emily and Giandonato, Teresa and Solow, Andrew R and Thompson, Michael A and Hong, Peter and Baumann, Hannes and Kaufman, Les and Llopiz, Joel K}, + month = jul, + year = {2021}, + pages = {1023--1037}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\6ZKVXN2A\\Suca et al. - 2021 - Sensitivity of sand lance to shifting prey and hyd.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\IADSPKUY\\6120244.html:text/html}, +} + +@article{lambert_energetic_2000, + title = {Energetic consequences of reproduction in {Atlantic} cod ({Gadus} morhua) in relation to spawning level of somatic energy reserves}, + volume = {57}, + issn = {0706-652X}, + url = {https://cdnsciencepub.com/doi/10.1139/f00-022}, + doi = {10.1139/f00-022}, + abstract = {The influence of a lower condition on reproductive investment, somatic energy losses, and postspawning condition of Atlantic cod (Gadus morhua) was examined under the hypothesis that females, in response to lower available energy reserves, would reduce reproductive investment in order to limit somatic energy losses. Laboratory experiments revealed that female cod with high prespawning condition factors ended reproduction in better condition than females with low prespawning condition factors. Fecundity and total egg dry weight were significantly lower in poor-condition females. The loss in somatic mass and energy in these poor-condition females was nevertheless higher, in relative terms, than the losses experienced by females in good condition. Consequently, energy reserves invested in reproduction by poor-condition females increase their risk of mortality. In the northern Gulf of St. Lawrence during the early 1990s, reproductive females had lower fecundities and were in worse prespawning and postspawning condition. The condition of spent females suggested a greater impact of changes in environmental conditions on adult than on immature cod. Reproductive potential and possibly recruitment may have suffered from that situation and could have contributed to the failure of that stock to recover despite the moratorium on commercial fishing.}, + number = {4}, + urldate = {2024-02-09}, + journal = {Canadian Journal of Fisheries and Aquatic Sciences}, + author = {Lambert, Yvan and Dutil, Jean-Denis}, + month = apr, + year = {2000}, + note = {Publisher: NRC Research Press}, + pages = {815--825}, +} + +@article{mcbride_energy_2015, + title = {Energy acquisition and allocation to egg production in relation to fish reproductive strategies}, + volume = {16}, + copyright = {Published 2013. This article is a US Government work and is in the public domain in the USA.}, + issn = {1467-2979}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/faf.12043}, + doi = {10.1111/faf.12043}, + abstract = {Oogenesis in fishes follows a universal plan; yet, due to differences in the synchrony and rate of egg development, spawning frequency varies from daily to once in a lifetime. Some species spawn and feed in separate areas, during different seasons, by storing energy and drawing on it later for reproduction (i.e. capital breeding). Other species spawn using energy acquired locally, throughout a prolonged spawning season, allocating energy directly to reproduction (i.e. income breeding). Capital breeders tend to ovulate all at once and are more likely to be distributed at boreal latitudes. Income breeding allows small fish to overcome allometric constraints on egg production. Income breeders can recover more quickly when good-feeding conditions are re-established, which is a benefit to adults regarding bet-hedging spawning strategies. Many species exhibit mixed capital- and income-breeding patterns. An individual's position along this capital–income continuum may shift with ontogeny or in relation to environmental conditions, so breeding patterns are a conditional reproductive strategy. Poor-feeding environments can lead to delayed maturation, skipped spawning, fewer spawning events per season or fewer eggs produced per event. In a few cases, variations in feeding environments appear to affect recruitment variability. These flexible processes of energy acquisition and allocation allow females to prioritize their own condition over their propagules' condition at any given spawning opportunity, thereby investing energy cautiously to maximize lifetime reproductive value. These findings have implications for temporal and spatial sampling designs, for measurement and interpretation of fecundity, and for interpreting fishery and ecosystem assessments.}, + language = {en}, + number = {1}, + urldate = {2024-02-09}, + journal = {Fish and Fisheries}, + author = {McBride, Richard S and Somarakis, Stylianos and Fitzhugh, Gary R and Albert, Anu and Yaragina, Nathalia A and Wuenschel, Mark J and Alonso-Fernández, Alexandre and Basilone, Gualtiero}, + year = {2015}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/faf.12043}, + keywords = {Allocation of surplus energy, capital breeding, income breeding, lifetime fecundity, oogenesis, reproductive strategy, reproductive tactic}, + pages = {23--57}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\UMK3TW5V\\faf.html:text/html;Submitted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\ZWRGTURD\\McBride et al. - 2015 - Energy acquisition and allocation to egg productio.pdf:application/pdf}, +} + +@article{robb_histological_1982, + title = {Histological observations on the reproductive biology of the haddock, {Melanogrammus} aeglefinus ({L}.)}, + volume = {20}, + issn = {1095-8649}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1095-8649.1982.tb03933.x}, + doi = {10.1111/j.1095-8649.1982.tb03933.x}, + abstract = {Histological observations were made on the changes occurring in the haddock ovary during maturation. The influence of varying food rations on the numbers of eggs released was considered. Oocyte development was found to be a continuous asynchronous process until the beginning of spawing, i.e. stage V when vitellogenesis became synchronized and there was no further initiation of new development. It is suggested that the upper limit of potential fecundity was determined before stage II, however the actual numbers of eggs spawned could still be reduced during later stages in response to a poor food supply.}, + language = {en}, + number = {4}, + urldate = {2024-02-09}, + journal = {Journal of Fish Biology}, + author = {Robb, A. P.}, + year = {1982}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1095-8649.1982.tb03933.x}, + pages = {397--408}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\N4YF6YCY\\j.1095-8649.1982.tb03933.html:text/html}, +} + +@article{tobin_timing_2010, + title = {Timing of the maturation transition in haddock {Melanogrammus} aeglefinus}, + volume = {77}, + issn = {1095-8649}, + doi = {10.1111/j.1095-8649.2010.02739.x}, + abstract = {The timing of maturation in haddock Melanogrammus aeglefinus was examined using changes in gonad development, follicle stimulating hormone β (FSH-β) transcript expression profile, growth and condition of 1 year old females held under a common environment between the summer and winter solstices. The circumnuclear ring, cortical alveolus and vitellogenic oocyte stages were first observed in August, October and November, respectively. FSH-β transcript levels did not change significantly until September but increased markedly thereafter in maturing fish. A combined analysis of the mean oocyte diameter of the leading cohort, histological staging and FSH-β transcript profile provided evidence of a commitment to maturation by October or November. Contrary to that previously proposed for gadoid species, histological analysis of field-caught immature M. aeglefinus during the spawning season indicated that cortical alveolar, rather than circumnuclear ring, stage oocytes provided definitive evidence of maturation. A decrease in relative liver size following the summer solstice suggested a possible link between energy status and maturation.}, + language = {eng}, + number = {6}, + journal = {Journal of Fish Biology}, + author = {Tobin, D. and Wright, P. J. and O'Sullivan, M.}, + month = oct, + year = {2010}, + pmid = {21039503}, + keywords = {Animals, Female, Follicle Stimulating Hormone, beta Subunit, Gadiformes, Gene Expression Profiling, Oocytes, Ovary}, + pages = {1252--1267}, +} + +@article{trippel_age_1995, + title = {Age at {Maturity} as a {Stress} {Indicator} in {Fisheries}: {Biological} processes related to reproduction in northwest {Atlantic} groundfish populations that have undergone declines}, + volume = {45}, + issn = {0006-3568}, + shorttitle = {Age at {Maturity} as a {Stress} {Indicator} in {Fisheries}}, + url = {https://doi.org/10.2307/1312628}, + doi = {10.2307/1312628}, + number = {11}, + urldate = {2024-02-09}, + journal = {BioScience}, + author = {Trippel, Edward A.}, + month = dec, + year = {1995}, + pages = {759--771}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\MFK3QTNF\\268371.html:text/html}, +} + +@article{trippel_maternal_2011, + title = {Maternal and seasonal differences in egg sizes and spawning activity of northwest {Atlantic} haddock ({Melanogrammus} aeglefinus) in relation to body size and condition}, + volume = {61}, + doi = {10.1139/f04-125}, + abstract = {Egg and larval production of 22 captive spawning pairs of northwest Atlantic haddock (Melanogrammus aeglefinus) were monitored. Females spawned an average of nine egg batches (range 3–16) with a mean batch fecundity of 60 000 eggs and mean total fecundity of 535 000 eggs. Mean spawning duration was 37 days with a mean batch interval of 5.4 days. In multiple linear regression, male Fulton's condition factor (range 1.10–1.55) and mean batch interval explained 56\% of variation in fertilization rate (33\% and 23\%, respectively). Seasonal composite egg diameter spanned 1.37–1.53 mm among females. Mean egg diameter within females declined seasonally by an average of 10.4\% (37\% by volume). Females produced 46 larvae per gram body weight. Body weight was the single best predictor of fecundity (r2 = 0.57), with Fulton's condition factor (range 1.04–1.76) explaining no significant additional variation over length or weight. Length and condition explained 39\% of variation in seasonal composite egg diameter (22\% and 17\%, respectively) and body weight independently explained 32\%. Sex-specific parental condition and body size acted through large egg size and elevated fertility to enhance reproductive output. Male spawning success was more sensitive than egg production to changes in condition.}, + journal = {Canadian Journal of Fisheries and Aquatic Sciences}, + author = {Trippel, Edward and Neil, Steven}, + month = apr, + year = {2011}, + pages = {2097--2110}, +} + +@article{wuenschel_reproductive_2019, + title = {The {Reproductive} {Biology} of {Female} {Atlantic} {Herring} in {U}.{S}. {Waters}: {Validating} {Classification} {Schemes} for {Assessing} the {Importance} of {Spring} and {Skipped} {Spawning}}, + volume = {11}, + copyright = {Published 2019. This article is a U.S. Government work and is in the public domain in the USA. Marine and Coastal Fisheries published by Wiley Periodicals, Inc. on behalf of American Fisheries Society.}, + issn = {1942-5120}, + shorttitle = {The {Reproductive} {Biology} of {Female} {Atlantic} {Herring} in {U}.{S}. {Waters}}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mcf2.10099}, + doi = {10.1002/mcf2.10099}, + abstract = {Atlantic Herring Clupea harengus are iteroparous (repeat spawners) with group-synchronous oocyte development and determinate fecundity, and they are total spawners. However, they also exhibit plasticity in other aspects of their reproductive biology including spawning seasonality and skipped spawning. Previous studies in other regions have reported skipped spawning and errors in macroscopic classifications of maturity, both of which could bias estimates of reproductive potential, but a critical assessment of these in U.S. waters is lacking. In the Gulf of Maine and Georges Bank, herring are assessed as a single stock complex, where females typically mature as 3 to 4 year olds and may live up to 11 years. To evaluate the magnitude of skipped spawning, we collected the ovaries of Atlantic Herring from fishery-dependent and fishery-independent sources over multiple seasons and evaluated them histologically to assess imminent (indicated by vitellogenic or maturing oocytes) or recent spawning (evidenced by the presence of postovulatory follicles). Gonad histology allowed us to determine spawning seasonality and skipped spawning. Macroscopic maturity classification was more accurate in fall (1–4\% incorrect maturity) than in spring (7\% incorrect maturity). The spatial distributions of immature and mature fish from both fishery-dependent and fishery-independent sources differed, which affected the estimation of maturity at length and age. We estimated 9–14\% spring spawners in the region, but we did not find evidence of skipped spawning. The time series of the macroscopic data that were available (1987–2018) showed increases in spring spawning with latitude, but the proportions have not changed much over recent decades. The effects of up to 30\% spring or skipped spawning on a stock assessment of Atlantic Herring were evaluated. Spring spawning had little effect relative to assuming 100\% fall spawning (the current assumption), and skipped spawning decreased the scale of spawning stock biomass (SSB) and related reference points, with the degree of change increasing with skipped spawning rates, but it had few consequences otherwise.}, + language = {en}, + number = {6}, + urldate = {2024-02-09}, + journal = {Marine and Coastal Fisheries}, + author = {Wuenschel, Mark J. and Deroba, Jonathan J.}, + year = {2019}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mcf2.10099}, + pages = {487--505}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\VAWQW4N2\\Wuenschel and Deroba - 2019 - The Reproductive Biology of Female Atlantic Herrin.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\72PLE7B5\\mcf2.html:text/html}, +} + +@article{wuenschel_measuring_2019, + title = {Measuring fish condition: an evaluation of new and old metrics for three species with contrasting life histories}, + volume = {76}, + issn = {0706-652X}, + shorttitle = {Measuring fish condition}, + url = {https://cdnsciencepub.com/doi/abs/10.1139/cjfas-2018-0076}, + doi = {10.1139/cjfas-2018-0076}, + abstract = {Measuring fish condition should link ecosystem drivers with population dynamics, if the underlying physiological basis for variations in condition indices are understood. We evaluated traditional (K, Kn, hepatosomatic index, gonadosomatic index, energy density, and percent dry weight of muscle (\%DWM) and liver (\%DWL)) and newer (bioelectrical impedance analysis (BIA) and scaled mass index (SMI)) condition indices to track seasonal cycles in three flatfishes — winter founder (Pseudopleuronectes americanus; three stocks), yellowtail flounder (Limanda ferruginea; three stocks), and summer flounder (Paralichthys dentatus; one stock) — with contrasting life histories in habitat, feeding, and reproduction. The \%DWM and \%DWL were good proxies for energy density (r2 {\textgreater} 0.96) and more strongly related to K, Kn, and SMI than to BIA metrics. Principal component analysis indicated many metrics performed similarly across species; some were confounded by size, sex, and maturity along PC1, while others effectively characterized condition along PC2. Stock differences were along PC1 in winter flounder, reflecting different sizes across stocks, whereas in yellowtail flounder differences occurred along PC2 related to condition. These comparisons, within and across species, highlight the broad applicability of some metrics and limitations in others.}, + number = {6}, + urldate = {2024-02-09}, + journal = {Canadian Journal of Fisheries and Aquatic Sciences}, + author = {Wuenschel, Mark J. and McElroy, W. David and Oliveira, Kenneth and McBride, Richard S.}, + month = jun, + year = {2019}, + note = {Publisher: NRC Research Press}, + pages = {886--903}, + file = {Accepted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\ID2HUZ2X\\Wuenschel et al. - 2019 - Measuring fish condition an evaluation of new and.pdf:application/pdf}, +} + +@misc{obrien_maturation_1993, + title = {Maturation of nineteen species of finfish off the {Northeast} coast of the {United} {States}, 1985-1990}, + publisher = {NOAA Technical Report NMFS 113.}, + author = {O'Brien, L and Burnett, J and Mayo, R. K.}, + year = {1993}, +} + +@misc{noauthor_r_nodate, + title = {R {Companion} {3E}}, + url = {https://www.john-fox.ca/Companion/index.html}, + urldate = {2024-02-09}, + file = {R Companion 3E:C\:\\Users\\andrew.beet\\Zotero\\storage\\3ITB7HMS\\index.html:text/html}, +} + +@article{ludecke_ggeffects_2018, + title = {ggeffects: {Tidy} {Data} {Frames} of {Marginal} {Effects} from {Regression} {Models}}, + volume = {3}, + issn = {2475-9066}, + shorttitle = {ggeffects}, + url = {https://joss.theoj.org/papers/10.21105/joss.00772}, + doi = {10.21105/joss.00772}, + abstract = {Lüdecke, (2018). ggeffects: Tidy Data Frames of Marginal Effects from Regression Models +. Journal of Open Source Software, 3(26), 772, https://doi.org/10.21105/joss.00772}, + language = {en}, + number = {26}, + urldate = {2024-02-09}, + journal = {Journal of Open Source Software}, + author = {Lüdecke, Daniel}, + month = jun, + year = {2018}, + pages = {772}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\EEGCMHXZ\\Lüdecke - 2018 - ggeffects Tidy Data Frames of Marginal Effects fr.pdf:application/pdf}, +} + +@article{manning_maternal_1998, + title = {Maternal and interannual comparison of the ovulatory periodicity, egg production and egg quality of the batch-spawning yellowtail flounder}, + volume = {53}, + issn = {1095-8649}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1095-8649.1998.tb00456.x}, + doi = {10.1111/j.1095-8649.1998.tb00456.x}, + abstract = {Estimates of the ovulatory periodicity of yellowtail flounder indicate that a 1-day interval, which predominated over all other intervals, may characterize regular ovulation patterns. Females produced a mean number of 14–22 batches in 1994 and 1995, respectively. Batch fecundities usually remained within a range of 10 000–60 000 eggs. Mean egg production increased from 549 756 eggs per female in 1994 to 1 186 881 eggs in 1995. Mean fertilization rates rose interannually from 38 to 57\%, while hatching rates, tested in 1994, had a mean of 63\%. Maternal variation in egg production and egg quality was large and independent of size differences among females. Some females had disrupted ovulation patterns which affected the realization of potential fecundity contained within the prespawning ovary. High interbatch variation in egg quality was not related to progressive decreases in egg diameter and dry weight over time. Batches with high survival rates appeared at random within a female's duration of ovulation.}, + language = {en}, + number = {5}, + urldate = {2024-02-09}, + journal = {Journal of Fish Biology}, + author = {Manning, A. J. and Crim, L. W.}, + year = {1998}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1095-8649.1998.tb00456.x}, + keywords = {batch-spawning, egg production, egg quality, maternal, ovulatory periodicity, yellowtail flounder}, + pages = {954--972}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\UG6342VZ\\j.1095-8649.1998.tb00456.html:text/html}, +} + +@article{mcbride_latitudinal_2013, + title = {Latitudinal and stock-specific variation in size- and age-at-maturity of female winter flounder, {Pseudopleuronectes} americanus, as determined with gonad histology}, + volume = {75}, + issn = {1385-1101}, + url = {https://ui.adsabs.harvard.edu/abs/2013JSR....75...41M}, + doi = {10.1016/j.seares.2012.04.005}, + abstract = {Female winter flounder were examined using gonad histology to determine the adequacy of routine macroscopic maturity classification methods and to determine the spatial variation in size and age of maturity in U.S. waters. Sampling occurred in spring and autumn, which was adequate to collect immature, mature, spawning-active, and non-active females. Females were collected in coastal waters from Delaware Bay, USA, to the Scotian Shelf, Canada, including in Long Island Sound and on Georges Bank, which covered all U.S. stock areas. Mature fish spawned in spring, when gonads comprised up to 30\% of the total body weight. Direct comparisons of maturity assignment by macroscopic versus microscopic methods demonstrated that both schemes are compatible, but the more cost-effective macroscopic method had trouble distinguishing larger immature from smaller resting females. Spatial comparisons, using gonad histology only, supported the existence of three stocks in U.S. waters, but also revealed significant variation in age at maturity within the two coastal stocks. Age-at-maturity was more variable than size-at-maturity, which is consistent with known stock-specific patterns of growth rates and a postulated life history tradeoff to delay maturity until a size threshold is reached. The within-stock variation in median age at maturity, about one year for coastal stocks, recommends further investigation of using static, stock-specific maturity ogives to calculate reference points for management.}, + urldate = {2024-02-09}, + journal = {Journal of Sea Research}, + author = {McBride, Richard S. and Wuenschel, Mark J. and Nitschke, Paul and Thornton, Grace and King, Jeremy R.}, + month = jan, + year = {2013}, + note = {ADS Bibcode: 2013JSR....75...41M}, + pages = {41--51}, +} + +@article{mcelroy_spatial_2016, + title = {Spatial and annual variation in fecundity and oocyte atresia of yellowtail flounder, {Limanda} ferruginea, in {U}.{S}. waters}, + volume = {107}, + issn = {13851101}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S1385110115300174}, + doi = {10.1016/j.seares.2015.06.015}, + language = {en}, + urldate = {2024-02-09}, + journal = {Journal of Sea Research}, + author = {McElroy, W. David and Wuenschel, Mark J. and Towle, Emilee K. and McBride, Richard S.}, + month = jan, + year = {2016}, + pages = {76--89}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\7FY3FDD2\\McElroy et al. - 2016 - Spatial and annual variation in fecundity and oocy.pdf:application/pdf}, +} + +@article{berghoff_physical_2023, + title = {Physical and biological effects on the carbonate system during summer in the {Northern} {Argentine} {Continental} {Shelf} ({Southwestern} {Atlantic})}, + volume = {237}, + issn = {09247963}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0924796322001294}, + doi = {10.1016/j.jmarsys.2022.103828}, + language = {en}, + urldate = {2024-02-09}, + journal = {Journal of Marine Systems}, + author = {Berghoff, Carla F. and Pierrot, Denis and Epherra, Lucía and Silva, Ricardo I. and Segura, Valeria and Negri, Rubén M. and Hozbor, M. Constanza and Carignan, Mario O. and Barbero, Leticia and Lutz, Vivian A.}, + month = jan, + year = {2023}, + pages = {103828}, + file = {Submitted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\YBKE2EJH\\Berghoff et al. - 2023 - Physical and biological effects on the carbonate s.pdf:application/pdf}, +} + +@article{bacheler_patterns_2019, + title = {Patterns in fish biodiversity associated with temperate reefs on the southeastern {US} continental shelf}, + volume = {49}, + issn = {1867-1616, 1867-1624}, + url = {http://link.springer.com/10.1007/s12526-019-00981-9}, + doi = {10.1007/s12526-019-00981-9}, + language = {en}, + number = {5}, + urldate = {2024-02-09}, + journal = {Marine Biodiversity}, + author = {Bacheler, Nathan M. and Schobernd, Zebulon H. and Gregalis, Kevan C. and Schobernd, Christina M. and Teer, Bradford Z. and Gillum, Zachary and Glasgow, Dawn M. and McNeill, Neil and Burton, Michael and Muñoz, Roldan}, + month = oct, + year = {2019}, + pages = {2411--2428}, +} + +@misc{murua_female_nodate, + title = {Female {Reproductive} {Strategies} of {Marine} {Fish} {Species} of the {North} {Atlantic}}, + url = {https://journal.nafo.int/Volumes/Articles/ID/561/Female-Reproductive-Strategies-of-Marine-Fish-Species-of-the-North-Atlantic}, + abstract = {Download the PDF (246 kb)}, + language = {en-US}, + urldate = {2024-02-10}, + author = {Murua, H and Saborido-Rey, F}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\2XDTSXZL\\Female-Reproductive-Strategies-of-Marine-Fish-Species-of-the-North-Atlantic.html:text/html}, +} + +@misc{noauthor_modern_nodate, + title = {Modern {Applied} {Statistics} with {S}, 4th ed}, + url = {https://www.stats.ox.ac.uk/pub/MASS4/}, + urldate = {2024-02-10}, + file = {Modern Applied Statistics with S, 4th ed:C\:\\Users\\andrew.beet\\Zotero\\storage\\8UJFI6UK\\MASS4.html:text/html}, +} + +@misc{zamarro_batch_1991, + title = {Batch fecundity and spawning frequency of yellowtail flounder ({Limanda} ferruginea) on the {Grand} {Bank}.}, + publisher = {Northwest Atl. Fish. Organ. Sci. Counc. Stud. 15, 43–51.}, + author = {Zamarro, J}, + year = {1991}, +} + +@article{howell_seasonal-changes_1983, + title = {Seasonal-{Changes} in the {Ovaries} of {Adult} {Yellowtail} {Flounder}, {Limanda}- {Ferruginea}}, + author = {Howell, W H}, + year = {1983}, +} + +@article{howell_fecundity_nodate, + title = {Fecundity of {Southern} {New} {England} {Stock} of {Yellowtail} {Flounder}, {Limanda}-{Ferruginea}}, + author = {Howell, W H and Kesler, D H}, +} + +@article{anderson_identification_2005, + series = {The {Ecology} and {Oceanography} of {Toxic} {Blooms} in the {Gulf} of {Maine}}, + title = {Identification and enumeration of \textit{{Alexandrium}} spp. from the {Gulf} of {Maine} using molecular probes}, + volume = {52}, + issn = {0967-0645}, + url = {https://www.sciencedirect.com/science/article/pii/S0967064505001621}, + doi = {10.1016/j.dsr2.2005.06.015}, + abstract = {Three different molecular methods were used with traditional brightfield microscope techniques to enumerate the toxic dinoflagellate Alexandrium fundyense in samples collected in the Gulf of Maine in 1998, 2000, 2001, and 2003. Two molecular probes were used in fluorescent whole-cell (WC) microscopic assays: a large-subunit ribosomal RNA (LSU rRNA) oligonucleotide probe (NA1) and a monoclonal antibody probe thought to be specific for Alexandrium spp. within the tamarense/catenella/fundyense complex. Cell abundance estimates also were obtained using the NA1 oligonucleotide probe in a semi-quantitative sandwich hybridization assay (SHA) that quantified target rRNA in cell lysates. Here we compare and contrast the specificity and utility of these probe types and assay approaches. WC counts of the 1998 field samples demonstrated that A. fundyense cell densities estimated using the antibody approach were higher than those using either the NA1 oligonucleotide or brightfield microscopy due to the co-occurrence of A. ostenfeldii with A. fundyense, and the inability of the antibody to discriminate between these two species. An approach using cell size and the presence or absence of food vacuoles allowed more accurate immunofluorescent cell counts of both species, but small cells of A. ostenfeldii that did not contain food vacuoles were still mistakenly counted as A. fundyense. For 2001, a dual-labeling procedure using two oligonucleotide probes was used to separately enumerate A. ostenfeldii and A. fundyense in the WC format. In addition, the SHA was used in 2001 and 2003 to enumerate A. fundyense. Some agreement was observed between the two oligonucleotide methods, but there were differences as well. Not including samples with cell numbers below empirically determined detection limits of 25cellsl–1, good correlation was observed for surface samples and vertical profiles in May 2001 and June 2003 when the SHA estimates were, on average, equivalent, and 1.5× the WC counts, respectively. The worst correlations were for virtually all samples from the June 2001 cruise where the SHA both over- and under-estimated the WC counts. Some differences were expected, since the SHA and the WC assays measure different, but related parameters. The former quantifies intact cells and particulate material that might contain non-viable cells or fragments, whereas the latter measures only intact cells that survive sample processing and are visible in a sample matrix. A variety of factors can thus affect results, particularly with the WC method, including variable uptake of the oligonucleotide probe due to cell permeability changes, cell lysis during sampling, preservation and processing; variable rRNA content or accessibility due to nutritional or environmental factors; and the variable detection of intact cells or cell fragments in fecal pellets and detritus. The SHA offers dramatic increases in sample throughput, but introduces uncertainties, such as those due to sample matrix effects (non-specific labeling and cross-reactions), variable rRNA levels in intact cells or to the possible presence of target rRNA in cell fragments, fecal pellets, or detritus. Molecular probes are powerful tools for monitoring and research applications, but more work is needed to compare and refine these different cell enumeration methods on field samples, as well as to assess the general validity of brightfield or fluorescent WC approaches.}, + number = {19}, + urldate = {2024-02-12}, + journal = {Deep Sea Research Part II: Topical Studies in Oceanography}, + author = {Anderson, Donald M. and Kulis, David M. and Keafer, Bruce A. and Gribble, Kristin E. and Marin, Roman and Scholin, Christopher A.}, + month = sep, + year = {2005}, + keywords = {Antibody, HAB, Molecular probes, Oligonucleotide}, + pages = {2467--2490}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\TNKJK69L\\S0967064505001621.html:text/html}, +} + +@article{anderson_bloom_1997, + title = {Bloom dynamics of toxic {Alexandrium} species in the northeastern {U}.{S}}, + volume = {42}, + copyright = {© 1997, by the Association for the Sciences of Limnology and Oceanography, Inc.}, + issn = {1939-5590}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.1997.42.5_part_2.1009}, + doi = {10.4319/lo.1997.42.5_part_2.1009}, + abstract = {Coastal waters of the northeastern U.S. are subject to recurrent outbreaks of paralytic shellfish poisoning (PSP) caused by toxic dinoflagellates in the genus Alexandrium. PSP is not uniform across the large region, but instead reflects Alexandrium growth and toxin accumulation in five separate habitats or zones defined by circulation patterns and the discontinuous distribution of the dinoflagellates. Each of these habitats has a unique set of environmental and oceanographic forcings that determine the timing and extent of bloom development and transport and that regulate the extent of genetic exchange with adjacent populations. Several habitats (e.g. the southwestern Gulf of Maine, Massachusetts Bay, and Georges Bank) are linked hydrographically and may share the same Alexandrium population via large-scale transport in a. coastal current, whereas the other two habitats (eastern Maine and southern salt ponds-embayments) seem to be isolated and have little or no hydrographic or genetic linkage to adjacent regions during bloom seasons. My paper provides an overview of the regional ecology and oceanography of Alexandrium through a focus on these five subpopulations. Issues that relate to PSP and Alexandrium dynamics throughout the world are highlighted, including species dispersal, the role of cysts and “initiation zones” in bloom development, and the influence of large- and small-scale hydrography on population development and transport. The ability of Alexandrium to colonize multiple habitats and to persist over a large region is emphasized in recognition of the adaptability and resilience of this important organism.}, + language = {en}, + number = {5part2}, + urldate = {2024-02-12}, + journal = {Limnology and Oceanography}, + author = {Anderson, Donald M.}, + year = {1997}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.1997.42.5\_part\_2.1009}, + pages = {1009--1022}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\QRC7KZ45\\Anderson - 1997 - Bloom dynamics of toxic Alexandrium species in the.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\B9UZEQGJ\\lo.1997.42.5_part_2.html:text/html}, +} + +@article{jin_value_2008, + title = {The value of harmful algal bloom predictions to the nearshore commercial shellfish fishery in the {Gulf} of {Maine}}, + volume = {7}, + issn = {1568-9883}, + url = {https://www.sciencedirect.com/science/article/pii/S1568988308000280}, + doi = {10.1016/j.hal.2008.03.002}, + abstract = {In this study, we develop a framework for measuring the value of harmful algal bloom (HAB) predictions. The framework captures the effects of both private and public responses to HABs. Using data from the New England nearshore commercial shellfish fishery and impact estimates for a large-scale HAB event in 2005, we illustrate how the potential value of HAB forecasts may be estimated. The results of our study suggest that the long-term value of a HAB prediction and tracking system for the Gulf of Maine is sensitive to the frequency of HAB events, the accuracy of predictions, the choice of HAB impact measures, and the effectiveness of public and private responses.}, + number = {6}, + urldate = {2024-02-12}, + journal = {Harmful Algae}, + author = {Jin, Di and Hoagland, Porter}, + month = oct, + year = {2008}, + keywords = {Fisheries, Forecast, Harmful algal bloom (HAB), Marine scientific research, Red tide, Value of information}, + pages = {772--781}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\NKMYAEA7\\S1568988308000280.html:text/html;Submitted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\VNVAAETE\\Jin and Hoagland - 2008 - The value of harmful algal bloom predictions to th.pdf:application/pdf}, +} + +@article{li_investigation_2009, + title = {Investigation of the 2006 \textit{{Alexandrium} fundyense} bloom in the {Gulf} of {Maine}: {In}-situ observations and numerical modeling}, + volume = {29}, + issn = {0278-4343}, + shorttitle = {Investigation of the 2006 \textit{{Alexandrium} fundyense} bloom in the {Gulf} of {Maine}}, + url = {https://www.sciencedirect.com/science/article/pii/S0278434309002301}, + doi = {10.1016/j.csr.2009.07.012}, + abstract = {In-situ observations and a coupled bio-physical model were used to study the germination, initiation, and development of the Gulf of Maine (GOM) Alexandrium fundyense bloom in 2006. Hydrographic measurements and comparisons with GOM climatology indicate that 2006 was a year with normal coastal water temperature, salinity, current and river runoff conditions. A. fundyense cyst abundance in bottom sediments preceding the 2006 bloom was at a moderate level compared to other recent annual cyst survey data. We used the coupled bio-physical model to hindcast coastal circulation and A. fundyense cell concentrations. Field data including water temperature, salinity, velocity time series and surface A. fundyense cell concentration maps were applied to gauge the model's fidelity. The coupled model is capable of reproducing the hydrodynamics and the temporal and spatial distributions of A. fundyense cell concentration reasonably well. Model hindcast solutions were further used to diagnose physical and biological factors controlling the bloom dynamics. Surface wind fields modulated the bloom's horizontal and vertical distribution. The initial cyst distribution was found to be the dominant factor affecting the severity and the interannual variability of the A. fundyense bloom. Initial cyst abundance for the 2006 bloom was about 50\% of that prior to the 2005 bloom. As the result, the time-averaged gulf-wide cell concentration in 2006 was also only about 60\% of that in 2005. In addition, weaker alongshore currents and episodic upwelling-favorable winds in 2006 reduced the spatial extent of the bloom as compared with 2005.}, + number = {17}, + urldate = {2024-02-12}, + journal = {Continental Shelf Research}, + author = {Li, Yizhen and He, Ruoying and McGillicuddy, Dennis J. and Anderson, Donald M. and Keafer, Bruce A.}, + month = sep, + year = {2009}, + keywords = {Gulf of Maine, Bio-physical interaction, Coastal circulation, Harmful algal bloom}, + pages = {2069--2082}, + file = {Accepted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\BJ56MWNF\\Li et al. - 2009 - Investigation of the 2006 Alexandrium fundyense.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\887TXE7J\\S0278434309002301.html:text/html}, +} + +@article{li_dynamics_2020, + title = {Dynamics of an intense \textit{{Alexandrium} catenella} red tide in the {Gulf} of {Maine}: satellite observations and numerical modeling}, + volume = {99}, + issn = {1568-9883}, + shorttitle = {Dynamics of an intense \textit{{Alexandrium} catenella} red tide in the {Gulf} of {Maine}}, + url = {https://www.sciencedirect.com/science/article/pii/S1568988320302067}, + doi = {10.1016/j.hal.2020.101927}, + abstract = {In July 2009, an unusually intense bloom of the toxic dinoflagellate Alexandrium catenella occurred in the Gulf of Maine. The bloom reached high concentrations (from hundreds of thousands to one million cells L−1) that discolored the water and exceeded normal bloom concentrations by a factor of 1000. Using Medium Resolution Imaging Spectrometer (MERIS) imagery processed to target chlorophyll concentrations ({\textgreater}2 µg L−1), patches of intense A. catenella concentration were identified that were consistent with the highly localized cell concentrations observed from ship surveys. The bloom patches were generally aligned with the edge of coastal waters with high-absorption. Dense bloom patches moved onshore in response to a downwelling event, persisted for approximately one week, then dispersed rapidly over a few days and did not reappear. Coupled physical-biological model simulations showed that wind forcing was an important factor in transporting cells onshore. Upward swimming behavior facilitated the horizontal cell aggregation, increasing the simulated maximum depth-integrated cell concentration by up to a factor of 40. Vertical convergence of cells, due to active swimming of A. catenella from the subsurface to the top layer, could explain the additional 25-fold intensification (25 × 40=1000-fold) needed to reach the bloom concentrations that discolored the water. A model simulation that considered upward swimming overestimated cell concentrations downstream of the intense aggregation. This discrepancy between model and observed concentrations suggested a loss of cells from the water column at a time that corresponded to the start of encystment. These results indicated that the joint effect of upward swimming, horizontal convergence, and wind-driven flow contributed to the red water event, which might have promoted the sexual reproduction event that preceded the encystment process.}, + urldate = {2024-02-12}, + journal = {Harmful Algae}, + author = {Li, Yizhen and Stumpf, Richard P. and McGillicuddy, D. J. and He, Ruoying}, + month = nov, + year = {2020}, + keywords = {Bloom patches, Cell accumulation, Coastal upwelling, Red water, Upward swimming}, + pages = {101927}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\DQNE9A9X\\Li et al. - 2020 - Dynamics of an intense Alexandrium catenellai.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\WHRSDAKF\\S1568988320302067.html:text/html}, +} + +@article{mcgillicuddy_suppression_2011, + title = {Suppression of the 2010 {Alexandrium} fundyense bloom by changes in physical, biological, and chemical properties of the {Gulf} of {Maine}}, + volume = {56}, + copyright = {© 2011, by the Association for the Sciences of Limnology and Oceanography, Inc.}, + issn = {1939-5590}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.2011.56.6.2411}, + doi = {10.4319/lo.2011.56.6.2411}, + abstract = {For the period 2005–2009, the abundance of resting cysts in bottom sediments from the preceding autumn was a first-order predictor of the overall severity of spring–summer blooms of Alexandrium fundyense in the western Gulf of Maine and southern New England. Cyst abundance off mid-coast Maine was significantly higher in autumn 2009 than it was preceding a major regional bloom in 2005. A seasonal ensemble forecast was computed using a range of forcing conditions for the period 2004–2009, suggesting that a large bloom was likely in the western Gulf of Maine in 2010. This did not materialize, perhaps because environmental conditions in spring–summer 2010 were not favorable for growth of A. fundyense. Water mass anomalies indicate a regional-scale change in circulation with direct influence on A. fundyense's niche. Specifically, near-surface waters were warmer, fresher, more stratified, and had lower nutrients than during the period of observations used to construct the ensemble forecast. Moreover, a weaker-than-normal coastal current lessened A. fundyense transport into the western Gulf of Maine and Massachusetts Bay. Satellite ocean color observations indicate the 2010 spring phytoplankton bloom was more intense than usual. Early season nutrient depletion may have caused a temporal mismatch with A. fundyense's endogenous clock that regulates the timing of cyst germination. These findings highlight the difficulties of ecological forecasting in a changing oceanographic environment, and underscore the need for a sustained observational network to drive such forecasts.}, + language = {en}, + number = {6}, + urldate = {2024-02-12}, + journal = {Limnology and Oceanography}, + author = {McGillicuddy, D. J., Jr. and Townsend, D. W. and He, R. and Keafer, B. A. and Kleindinst, J. L. and Li, Y. and Manning, J. P. and Mountain, D. G. and Thomas, M. A. and Anderson, D. M.}, + year = {2011}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.4319/lo.2011.56.6.2411}, + pages = {2411--2426}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\YRAWY6TP\\McGillicuddy et al. - 2011 - Suppression of the 2010 Alexandrium fundyense bloo.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\LUMZ3UNG\\lo.2011.56.6.html:text/html}, +} + +@misc{li_noaanos_nodate, + title = {{NOAA}/{NOS} {NCCOS} {Stressor} {Detection} and {Impacts} {Division}}, + url = {https://www.fisheries.noaa.gov/inport/item/45863}, + author = {Li, Yizhen}, +} + +@article{kleindinst_categorizing_2014, + title = {Categorizing the severity of paralytic shellfish poisoning outbreaks in the {Gulf} of {Maine} for forecasting and management}, + volume = {103}, + issn = {0967-0645}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4112480/}, + doi = {10.1016/j.dsr2.2013.03.027}, + abstract = {Development of forecasting systems for harmful algal blooms (HABs) has been a long-standing research and management goal. Significant progress has been made in the Gulf of Maine, where seasonal bloom forecasts are now being issued annually using Alexandrium fundyense cyst abundance maps and a population dynamics model developed for that organism. Thus far, these forecasts have used terms such as “significant”, “moderately large” or “moderate” to convey the extent of forecasted paralytic shellfish poisoning (PSP) outbreaks. In this study, historical shellfish harvesting closure data along the coast of the Gulf of Maine were used to derive a series of bloom severity levels that are analogous to those used to define major storms like hurricanes or tornados. Thirty-four years of PSP-related shellfish closure data for Maine, Massachusetts and New Hampshire were collected and mapped to depict the extent of coastline closure in each year. Due to fractal considerations, different methods were explored for measuring length of coastline closed. Ultimately, a simple procedure was developed using arbitrary straight-line segments to represent specific sections of the coastline. This method was consistently applied to each year’s PSP toxicity closure map to calculate the total length of coastline closed. Maps were then clustered together statistically to yield distinct groups of years with similar characteristics. A series of categories or levels was defined (“Level 1: Limited”, “Level 2: Moderate”, and “Level 3: Extensive”) each with an associated range of expected coastline closed, which can now be used instead of vague descriptors in future forecasts. This will provide scientifically consistent and simply defined information to the public as well as resource managers who make decisions on the basis of the forecasts.}, + urldate = {2024-02-12}, + journal = {Deep-sea research. Part II, Topical studies in oceanography}, + author = {Kleindinst, Judith L. and Anderson, Donald M. and McGillicuddy, Dennis J. and Stumpf, Richard P. and Fisher, Kathleen M. and Couture, Darcie A. and Hickey, J. Michael and Nash, Christopher}, + month = may, + year = {2014}, + pmid = {25076815}, + pmcid = {PMC4112480}, + pages = {277--287}, + file = {PubMed Central Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\69EZCIUS\\Kleindinst et al. - 2014 - Categorizing the severity of paralytic shellfish p.pdf:application/pdf}, +} + +@incollection{fath_eutrophication_2019, + address = {Oxford}, + edition = {Second Edition}, + title = {Eutrophication}, + isbn = {978-0-444-64130-4}, + url = {http://www.sciencedirect.com/science/article/pii/B9780124095489109571}, + booktitle = {Encyclopedia of {Ecology} ({Second} {Edition})}, + publisher = {Elsevier}, + author = {Lemley, Daniel A. and Adams, Janine B.}, + editor = {Fath, Brian}, + year = {2019}, + doi = {https://doi.org/10.1016/B978-0-12-409548-9.10957-1}, + keywords = {Anthropogenic nutrient loading, Assessment frameworks, Coastal eutrophication, Ecosystem approach, Estuaries, Indicators, Management, Primary producers, Rehabilitation}, + pages = {86 -- 90}, +} + +@article{wallace_understanding_2000, + title = {Understanding fisheries management: a manual for understanding the federal fisheries management process, including analysis of the 1996 {Sustainable} {Fisheries} {Act}}, + author = {Wallace, Richard Kent and Fletcher, Kristen Michele}, + year = {2000}, +} + +@book{council_sharing_1999, + address = {Washington, DC}, + title = {Sharing the {Fish}: {Toward} a {National} {Policy} on {Individual} {Fishing} {Quotas}}, + isbn = {978-0-309-06330-2}, + url = {https://www.nap.edu/catalog/6335/sharing-the-fish-toward-a-national-policy-on-individual-fishing}, + publisher = {The National Academies Press}, + author = {Council, National Research}, + year = {1999}, + doi = {10.17226/6335}, +} + +@book{united_states_noaa_2005, + title = {{NOAA} fisheries glossary}, + url = {https://repository.library.noaa.gov/view/noaa/12856}, + author = {United States, National Oceanic and Administration, Atmospheric}, + year = {2005}, +} + +@book{dynamics_ecosystem_nodate, + title = {Ecosystem {Status} {Report}}, + url = {https://www.nefsc.noaa.gov/ecosys/ecosystem-status-report/glossary.html}, + author = {Dynamics, Ecosystem and Branch, Assessment}, +} + +@book{service_nmfs_2004, + series = {{NOAA} tech. memo. {NMFS}-{F}/{SPO}}, + title = {{NMFS} {Strategic} {Plan} for {Fisheries} {Research}}, + url = {https://books.google.com/books?id=IkbxAAAAMAAJ}, + publisher = {U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service}, + author = {Service, United States National Marine Fisheries}, + year = {2004}, + lccn = {2004368308}, +} + +@book{wallace_fisheries_1994, + title = {Fisheries management for fisherman: a manual for helping fisherman understand the federal management process}, + url = {http://hdl.handle.net/1969.3/23692}, + author = {Wallace, Richard K, William Hosking and Szedlmayer, Stephen T.}, + year = {1994}, +} + +@article{madden_framework_2004, + title = {A framework for a {Coastal}/{Marine} {Ecological} {Classification} {Standard} ({CMECS})}, + journal = {Special Paper - Geological Association of Canada}, + author = {Madden, Chris and Grossman, Dennis}, + month = jan, + year = {2004}, + pages = {185--209}, +} + +@book{noauthor_northwest_nodate, + title = {Northwest {Fisheries} {Science} {Center}. {Glossary}}, + url = {https://www.nwfsc.noaa.gov/research/divisions/fe/estuarine/oeip/ic-glossary.cfm}, +} + +@book{noauthor_united_nodate, + title = {United {Nations} {Food} and {Agricultural} {Organization}. {Fisheries} {Glossary}}, + url = {http://www.fao.org/fi/glossary/default.asp}, +} + +@article{pauly_primary_1995, + title = {Primary production required to sustain global fisheries}, + url = {https://doi.org/10.1038/374255a0}, + doi = {10.1038/374255a0}, + journal = {Nature}, + author = {Pauly, D and Christensen, V}, + year = {1995}, +} + +@book{noauthor_technical_nodate, + title = {Technical {Documentation}: {State} of the {Ecosystem}}, + url = {https://doi.org/10.25923/64pf-sc70}, +} + +@book{jewett_noaa_2020, + title = {{NOAA} {Ocean} and {Great} {Lakes} {Acidification} {Research} {Plan} 2020-2029}, + url = {https://sab.noaa.gov/sites/SAB/Meetings/2019_Documents/Dec_Meeting/2020%20OA%20Research%20Plan%20DRAFT%20External%20Review.pdf}, + author = {Jewett, Elizabeth and Osborne, Emily and Wanninkhof, Rik and DeAngelo, Benjamin and Arzayus, Krisa and Osgood, Kenric}, + year = {2020}, +} + +@article{balk_assumption-free_2010, + title = {An {Assumption}-{Free} {Framework} for {Measuring} {Productivity} {Change}}, + volume = {56}, + copyright = {© 2010 The Author. Journal compilation © International Association for Research in Income and Wealth 2010}, + issn = {1475-4991}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1475-4991.2010.00388.x}, + doi = {10.1111/j.1475-4991.2010.00388.x}, + abstract = {The measurement of productivity change (or difference) is usually based on models that make use of strong assumptions such as competitive behavior and constant returns to scale. This survey discusses the basics of productivity measurement and shows that one can dispense with most if not all of the usual, neoclassical assumptions. By virtue of its structural features, the measurement model is applicable to individual establishments and aggregates such as industries, sectors, or economies.}, + language = {en}, + number = {s1}, + urldate = {2024-02-12}, + journal = {Review of Income and Wealth}, + author = {Balk, Bert M.}, + year = {2010}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1475-4991.2010.00388.x}, + pages = {S224--S256}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\PBEUPQVD\\j.1475-4991.2010.00388.html:text/html}, +} + +@article{cross_value_2009, + title = {Value data and the {Bennet} price and quantity indicators}, + volume = {102}, + issn = {0165-1765}, + url = {https://www.sciencedirect.com/science/article/pii/S0165176508002796}, + doi = {10.1016/j.econlet.2008.10.003}, + abstract = {We derive value-based Bennet indicators, expressed in terms of revenues and expenditures (values) and relative prices, rather than prices and quantities. We then show how relative price information is recoverable from value data when agents are rational.}, + number = {1}, + urldate = {2024-02-12}, + journal = {Economics Letters}, + author = {Cross, Robin M. and Färe, Rolf}, + month = jan, + year = {2009}, + keywords = {Bennet indicators, Prices, Weak axiom}, + pages = {19--21}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\FZZATZZE\\S0165176508002796.html:text/html}, +} + +@article{georgianna_contrasting_2017, + title = {Contrasting trends in the {Northeast} {United} {States} groundfish and scallop processing industries}, + volume = {85}, + issn = {0308-597X}, + url = {https://www.sciencedirect.com/science/article/pii/S0308597X1730266X}, + doi = {10.1016/j.marpol.2017.08.025}, + abstract = {In the northeast United States, groundfish landings have declined almost continually since 1983. Fresh groundfish processors have redeployed their marketing capital by substituting other seafood products in place of regional groundfish and expanding into different markets. Meanwhile, northeast U.S. Atlantic sea scallop landings sharply increased starting in 1998, peaked in 2004, remained relatively constant until declining in 2013 and 2014. In order to leverage their favorable access to increased landings, scallop processors invested in development of new products, such as individually quick frozen (IQF) scallops, and invested in marketing activities to attract new customers, including those located in foreign nations. This behavior is consistent with Montgomery and Wernerfelt's theory of diversification in response to excess capacity of firm-specific resources and investments in product development and marketing in firm specific resources. Price and quantity indicators were used to examine the effects of shifting landings and composition of landings on exvessel values. These indicators show that declines in groundfish exvessel values were driven by declining quantities, which is consistent with processors substituting other products rather than biding up exvessel prices. Increases in scallop exvessel values were driven by both increasing prices for all sizes and by increasing quantities for large scallops, which is consistent with investment in marketing for larger scallops.}, + urldate = {2024-02-12}, + journal = {Marine Policy}, + author = {Georgianna, Daniel and Lee, Min-Yang and Walden, John}, + month = nov, + year = {2017}, + pages = {100--106}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\88H29DAT\\S0308597X1730266X.html:text/html}, +} + +@article{grifell-tatje_decomposing_2004, + title = {Decomposing the dividend}, + volume = {32}, + issn = {0147-5967}, + url = {https://www.sciencedirect.com/science/article/pii/S0147596704000484}, + doi = {10.1016/j.jce.2004.05.002}, + abstract = {Most theoretical and empirical research on cooperatives focuses on the comparative statics behavior of optimizing cooperatives. We focus on the magnitude and sources of variation in the dividend that cooperatives are presumed to maximize. Variation in the dividend leads to an inefficient allocation of labor among cooperatives. For a panel of 59 Spanish cooperative financial institutions, from 1994 to 2001, we decompose dividend variation into mutually exclusive and exhaustive sources. We also compare the performance of all other cooperative financial institutions to that of Caja Laboral Popular, which provides financial services to the Mondragón group of cooperatives. Journal of Comparative Economics 32 (3) (2004) 500–518.}, + number = {3}, + urldate = {2024-02-12}, + journal = {Journal of Comparative Economics}, + author = {Grifell-Tatjé, E. and Lovell, C. A. Knox}, + month = sep, + year = {2004}, + pages = {500--518}, + file = {ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\IWZTHYDU\\S0147596704000484.html:text/html;Submitted Version:C\:\\Users\\andrew.beet\\Zotero\\storage\\ZGRVRMWS\\Grifell-Tatjé and Lovell - 2004 - Decomposing the dividend.pdf:application/pdf}, +} + +@article{lim_profit_2009, + title = {Profit and productivity of {US} {Class} {I} railroads}, + volume = {30}, + copyright = {Copyright © 2009 John Wiley \& Sons, Ltd.}, + issn = {1099-1468}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mde.1462}, + doi = {10.1002/mde.1462}, + abstract = {This paper examines how productivity changes and price changes have contributed to short-run profit change in the railroad industry. Using an unbalanced panel of US Class I railroads for the period 1996–2003, a short-run profit change decomposition model is used to attribute intertemporal profit change to its causal factors. We find that productivity improvements and an increased scale of production contributed to increases in profit, and that variation in operating efficiency had a mixed impact on profit. We also find that relative changes in rail rates and variable input prices exerted downward pressure on profit. Copyright © 2009 John Wiley \& Sons, Ltd.}, + language = {en}, + number = {7}, + urldate = {2024-02-12}, + journal = {Managerial and Decision Economics}, + author = {Lim, Siew Hoon and Lovell, C.A. Knox}, + year = {2009}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mde.1462}, + pages = {423--442}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\GQYMZUT3\\mde.html:text/html}, +} + +@article{walden_measuring_2017, + title = {Measuring change in productivity of a fishery with the {Bennet}–{Bowley} indicator}, + volume = {115}, + issn = {00900656}, + url = {http://spo.nmfs.noaa.gov/content/fishery-bulletin/measuring-change-productivity-fishery-bennet%E2%80%93bowley-indicator}, + doi = {10.7755/FB.115.3.1}, + number = {3}, + urldate = {2024-02-12}, + journal = {Fishery Bulletin}, + author = {Walden, John B. and Färe, Rolf and Grosskopf, Shawna}, + month = apr, + year = {2017}, + pages = {273--283}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\NBN6KIUH\\Walden et al. - 2017 - Measuring change in productivity of a fishery with.pdf:application/pdf}, +} + +@article{friedland_forage_2023, + title = {Forage {Fish} {Species} {Prefer} {Habitat} within {Designated} {Offshore} {Wind} {Energy} {Areas} in the {U}.{S}. {Northeast} {Shelf} {Ecosystem}}, + volume = {15}, + copyright = {© 2023 The Authors. Marine and Coastal Fisheries published by Wiley Periodicals LLC on behalf of American Fisheries Society.}, + issn = {1942-5120}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mcf2.10230}, + doi = {10.1002/mcf2.10230}, + abstract = {As the world develops sources of renewable energy, there is an intensifying interest in offshore wind energy production. The Northeast U.S. Continental Shelf (NES) ecosystem has favorable wind dynamics, with active development of wind energy. In this study, we present species distribution models that consider both occupancy and biomass responses for a broad spectrum of fish and macroinvertebrate taxa (n = 177). Building upon prior analyses, habitat was differentiated into overall and core habitats based on statistical distributions of habitat scores. Overall habitat was used to show each species' regional distribution based on fishery-independent survey captures between 1976 and 2019, whereas core habitat represented where the focus of the species' abundance was located as a subset of overall habitat. Wind energy developments may modify the water column in ways that impact lower-trophic-level productivity; therefore, added attention was given to the response of forage species. Over 20\% of species showed preferential use of putative and potential wind development areas, including a disproportionate number of forage taxa. Principal usage varied by season, with forage species like Atlantic Menhaden Brevoortia tyrannus and Atlantic Mackerel Scomber scombrus preferentially using the lease areas in spring and Round Herring Etrumeus teres and longfin inshore squid Doryteuthis pealeii using lease areas in autumn. For species with relatively low usage of the lease areas, there was a tendency for the usage related to overall habitat to be lower than usage for core habitat; in contrast, for species with high usage of the lease areas, that usage was higher for overall habitat than for core habitat. The area of habitat tended to have positive trends across species, with these positive trends being disproportionately higher among forage taxa. These results frame the importance of wind lease areas for species in the NES, particularly forage taxa that fulfill many important ecological functions.}, + language = {en}, + number = {2}, + urldate = {2023-08-07}, + journal = {Marine and Coastal Fisheries}, + author = {Friedland, Kevin D. and Adams, Evan M. and Goetsch, Chandra and Gulka, Julia and Brady, Damian C. and Rzeszowski, Everett and Crear, Daniel P. and Gaichas, Sarah and Gill, Andrew B. and McManus, M. Conor and Methratta, Elizabeth T. and Morano, Janelle L. and Staudinger, Michelle D.}, + year = {2023}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mcf2.10230}, + pages = {e10230}, + annote = {e10230 UMCF-2022-0033.R1}, + annote = {e10230 UMCF-2022-0033.R1}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\MJ75YPT5\\Friedland et al. - 2023 - Forage Fish Species Prefer Habitat within Designat.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\8DSLKU44\\mcf2.html:text/html}, +} + +@article{lelli_seal_2009, + title = {Seal {Bounties} in {Maine} and {Massachusetts}, 1888 to 1962}, + volume = {16}, + issn = {1092-6194}, + url = {https://www.jstor.org/stable/27744561}, + abstract = {Maine and Massachusetts paid bounties on seals during the 19th and 20th centuries. To determine the number of seals killed for bounty, we examined historical records of bounty claims, and used geographic information systems and multiple linear regression to find predictors of places where large numbers of bounties were paid. We found records of 24,831 bounties paid in Maine (1891–1945) and 15,690 in Massachusetts (1888–1962). Considering possible fraud, missing data, and seals struck and lost, this suggests that 72,284 to 135,498 seals were killed in the bounty hunt, probably enough to account for regional declines in seal populations. Larger numbers of bounties were paid where there were more seals and a higher human population.}, + number = {2}, + urldate = {2024-02-12}, + journal = {Northeastern Naturalist}, + author = {Lelli, Barbara and Harris, David E. and Aboueissa, AbouEl-Makarim}, + year = {2009}, + note = {Publisher: Eagle Hill Institute}, + pages = {239--254}, + file = {JSTOR Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\2YW4R6HR\\Lelli et al. - 2009 - Seal Bounties in Maine and Massachusetts, 1888 to .pdf:application/pdf}, +} + +@article{andrews_gray_1967, + title = {Gray {Seals} at {Nantucket}, {Massachusetts}}, + volume = {48}, + issn = {0022-2372}, + url = {https://www.jstor.org/stable/1377597}, + doi = {10.2307/1377597}, + number = {4}, + urldate = {2024-02-12}, + journal = {Journal of Mammalogy}, + author = {Andrews, J. Clinton and Mott, Peter Rhoades}, + year = {1967}, + note = {Publisher: [American Society of Mammalogists, Oxford University Press]}, + pages = {657--658}, + file = {JSTOR Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\GXTZD39A\\Andrews and Mott - 1967 - Gray Seals at Nantucket, Massachusetts.pdf:application/pdf}, +} + +@book{wood_gray_2022, + title = {Gray seal ({Halichoerus} grypus) pupping trends and 2021 population estimate in {U}.{S}. waters. {Northeast} {Fisheries} {Science} {Center} {Reference} {Document} 22-14}, + abstract = {The Northeast Fisheries Science Center (NEFSC) conducts aerial surveys of the Northwest +Atlantic stock of gray seals approximately every 5 years to monitor the size, distribution, and rate +of growth of the population in U.S waters. We flew aerial surveys in January 2021 to obtain +updated information on gray seal (Halichoerus grypus) pupping in U.S. waters, relative to the +previous survey flown in 2016. Nine known pupping sites were surveyed along with +reconnaissance flights in Maine, New Hampshire, and New York to look for newly established +sites. Two image processing approaches were taken: the first created layers of individual seals on +a base-map satellite image of the islands, while the second marked individual seals on large, +stitched images. We counted a total of 6,469 pups and 7,890 adults which produced a population +estimate of 27,911 gray seals in U.S. waters and a minimum population estimate of 23,624. We +also fit an autoregressive state-space model to calculate pup growth rates at 4 sites, 3 in +Massachusetts (Monomoy Island, Muskeget Island, and Nomans Land) and 1 in Maine (Seal +Island). All 4 sites had positive long-term growth rates although we found high variability among +the sites and a pattern of rapid growth early in recolonization that then slowed over time.}, + author = {Wood, Stephanie A and Josephson, Elizabeth and Precoda, Kristin and Murray, Kimberly T}, + year = {2022}, +} + +@article{garrison_dietary_2000, + title = {Dietary guild structure of the fish community in the {Northeast} {United} {States} continental shelf ecosystem}, + volume = {202}, + issn = {0171-8630, 1616-1599}, + url = {http://www.int-res.com/abstracts/meps/v202/p231-240/}, + doi = {10.3354/meps202231}, + abstract = {Trophic guild analysis identifies groups of species that use similar resources within a community. We evaluated the trophic guild structure in an assemblage of 40 fish species in the Northeast United States shelf ecosystem using a 25 yr database of food habits. We explicitly accounted for ontogenetic diet shifts by separating predator species into size classes. There were 14 significant trophic guilds. These distinguished predators based upon prey size and location in the water column (i.e., benthic to pelagic feeding). Ontogenetic diet shifts were important in guild structure, particularly within dominant piscivores. The mean dietary overlap both between and within guilds was notably lower than in other fish communities due to the broad spatial and temporal scale of the study, the diversity of prey types consumed, and the generalist nature of predators in this system. The guild concept is a useful framework to simplify highly connected, complex ecosystems like the Northeast US continental shelf and identify ecologically similar functional units.}, + language = {en}, + urldate = {2018-10-22}, + journal = {Marine Ecology Progress Series}, + author = {Garrison, Lp and Link, Js}, + year = {2000}, + pages = {231--240}, + file = {Garrison and Link - 2000 - Dietary guild structure of the fish community in t.pdf:C\:\\Users\\andrew.beet\\Zotero\\storage\\S2JCYD5S\\Garrison and Link - 2000 - Dietary guild structure of the fish community in t.pdf:application/pdf}, +} + +@book{smith_trophic_2010, + title = {The {Trophic} {Dynamics} of 50 {Finfish} and 2 {Squid} {Species} on the {Northeast} {US} {Continental} {Shelf}. {NOAA} {Technichal} {Memorandum} {NMFS}-{NE}-216}, + url = {http://www.nefsc.noaa.gov/publications/tm/tm216/}, + urldate = {2016-04-26}, + publisher = {National Marine Fisheries Service, 166 Water Street, Woods Hole, MA 02543-1026}, + author = {Smith, Brian E. and Link, Jason S.}, + year = {2010}, + file = {NOAA Tech Memo 216:C\:\\Users\\andrew.beet\\Zotero\\storage\\V8WM7TZC\\tm216.html:text/html}, +} + +@article{goldsmith_scientific_2019, + title = {Scientific considerations for acidification monitoring in the {U}.{S}. {Mid}-{Atlantic} {Region}}, + volume = {225}, + issn = {0272-7714}, + url = {https://www.sciencedirect.com/science/article/pii/S0272771418308679}, + doi = {10.1016/j.ecss.2019.04.023}, + abstract = {Coastal and ocean acidification has the potential to cause significant environmental and societal impacts. Monitoring carbonate chemistry parameters over spatial and temporal scales is challenging, especially with limited resources. A lack of monitoring data can lead to a limited understanding of real-world conditions. Without such data, robust experimental and model design is challenging, and the identification and understanding of episodic acidification events is nearly impossible. We present considerations for resource managers, academia, and industry professionals who are currently developing acidification monitoring programs in the Mid-Atlantic region. We highlight the following considerations for deliberation: 1) leverage existing infrastructure to include multiple carbonate chemistry parameters as well as other water quality measurements, 2) direct monitoring efforts in subsurface waters rather than limiting monitoring to surface waters, 3) identify the best available sensor technology for long-term, in-situ monitoring, 4) monitor across a salinity gradient to account for the complexity of estuarine, coastal, and ocean environments, and identify potential areas of enhanced vulnerability, 5) increase sampling frequency to capture variability, 6) consider other drivers (e.g., freshwater discharge, nutrients, physiochemical parameters) that may affect acidification, and 7) conduct or continue monitoring in specific ecological and general regions that may have enhanced vulnerability. Through the incorporation of these considerations, individual monitoring programs can more efficiently and effectively leverage resources and build partnerships for a more comprehensive data collection in the region. While these considerations focus on the Mid-Atlantic region), similar strategies can be used to leverage resources in other locations.}, + urldate = {2024-02-12}, + journal = {Estuarine, Coastal and Shelf Science}, + author = {Goldsmith, Kaitlin A. and Lau, Sherilyn and Poach, Matthew E. and Sakowicz, Gregg P. and Trice, T. Mark and Ono, C. Ryan and Nye, Janet and Shadwick, Elizabeth H. and StLaurent, Kari A. and Saba, Grace K.}, + month = sep, + year = {2019}, + pages = {106189}, + file = {Full Text:C\:\\Users\\andrew.beet\\Zotero\\storage\\M4898B4I\\Goldsmith et al. - 2019 - Scientific considerations for acidification monito.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\HXYZFD6Z\\S0272771418308679.html:text/html}, +} + +@article{xu_long-term_2020, + title = {Long-{Term} {Changes} of {Carbonate} {Chemistry} {Variables} {Along} the {North} {American} {East} {Coast}}, + volume = {125}, + copyright = {©2020. The Authors.}, + issn = {2169-9291}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JC015982}, + doi = {10.1029/2019JC015982}, + abstract = {Decadal variability of carbonate chemistry variables has been studied for the open ocean using observations and models, but less is known about the variations in the coastal ocean due to observational gaps and the more complex environments. In this work, we use a Bayesian-neural-network approach to reconstruct surface carbonate chemistry variables for the Mid-Atlantic Bight (MAB) and the South Atlantic Bight (SAB) along the North American East Coast from 1982 to 2015. The reconstructed monthly time series data suggest that the rate of fCO2 increase in the MAB (18 ± 1 μatm per decade) is faster than those in the SAB (14 ± 1 μatm per decade) and the open ocean (14 ± 1 μatm per decade). Correspondingly, pH decreases faster in the MAB. The observed stagnation in the aragonite saturation state, Ωarag decrease during 2005–2015 in the MAB, is attributed to the intrusion of water from southern and offshore regions with high Ωarag, which offsets the decrease expected from anthropogenic CO2 uptake. Furthermore, seasonal asymmetry in the evolution of long-term change leads to the faster change in the amplitudes of the seasonal cycle in carbonate chemistry variables in coastal waters than those in the open ocean. In particular, the increase in the seasonal-cycle amplitude of dissolved inorganic carbon in the MAB is 2.9 times larger than that of the open ocean. This leads to the faster increase in the season-cycle amplitude of Ωarag and earlier occurrence of undersaturation in coastal waters as acidification continues.}, + language = {en}, + number = {7}, + urldate = {2024-02-12}, + journal = {Journal of Geophysical Research: Oceans}, + author = {Xu, Yuan-Yuan and Cai, Wei-Jun and Wanninkhof, Rik and Salisbury, Joseph and Reimer, Janet and Chen, Baoshan}, + year = {2020}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019JC015982}, + keywords = {carbonate chemistry, Mid-Atlantic Bight, coastal acidification, long-term change, South Atlantic Bight}, + pages = {e2019JC015982}, + annote = {e2019JC015982 2019JC015982}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\P7Q6U9D6\\Xu et al. - 2020 - Long-Term Changes of Carbonate Chemistry Variables.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\229QKYYQ\\2019JC015982.html:text/html}, +} + +@article{nye_gulf_2011, + title = {Gulf {Stream} position determines spatial distribution of silver hake}, + volume = {2}, + shorttitle = {Gulf {Stream} position determines spatial distribution of silver hake}, + doi = {10.1038/ncomms1420}, + number = {412}, + journal = {Nature Communications}, + author = {Nye, J.A. and Joyce, T.M. and Kwon, Y.-O. and Link, J.S.}, + year = {2011}, + keywords = {Climate science, Earth Sciences, Ecology, Oceanography, Zoology}, + annote = {10.1038/ncomms1420}, + file = {Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\5XTGJRZH\\ncomms1420.html:text/html}, +} + +@article{joyce_meridional_2019, + title = {Meridional {Gulf} {Stream} {Shifts} {Can} {Influence} {Wintertime} {Variability} in the {North} {Atlantic} {Storm} {Track} and {Greenland} {Blocking}}, + volume = {46}, + copyright = {©2019. American Geophysical Union. All Rights Reserved.}, + issn = {1944-8007}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2018GL081087}, + doi = {10.1029/2018GL081087}, + abstract = {After leaving the U.S. East Coast, the northward flowing Gulf Stream (GS) becomes a zonal jet and carries along its frontal characteristics of strong flow and sea surface temperature gradients into the North Atlantic at midlatitudes. The separation location where it leaves the coast is also an anchor point for the wintertime synoptic storm track across North America to continue to develop and head across the ocean. We examine the meridional variability of the separated GS path on interannual to decadal time scales as an agent for similar changes in the storm track and blocking variability at midtroposphere from 1979 to 2012. We find that periods of northerly (southerly) GS path are associated with increased (suppressed) excursions of the synoptic storm track to the northeast over the Labrador Sea and reduced (enhanced) Greenland blocking. In both instances, GS shifts lead those in the midtroposphere by a few months.}, + language = {en}, + number = {3}, + urldate = {2024-02-12}, + journal = {Geophysical Research Letters}, + author = {Joyce, Terrence M. and Kwon, Young-Oh and Seo, Hyodae and Ummenhofer, Caroline C.}, + year = {2019}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018GL081087}, + keywords = {Greenland blocking, Gulf Stream path changes, intrerannual variability, wintertime atmospheric storm track}, + pages = {1702--1708}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\J8DQ2K4X\\Joyce et al. - 2019 - Meridional Gulf Stream Shifts Can Influence Winter.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\H6FHSTRP\\2018GL081087.html:text/html}, +} + +@article{joyce_relationship_2009, + title = {On the {Relationship} between {Synoptic} {Wintertime} {Atmospheric} {Variability} and {Path} {Shifts} in the {Gulf} {Stream} and the {Kuroshio} {Extension}}, + volume = {22}, + issn = {0894-8755, 1520-0442}, + url = {https://journals.ametsoc.org/view/journals/clim/22/12/2008jcli2690.1.xml}, + doi = {10.1175/2008JCLI2690.1}, + abstract = {Abstract Coherent, large-scale shifts in the paths of the Gulf Stream (GS) and the Kuroshio Extension (KE) occur on interannual to decadal time scales. Attention has usually been drawn to causes for these shifts in the overlying atmosphere, with some built-in delay of up to a few years resulting from propagation of wind-forced variability within the ocean. However, these shifts in the latitudes of separated western boundary currents can cause substantial changes in SST, which may influence the synoptic atmospheric variability with little or no time delay. Various measures of wintertime atmospheric variability in the synoptic band (2–8 days) are examined using a relatively new dataset for air–sea exchange [Objectively Analyzed Air–Sea Fluxes (OAFlux)] and subsurface temperature indices of the Gulf Stream and Kuroshio path that are insulated from direct air–sea exchange, and therefore are preferable to SST. Significant changes are found in the atmospheric variability following changes in the paths of these currents, sometimes in a local fashion such as meridional shifts in measures of local storm tracks, and sometimes in nonlocal, broad regions coincident with and downstream of the oceanic forcing. Differences between the North Pacific (KE) and North Atlantic (GS) may be partly related to the more zonal orientation of the KE and the stronger SST signals of the GS, but could also be due to differences in mean storm-track characteristics over the North Pacific and North Atlantic.}, + language = {EN}, + number = {12}, + urldate = {2024-02-12}, + journal = {Journal of Climate}, + author = {Joyce, Terrence M. and Kwon, Young-Oh and Yu, Lisan}, + month = jun, + year = {2009}, + note = {Publisher: American Meteorological Society +Section: Journal of Climate}, + pages = {3177--3192}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\GTM3IWKM\\Joyce et al. - 2009 - On the Relationship between Synoptic Wintertime At.pdf:application/pdf}, +} + +@article{chi_distinction_2019, + title = {The {Distinction} {Between} the {Gulf} {Stream} and {Its} {North} {Wall}}, + volume = {46}, + copyright = {©2019. American Geophysical Union. All Rights Reserved.}, + issn = {1944-8007}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2019GL083775}, + doi = {10.1029/2019GL083775}, + abstract = {Downstream of Cape Hatteras, the Gulf Stream (GS) is bounded to the north by a sharp temperature front known as the North Wall (NW). Previous studies have generally assumed that variations of the NW and GS are equivalent. Using satellite sea surface height to identify the GS and the 15 °C isotherm at 200-m depth to represent the NW, this paper examines their similarities and differences during 1993–2016. The NW and GS are geographically close and vary similarly only to the west of 71°W. Downstream of that, they rapidly diverge—and the variances of their latitudes increase by more than a factor of 2—as the GS flows past the New England Seamounts. Evidence is presented to show that the difference in properties of the NW and the GS is related to the presence of mesoscale eddies in the region separating them.}, + language = {en}, + number = {15}, + urldate = {2024-02-12}, + journal = {Geophysical Research Letters}, + author = {Chi, Lequan and Wolfe, Christopher L. P. and Hameed, Sultan}, + year = {2019}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019GL083775}, + pages = {8943--8951}, + file = {Full Text PDF:C\:\\Users\\andrew.beet\\Zotero\\storage\\2JPUG2SB\\Chi et al. - 2019 - The Distinction Between the Gulf Stream and Its No.pdf:application/pdf;Snapshot:C\:\\Users\\andrew.beet\\Zotero\\storage\\3ZDAIUTZ\\2019GL083775.html:text/html}, +} + +@article{de_lafontaine_pelagic_1991, + title = {Pelagic food web interactions and productivity in the {Gulf} of {St}. {Lawrence}: a perspective.}, + volume = {113:}, + journal = {Can. Spec. Publ. Fish. Aquat. Sci}, + author = {de Lafontaine, Y and Demers, S and Runge, J. A.}, + year = {1991}, + pages = {99--124.}, +} diff --git a/bibliography/introduction.bib b/bibliography/introduction.bib index e07bb11d..f4dc0d72 100644 --- a/bibliography/introduction.bib +++ b/bibliography/introduction.bib @@ -146,3 +146,21 @@ @article{Hare2016 file = {} } +@article{gangopadhyay_observed_2019, + title = {An {Observed} {Regime} {Shift} in the {Formation} of {Warm} {Core} {Rings} from the {Gulf} {Stream}}, + volume = {9}, + copyright = {2019 The Author(s)}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-019-48661-9}, + doi = {10.1038/s41598-019-48661-9}, + abstract = {We present observational evidence that a significant regime change occurred around the year 2000 in the formation of Warm Core Rings (WCRs) from the Gulf Stream (GS) between 75° and 55°W. The dataset for this study is a set of synoptic oceanographic charts available over the thirty-eight-year period of 1980–2017. The upward regime change shows an increase to 33 WCRs per year during 2000–2017 from an average of 18 WCRs during 1980 to 1999. A seasonal analysis confirms May-June-July as the peak time for WCR births in agreement with earlier studies. The westernmost region (75°-70°W) is least ring-productive, while the region from 65°W to 60°W is most productive. This regime shift around 2000 is detected in WCR formation for all of the four 5-degree wide sub-regions and the whole region (75°-55°W). This might be related to a reduction of the deformation radius for ring formation, allowing unstable meanders to shed more frequent rings in recent years. A number of possible factors resulting in such a regime shift related to the possible changes in reduced gravity, instability, transport of the GS, large-scale changes in the wind system and atmospheric fluxes are outlined, which suggest new research directions. The increase in WCRs has likely had an impact on the marine ecosystem since 2000, a topic worthy for future studies.}, + language = {en}, + number = {1}, + urldate = {2020-02-10}, + journal = {Scientific Reports}, + author = {Gangopadhyay, Avijit and Gawarkiewicz, Glen and Silva, E. Nishchitha S. and Monim, M. and Clark, Jenifer}, + month = aug, + year = {2019}, + pages = {1--9}, + file = {Full Text PDF:/Users/sarah.gaichas/Zotero/storage/V9GUKHJ2/Gangopadhyay et al. - 2019 - An Observed Regime Shift in the Formation of Warm .pdf:application/pdf;Snapshot:/Users/sarah.gaichas/Zotero/storage/XKY6AXDH/s41598-019-48661-9.html:text/html}, +} diff --git a/chapters/HMS_species_distribution.rmd b/chapters/HMS_species_distribution.rmd index 6e623d4a..1ab391b8 100644 --- a/chapters/HMS_species_distribution.rmd +++ b/chapters/HMS_species_distribution.rmd @@ -16,7 +16,9 @@ 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 et al. (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, 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. + +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. @@ -25,7 +27,7 @@ For species that showed a clear displacement of the weighted centroid, the avera ```{r plot_HMS_species_distributionMAB} # Plot indicator ggplotObject <- ecodata::plot_HMS_species_distribution(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -45,7 +47,7 @@ Temporal scale: By each of the 4 seasons in 2010 and in 2017. ``` ## 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. +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 @@ -55,8 +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 @@ -82,5 +84,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/SAV.rmd b/chapters/SAV.rmd index e79522c4..f351c6cd 100644 --- a/chapters/SAV.rmd +++ b/chapters/SAV.rmd @@ -26,7 +26,7 @@ SAV increased in the Mesohaline and Polyhaline zones, where SAV continued to rec ```{r plot_SAVMAB} # Plot indicator ggplotObject <- ecodata::plot_SAV(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -88,5 +88,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/abc_acl.rmd b/chapters/abc_acl.rmd index 86266aaf..f73d7052 100644 --- a/chapters/abc_acl.rmd +++ b/chapters/abc_acl.rmd @@ -27,13 +27,13 @@ Catch divided by ABC/ACL for MAFMC managed fisheries. Red line indicates the med ```{r plot_abc_aclMidAtlanticStacked} # Plot indicator ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Stacked') -print(ggplotObject) +ggplotObject ``` ```{r plot_abc_aclMidAtlanticCatch} # Plot indicator ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Catch') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -41,13 +41,13 @@ print(ggplotObject) ```{r plot_abc_aclNewEnglandStacked} # Plot indicator ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Stacked') -print(ggplotObject) +ggplotObject ``` ```{r plot_abc_aclNewEnglandCatch} # Plot indicator ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Catch') -print(ggplotObject) +ggplotObject ``` @@ -108,5 +108,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/aggregate_biomass.rmd b/chapters/aggregate_biomass.rmd index de750590..b45b3565 100644 --- a/chapters/aggregate_biomass.rmd +++ b/chapters/aggregate_biomass.rmd @@ -26,7 +26,7 @@ Aggregate biomass levels have been relatively stable over time. ```{r plot_aggregate_biomassMAB} # Plot indicator ggplotObject <- ecodata::plot_aggregate_biomass(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` ### GB @@ -34,7 +34,7 @@ print(ggplotObject) ```{r plot_aggregate_biomassNEGB} # Plot indicator ggplotObject <- ecodata::plot_aggregate_biomass(report='NewEngland',EPU='GB') -print(ggplotObject) +ggplotObject ``` ### GOM @@ -42,7 +42,7 @@ print(ggplotObject) ```{r plot_aggregate_biomassNEGOM} # Plot indicator ggplotObject <- ecodata::plot_aggregate_biomass(report='NewEngland',EPU='GOM') -print(ggplotObject) +ggplotObject ``` @@ -109,5 +109,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/aquaculture.rmd b/chapters/aquaculture.rmd index adba877e..46d25d75 100644 --- a/chapters/aquaculture.rmd +++ b/chapters/aquaculture.rmd @@ -26,7 +26,7 @@ Overall oyster production is increasing and production per area is also improvin ```{r plot_aquacultureMAB} # Plot indicator ggplotObject <- ecodata::plot_aquaculture(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -83,5 +83,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/bennet.rmd b/chapters/bennet.rmd index 2f51ae9f..4c38224b 100644 --- a/chapters/bennet.rmd +++ b/chapters/bennet.rmd @@ -17,21 +17,9 @@ 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 and Färe 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 2010), and has been used to examine revenue changes in Swedish pharmacies, productivity change in U.S. railroads (Lim and Lovell 2009), and dividend changes in banking operations (Grifell-Tatjé and Lovell 2004). An attractive feature of the BI is that the overall indicator is equal to the sum of its subcomponents (Balk 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 times prices received from all species groups (or guilds) within the EPU. At time t, revenue (R) in an EPU is defined as: +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. -Rt=j=1Jpjtyjt -where pj is the price for species group j, and yj is the quantity landed of species group j. Revenue change between any two time periods, say t+1 and t, is then Rt+1-Rt which can also be expressed as: - -R=j=1Jpjt+1yjt+1-j=1Jpjtyjt. - -This change can be decomposed further, yielding a VI and PI. The VI is calculated using the following formula (Georgianna, Lee, and Walden 2017): - -VI=12(j=1Jpjt+1yjt+1-j=1Jpjt+1yjt+j=1Jpjtyjt+1-j=1Jpjtyjt) - -The price indicator (PI) is calculated as follows: - -PI=12(j=1Jyjt+1pjt+1-j=1Jyjt+1pjt+j=1Jyjtpjt+1-j=1Jyjtpjt) +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. @@ -45,19 +33,19 @@ In the Mid-Atlantic region revenue was positive compared to 1982 levels for all ```{r plot_bennetMidAtlanticguildMAB} # Plot indicator ggplotObject <- ecodata::plot_bennet(report= 'MidAtlantic', varName= 'guild' ,EPU= 'MAB') -print(ggplotObject) +ggplotObject ``` ```{r plot_bennetMidAtlantictotalMAB} # Plot indicator ggplotObject <- ecodata::plot_bennet(report= 'MidAtlantic', varName= 'total' ,EPU= 'MAB') -print(ggplotObject) +ggplotObject ``` ```{r plot_bennetMidAtlantictotal_guildMAB} # Plot indicator ggplotObject <- ecodata::plot_bennet(report= 'MidAtlantic', varName= 'total_guild' ,EPU= 'MAB') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -65,37 +53,37 @@ print(ggplotObject) ```{r plot_bennetNewEnglandguildGB} # Plot indicator ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'guild' ,EPU= 'GB') -print(ggplotObject) +ggplotObject ``` ```{r plot_bennetNewEnglandguildGOM} # Plot indicator ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'guild' ,EPU= 'GOM') -print(ggplotObject) +ggplotObject ``` ```{r plot_bennetNewEnglandtotalGB} # Plot indicator ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total' ,EPU= 'GB') -print(ggplotObject) +ggplotObject ``` ```{r plot_bennetNewEnglandtotalGOM} # Plot indicator ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total' ,EPU= 'GOM') -print(ggplotObject) +ggplotObject ``` ```{r plot_bennetNewEnglandtotal_guildGB} # Plot indicator ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total_guild' ,EPU= 'GB') -print(ggplotObject) +ggplotObject ``` ```{r plot_bennetNewEnglandtotal_guildGOM} # Plot indicator ggplotObject <- ecodata::plot_bennet(report= 'NewEngland', varName= 'total_guild' ,EPU= 'GOM') -print(ggplotObject) +ggplotObject ``` @@ -157,5 +145,3 @@ Please email john.walden@noaa.gov for further information and queries of Bennet **tech-doc link** -## References - diff --git a/chapters/bottom_temp.rmd b/chapters/bottom_temp.rmd index 79e67faa..b40fe514 100644 --- a/chapters/bottom_temp.rmd +++ b/chapters/bottom_temp.rmd @@ -26,7 +26,7 @@ _No response_ ```{r plot_bottom_tempMAB} # Plot indicator ggplotObject <- ecodata::plot_bottom_temp(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` ### NE @@ -34,7 +34,7 @@ print(ggplotObject) ```{r plot_bottom_tempNE} # Plot indicator ggplotObject <- ecodata::plot_bottom_temp(report='NewEngland') -print(ggplotObject) +ggplotObject ``` @@ -89,5 +89,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/bottom_temp_comp.rmd b/chapters/bottom_temp_comp.rmd index c886e1af..0aef2074 100644 --- a/chapters/bottom_temp_comp.rmd +++ b/chapters/bottom_temp_comp.rmd @@ -30,13 +30,13 @@ Time series plots for seasonal bottom temperature anomaly for each EPU shows a l ```{r plot_bottom_temp_compMidAtlanticseasonalMAB} # Plot indicator ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'MidAtlantic', varName= 'seasonal' ,EPU= 'MAB') -print(ggplotObject) +ggplotObject ``` ```{r plot_bottom_temp_compMidAtlanticannualMAB} # Plot indicator ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'MidAtlantic', varName= 'annual' ,EPU= 'MAB') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -44,25 +44,25 @@ print(ggplotObject) ```{r plot_bottom_temp_compNewEnglandseasonalGB} # Plot indicator ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GB') -print(ggplotObject) +ggplotObject ``` ```{r plot_bottom_temp_compNewEnglandseasonalGOM} # Plot indicator ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GOM') -print(ggplotObject) +ggplotObject ``` ```{r plot_bottom_temp_compNewEnglandannualGB} # Plot indicator ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'annual' ,EPU= 'GB') -print(ggplotObject) +ggplotObject ``` ```{r plot_bottom_temp_compNewEnglandannualGOM} # Plot indicator ggplotObject <- ecodata::plot_bottom_temp_comp(report= 'NewEngland', varName= 'annual' ,EPU= 'GOM') -print(ggplotObject) +ggplotObject ``` @@ -93,7 +93,7 @@ Bottom temperature is an important driver for benthic and demersal species growt 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 from du Pontavice et al, 2023), GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature), PSY (CMEM’s PSY global forecast bottom temperature) +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 @@ -122,5 +122,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/bottom_temp_seasonal_gridded.rmd b/chapters/bottom_temp_seasonal_gridded.rmd index aea392d7..b699df01 100644 --- a/chapters/bottom_temp_seasonal_gridded.rmd +++ b/chapters/bottom_temp_seasonal_gridded.rmd @@ -26,7 +26,7 @@ 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') -print(ggplotObject) +ggplotObject ``` @@ -55,8 +55,7 @@ Bottom temperature is a key environmental parameter in defining the habitat and **Variable definitions** -- Time: year - Lat: latitude - Lon: longitude - Variable: season -- Value: bottom temperature (degrees Celcius) +- Time: year - Lat: latitude - Lon: longitude - Variable: season - Value: bottom temperature (degrees Celcius) ```{r vars_bottom_temp_seasonal_gridded} # Pull all var names @@ -84,5 +83,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/calanus_variation.rmd b/chapters/calanus_variation.rmd index ec4684da..eecf65c7 100644 --- a/chapters/calanus_variation.rmd +++ b/chapters/calanus_variation.rmd @@ -16,6 +16,13 @@ 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 @@ -43,7 +50,7 @@ Temporal scale: NOAA EcoMon/MARMAP data between 1977 and 2019, collected during ``` ## 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 et al. 2019; Meyer-Gutbrod et al. 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 et al. 2021; Pershing and Kemberling 2023). +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: @@ -51,44 +58,20 @@ A NERACOOS-ISMN Marine Biodiversity Observation Network report: Status of the zo 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 et al. 2023; Johnson et al. NW Atlantic Zooplankton Atlas, in prep.) +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 et al. 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 et al. 2023). 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 et al. 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 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 et al. 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 et al. 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 et al.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 et al. 2005; Grieve et al. 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. - -References -Casault, B., C. Johnson, E. Devred, E. Head, and L. Beazley. 2023. Optical, Chemical, and -Biological Oceanographic Conditions on the Scotian Shelf and in the eastern Gulf of Maine -during 2021. DFO Can. Sci. Advis. Sec. Res. Doc. 2023/016. v + 74 p. - -Dullaert, E. et al. The response of the zooplankton community in the western Gulf of Maine to a shift in oceanographic conditions: 2005-2017. Manuscript in preparation. - -Grieve, B.D., Hare, J.A. and Saba, V.S., 2017. Projecting the effects of climate change on Calanus finmarchicus distribution within the US Northeast Continental Shelf. Scientific Reports, 7(1): 1-12. - -Ji, R., J. A. Runge, C.S. Davis and P. Wiebe. 2022. Drivers of variability of Calanus finmarchicus in the Gulf of Maine: roles of internal production and external exchange. ICES Journal of Marine Science. 79 (3): 775–784. https://doi.org/10.1093/icesjms/fsab147. - -Meyer-Gutbrod EL, Greene CH, Davies KTA, Johns DG. 2021. Ocean regime shift is driving collapse of the North Atlantic right whale population. Oceanography 34(3):22–31. - -Pershing, A.J., Greene, C.H., Jossi, J.W., O'Brien, L., Brodziak, J.K. and Bailey, B.A., 2005. Interdecadal variability in the Gulf of Maine zooplankton community, with potential impacts on fish recruitment. ICES Journal of Marine Science, 62(7): 1511-1523. - -Record, N.R., J.A. Runge, D.E. Pendleton, W.A. Balch, K.T.A. Davies, A.J. Pershing, C. Johnson, K. Stamieszkin, R. Ji, Z. Feng, S.D. Kraus, R.D. Kenney, C. Hudak, C.A. Mayo, C. Chen, J. Salisbury and C.R.S. -Thompson. 2019. Climate-driven circulation changes threaten conservation of endangered North Atlantic right whales. Oceanography. 32(2): 162-169. doi.org/10.5670/ oceanog.2019.201. - -Runge J, Karp Boss L, Dullaert E, Ji, R, Motyka J, Young-Morse R, Pugh, D, Shellito S, Vandemark D. 2023. Sustained monitoring of zooplankton populations at the Coastal Maine Time Series (CMTS) and Wilkinson Basin Time Series (WBTS) stations in the western Gulf of Maine: Results from 2005-2022. Sterling (VA): U.S. Department of the Interior, Bureau of Ocean Energy Management. 40 p. Report No.: OCS Study BOEM 2023-015. Contract No.: CA M19AC00022. - -Runge, J., E. Dullaert, S. Shellito, L. Karp Boss, I. Honda, R. Ji, J. Motyka, D. Pugh, C. Thompson, R. Young-Morse and D. Vandemark. Fixed time series station observations of zooplankton responses to changing oceanographic conditions in the western Gulf of Maine: the Calanus Index. Manuscript in preparation. - -Suca J., D. Wiley, T. Silva, A. Robuck, D. Richardson, S.Glancy, E. Clancey, T.Giandonato, A. Solow, M. Thompson, P. Hong, H. Baumann, L. Kaufman and J. Llopiz. 2021. Sensitivity of sand lance to shifting prey and hydrography indicates forthcoming change to the northeast US shelf forage fish complex. ICES Jour. Mar. Sci. 78(3): 1023-1037. +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} +**Point of contact**: [Jeffrey Runge (jeffrey.runge@maine.edu)](mailto:Jeffrey Runge (jeffrey.runge@maine.edu)){.email} **ecodata name**: No dataset @@ -114,8 +97,3 @@ Source data are publicly available. _No response_ -**tech-doc link** - - -## References - diff --git a/chapters/ch_bay_sal.rmd b/chapters/ch_bay_sal.rmd index 72c184cd..4ddca77d 100644 --- a/chapters/ch_bay_sal.rmd +++ b/chapters/ch_bay_sal.rmd @@ -26,7 +26,7 @@ The key results are the state of the water temperature and the salinity ```{r plot_ch_bay_salMAB} # Plot indicator ggplotObject <- ecodata::plot_ch_bay_sal(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -81,5 +81,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/ch_bay_temp.rmd b/chapters/ch_bay_temp.rmd index e81c9154..a0cd3867 100644 --- a/chapters/ch_bay_temp.rmd +++ b/chapters/ch_bay_temp.rmd @@ -26,7 +26,7 @@ The key results are the state of the water temperature and the salinity ```{r plot_ch_bay_tempMAB} # Plot indicator ggplotObject <- ecodata::plot_ch_bay_temp(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -81,5 +81,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/ches_bay_sst.rmd b/chapters/ches_bay_sst.rmd index 5f0c84b0..1acc9459 100644 --- a/chapters/ches_bay_sst.rmd +++ b/chapters/ches_bay_sst.rmd @@ -24,7 +24,7 @@ For 2023, winter SST’s show conditions to be roughly 1 degree Celsius warmer t ```{r plot_ches_bay_sstMAB} # Plot indicator ggplotObject <- ecodata::plot_ches_bay_sst(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -61,8 +61,7 @@ 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} @@ -90,5 +89,3 @@ For seasonal SST anomaly data files (including the SST long-term climatology), a **tech-doc link** -## References - diff --git a/chapters/ches_bay_synthesis.rmd b/chapters/ches_bay_synthesis.rmd index be4b8a9c..6013173e 100644 --- a/chapters/ches_bay_synthesis.rmd +++ b/chapters/ches_bay_synthesis.rmd @@ -72,8 +72,3 @@ Source data are publicly available. _No response_ -**tech-doc link** - - -## References - diff --git a/chapters/ches_bay_wq.rmd b/chapters/ches_bay_wq.rmd index cd5770a7..44c9ea36 100644 --- a/chapters/ches_bay_wq.rmd +++ b/chapters/ches_bay_wq.rmd @@ -16,31 +16,23 @@ 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) (U.S. Environmental Protection Agency, 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 et al., 2020; U.S. Environmental Protection Agency, 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. +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 et al. (2018). +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 (U.S. Environmental Protection Agency, 2017). For more details, refer to the Methods section of Zhang et al. (2018). - -Hernandez-Cordero AL, Tango PJ, Batiuk RA. Development of a multimetric water quality Indicator for tracking progress towards the achievement of Chesapeake Bay water quality standards. Environmental Monitoring and Assessment 2020; 192. - -U.S. Environmental Protection Agency. Ambient water quality criteria for dissolved oxygen, water clarity and chlorophyll-a for the Chesapeake Bay and its tidal tributaries. USEPA Region III Chesapeake Bay Program Office, Annapolis, Maryland, 2003. - -U.S. Environmental Protection Agency. Ambient water quality criteria for dissolved oxygen, water clarity and chlorophyll-a for the Chesapeake Bay and its tidal tributaries: 2017 addendum. USEPA Region III Chesapeake Bay Program Office, Annapolis, Maryland, 2017. - -Zhang Q, Murphy RR, Tian R, Forsyth MK, Trentacoste EM, Keisman J, et al. Chesapeake Bay's water quality condition has been recovering: Insights from a multimetric indicator assessment of thirty years of tidal monitoring data. Science of the Total Environment 2018; 637-638: 1617-1625. +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 et al., 2018). +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') -print(ggplotObject) +ggplotObject ``` @@ -59,7 +51,7 @@ Temporal scale: 3-year assessment period between 1985-1987 and 2019-2021. ``` ## Implications -Patterns of attainment of individual designated uses are variable (Figure 2). According to Mann-Kendall trend analysis extended from Zhang et al. (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. +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 @@ -69,8 +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 @@ -102,5 +94,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/chl_pp.rmd b/chapters/chl_pp.rmd index ed6916db..a6b98fd5 100644 --- a/chapters/chl_pp.rmd +++ b/chapters/chl_pp.rmd @@ -1,4 +1,4 @@ -# Phytoplankton chlorophyll, size class and primary production +# Chlorophyll and Primary Production **Description**: Satellite derived phytoplankton data including chlorophyll concentration, phytoplankton size class, and primary production for the Northeast Continental Shelf and ecological production units. @@ -30,37 +30,37 @@ Phytoplankton are the foundation of the marine food web and are the primary food ```{r plot_chl_ppMidAtlanticchlweekly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'chl' ,plottype= 'weekly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppMidAtlanticchlmonthly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'chl' ,plottype= 'monthly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppMidAtlanticchlanomaly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'chl' ,plottype= 'anomaly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppMidAtlanticppweekly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'pp' ,plottype= 'weekly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppMidAtlanticppmonthly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'pp' ,plottype= 'monthly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppMidAtlanticppanomaly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'MidAtlantic', varName= 'pp' ,plottype= 'anomaly') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -68,37 +68,37 @@ print(ggplotObject) ```{r plot_chl_ppNewEnglandchlweekly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'chl' ,plottype= 'weekly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppNewEnglandchlmonthly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'chl' ,plottype= 'monthly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppNewEnglandchlanomaly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'chl' ,plottype= 'anomaly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppNewEnglandppweekly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'pp' ,plottype= 'weekly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppNewEnglandppmonthly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'pp' ,plottype= 'monthly') -print(ggplotObject) +ggplotObject ``` ```{r plot_chl_ppNewEnglandppanomaly} # Plot indicator ggplotObject <- ecodata::plot_chl_pp(report= 'NewEngland', varName= 'pp' ,plottype= 'anomaly') -print(ggplotObject) +ggplotObject ``` @@ -168,5 +168,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/cold_pool.rmd b/chapters/cold_pool.rmd index 6c0e515a..1e85d77c 100644 --- a/chapters/cold_pool.rmd +++ b/chapters/cold_pool.rmd @@ -16,7 +16,9 @@ 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. +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. @@ -26,19 +28,19 @@ Time series plots of the three cold pool indices. Cold pool index shows the mean ```{r plot_cold_poolMidAtlanticcold_pool} # Plot indicator ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'cold_pool') -print(ggplotObject) +ggplotObject ``` ```{r plot_cold_poolMidAtlanticpersistence} # Plot indicator ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'persistence') -print(ggplotObject) +ggplotObject ``` ```{r plot_cold_poolMidAtlanticextent} # Plot indicator ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'extent') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -46,19 +48,19 @@ print(ggplotObject) ```{r plot_cold_poolNewEnglandcold_pool} # Plot indicator ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'cold_pool') -print(ggplotObject) +ggplotObject ``` ```{r plot_cold_poolNewEnglandpersistence} # Plot indicator ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'persistence') -print(ggplotObject) +ggplotObject ``` ```{r plot_cold_poolNewEnglandextent} # Plot indicator ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'extent') -print(ggplotObject) +ggplotObject ``` @@ -77,7 +79,9 @@ Temporal scale: annual ``` ## 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 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 @@ -87,12 +91,10 @@ Changes in cold pool indicators can be signs of changes in regional/seasonal oce **Variable definitions** -1) Source: ROMS (bias-corrected ROMS-NWA bottom temperature from du Pontavice et al, 2023), GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature), PSY (CMEM’s PSY global forecast bottom temperature) +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 +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} @@ -120,5 +122,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/comdat.rmd b/chapters/comdat.rmd index ba280a7d..bb00988e 100644 --- a/chapters/comdat.rmd +++ b/chapters/comdat.rmd @@ -29,25 +29,25 @@ Commercial revenue by managed species has generally been down. The exception is ```{r plot_comdatMidAtlanticlandingstotal} # Plot indicator ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'total') -print(ggplotObject) +ggplotObject ``` ```{r plot_comdatMidAtlanticlandingsguild} # Plot indicator ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'guild') -print(ggplotObject) +ggplotObject ``` ```{r plot_comdatMidAtlanticrevenuetotal} # Plot indicator ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'total') -print(ggplotObject) +ggplotObject ``` ```{r plot_comdatMidAtlanticrevenueguild} # Plot indicator ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'guild') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -55,25 +55,25 @@ print(ggplotObject) ```{r plot_comdatNewEnglandlandingstotal} # Plot indicator ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'total') -print(ggplotObject) +ggplotObject ``` ```{r plot_comdatNewEnglandlandingsguild} # Plot indicator ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'guild') -print(ggplotObject) +ggplotObject ``` ```{r plot_comdatNewEnglandrevenuetotal} # Plot indicator ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'total') -print(ggplotObject) +ggplotObject ``` ```{r plot_comdatNewEnglandrevenueguild} # Plot indicator ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'guild') -print(ggplotObject) +ggplotObject ``` @@ -104,8 +104,7 @@ Revenue is a key indicator of the State of the Ecosystem report. Revenue is larg **Variable definitions** -*Landings* -1) Name: Landings; Definition: Total landings for a region; Units: metric tons. +*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 @@ -141,5 +140,3 @@ Please email Andrew.Beet@NOAA.gov for further questions. Access to data will re **tech-doc link** -## References - diff --git a/chapters/commercial_div.rmd b/chapters/commercial_div.rmd index f05965c1..77cf2384 100644 --- a/chapters/commercial_div.rmd +++ b/chapters/commercial_div.rmd @@ -22,27 +22,26 @@ Diversity estimates have been developed to understand whether specialization, or ## 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). +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') -print(ggplotObject) +ggplotObject ``` ```{r plot_commercial_divMidAtlanticFleetdiversityinrevenue} # Plot indicator ggplotObject <- ecodata::plot_commercial_div(report= 'MidAtlantic', varName= 'Fleet diversity in revenue') -print(ggplotObject) +ggplotObject ``` ```{r plot_commercial_divMidAtlanticPermitrevenuespeciesdiversity} # Plot indicator ggplotObject <- ecodata::plot_commercial_div(report= 'MidAtlantic', varName= 'Permit revenue species diversity') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -50,19 +49,19 @@ print(ggplotObject) ```{r plot_commercial_divNewEnglandFleetcount} # Plot indicator ggplotObject <- ecodata::plot_commercial_div(report= 'NewEngland', varName= 'Fleet count') -print(ggplotObject) +ggplotObject ``` ```{r plot_commercial_divNewEnglandFleetdiversityinrevenue} # Plot indicator ggplotObject <- ecodata::plot_commercial_div(report= 'NewEngland', varName= 'Fleet diversity in revenue') -print(ggplotObject) +ggplotObject ``` ```{r plot_commercial_divNewEnglandPermitrevenuespeciesdiversity} # Plot indicator ggplotObject <- ecodata::plot_commercial_div(report= 'NewEngland', varName= 'Permit revenue species diversity') -print(ggplotObject) +ggplotObject ``` @@ -83,7 +82,7 @@ Temporal scale: Annual ``` ## 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 [1]. 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. +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. @@ -123,5 +122,3 @@ Request data from GARFO **tech-doc link** -## References - diff --git a/chapters/condition.rmd b/chapters/condition.rmd index 71781111..9edd292f 100644 --- a/chapters/condition.rmd +++ b/chapters/condition.rmd @@ -16,28 +16,31 @@ 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 1951). Heavier and fatter fish at a given length have higher relative condition which is expected to improve growth, reproductive output, and survival. +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 (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. (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. +Le Cren’s [@le_cren_length-weight_1951] relative condition (Kn) is used in this study: -Le Cren ED. The Length-Weight Relationship and Seasonal Cycle in Gonad Weight and Condition in the Perch (Perca fuviatilis). Journal of Animal Ecology. 1951;20: 201–219. doi:10.2307/1540 +Kn = W/W’ -Wigley, S.E., H.M. McBride, N.J. McHugh. 2003. Length-Weight Relationships for 74 Fish Species Collected during NEFSC Research Vessel Bottom Trawl Surveys, 1992-99. NOAA Tech. Memo. NMFS-NE-171. +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 -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. +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 -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 2022, but a number of species improved in condition from the relatively low condition year in 2021 (Fig. 43). Preliminary results of synthetic analyses show that changes in temperature, zooplankton, fishing pressure, and population size influence the condition of different fish species. +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') -print(ggplotObject) +ggplotObject ``` ### GB @@ -45,7 +48,7 @@ print(ggplotObject) ```{r plot_conditionNEGB} # Plot indicator ggplotObject <- ecodata::plot_condition(report='NewEngland',EPU='GB') -print(ggplotObject) +ggplotObject ``` ### GOM @@ -53,7 +56,7 @@ print(ggplotObject) ```{r plot_conditionNEGOM} # Plot indicator ggplotObject <- ecodata::plot_condition(report='NewEngland',EPU='GOM') -print(ggplotObject) +ggplotObject ``` @@ -83,8 +86,7 @@ These changes in condition have direct implications for stock assessments, catch **Variable definitions** -Species: common name for fish species EPU: Ecological Production Unit -YEAR: year of condition data +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} @@ -111,5 +113,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/energy_density.rmd b/chapters/energy_density.rmd index c83a62e0..5f96d5e2 100644 --- a/chapters/energy_density.rmd +++ b/chapters/energy_density.rmd @@ -1,6 +1,6 @@ # Forage Fish Energy Density -**Description**: 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 int he lab to estimate energy content. **Indicator family**: @@ -16,15 +16,17 @@ 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. +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 density of Atlantic Herring has been well below the available estimates from the 1980s and 1990s, but is showing some signs of improving. +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') -print(ggplotObject) +ggplotObject ``` @@ -44,7 +46,7 @@ Temporal scale: Spring and Fall Bottom Trawl Survey ``` ## Implications -_No response_ +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 @@ -80,5 +82,3 @@ Email mark.wuenschel@noaa.gov for further information. Data tables are beign cre **tech-doc link** -## References - diff --git a/chapters/engagement.rmd b/chapters/engagement.rmd index e6f2c7e0..fcde7642 100644 --- a/chapters/engagement.rmd +++ b/chapters/engagement.rmd @@ -18,23 +18,95 @@ 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. +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 -Results narratives are currently under review. +#### Mid Atlantic + +##### 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. + +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. + +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') -print(ggplotObject) +ggplotObject ``` ```{r plot_engagementMidAtlanticRecreational} # Plot indicator ggplotObject <- ecodata::plot_engagement(report= 'MidAtlantic', varName= 'Recreational') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -42,13 +114,13 @@ print(ggplotObject) ```{r plot_engagementNewEnglandCommercial} # Plot indicator ggplotObject <- ecodata::plot_engagement(report= 'NewEngland', varName= 'Commercial') -print(ggplotObject) +ggplotObject ``` ```{r plot_engagementNewEnglandRecreational} # Plot indicator ggplotObject <- ecodata::plot_engagement(report= 'NewEngland', varName= 'Recreational') -print(ggplotObject) +ggplotObject ``` @@ -67,7 +139,9 @@ Temporal scale: Year of 2021 ``` ## Implications -These indicators are used to identify top fishing communities and those with environmental justice concerns based on 2021data. Highlighted communities may be vulnerable to changes in fishing patterns due to regulations and/or climate change. When any of these communities also experience environmental justice issues, they may have lower ability to successfully respond/adapt to change. +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 @@ -77,16 +151,14 @@ These indicators are used to identify top fishing communities and those with env **Variable definitions** -1) Name: Community Name: name of the community. -2) Commercial Engagement Index: commercial engagement factor score. +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. +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**: @@ -99,10 +171,8 @@ Source data are NOT publicly available. ## Accessibility and Constraints -Please email lisa.l.colburnl@noaa.gov for further information and queries of fishing and environmental justice indicator source data. +Please email lisa.l.colburn@noaa.gov for further information and queries of fishing and environmental justice indicator source data. **tech-doc link** -## References - diff --git a/chapters/exp_n.rmd b/chapters/exp_n.rmd index eafaf577..133be309 100644 --- a/chapters/exp_n.rmd +++ b/chapters/exp_n.rmd @@ -27,13 +27,13 @@ Due to the shift to the NOAA vessel Henry B. Bigelow in 2009 and the inability t ```{r plot_exp_nMidAtlanticfall} # Plot indicator ggplotObject <- ecodata::plot_exp_n(report= 'MidAtlantic', varName= 'fall') -print(ggplotObject) +ggplotObject ``` ```{r plot_exp_nMidAtlanticspring} # Plot indicator ggplotObject <- ecodata::plot_exp_n(report= 'MidAtlantic', varName= 'spring') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -41,13 +41,13 @@ print(ggplotObject) ```{r plot_exp_nNewEnglandfall} # Plot indicator ggplotObject <- ecodata::plot_exp_n(report= 'NewEngland', varName= 'fall') -print(ggplotObject) +ggplotObject ``` ```{r plot_exp_nNewEnglandspring} # Plot indicator ggplotObject <- ecodata::plot_exp_n(report= 'NewEngland', varName= 'spring') -print(ggplotObject) +ggplotObject ``` @@ -68,7 +68,7 @@ Temporal scale: Spring (March-May) and Fall (September-November) ``` ## 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. 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. +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 @@ -105,5 +105,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/forage_index.rmd b/chapters/forage_index.rmd index 3d7d0ec1..6cb9c44f 100644 --- a/chapters/forage_index.rmd +++ b/chapters/forage_index.rmd @@ -16,23 +16,25 @@ 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. +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 trends reported previously for 1985-2021. +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') -print(ggplotObject) +ggplotObject ``` ```{r plot_forage_indexMidAtlanticcog} # Plot indicator ggplotObject <- ecodata::plot_forage_index(report= 'MidAtlantic', varName= 'cog') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -40,13 +42,13 @@ print(ggplotObject) ```{r plot_forage_indexNewEnglandindex} # Plot indicator ggplotObject <- ecodata::plot_forage_index(report= 'NewEngland', varName= 'index') -print(ggplotObject) +ggplotObject ``` ```{r plot_forage_indexNewEnglandcog} # Plot indicator ggplotObject <- ecodata::plot_forage_index(report= 'NewEngland', varName= 'cog') -print(ggplotObject) +ggplotObject ``` @@ -65,7 +67,11 @@ Temporal scale: Spring (January-June), Fall (July-December) ``` ## Implications -While the resulting indices show no long term trends in the Mid-Atlantic, they do show overall higher forage fish in fall relative to spring (Fig. \@ref(fig:MAforagebio)), with highest forage biomass during fall in the mid-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 decreasing trend in the fall for GB and an increasing trend in the spring on GOM. Changes in the distribution of forage biomass also affects predator distribution. Spatial subsets of this index were included in the bluefish research track stock assessment to investigate forage-driven changes in bluefish availability to recreational fisheries and surveys. +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 @@ -78,7 +84,12 @@ While the resulting indices show no long term trends in the Mid-Atlantic, they d 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 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 @@ -104,5 +115,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/glossary.Rmd b/chapters/glossary.Rmd index ad2c7ce5..7cd16748 100644 --- a/chapters/glossary.Rmd +++ b/chapters/glossary.Rmd @@ -8,13 +8,13 @@ Predators with no natural predators of their own, such as large sharks, toothed Predator feeding on bottom-dwelling prey, such as lobster and haddock. **Benthos:** -Organisms that live on or in the sea bottom (@madden2004), such as scallop and quahog. +Organisms that live on or in the sea bottom (@madden_framework_2004), such as scallop and quahog. **Bmsy:** -The weight (biomass) of a group of fish necessary to produce maximum sustainable yield (MSY) (@nwfsc). +The weight (biomass) of a group of fish necessary to produce maximum sustainable yield (MSY) (@noauthor_northwest_nodate). **Catch:** -The total number (or weight) of fish caught by fishing operations. The component of fish that comes into contact with fishing gear, which is retained by the gear (@unfao). +The total number (or weight) of fish caught by fishing operations. The component of fish that comes into contact with fishing gear, which is retained by the gear (@noauthor_united_nodate). **Climate Vulnerability:** The degree to which the habitat/species are unable to cope with negative impacts of climate change. @@ -32,32 +32,32 @@ Large-scale industry selling fish, shellfish and other aquatic animals. A mathematical measure of how engaged a community is in commercial fisheries. This index includes the amount of landings, dealers and permits. **Conceptual Model:** -A representation of the most current understanding of the major system features and processes of a particular environment (@madden2004). +A representation of the most current understanding of the major system features and processes of a particular environment (@madden_framework_2004). **Condition:** -A mathematical measurement of the “plumpness,” or the general health of a fish or group of fishes (@wallace1994). +A mathematical measurement of the “plumpness,” or the general health of a fish or group of fishes (@wallace_fisheries_1994). **Continental Shelf:** -Underwater portion (shelf) of the continent, extending seaward from the shore to the edge of the continental slope where the depth increases rapidly (@unfao). +Underwater portion (shelf) of the continent, extending seaward from the shore to the edge of the continental slope where the depth increases rapidly (@noauthor_united_nodate). **Continental Slope:** -Part of the continental margin; the ocean floor from the continental shelf to the continental rise (@madden2004). +Part of the continental margin; the ocean floor from the continental shelf to the continental rise (@madden_framework_2004). **Ecological Production Unit (EPU):** A specific geographic region of similar physical features and plankton characteristics supporting an ecological community within a large marine ecosystem (LME). **Ecosystem Assessment:** -A social process through which the findings of science concerning the causes of ecosystem change, their consequences for human well-being, and management and policy options are presented to decision makers (@unfao). +A social process through which the findings of science concerning the causes of ecosystem change, their consequences for human well-being, and management and policy options are presented to decision makers (@noauthor_united_nodate). **Effort:** -The amount of time and fishing power used to harvest fish; includes gear size, boat size, and horsepower (@wallace1994). +The amount of time and fishing power used to harvest fish; includes gear size, boat size, and horsepower (@wallace_fisheries_1994). **Elasmobranch:** -Describes a group of fish without a hard bony skeleton, including sharks, skates, and rays (@unfao). +Describes a group of fish without a hard bony skeleton, including sharks, skates, and rays (@noauthor_united_nodate). **Endangered Species:** -A species as defined in the US Endangered Species Act, that is in danger of extinction through a significant portion of its range (@noaaglos). +A species as defined in the US Endangered Species Act, that is in danger of extinction through a significant portion of its range (@united_states_noaa_2005). **Energy Density:** @@ -70,35 +70,35 @@ Coastal body of brackish water which may be an important nursery habitat for man Conditions found in an estuary: shallow water, high variability in water temperature, salt content, nutrients, and oxygen level. **Eutrophication:** -The enrichment of water by nutrients causing increased growth of algae and higher forms of plant life creating an imbalance of organisms present in the water and to the quality of the water they live in (@ospar2003). +The enrichment of water by nutrients causing increased growth of algae and higher forms of plant life creating an imbalance of organisms present in the water and to the quality of the water they live in (@fath_eutrophication_2019). **Exclusive Economic Zone:** -The EEZ is the area that extends from the seaward boundaries of the coastal states 3 to 200 nautical miles off the U.S. coast. Within this area, the United States claims exclusive fishery management authority over all fishery resources (@nmfs2004). +The EEZ is the area that extends from the seaward boundaries of the coastal states 3 to 200 nautical miles off the U.S. coast. Within this area, the United States claims exclusive fishery management authority over all fishery resources (@service_nmfs_2004). **Feeding Guild:** A group of species consuming similar prey species; for example, planktivores are different species that all eat plankton. **Fishery:** -The combination of fish and fishers in a region, the latter fishing for similar or the same species with similar or the same gear types (@madden2004). +The combination of fish and fishers in a region, the latter fishing for similar or the same species with similar or the same gear types (@madden_framework_2004). **Fishery-Dependent Data:** -Data collected directly on a fish or fishery from commercial or sport fishermen and seafood dealers. Common methods include logbooks, trip tickets, port sampling, fishery observers, and phone surveys (@wallace1994). +Data collected directly on a fish or fishery from commercial or sport fishermen and seafood dealers. Common methods include logbooks, trip tickets, port sampling, fishery observers, and phone surveys (@wallace_fisheries_1994). **Fishery-Independent Data:** -Stock/habitat/environmental data collected independently of the activity of the fishing sector usually on a research vessel (@unfao). +Stock/habitat/environmental data collected independently of the activity of the fishing sector usually on a research vessel (@noauthor_united_nodate). **Fmsy:** -The rate of removal of fish from a population by fishing that, if applied constantly, would result in maximum sustainable yield (MSY) (@unfao). +The rate of removal of fish from a population by fishing that, if applied constantly, would result in maximum sustainable yield (MSY) (@noauthor_united_nodate). **Forage Species:** -Species used as prey by a larger predator for its food. Includes small schooling fishes such as anchovies, sardines, herrings, capelin, smelts, and menhaden (@unfao). +Species used as prey by a larger predator for its food. Includes small schooling fishes such as anchovies, sardines, herrings, capelin, smelts, and menhaden (@noauthor_united_nodate). **GB:** -George’s Bank Ecological Production Unit (@techdoc). +George’s Bank Ecological Production Unit (@noauthor_technical_nodate). **GOM:** -Gulf of Maine Ecological Production Unit (@techdoc). +Gulf of Maine Ecological Production Unit (@noauthor_technical_nodate). **Groundfish:** Group of commercially harvested ocean bottom-oriented fish in cooler regions of the Northern Hemisphere including cods, flounders, and other associated species. The exact species list varies regionally. @@ -108,35 +108,35 @@ Group of commercially harvested ocean bottom-oriented fish in cooler regions of A warm ocean current flowing northward along the eastern United States. **Habitat:** -1. The environment in which the fish live, including everything that surrounds and affects its life, e.g. water quality, bottom, vegetation, associated species (including food supplies); 2. The site and particular type of local environment occupied by an organism (@unfao). +1. The environment in which the fish live, including everything that surrounds and affects its life, e.g. water quality, bottom, vegetation, associated species (including food supplies); 2. The site and particular type of local environment occupied by an organism (@noauthor_united_nodate). **Harvest:** -The total number or weight of fish caught and kept from an area over a period of time (@wallace1994). +The total number or weight of fish caught and kept from an area over a period of time (@wallace_fisheries_1994). **Highly Migratory Species:** -Marine species whose life cycle includes lengthy migrations, usually through the exclusive economic zones of two or more countries as well as into international waters. This term usually is used to denote tuna and tuna-like species, sharks, swordfish, and billfish (@unfao). +Marine species whose life cycle includes lengthy migrations, usually through the exclusive economic zones of two or more countries as well as into international waters. This term usually is used to denote tuna and tuna-like species, sharks, swordfish, and billfish (@noauthor_united_nodate). **Ichthyoplankton:** -Fish eggs and larvae belonging to the planktonic community (@unfao). +Fish eggs and larvae belonging to the planktonic community (@noauthor_united_nodate). **Indicator:** -1. A variable, pointer, or index. Its fluctuation reveals the variations in key elements of a system. The position and trend of the indicator in relation to reference points or values indicate the present state and dynamics of the system. Indicators provide a bridge between objectives and action (@unfao). +1. A variable, pointer, or index. Its fluctuation reveals the variations in key elements of a system. The position and trend of the indicator in relation to reference points or values indicate the present state and dynamics of the system. Indicators provide a bridge between objectives and action (@noauthor_united_nodate). **Landings:** -1. The number or weight of fish unloaded by commercial fishermen or brought to shore by recreational fishermen for personal use. Landings are reported at the locations at which fish are brought to shore (@wallace1994). +1. The number or weight of fish unloaded by commercial fishermen or brought to shore by recreational fishermen for personal use. Landings are reported at the locations at which fish are brought to shore (@wallace_fisheries_1994). **Large Marine Ecosystem (LME):** -A geographic area of an ocean that has distinct physical and oceanographic characteristics, productivity, and trophically dependent populations (@unfao). +A geographic area of an ocean that has distinct physical and oceanographic characteristics, productivity, and trophically dependent populations (@noauthor_united_nodate). **MAB:** -Mid-Atlantic Bight Ecological Production Unit (@techdoc). +Mid-Atlantic Bight Ecological Production Unit (@noauthor_technical_nodate). **Marine Heatwave:** -Period of five or more days where sea surface temperature is warmer than 90% of all previously measured temperatures based on a 30-year historical baseline period (@hobday2016). +Period of five or more days where sea surface temperature is warmer than 90% of all previously measured temperatures based on a 30-year historical baseline period (@hobday_hierarchical_2016). **Marine Mammals:** -Warm-blooded animals that live in marine waters and breathe air directly. These include porpoises, dolphins, whales, seals, and sea lions (@wallace2000). +Warm-blooded animals that live in marine waters and breathe air directly. These include porpoises, dolphins, whales, seals, and sea lions (@wallace_understanding_2000). **Mortality Event: ** The death of one or more individuals of a species. @@ -148,13 +148,13 @@ The Northeast U.S. Continental Shelf Large Marine Ecosystem (NES LME). The regio Global-scale changes in ocean marine carbonate chemistry driven by ocean uptake of atmospheric carbon dioxide (CO2). Human-induced ocean acidification specifically refers to the significant present shifts in the marine carbonate system that are a direct result of the exponential increase in atmospheric CO2 concentrations associated with human activities like fossil fuel use. *(CITE????)* **Overfished:** -When a stock’s biomass is below the point at which stock can produce sustainable yield. The term is used when biomass has been estimated to be below a limit biological reference point: in the US when biomass is less than ½ of Bmsy (@unfao). +When a stock’s biomass is below the point at which stock can produce sustainable yield. The term is used when biomass has been estimated to be below a limit biological reference point: in the US when biomass is less than ½ of Bmsy (@noauthor_united_nodate). **Overfishing:** -Whenever a stock is subjected to a fishing morality greater than the fishing mortality that produces maximum sustainable yield (MSY) on a continuing basis (@unfao). +Whenever a stock is subjected to a fishing morality greater than the fishing mortality that produces maximum sustainable yield (MSY) on a continuing basis (@noauthor_united_nodate). **Phytoplankton:** -Microscopic single-celled, free-floating algae (plants) that take up carbon dioxide and use nutrients and sunlight to produce biomass and form the base of the food web (@unfao). +Microscopic single-celled, free-floating algae (plants) that take up carbon dioxide and use nutrients and sunlight to produce biomass and form the base of the food web (@noauthor_united_nodate). **Piscivore:** Predator whose diet primarily consists of fish and squid, such as cod and striped bass. @@ -163,10 +163,10 @@ Predator whose diet primarily consists of fish and squid, such as cod and stripe Predator whose diet primarily consists of plankton, such as herring and mackerel. **Primary Production:** -The amount of energy produced by the assimilation and fixation of inorganic carbon and other nutrients by autotrophs (plants and certain bacteria) (@unfao). +The amount of energy produced by the assimilation and fixation of inorganic carbon and other nutrients by autotrophs (plants and certain bacteria) (@noauthor_united_nodate). **Primary Production Required:** -Indicator expressing the total amount of fish removed from an area as a fraction of the total primary production in the area (@pauly1995). +Indicator expressing the total amount of fish removed from an area as a fraction of the total primary production in the area (@pauly_primary_1995). **Primary Productivity:** The rate at which food energy is generated, or fixed, by photosynthesis or chemosynthesis. @@ -175,7 +175,7 @@ The rate at which food energy is generated, or fixed, by photosynthesis or chemo The modelled chance of a species being likely to occur in a specific area. **Productivity:** -Relates to the birth, growth and death rates of a stock. A highly productive stock is characterized by high birth, growth, and mortality rates, and as a consequence, a high turnover and production to biomass ratios (P/B) (@unfao). +Relates to the birth, growth and death rates of a stock. A highly productive stock is characterized by high birth, growth, and mortality rates, and as a consequence, a high turnover and production to biomass ratios (P/B) (@noauthor_united_nodate). **Recreational Fishery:** Fishing for fun or competition instead of profit like a commercial fishery. Includes for-hire charter and party boats, private boats, and shore-based fishing activities. @@ -187,42 +187,42 @@ The number of young fish entering the population each year at the age first caug The dollar value commercial fishermen receive for selling landed fish. **Salinity:** -The total mass of salts dissolved in seawater per unit of water; generally expressed in parts per thousands (ppt) or practical salinity units (psu) (@madden2004). +The total mass of salts dissolved in seawater per unit of water; generally expressed in parts per thousands (ppt) or practical salinity units (psu) (@madden_framework_2004). **Satellite Imagery:** -Imagery of the ocean surface gathered by earth-orbiting satellites (@unfao). +Imagery of the ocean surface gathered by earth-orbiting satellites (@noauthor_united_nodate). **Slopewater Proportion:** -The proportion of deep water entering the Gulf of Maine through the Northeast channel from two main water sources. The Labrador slope water is colder water moving south from Canada and Warm slope water is warmer water moving north from the southern U.S. (@techdoc). +The proportion of deep water entering the Gulf of Maine through the Northeast channel from two main water sources. The Labrador slope water is colder water moving south from Canada and Warm slope water is warmer water moving north from the southern U.S. (@noauthor_technical_nodate). **Socio-Economic:** -The combination or interaction of social and economic factors and involves topics such as distributional issues, labor market structure, social and opportunity costs, community dynamics, and decision-making processes (@unfao). +The combination or interaction of social and economic factors and involves topics such as distributional issues, labor market structure, social and opportunity costs, community dynamics, and decision-making processes (@noauthor_united_nodate). **SS:** -Scotian Shelf Ecological Production Unit. (@techdoc) +Scotian Shelf Ecological Production Unit. (@noauthor_technical_nodate) **Stock:** -A part of a fish population usually with a particular migration pattern, specific spawning grounds, and subject to a distinct fishery. Total stock refers to both juveniles and adults, either in numbers or by weight (@unfao). +A part of a fish population usually with a particular migration pattern, specific spawning grounds, and subject to a distinct fishery. Total stock refers to both juveniles and adults, either in numbers or by weight (@noauthor_united_nodate). **Trophic Level:** -Position in the food chain determined by the number of energy-transfer steps to that level. Primary producers constitute the lowest level, followed by zooplankton, etc. (@unfao). +Position in the food chain determined by the number of energy-transfer steps to that level. Primary producers constitute the lowest level, followed by zooplankton, etc. (@noauthor_united_nodate). **Warm Core Ring:** A clockwise turning eddy of cold water surrounding warm water in the center that breaks away from the Gulf Stream as it meanders. **Water Quality:** -The chemical, physical, and biological characteristics of water in respect to its suitability for a particular purpose (@noaaglos). +The chemical, physical, and biological characteristics of water in respect to its suitability for a particular purpose (@united_states_noaa_2005). **Zooplankton:** Plankton consisting of small animals and the immature stages of larger animals, ranging from microscopic organisms to large species, such as jellyfish. -(ref:neusmap) Map of Northeast U.S. Continental Shelf Large Marine Ecosystem from @Hare2016. +(ref:neusmap1) Map of Northeast U.S. Continental Shelf Large Marine Ecosystem from @hare_vulnerability_2016 -```{r neusmap1, message = FALSE, warning=FALSE, fig.align='center', fig.height=6, echo = F, fig.cap='(ref:neusmap)'} +```{r neusmap1, message = FALSE, warning=FALSE, fig.align='center',out.width="75%", echo = F, fig.cap='(ref:neusmap1)'} knitr::include_graphics("images/journal.pone.0146756.g002.PNG") ``` \ No newline at end of file diff --git a/chapters/gom_salmon.rmd b/chapters/gom_salmon.rmd index 4004792d..e1696cdd 100644 --- a/chapters/gom_salmon.rmd +++ b/chapters/gom_salmon.rmd @@ -17,7 +17,7 @@ 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 et al. 2019). Together, abundance and return rates allow monitoring of population status (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. @@ -26,7 +26,7 @@ Atlantic salmon adult returns in 2022 were estimated at 1,520 with 85% originati ```{r plot_gom_salmonMAB} # Plot indicator ggplotObject <- ecodata::plot_gom_salmon(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -84,5 +84,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/grayseal.rmd b/chapters/grayseal.rmd index 9ccf14a7..6f7fdb2a 100644 --- a/chapters/grayseal.rmd +++ b/chapters/grayseal.rmd @@ -25,7 +25,7 @@ Marine mammal species specific bycatch estimates from 2022 are below current PBR ```{r plot_graysealMAB} # Plot indicator ggplotObject <- ecodata::plot_grayseal(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -86,5 +86,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/gsi.rmd b/chapters/gsi.rmd index 574bd067..c9a4c10b 100644 --- a/chapters/gsi.rmd +++ b/chapters/gsi.rmd @@ -1,6 +1,6 @@ # Gulf Stream Index -**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 et al. (2009). +**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**: @@ -16,7 +16,7 @@ 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 et al., 2019; Chi et al., 2019). +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. @@ -26,13 +26,13 @@ The Gulf Stream Index suggest that recent years (2021-2022) the GS almost mainta ```{r plot_gsiMidAtlanticgsi} # Plot indicator ggplotObject <- ecodata::plot_gsi(report= 'MidAtlantic', varName= 'gsi') -print(ggplotObject) +ggplotObject ``` ```{r plot_gsiMidAtlanticwestgsi} # Plot indicator ggplotObject <- ecodata::plot_gsi(report= 'MidAtlantic', varName= 'westgsi') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -40,13 +40,13 @@ print(ggplotObject) ```{r plot_gsiNewEnglandgsi} # Plot indicator ggplotObject <- ecodata::plot_gsi(report= 'NewEngland', varName= 'gsi') -print(ggplotObject) +ggplotObject ``` ```{r plot_gsiNewEnglandwestgsi} # Plot indicator ggplotObject <- ecodata::plot_gsi(report= 'NewEngland', varName= 'westgsi') -print(ggplotObject) +ggplotObject ``` @@ -65,7 +65,7 @@ Temporal scale: Monthly from 1954 to 2022 ``` ## 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 et al., 2011). +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 @@ -103,5 +103,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/habitat_diversity.rmd b/chapters/habitat_diversity.rmd new file mode 100644 index 00000000..d899b5e5 --- /dev/null +++ b/chapters/habitat_diversity.rmd @@ -0,0 +1,106 @@ +# Species Richness + +**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** + + diff --git a/chapters/habs.rmd b/chapters/habs.rmd index a6cfc2da..6a40d855 100644 --- a/chapters/habs.rmd +++ b/chapters/habs.rmd @@ -16,9 +16,9 @@ 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 et al. 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. +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 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. These data were provided by Yizhen Li, NOAA/NOS NCCOS Stressor Detection and Impacts Division, HAB Forecasting Branch, Silver Spring, MD. 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. +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. @@ -31,13 +31,13 @@ A three-tier column graph illustrates three different metrics of shellfish bed c ```{r plot_habsMidAtlanticAlexandrium} # Plot indicator ggplotObject <- ecodata::plot_habs(report= 'MidAtlantic', varName= 'Alexandrium') -print(ggplotObject) +ggplotObject ``` ```{r plot_habsMidAtlanticPSP} # Plot indicator ggplotObject <- ecodata::plot_habs(report= 'MidAtlantic', varName= 'PSP') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -45,13 +45,13 @@ print(ggplotObject) ```{r plot_habsNewEnglandAlexandrium} # Plot indicator ggplotObject <- ecodata::plot_habs(report= 'NewEngland', varName= 'Alexandrium') -print(ggplotObject) +ggplotObject ``` ```{r plot_habsNewEnglandPSP} # Plot indicator ggplotObject <- ecodata::plot_habs(report= 'NewEngland', varName= 'PSP') -print(ggplotObject) +ggplotObject ``` @@ -70,9 +70,9 @@ Temporal scale: Alexandrium results are aggregated annually, and reported for 20 ``` ## 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 et al. 2005, Li et al. 2009 & 2020, McGillicuddy et al. 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 and Hoagland 2008). Impacts to offshore shellfisheries can occur as well, for example surf clam and ocean quahog on Georges Bank 1988-1990 (Anderson et al. 1997). +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 et al. 2014). Economic impacts to inshore shellfisheries (mussels, softshell clams, quahogs) can be substantial (Jin and Hoagland 2008). +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 @@ -84,9 +84,8 @@ Alexandrium bloom events in the Gulf of Maine can result in Paralytic Shellfish 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: 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. @@ -115,5 +114,3 @@ Data were provided by consultation with Yizhen Li. Data are also used in operat **tech-doc link** -## References - diff --git a/chapters/harborporpoise.rmd b/chapters/harborporpoise.rmd index e8dcc99c..7ac0c754 100644 --- a/chapters/harborporpoise.rmd +++ b/chapters/harborporpoise.rmd @@ -25,7 +25,7 @@ Marine mammal species specific bycatch estimates from 2022 are below current PBR ```{r plot_harborporpoiseMAB} # Plot indicator ggplotObject <- ecodata::plot_harborporpoise(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -86,5 +86,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/heatwave.rmd b/chapters/heatwave.rmd index 04b7043f..2b8b66a3 100644 --- a/chapters/heatwave.rmd +++ b/chapters/heatwave.rmd @@ -1,4 +1,4 @@ -# Marine Heatwaves - Surface +# Annual Heatwave Intensity **Description**: Surface and bottom MHWs for 2023. @@ -16,7 +16,7 @@ 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 by Jacox et al. 2020 and Jacox et al. 2022 have modified the MHW methodology originally developed by Hobday et al. 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 et al. 2020). 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. +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 @@ -48,13 +48,13 @@ In 2023, the Middle Atlantic Bight did not experience any bottom MHWs. The stron ```{r plot_heatwaveMidAtlanticSurface} # Plot indicator ggplotObject <- ecodata::plot_heatwave(report= 'MidAtlantic', varName= 'Surface') -print(ggplotObject) +ggplotObject ``` ```{r plot_heatwaveMidAtlanticBottom} # Plot indicator ggplotObject <- ecodata::plot_heatwave(report= 'MidAtlantic', varName= 'Bottom') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -62,20 +62,20 @@ print(ggplotObject) ```{r plot_heatwaveNewEnglandSurface} # Plot indicator ggplotObject <- ecodata::plot_heatwave(report= 'NewEngland', varName= 'Surface') -print(ggplotObject) +ggplotObject ``` ```{r plot_heatwaveNewEnglandBottom} # Plot indicator ggplotObject <- ecodata::plot_heatwave(report= 'NewEngland', varName= 'Bottom') -print(ggplotObject) +ggplotObject ``` ## Indicator statistics Spatial scale: EPU -Temporal scale: Daily +Temporal scale: Annual **Synthesis Theme**: @@ -124,5 +124,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/heatwave_year.rmd b/chapters/heatwave_year.rmd index 3b0e8962..887cf8bc 100644 --- a/chapters/heatwave_year.rmd +++ b/chapters/heatwave_year.rmd @@ -1,4 +1,4 @@ -# Marine Heatwaves - Bottom +# Marine Heatwave Events **Description**: Surface and bottom MHWs for 2023. @@ -16,7 +16,7 @@ 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 by Jacox et al. 2020 and Jacox et al. 2022 have modified the MHW methodology originally developed by Hobday et al. 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 et al. 2020). 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. +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 @@ -33,7 +33,7 @@ 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. +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 @@ -48,13 +48,13 @@ In 2023, the Middle Atlantic Bight did not experience any bottom MHWs. The stron ```{r plot_heatwave_yearMidAtlanticSurface} # Plot indicator ggplotObject <- ecodata::plot_heatwave_year(report= 'MidAtlantic', varName= 'Surface') -print(ggplotObject) +ggplotObject ``` ```{r plot_heatwave_yearMidAtlanticBottom} # Plot indicator ggplotObject <- ecodata::plot_heatwave_year(report= 'MidAtlantic', varName= 'Bottom') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -62,13 +62,13 @@ print(ggplotObject) ```{r plot_heatwave_yearNewEnglandSurface} # Plot indicator ggplotObject <- ecodata::plot_heatwave_year(report= 'NewEngland', varName= 'Surface') -print(ggplotObject) +ggplotObject ``` ```{r plot_heatwave_yearNewEnglandBottom} # Plot indicator ggplotObject <- ecodata::plot_heatwave_year(report= 'NewEngland', varName= 'Bottom') -print(ggplotObject) +ggplotObject ``` @@ -124,5 +124,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/hms_cpue.rmd b/chapters/hms_cpue.rmd index 514f50b9..40ffa20a 100644 --- a/chapters/hms_cpue.rmd +++ b/chapters/hms_cpue.rmd @@ -26,13 +26,13 @@ TBD; data may not be correctly displayed in graphs. ```{r plot_hms_cpueMidAtlanticshark} # Plot indicator ggplotObject <- ecodata::plot_hms_cpue(report= 'MidAtlantic', varName= 'shark') -print(ggplotObject) +ggplotObject ``` ```{r plot_hms_cpueMidAtlantictuna} # Plot indicator ggplotObject <- ecodata::plot_hms_cpue(report= 'MidAtlantic', varName= 'tuna') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -40,13 +40,13 @@ print(ggplotObject) ```{r plot_hms_cpueNewEnglandshark} # Plot indicator ggplotObject <- ecodata::plot_hms_cpue(report= 'NewEngland', varName= 'shark') -print(ggplotObject) +ggplotObject ``` ```{r plot_hms_cpueNewEnglandtuna} # Plot indicator ggplotObject <- ecodata::plot_hms_cpue(report= 'NewEngland', varName= 'tuna') -print(ggplotObject) +ggplotObject ``` @@ -101,5 +101,3 @@ Pelagic observer data is considered confidential data, and must be screened to e **tech-doc link** -## References - diff --git a/chapters/hms_landings.rmd b/chapters/hms_landings.rmd index 63ef8dca..b618380d 100644 --- a/chapters/hms_landings.rmd +++ b/chapters/hms_landings.rmd @@ -26,13 +26,13 @@ TBD - recommend including a version of the plots or tables generated in the Tech ```{r plot_hms_landingsMidAtlanticLandings} # Plot indicator ggplotObject <- ecodata::plot_hms_landings(report= 'MidAtlantic', varName= 'Landings') -print(ggplotObject) +ggplotObject ``` ```{r plot_hms_landingsMidAtlanticRevenue} # Plot indicator ggplotObject <- ecodata::plot_hms_landings(report= 'MidAtlantic', varName= 'Revenue') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -40,13 +40,13 @@ print(ggplotObject) ```{r plot_hms_landingsNewEnglandLandings} # Plot indicator ggplotObject <- ecodata::plot_hms_landings(report= 'NewEngland', varName= 'Landings') -print(ggplotObject) +ggplotObject ``` ```{r plot_hms_landingsNewEnglandRevenue} # Plot indicator ggplotObject <- ecodata::plot_hms_landings(report= 'NewEngland', varName= 'Revenue') -print(ggplotObject) +ggplotObject ``` @@ -65,7 +65,9 @@ Temporal scale: Annual ``` ## Implications -TBD – pending finalization of dataset for 2024 report +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 @@ -101,5 +103,3 @@ Data that has been properly screened to meet data standards and confidentiality **tech-doc link** -## References - diff --git a/chapters/hms_stock_status.rmd b/chapters/hms_stock_status.rmd index 19c6d7b3..52fffecf 100644 --- a/chapters/hms_stock_status.rmd +++ b/chapters/hms_stock_status.rmd @@ -24,7 +24,7 @@ TBD – we have a meeting in mid-November to finalize decisions regarding this c ```{r plot_hms_stock_statusMAB} # Plot indicator ggplotObject <- ecodata::plot_hms_stock_status(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -79,5 +79,3 @@ All stock assessment results are publicly available (see Data Sources). Summariz **tech-doc link** -## References - diff --git a/chapters/long_term_sst.rmd b/chapters/long_term_sst.rmd index 881a78f0..062e76ce 100644 --- a/chapters/long_term_sst.rmd +++ b/chapters/long_term_sst.rmd @@ -24,7 +24,7 @@ Since the 1860’s, the Northeast US shelf sea surface temperature (SST) has exh ```{r plot_long_term_sstMAB} # Plot indicator ggplotObject <- ecodata::plot_long_term_sst(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -79,5 +79,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/mab_inshore_survey.rmd b/chapters/mab_inshore_survey.rmd index a2d05e19..5865331a 100644 --- a/chapters/mab_inshore_survey.rmd +++ b/chapters/mab_inshore_survey.rmd @@ -1,4 +1,4 @@ -# Inshore bottom trawl surveys +# Inshore Survey (Mid Atlantic) **Description**: Biomass time series for aggregate species groups from *three* inshore bottom trawl surveys conducted throughout the NE US: @@ -32,7 +32,7 @@ Each survey shows trends by aggregate group. ```{r plot_mab_inshore_surveyMAB} # Plot indicator ggplotObject <- ecodata::plot_mab_inshore_survey(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -88,5 +88,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/mass_inshore_survey.rmd b/chapters/mass_inshore_survey.rmd new file mode 100644 index 00000000..b4ab517c --- /dev/null +++ b/chapters/mass_inshore_survey.rmd @@ -0,0 +1,90 @@ +# Inshore Survey (Massachusetts) + +**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 b897441b..70215fa9 100644 --- a/chapters/narw.rmd +++ b/chapters/narw.rmd @@ -18,7 +18,7 @@ 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. 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. +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). @@ -26,21 +26,20 @@ The most recent estimate of total population size in 2022 was 356 whales, with a 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 35 dead stranded whales, 14 in the US and 21 in Canada. When alive but seriously injured whales (22) and sublethal injuries or ill whales (37) are taken into account, 94 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 [3]. The primary cause of death is “human interaction” from entanglements or vessel strikes. +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') -print(ggplotObject) +ggplotObject ``` ```{r plot_narwMidAtlanticcalf} # Plot indicator ggplotObject <- ecodata::plot_narw(report= 'MidAtlantic', varName= 'calf') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -48,13 +47,13 @@ print(ggplotObject) ```{r plot_narwNewEnglandadult} # Plot indicator ggplotObject <- ecodata::plot_narw(report= 'NewEngland', varName= 'adult') -print(ggplotObject) +ggplotObject ``` ```{r plot_narwNewEnglandcalf} # Plot indicator ggplotObject <- ecodata::plot_narw(report= 'NewEngland', varName= 'calf') -print(ggplotObject) +ggplotObject ``` @@ -74,7 +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 [5]. 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 [5–7]. 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 [8,9]. This area is also a primary right whale winter foraging habitat. Additional information can be found in the offshore wind section. +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. 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. @@ -86,14 +85,12 @@ 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 @@ -119,5 +116,3 @@ Source data are available from the New England Aquarium upon request. Estimates **tech-doc link** -## References - diff --git a/chapters/ne_inshore_survey.rmd b/chapters/ne_inshore_survey.rmd new file mode 100644 index 00000000..abf1fd5f --- /dev/null +++ b/chapters/ne_inshore_survey.rmd @@ -0,0 +1,90 @@ +# Inshore Survey (New England) + +**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 new file mode 100644 index 00000000..6a54e411 --- /dev/null +++ b/chapters/observation_synthesis.rmd @@ -0,0 +1,97 @@ +# 2023 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_ + diff --git a/chapters/ocean_acidification.rmd b/chapters/ocean_acidification.rmd index 7ac7c2b9..dd9b665b 100644 --- a/chapters/ocean_acidification.rmd +++ b/chapters/ocean_acidification.rmd @@ -16,9 +16,9 @@ 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 et al. 2013, Saba et al. 2019). However, sensitivity levels vary, and some organisms exhibit negative responses to calcification and other processes when ΩArag is as low as 3. +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 et al. 2019, Wright-Fairbanks et al. 2020, Xu et al. 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. +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. @@ -34,6 +34,8 @@ With high temporal resolution (> seasonal) glider sampling off the coast of New 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. @@ -45,9 +47,13 @@ Healthy dissolved oxygen, pH, and aragonite saturation state levels were restore 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. @@ -55,6 +61,8 @@ Using available quality-controlled vessel- and glider-based datasets accompanyin 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). @@ -62,7 +70,9 @@ Using the bottom water aragonite saturation state data, maps depicting locations 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 et al. (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 Zafroff et al. (2019) and Zafroff & Mooney (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. 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 @@ -92,8 +102,10 @@ 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 @@ -111,8 +123,3 @@ Source data are publicly available. _No response_ -**tech-doc link** - - -## References - diff --git a/chapters/osw_survey_impact.rmd b/chapters/osw_survey_impact.rmd new file mode 100644 index 00000000..dc0d48c9 --- /dev/null +++ b/chapters/osw_survey_impact.rmd @@ -0,0 +1,73 @@ +# Survey Impacts from Offshore Wind Development + +**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 d377b386..edb24428 100644 --- a/chapters/persistent_hotspots.rmd +++ b/chapters/persistent_hotspots.rmd @@ -1,4 +1,4 @@ -# persistent_hotspots +# Persistent annual hotspots **Description**: Integrated persistent annual hotspots derived from at-sea observations of seabirds, cetaceans and sea turtles collected on systematic ship and aerial surveys @@ -19,7 +19,9 @@ library(ecodata) Locations of high densities through time of seabirds, marine mammals, and sea turtles ## Key Results and Visualizations -NA +![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 @@ -66,8 +68,3 @@ Source data are publicly available. _No response_ -**tech-doc link** - - -## References - diff --git a/chapters/phyto_size.rmd b/chapters/phyto_size.rmd new file mode 100644 index 00000000..d056526f --- /dev/null +++ b/chapters/phyto_size.rmd @@ -0,0 +1,118 @@ +# Phytoplankton Size Class + +**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** + + diff --git a/chapters/ppr.rmd b/chapters/ppr.rmd index 992fcb4f..5537367d 100644 --- a/chapters/ppr.rmd +++ b/chapters/ppr.rmd @@ -17,14 +17,14 @@ 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 ([JS Link and Watson 2019](https://noaa-edab.github.io/tech-doc/ecosystem-overfishing.html#ref-link2019eof)). 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 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 ([JS Link and Watson 2019](https://noaa-edab.github.io/tech-doc/ecosystem-overfishing.html#ref-link2019eof)). The units are parts per thousand. +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" ([JS Link and Watson 2019](https://noaa-edab.github.io/tech-doc/ecosystem-overfishing.html#ref-link2019eof)). When accounting for regional primary prductivity the Fogarty index shows low levels of fishing relative to these global thresholds. +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 @@ -33,37 +33,37 @@ Thresholds based on regional estimates of primary productivity, are not yet avai ```{r plot_pprMidAtlanticppglobal} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'pp' ,threshold= 'global') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprMidAtlanticppregional} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'pp' ,threshold= 'regional') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprMidAtlanticfogartyglobal} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'fogarty' ,threshold= 'global') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprMidAtlanticfogartyregional} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'fogarty' ,threshold= 'regional') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprMidAtlanticrytherglobal} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'ryther' ,threshold= 'global') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprMidAtlanticrytherregional} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'MidAtlantic', varName= 'ryther' ,threshold= 'regional') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -71,37 +71,37 @@ print(ggplotObject) ```{r plot_pprNewEnglandppglobal} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'pp' ,threshold= 'global') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprNewEnglandppregional} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'pp' ,threshold= 'regional') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprNewEnglandfogartyglobal} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'fogarty' ,threshold= 'global') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprNewEnglandfogartyregional} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'fogarty' ,threshold= 'regional') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprNewEnglandrytherglobal} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'ryther' ,threshold= 'global') -print(ggplotObject) +ggplotObject ``` ```{r plot_pprNewEnglandrytherregional} # Plot indicator ggplotObject <- ecodata::plot_ppr(report= 'NewEngland', varName= 'ryther' ,threshold= 'regional') -print(ggplotObject) +ggplotObject ``` @@ -132,8 +132,7 @@ There is insufficient evidence to determine whether ecosystem overfishing in occ **Variable definitions** -1. Ryther; The Ryther index; mt km^-2 y^-1 -2. Fogarty; The Fogarty Index; Parts per thousand 0/00 +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} @@ -160,5 +159,3 @@ Please email andrew.beet@noaa.gov for further information and queries of source **tech-doc link** -## References - diff --git a/chapters/productivity_anomaly.rmd b/chapters/productivity_anomaly.rmd index ee0a66a2..3db53874 100644 --- a/chapters/productivity_anomaly.rmd +++ b/chapters/productivity_anomaly.rmd @@ -18,7 +18,7 @@ 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 Charles Perretti et al. (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. +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. @@ -28,13 +28,13 @@ The small fish per large fish anomaly indicator, derived from NEFSC bottom trawl ```{r plot_productivity_anomalyMidAtlanticanomalyMAB} # Plot indicator ggplotObject <- ecodata::plot_productivity_anomaly(report= 'MidAtlantic', varName= 'anomaly' ,EPU= 'MAB') -print(ggplotObject) +ggplotObject ``` ```{r plot_productivity_anomalyMidAtlanticassessmentMAB} # Plot indicator ggplotObject <- ecodata::plot_productivity_anomaly(report= 'MidAtlantic', varName= 'assessment' ,EPU= 'MAB') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -42,25 +42,25 @@ print(ggplotObject) ```{r plot_productivity_anomalyNewEnglandanomalyGB} # Plot indicator ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'anomaly' ,EPU= 'GB') -print(ggplotObject) +ggplotObject ``` ```{r plot_productivity_anomalyNewEnglandanomalyGOM} # Plot indicator ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'anomaly' ,EPU= 'GOM') -print(ggplotObject) +ggplotObject ``` ```{r plot_productivity_anomalyNewEnglandassessmentGB} # Plot indicator ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'assessment' ,EPU= 'GB') -print(ggplotObject) +ggplotObject ``` ```{r plot_productivity_anomalyNewEnglandassessmentGOM} # Plot indicator ggplotObject <- ecodata::plot_productivity_anomaly(report= 'NewEngland', varName= 'assessment' ,EPU= 'GOM') -print(ggplotObject) +ggplotObject ``` @@ -89,13 +89,10 @@ The apparent decline in productivity across multiple managed species in the MAB, **Variable definitions** -Variable names are organized using this format: -[region] stock name - variable type and source +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 +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. @@ -124,5 +121,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/recdat.rmd b/chapters/recdat.rmd index 622f4be8..c49d70b9 100644 --- a/chapters/recdat.rmd +++ b/chapters/recdat.rmd @@ -31,25 +31,25 @@ In New England, recreational species catch diversity has been above the time ser ```{r plot_recdatMidAtlanticlandings} # Plot indicator ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'landings') -print(ggplotObject) +ggplotObject ``` ```{r plot_recdatMidAtlanticeffortdiversity} # Plot indicator ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'effortdiversity') -print(ggplotObject) +ggplotObject ``` ```{r plot_recdatMidAtlanticcatchdiversity} # Plot indicator ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'catchdiversity') -print(ggplotObject) +ggplotObject ``` ```{r plot_recdatMidAtlanticeffort} # Plot indicator ggplotObject <- ecodata::plot_recdat(report= 'MidAtlantic', varName= 'effort') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -57,25 +57,25 @@ print(ggplotObject) ```{r plot_recdatNewEnglandlandings} # Plot indicator ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'landings') -print(ggplotObject) +ggplotObject ``` ```{r plot_recdatNewEnglandeffortdiversity} # Plot indicator ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'effortdiversity') -print(ggplotObject) +ggplotObject ``` ```{r plot_recdatNewEnglandcatchdiversity} # Plot indicator ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'catchdiversity') -print(ggplotObject) +ggplotObject ``` ```{r plot_recdatNewEnglandeffort} # Plot indicator ggplotObject <- ecodata::plot_recdat(report= 'NewEngland', varName= 'effort') -print(ggplotObject) +ggplotObject ``` @@ -96,8 +96,7 @@ Temporal scale: Annual ``` ## 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. +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. @@ -142,5 +141,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/references.Rmd b/chapters/references.Rmd index c17d1140..39ccb1fb 100644 --- a/chapters/references.Rmd +++ b/chapters/references.Rmd @@ -1,9 +1,9 @@ # References {-} -```{r include=FALSE} -#`r if (knitr::is_html_output()) '# References {-}'` -knitr::write_bib(c( - .packages(), 'bookdown', 'knitr', 'rmarkdown', 'htmlwidgets', 'webshot', 'DT', - 'miniUI', 'tufte', 'servr', 'citr', 'rticles' -), 'packages.bib') -``` + + + + + + + diff --git a/chapters/seabird_ne.rmd b/chapters/seabird_ne.rmd index 62fbec5d..693a70a5 100644 --- a/chapters/seabird_ne.rmd +++ b/chapters/seabird_ne.rmd @@ -26,19 +26,19 @@ GOM common tern average productivity (fledglings per nest) across 7 colonies has ```{r plot_seabird_neMidAtlanticdiversity} # Plot indicator ggplotObject <- ecodata::plot_seabird_ne(report= 'MidAtlantic', varName= 'diversity') -print(ggplotObject) +ggplotObject ``` ```{r plot_seabird_neMidAtlanticproductivity} # Plot indicator ggplotObject <- ecodata::plot_seabird_ne(report= 'MidAtlantic', varName= 'productivity') -print(ggplotObject) +ggplotObject ``` ```{r plot_seabird_neMidAtlanticprey} # Plot indicator ggplotObject <- ecodata::plot_seabird_ne(report= 'MidAtlantic', varName= 'prey') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -46,19 +46,19 @@ print(ggplotObject) ```{r plot_seabird_neNewEnglanddiversity} # Plot indicator ggplotObject <- ecodata::plot_seabird_ne(report= 'NewEngland', varName= 'diversity') -print(ggplotObject) +ggplotObject ``` ```{r plot_seabird_neNewEnglandproductivity} # Plot indicator ggplotObject <- ecodata::plot_seabird_ne(report= 'NewEngland', varName= 'productivity') -print(ggplotObject) +ggplotObject ``` ```{r plot_seabird_neNewEnglandprey} # Plot indicator ggplotObject <- ecodata::plot_seabird_ne(report= 'NewEngland', varName= 'prey') -print(ggplotObject) +ggplotObject ``` @@ -115,5 +115,3 @@ Please email dlyons@audubon.org for further information and queries on this indi **tech-doc link** -## References - diff --git a/chapters/seal_pups.rmd b/chapters/seal_pups.rmd index fccdc32e..63964fde 100644 --- a/chapters/seal_pups.rmd +++ b/chapters/seal_pups.rmd @@ -16,18 +16,18 @@ 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 and Mott 1967; Lelli et al. 2009). Since the late 1980s, ground and aerial surveys have documented the recovery and recolonization of pupping sites in northeast U.S. waters (Wood et al. 2022). This recovery is due in large part to the protection provided by the Marine Mammal Protection Act (MMPA) of 1972. +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%; [4] Fig. x). +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') -print(ggplotObject) +ggplotObject ``` @@ -82,5 +82,3 @@ Reach out to Stephanie Wood (stephanie.wood@noaa.gov) for data. **tech-doc link** -## References - diff --git a/chapters/seasonal_oisst_anom.rmd b/chapters/seasonal_oisst_anom.rmd index 8f5ed0f9..77a64aa5 100644 --- a/chapters/seasonal_oisst_anom.rmd +++ b/chapters/seasonal_oisst_anom.rmd @@ -27,7 +27,7 @@ Since 1982, SST has been increasing in all seasons in all three EPUs. 2023 was t ```{r plot_seasonal_oisst_anomMAB} # Plot indicator ggplotObject <- ecodata::plot_seasonal_oisst_anom(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` ### GB @@ -35,7 +35,7 @@ print(ggplotObject) ```{r plot_seasonal_oisst_anomNEGB} # Plot indicator ggplotObject <- ecodata::plot_seasonal_oisst_anom(report='NewEngland',EPU='GB') -print(ggplotObject) +ggplotObject ``` ### GOM @@ -43,7 +43,7 @@ print(ggplotObject) ```{r plot_seasonal_oisst_anomNEGOM} # Plot indicator ggplotObject <- ecodata::plot_seasonal_oisst_anom(report='NewEngland',EPU='GOM') -print(ggplotObject) +ggplotObject ``` @@ -98,5 +98,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/seasonal_sst_anomaly_gridded.rmd b/chapters/seasonal_sst_anomaly_gridded.rmd index c4116674..f7400da1 100644 --- a/chapters/seasonal_sst_anomaly_gridded.rmd +++ b/chapters/seasonal_sst_anomaly_gridded.rmd @@ -25,7 +25,7 @@ Despite record setting ocean temperatures in 2023, the Northeast US shelf had bo ```{r plot_seasonal_sst_anomaly_griddedMAB} # Plot indicator ggplotObject <- ecodata::plot_seasonal_sst_anomaly_gridded(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -72,5 +72,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/slopewater.rmd b/chapters/slopewater.rmd index 6e1ad5a7..58b91b59 100644 --- a/chapters/slopewater.rmd +++ b/chapters/slopewater.rmd @@ -16,7 +16,7 @@ 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 David G. Mountain (2012a). 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. +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_ @@ -26,7 +26,7 @@ _No response_ ```{r plot_slopewaterNE} # Plot indicator ggplotObject <- ecodata::plot_slopewater(report='NewEngland') -print(ggplotObject) +ggplotObject ``` @@ -81,5 +81,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/spawn_timing.rmd b/chapters/spawn_timing.rmd index 6e237b1d..36918550 100644 --- a/chapters/spawn_timing.rmd +++ b/chapters/spawn_timing.rmd @@ -25,7 +25,7 @@ These indicators were presented as working papers to the haddock and yellowtail ## 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 et al. 2010), 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. +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. @@ -37,7 +37,7 @@ When summarized by decade, the most recent decade (2010s) sampled fish at slight **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 et al. 2010), 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. +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. @@ -45,7 +45,7 @@ The multinomial model indicated significant effects of bottom temperature, week **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 et al. 2010), 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. +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. @@ -53,7 +53,7 @@ The multinomial model indicated significant effects of bottom temperature, week **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 et al. 2010), 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. +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. @@ -64,43 +64,43 @@ The multinomial model indicated significant effects of bottom temperature, week ```{r plot_spawn_timingMidAtlanticResting} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Resting') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingMidAtlanticRipe} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Ripe') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingMidAtlanticSpent} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Spent') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingMidAtlanticDeveloping} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'Developing') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingMidAtlanticMF} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'MF') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingMidAtlanticmeanTEMP} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'meanTEMP') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingMidAtlanticmeanJDAY} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'MidAtlantic', varName= 'meanJDAY') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -108,43 +108,43 @@ print(ggplotObject) ```{r plot_spawn_timingNewEnglandResting} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Resting') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingNewEnglandRipe} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Ripe') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingNewEnglandSpent} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Spent') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingNewEnglandDeveloping} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'Developing') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingNewEnglandMF} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'MF') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingNewEnglandmeanTEMP} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'meanTEMP') -print(ggplotObject) +ggplotObject ``` ```{r plot_spawn_timingNewEnglandmeanJDAY} # Plot indicator ggplotObject <- ecodata::plot_spawn_timing(report= 'NewEngland', varName= 'meanJDAY') -print(ggplotObject) +ggplotObject ``` @@ -163,11 +163,13 @@ Temporal scale: Spring and Fall ``` ## Implications -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 and Dutil 2000) and flatfishes (Wuenschel et al. 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 et al. 2015). Trippel and Neil (2004) 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. +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. -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 et al. 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). +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. -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 and Dutil 2000) and flatfishes (Wuenschel et al. 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 et al. 2015). Manning and Crim (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. +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 @@ -179,8 +181,7 @@ The post spawning Yellowtail Flounder would be expected to have lower relative c 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 +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 @@ -209,5 +210,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/species_dist.rmd b/chapters/species_dist.rmd new file mode 100644 index 00000000..ca51163a --- /dev/null +++ b/chapters/species_dist.rmd @@ -0,0 +1,106 @@ +# Species Distribution Indicators + +**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 7ec70cb2..b7673922 100644 --- a/chapters/species_groupings.rmd +++ b/chapters/species_groupings.rmd @@ -19,7 +19,7 @@ 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. *add refs:* Garrison and Link 2000, Smith and Link 2010, [NEFSC food habits online](https://fwdp.shinyapps.io/tm2020/) +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. @@ -29,7 +29,7 @@ Each management body (MAFMC, NEFSC, State etc) has its managed species associate ```{r plot_species_groupingsMidAtlantic2024} # Plot indicator ggplotObject <- ecodata::plot_species_groupings(report= 'MidAtlantic', varName= '2024') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -37,7 +37,7 @@ print(ggplotObject) ```{r plot_species_groupingsNewEngland2024} # Plot indicator ggplotObject <- ecodata::plot_species_groupings(report= 'NewEngland', varName= '2024') -print(ggplotObject) +ggplotObject ``` @@ -88,5 +88,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/stock_status.rmd b/chapters/stock_status.rmd index d9e3a852..b56ce3f8 100644 --- a/chapters/stock_status.rmd +++ b/chapters/stock_status.rmd @@ -31,7 +31,7 @@ Single species management objectives (1. maintaining biomass above minimum thres ```{r plot_stock_statusMAB} # Plot indicator ggplotObject <- ecodata::plot_stock_status(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` ### NE @@ -39,7 +39,7 @@ print(ggplotObject) ```{r plot_stock_statusNE} # Plot indicator ggplotObject <- ecodata::plot_stock_status(report='NewEngland') -print(ggplotObject) +ggplotObject ``` @@ -104,5 +104,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/thermal_habitat_area.rmd b/chapters/thermal_habitat_area.rmd index 210511aa..7794bd4f 100644 --- a/chapters/thermal_habitat_area.rmd +++ b/chapters/thermal_habitat_area.rmd @@ -27,7 +27,7 @@ For each EPU, bottom temperature data were separate by depth bin (0-25m; 25m-100 ```{r plot_thermal_habitat_areaMAB} # Plot indicator ggplotObject <- ecodata::plot_thermal_habitat_area(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` ### GB @@ -35,7 +35,7 @@ print(ggplotObject) ```{r plot_thermal_habitat_areaNEGB} # Plot indicator ggplotObject <- ecodata::plot_thermal_habitat_area(report='NewEngland',EPU='GB') -print(ggplotObject) +ggplotObject ``` ### GOM @@ -43,7 +43,7 @@ print(ggplotObject) ```{r plot_thermal_habitat_areaNEGOM} # Plot indicator ggplotObject <- ecodata::plot_thermal_habitat_area(report='NewEngland',EPU='GOM') -print(ggplotObject) +ggplotObject ``` @@ -75,8 +75,7 @@ If a large proportion of species current habitat become thermally inhospitable, 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 +area: area exceeding temperature threshold (m2) area.prop: proportion of EPU area exceeding temperature threshold ```{r vars_thermal_habitat_area} # Pull all var names @@ -102,5 +101,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/thermal_habitat_persistence.rmd b/chapters/thermal_habitat_persistence.rmd index b4cfff1f..dce5b829 100644 --- a/chapters/thermal_habitat_persistence.rmd +++ b/chapters/thermal_habitat_persistence.rmd @@ -22,6 +22,12 @@ Many deep water benthic and demersal exhibit thermal preferences for metabolic, ## 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 @@ -78,5 +84,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/timing_shifts.rmd b/chapters/timing_shifts.rmd new file mode 100644 index 00000000..f117d103 --- /dev/null +++ b/chapters/timing_shifts.rmd @@ -0,0 +1,74 @@ +# Timing shifts: Risks to Seasonal Management + +**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_ + diff --git a/chapters/trans_dates.rmd b/chapters/trans_dates.rmd index 137274c7..c108d8f7 100644 --- a/chapters/trans_dates.rmd +++ b/chapters/trans_dates.rmd @@ -26,13 +26,13 @@ Ocean summer length in Mid-Atlantic: the annual total number of days between the ```{r plot_trans_datesMidAtlantictiming} # Plot indicator ggplotObject <- ecodata::plot_trans_dates(report= 'MidAtlantic', varName= 'timing') -print(ggplotObject) +ggplotObject ``` ```{r plot_trans_datesMidAtlanticlength} # Plot indicator ggplotObject <- ecodata::plot_trans_dates(report= 'MidAtlantic', varName= 'length') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -40,13 +40,13 @@ print(ggplotObject) ```{r plot_trans_datesNewEnglandtiming} # Plot indicator ggplotObject <- ecodata::plot_trans_dates(report= 'NewEngland', varName= 'timing') -print(ggplotObject) +ggplotObject ``` ```{r plot_trans_datesNewEnglandlength} # Plot indicator ggplotObject <- ecodata::plot_trans_dates(report= 'NewEngland', varName= 'length') -print(ggplotObject) +ggplotObject ``` @@ -65,7 +65,7 @@ Temporal scale: Annual time series (1982 to 2023) ``` ## Implications -To be determined. +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 @@ -101,5 +101,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/wbts_mesozooplankton.rmd b/chapters/wbts_mesozooplankton.rmd index ac7efc09..0db7a14f 100644 --- a/chapters/wbts_mesozooplankton.rmd +++ b/chapters/wbts_mesozooplankton.rmd @@ -1,4 +1,4 @@ -# Mesozooplankton Biomass at Wilkson Basin +# Mesozooplankton Biomass at Wilkinson Basin **Description**: Mesozooplankton biomass at the Wilkinson Basin Time Series Station (WBTS): 2005-2022 @@ -25,9 +25,9 @@ Note: mesozooplankton biomass figure uploaded in data folder (not sure where to 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 (deLafontaine et al 1991; Sorochan et al. 2019), Scotian Shelf (Casault et al. 2023) and the Norwegian and Barents Seas (Melle et al. 2014; Skjodal et al. 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. +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 (Dullaert et al. in prep). +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 @@ -47,11 +47,11 @@ Temporal scale: Monthly samples (with data gaps) between 2005-2022 ``` ## 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 (Dullaert et al. in press). +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 et al. (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. +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 et al. 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. +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 @@ -79,8 +79,3 @@ Source data are NOT publicly available. 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 -**tech-doc link** - - -## References - diff --git a/chapters/wcr.rmd b/chapters/wcr.rmd index 3747a274..360dd4d7 100644 --- a/chapters/wcr.rmd +++ b/chapters/wcr.rmd @@ -25,7 +25,7 @@ Prior to 2000, an average of 18 warm core rings were formed by the Gulf Stream o ```{r plot_wcrMAB} # Plot indicator ggplotObject <- ecodata::plot_wcr(report='MidAtlantic') -print(ggplotObject) +ggplotObject ``` @@ -81,5 +81,3 @@ Please contact Kimberly.Hyde@noaa.gov **tech-doc link** -## References - diff --git a/chapters/wind_dev_speed.rmd b/chapters/wind_dev_speed.rmd index 29e71054..b5ea7f29 100644 --- a/chapters/wind_dev_speed.rmd +++ b/chapters/wind_dev_speed.rmd @@ -17,15 +17,17 @@ 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. +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 -NA +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') -print(ggplotObject) +ggplotObject ``` @@ -80,5 +82,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/wind_port.rmd b/chapters/wind_port.rmd index 34e2859b..e208f139 100644 --- a/chapters/wind_port.rmd +++ b/chapters/wind_port.rmd @@ -20,14 +20,14 @@ library(ecodata) NA ## Key Results and Visualizations -NA +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') -print(ggplotObject) +ggplotObject ``` ### NE @@ -35,7 +35,7 @@ print(ggplotObject) ```{r plot_wind_portNE} # Plot indicator ggplotObject <- ecodata::plot_wind_port(report='NewEngland') -print(ggplotObject) +ggplotObject ``` @@ -54,7 +54,7 @@ Temporal scale: 2008-2022 ``` ## Implications -NA +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 @@ -90,5 +90,3 @@ _No response_ **tech-doc link** -## References - diff --git a/chapters/wind_revenue.rmd b/chapters/wind_revenue.rmd index e01098ae..5a034e6b 100644 --- a/chapters/wind_revenue.rmd +++ b/chapters/wind_revenue.rmd @@ -27,25 +27,25 @@ Figures include annual landings and revenue for each managed species caught with ```{r plot_wind_revenueMidAtlanticlandingfacets} # Plot indicator ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'landing' ,plottype= 'facets') -print(ggplotObject) +ggplotObject ``` ```{r plot_wind_revenueMidAtlanticlandingnofacets} # Plot indicator ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'landing' ,plottype= 'nofacets') -print(ggplotObject) +ggplotObject ``` ```{r plot_wind_revenueMidAtlanticvaluefacets} # Plot indicator ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'value' ,plottype= 'facets') -print(ggplotObject) +ggplotObject ``` ```{r plot_wind_revenueMidAtlanticvaluenofacets} # Plot indicator ggplotObject <- ecodata::plot_wind_revenue(report= 'MidAtlantic', varName= 'value' ,plottype= 'nofacets') -print(ggplotObject) +ggplotObject ``` ### NewEngland @@ -53,25 +53,25 @@ print(ggplotObject) ```{r plot_wind_revenueNewEnglandlandingfacets} # Plot indicator ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'landing' ,plottype= 'facets') -print(ggplotObject) +ggplotObject ``` ```{r plot_wind_revenueNewEnglandlandingnofacets} # Plot indicator ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'landing' ,plottype= 'nofacets') -print(ggplotObject) +ggplotObject ``` ```{r plot_wind_revenueNewEnglandvaluefacets} # Plot indicator ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'value' ,plottype= 'facets') -print(ggplotObject) +ggplotObject ``` ```{r plot_wind_revenueNewEnglandvaluenofacets} # Plot indicator ggplotObject <- ecodata::plot_wind_revenue(report= 'NewEngland', varName= 'value' ,plottype= 'nofacets') -print(ggplotObject) +ggplotObject ``` @@ -132,5 +132,3 @@ Please email douglas.christel@noaa.gov for further information or request suppor **tech-doc link** -## References - diff --git a/chapters/zoo_abundance_anom.rmd b/chapters/zoo_abundance_anom.rmd new file mode 100644 index 00000000..008a4b9f --- /dev/null +++ b/chapters/zoo_abundance_anom.rmd @@ -0,0 +1,112 @@ +# Zooplankton Abundance Anomalies + +**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 new file mode 100644 index 00000000..4339873d --- /dev/null +++ b/chapters/zoo_diversity.rmd @@ -0,0 +1,96 @@ +# Zooplankton 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** + + diff --git a/index.Rmd b/index.Rmd index ad24070e..cbd8d6cf 100644 --- a/index.Rmd +++ b/index.Rmd @@ -5,10 +5,12 @@ date: "`r format(Sys.Date(), '%e %B %Y')`" site: bookdown::bookdown_site documentclass: book bibliography: - - bibliography/introduction.bib + - bibliography/StateOftheEcosystem.bib #geometry: "left=1.0in, right=1.0in, top=1.0in, bottom=1.0in, includefoot" biblio-style: apalike -csl: chicago-fullnote-bibliography.csl +#csl: chicago-fullnote-bibliography.csl +csl: plos.csl +always_allow_html: true link-citations: true github-repo: NOAA-EDAB/catalog url: https://noaa-edab.github.io/catalog/ @@ -18,10 +20,6 @@ description: "This catalogue holds information on available datasets and indicat # Introduction {-} -* THIS BOOK IS IN DEVELOPEMENT. * - - - The purpose of this document is to collate the indicator information and methods used to inform the data driven products on the Northeast Shelf Large Marine Ecosystem by the Ecosystem Dynamics and Assessment Branch at the Northeast Fisheries Science Center (see figure, below). The information included in this document is contributed by a large variety of callaborators and contact information is available in each chapter. When possible, provided data is available in `ecodata`, an R package that holds the lastest version of the data available. @@ -43,7 +41,7 @@ knitr::opts_chunk$set(echo = F, ``` -(ref:neusmap) Map of Northeast U.S. Continental Shelf Large Marine Ecosystem from @Hare2016. +(ref:neusmap) Map of Northeast U.S. Continental Shelf Large Marine Ecosystem from @hare_vulnerability_2016 ```{r neusmap, message = FALSE, warning=FALSE, fig.align='center',out.width="75%", echo = F, fig.cap='(ref:neusmap)'} knitr::include_graphics("images/journal.pone.0146756.g002.PNG") diff --git a/plos.csl b/plos.csl new file mode 100644 index 00000000..090e5f72 --- /dev/null +++ b/plos.csl @@ -0,0 +1,316 @@ + + \ No newline at end of file