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-["index.html", "Technical Documentation, State of the Ecosystem Report Introduction", " Technical Documentation, State of the Ecosystem Report Northeast Fisheries Science Center 13 February 2020 Introduction The purpose of this document is to collate the methods used to access, collect, process, and analyze derived data (“indicators”) used to describe the status and trend of social, economical, ecological, and biological conditions in the Northeast Shelf Large Marine Ecosystem (see figure, below). These indicators are further synthesized in State of the Ecosystem Reports produced annually by the Northeast Fisheries Science Center for the New England Fisheries Management Council and the Mid-Atlantic Fisheries Management Council. The metadata for each indicator (in accordance with the Public Access to Research Results (PARR) directive) and the methods used to construct each indicator are described in the subsequent chapters, with each chapter title corresponding to an indicator or analysis present in State of the Ecosystem Reports. The most recent and usable html version of this document can be found at the NOAA EDAB Github. The PDF version of this document is for archiving only. Indicators included in this document were selected to clearly align with management objectives, which is required for integrated ecosystem assessment (Levin et al. 2009), and has been advised many times in the literature (Degnbol and Jarre 2004; Jennings 2005; Rice and Rochet 2005; Link 2005). A difficulty with practical implementation of this in ecosystem reporting can be the lack of clearly specified ecosystem-level management objectives (although some have been suggested (Murawski 2000)). In our case, considerable effort had already been applied to derive both general goals and operational objectives from both US legislation such as the Magnuson-Stevens Fisheries Conservation and Management Act (MSA) and regional sources (DePiper et al. 2017). These objectives are somewhat general and would need refinement together with managers and stakeholders, however, they serve as a useful starting point to structure ecosystem reporting. Figure 0.1: Map of Northeast U.S. Continental Shelf Large Marine Ecosystem from Hare et al. (2016). References "],
-["erddap.html", "1 Data and Code Access", " 1 Data and Code Access 1.0.1 About The Technical Documentation for the State of the Ecosystem (SOE) reports is a bookdown document; hosted on the NOAA Northeast Fisheries Science Center (NEFSC) Ecosystems Dynamics and Assessment Branch Github page, and developed in R. Derived data used to populate figures in this document are queried directly from the ecodata R package or the NEFSC ERDDAP server. ERDDAP queries are made using the R package rerddap. 1.0.2 Accessing data and build code In this technical documentation, we hope to shine a light on the processing and analytical steps involved to get from source data to final product. This means that whenever possible, we have included the code involved in source data extraction, processing, and analyses. We have also attempted to thoroughly describe all methods in place of or in supplement to provided code. Example plotting code for each indicator is presented in sections titled “Plotting”, and these code chunks can be used to recreate the figures found in ecosystem reporting documents where each respective indicator was included1. Source data for the derived indicators in this document are linked to in the text unless there are privacy concerns involved. In that case, it may be possible to access source data by reaching out to the Point of Contact associated with that data set. Derived data sets make up the majority of the indicators presented in ecosystem reporting documents, and these data sets are available for download through the ecodata R package. 1.0.3 Building the document Start a local build of the SOE bookdown document by first cloning the project’s associated git repository. Next, if you would like to build a past version of the document, use git checkout [version_commit_hash] to revert the project to a past commit of interest, and set build_latest <- FALSE in this code chunk. This will ensure the project builds from a cached data set, and not the most updated versions present on the NEFSC ERDDAP server. Once the tech-doc.Rproj file is opened in RStudio, run bookdown::serve_book() from the console to build the document. 1.0.3.1 A note on data structures The majority of the derived time series used in State of the Ecosystem reports are in long format. This approach was taken so that all disparate data sets could be “bound” together for ease of use in our base plotting functions. There are multiple R scripts sourced throughout this document in an attempt to keep code concise. These scripts include BasePlot_source.R, GIS_source.R, and get_erddap.R. The scripts BasePlot_source.R and GIS_source.R refer to deprecated code used prior to the 2019 State of the Ecosystem reports. Indicators that were not included in reports after 2018 make use of this syntax, whereas newer indicators typically use ggplot2 for plotting.↩︎ "],
-["aggroups.html", "2 Aggregate Groups 2.1 Methods", " 2 Aggregate Groups Description: Mappings of species into aggregate group categories for different analyses Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2018, 2019, 2020) Indicator category: Synthesis of published information Contributor(s): Geret DePiper, Sarah Gaichas, Sean Hardison, Sean Lucey Data steward: Sean Lucey Sean.Lucey@noaa.gov Point of contact: Sean Lucey Sean.Lucey@noaa.gov Public availability statement: Source data is available to the public (see Data Sources). 2.1 Methods The State of the Ecosystem (SOE) reports are delivered to the New England Fishery Management Council (NEFMC) and Mid-Atlantic Fishery Management Council (MAFMC) to provide ecosystems context. To better understand that broader ecosystem context, many of the indicators are reported at an aggregate level rather than at a single species level. Species were assigned to an aggregate group following the classification scheme of Garrison and Link (2000) and Link et al. (2006). Both works classified species into feeding guilds based on food habits data collected at the Northeast Fisheries Science Center (NEFSC). In 2017, the SOE used seven specific feeding guilds (plus an “other” category; Table 2.1). These seven were the same guilds used in Garrison and Link (2000), which also distinguished ontogentic shifts in species diets. For the purposes of the SOE, species were only assigned to one category based on the most prevalent size available to commercial fisheries. However, several of those categories were confusing to the management councils, so in 2018 those categories were simplified to five (plus “other”; Table 2.2) along the lines of Link et al. (2006). In addition to feeding guilds, species managed by the councils have been identified. This is done to show the breadth of what a given council is responsible for within the broader ecosystem context. In the 2020 report, squids were moved from planktivores to piscivores. Table 2.1: Aggregate groups use in 2017 SOE. Classifications are based on Garrison and Link (2000) . Feeding.Guild Description Apex Predator Top of the food chain Piscivore Fish eaters Macrozoo-piscivore Shrimp and small fish eaters Macroplanktivore Amphipod and shrimp eaters Mesoplanktivore Zooplankton eaters Benthivore Bottom eaters Benthos Things that live on the bottom Other Things not classified above Table 2.2: Aggregate groups use in 2018 SOE. Classifications are based on Link et al. (2006). Feeding.Guild Description Apex Predator Top of the food chain Piscivore Fish eaters Planktivore Zooplankton eaters Benthivore Bottom eaters Benthos Things that live on the bottom Other Things not classified above 2.1.1 Data sources In order to match aggregate groups with various data sources, a look-up table was generated which includes species’ common names (COMNAME) along with their scientific names (SCINAME) and several species codes. SVSPP codes are used by the NEFSC Ecosystems Surveys Branch (ESB) in their fishery-independent Survey Database (SVDBS), while NESPP3 codes refer to the codes used by the Commercial Fisheries Database System (CFDBS) for fishery-dependent data. A third species code provided is the ITISSPP, which refers to species identifiers used by the Integrated Taxonomic Information System (ITIS). Digits within ITIS codes are hierarchical, with different positions in the identifier referring to higher or lower taxonomic levels. More information about the SVDBS, CFDBS, and ITIS species codes are available in the links provided below. Management responsibilities for different species are listed under the column “Fed.managed” (NEFMC, MAFMC, or JOINT for jointly managed species). More information about these species is available on the FMC websites listed below. Species groupings listed in the “NEIEA” column were developed for presentation on the Northeast Integrated Ecosystem Assessment (NE-IEA) website. These groupings are based on EMAX groupings (Link et al. 2006), but were adjusted based on conceptual models developed for the NE-IEA program that highlight focal components in the Northeast Large Marine Ecosystem (i.e. those components with the largest potential for perturbing ecosystem dynamics). NE-IEA groupings were further simplified to allow for effective communication through the NE-IEA website. 2.1.1.1 Supplemental information See the following links for more information regarding the NEFSC ESB Bottom Trawl Survey, CFDBS, and ITIS: https://www.itis.gov/ https://inport.nmfs.noaa.gov/inport/item/22561 https://inport.nmfs.noaa.gov/inport/item/22560 https://inport.nmfs.noaa.gov/inport/item/27401 More information about the NE-IEA program is available here. More information about the New Engalnd Fisheries Management Council is available here. More information about the Mid-Atlantic Fisheries Management Council is available here. 2.1.2 Data extraction Species lists are pulled from SVDBS and CFDBS. They are merged using the ITIS code. Classifications from Garrison and Link (Garrison and Link 2000) and Link et al. (Link et al. 2006) are added manually. The R code used in the extraction process can be found here. References "],
-["annual-sst-cycles.html", "3 Annual SST Cycles 3.1 Methods", " 3 Annual SST Cycles Description: Annual SST Cycles Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull with analysis Contributor(s): Sean Hardison, Vincent Saba Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Source data are available here. 3.1 Methods 3.1.1 Data sources Data for annual sea surface tempature (SST) cycles were derived from the NOAA optimum interpolation sea surface temperature (OISST) high resolution dataset (NOAA OISST V2 dataset) provided by NOAA’s Earth System Research Laboratory’s Physical Sciences Devision, Boulder, CO. The data extend from 1981 to present, and provide a 0.25° x 0.25° global grid of SST measurements (Reynolds et al. 2007). Gridded SST data were masked according to the extent of Ecological Production Units (EPU) in the Northeast Large Marine Ecosystem (NE-LME) (See “EPU_Extended” shapefiles). 3.1.2 Data extraction Daily mean sea surface temperature data for 2017 and for each year during the period of 1981-2012 were downloaded from the NOAA OI SST V2 site to derive the long-term climatological mean for the period. The use of a 30-year climatological reference period is a standard procedure for metereological observing (WMO 2017). These reference periods serve as benchmarks for comparing current or recent observations, and for the development of standard anomaly data sets. The reference period of 1982-2012 was chosen to be consistent with previous versions of the State of the Ecosystem report. R code used in extraction and processing can be found here 3.1.3 Data analysis We calculated the long-term mean and standard deviation of SST over the period of 1982-2012 for each EPU, as well as the daily mean for 2017. R code used for analysis and plotting can be found here Figure 3.1: Long-term mean SSTs for the Mid-Atlantic Bight (A), Georges Bank (B), and Gulf of Maine (C). Orange and cyan shading show where the 2017 daily SST values were above or below the long-term mean respectively; red and dark blue shades indicate days when the 2017 mean exceeded +/- 1 standard deviation from the long-term mean. References "],
-["aquaculture.html", "4 Aquaculture 4.1 Methods", " 4 Aquaculture Description: Aquaculture indicators Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019) Indicator category: Synthesis of published information Contributor(s): Sean Hardison, Lisa Calvo, Karl Roscher Data steward: Kimberly Bastille kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille kimberly.bastille@noaa.gov Public availability statement: Source data are publicly available in referenced reports, and are also available for download here. 4.1 Methods Aquaculture data included in the State of the Ecosystem (SOE) report were time series of number of oysters sold in Virginia, Maryland, and New Jersey. 4.1.1 Data sources Virginia oyster harvest data are collected from mail and internet-based surveys of active oyster aquaculture operations on both sides of the Chesapeake Bay, which are then synthesized in an annual report (Hudson 2017). In Maryland, shellfish aquaculturists are required to report their monthly harvests to the Maryland Department of Natural Resources (MD-DNR). The MD-DNR then aggregates the harvest data for release in the Maryland Aquaculture Coordinating Council Annual Report (ACC 2017), from which data were collected. Similar to Virginia, New Jersey releases annual reports synthesizing electronic survey results from lease-holding shellfish growers. Data from New Jersey reflects cage reared oysters grown from hatchery seed (Calvo 2017). 4.1.2 Data extraction Data were collected directly from state aquaculture reports. Oyster harvest data in MD was reported in bushels which were then converted to individual oysters by an estimate of 300 oysters bushel\\(^{-1}\\). View processing code for this indicator here. 4.1.3 Data analysis No data analyses occurred for this indicator. 4.1.4 Data processing Aquaculture data were formatted for inclusion in the ecodata R package using the code found here. 4.1.5 Plotting Code for plotting data included in the State of the Ecosystem report can be found here. Figure 4.1: Oyster aquaculture production in terms of number of oysters sold from Virginia, Maryland, and New Jersey. References "],
-["bennet-indicator.html", "5 Bennet Indicator 5.1 Methods", " 5 Bennet Indicator Description: Bennet Indicator Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2018, 2019, 2020) Indicator category: Database pull with analysis Contributor(s): John Walden Data steward:Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: John Walden, john.walden@noaa.gov Public availability statement: Derived CFDBS data are available for this analysis (see Comland). 5.1 Methods 5.1.1 Data sources Data used in the Bennet Indicator were derived from the Comland data set; a processed subset of the Commercial Fisheries Database System (CFDBS). The derived Comland data set is available for download here. 5.1.2 Data extraction For information regarding processing of CFDBS, please see Comland methods. The Comland dataset containing seafood landings data was subsetted to US landings after 1964 where revenue was \\(\\ge\\) 0 for each Ecological Production Unit (i.e. Mid-Atlantic Bight, Georges Bank, and Gulf of Maine). Each EPU was run in an individual R script, and the code specific to Georges Bank is shown [here](. 5.1.3 Data analysis Revenue earned by harvesting resources from a Large Marine Ecosystem (LME) at time t is a function of both the quantity landed of each species and the prices paid for landings. Changes in revenue between any two years depends on both prices and quantities in each year, and both may be changing simultaneously. For example, an increase in the harvest of higher priced species, such as scallops can lead to an overall increase in total revenue from an LME between time periods even if quantities landed of other species decline. Although measurement of revenue change is useful, the ability to see what drives revenue change, whether it is changing harvest levels, the mix of species landed, or price changes provides additional valuable information. Therefore, it is useful to decompose revenue change into two parts, one which is due to changing quantities (or volumes), and a second which is due to changing prices. In an LME, the quantity component will yield useful information about how the species mix of harvests are changing through time. A Bennet indicator (BI) is used to examine revenue change between 1964 and 2015 for two major LME regions. It is composed of a volume indicator (VI), which measures changes in quantities, and a price indicator (PI) which measures changes in prices. 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 one to examine what component of overall revenue is responsible for change between time periods. This allows us to examine whether changing quantities or prices of separate species groups are driving revenue change in each EPU between 1964 and 2015. Revenue in a given year for any species group is the product of quantity landed times price, and the sum of revenue from all groups is total revenue from the LME. In any year, both prices and quantities can change from prior years, leading to total revenue change. At time t, revenue (R) is defined as \\[R^{t} = \\sum_{j=1}^{J}p_{j}^{t}y_{j}^{t},\\] where \\(p_{j}\\) is the price for species group \\(j\\), and \\(y_{j}\\) is the quantity landed of species group \\(j\\). Revenue change between any two time periods, say \\(t+1\\) and \\(t\\), is then \\(R^{t+1}-R^{t}\\), which can also be expressed as: \\[\\Delta R = \\sum_{j=1}^{J}p_{j}^{t+1}y_{j}^{t+1}-\\sum_{j=1}^{J}p_{j}^{t}y_{j}^{t}.\\] 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 = \\frac{1}{2}(\\sum_{j=1}^{J}p_{j}^{t+1}y_{j}^{t+1} - \\sum_{j=1}^{J}p_{j}^{t+1}y_{j}^{t} + \\sum_{j=1}^{J}p_{j}^{t}y_{j}^{t+1} - \\sum_{j=1}^{J}p_{j}^{t}y_{j}^{t})\\] The price indicator (PI) is calculated as follows: \\[PI = \\frac{1}{2}(\\sum_{j=1}^{J}y_{j}^{t+1}p_{j}^{t+1} - \\sum_{j=1}^{J}y_{j}^{t+1}p_{j}^{t} + \\sum_{j=1}^{J}y_{j}^{t}p_{j}^{t+1} - \\sum_{j=1}^{J}y_{j}^{t}p_{j}^{t})\\] Total revenue change between time \\(t\\) and \\(t+1\\) is the sum of the VI and PI. Since revenue change is being driven by changes in the individual prices and quantities landed of each species group, changes at the species group 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 the VI for each group will equal the overall VI, and the sum of the PI for each group will equal the overall PI. 5.1.4 Data processing Bennet indicator time series were formatted for inclusion in the ecodata R package using the R code found here 5.1.5 Plotting Code for plotting the bennet indicator can be found here. Figure 5.1: Revenue change from the long-term mean in 2015 dollars (black), Price (PI), and Volume Indicators (VI) for commercial landings in the Mid-Atlantic. References "],
-["bottom-temperatures.html", "6 Bottom temperatures 6.1 Methods", " 6 Bottom temperatures Description: Time series of annual in situ bottom temperatures on the Northeast Continental Shelf. Indicator category: Extensive analysis; not yet published Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2019, 2020); State of the Ecosystem - Mid-Atlantic Bight (2019, 2020) Contributor(s): Paula Fratantoni, paula.fratantoni@noaa.gov Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Paula Fratantoni, paula.fratantoni@noaa.gov Public availability statement: Source data are publicly available at ftp://ftp.nefsc.noaa.gov/pub/hydro/matlab_files/yearly and in the World Ocean Database housed at http://www.nodc.noaa.gov/OC5/SELECT/dbsearch/dbsearch.html under institute code number 258. 6.1 Methods 6.1.1 Data sources The bottom temperature index incorporates near-bottom temperature measurements collected on Northeast Fisheries Science Center (NEFSC) surveys between 1977-present. Early measurements were made using surface bucket samples, mechanical bathythermographs and expendable bathythermograph probes, but by 1991 the CTD – an acronym for conductivity temperature and depth – became standard equipment on all NEFSC surveys. Near-bottom refers to the deepest observation at each station that falls within 10 m of the reported water depth. Observations encompass the entire continental shelf area extending from Cape Hatteras, NC to Nova Scotia, Canada, inclusive of the Gulf of Maine and Georges Bank. 6.1.2 Data extraction While all processed hydrographic data are archived in an Oracle database (OCDBS), we work from Matlab-formatted files stored locally. 6.1.3 Data analysis Ocean temperature on the Northeast U.S. Shelf varies significantly on seasonal timescales. Any attempt to resolve year-to-year changes requires that this seasonal variability be quantified and removed to avoid bias. This process is complicated by the fact that NEFSC hydrographic surveys conform to a random stratified sampling design meaning that stations are not repeated at fixed locations year after year so that temperature variability cannot be assessed at fixed station locations. Instead, we consider the variation of the average bottom temperature within four Ecological Production Units (EPUs): Middle Atlantic Bight, Georges Bank, Gulf of Maine and Scotian Shelf. Within each EPU, ocean temperature observations are extracted from the collection of measurements made within 10 m of the bottom on each survey and an area-weighted average temperature is calculated. The result of this calculation is a timeseries of regional average near-bottom temperature having a temporal resolution that matches the survey frequency in the database. Anomalies are subsequently calculated relative to a reference annual cycle, estimated using a multiple linear regression model to fit an annual harmonic (365-day period) to historical regional average temperatures from 1981-2010. The curve fitting technique to formulate the reference annual cycle follows the methodologies outlined by Mountain (1991). The reference period was chosen because it is the standard climatological period adopted by the World Meteorological Organization. The resulting anomaly time series represents the difference between the time series of regional mean temperatures and corresponding reference temperatures predicted by a reference annual cycle for the same time of year. Finally, a reference annual average temperature (calculated as the average across the reference annual cycle) is added back into the anomaly timeseries to convert temperature anomalies back to ocean bottom temperature. 6.1.4 Data processing Derived bottom temperature data were formatted for inclusion in the ecodata R package using the R code found here. 6.1.5 Plotting Code for plotting Georges Bank and Gulf of Maine bottom temperature time series can be found here. References "],
-["catch-and-fleet-diversity.html", "7 Catch and Fleet Diversity 7.1 Methods", " 7 Catch and Fleet Diversity Description: Permit-level species diversity and Council-level fleet diversity. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull with analysis; Published methods Contributor(s): Geret DePiper, Min-Yang Lee Data steward: Geret DePiper, geret.depiper@noaa.gov Point of contact: Geret DePiper, geret.depiper@noaa.gov Public availability statement: Source data is not publicly availabe due to PII restrictions. Derived time series are available for download here. 7.1 Methods Diversity estimates have been developed to understand whether specialization, or alternatively stovepiping, is occurring in fisheries of the Northeastern Large Marine Ecosystem. We use the average effective Shannon indices for species revenue at the permit level, for all permits landing any amount of NEFMC or MAFMC Fishery Management Plan (FMP) species within a year (including both Monkfish and Spiny Dogfish). We also use the effective Shannon index of fleet revenue diversity and count of active fleets to assess the extent to which the distribution of fishing changes across fleet segments. 7.1.1 Data sources Data for these diversity estimates comes from a variety of sources, including the Commercial Fishery Dealer Database, Vessel Trip Reports, Clam logbooks, vessel characteristics from Permit database, WPU series producer price index. These data are typically not available to the public. 7.1.2 Data extraction The following describes both the permit-level species and fleet diversity data generation. Price data was extracted from the Commercial Fishery Dealer database (CFDERS) and linked to Vessel Trip Reports by a heirarchical matching algorithm that matched date and port of landing at its highest resolution. Code used in these analyses is available upon request. Output data was then matched to vessel characteristics from the VPS VESSEL data set. For the permit-level estimate, species groups are based off of a slightly refined NESPP3 code (Table 7.1), defined in the data as “myspp”, which is further developed in the script to rectify inconsistencies in the data. Table 7.1: Species grouping Group NESPP3 Common Name Scientific Name Highly Migratory Species 470 ALBACORE THUNNUS ALALUNGA 494 ATLANTIC SHARPNOSE SHARK RHIZOPRIONODON TERRAENOVAE 354 BIGEYE THRESHER SHARK ALOPIAS SUPERCILIOSUS 469 BIGEYE TUNA THUNNUS OBESUS 487 BLACKTIP SHARK CARCHARHINUS LIMBATUS 493 BLUE SHARK PRIONACE GLAUCA 467 BLUEFIN TUNA THUNNUS THYNNUS 468 LITTLE TUNNY EUTHYNNUS ALLETTERATUS 358 LONGFIN MAKO ISURUS PAUCUS 481 PORBEAGLE SHARK LAMNA NASUS 349 SAND TIGER CARCHARIAS TAURUS 482 SANDBAR SHARK CARCHARHINUS PLUMBEUS 359 SHARK,UNC CHONDRICHTHYES 355 SHORTFIN MAKO ISURUS OXYRINCHUS 466 SKIPJACK TUNA KATSUWONUS PELAMIS 432 SWORDFISH XIPHIAS GLADIUS 353 THRESHER SHARK ALOPIAS VULPINUS 491 TIGER SHARK GALEOCERDO CUVIER 471 YELLOWFIN TUNA THUNNUS ALBACARES Monkfish in Mid-Atlantic Waters 11 GOOSEFISH LOPHIUS AMERICANUS 12 GOOSEFISH LOPHIUS AMERICANUS Atlantic Scallops 800 SEA SCALLOP PLACOPECTEN MAGELLANICUS Shrimp 737 MANTIS SHRIMP UNCL STOMATOPODA 737 MANTIS SHRIMPS STOMATOPODA 736 NORTHERN SHRIMP PANDALUS BOREALIS 738 SHRIMP,ATLANTIC & GULF,BROWN PANAEIDAE 735 SHRIMP,UNC (CARIDEA) CARIDEA Skates 368 BARNDOOR SKATE DIPTURUS LAEVIS 372 CLEARNOSE SKATE RAJA EGLANTERIA 366 LITTLE SKATE LEUCORAJA ERINACEA 365 OCELLATE SKATES RAJA 365 SKATES RAJIDAE 373 SKATES,LITTLE/WINTER MIXED LEUCORAJA 369 SMOOTH SKATE MALACORAJA SENTA 370 THORNY SKATE AMBLYRAJA RADIATA 367 WINTER SKATE LEUCORAJA OCELLATA Herring 168 ATLANTIC HERRING CLUPEA HARENGUS Ocean Quahog 754 OCEAN QUAHOG ARCTICA ISLANDICA Surf Clam 769 ATLANTIC SURFCLAM SPISULA SOLIDISSIMA Tilefish 444 BLUELINE TILEFISH CAULOLATILUS MICROPS 445 SAND TILEFISH MALACANTHUS PLUMIERI 446 TILEFISH LOPHOLATILUS CHAMAELEONTICEPS 447 TILEFISH,UNC MALACANTHIDAE Fluke & Black Seabass 335 BLACK SEA BASS CENTROPRISTIS STRIATA 121 SUMMER FLOUNDER PARALICHTHYS DENTATUS Butterfish & Hake 51 BUTTERFISH PEPRILUS TRIACANTHUS 152 RED HAKE UROPHYCIS CHUSS 509 SILVER HAKE MERLUCCIUS BILINEARIS Bluefish in Mid-Atlantic Waters 23 BLUEFISH POMATOMUS SALTATRIX Spiny Dogfish 352 SPINY DOGFISH SQUALUS ACANTHIAS Northern Shortfin Squid 802 NORTHERN SHORTFIN SQUID ILLEX ILLECEBROSUS American Lobster 727 AMERICAN LOBSTER HOMARUS AMERICANUS Longfin Squid 801 LONGFIN SQUID LOLIGO PEALEII Menhaden 221 MENHADEN BREVOORTIA Offshore Hake 508 OFFSHORE HAKE MERLUCCIUS ALBIDUS Scup in Mid-Atlantic Waters 329 SCUP STENOTOMUS CHRYSOPS Windowpane Flounder in New England Waters 125 WINDOWPANE SCOPHTHALMUS AQUOSUS Ocean Pout in New England Waters 250 OCEAN POUT ZOARCES AMERICANUS Wolffish 512 ATLANTIC WOLFFISH ANARHICHAS LUPUS Winter Flounder in Mid-Atlantic Waters 120 WINTER FLOUNDER PSEUDOPLEURONECTES AMERICANUS Yellowtail Flounder in Mid-Atlantic Waters 123 YELLOWTAIL FLOUNDER LIMANDA FERRUGINEA Unclassified Hake 155 Unclassified Hake White Hake in Mid-Atlantic Waters 153 WHITE HAKE UROPHYCIS TENUIS Bluefish & Scup in New England Waters 23 BLUEFISH POMATOMUS SALTATRIX 329 SCUP STENOTOMUS CHRYSOPS Halibut in New England Waters 159 ATLANTIC HALIBUT HIPPOGLOSSUS HIPPOGLOSSUS Groundfish in New England Waters 240 ACADIAN REDFISH SEBASTES FASCIATUS 124 AMERICAN PLAICE HIPPOGLOSSOIDES PLATESSOIDES 81 ATLANTIC COD GADUS MORHUA 11 GOOSEFISH LOPHIUS AMERICANUS 12 GOOSEFISH LOPHIUS AMERICANUS 147 HADDOCK MELANOGRAMMUS AEGLEFINUS 269 POLLOCK POLLACHIUS VIRENS 153 WHITE HAKE UROPHYCIS TENUIS 120 WINTER FLOUNDER PSEUDOPLEURONECTES AMERICANUS 122 WITCH FLOUNDER GLYPTOCEPHALUS CYNOGLOSSUS 123 YELLOWTAIL FLOUNDER LIMANDA FERRUGINEA Groundfish in Mid-Atlantic Waters 240 ACADIAN REDFISH SEBASTES FASCIATUS 124 AMERICAN PLAICE HIPPOGLOSSOIDES PLATESSOIDES 81 ATLANTIC COD GADUS MORHUA 159 ATLANTIC HALIBUT HIPPOGLOSSUS HIPPOGLOSSUS 512 ATLANTIC WOLFFISH ANARHICHAS LUPUS 147 HADDOCK MELANOGRAMMUS AEGLEFINUS 269 POLLOCK POLLACHIUS VIRENS 122 WITCH FLOUNDER GLYPTOCEPHALUS CYNOGLOSSUS 155 Unclassified Hake Windowpane Flounder & Ocean Pout in Mid-Atlantic Waters 250 OCEAN POUT ZOARCES AMERICANUS 125 WINDOWPANE SCOPHTHALMUS AQUOSUS For the fleet diversity metric, gears include scallop dredge (gearcodes DRS, DSC, DTC, and DTS), other dredges (gearcodes DRM, DRO, and DRU), gillnet (gearcodes GND, GNT, GNO, GNR, and GNS), hand (gearcode HND), longline (gearcodes LLB and LLP), bottom trawl (gearcodes OTB, OTF, OTO, OTC. OTS, OHS, OTR, OTT, and PTB), midwater trawls (gearcode OTM and PTM), pot (gearcodes PTL, PTW, PTC, PTE, PTF, PTH, PTL, PTO, PTS, and PTX), purse seine (gearcode PUR), and hydraulic clam dredge (gearcode DRC).Vessels were further grouped by length categories of less than 30 feet, 30 to 50 feet, 50 to 75 feet, and 75 feet and above. All revenue was deflated to real dollars using the “WPU0223” Producer Price Index with a base of January 2015. Stata code for data processing is available here. 7.1.3 Data analysis This permit-level species effective Shannon index is calculated as \\[exp(-\\sum_{i=1}^{N}p_{ijt}ln(p_{ijt}))\\] for all \\(j\\), with \\(p_{ijt}\\) representing the proportion of revenue generated by species or species group \\(i\\) for permit \\(j\\) in year \\(t\\), and is a composite of richness (the number of species landed) and abundance (the revenue generated from each species). The annual arithmetic mean value of the effective Shannon index across permits is used as the indicator of permit-level species diversity. In a similar manner, the fleet diversity metric is estimated as \\[exp(-\\sum_{i=1}^{N}p_{kt}ln(p_{kt})) \\] for all \\(k\\), where \\(p_{kt}\\) represents the proportion of total revenue generated by fleet segment \\(k\\) (gear and length combination) per year \\(t\\). The indices each run from 1996 to 2017. A count of the number of fleets active in every year is also provided to assess whether changes in fleet diversity are caused by shifts in abundance (number of fleets), or evenness (concentration of revenue). The work is based off of analysis conducted in Thunberg and Correia (2015) and published in Gaichas et al. (2016). 7.1.4 Data processing Catch and fleet diversity indicators were formatted for inclusion in the ecodata R package using the R script found here. 7.1.5 Plotting Code for plotting the catch and fleet diversity indicator can be found here. Figure 7.1: Fleet diversity and fleet count in the Mid-Atlantic. References "],
-["chesapeake-bay-salinity.html", "8 Chesapeake Bay Salinity 8.1 Methods", " 8 Chesapeake Bay Salinity Description: Chesapeake Bay Salinity Found in: State of the Ecosystem - Mid-Atlantic (2020) Indicator category: Contributor(s): Bruce Vogt Data steward: Bruce Vogt Point of contact: Bruce Vogt, bruce.vogt@noaa.gov Public availability statement: Source data are publicly available. 8.1 Methods 8.1.1 Data sources Low salinity levels recorded by NOAA Chesapeake Bay Office’s Chesapeake Bay Interpretive Buoy System (CBIBS). NOAA Chesapeake Bay Office https://buoybay.noaa.gov/node/160 Chesapeake Bay Program https://www.chesapeakebay.net/news/blog/what_this_summers_rainfall_could_mean_for_the_bay Maryland Department of Natural Resources (Jodi Baxter, Lynn Fegley) Virginia Marine Resources Commission (Andrew Button) University of Maryland Center for Environmental Science, Horn Point Laboratory Oyster Hatchery (Stephanie Tobash Alexander) 8.1.2 Data extraction 8.1.3 Data analysis 8.1.4 Data processing Code for processing salinity data can be found here. 8.1.5 Plotting "],
-["chesapeake-bay-water-quality-standards-attainment.html", "9 Chesapeake Bay Water Quality Standards Attainment 9.1 Methods", " 9 Chesapeake Bay Water Quality Standards Attainment Description: A multimetric indicator describing the attainment status of Chesapeake Bay with respect to three water quality standards criteria, namely, dissolved oxygen, chlorophyll-a, and water clarity/submerged aquatic vegetation. Indicator category: Published method; Database pull with analysis Found in: State of the Ecosystem - Mid-Atlantic (2019) Contributor(s): Qian Zhang, Rebecca Murphy, Richard Tian, Melinda Forsyth, Emily Trentacoste, Jeni Keisman, and Peter Tango. Data steward: Qian Zhang, qzhang@chesapeakebay.net Point of contact: Qian Zhang, qzhang@chesapeakebay.net Public availability statement: Data are publicly available (see Data Sources below). 9.1 Methods 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) (USEPA 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 (USEPA 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. 9.1.1 Data sources 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. 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. 9.1.2 Data analysis Criteria attainment assessment 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). Indicator calculation 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 2015-2017. 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 (USEPA 2017). For more details, refer to the Methods section of Zhang et al. (2018). The indicator provides an integrated measure of Chesapeake Bay’s water quality condition (Figure 1). In 2015-2017, 42% of all tidal water segment-DU-criterion combinations are estimated to have met or exceeded applicable water quality criteria thresholds, which marks the best 3-year status since 1985-1987. The indicator has a positive and statistically significant trend from 1985 to 2017, which shows that Chesapeake Bay is on a positive trajectory toward recovery. This pattern was statistically linked to total nitrogen reduction, indicating responsiveness of attainment status to management actions implemented to reduce nutrients in the system. Figure 9.1: Time series of the multimetric indicator score for estimated Chesapeake Bay water quality standards attainment for each 3-year assessment period between 1985-1987 and 2015-2017. A significant positive trend for the time series is shown by the orange line (p < 0.05). Patterns of attainment of individual DUs are variable (Figure 2). Changes in OW-DO, DC-DO, and water clarity/SAV have shown long-term improvements, which have contributed to overall attainment indicator improvement. By contrast, the MSN-DO attainment experienced a sharp spike in the first few assessment periods but generally degraded after the 1997-1999, which has implications to the survival, growth, and reproduction of the migratory and resident tidal freshwater fish during spawning and nursery season in the tidal freshwater to low-salinity habitats. The status and trends of tidal segments’ attainment may be used to inform siting decisions of aquaculture operations in Chesapeake Bay. Figure 9.2: Time series of the estimated attainment of water quality standards (i.e., DO: dissolved oxygen; CHLA: chlorophyll-a; Clarity/SAV: water clarity/submerged aquatic vegetation) for five Chesapeake Bay designated uses (MSN: migratory spawning and nursery; OW: open water; DW: deep water; DC: deep channel; SW: shallow water) for each 3-year assessment period between 1985-1987 and 2015-2017. 9.1.3 Data processing The indicator data set was formatted for inclusion in the ecodata R package using the R script found here. References "],
-["chl-pp.html", "10 Chlorophyll a and Primary Production 10.1 Methods", " 10 Chlorophyll a and Primary Production Description: Chlorophyll a and Primary Production Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2018, 2019, 2020) Indicator category: Database pull; Database pull with analysis; Published methods Contributor(s): Kimberly Hyde Data steward: Kimberly Hyde, kimberly.hyde@noaa.gov Point of contact: Kimberly Hyde, kimberly.hyde@noaa.gov Public availability statement: Source data used in these analyses will be made publicly available. Derived data used in State of the Ecosystem Reports can be found here. 10.1 Methods 10.1.1 Data sources Level 1A ocean color remote sensing data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (NASA Ocean Biology Processing Group 2018) on the OrbView-2 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) (NASA Ocean Biology Processing Group 2017) on the Aqua satellite were acquired from the NASA Ocean Biology Processing Group (OBPG). Sea Surface Temperature (SST) data included the 4 km nighttime NOAA Advanced Very High Resolution Radiometer (AVHRR) Pathfinder (Casey et al. 2010; Saha et al. 2018) and the Group for High Resolution Sea Surface Temperature (GHRSST) Multiscale Ultrahigh Resolution (MUR, version 4.1) Level 4 (Chin, Vazquez-Cuervo, and Armstrong 2017; Project 2015) data. 10.1.2 Data extraction NA 10.1.3 Data analysis The SeaWiFS and MODIS L1A files were processed using the NASA Ocean Biology Processing Group SeaDAS software version 7.4. All MODIS files were spatially subset to the U.S. East Coast (SW longitude=-82.5, SW latitude=22.5, NE longitude=-51.5, NE latitude=48.5) using L1AEXTRACT_MODIS. SeaWiFS files were subset using the same coordinates prior to begin downloaded from the Ocean Color Web Browser. SeaDAS’s L2GEN program was used to generate Level 2 (L2) files using the default settings and optimal ancillary files, and the L2BIN program spatially and temporally aggregated the L2 files to create daily Level 3 binned (L3B) files. The daily files were binned at 2 km resolution that are stored in a global, nearly equal-area, integerized sinusoidal grids and use the default L2 ocean color flag masks. The global SST data were also subset to the same East Coast region and remapped to the same sinusoidal grid. The L2 files contain several ocean color products including the default chlorophyll a; product (CHL-OCI), photosynthetic available radiation (PAR), remote sensing reflectance \\((R_{rs}(\\lambda))\\), and several inherent optical property products (IOPs). The CHL-OCI product combines two algorithms, the O’Reilly band ratio (OCx) algorithm (O’Reilly et al. 1998) and the Hu color index (CI) algorithm (Hu, Lee, and Franz 2012). The SeaDAS default CHL-OCI algorithm diverges slightly from Hu, Lee, and Franz (2012) in that the transition between CI and OCx occurs at 0.15 < CI < 0.2 mg m-3 to ensure a smooth transition. The regional chlorophyll a algorithm by Pan et al. (2008) was used to create a second chlorophyll product (CHL-PAN). CHL-PAN is an empirical algorithm derived from in situ sampling within the Northeast Large Marine Ecosystem (NE-LME) and demonstrated significant improvements from the standard NASA operational algorithm in the NES-LME (Pan et al. 2010). A 3rd-order polynomial function (Equation (10.1)) is used to derive [CHL-PAN] from Rrs band ratios (RBR): \\[\\begin{equation} log[\\textrm{CHL-PAN}] = A_{0} + A_{1}X + A_{2}X^{2} + A_{3}X^{3}, \\tag{10.1} \\end{equation}\\] where \\(X = log(R_{rs}(\\lambda_{1})/R_{rs}(\\lambda_{2}))\\) and \\(A_{i} (i = 0, 1, 2, \\textrm{or } 3)\\) are sensor and RBR specific coefficients: If SeaWiFS and RBR is \\(R_{rs}(490)/R_{rs}(555)(R_{^3{\\mskip -5mu/\\mskip -3mu}_5})\\) then: \\(A_0=0.02534, A_1=-3.033, A_2=2.096, A_3=-1.607\\) If SeaWiFS and RBR is \\(R_{rs}(490)/R_{rs}(670)(R_{^3{\\mskip -5mu/\\mskip -3mu}_6})\\) then: \\(A_0=1.351, A_1=-2.427, A_2=0.9395, A_3=-0.2432\\) If MODIS and RBR is \\(R_{rs}(488)/R_{rs}(547)(R_{^3{\\mskip -5mu/\\mskip -3mu}_5})\\) then: \\(A_0=0. 03664, A_1=-3.451, A_2=2.276, A_3=-1.096\\) If MODIS and RBR is \\(R_{rs}(488)/R_{rs}(667)(R_{^3{\\mskip -5mu/\\mskip -3mu}_6})\\) then: \\(A_0=1.351, A_1=-2.427, A_2=0.9395, A_3=-0.2432\\) C3/5 and C3/6 were calculated for each sensor specific RBR (R3/5 and R3/6 respectively) and then the following criteria were used to determine to derive CHL-PAN: If \\(R_{^3{\\mskip -5mu/\\mskip -3mu}_5}>0.15\\) or \\(R_{6} <0.0001\\) then \\(\\textrm{CHL-PAN} = C_{^3{\\mskip -5mu/\\mskip -3mu}_5};\\) Otherwise, \\(\\textrm{CHL-PAN} = \\textrm{max}(C_{^3{\\mskip -5mu/\\mskip -3mu}_5}, C_{^3{\\mskip -5mu/\\mskip -3mu}_6})\\), where \\(R_6\\) is \\(R_{rs}(670)\\) (SeaWiFS) or \\(R_{rs}(667)\\) (Pan et al. 2010). The Vertically Generalized Production Model (VGPM) estimates net primary production (PP) as a function of chlorophyll a, photosynthetically available light and the photosynthetic efficiency (Behrenfeld and Falkowski 1997). In the VGPM-Eppley version, the original temperature-dependent function to estimate the chlorophyll-specific photosynthetic efficiency is replaced with the exponential “Eppley” function (equation PP1) as modified by Morel (1991). The VGPM calculates the daily amount of carbon fixed based on the maximum rate of chlorophyll-specific carbon fixation in the water column, sea surface daily photosynthetically available radiation, the euphotic depth (the depth where light is 1% of that at the surface), chlorophyll a concentration, and the number of daylight hours (Equation (10.2)). \\[\\begin{equation} P_{max}^{b}(SST) = 4.6 * 1.065^{SST-20^{0}} \\tag{10.2} \\end{equation}\\] Where \\(P_{max}^{b}\\) is the maximum carbon fixation rate and SST is sea surface temperature. \\[\\begin{equation} PP_{eu} = 0.66125 * P_{max}^{b} * \\frac{I_{0}}{I_{0}+4.1} * Z_{eu} * \\textrm{CHL} * \\text{DL} \\tag{10.3} \\end{equation}\\] Where \\(PP_{eu}\\) is the daily amount of carbon fixed integrated from the surface to the euphotic depth (mgC m-2 day-1), \\(P_{max}^{b}\\) is the maximum carbon fixation rate within the water column (mgC mgChl-1 hr-1), \\(I_{0}\\) is the daily integrated molar photon flux of sea surface PAR (mol quanta m-2 day-1), Zeu is the euphotic depth (m), CHL is the daily interpolated CHIi-OCI (mg m-3), and DL is the photoperiod (hours) calculated for the day of the year and latitude according to Kirk (1994). The light dependent function \\((I_{0}/(I_{0}+4.1))\\) describes the relative change in the light saturation fraction of the euphotic zone as a function of surface PAR (\\(I_0\\)). Zeu is derived from an estimate of the total chlorophyll concentration within the euphotic layer (CHLeu) based on the Case I models of Morel and Berthon (1989): For \\(\\textrm{CHL}_{eu} > 10.0\\;\\;\\;\\;\\;Z_{eu} = 568.2 * \\textrm{CHL}_{eu}^{-0.746}\\) For \\(\\textrm{CHL}_{eu} \\leq 10.0\\;\\;\\;\\;\\;Z_{eu} = 200.0 * \\textrm{CHL}_{eu}^{-0.293}\\) For \\(\\textrm{CHL}_{0} \\leq 1.0\\;\\;\\;\\;\\;\\textrm{CHL}_{eu} = 38.0 * \\textrm{CHL}_{0}^{0.425}\\) For \\(\\textrm{CHL}_{0} > 1.0\\;\\;\\;\\;\\;\\textrm{CHL}_{eu} = 40.2 * \\textrm{CHL}_{0}^{0.507}\\) Where \\(\\textrm{CHL}_0\\) is the surface chlorophyll concentration. Prior to being input into the VGPM-Eppley model, the daily CHL-OCI and AVHRR SST data were temporally interpolated and smoothed (CHL-OCIINT and SSTINT respectively) to increase the data coverage and better match data collected from different sensors and different times. The daily PAR data are not affected by cloud cover and MUR SST data is a blended/gap free data product so these products were not interpolated. Daily data at each pixel location covering the entire date range were extracted to create a pixel time series \\((D_{x,y})\\). \\((D_{x,y})\\) are linearly interpolated based on days in the time series using interpx.pro. Prior to interpolation, the CHL data are log-transformed to account for the log-normal distribution of chlorophyll data (Campbell 1995). Interpolating the entire times series requires a large amount of processing time so the series was processed one year at a time. Each yearly series included 60 days from the previous year and 60 days from the following year to improve the interpolation at the beginning and end of the year. Following interpolation, the data are smoothed with a tri-cube filter (width=7) using IDL’s CONVOL program. In order to avoid over interpolating data when there were several days of missing data in the time series, the interpolated data were removed and replaced with blank data if the window of interpolation spanned more than 7 days for CHL or 10 days for SST. After all Dx,y pixels had been processed, the one-dimensional pixel time series were converted back to two-dimensional daily files. Statistics, including the arithmetic mean, geometric mean (for CHL and PP), standard deviation, and coefficient of variation were calculated at daily (3 and 8-day running means), weekly, monthly, and annual time steps and for several climatological periods. Annual statistics used the monthly means as inputs to avoid a summer time bias when more data is available due to reduced cloud cover. The daily, weekly, monthly and annual climatological statistics include the entire time series for each specified period. For example, the climatological January uses the monthly mean from each January in the time series and the climatological annual uses the annual mean from each year. The CHL and PP climatological statistics include data from both SeaWiFS (1997-2007) and MODIS (2008-2017). Weekly, monthly and annual anomalies were calculated for each product by taking the difference between the mean of the input time period (i.e. week, month, year) and the climatological mean for the same period. Because bio-optical data are typically log-normally distributed (Campbell 1995), the CHL and PP data were first log-transformed prior to taking the difference and then untransformed, resulting in an anomaly ratio. The ecological production unit (EPU) shapefile that excludes the estuaries was used to spatially extract all data location within an ecoregion from the statistic and anomaly files. The median values, which are equivalent to the geometric mean, were used for the CHL and PP data. For the extended time series, the 1998-2007 data use the SeaWiFS ocean color products and MODIS-Aqua products were used from 2008 to 2017. Prior to June 2002, AVHRR Pathfinder data are used as the SST source and MUR SST in subsequent years. 10.1.4 Data processing CHL and PPD time series were formatted for inclusion in the ecodata R package using the R code found here. 10.1.5 Plotting Chl a and primary production data were also examined in relation to the long-term means of each series. The figures below show data specific to the Mid-Atlantic Bight. The code for the plots can be found here. Figure 10.1: Weekly chlorophyll concentrations in the Mid-Atlantic are shown by the colored line for 2019. The long-term mean is shown in black, and shading indicates +/- 1 sample SD. In the figure below, we show monthly primary productivity on an annual time step in the Mid-Atlantic Bight. The code for this can be found here Figure 10.2: Monthly primary production trends show the annual cycle (i.e. the peak during the summer months) and the changes over time for each month. References "],
-["fishing-community-climate-vulnerability.html", "11 Fishing Community Climate Vulnerability 11.1 Methods", " 11 Fishing Community Climate Vulnerability Description: Community climate vulnerability Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull with analysis Contributor(s): Lisa L. Colburn Data steward: Lisa L. Colburn, lisa.L.colburn@noaa.gov Point of contact: Lisa L. Colburn, lisa.L.colburn@noaa.gov Public availability statement: The fisheries data used for this analysis includes confidential information and is not available to the public. 11.1 Methods 11.1.1 Data sources The data used in community climate vulnerability analyses were derived from the following sources in partnership with the Atlantic Coastal Cooperative Statistics Program’s (ACCSP) Standard Atlantic Fisheries Information System (SAFIS). Database Name Description Cfdersyyyy The dealer data are transaction-level pricing at the level of the “market-category.” These data are primarily generated through mandatory reporting by federally-permitted fish dealers. The federal reporting is supplemented with data from non-federally-permitted (state-only) fish dealers. Data are currently reported electronically in partnership with ACCSP through SAFIS. Cfvessyyy A related database that contains permit information. In these databases, the variable “port” contains the post associated with the vessel. The variable “Statenm” refers to the state of the mailing address of the owner. 11.1.2 Data extraction Code for plotting the community climate vulnerability indicator can be found here. 11.1.3 Data analysis The results described below were developed using the methodology described in Colburn et al. (2016). Mapping community climate vulnerability - The map was produced using two variables: total value landed in a community and community species vulnerability, defined below: Communities were grouped based on total value of landings into the following categories: 1 (<$ 200,000), 2 ($200,000-$9,999,999), 3 ($10,000,000-$49, 999,999), and 4 ($50,000,000 and above). Only communities with a total value landed of $200,000 or more were selected for the mapping process. Community climate vulnerability is determined by the percent contribution of species classified as very high, high, moderate, or low climate vulnerability in a community. The percent contribution of species is calculated as following: % VH & H = value of landing contributed by species classified as having very high or high climate change vulnerability/total value of landings * 100 % M = value of landing contributed by species classified as having moderate climate change vulnerability/total value of landings * 100 % L = value of landing contributed by species classified as having low climate change vulnerability/ total value of landings * 100 If a community received a dominant score (50% or more) for any of the above categories, % VH &, %M, or %L, then the community received a respective community species vulnerability ranking of High, Moderate, or Low. For example, if 90% of the total value landed a community is contributed by species classified as having very high or high climate change vulnerability, then this community gets “Very High/High” community species vulnerability. In case of no dominant percentage identified, the community gets a “Mixed” community species vulnerability ranking. Pie charts - The pie charts were created using the National Marine Fisheries Service (NMFS) landings data pulled from New England Fisheries Science Center (NEFSC) databases in Woods Hole, MA. The percent contribution of each species was calculated by dividing the total value of landings in each port by each species’ landed value. Data was calculated and graphed in a pie chart in Excel and given the colors that represent High (red), Moderate (blue), Low (yellow) climate vulnerability. The “other” category consists of species with low landings and/or those that do not have a vulnerability ranking based on Hare et al. (2016). These species were aggregated and given the color gray. 11.1.4 Plotting Figure 11.1: Commercial species vulnerability to climate change in in New England fishing communities. References "],
-["conceptual-models.html", "12 Conceptual Models 12.1 Methods", " 12 Conceptual Models Description: Conceptual models for the New England (Georges Bank and Gulf of Maine) and Mid-Atlantic regions of the Northeast US Large Marine Ecosystem Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2018, 2019, 2020) Indicator category: Synthesis of published information, Extensive analysis; not yet published Contributor(s): Sarah Gaichas, Patricia Clay, Geret DePiper, Gavin Fay, Michael Fogarty, Paula Fratantoni, Robert Gamble, Sean Lucey, Charles Perretti, Patricia Pinto da Silva, Vincent Saba, Laurel Smith, Jamie Tam, Steve Traynor, Robert Wildermuth Data steward: Sarah Gaichas, sarah.gaichas@noaa.gov Point of contact: Sarah Gaichas, sarah.gaichas@noaa.gov Public availability statement: All source data aside from confidential commercial fisheries data (relevant only to some components of the conceptual models) are available to the public (see Data Sources below). 12.1 Methods Conceptual models were constructed to facilitate multidisciplinary analysis and discussion of the linked social-ecological system for integrated ecosystem assessment. The overall process was to first identify the components of the model (focal groups, human activities, environmental drivers, and objectives), and then to document criteria for including groups and linkages and what the specific links were between the components. The prototype conceptual model used to design Northeast US conceptual models for each ecosystem production unit (EPU) was designed by the California Current IEA program. The California Current IEA developed an overview conceptual model for the Northern California Current Large Marine Ecosystem (NCC), with models for each focal ecosystem component that detailed the ecological, environmental, and human system linkages. Another set of conceptual models outlined habitat linkages. An inital conceptual model for Georges Bank and the Gulf of Maine was outlined at the 2015 ICES WGNARS meeting. It specified four categories: Large scale drivers, focal ecosystem components, human activities, and human well being. Strategic management objectives were included in the conceptual model, which had not been done in the NCC. Focal ecosystem components were defined as aggregate species groups that had associated US management objectives (outlined within WGNARS for IEAs, see DePiper et al. (2017)): groundfish, forage fish, fished invertebrates, living habitat, and protected species. These categories roughly align with Fishery Managment Plans (FMPs) for the New England Fishery Management Council. The Mid-Atlantic conceptual model was developed along similar lines, but the focal groups included demersals, forage fish, squids, medium pelagics, clams/quahogs, and protected species to better align with the Mid Atlantic Council’s FMPs. After the initial draft model was outlined, working groups were formed to develop three submodels following the CCE example: ecological, environmental, and human dimensions. The general approach was to specify what was being included in each group, what relationship was represented by a link between groups, what threshold of the relationship was used to determine whether a relationship was significant enough to be included (we did not want to model everything), the direction and uncertainty of the link, and documentation supporting the link between groups. This information was recorded in a spreadsheet. Submodels were then merged together by common components using the “merge” function in the (currently unavailable) desktop version of Mental Modeler (http://www.mentalmodeler.org/#home; Gray et al. (2013)). The process was applied to Georges Bank (GB), the Gulf of Maine (GOM), and the Mid-Atlantic Bight (MAB) Ecological Production Units. 12.1.1 Data sources 12.1.1.1 Ecological submodels Published food web (EMAX) models for each subregion (J. S. Link et al. 2006; Link et al. 2008), food habits data collected by NEFSC trawl surveys (Smith and Link 2010), and other literature sources (Smith et al. 2015) were consulted. Expert judgement was also used to adjust historical information to current conditions, and to include broad habitat linkages to Focal groups. 12.1.1.2 Environmental submodels Published literature on the primary environmental drivers (seasonal and interannual) in each EPU was consulted. Sources for Georges Bank included Backus and Bourne (1987) and Townsend et al. (2006). Sources for the Gulf of Maine included Smith (1983), Smith et al. (2001), Mupparapu and Brown (2002), Townsend et al. (2006), Smith et al. (2012), and Mountain (2012). Sources for the Mid Atlantic Bight included Houghton et al. (1982), Beardsley et al. (1985), Lentz (2003), Mountain (2003), Glenn et al. (2004), Sullivan, Cowen, and Steves (2005), Castelao et al. (2008), Shearman and Lentz (2009), Castelao, Glenn, and Schofield (2010), Gong, Kohut, and Glenn (2010), Gawarkiewicz et al. (2012), Forsyth, Andres, and Gawarkiewicz (2015), Fratantoni, Holzwarth-Davis, and Taylor (2015), Zhang and Gawarkiewicz (2015), Miller, Hare, and Alade (2016), and Lentz (2017). 12.1.1.3 Human dimensions submodels Fishery catch and bycatch information was drawn from multiple regional datasets, incuding the Greater Atlantic Regional Office Vessel Trip Reports & Commercial Fisheries Dealer databases, Northeast Fishery Observer Program & Northeast At-Sea Monitoring databases, Northeast Fishery Science Center Social Sciences Branch cost survey, and the Marine Recreational Informational Program database. Further synthesis of human welfare derived from fisheries was drawn from Färe, Kirkley, and Walden (2006), Walden et al. (2012), Lee and Thunberg (2013), Lee (2014), and Lee, Steinback, and Wallmo (2017). Bycatch of protected species was taken from Waring et al. (2015), with additional insights from Bisack and Magnusson (2014). The top 3 linkages were drawn for each node. For example, the top 3 recreational species for the Mid-Atlantic were used to draw linkages between the recreational fishery and species focal groups. A similar approach was used for relevant commercial fisheries in each region. Habitat-fishery linkages were drawn from unpublished reports, including: Mid-Atlantic Fishery Management Council. 2016. Amendment 16 to the Atlantic Mackerel, Squid, and Butterfish Fishery Management Plan: Measures to protect deep sea corals from Impacts of Fishing Gear. Environmental Assessment, Regulatory Impact Review, and Initial Regulatory Flexibility Analysis. Dover, DE. August, 2016. NOAA. 2016. Deep sea coral research and technology program 2016 Report to Congress. http://www.habitat.noaa.gov/protection/corals/deepseacorals.html retrieved February 8, 2017. New England Fishery Management Council. 2016. Habitat Omnibus Deep-Sea Coral Amendment: Draft. http://www.nefmc.org/library/omnibus-deep-sea-coral-amendment Retrieved Feb 8, 2017. Bachman et al. 2011. The Swept Area Seabed Impact (SASI) Model: A Tool for Analyzing the Effects of Fishing on Essential Fish Habitat. New England Fisheries Management Council Report. Newburyport, MA. Tourism and habitat linkages were drawn from unpublished reports, including: http://neers.org/RESOURCES/Bibliographies.html Great Bay (GoM) resources http://greatbay.org/about/publications.htm Meaney, C.R. and C. Demarest. 2006. Coastal Polution and New England Fisheries. Report for the New England Fisheries Management Council. Newburyport, MA. List of valuation studies, by subregion and/or state, can be found at http://www.oceaneconomics.org/nonmarket/valestim.asp. Published literature on human activities in each EPU was consulted. Sources for protected species and tourism links included Hoagland and Meeks (2000) and Lee (2010). Sources for links between environmental drivers and human activities included Adams (1973), Matzarakis and Freitas (2001), Scott, McBoyle, and Schwartzentruber (2004), Hess, Malilay, and Parkinson (2008), Colburn and Jepson (2012), Jepson and Colburn (2013), and Colburn et al. (2016). Sources for cultural practices and attachments links included Pauly (1997), McGoodwin (2001), St Martin (2001), Norris-Raynbird (2004), Pollnac et al. (2006), Clay and Olson (2007), Clay and Olson (2008), Everett and Aitchison (2008), Donkersloot (2010), Lord (2011), Halpern et al. (2012), Wynveen, Kyle, and Sutton (2012), Cortes-Vazquez and Zedalis (2013), Koehn, Reineman, and Kittinger (2013), Potschin and Haines-Young (2013), Reed et al. (2013), Urquhart and Acott (2013), Blasiak et al. (2014), Klain, Satterfield, and Chan (2014), Poe, Norman, and Levin (2014), Brown (2015), Donatuto and Poe (2015), Khakzad and Griffith (2016), Oberg et al. (2016), and Seara, Clay, and Colburn (2016). 12.1.2 Data extraction 12.1.2.1 Ecological submodels “Data” included model estimated quantities to determine whether inclusion thresholds were met for each potential link in the conceptual model. A matrix with diet composition for each modeled group is an input to the food web model. A matrix of mortalities caused by each predator and fishery on each modeled group is a direct ouput of a food web model (e.g. Ecopath). Food web model biomasss flows between species, fisheries, and detritus were summarized using algorithms implemented in visual basic by Kerim Aydin, NOAA NMFS Alaska Fisheries Science Center. Because EMAX model groups were aggregated across species, selected diet compositions for individual species were taken from the NEFSC food habits database using the FEAST program for selected species (example query below). These diet queries were consulted as supplemental information. Example FEAST sql script for Cod weighted diet on Georges Bank can be found here. Queries for different species are standardized by the FEAST application and would differ only in the svspp code. 12.1.2.2 Environmental submodels Information was synthesized entirely from published sources and expert knowledge; no additional data extraction was completed for the environmental submodels. 12.1.2.3 Human dimensions submodels Recreational fisheries data were extracted from the 2010-2014 MRIP datasets. Original data can be found here for each region (New England or Mid-Atlantic as defined by states). Commercial fishing data was developed as part of the State of the Ecosystem Report, including revenue and food production estimates, with data extraction metodology discussed in the relevant sections of the technical document. In addition, the Northeast Regional Input/Output Model (Steinback and Thunberg 2006) was used as the basis for the strength of the employment linkages. 12.1.3 Data analysis 12.1.3.1 Ecological submodels Aggregated diet and mortality information was examined to determine the type of link, direction of link, and which links between which groups should be inclded in the conceptual models. Two types of ecological links were defined using food web models: prey links and predation/fishing mortality links. Prey links resulted in positve links between the prey group and the focal group, while predation/fishing mortality links resulted in negative links to the focal group to represent energy flows. The intent was to include only the most important linkages between focal groups and with other groups supporting or causing mortality on focal species groups. Therefore, threshold levels of diet and mortality were established (based on those that would select the top 1-3 prey and predators of each focal group): 10% to include a link (or add a linked group) in the model and 20% to include as a strong link. A Primary Production group was included in each model and linked to pelagic habitat to allow environmental effects on habitat to be connected to the ecologial submodel. Uncertainty for the inclusion of each link and for the magnitude of each link was qualitatively assessed and noted in the spreadsheet. Four habitat categories (Pelagic, Seafloor and Demersal, Nearshore, and Freshwater and Estuarine) were included in ecological submodels as placeholders to be developed further along with habitat-specific research. Expert opinion was used to include the strongest links between each habitat type and each Focal group (noting that across species and life stages, members of these aggregate groups likely occupy many if not all of the habitat types). Link direction and strength were not specified. Environmental drivers were designed to link to habitats, rather than directly to Focal groups, to represent each habitat’s important mediation function. EMAX model groups were aggregated to focal groups for the Georges Bank (GB), Gulf of Maine (GOM) and Mid-Atlantic Bight (MAB) conceptual models according to Table 12.1. “Linked groups” directly support or impact the Focal groups as described above. Table 12.1: Relationship between food web model groups and conceptual model focal groups. Pinnipeds not included in GB and Seabirds not included in MAB. Group Type Region Conceptual model group EMAX group(s) Focal GB Commercial Fishery Fishery Focal GB Fished Inverts Megabenthos filterers Focal GB Forage Fish Sum of Small pelagics-commercial, other, anadromous, and squids Focal GB Groundfish Sum of Demersals-omnivores, benthivores, and piscivores Focal GB Protected Species Sum of Baleen Whales, Odontocetes, and Seabirds Linked GB Benthos Sum of Macrobenthos-polychaetes, crustaceans, molluscs, other and Megabenthos-other Linked GB Copepods and Micronecton Sum of Copepods-small and large, and Micronekton Linked GB Detritus and Bacteria Sum of Bacteria and Detritus-POC Linked GB Gelatinous zooplankton Gelatinous zooplankton Linked GB Primary Production Phytoplankton-Primary production Focal GOM Commercial Fishery Fishery Focal GOM Fished Inverts Megabenthos filterers Focal GOM Forage Fish Sum of Small pelagics-commercial, other, anadromous, and squids Focal GOM Groundfish Sum of Demersals-omnivores, benthivores, and piscivores Focal GOM Protected Species Sum of Baleen Whales, Odontocetes, Pinnipeds, and Seabirds Linked GOM Benthos Sum of Macrobenthos-polychaetes, crustaceans, molluscs, other and Megabenthos-other Linked GOM Copepods and Micronecton Sum of Copepods-small and large, and Micronekton Linked GOM Detritus and Bacteria Sum of Bacteria and Detritus-POC Linked GOM Gelatinous zooplankton Gelatinous zooplankton Linked GOM Primary Production Phytoplankton-Primary production Focal MAB Clams Quahogs Megabenthos filterers Focal MAB Commercial Fishery Fishery Focal MAB Demerals Sum of Demersals-omnivores, benthivores, and piscivores Focal MAB Forage Fish Sum of Small pelagics-commercial, other, and anadromous Focal MAB Medium Pelagics Medium pelagics Focal MAB Protected Species Sum of Baleen whales and Odontocetes Focal MAB Squids Small pelagics-squids Linked MAB Benthos Sum of Macrobenthos-polychaetes, crustaceans, molluscs, other Linked MAB Copepods and Micronecton Sum of Copepods-small and large, and Micronekton Linked MAB Detritus and Bacteria Sum of Bacteria and Detritus-POC Linked MAB Gelatinous zooplankton Gelatinous zooplankton Linked MAB Primary Production Phytoplankton-Primary production Linked MAB Sharks Sum of Sharks-pelagic and coastal Ecological submodels were constructed and visualized in Mental Modeler (Fig. 12.1). Here, we show only the Gulf of Maine submodels as examples. Figure 12.1: Gulf of Maine Ecological submodel 12.1.3.2 Environmental submodels Environmental submodels were designed to link key oceanographic processes in each ecosystem production unit to the four general habitat categories (Pelagic, Seafloor and Demersal, Nearshore, and Freshwater and Estuarine) with emphasis on the most important physical processes in each ecosystem based on expert knowledge as supported by literature review. The basis of each submodel were environmental variables observable at management-relevant scales as identified by WGNARS: Surface and Bottom Water Temperature and Salinity, Freshwater Input, and Stratification (as well as sea ice timing and cover, which is not relevant to the northeast US shelf). Key drivers changing these observable variables and thus structuring habitat dynamics in each Ecological Production Units were added to the model using expert consensus. Environmental submodels were initially constructed and visualized in Mental Modeler (Fig. 12.2). Figure 12.2: Gulf of Maine Environmental submodel 12.1.3.3 Human dimensions submodels The top 3 species from each mode of recreational fishing (shoreside, private boat, party/charter) were used to assess the potential for missing links between the recreational fishing activity and biological focal components. Given the predominance of Mid-Atlantic groundfish in recreational fishing off New England (summer flounder, bluefish, striped bass), a Mid-Atlantic groundfish focal component was added to the Georges Bank EPU model. The magnitude of benefits generated from recreational fishing was scaled to reflect expert knowledge of target species, coupled with the MRIP data highlighted above. Scales were held consistent across the focal components within recreational fishing. No additional biological focal components were added to the commercial fishing activity, beyond what was developed in the ecological submodel. Benefits derived from commercial fishing were scaled to be consistent with the State of the Ecosystem revenue estimates, as modulated by expert knowledge and additional data sources. For example,the percentage of landings sold as food was used to map fishing activity to the commercial fishery food production objective, and the Northeast Regional Input/Output Model (Steinback and Thunberg 2006) was used to define the strength of the employment linkages. For profitability, expert knowledge was used to reweight revenue landings, based on ancillary cost data available (Das, Chhandita 2013, 2014). Human activities and objectives for the conceptual sub model are defined in DePiper et al. (2017). As shown in Figure 12.3, human dimensions submodels were also initially constructed and visualized in Mental Modeler. Figure 12.3: Gulf of Maine Human dimensions submodel 12.1.3.4 Merged models All links and groups from each submodel were preserved in the full merged model for each system. Mental modeler was used to merge the submodels. Full models were then re-drawn in Dia (http://dia-installer.de/) with color codes for each model component type for improved readability. Examples for each system are below. Figure 12.4: Georges Bank conceptual model Figure 12.5: Gulf of Maine conceptual model Figure 12.6: Mid-Atlantic Bight conceptual model 12.1.3.5 Communication tools The merged models were redrawn for use in communications with the public. These versions lead off the State of the Ecosystem reports for both Fishery Management Councils to provide an overview of linkages between environmental drivers, ecological, and human systems. Figure 12.7: New England conceptual model for public communication Figure 12.8: Mid-Atlantic conceptual model for public communication References "],
-["fish-condition-indicator.html", "13 Fish Condition Indicator 13.1 Methods", " 13 Fish Condition Indicator Description: Relative condition Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018,2019, 2020), State of the Ecosystem - Mid-Atlantic (2018,2019, 2020) Indicator category: Database pull with analysis Contributor(s): Laurel Smith Data steward: Laurel Smith, laurel.smith@noaa.gov Point of contact: Laurel Smith, laurel.smith@noaa.gov Public availability statement: NEFSC survey data used in these analyses are available upon request (see BTS metadata for access procedures). Derived condition data are available here. 13.1 Methods Relative condition (Kn) was introduced by Cren (1951) as a way to remove the influence of length on condition, and Blackwell, Brown, and Willis (2000) noted that Kn may be useful in detecting prolonged physical stress on a fish populations. Relative condition is calculated as \\[Kn = W/W',\\] where \\(W\\) is the weight of an individual fish and \\(W'\\) is the predicted length-specific mean weight for the fish population in a given region. \"Here, relative condition was calculated for finfish stocks commonly caught on the Northeast Fisheries Science Center’s (NEFSC) autumn and spring bottom trawl surveys, from 1992-present. Where data allowed, predicted length-weight parameters were calculated for \\(W’\\) by species, sex and season over the time period 1992-2012. When sample sizes of individual fish weights and lengths were too low, parameters were calculated for aggregated spring and fall survey data over the same time period. Fall survey relative condition was calculated by sex for those species that exhibited differences in growth between sexes and aggregated across sex for those that did not. The Condition package used for calculations and plotting of fish condition factor can be found on GitHub. 13.1.1 Data sources Individual fish lengths (to the nearest 0.5 cm) and weights (grams) were collected on the NEFSC bottom trawl surveys from 1992-present aboard RVs Albatross IV, Delaware II and the Henry B. Bigelow (see Survdat). A small number of outlier values were removed when calculating the length-weight parameters. 13.1.2 Data extraction Data were extracted from NEFSC’s survey database (SVDBS) using the R script found here 13.1.3 Data analysis The following growth curve was fit through individual fish lengths and weights from the NEFSC bottom trawl survey data from 1992-2012 to produce reference length-weight parameters: \\[\\textrm{Weight} = e^{Fall_{coef}} * \\textrm{Length}^{Fall_{exp}},\\] where length is in cm and weight is in kg. Fall survey data were used where sample sizes allowed for growth curve estimation, otherwise data from spring and fall seasons were combined. Individual fish lengths from NEFSC fall bottom trawl survey from 1992-2017 were then used to calculate predicted weights using the reference length-weight parameters. Relative condition (\\(Kn\\)) was calculated annually by species and sex (for sexually dimorphic species) by dividing individual fish weights by the predicted weight. The code found here was used in the analysis of fish condition. 13.1.4 Plotting Code for plotting the fish condition indicator can be found here. Figure 13.1: Condition factor for fish species in the MAB. MAB data are missing for 2017 due to survey delays. References "],
-["epu.html", "14 Ecological Production Units 14.1 Methods", " 14 Ecological Production Units Description: Ecological Production Units Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2018, 2019, 2020) Indicator category: Extensive analysis, not yet published Contributor(s): Robert Gamble Data steward: NA Point of contact: Robert Gamble, robert.gamble@noaa.gov Public availability statement: Ecological production unit (EPU) shapefiles are available here. More information about source data used to derive EPUs can be found here. 14.1 Methods To define ecological production units (EPUs), we assembled a set of physiographic, oceanographic and biotic variables on the Northeast U.S. Continental Shelf, an area of approximately 264,000 km within the 200 m isobath. The physiographic and hydrographic variables selected have been extensively used in previous analyses of oceanic provinces and regions (e.g Roff and Taylor 2000). Primary production estimates have also been widely employed for this purpose in conjunction with physical variables (Longhurst 2007) to define ecological provinces throughout the world ocean. We did not include information on zooplankton, benthic invertebrates, fish, protected species, or fishing patterns in our analysis. The biomass and production of the higher trophic level groups in this region has been sharply perturbed by fishing and other anthropogenic influences. Similarly, fishing patterns are affected by regulatory change, market and economic factors and other external influences. Because these malleable patterns of change are often unconnected with underlying productivity, we excluded factors directly related to fishing practices. The physiographic variables considered in this analysis are listed in Table 14.1. They include bathymetry and surficial sediments. The physical oceanographic and hydrographic measurements include sea surface temperature, annual temperature span, and temperature gradient water derived from satellite observations for the period 1998 to 2007. 14.1.1 Data sources Shipboard observations for surface and bottom water temperature and salinity in surveys conducted in spring and fall. Daily sea surface temperature (SST, °C) measurements at 4 km resolution were derived from nighttime scenes composited from the AVHRR sensor on NOAA’s polar-orbiting satellites and from NASA’s MODIS TERRA and MODIS AQUA sensors. We extracted information for the annual mean SST, temperature span, and temperature gradients from these sources. The latter metric provides information on frontal zone locations. Table 14.1: Variables used in derivation of Ecological Production Units. Variables Sampling Method Units Surficial Sediments Benthic Grab Krumbian Scale Sea Surface Temperature Satellite Imagery (4km grid) °C annual average Sea Surface Temperature Satellite Imagery (4km grid) dimensionless Sea Surface Temperature Satellite Imagery (4km grid) °C annual average Surface Temperature Shipboard hydrography (point) °C (Spring and Fall) Bottom Temperature Shipboard hydrography (point) °C (Spring and Fall) Surface Salinity Shipboard hydrography (point) psu (Spring and Fall) Bottom Salinity Shipboard hydrography (point) psu (Spring and Fall) Stratification Shipboard hydrography (point) Sigma-t units (Spring and Fall) Chlorophyll-a Satellite Imagery (1.25 km grid) mg/C/m3 (annual average) Chlorophyll-a gradient Satellite Imagery (1.25 km grid) dimensionless Chlorophyll-a span Satellite Imagery (1.25 km grid) mg/C/m3 (annual average) Primary Production Satellite Imagery (1.25 km grid) gC/m3/year (cumulative) Primary Production gradient Satellite Imagery (1.25 km grid) dimensionless Primary Production span Satellite Imagery (1.25 km grid) gC/m3/year (cumulative) The biotic measurements included satellite-derived estimates of chlorophyll a (CHLa) mean concentration, annual span, and CHLa gradients and related measures of primary production. Daily merged SeaWiFS/MODIS-Aqua CHLa (CHL, mg m-3) and SeaiWiFS photosynthetically available radiation (PAR, Einsteins m-2 d-1) scenes at 1.25 km resolution were obtained from NASA Ocean Biology Processing Group. 14.1.2 Data extraction NA 14.1.3 Data analysis In all cases, we standardized the data to common spatial units by taking annual means of each observation type within spatial units of 10’ latitude by 10’ longitude to account for the disparate spatial and temporal scales at which these observations are taken. There are over 1000 spatial cells in this analysis. Shipboard sampling used to obtain direct hydrographic measurements is constrained by a minimum sampling depth of 27 m specified on the basis of prescribed safe operating procedures. As a result nearshore waters are not fully represented in our initial specifications of ecological production units. The size of the spatial units employed further reflects a compromise between retaining spatial detail and minimizing the need for spatial interpolation of some data sets. For shipboard data sets characterized by relatively coarse spatial resolution, where necessary, we first constructed an interpolated map using an inverse distance weighting function before including it in the analysis. Although alternative interpolation schemes based on geostatistical approaches are possible, we considered the inverse distance weighting function to be both tractable and robust for this application. We first employed a spatial principal components analysis (PCA; e.g. Pielou 1984; Legendre and Legendre 1998) to examine the multivariate structure of the data and to account for any inter-correlations among the variables to be used in subsequent analysis. The variables included in the analysis exhibited generally skewed distributions and we therefore transformed each to natural logarithms prior to analysis. The PCA was performed on the correlation matrix of the transformed observations. We selected the eigenvectors associated with eigenvalues of the dispersion matrix with scores greater than 1.0 (the Kaiser-Guttman criterion; Legendre and Legendre 1998) for all subsequent analysis. These eigenvectors represent orthogonal linear combinations of the original variables used in the analysis. We delineated ecological subunits by applying a disjoint cluster based on Euclidean distances using the K-means procedure (Legendre and Legendre 1998) on the principal component scores The use of non-independent variables can strongly influence the results of classification analyses of this type (Pielou 1984), hence the interest in using the PCA results in the cluster. The eigenvectors were represented as standard normal deviates. We used a Pseudo-F Statistic described by Milligan and Cooper (1985) to objectively define the number of clusters to use in the analysis. The general approach employed is similar to that of Host et al. (1996) for the development of regional ecosystem classifications for terrestrial systems. After the analyses were done, we next considered options for interpolation of nearshore boundaries resulting from depth-related constraints on shipboard observations. For this, we relied on information from satellite imagery. For the missing nearshore areas in the Gulf of Maine and Mid-Atlantic Bight, the satellite information for chlorophyll concentration and sea surface temperature indicated a direct extension from adjacent observations. For the Nantucket Shoals region south of Cape Cod, similarities in tidal mixing patterns reflected in chlorophyll and temperature observations indicated an affinity with Georges Bank and the boundaries were changed accordingly. Finally, we next considered consolidation of ecological subareas so that nearshore regions are considered to be special zones nested within the adjacent shelf regions. Similar consideration led to nesting the continental slope regions within adjacent shelf regions in the Mid-Atlantic and Georges Bank regions. This led to four major units: Mid-Atlantic Bight, Georges Bank, Western-Central Gulf of Maine (simply “Gulf of Maine” in the State of the Ecosystem), and Scotian Shelf-Eastern Gulf of Maine. As the State of the Ecosystem reports are specific to FMC managed regions, the Scotian Shelf-Eastern Gulf of Maine EPU is not considered in SOE indicator analyses. Figure 14.1: Map of the four Ecological Production Units, including the Mid-Atlantic Bight (light blue), Georges Bank (red), Western-Central Gulf of Maine (or Gulf of Maine; green), and Scotian Shelf-Eastern Gulf of Maine (dark blue) 14.1.4 Data processing Shapefiles were converted to sf objects for inclusion in the ecodata R package using the R code found here. References "],
-["forage-fish-energy-density.html", "15 Forage Fish Energy Density 15.1 Methods", " 15 Forage Fish Energy Density Description: Forage Engery Density indicators Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2020), State of the Ecosystem - Mid-Atlantic (2020) Indicator category: Contributor(s): Mark Wuenschel, Ken Oliveira and Kelcie Bean Data steward: Mark Wuenschel mark.wuenschel@noaa.gov Point of contact: Mark Wuenschel mark.wuenschel@noaa.gov Public availability statement: Source data are publicly available. 15.1 Methods A collaborative project between UMASS Dartmouth Biology Department (Dr. Ken Oliveira, M.S student Kelcie Bean) and NEFSC Population Biology Branch (Mark Wuenschel) is underway to evaluate energy content of forage species. The study focuses on the following species; Atlantic herring, alewife, silver hake, butterfish, northern sandlance, Atlantic mackerel, longfin squid, and northern shortfin squid. Samples have been analyzed for proximate composition and energy density from 2017 and 2018 NEFSC spring and fall bottom trawl surveys (Bean 2019, table below). Predictive relationships between the percent dry weight of samples and energy density were developed, and samples collected from 2019 surveys are currently being analyzed for percentage dry weight to enable estimation of energy content. The energy density of forage species differed from prior studies in the 1980s and 1990s (Steimle and Terranova (1985), Lawson, Magalhães, and Miller (1998)). 15.1.1 Data sources 2017 and 2018 NEFSC spring and fall bottom trawl surveys. 15.1.2 Data extraction 15.1.3 Data analysis Sampling and laboratory analysis is ongoing, with the goal of continuing routine monitoring of energy density of these species. 15.1.4 Data processing Code for building the table used in the SOE can be found here. Table 15.1: Forage fish energy content 2017 2018 Total Steimle and Terranove (1985) Lawson et al. (1998) Spring Fall Spring Fall Species Mean ED (SD) N Mean ED (SD) N Mean ED (SD) N Mean ED (SD) N Mean ED (SD) N Mean ED Mean ED (SD) Alewife 6.84 (1.62) 128 8.12 (1.46) 50 6.45 (1.21) 47 7.41 (1.6) 42 7.1 (1.62) 267 6.4 Atl. Herring 5.34 (0.94) 122 5.77 (1.31) 52 6.69 (0.85) 51 5.41 (1.34) 50 5.69 (1.19) 275 10.6 9.4 (1.4) Atl. Mackerel NA 7.24 (1.13) 50 5.33 (0.86) 51 6.89 (1.07) 50 6.48 (1.32) 151 6.0 Butterfish 7.13 (1.59) 65 7.31 (1.45) 89 4.91 (1.12) 53 8.1 (2.7) 50 6.92 (2.04) 257 6.2 Illex 5.54 (0.4) 77 5.43 (0.51) 52 5.5 (0.52) 50 4.76 (0.79) 50 5.33 (0.63) 229 7.1 5.9 (0.56) Loligo 5.22 (0.36) 83 5.24 (0.26) 60 4.84 (0.63) 52 4.6 (0.72) 50 5.02 (0.56) 245 5.6 Sand lance 6.66 (0.54) 18 NA 5.78 (0.34) 60 7.99 (0.74) 8 6.17 (0.81) 86 6.8 4.4 (0.82) Silver hake 4.25 (0.39) 189 4.42 (0.45) 50 4.19 (0.39) 50 4.55 (0.63) 50 4.31 (0.46) 339 4.6 References "],
-["gulf-stream-index.html", "16 Gulf Stream Index 16.1 Methods", " 16 Gulf Stream Index Description: Annual time series of the Gulf Stream index Indicator category: Published method Found in: State of the Ecosystem - New England (2019, 2020) Contributor(s): Terry Joyce, Rong Zhang Data steward: Vincent Saba, vincent.saba@noaa.gov Point of contact: Vincent Saba, vincent.saba@noaa.gov Public availability statement: Source data are publicly available 16.1 Methods 16.1.1 Data sources Ocean temperature data from NOAA’s National Centers for Environmental Information (NCEI) Ocean temperatures at 200 m are available at https://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/. 16.1.2 Data analysis Summarized from Joyce et al. (2019), ocean temperature data from NOAA’s National Centers for Environmental Information (NCEI) were sorted by latitude, longitude, and time using a resolution of 1° of longitude, latitude, and 3 months of time, respectively, with a Gaussian squared weighting from the selected desired point in a window twice the size of the desired resolution. Editing was used to reject duplicate samples and 3\\(\\sigma\\) outliers from each selected sample point prior to performing the weighting and averaging; the latter was only carried out when there were at least three data points in the selected interval for each sample point. Typically, 50 or more data values were available. The resulting temperature field was therefore smoothed. Data along the Gulf Stream north wall at nine data points were used to assemble a spatial/temporal sampling of the temperature at 200m data along the north wall from 75°W to 55°W. The leading mode of temperature variability of the Gulf Stream is equivalent to a north‐south shift of 50–100 km, which is zonally of one sign and amounts to 50% of the seasonal‐interannual variance between 75°W and 55°W. The temporal behavior of this mode (PC1) shows the temporal shift of the Gulf Stream path with a dominant approximately 8‐ to 10‐year periodicity over much of the period. For detailed analytical methods, see Joyce et al. (2019). 16.1.3 Data Processing The Gulf Stream index data set was formatted for inclusion in the ecodata R package with the code found here. 16.1.4 Plotting The plot below was built using the code found here. Figure 16.1: Gulf Stream Index References "],
-["harbor-porpoise-bycatch.html", "17 Harbor Porpoise Bycatch 17.1 Methods", " 17 Harbor Porpoise Bycatch Description: Harbor Porpoise Indicator Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019), State of the Ecosystem - Mid-Atlantic (2018, 2019) Indicator category: Synthesis of published information; Published methods Contributor(s): Christopher D. Orphandies Data steward: Chris Orphanides, chris.orphanides@noaa.gov Point of contact: Chris Orphanides, chris.orphanides@noaa.gov Public availability statement: Source data are available in public stock assessment reports (2018 report in-press). Derived data as shown in the 2018 SOE reports are available here 17.1 Methods 17.1.1 Data sources Reported harbor porpoise bycatch estimates and potential biological removal levels can be found in publicly available documents; detailed here. The most recent bycatch estimates for 2016 were taken from the 2018 stock assessment (in-press). More detailed documentation as to the methods employed can be found in NOAA Fisheries Northeast Fisheries Science Center (NEFSC) Center Reference Documents (CRDs) found on the NEFSC publications page. The document for the 2016 estimates (CRD 19-04) is available here. Additional methodological details are available for previous year’s estimates and are documented in numerous published CRDs: CRD 17-18, CRD-16-05, CRD 15-15, CRD 14-02, CRD 13-13, CRD 11-08, CRD 10-10, CRD 07-20, CRD 06-13, CRD 03-18, CRD 01-15, and CRD 99-17. 17.1.2 Data extraction Annual gillnet bycatch estimates are documented in a CRD (see sources above). These feed into the Stock Assessment Reports which report both the annual bycatch estimate and the mean 5-year estimate. The 5-year estimate is the one used for management purposes, so that is the one provided for the SOE plot. 17.1.3 Data analysis Bycatch estimates as found in stock assessment reports were plotted along with confidence intervals. The confidence intervals were calculated from published CVs assuming a normal distribution (\\(\\sigma = \\mu CV\\); \\(CI = \\bar{x} \\pm \\sigma * 1.96\\)). Data were analyzed and formatted for inclusion in the ecodata R package using the R code found here. 17.1.4 Plotting Code used to plot harbor porpoise data can be found here "],
-["highly-migratory-species-landings.html", "18 Highly Migratory Species Landings 18.1 Methods", " 18 Highly Migratory Species Landings Description: Highly Migratory Species Landings Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2020), State of the Ecosystem - Mid-Atlantic (2020) Indicator category: Synthesis of published information, Database pull with analysis Contributor(s): George Silva, Heather Baertlein, and Cliff Hutt Data steward: Kimberly Bastille Point of contact: George Silva george.silva@noaa.gov Public availability statement: Source data are publicly available. 18.1 Methods 18.1.1 Data sources Data from eDealer database (website) and Bluefin Tuna Dealer reports on SAFIS. 18.1.2 Data extraction Data was processed for Fisheries of the United States and then aggregated by region to avoid confidentiality issues. 18.1.3 Data analysis High migratory landings include 19 species of tunas, sharks and swordfish. Data was analyzed using Excel pivot tables. 18.1.4 Data processing HMS landings data were formatted for inclusion in the ecodata R package using the R code found here. 18.1.5 Plotting The plot below was built using the code found here. Figure 18.1: Top plot shows HMS landings from 2016-2018 broken out by group (sharks, tunas or swordfish. "],
-["ichthyoplankton-diversity.html", "19 Ichthyoplankton Diversity 19.1 Methods", " 19 Ichthyoplankton Diversity Description: NOAA NEFSC Oceans and Climate branch public ichthyoplankton dataset Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019), State of the Ecosystem - Mid-Atlantic (2018, 2019) Indicator category: Database pull with analysis Contributor(s): Harvey J. Walsh Data steward: Harvey Walsh, harvey.walsh@noaa.gov Point of contact: Harvey Walsh, harvey.walsh@noaa.gov Public availability statement: Source data are available to the public here. Derived data for this indicator are available here. 19.1 Methods Data from the NOAA Northeast Fisheries Science Center (NEFSC) Oceans and Climate branch (OCB) public dataset were used to examine changes in diversity of abundance among 45 ichthyoplankton taxa. The 45 taxa were established (Walsh et al. 2015), and include the most abundant taxa from the 1970s to present that represent consistency in the identification of larvae. 19.1.1 Data sources Multi-species plankton surveys cover the entire Northeast US shelf from Cape Hatteras, North Carolina, to Cape Sable, Nova Scotia, four to six times per year. A random-stratified design based on the NEFSC bottom trawl survey design (Azarovitz 1981) is used to collect samples from 47 strata. The number of strata is lower than the trawl survey as many of the narrow inshore and shelf-break strata are combined in the EcoMon design. The area encompassed by each stratum determined the number of samples in each stratum. Samples were collected both day and night using a 61 cm bongo net. Net tow speed was 1.5 knots and maximum sample depth was 200 m. Double oblique tows were a minimum of 5 mintues in duration, and fished from the surface to within 5 m of the seabed or to a maximum depth of 200 m. The volume filtered of all collections was measured with mechanical flowmeters mounted across the mouth of each net. Processing of most samples was conducted at the Morski Instytut Rybacki (MIR) in Szczecin, Poland; the remaining samples were processed at the NEFSC or the Atlantic Reference Center, St Andrews, Canada. Larvae were identified to the lowest possible taxa and enumerated for each sample. Taxon abundance for each station was standardized to number under 10 m-2 sea surface. 19.1.2 Data extraction Data retrieved from NOAA NEFSC Oceans and Climate branch public dataset (Filename: “EcoMon_Plankton_Data_v3_0.xlsx”, File Date: 10/20/2016). 19.1.3 Data analysis All detailed data processing steps are not currently included in this document, but general steps are outlined. Data were grouped into seasons: spring = February, March, April and fall = September, October, November. Stratified weighted mean abundance was calculated for each taxon for each year and season across all plankton strata (n = 47) for 17 years (1999 to 2015). Shannon Diversity Index and count of positive taxon was calculated for each season and year. MATLAB code used to calculate diversity indices: 19.1.4 Data processing Ichthyoplankton diversity data sets were formatted for inclusion in the ecodata R package using the R code found here. 19.1.5 Plotting Code used to plot ichthyoplankton diversity can be found here. References "],
-["inshoresurvdat.html", "20 Inshore bottom trawl surveys 20.1 Methods", " 20 Inshore bottom trawl surveys Description: Inshore surveys include the Northeast Area Monitoring and Assessment Program (NEAMAP) survey, Massachusetts Division of Marine Fisheries Bottom Trawl Survey, and Maine/New Hampshire Inshore Trawl Survey. Indicator category: Database pull with analysis Found in: State of the Ecosystem - Mid-Atlantic (2019,2020), State of the Ecosystem - New England (2019, 2020) Contributor(s): James Gartland, Matt Camisa, Rebecca Peters, Sean Lucey Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Points of contact: James Gartland (NEAMAP), jgartlan@vims.edu; Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov; Sean Lucey (MA Inshore Survey), sean.lucey@noaa.gov Public availability statement: Data are available upon request. 20.1 Methods 20.1.1 Data Sources All inshore bottom trawl survey data sets were derived from raw survey data. NEAMAP source data are available for download here. More detailed information describing NEAMAP survey methods is available on the NEAMAP website. ME/NH inshore survey data are available upon request (see Points of Contact). Technical documentation for ME/NH survey methods and survey updates are made available through the Maine Department of Marine Resources. Data from the MA Inshore Bottom Trawl Survey are stored on local servers at the Northeast Fisheries Science Center (Woods Hole, MA), and are also available upon request. More information about the MA Inshore Bottom Trawl Survey is available here. 20.1.2 Data extraction Source data from the Massachusetts DMF Bottom Trawl Survey were extracted using this R script) . 20.1.3 Data Processing The following R code was used to process inshore bottom trawl data into the ecodata R package. New England https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_inshore_survdat.R Massachusetts https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_mass_inshore_survey.R Mid-Atlantic (NEAMAP) https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_mab_inshore_survey.R 20.1.4 Data Analysis Biomass indices were provided as stratified mean biomass (kg tow-1) for all inshore surveys. Time series of stratified mean biomass were calculated for ME/NH and NEAMAP surveys through the following procedure: All species catch weights were summed for each tow and for each feeding guild category. The average weight per tow, associated variances and standard deviation for each survey, region, stratum, and feeding guild was calculated. Stratified mean biomass was then calculated as the sum of the weighted averages of the strata, where the weight of a given stratum was the proportion of the survey area accounted for by that stratum. Stratified mean biomass was also calculated for the MA Inshore Bottom Trawl Survey. These calculations followed those used to find stratified mean biomass by feeding guild in the NEFSC Bottom Trawl Survey and are described in greater detail there. The R code used to derive the stratified mean biomass indices for MA Inshore time series is provided below. R code used for analysis can be found here 20.1.5 Plotting 20.1.5.1 NEAMAP Figure 20.1: Spring (left) and fall (right) surveyed biomass in the Mid-Atlantic Bight. Data from the NEFSC Bottom Trawl Survey are shown in black, with NEAMAP shown in red. 20.1.5.2 Massechusetts Figure 20.2: Spring (left) and fall (right) surveyed biomass from the MA state inshore bottom trawl survey. 20.1.5.3 Maine-New Hampshire Figure 20.3: Spring (left) and fall(right) surveyed biomass from the ME/NH state inshore bottom trawl survey. "],
-["comdat.html", "21 Commercial Landings Data 21.1 Methods", " 21 Commercial Landings Data Description: Commercial landings data pull Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019,2020), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019,2020) Indicator category: Database pull Contributor(s): Sean Lucey Data steward: Sean Lucey, Sean.Lucey@noaa.gov Point of contact: Sean Lucey, Sean.Lucey@noaa.gov Public availability statement: Raw data are not publically available due to confidentiality of individual fishery participants. Derived indicator outputs are available here. 21.1 Methods Fisheries dependent data for the Northeast Shelf extend back several decades. Data from the 1960s on are housed in the Commercial database (CFDBS) of the Northeast Fisheries Science Center which contains the commercial fisheries dealer purchase records (weigh-outs) collected by National Marine Fisheries Service (NMFS) Statistical Reporting Specialists and state agencies from Maine to Virginia. The data format has changed slightly over the time series with three distinct time frames as noted in Table 21.1 below. Table 21.1: Data formats Table Years WOLANDS 1964 - 1981 WODETS 1982 - 1993 CFDETS_AA > 1994 Comlands is an R database pull that consolidates the landings records from 1964 on and attempts to associate them with NAFO statistical areas (Figure 21.1). The script is divided into three sections. The first pulls domestic landings data from the yearly landings tables and merges them into a single data source. The second section applies an algorithm to associate landings that are not allocated to a statistical area using similar characteristics of the trip to trips with known areas. The final section pulls foreign landings from the Northwest Atlantic Fisheries Organization website and rectifies species and gear codes so they can be merged along with domestic landings. Figure 21.1: Map of the North Atlantic Fisheries Organization (NAFO) Statistical Areas. Colors represent the Ecological Production Unit (EPU) with which the statistical area is associated. During the first section, the Comlands script pulls the temporal and spatial information as well as vessel and gear characteristics associated with the landings in addition to the weight, value, and utilization code of each species in the landings record. The script includes a toggle to use landed weights as opposed to live weights. For all but shellfish species, live weights are used for the State of the Ecosystem report. Due to the volume of data contained within each yearly landings table, landings are aggregated by species, utilization code, and area as well as by month, gear, and tonnage class. All weights are then converted from pounds to metric tons. Landings values are also adjusted for inflation using the Producer Price Index by Commodity for Processed Foods and Feeds: Unprocessed and Packaged Fish. Inflation is based on January of the terminal year of the data pull ensuring that all values are in current dollar prices. Table 21.2: Gear types used in commercial landings Major gear 1 Otter Trawls 2 Scallop Dredges 3 Other Dredges 4 Gillnets 5 Longlines 6 Seines 7 Pots/Traps 8 Midwater 9 Other Several species have additional steps after the data is pulled from CFDBS. Skates are typically landed as a species complex. In order to segregate the catch into species, the ratio of individual skate species in the NEFSC bottom trawl survey is used to disaggregate the landings. A similar algorithm is used to separate silver and offshore hake which can be mistaken for one another. Finally, Atlantic herring landings are pulled from a separate database as the most accurate weights are housed by the State of Maine. Comlands pulls from the State database and replaces the less accurate numbers from the federal database. The majority of landings data are associated with a NAFO Statistical Area. For those that are not, Comlands attempts to assign them to an area using similar characteristics of trips where the area is known. To simplify this task, landings data are further aggregated into quarter and half year, small and large vessels, and eight major gear categories (Table 21.2). Landings are then proportioned to areas that meet similar characteristics based on the proportion of landings in each area by that temporal/vessel/gear combination. If a given attribute is unknown, the algorithm attempts to assign it one, once again based on matched characteristics of known trips. Statistical areas are then assigned to their respective Ecological Production Unit (Table 21.3). Table 21.3: Statistical areas making up each EPU EPU Stat Areas Gulf of Maine 500, 510, 512, 513, 514, 515 Georges Bank 521, 522, 523, 524, 525, 526, 551, 552, 561, 562 Mid-Atlantic 537, 539, 600, 612, 613, 614, 615, 616, 621, 622, 625, 626, 631, 632 The final step of Comlands is to pull the foreign landings from the NAFO database. US landings are removed from this extraction so as not to be double counted. NAFO codes and CFDBS codes differ so the script rectifies those codes to ensure that the data is seamlessly merged into the domestic landings. Foreign landings are flagged so that they can be removed if so desired. 21.1.1 Data sources Comland is a database query of the NEFSC commercial fishery database (CFDBS). More information about the CFDBS is available here. 21.1.2 Data extraction R code used in the extraction process described above can be found here. 21.1.2.1 Data Processing The landings data were formatted for inclusion in the ecodata R package with this R code. 21.1.3 Data analysis Fisheries dependent data from Comlands is used in several indicators for the State of the Ecosystem report; the more complicated analyses are detailed in their own sections. The most straightforward use of this data are the aggregate landings indicators. These are calculated by first assigning the various species into aggregate groups. Species are also marked by which management body manages them. Landings are then summed by year, EPU, aggregate group, and whether they are managed or not. Both managed and unmanaged totals are added together to get the final amount of total landings for that aggregate group within its respective region. Both the total and those landings managed by the management body receiving the report are reported. Proportions of managed landings to total landings are also reported in tabular form. 21.1.4 Plotting The plot below was built using the code found here. Figure 21.2: Mid-Atlantic commercial landings. "],
-["long-term-sea-surface-temperature.html", "22 Long-term Sea Surface Temperature 22.1 Methods", " 22 Long-term Sea Surface Temperature Description: Long-term sea-surface temperatures Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019, 2020) Indicator category: Database pull Contributor(s): Kevin Friedland Data steward: Kevin Friedland, kevin.friedland@noaa.gov Point of contact: Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are available here. 22.1 Methods Data for long-term sea-surface temperatures were derived from the Noational Oceanographic and Atmospheric Administration (NOAA) extended reconstructed sea surface temperature data set (ERSST V5). The ERSST V5 dataset is parsed into 2° x 2° gridded bins between 1854-present with monthly temporal resolution. Data were interpolated in regions with limited spatial coverage, and heavily damped during the period between 1854-1880 when collection was inconsistent (Huang, Thorne, et al. 2017a, 2017b). For this analysis, 19 bins were selected that encompassed the Northeast US Continental Shelf region (see Friedland and Hare 2007). 22.1.1 Data sources This indicator is derived from the NOAA ERSST V5 dataset (Huang, Thorne, et al. 2017a). 22.1.2 Data extraction Table 22.1: Coordinates used in NOAA ERSST V5 data extraction. Longitude Latitude -74 40 -74 38 -72 40 -70 44 -70 42 -70 40 -68 44 -68 42 R code used in extracting time series of long-term SST data can be found here. 22.1.3 Data Processing Data were formatted for inclusion in the ecodata R package with the R code found here. 22.1.4 Plotting The plot below was built using the code found here. Figure 22.1: Long-term sea surface temperatures on the Northeast Continental Shelf. References "],
-["mid-atlantic-harmful-algal-bloom-indicator.html", "23 Mid-Atlantic Harmful Algal Bloom Indicator 23.1 Methods", " 23 Mid-Atlantic Harmful Algal Bloom Indicator Description: An aggregation of reported algal bloom data in Chesapeake Bay between 2007-2017. Found in: State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull Contributor(s): Sean Hardison, Virginia Department of Health Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Source data for this indicator are available here. Processed time series can be found here. 23.1 Methods We presented two indicator time series for reports of algal blooms in the southern portion of Chesapeake Bay between 2007-2017. The first indicator was observations of algal blooms above 5000 cell ml-1. This threshold was developed by the Virginia Department of Health (VDH) for Microcystis spp. algal blooms based on World Health Organization guidelines (Organization 2003; Health 2011). VDH also uses this same threshold for other algal species blooms in Virginia waters. When cell concentrations are above 5000 cell ml-1, VDH recommends initiation of biweekly water sampling and that relevant local agencies be notified of the elevated cell concentrations. The second indicator we reported, blooms of Cochlodinium polykrikoides at cell concentrations >300 cell ml-1, was chosen due to reports of high ichthyotoxicity seen at these levels. Tang and Gobler (2009) showed that fish exposed to cultured C. polykrikoides at densities as low 330 cells ml-1 saw 100% mortality within 1 hour, which if often far less than C. polykrikoides cell concentrations seen in the field. Algal bloom data were not available for 2015 nor 2010. The algal bloom information presented here are a synthesis of reported events, and has been updated to include data not presented in the 2018 State of the Ecosystem Report. 23.1.1 Data sources Source data were obtained from VDH. Sampling, identification, and bloom characterization was completed by the VDH, Phytoplankton Analysis Laboratory at Old Dominion University (ODU), Reece Lab at the Virginia Institute of Marine Science (VIMS), and Virginia Department of Environmental Quality. Problem algal species were targeted for identification via light microscopy followed by standard or quantitative PCR assays and/or enzyme-linked immunosorbent assay (ELISA). Reports specifying full methodologies from ODU, VIMS, and VDH source data are available upon request. 23.1.2 Data extraction Data were extracted from a series of spreadsheets provided by the VDH. We quantified the number of algal blooms in each year reaching target cell density thresholds in the southern Chesapeake Bay. R code used in extracting harmful algal bloom data can be found here. 23.1.3 Data analysis No data analysis steps took place for this indicator. References "],
-["new-england-harmful-algal-bloom-indicator.html", "24 New England Harmful Algal Bloom Indicator 24.1 Methods", " 24 New England Harmful Algal Bloom Indicator Description: Regional incidence of shellfish bed closures due to presence of toxins associated with harmful algae. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018) Indicator Category: Synthesis of published information Contributor(s): Dave Kulis, Donald M Anderson, Sean Hardison Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Data are publicly available (see Data Sources below). 24.1 Methods The New England Harmful Algal Bloom (HAB) indicator is a synthesis of shellfish bed closures related to the presence of HAB-associated toxins above threshold levels from 2007-2016 (Figure ??). Standard detection methods were used to identify the presence of toxins associated with Amnesic Shellfish Poisoning (ASP), Paralytic Shellfish Poisoning (PSP), and Diarrhetic Shellfish Poisoning (DSP) by state and federal laboratories. 24.1.0.1 Paralytic Shellfish Poisoning The most common cause of shellfish bed closures in New England is the presence of paralytic shellfish toxins (PSTs) produced by the dinoflagellate Alexandrium catenella. All New England states except Maine relied on the Association of Official Analytical Chemists (AOAC) approved mouse bioassay method to detect PSTs in shellfish during the 2007-2016 period reported here (International 2005). In Maine, PST detection methods were updated in May 2014 when the state adopted the hydrophilic interaction liquid chromatography (HILIC) UPLC-MS/MS protocol (Boundy et al. 2015) in concordance with National Shellfish Sanitation Program (NSSP) requirements. Prior to this, the primary method used to detect PST in Maine was with the mouse bioassay. 24.1.0.2 Amnesic Shellfish Poisoning Amnesic shellfish poisoning (ASP) is caused by the toxin domoic acid (DA), which is produced by several phytoplankton species belonging to the genus Pseudo-nitzchia. In New England, a UV-HPLC method (Quilliam, Xie, and Hardstaff 1995), which specifies a HPLC-UV protocol, is used for ASP detection. 24.1.0.3 Diarrhetic Shellfish Poisoning Diarrhetic Shellfish Poisoning (DSP) is rare in New England waters, but the presence of the DSP-associated okadaic acid (OA) in mussels was confirmed in Massachusetts in 2015 (J. Deeds, personal communication, July 7, 2018). Preliminary testing for OA in Massachusetts utilized the commercially available Protein Phosphatase Inhibition Assay (PPIA) and these results are confirmed through LC-MS/MS when necessary (Smienk et al. 2012; Stutts and Deeds 2017). 24.1.1 Data sources Data used in this indicator were drawn from the 2017 Report on the ICES-IOC Working Group on Harmful Algal Bloom Dynamics (WGHABD). The report and data are available here. Closure information was collated from information provided by the following organizations: Table 24.1: Shellfish closure information providers. State Source Organization Maine Maine Department of Marine Resources New Hampshire New Hampshire Department of Environmental Services Massachusetts Massachusetts Division of Marine Fisheries Rhode Island Rhode Island Department of Environmental Management Connecticut Connecticut Department of Agriculture 24.1.2 Data extraction Data were extracted from the original report visually and accuracy confirmed with report authors. 24.1.3 Data analysis No data analysis steps took place for this indicator. 24.1.4 Plotting The script used to develop the figure in the SOE report can be found here. References "],
-["marine-heatwave.html", "25 Marine Heatwave 25.1 Methods", " 25 Marine Heatwave Description: Marine Heatwave Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2020), Mid-Atlantic (2020) Indicator category: Published methods, Database pull with analysis Contributor(s): Vincent Saba Data steward: Kimberly Bastille kimberly.bastille@noaa.gov Point of contact: Vincent Saba vincent.saba@noaa.gov Public availability statement: 25.1 Methods Marine heatwave analysis for Georges Bank, Gulf of Maine, and the Middle Atlantic Bight according to the definition in Hobday et al. (2016). 25.1.1 Data sources NOAA high-res OISST (daily, 25-km, 1982-2019) https://www.esrl.noaa.gov/psd/cgi-bin/db_search/DBListFiles.pl?did=132&tid=79458&vid=2423 25.1.2 Data extraction Each yearly file (global) was downloaded, concatenated into a single netcdf file using nco (Unix), and then cropped to the USNES region using Ferret. Each EPU’s time-series of SST was averaged using .shp file boundaries for the MAB, GB, and GOM (also done in Ferret) and saved to the three .csv files. 25.1.3 Data analysis The marine heatwave metrics Maximum Intensity [deg. C] and Cumulative Intensity [deg. C x days] are calculated using NOAA OISST daily sea surface temperature data (25-km resolution) from January 1982 to December 2019. The heatwaves are calculated based on the algorithms in Hobday et al. 2016 and by using a climatology of 1982-2010. These metrics were run R using https://robwschlegel.github.io/heatwaveR/ 25.1.4 Data processing Marine Heatwave data were formatted for inclusion in the ecodata R package using this R code. 25.1.5 Plotting Code used for the plots below can be found here. Figure 25.1: Cumulative and maximum marine heatwave in the Mid-Atlantic References "],
-["verified-records-of-southern-kingfish.html", "26 Verified Records of Southern Kingfish 26.1 Methods", " 26 Verified Records of Southern Kingfish Description: Fisheries Observer Data – Verified Records of Southern Kingfish Found in: State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull Contributor(s): Debra Duarte, Loren Kellogg Data steward: Gina Shield, gina.shield@noaa.gov Point of contact: Gina Shield, gina.shield@noaa.gov Public availability statement: Due to PII concerns data for this indicator are not publicly available. 26.1 Methods 26.1.1 Data sources The Fisheries Sampling Branch deploys observers on commercial fisheries trips from Maine to North Carolina. On observed tows, observers must fully document all kept and discarded species encountered. Observers must comply with a Species Verification Program (SVP), which requires photo or sample submissions of high priority species at least once per quarter. Photos and samples submitted for verification are identified independently by at least two reviewers. The derived data presented in the Mid-Atlantic State of the Ecosystem report for southern kingfish include records verified by the SVP program only. The occurrence of southern kingfish in SVP records were chosen for inclusion in the report due to the recent increases of the species in SVP observer records since 2010. These data are not a complete list from the New England Fisheries Observer Program (NEFOP). Southern Kingfish are less common than Northern Kingfish in observer data and are possibly misidentified so we have initially included records here only when a specimen record was submitted to and verified through the SVP (see Data extraction). 26.1.2 Data extraction SQL query for observer data extraction can be found here. 26.1.3 Data analysis Time series were summed by year and plotted, and mapped data for individual records were plotted according to the location where gear was hauled. As coordinate data were not always available for each record, the map does not include all occurrences of southern kingfish, but was included for spatial context. 26.1.4 Plotting Code used to produce the plot below can be found here. Figure 26.1: Verified records of Southern Kingfish occurrence in the Mid-Atlantic. "],
-["habitat-occupancy-models.html", "27 Habitat Occupancy Models 27.1 Methods", " 27 Habitat Occupancy Models Description: Habitat Occupancy Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull with analysis; Extensive analysis; not yet published; Published methods Contributor(s): Kevin Friedland Data steward: Kevin Friedland, kevin.friedland@noaa.gov Point of contact: Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are available upon request (see Survdat, CHL/PP, and Data Sources below for more information). Model-derived time series are available here. 27.1 Methods Habitat area with a probability of occupancy greater than 0.5 was modeled for many species throughout the Northeast Large Marine Ecosystem (NE-LME) using random forest decision tree models. 27.1.1 Data sources Models were parameterized using a suite of static and dynamic predictor variables, with occurrence and catch per unit effort (CPUE) of species from spring and fall Northeast Fisheries Science Center (NEFSC) bottom trawl surveys (BTS) serving as response variables. Sources of variables used in the analyses are described below. 27.1.1.1 Station depth The NEFSC BTS data included depth observations made concurrently with trawls at each station. Station depth was a static variable for these analyses. 27.1.1.2 Ocean temperature and salinity Sea surface and bottom water temperature and salinity measurements were included as dynamic predictor variables in the model, and were collected using Conductivity/Temperature/Depth (CTD) instruments. Ocean temperature and salinity measurements had the highest temporal coverage during the spring (February-April) and fall (September-November) months. Station salinity data were available between 1992-2016. 27.1.1.3 Habitat descriptors A variety of benthic habitat descriptors were incorporated as predictor variables in occupancy models (Table 27.1). The majority of these parameters are based on depth (e.g. BPI, VRM, Prcury, rugosity, seabedforms, slp, and slpslp). The vorticity variable is based on current estimates, and the variable soft_sed based on sediment grain size. Table 27.1: Habitat descriptors used in model parameterization. Variables Notes References Namera_vrm Vector Ruggedness Measure (VRM) measures terrain ruggedness as the variation in three-dimensional orientation of grid cells within a neighborhood based on The Nature Conservancy Northwest Atlantic Marine Ecoregional Assessment (“NAMERA”) data. Hobson (1972); Sappington, Longshore, and Thompson (2007) Prcurv (2 km, 10 km, and 20 km) Benthic profile curvature at 2km, 10km and 20 km spatial scales was derived from depth data. Winship et al. (2018) Rugosity A measure of small-scale variations of amplitude in the height of a surface, the ratio of the real to the geometric surface area. Friedman et al. (2012) seabedforms Seabed topography as measured by a combination of seabed position and slope. http://www.northeastoceandata.org/ Slp (2 km, 10 km, and 20 km) Benthic slope at 2km, 10km and 20km spatial scales. Winship et al. (2018) Slpslp (2 km, 10 km, and 20 km) Benthic slope of slope at 2km, 10km and 20km spatial scales Winship et al. (2018) soft_sed Soft-sediments is based on grain size distribution from the USGS usSeabed: Atlantic coast offshore surficial sediment data. http://www.northeastoceandata.org/ Vort (fall - fa; spring - sp; summer - su; winter - wi) Benthic current vorticity at a 1/6 degree (approx. 19 km) spatial scale. Kinlan et al. (2016) 27.1.1.4 Zooplankton Zooplankton data are acquired through the NEFSC Ecosystem Monitoring Program (“EcoMon”). For more information regarding the collection process for these data, see Kane (2007), Kane (2011), and Morse et al. (2017). The bio-volume of the 18 most abundant zooplankton taxa were considered as potential predictor variables. 27.1.1.5 Remote sensing data Both chlorophyll concentration and sea surface temperature (SST) from remote sensing sources were incorporated as static predictor variables in the model. During the period of 1997-2016, chlorophyll concentrations were derived from observations made by the Sea-viewing Wide Field of View Sensor (SeaWIFS), Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua), Medium Resolution Imaging Spectrometer (MERIS), and Visible and Infrared Imaging/Radiometer Suite (VIIRS). 27.1.2 Data processing 27.1.2.1 Zooplankton Missing values in the EcoMon time series were addressed by summing data over five-year time steps for each seasonal time frame and interpolating a complete field using ordinary kriging. Missing values necessitated interpolation for spring data in 1989, 1990, 1991, and 1994. The same was true of the fall data for 1989, 1990, and 1992. 27.1.2.2 Remote sensing data An overlapping time series of observations from the four sensors listed above was created using a bio-optical model inversion algorithm (Maritorena et al. 2010). Monthly SST data were derived from MODIS-Terra sensor data (available here). 27.1.2.3 Ocean temperature and salinity Date of collection corrections for ocean temperature data were developed using linear regressions for the spring and fall time frames; standardizing to collection dates of April 3 and October 11 for spring and fall. No correction was performed for salinity data. Annual data for ocean temperature and salinity were combined with climatology by season through an optimal interpolation approach. Specifically, mean bottom temperature or salinity was calculated by year and season on a 0.5° grid across the ecosystem, and data from grid cells with >80% temporal coverage were used to calculate a final seasonal mean. Annual seasonal means were then used to calculate combined anomalies for seasonal surface and bottom climatologies. An annual field was then estimated using raw data observations for a year, season, and depth using universal kriging (Hiemstra et al. 2008), with depth included as a covariate (on a standard 0.1° grid). This field was then combined with the climatology anomaly field and adjusted by the annual mean using the variance field from kriging as the basis for a weighted mean between the two. The variance field was divided into quartiles with the lowest quartile assigned a weighting of 4:1 between the annual and climatology values. The optimally interpolated field at these locations was therefore skewed towards the annual data, reflecting their proximity to actual data locations and associated low kriging variance. The highest kriging variance quartile (1:1) reflected less information from the annual field and more from the climatology. 27.1.3 Data analysis 27.1.3.1 Occupancy models Prior to fitting the occupancy models, predictor variables were tested for multi-collinearity and removed if found to be correlated. The final model variables were then chosen utilizing a model selection process as shown by Murphy, Evans, and Storfer (2010) and implemented with the R package rfUtilities (Evans and Murphy 2018). Occupancy models were then fit as two-factor classification models (absence as 0 and presence as 1) using the randomForest R package (Liaw and Wiener 2002). 27.1.3.2 Selection criteria and variable importance The irr R package (Gamer, Lemon, and Singh 2012) was used to calculate Area Under the ROC Curve (AUC) and Cohen’s Kappa for assessing accuracy of occupancy habitat models. Variable importance was assessed by plotting the occurrence of a variable as a root variable versus the mean minimum node depth for the variable (Paluszynska and Biecek 2017), as well as by plotting the Gini index decrease versus accuracy decrease. 27.1.4 Plotting Figure 27.1: Summer flounder spring (A) and fall (B) occupancy habitat area in the Northeast Large Marine Ecosystem. References "],
-["primary-production-required.html", "28 Primary Production Required 28.1 Methods", " 28 Primary Production Required Description: Time Series of Primary Production Required to sustain reported landings. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2020+), State of the Ecosystem - Mid-Atlantic (2020+) Indicator category: Database pull with analysis; Published methods Contributor(s): Michael Fogarty, Andrew Beet Data steward: Andrew Beet, andrew.beet@noaa.gov Point of contact: Andrew Beet, andrew.beet@noaa.gov Public availability statement: Source data is not publicly availabe due to PII restrictions. 28.1 Methods The index is a measure of the impact of fishing on the base of the foodweb. The amount of potential yield we can expect from a marine ecosystem depends on the amount of production entering at the base of the food web, primarily in the form of phytoplankton; the pathways this energy follows to reach harvested species; the efficiency of transfer of energy at each step in the food web; and the fraction of this production that is removed by the fisheries. Species such as scallops and clams primarily feed directly on larger phytoplankton species and therefore require only one step in the transfer of energy. The loss of energy at each step can exceed 80-90%. For many fish species, as many as 2-4 steps may be necessary. Given the trophic level and the efficiency of energy transfer of the species in the ecosystem the amount phytoplankton production required (PPR) to account for the observed catch can be estimated. The index for Primary Production Required (PPR) was adapted from (Pauly and Christensen 1995). \\[PPR_t = \\sum_{i=1}^{n_t} \\left(\\frac{landings_{t,i}}{9}\\right) \\left(\\frac{1}{TE}\\right)^{TL_i-1}\\] where \\(n_t\\) = number of species in time \\(t\\), \\(landings_{t,i}\\) = landings of species \\(i\\) in time \\(t\\), \\(TL_i\\) is the trophic level of species \\(i\\), \\(TE\\) = Trophic efficiency. The PPR estimate assumes a 9:1 ratio for the conversion of wet weight to carbon and a 15% transfer efficiency per trophic level, (\\(TE\\) = 0.15) The index is presented as a percentage of estimated primary production (PP) available over the geographic region of interest, termed an Ecological Production Unit (EPU). The scaled index is estimated by dividing the PPR index in year \\(t\\) by the estimated primary production in time \\(t\\). \\[scaledPPR_t = \\frac{PPR_t}{PP_t}\\] The species selected in each year were determined by their cumulative contribution to total landings. A threshold of at least 80% of the total landings is used. 28.1.1 Data sources Data for this index come from a variety of sources. The landings data come from the Commercial Fishery Database (CFDBS), species trophic level information come from fishbase and sealifebase, and primary production estimates are derived from satellites. Some of these data are typically not available to the public. 28.1.2 Data extraction Landings are extracted from the commercial fisheries database (CFDBS) using the methods described in the chapter Commercial Landings Data. Trophic level information for each species is obtained from fishbase and sealifebase using the R package rfishbase (Froese and Pauly 2019) in tandem with the package indexPPR. Primary Production is estimated using the methods described in the chapter Chlorophyll a and Primary Production. 28.1.3 Data analysis Annual (wet weight) landings are calculated for each species for a given EPU. For each year the landings are sorted in descending order by species and the cumulative landings are calculated. The top species that accounted for 80% of total cumulative landings are selected. The trophic level for each of these species are then obtained from fishbase/sealifebase. At this point the PPR index is calculated. The units of the index are \\(gCyear^{-1}\\) for the EPU. The index is converted to \\(gCm^{-2}year^{-1}\\) by dividing by the area (in \\(m^2\\)) of the EPU. To normalize the index the total Primiary Production for the given EPU is required. This is calculated as described in the chapter Chlorophyll a and Primary Production. The units are also converted to \\(gCm^{-2}year^{-1}\\). The index is then normalized by dividing the index in year t by the total primary production in time \\(t\\). 28.1.4 Plotting Four plots are produced for each EPU: The normalized PPR index (along with the associated landings). Total primary production Mean trophic level of the species included in the index (weighted by their landings) Species composition of landings All created using the indexPPR See the workedExample vignette in the indexPPR package for plotting code. 28.1.4.1 Georges Bank (GB) 28.1.4.2 Gulf of Maine (GOM) 28.1.4.3 Mid-Atlantic Bight (MAB) References "],
-["fish-productivity-indicator.html", "29 Fish Productivity Indicator 29.1 Methods", " 29 Fish Productivity Indicator Description: Groundfish productivity estimated as the ratio of small fish to large fish Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2020), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019, 2020) Indicator category: Database pull with analysis; Published methods Contributor(s): Charles Perretti Data steward: Charles Perretti, charles.perretti@noaa.gov Point of contact: Charles Perretti, charles.perretti@noaa.gov Public availability statement: Source data are available upon request. 29.1 Methods 29.1.1 Data sources Survey data from the Northeast Fisheries Science Center (NEFSC) trawl database. These data in their derived form are available through Survdat. 29.1.2 Data extraction Data were extracted from Survdat. 29.1.3 Data analysis We defined size thresholds separating small and large fish for each species based on the 20th percentile of the length distribution across all years. This threshold was then used to calculate a small and large fish index (numbers below and above the threshold, respectively) each year. Although the length percentile corresponding to age-1 fish will vary with species, we use the 20th percentile as an approximation. Biomass was calculated using length–weight relationships directly from the survey data. Following Wigley, McBride, and McHugh (2003), the length-weight relationship was modeled as \\[\\ln W = \\ln a + b \\ln L\\] where \\(W\\) is weight (kg), \\(L\\) is length (cm), and \\(a\\) and \\(b\\) are parameters fit via linear regression. The ratio of small fish numbers of the following year to larger fish biomass in the current year was used as the index of recruitment success. The fall and spring recruitment success anomalies were averaged to provide an annual index of recruitment success. Further details of methods described in Perretti et al. (2017a). 29.1.4 Data processing Productivity data were formatted for inclusion in the ecodata R package using the R code found here. 29.1.5 Plotting Figure 29.1: Groundfish productivity across all stocks in the Mid-Atlantic Bight. References "],
-["recreational-fishing-indicators.html", "30 Recreational Fishing Indicators 30.1 Methods", " 30 Recreational Fishing Indicators Description: A variety of indicators derived from MRIP Recreational Fisheries Statistics, including total recreational catch, total angler trips by region, annual diversity of recreational fleet effort, and annual diversity of managed species. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019, 2020) Indicator category: Database pull with analysis Contributor(s): Geret DePiper, Scott Steinbeck Data steward: Geret DePiper, geret.depiper@noaa.gov Point of contact: Geret DePiper, geret.depiper@noaa.gov Public availability statement: Data sets are publicly available (see Data Sources below). 30.1 Methods We used total recreational harvest as an indicator of seafood production and total recreational trips and total recreational anglers as proxies for recreational value generated from the Mid-Atlantic and New England regions respectively. We estimated both recreational catch diversity in species manages by the Fisheries Management Councils; Mid-Atlantic (MAFMC), New England (NEFMC) and Atlantic States (ASFMC) and fleet effort diversity using the effective Shannon index. 30.1.1 Data sources All recreational fishing indicator data, including number of recreationally harvested fish, number of angler trips, and number of anglers, were downloaded from the Marine Recreational Information Program MRIP Recreational Fisheries Statistics Queries portal. Relevant metadata including information regarding data methodology updates are available at the query site. Note that 2017 data were considered preliminary at the time of the data pull. Data sets were queried by region on the MRIP site, and for the purposes of the State of the Ecosystem reports, the “NORTH ATLANTIC” and “MID-ATLANTIC” regions were mapped to the New England and Mid-Atlantic report versions respectively. All query pages are accessible through the MRIP Recreational Fisheries Statistics site. The number of recreationally harvested fish was found by selecting “TOTAL HARVEST (A + B1)” on the Catch Time Series Query page. Catch diversity estimates were also derived from the total catch time series (see below). Species included in the diversity of catch analysis included American eel Anguilla rostrata, Atlantic cod Gadus morhua, Atlantic mackerel Scomber scombrus, Atlantic sturgeon Acipenser oxyrinchus, black drum Pogonias cromis, black sea bass Centropristis striata , bluefish Pomatomus saltatrix , cobia Rachycentron canadum, haddock Melanogrammus aeglefinus , pollock Pollachius virens, red drum Sciaenops ocellatus, scup Stenotomus chrysops, Spanish mackerel Scomberomorus maculatus, spiny dogfish Squalus acanthias, spot Leiostomus xanthurus , spotted seatrout Cynoscion nebulosus, striped bass Morone saxatilis, summer flounder Paralichthys dentatus, tautog Tautoga onitis , tilefish Lopholatilus chamaeleonticeps, weakfish Cynoscion regalis, winter flounder Pseudopleuronectes americanus, and “All Other Species”. Angler trips (listed as “TOTAL” trips) were pulled from the MRIP Effort Time Series Query page, and included data from 1981 - 2019. Time series of recreational fleet effort diversity were calculated from this data set (see below). The number of anglers was total number of anglers from the Marine Recreational Fishery Statistics Survey (MRFSS) Participation Time Series Query, and includes data from 1981 - 2016. 30.1.2 Data analysis Recreational fleet effort diversity Code used to for effort diversity data analysis can be found here. Recreational catch diversity Code used to for catch diversity data analysis can be found here. 30.1.3 Data processing Recreational fishing indicators were formatted for inclusion in the ecodata R package using this code. 30.1.4 Plotting Figure 30.1: Recreational effort diversity and diversity of recreational catch in the Mid-Atlantic. Figure 30.2: Total recreational seafood harvest in the Mid-Atlantic. "],
-["right-whale-abundance.html", "31 Right Whale Abundance 31.1 Methods", " 31 Right Whale Abundance Description: Right Whale Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019, 2020) Indicator category: Synthesis of published information; Published methods Contributor(s): Christopher D. Orphanides Data steward: Chris Orphanides, chris.orphanides@noaa.gov Point of contact: Richard Pace, richard.pace@noaa.gov Public availability statement: Source data are available from the New England Aquarium upon request. Derived data are available here 31.1 Methods 31.1.1 Data sources The North Atlantic right whale abundance estimates were taken from a published document (see Pace, Corkeron, and Kraus 2017), except for the most recent 2016 and 2017 estimates. Abundance estimates from 2016 and 2017 were taken from the 2016 National Oceanographic and Atmospheric Administration marine mammal stock assessment (Hayes et al. 2017) and an unpublished 2017 stock assessment. Calves?? 31.1.2 Data extraction Data were collected from existing reports and validated by report authors. 31.1.3 Data analysis Analysis for right whale abundance estimates is provided by Pace, Corkeron, and Kraus (2017), and code can be found in the supplemental materials. 31.1.4 Data processing Time series of right whale and calf abundance estimates were formatted for inclusion in the ecodata R package using this R code. 31.1.5 Plotting Code used create the plots below can be found at these links, NARW population estimates and Calf births. Figure 31.1: North Atlantic right whale population estimates shown with 95% credible intervals. Figure 31.2: North Atlantic right whale calf births. References "],
-["ne-seabird-diet-and-productivity.html", "32 NE Seabird diet and productivity 32.1 Methods", " 32 NE Seabird diet and productivity Description: Common tern annual diet and productivity at seven Gulf of Maine colonies managed by the National Audubon Society’s Seabird Restoration Program Indicator category: Published method Found in: State of the Ecosystem - New England (2019, 2020) Contributor(s): Don Lyons, Steve Kress, Paula Shannon, Sue Schubel Data steward: Don Lyons, dlyons@audubon.org Point of contact: Don Lyons, dlyons@audubon.org Public availability statement: Please email dlyons@audubon.org for further information and queries on this indicator source data. 32.1 Methods Chick diet Common tern (Sterna hirundo) chick diet was quantified at each of the seven nesting sites (Fig. ?? ) by observing chick provisioning from portable observation blinds. The locations of observation blinds within each site were chosen to maximize the number of visible nests, and provisioning observations took place between mid-June and early August annually. Observations of chick diet were made during one or two, three to four hour periods throughout the day, but typically proceed according to nest activity levels (moreso in the morning hours). Observations began with chicks as soon as they hatched, and continue until the chicks fledged or died. Most common tern prey species were identifiable to the species level due to distinct size, color and shape. However, when identification was not possible or was unclear, prey species were listed as “unknown” or “unknown fish”. More detailed methods can be found in Hall, Kress, and Griffin (2000). Nest productivity Common tern nest productivity, in terms of the number of fledged chicks per nest, was collected annually from fenced enclosures at island nesting sites (known as “productivity plots”). Newly hatched chicks within these enclosures were weighed, marked or banded, and observed until fledging, death, or until a 15 day period had passed when chicks were assumed to have fledged. Productivity was also quantified from observer blinds for nests outside of the productivity plots where chicks were marked for identification. More detailed methods for quantifying nest productivity can be found in Hall and Kress (2004) 32.1.1 Data sources Common tern diet and nest productivity data were provided by the National Audubon Society’s Seabird Restoration Program. 32.1.2 Data processing Diet and productivity data were formatted for inclusion in the ecodata R package using this R code. 32.1.3 Data analysis Raw diet data were used to create time series of mean shannon diversity through time and across study sites using the vegan R package (Oksanen et al. 2019). Code for this calculation can be found here. Diet diversity is presented along with nest productivity (+/- 1 SE). Code used to create the figures below can be found at these links, diet diversity, prey frequencies and common tern productivity 32.1.4 Plotting 32.1.4.1 Diet diversity Figure 32.1: Shannon diversity of common tern diets observed at nesting sites in Gulf of Maine. Diversity of common tern diets has been predominantly above the long-term mean since 2006. 32.1.4.2 Prey frequencies Figure 32.2: Prey frequencies in the diets of common tern observed across the seven colonies in the Gulf of Maine. 32.1.4.3 Common tern productivity Figure 32.3: Common terns: Mean common tern productivity at nesting sites in Gulf of Maine. Error bars show +/- 1 SE of the mean. References "],
-["ma-seabird-productivity.html", "33 MA Seabird productivity 33.1 Methods", " 33 MA Seabird productivity Description: Virginia seabird data Indicator category: Published method Found in: State of the Ecosystem - Mid-Atantic (2020) Contributor(s): Ruth Boettcher Data steward: Kimberly Bastille kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille kimberly.bastille@noaa.gov Public availability statement: 33.1 Methods 33.1.1 Data sources Virginia seabird breeding pair population estimates derived from table 4 of “Status and distribution of colonial waterbirds in coastal Virginia: 2018 breeding season.” Center for Conservation Biology Technical Report Series, CCBTR-19-06. College of William and Mary & Virginia Commonwealth University, Williamsburg, VA. 28 pp. Available at: https://ccbbirds.org/wp-content/uploads/CCBTR-19-06_Colonial-waterbirds-in-coastal-Virginia-2018.pdf 33.1.2 Data processing VA seabird data were formatted for inclusion in the ecodata R package using this R code. 33.1.3 Data analysis NA 33.1.4 Plotting Code used to create the figure below can be found here Figure 33.1: Functional group population estimated derived from Table 4 of Watts, B. D., B. J. Paxton, R. Boettcher, and A. L. Wilke. 2019. "],
-["seasonal-sst-anomalies.html", "34 Seasonal SST Anomalies 34.1 Methods", " 34 Seasonal SST Anomalies Description: Seasonal SST Anomalies Indicator category: Database pull with analysis Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2018, 2019, 2020) Contributor(s): Sean Hardison, Vincent Saba Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Source data are available here. 34.1 Methods 34.1.1 Data sources Data for seasonal sea surface tempature anomalies (Fig. 34.1) were derived from the National Oceanographic and Atmospheric Administartion optimum interpolation sea surface temperature high resolution data set (NOAA OISST V2) provided by NOAA Earth System Research Laboratory’s Physical Science Division, Boulder, CO. The data extend from 1981 to present, and provide a 0.25° x 0.25° global grid of SST measurements (Reynolds et al. 2007). 34.1.2 Data extraction Individual files containing daily mean SST data for each year during the period of 1981-present were downloaded from the OI SST V5 site. Yearly data provided as layered rasters were masked according to the extent of Northeast US Continental Shelf. Data were split into three month seasons for (Winter = Jan, Feb, Mar; Spring = Apr, May, Jun; Summer = July, August, September; Fall = Oct, Nov, Dec). 34.1.3 Data analysis We calculated the long-term mean (LTM) for each season-specific stack of rasters over the period of 1982-2010, and then subtracted the (LTM) from daily mean SST values to find the SST anomaly for a given year. The use of climatological reference periods is a standard procedure for the calculation of meteorological anomalies (WMO 2017). Prior to 2019 State of the Ecosystem reports, SST anomaly information made use of a 1982-2012 reference period. A 1982-2010 reference period was adopted to facilitate calculating anomalies from a standard NOAA ESRL data set. R code used in extraction and processing gridded and timeseries data can found in the ecodata package. 34.1.4 Plotting Code used to build the figure below can be found here. Figure 34.1: MAB seasonal sea surface temperature time series overlaid onto 2019 seasonal spatial anomalies. References "],
-["stockstatus.html", "35 Single Species Status Indicator 35.1 Methods", " 35 Single Species Status Indicator Description: Summary of the most recent stock assessment results for each assessed species. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019, 2020) Indicator category: Synthesis of published information Contributor(s): Sarah Gaichas, based on code and spreadsheets originally provided by Chris Legault Data steward: Sarah Gaichas sarah.gaichas@noaa.gov Point of contact: Sarah Gaichas sarah.gaichas@noaa.gov Public availability statement: All stock assessment results are publicly available (see Data Sources). Summarized data are available here. 35.1 Methods 35.1.1 Data sources “Data” used for this indicator are the outputs of stock assessment models and review processes, including reference points (proxies for fishing mortality limits and stock biomass targets and limits), and the current fishing mortality rate and biomass of each stock. The spreadsheet summarizes the most recent stock assessment updates for each species, which are available on the Northeast Fisheries Science Center (NEFSC) website at: https://www.nefsc.noaa.gov/saw/reports.html https://www.nefsc.noaa.gov/publications/crd/crd1717/ Additional assessments are reported directly to the New England Fishery Management Council (NEFMC): http://s3.amazonaws.com/nefmc.org/Document-2-SAFE-Report-for-Fishing-Year-2016.pdf http://s3.amazonaws.com/nefmc.org/4_NEFSC_SkateMemo_July_2017.pdf 35.1.2 Data extraction Each assessment document was searched to find the following information (often but not always summarized under a term of reference to determine stock status in the executive summary): Bcur: current year biomass, (most often spawning stock biomass (SSB) or whatever units the reference points are in) Fcur: current year fishing mortality, F Bref: biomass reference point, a proxy of Bmsy (the target) Fref: fishing mortality reference point, a proxy of Fmsy 35.1.3 Data processing R code used to process the stock status data set for inclusion in the ecodata R package can be found here. 35.1.4 Data analysis For each assessed species, Bcur is divided by Bref and Fcur is divided by Fref. They are then plotted for each species on an x-y plot, with Bcur/Bref on the x axis, and Fcur/Fref on the y axis. 35.1.5 Plotting The script used to develop the figure in the State of the Ecosystem report can be found here. Figure 35.1: Summary of single species status for MAFMC and jointly managed stocks. "],
-["slopewater-proportions.html", "36 Slopewater proportions 36.1 Methods", " 36 Slopewater proportions Description: Percent total of water type observed in the deep Northeast Channel (150-200 m water depth). Indicator category: Published methods Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2019, 2020) Contributors: Paula Fratantoni, paula.fratantoni@noaa.gov; David Mountain, NOAA Fisheries, retired. Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Paula Fratantoni, paula.fratantoni@noaa.gov Public availability statement: Source data are publicly available at ftp://ftp.nefsc.noaa.gov/pub/hydro/matlab_files/yearly and in the World Ocean Database housed at http://www.nodc.noaa.gov/OC5/SELECT/dbsearch/dbsearch.html under institute code 258 36.1 Methods 36.1.1 Data sources The slope water composition index incorporates temperature and salinity measurements collected on Northeast Fisheries Science Center surveys between 1977-present within the geographic confines of the Northeast Channel in the Gulf of Maine. Early measurements were made using water samples collected primarily with Niskin bottles at discreet depths, mechanical bathythermographs and expendable bathythermograph probes, but by 1991 the CTD – an acronym for conductivity temperature and depth – became standard equipment on all NEFSC surveys. 36.1.2 Data extraction While all processed hydrographic data are archived in an Oracle database (OCDBS), we work from Matlab-formatted files stored locally. 36.1.3 Data analysis Temperature and salinity measurements are examined to assess the composition of the waters entering the Gulf of Maine through the Northeast Channel. The analysis closely follows the methodology described by D. G. Mountain (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. For each survey in the hydrographic archive, ocean temperature and salinity observations sampled in the area just inside the Northeast Channel (bounded by 42.2-42.6° latitude north and 66-66.8° longitude west) and between 150 - 200 meters depth are extracted and a volume-weighted average temperature and salinity is calculated. The volume weighting is accomplished by apportioning the area within the Northeast Channel polygon among the stations occupying the region, based on inverse distance squared weighting. The result of this calculation is a timeseries of volume-average temperature and salinity having a temporal resolution that matches the survey frequency in the database. The average temperature and salinity observed at depth in the Northeast Channel is assumed to be the product of mixing between three distinct sources having the following temperature and salinity characteristics: (1) Warm Slope Water (T=10 °C, S=35), (2) Labrador Slope Water (T=6 °C, S=34.7) and (3) Scotian Shelf Water (T=2 °C, S=32). As described by D. G. Mountain (2012), the relative proportion of each source is determined via a rudimentary 3-point mixing algorithm. On a temperature-salinity diagram, lines connecting the T-S coordinates for these three sources form a triangle, the sides of which represent mixing lines between the sources. A water sample that is a mixture of two sources will have a temperature and salinity that falls somewhere along the line connecting the two sources on the temperature-salinity diagram. Observations of temperature and salinity collected within the Northeast Channel would be expected to fall within the triangle if the water sampled is a mixture of the three sources. Simple geometry allows us to calculate the relative proportion of each source in a given measurement. As an example, a line drawn from the T-S point representing shelf water through an observed T-S in the center of the triangle will intersect the opposite side of the triangle (the mixing line connecting the coordinates of the two slope water sources). This intersecting T-S value may then be used to calculate the relative proportions (percentage) of the two slope water sources. Using this method, the percentage of Labrador slope water and Warm slope water are determined for the timeseries of volume-average temperature and salinity. It should be noted that our method assumes that the temperature and salinity properties associated with the source watermasses are constant. In reality, these may vary from year to year, modified by atmospheric forcing, mixing and/or advective processes. Likewise, other sources are periodically introduced into the Northeast Channel, including intrusions of Gulf Stream water flowing into the Gulf of Maine and modified shelf water flowing out of the Gulf of Maine along the flank of Georges Bank. These sources are not explicitely considered in the 3-point mixing algorithm and may introduce errors in the proportional estimates. Code used to calculate slopewater proportions can be found here. 36.1.4 Data processing Source data were formatted for inclusion in the ecodata R package using the R code found here. 36.1.5 Plotting Code used to create the figure below can be found here. Figure 36.1: Proportion of warm slope water (WSW) and Labrador slope water (LSLW) entering the GOM through the Northeast Channel. References "],
-["species-density-estimates.html", "37 Species Density Estimates 37.1 Methods", " 37 Species Density Estimates Description: Current and Historical Species Distributions Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018), State of the Ecosystem - Mid-Atlantic (2017, 2018) Indicator category: Database pull; Database pull with analysis Contributor: Kevin Friedland Data steward: Kevin Friedland Point of contact: Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are publicly available. 37.1 Methods We used kernel density plots to depict shifts in species’ distributions over time. These figures characterize the probability of a species occurring in a given area based on Northeast Fisheries Science Center (NEFSC) Bottom Trawl Survey data. Kernel density estimates (KDEs) of distributions are shown for the period of 1970-1979 (shaded blue) and most recent three years of survey data (shaded red) (e.g. Figure 37.1). Results are typically visualized for spring and fall bottom trawl surveys seperately. Three probability levels (25%, 50%, 75%) are shown for each time period, where the 25% region depicts the core area of the distribution and the 75% region shows the area occupied more broadly by the species. A wide array of KDEs for many ecologically and economically important species on the Northeast US Continental Shelf are available here. 37.1.1 Data sources Current and historical species distributions are based on the NEFSC Bottom Trawl Survey data (aka “Survdat”) and depth strata. Strata are available as shapefiles that can be downloaded here (listed as “strata.shp”). 37.1.2 Data analysis Code used for species density analysis can be found here. 37.1.3 Plotting Figure 37.1: Current and historical sea scallop kernel density estimates derived from spring survey data. Current estimates derived from 2016-2018 data. "],
-["species-distribution-indicators.html", "38 Species Distribution Indicators 38.1 Methods", " 38 Species Distribution Indicators Description: Species mean depth, along-shelf distance, and distance to coastline Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019, 2020) Indicator category: Extensive analysis; not yet published Contributor(s): Kevin Friedland Data steward: Kevin Friedland, kevin.friedland@noaa.gov Point of contact: Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are available upon request (read more here). Derived data may be downloaded here. 38.1 Methods Three metrics quantifying spatial-temporal distribution shifts within fish populations were developed by Friedland et al. (2018), including mean depth, along-shelf distance, and distance to coastline. 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. Values in the derived time series correspond to mean distance in km from the southwest origin of the along-shelf axis at 0 km. The along-shelf axis begins at 76.53°W 34.60°N and terminates at 65.71°W 43.49°N. Once mean distance is found, depth of occurrence and distance to coastline can be calculated for each species’ positional center. Analyses present in the State of the Ecosystem (SOE) reports include mean depth and along-shelf distance for Atlantic cod, sea scallop, summer flounder, and black sea bass. 38.1.1 Data sources Data for these indicators were derived from fishery-independent bottom trawl survey data collected by the Northeast Fisheries Science Center (NEFSC). 38.1.2 Data analysis Species distribution indicators were derived using the R code found here. 38.1.3 Data processing Distribution indicators were further formatted for inclusion in the ecodata R package using the R code found here. 38.1.4 Plotting Code used to create the figure below can be found here. Figure 38.1: Aggregate species distribution depth along shelf distance (northward shift) and depth. References "],
-["survdat.html", "39 Survey Data 39.1 Methods", " 39 Survey Data Description: Survdat (Survey database) Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019, 2020), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019, 2020) Indicator category: Database pull Contributor(s): Sean Lucey Data steward: Sean Lucey sean.lucey@noaa.gov Point of contact: Sean Lucey sean.lucey@noaa.gov Public availability statement: Source data are available to qualified researchers upon request (see “Access Information” here). Derived data used in SOE reports are available here. 39.1 Methods The Northeast Fisheries Science Center (NEFSC) has been conducting standardized bottom trawl surveys in the fall since 1963 and spring since 1968. The surveys follow a stratified random design. Fish species and several invertebrate species are enumerated on a tow by tow basis (Azarovitz 1981). The data are housed in the NEFSC’s survey database (SVDBS) maintained by the Ecosystem Survey Branch. Direct pulls from the database are not advisable as there have been several gear modifications and vessel changes over the course of the time series (Miller et al. 2010). Survdat was developed as a database query that applies the appropriate calibration factors for a seamless time series since the 1960s. As such, it is the base for many of the other analyses conducted for the State of the Ecosystem report that involve fisheries independent data. The Survdat script can be broken down into two sections. The first pulls the raw data from SVDBS. While the script is able to pull data from more than just the spring and fall bottom trawl surveys, for the purposes of the State of the Ecosystem reports only the spring and fall data are used. Survdat identifies those research cruises associated with the seasonal bottom trawl surveys and pulls the station and biological data. Station data includes tow identification (cruise, station, and stratum), tow location and date, as well as several environmental variables (depth, surface/bottom salinity, and surface/bottom temperature). Stations are filtered for representativness using a station, haul, gear (SHG) code for tows prior to 2009 and a tow, operations, gear, and aquisition (TOGA) code from 2009 onward. The codes that correspond to a representative tow (SHG <= 136 or TOGA <= 1324) are the same used by assessment biologists at the NEFSC. Biological data includes the total biomass and abundance by species, as well as lengths and number at length. The second section of the Survdat script applies the calibration factors. There are four calibrartion factors applied (Table 39.1). Calibration factors are pulled directly from SVDBS. Vessel conversions were made from either the NOAA Ship Delaware II or NOAA Ship Henry Bigelow to the NOAA Ship Albatross IV which was the primary vessel for most of the time series. The Albatross was decommisioned in 2009 and the Bigelow is now the primary vessel for the bottom trawl survey. Table 39.1: Calibration factors for NEFSC trawl survey data Name Code Applied Door Conversion DCF <1985 Net Conversion GCF 1973 - 1981 (Spring) Vessel Conversion I VCF Delaware II records Vessel Conversion II BCF Henry Bigelow records The output from Survdat is an RData file that contains all the station and biological data, corrected as noted above, from the NEFSC Spring Bottom Trawl Survey and NEFSC Fall Bottom Trawl Survey. The RData file is a data.table, a powerful wrapper for the base data.frame (https://cran.r-project.org/web/packages/data.table/data.table.pdf). There are also a series of tools that have been developed in order to utilize the Survdat data set (https://github.com/slucey/RSurvey). 39.1.1 Data sources Survdat is a database query of the NEFSC survey database (SVDBS).These data are available to qualified researchers upon request. More information on the data request process is available under the “Access Information” field here. 39.1.2 Data extraction Extraction methods are described above. The R code found here was used in the survey data extraction process. 39.1.3 Data analysis The fisheries independent data contained within the Survdat is used in a variety of products; the more complicated analyses are detailed in their own sections. The most straightforward use of this data is for the resource species aggregate biomass indicators. For the purposes of the aggregate biomass indicators, fall and spring survey data are treated separately. Additionally, all length data is dropped and species seperated by sex at the catch level are merged back together. For the aggregate biomass indicators, Survdat is first post stratified into Ecological Production Units. Stations are labeled by the EPU they fall within using the over function from the rdga R package (Bivand, Keitt, and Rowlingson 2018). Next, the total number of stations within each EPU per year is counted using unique station records. Biomass is summed by species per year per EPU. Those sums are divided by the appropriate station count to get the EPU mean. Finally, the mean biomasses are summed by aggregate groups. These steps are encompassed in the processing code, which also includes steps taken to format the data set for inclusion in the ecodata R package. 39.1.4 Plotting Code used to create the figure below can be found here Figure 39.1: Spring (left) and fall (right) surveyed biomass in the Mid-Atlantic Bight. Data from the NEFSC Bottom Trawl Survey are shown in black, with NEAMAP shown in red. References "],
-["thermal-habitat-projections.html", "40 Thermal Habitat Projections 40.1 Methods", " 40 Thermal Habitat Projections Description: Species Thermal Habitat Projections Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Published methods Contributor(s): Vincent Saba Data steward: Vincent Saba, vincent.saba@noaa.gov Point of contact: Vincent Saba, vincent.saba@noaa.gov Public availability statement: Source data are available to the public. Model outputs for thermal habitat projections are available here. 40.1 Methods This indicator is based on work reported in Kleisner et al. (2017). 40.1.1 Data sources 40.1.1.1 Global Climate Model Projection We used National Oceanographic and Atmosheric Administration’s Geophysical Fluid Dynamics Laboratory (NOAA GFDL) CM2.6 simulation consisting of (1) a 1860 pre-industrial control, which brings the climate system into near-equilibrium with 1860 greenhouse gas concentrations, and (2) a transient climate response (2xCO2) simulation where atmospheric CO2 is increased by 1% per year, which results in a doubling of CO2 after 70 years. The climate change response from CM2.6 was based on the difference between these two experimental runs. Refer to Saba et al. (2016) for further details. 40.1.1.2 Modeling Changes in Suitable Thermal Habitat The NOAA Northeast Fisheries Science Center, U.S. Northeast Shelf (NES) bottom trawl survey, which has been conducted for almost 50-years in the spring and fall, provides a rich source of data on historical and current marine species distribution, abundance, and habitat, as well as oceanographic conditions (Azarovitz 1981). The survey was implemented to meet several objectives: (1) monitor trends in abundance, biomass, and recruitment, (2) monitor the geographic distribution of species, (3) monitor ecosystem changes, (4) monitor changes in life history traits (e.g., trends in growth, longevity, mortality, and maturation, and food habits), and (5) collect baseline oceanographic and environmental data. These data can be leveraged for exploring future changes in the patterns of abundance and distribution of species in the region. 40.1.2 Data analysis 40.1.2.1 Global Climate Model Projection The CM2.6 80-year projections can be roughly assigned to a time period by using the International Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs), which describe four different 21st century pathways of anthropogenic greenhouse gas emissions, air pollutant emissions, and land use (IPCC 2014). There are four RCPs, ranging from a stringent mitigation scenario (RCP2.6), two intermediate scenarios (RCP4.5 and RCP6.0), and one scenario with very high greenhouse gas emissions (RCP8.5). For RCP8.5, the global average temperature at the surface warms by 2C by approximately 2060-2070 relative to the 1986-2005 climatology (see Figure SPM.7a in IPCC, 2013). For CM2.6, the global average temperature warms by 2C by approximately years 60-80 (see Fig. 1 in Winton et al. (2014)). Therefore, the last 20 years of the transient climate response simulation roughly corresponds to 2060-2080 of the RCP8.5 scenario. Here, the monthly differences in surface and bottom temperatures (‘deltas’) for spring (February-April) and fall (September- November) are added to an average annual temperature climatology for spring and fall, respectively, derived from observed surface and bottom temperatures to produce an 80-year time series of future bottom and surface temperatures in both seasons. The observed temperatures come from the NEFSC spring and fall bottom trawl surveys conducted from 1968 to 2013 and represent approximately 30,000 observations over the time series. 40.1.2.2 Modeling Changes in Suitable Thermal Habitat We modeled individual species thermal habitat across the whole U.S. NES and not by sub-region because we did not want to assume that species would necessarily maintain these assemblages in the future. Indeed, the goal here is to determine future patterns of thermal habitat availability for species on the U.S. NES in more broad terms. We fit one generalizaed additive model (GAM) based on both spring and fall data (i.e., an annual model as opposed to separate spring and fall models) and use it to project potential changes in distribution and magnitude of biomass separately for each season for each species. By creating a single annual model based on temperature data from both spring and fall, we ensure that the full thermal envelope of each species is represented. For example, if a species with a wide thermal tolerance has historically been found in cooler waters in the spring, and in warmer waters in the fall, an annual model will ensure that if there are warmer waters in the spring in the future, that species will have the potential to inhabit those areas. Additionally, because the trawl survey data are subject to many zero observations, we use delta-lognormal GAMs (Wood 2011), which model presence-absence separately from logged positive observations. The response variables in each of the GAMs are presence/absence and logged positive biomass of each assemblage or individual species, respectively. A binomial link function is used in the presence/absence models and a Gaussian link function is used in the models with logged positive biomass. The predictor variables are surface and bottom temperature and depth (all measured by the survey at each station), fit with penalized regression splines, and survey stratum, which accounts for differences in regional habitat quality across the survey region. Stratum may be considered to account for additional information not explicitly measured by the survey (e.g., bottom rugosity). Predictions of species abundance are calculated as the product of the predictions from the presence-absence model, the exponentiated predictions from the logged positive biomass model, and a correction factor to account for the retransformation bias associated with the log transformation (Duan 1983; and see Pinsky et al. 2013). We calculated the suitable thermal habitat both in terms of changes in ‘suitable thermal abundance’, defined as the species density possible given appropriate temperature, depth and bathymetric conditions, and changes in ‘suitable thermal area’, defined as the size of the physical area potentially occupied by a species given appropriate temperature, depth and bathymetric conditions. Suitable thermal abundance is determined from the predictions from the GAMs (i.e., a prediction of biomass). However, this quantity should not be interpreted directly as a change in future abundance or biomass, but instead as the potential abundance of a species in the future given changes in temperature and holding all else (e.g., fishing effort, species interactions, productivity, etc.) constant. Suitable thermal area is determined as a change in the suitable area that a species distribution occupies in the future and is derived from the area of the kernel density of the distribution. To ensure that the estimates are conservative, we select all points with values greater than one standard deviation above the mean. We then compute the area of these kernels using the gArea function from the rgeos package in R (Bivand et al. 2011). 40.1.3 Plotting Figure 40.1: Current thermal habitat estimate (A), and 20-40 year thermal habitat projection (B) for summer flounder on the Northeast Continental Shelf. Note: The thermal habitat model output for all species presented in State of the Ecosystem reports is accessible through the NEFSC ERDDAP server. References "],
-["trend-analysis.html", "41 Trend Analysis 41.1 Methods", " 41 Trend Analysis Description: Time series trend analysis Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019), State of the Ecosystem - Mid-Atlantic (2018, 2019) Indicator category: Extensive analysis, not yet published Contributor(s): Sean Hardison, Charles Perretti, Geret DePiper Data steward: NA Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: NA 41.1 Methods Summarizing trends for ecosystem indicators is desirable, but the power of statistical tests to detect a trend is hampered by low sample size and autocorrelated observations (see Nicholson and Jennings 2004; Wagner et al. 2013; Storch 1999). Prior to 2018, time series indicators in State of the Ecosystem reports were presented with trend lines based on a Mann-Kendall test for monotonic trends to test significance (p < 0.05) of both long term (full time series) and recent (2007–2016) trends, although not all time series were considered for trend analysis due to limited series lengths. There was also concern that a Mann-Kendall test would not account for any autocorrelation present in State of the Ecosystem (SOE) indicators. In a simulation study (Hardison et al. 2019), we explored the effect of time series length and autocorrelation strength on statistical power of three trend detection methods: a generalized least squares model selection approach, the Mann-Kendall test, and Mann-Kendall test with trend-free pre-whitening. Methods were applied to simulated time series of varying trend and autocorrelation strengths. Overall, when sample size was low (N = 10) there were high rates of false trend detection, and similarly, low rates of true trend detection. Both of these forms of error were further amplified by autocorrelation in the trend residuals. Based on these findings, we selected a minimum series length of N = 30 for indicator time series before assessing trend. We also chose to use a GLS model selection (GLS-MS) approach to evaluate indicator trends in the 2018 (and future) State of the Ecosystem reports, as this approach performed best overall in the simulation study. GLS-MS also allowed for both linear and quadratic model fits and quantification of uncertainty in trend estimates. The model selection procedure for the GLS approach fits four models to each time series and selects the best fitting model using AICc. The models are, 1) linear trend with uncorrelated residuals, 2) linear trend with correlated residuals, 3) quadratic trend with uncorrelated residuals, and 4) quadratic trend with correlated residuals. I.e., the models are of the form \\[ Y_t = \\alpha_0 + \\alpha_1X_t + \\alpha_2X_t^2 + \\epsilon_t\\] \\[\\epsilon_t = \\rho\\epsilon_{t-1} + \\omega_t\\] \\[w_t \\sim N(0, \\sigma^2)\\] Where \\(Y_t\\) is the observation in time \\(t\\), \\(X_t\\) is the time index, \\(\\epsilon_t\\) is the residual in time \\(t\\), and \\(\\omega_t\\) is a normally distributed random variable. Setting \\(\\alpha_2 = 0\\) yields the linear trend model, and \\(\\rho = 0\\) yields the uncorrelated residuals model. The best fit model was tested against the null hypothesis of no trend through a likelihood ratio test (p < 0.05). All models were fit using the R package nlme (Pinheiro et al. 2017) and AICc was calculated using the R package AICcmodavg (Mazerolle 2017). In SOE time series figures, significant positive trends were colored orange, and negative trends purple. 41.1.1 Data source(s) NA 41.1.2 Data extraction NA 41.1.3 Data analysis Code used for trend analysis can be found here. Example plot References "],
-["wind-lease-areas-and-habitat-occupancy-overlap.html", "42 Wind lease areas and habitat occupancy overlap 42.1 Methods", " 42 Wind lease areas and habitat occupancy overlap Description: Wind lease areas and habitat occupancy Found in: State of the Ecosystem - Mid-Atlantic (2020) Indicator category: Contributor(s): Kevin Friedland Data steward: Kimberly Bastille kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille kimberly.bastille@noaa.gov Public availability statement: Source data are publicly available. 42.1 Methods 42.1.1 Data sources 42.1.2 Data extraction 42.1.3 Data analysis 42.1.4 Data processing 42.1.5 Plotting "],
-["zooabund.html", "43 Zooplankton 43.1 Methods", " 43 Zooplankton Description: Annual time series of zooplankton abundance Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019) Indicator category: Database pull with analysis; Synthesis of published information; Extensive analysis, not yet published; Published methods Contributor(s): Ryan Morse, Kevin Friedland Data steward: Harvey Walsh, harvey.walsh@noaa.gov; Mike Jones, michael.jones@noaa.gov Point of contact: Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are publicly available here. Derived data can be found here. 43.1 Methods 43.1.1 Data sources Zooplankton data are from the National Oceanographic and Atmospheric Administration Marine Resources Monitoring, Assessment and Prediction (MARMAP) program and Ecosystem Monitoring (EcoMon) cruises detailed extensively in Kane (2007), Kane (2011), and Morse et al. (2017). 43.1.2 Data extraction Data are from the publicly available zooplankton dataset on the NOAA File Transfer Protocol (FTP) server. The excel file has a list of excluded samples and cruises based on Kane (2007) and Kane (2011). R code used in extraction process. # load data URL = "ftp://ftp.nefsc.noaa.gov/pub/hydro/zooplankton_data/EcoMon_Plankton_Data_v3_0.xlsx" ZPD = openxlsx::read.xlsx(URL, sheet = "Data") 43.1.3 Data analysis Annual abundance anomalies Data are processed similarly to Kane (2007) and Perretti et al. (2017b), where a mean annual abundance by date is computed by area for each species meeting inclusion metrics set in Morse et al. (2017). This is accomplished by binning all samples for a given species to bi-monthly collection dates based on median cruise date and taking the mean, then fitting a spline interpolation between mean bi-monthly abundance to give expected abundance on any given day of the year. Abundance anomalies (Figure ??) are computed from the expected abundance on the day of sample collection. Abundance anomaly time series are constructed for Centropages typicus, Pseudocalanus spp., Calanus finmarchicus, and total zooplankton biovolume. The small-large copepod size index is computed by averaging the individual abundance anomalies of Pseudocalanus spp., Centropages hamatus, Centropages typicus, and Temora longicornis, and subtracting the abundance anomaly of Calanus finmarchicus. This index tracks the overall dominance of the small bodied copepods relative to the largest copepod in the Northeast U.S. region, Calanus finmarchicus. Code used for zooplankton data analysis can be found here. Seasonal abundance Time series of zooplankton abundance in the spring and fall months have been presented in the 2019 Mid-Atlantic State of the Ecosystem report. Raw abundance data were sourced from the EcoMon cruises referenced above, and ordinary kriging was used to estimate seasonal abundance over the Northeast Shelf. These data were then aggregated further into time series of mean abundance by Ecological Production Unit. These data are presented in Figure ??. 43.1.4 Data processing Zooplankton abundances indicators were formatted for inclusion in the ecodata R package using the code at these links, abundance anomaly and seasonal abundance 43.1.5 Plotting Code used to create the figures below can be found linked here, copepod abundance, Euphausiid and Cnidarian abundance and zooplankton diversity. Abundance anomaly Figure 43.1: Large (red) and small-bodied (blue) copepod abundance in the Mid-Atlantic Bight. Figure 43.2: Stratified abundance of cnidarians and euphausiids in Mid-Atlantic Bight. Zooplankton Diversity Figure 43.3: Zooplankton diversity in the Mid-Atlantic Bight. References "],
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+[["index.html", "Technical Documentation, State of the Ecosystem Report Introduction", " Technical Documentation, State of the Ecosystem Report Northeast Fisheries Science Center 25 March 2022 Introduction The purpose of this document is to collate the methods used to access, collect, process, and analyze derived data (indicators) used to describe the status and trend of social, economical, ecological, and biological conditions in the Northeast Shelf Large Marine Ecosystem (see figure, below). These indicators are further synthesized in State of the Ecosystem Reports produced annually by the Northeast Fisheries Science Center for the New England Fisheries Management Council and the Mid-Atlantic Fisheries Management Council. The metadata for each indicator (in accordance with the Public Access to Research Results (PARR) directive) and the methods used to construct each indicator are described in the subsequent chapters, with each chapter title corresponding to an indicator or analysis present in State of the Ecosystem Reports. The most recent and usable html version of this document can be found at the NOAA EDAB Github. The PDF version of this document is for archiving only. The PDF version from previous years is archived in NOAAs Institutional Repository. Indicators included in this document were selected to clearly align with management objectives, which is required for integrated ecosystem assessment (Levin et al. 2009), and has been advised many times in the literature (Degnbol and Jarre 2004; Jennings 2005; Rice and Rochet 2005; Jason S. Link 2005). A difficulty with practical implementation of this in ecosystem reporting can be the lack of clearly specified ecosystem-level management objectives (although some have been suggested (Murawski 2000)). In our case, considerable effort had already been applied to derive both general goals and operational objectives from both US legislation such as the Magnuson-Stevens Fisheries Conservation and Management Act (MSA) and regional sources (DePiper et al. 2017). These objectives are somewhat general and would need refinement together with managers and stakeholders, however, they serve as a useful starting point to structure ecosystem reporting. Figure 0.1: Map of Northeast U.S. Continental Shelf Large Marine Ecosystem from Hare et al. (2016). References "],["erddap.html", "1 Data and Code Access", " 1 Data and Code Access 1.0.1 About The Technical Documentation for the State of the Ecosystem (SOE) reports is a bookdown document; hosted on the NOAA Northeast Fisheries Science Center (NEFSC) Ecosystems Dynamics and Assessment Branch Github page, and developed in R. Derived data used to populate figures in this document are queried directly from the ecodata R package or the NEFSC ERDDAP server. ERDDAP queries are made using the R package rerddap. 1.0.2 Accessing data and build code In this technical documentation, we hope to shine a light on the processing and analytical steps involved to get from source data to final product. This means that whenever possible, we have included the code involved in source data extraction, processing, and analyses. We have also attempted to thoroughly describe all methods in place of or in supplement to provided code. Example plotting code for each indicator is presented in sections titled Plotting, and these code chunks can be used to recreate the figures found in ecosystem reporting documents where each respective indicator was included1. Source data for the derived indicators in this document are linked to in the text unless there are privacy concerns involved. In that case, it may be possible to access source data by reaching out to the Point of Contact associated with that data set. Derived data sets make up the majority of the indicators presented in ecosystem reporting documents, and these data sets are available for download through the ecodata R package. 1.0.3 Building the document Start a local build of the SOE bookdown document by first cloning the projects associated git repository. Next, if you would like to build a past version of the document, use git checkout [version_commit_hash] to revert the project to a past commit of interest, and set build_latest <- FALSE in this code chunk. This will ensure the project builds from a cached data set, and not the most updated versions present on the NEFSC ERDDAP server. Once the tech-doc.Rproj file is opened in RStudio, run bookdown::serve_book() from the console to build the document. 1.0.3.1 A note on data structures The majority of the derived time series used in State of the Ecosystem reports are in long format. This approach was taken so that all disparate data sets could be bound together for ease of use in our base plotting functions. There are multiple R scripts sourced throughout this document in an attempt to keep code concise. These scripts include BasePlot_source.R, GIS_source.R, and get_erddap.R. The scripts BasePlot_source.R and GIS_source.R refer to deprecated code used prior to the 2019 State of the Ecosystem reports. Indicators that were not included in reports after 2018 make use of this syntax, whereas newer indicators typically use ggplot2 for plotting. "],["mafmc-abcacl-and-catch.html", "2 MAFMC ABC/ACL and Catch 2.1 Methods", " 2 MAFMC ABC/ACL and Catch Description: The catch limit (either ABC or ACL) and total catch from 2012 - 2020 for all MAFMC species and sector (commercial or recreational), if appropriate. Found in: State of the Ecosystem - Mid-Atlantic (2022) Indicator category: Synthesis of published information, Database pull Contributor(s): Jessica Coakley, Kiley Dancy, Jose Montanez, Julia Beaty, Karson Coutre, Jason Didden Data steward: Brandon Muffley bmuffley@mafmc.org Point of contact: Brandon Muffley bmuffley@mafmc.org Public availability statement: Source data are publicly available 2.1 Methods 2.1.1 Data Sources These data were compiled from MAFMC Fishery Information Documents, Stock Assessment reports, SSC reports, GARFO catch/landings database, and MRIP queries. 2.1.2 Data Analysis Allowable Biological Catch (ABC) for each managed stock is set by the MAFMC Science and Statistical Committee(SSC), Annual Catch Limit (ACL) (if appropriate) developed by the Council; recreational data from MRIP, commercial catch from either the NEFSC assessment lead or GARFO database. Each species, depending upon data availability, sectors, fleets etc., goes through a different data processing process. 2.1.3 Data Processing Data were formatted for inclusion in the ecodata R package using the R code found here. 2.1.4 Plotting The plot below was built using the code found here. Figure 2.1: Total ABC or ACL means and ranges throughout the eight years of data avaliable. Figure 2.2: Fishing limit compared to catch for each avalaible managed stock. Dashed line is 1 where limit equals catch. Red line is the mean for all stocks. Figure 2.3: Cumulative limit for all stocks with limits greater that 10,000 lbs. "],["aggroups.html", "3 Aggregate Groups 3.1 Methods", " 3 Aggregate Groups Description: Mappings of species into aggregate group categories for different analyses Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) Indicator category: Synthesis of published information Contributor(s): Geret DePiper, Sarah Gaichas, Sean Hardison, Sean Lucey Data steward: Sean Lucey Sean.Lucey@noaa.gov Point of contact: Sean Lucey Sean.Lucey@noaa.gov Public availability statement: Source data is available to the public (see Data Sources). 3.1 Methods The State of the Ecosystem (SOE) reports are delivered to the New England Fishery Management Council (NEFMC) and Mid-Atlantic Fishery Management Council (MAFMC) to provide ecosystems context. To better understand that broader ecosystem context, many of the indicators are reported at an aggregate level rather than at a single species level. Species were assigned to an aggregate group following the classification scheme of Garrison and Link (2000) and Jason S. Link et al. (2006). Both works classified species into feeding guilds based on food habits data collected at the Northeast Fisheries Science Center (NEFSC). In 2017, the SOE used seven specific feeding guilds (plus an other category; Table 3.1). These seven were the same guilds used in Garrison and Link (2000), which also distinguished ontogentic shifts in species diets. For the purposes of the SOE, species were only assigned to one category based on the most prevalent size available to commercial fisheries. However, several of those categories were confusing to the management councils, so in 2018 those categories were simplified to five (plus other; Table 3.2) along the lines of Jason S. Link et al. (2006). In addition to feeding guilds, species managed by the councils have been identified. This is done to show the breadth of what a given council is responsible for within the broader ecosystem context. In the 2020 report, squids were moved from planktivores to piscivores based on the majority of their diet being either fish or other squid. Table 3.1: Aggregate groups use in 2017 SOE. Classifications are based on Garrison and Link (2000) . Feeding.Guild Description Apex Predator Top of the food chain Piscivore Fish eaters Macrozoo-piscivore Shrimp and small fish eaters Macroplanktivore Amphipod and shrimp eaters Mesoplanktivore Zooplankton eaters Benthivore Bottom eaters Benthos Things that live on the bottom Other Things not classified above Table 3.2: Aggregate groups use since 2018 SOE. Classifications are based on Jason S. Link et al. (2006). Feeding.Guild Description Apex Predator Top of the food chain Piscivore Fish eaters Planktivore Zooplankton eaters Benthivore Bottom eaters Benthos Things that live on the bottom Other Things not classified above 3.1.1 Data sources In order to match aggregate groups with various data sources, a look-up table was generated which includes species common names (COMNAME) along with their scientific names (SCINAME) and several species codes. SVSPP codes are used by the NEFSC Ecosystems Surveys Branch (ESB) in their fishery-independent Survey Database (SVDBS), while NESPP3 codes refer to the codes used by the Commercial Fisheries Database System (CFDBS) for fishery-dependent data. A third species code provided is the ITISSPP, which refers to species identifiers used by the Integrated Taxonomic Information System (ITIS). Digits within ITIS codes are hierarchical, with different positions in the identifier referring to higher or lower taxonomic levels. More information about the SVDBS, CFDBS, and ITIS species codes are available in the links provided below. Management responsibilities for different species are listed under the column Fed.managed (NEFMC, MAFMC, or JOINT for jointly managed species). More information about these species is available on the FMC websites listed below. Species groupings listed in the NEIEA column were developed for presentation on the Northeast Integrated Ecosystem Assessment (NE-IEA) website. These groupings are based on EMAX groupings (Jason S. Link et al. 2006), but were adjusted based on conceptual models developed for the NE-IEA program that highlight focal components in the Northeast Large Marine Ecosystem (i.e. those components with the largest potential for perturbing ecosystem dynamics). NE-IEA groupings were further simplified to allow for effective communication through the NE-IEA website. 3.1.1.1 Supplemental information See the following links for more information regarding the NEFSC ESB Bottom Trawl Survey, CFDBS, and ITIS: https://www.itis.gov/ https://inport.nmfs.noaa.gov/inport/item/22561 https://inport.nmfs.noaa.gov/inport/item/22560 https://inport.nmfs.noaa.gov/inport/item/27401 More information about the NE-IEA program is available here. More information about the New Engalnd Fisheries Management Council is available here. More information about the Mid-Atlantic Fisheries Management Council is available here. 3.1.2 Data extraction Species lists are pulled from SVDBS and CFDBS. They are merged using the ITIS code. Classifications from Garrison and Link (Garrison and Link 2000) and Link et al. (Jason S. Link et al. 2006) are added manually. The R code used in the extraction process can be found here. References "],["annual-sst-cycles.html", "4 Annual SST Cycles 4.1 Methods", " 4 Annual SST Cycles Description: Annual SST Cycles Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull with analysis Contributor(s): Sean Hardison, Vincent Saba Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Source data are available here. 4.1 Methods 4.1.1 Data sources Data for annual sea surface tempature (SST) cycles were derived from the NOAA optimum interpolation sea surface temperature (OISST) high resolution dataset (NOAA OISST V2 dataset) provided by NOAAs Earth System Research Laboratorys Physical Sciences Devision, Boulder, CO. The data extend from 1981 to present, and provide a 0.25° x 0.25° global grid of SST measurements (Reynolds et al. 2007). Gridded SST data were masked according to the extent of Ecological Production Units (EPU) in the Northeast Large Marine Ecosystem (NE-LME) (See EPU_Extended shapefiles). 4.1.2 Data extraction Daily mean sea surface temperature data for 2017 and for each year during the period of 1981-2012 were downloaded from the NOAA OI SST V2 site to derive the long-term climatological mean for the period. The use of a 30-year climatological reference period is a standard procedure for metereological observing (WMO 2017). These reference periods serve as benchmarks for comparing current or recent observations, and for the development of standard anomaly data sets. The reference period of 1982-2012 was chosen to be consistent with previous versions of the State of the Ecosystem report. R code used in extraction and processing can be found here 4.1.3 Data analysis We calculated the long-term mean and standard deviation of SST over the period of 1982-2012 for each EPU, as well as the daily mean for 2017. R code used for analysis and plotting can be found here Figure 4.1: Long-term mean SSTs for the Mid-Atlantic Bight (A), Georges Bank (B), and Gulf of Maine (C). Orange and cyan shading show where the 2017 daily SST values were above or below the long-term mean respectively; red and dark blue shades indicate days when the 2017 mean exceeded +/- 1 standard deviation from the long-term mean. References "],["aquaculture.html", "5 Aquaculture 5.1 Methods 5.2 Methods 2017-2019", " 5 Aquaculture Description: Aquaculture indicators Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018 (Different Methods), 2021+), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019) Indicator category: Synthesis of published information Contributor(s): Christopher Schillaci, Maine DMR, NH DES, MA DMF, RI CRMC, MD DNR Data steward: Chris Schillaci christopher.schillaci@noaa.gov Point of contact: Chris Schillaci christopher.schillaci@noaa.gov Public availability statement: Source data are publicly available 5.1 Methods 5.1.1 Data Sources Data was synthesized from state specific sources, listed below. State of Maine, Department of Marine Resources. State of New Hampshire, Marine Aquaculture Compendium State of Massachusetts, Division of Marine Fisheries State of Rhode Island, Coastal Resource Management Council State of Maryland, Aquaculture Coordinating Council 5.1.2 Data Extraction Production data was pulled from the state sources above. 5.1.3 Data analysis Divsion of production in pieces by shellfish lease area acres to calculate production per acre. 5.1.4 Data processing Aquaculture data were formatted for inclusion in the ecodata R package using the code found here. 5.1.5 Plotting Code for plotting data included in the State of the Ecosystem report can be found here. Figure 5.1: Total oyster production in peices from areas leased for New England states. 5.2 Methods 2017-2019 Aquaculture data included in the State of the Ecosystem (SOE) report were time series of number of oysters sold in Virginia, Maryland, and New Jersey. 5.2.1 Data sources Virginia oyster harvest data are collected from mail and internet-based surveys of active oyster aquaculture operations on both sides of the Chesapeake Bay, which are then synthesized in an annual report (Hudson 2017). In Maryland, shellfish aquaculturists are required to report their monthly harvests to the Maryland Department of Natural Resources (MD-DNR). The MD-DNR then aggregates the harvest data for release in the Maryland Aquaculture Coordinating Council Annual Report (ACC 2017), from which data were collected. Similar to Virginia, New Jersey releases annual reports synthesizing electronic survey results from lease-holding shellfish growers. Data from New Jersey reflects cage reared oysters grown from hatchery seed (Calvo 2017). 5.2.2 Data extraction Data were collected directly from state aquaculture reports. Oyster harvest data in MD was reported in bushels which were then converted to individual oysters by an estimate of 300 oysters bushel\\(^{-1}\\). View processing code for this indicator here. 5.2.3 Data analysis No data analyses occurred for this indicator. References "],["bennet-indicator.html", "6 Bennet Indicator 6.1 Methods", " 6 Bennet Indicator Description: Bennet Indicator Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019, 2020, 2021), State of the Ecosystem - Mid-Atlantic (2018, 2019, 2020, 2021) Indicator category: Database pull with analysis Contributor(s): John Walden Data steward:Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: John Walden, john.walden@noaa.gov Public availability statement: Derived CFDBS data are available for this analysis (see Comland). 6.1 Methods 6.1.1 Data sources Data used in the Bennet Indicator were derived from the Comland data set; a processed subset of the Commercial Fisheries Database System (CFDBS). The derived Comland data set is available for download here. 6.1.2 Data extraction For information regarding processing of CFDBS, please see Comland methods. The Comland dataset containing seafood landings data was subsetted to US landings after 1964 where revenue was \\(\\ge\\) 0 for each Ecological Production Unit (i.e. Mid-Atlantic Bight, Georges Bank, and Gulf of Maine). Each EPU was run in an individual R script, and the code specific to Georges Bank is shown [here](. 6.1.3 Data analysis Revenue earned by harvesting resources from a Large Marine Ecosystem (LME) at time t is a function of both the quantity landed of each species and the prices paid for landings. Changes in revenue between any two years depends on both prices and quantities in each year, and both may be changing simultaneously. For example, an increase in the harvest of higher priced species, such as scallops can lead to an overall increase in total revenue from an LME between time periods even if quantities landed of other species decline. Although measurement of revenue change is useful, the ability to see what drives revenue change, whether it is changing harvest levels, the mix of species landed, or price changes provides additional valuable information. Therefore, it is useful to decompose revenue change into two parts, one which is due to changing quantities (or volumes), and a second which is due to changing prices. In an LME, the quantity component will yield useful information about how the species mix of harvests are changing through time. A Bennet indicator (BI) is used to examine revenue change between 1964 and 2015 for two major LME regions. It is composed of a volume indicator (VI), which measures changes in quantities, and a price indicator (PI) which measures changes in prices. 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 one to examine what component of overall revenue is responsible for change between time periods. This allows us to examine whether changing quantities or prices of separate species groups are driving revenue change in each EPU between 1964 and 2015. Revenue in a given year for any species group is the product of quantity landed times price, and the sum of revenue from all groups is total revenue from the LME. In any year, both prices and quantities can change from prior years, leading to total revenue change. At time t, revenue (R) is defined as \\[R^{t} = \\sum_{j=1}^{J}p_{j}^{t}y_{j}^{t},\\] where \\(p_{j}\\) is the price for species group \\(j\\), and \\(y_{j}\\) is the quantity landed of species group \\(j\\). Revenue change between any two time periods, say \\(t+1\\) and \\(t\\), is then \\(R^{t+1}-R^{t}\\), which can also be expressed as: \\[\\Delta R = \\sum_{j=1}^{J}p_{j}^{t+1}y_{j}^{t+1}-\\sum_{j=1}^{J}p_{j}^{t}y_{j}^{t}.\\] 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 = \\frac{1}{2}(\\sum_{j=1}^{J}p_{j}^{t+1}y_{j}^{t+1} - \\sum_{j=1}^{J}p_{j}^{t+1}y_{j}^{t} + \\sum_{j=1}^{J}p_{j}^{t}y_{j}^{t+1} - \\sum_{j=1}^{J}p_{j}^{t}y_{j}^{t})\\] The price indicator (PI) is calculated as follows: \\[PI = \\frac{1}{2}(\\sum_{j=1}^{J}y_{j}^{t+1}p_{j}^{t+1} - \\sum_{j=1}^{J}y_{j}^{t+1}p_{j}^{t} + \\sum_{j=1}^{J}y_{j}^{t}p_{j}^{t+1} - \\sum_{j=1}^{J}y_{j}^{t}p_{j}^{t})\\] Total revenue change between time \\(t\\) and \\(t+1\\) is the sum of the VI and PI. Since revenue change is being driven by changes in the individual prices and quantities landed of each species group, changes at the species group 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 the VI for each group will equal the overall VI, and the sum of the PI for each group will equal the overall PI. 6.1.4 Data processing Bennet indicator time series were formatted for inclusion in the ecodata R package using the R code found here. 6.1.5 Plotting Code for plotting the bennet indicator can be found here. Figure 6.1: Revenue change from the long-term mean in 2015 dollars (black), Price (PI), and Volume Indicators (VI) for commercial landings in the Mid-Atlantic. References "],["bottom-temperature---in-situ.html", "7 Bottom temperature - in situ 7.1 Methods", " 7 Bottom temperature - in situ Description: Time series of annual in situ bottom temperatures on the Northeast Continental Shelf. Indicator category: Extensive analysis; not yet published Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2019+); State of the Ecosystem - Mid-Atlantic Bight (2019+) Contributor(s): Paula Fratantoni, paula.fratantoni@noaa.gov Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Paula Fratantoni, paula.fratantoni@noaa.gov Public availability statement: Source data are publicly available at ftp://ftp.nefsc.noaa.gov/pub/hydro/matlab_files/yearly and in the World Ocean Database housed at http://www.nodc.noaa.gov/OC5/SELECT/dbsearch/dbsearch.html under institute code number 258. 7.1 Methods 7.1.1 Data sources The bottom temperature index incorporates near-bottom temperature measurements collected on Northeast Fisheries Science Center (NEFSC) surveys between 1977-present. Early measurements were made using surface bucket samples, mechanical bathythermographs and expendable bathythermograph probes, but by 1991 the CTD  an acronym for conductivity temperature and depth  became standard equipment on all NEFSC surveys. Near-bottom refers to the deepest observation at each station that falls within 10 m of the reported water depth. Observations encompass the entire continental shelf area extending from Cape Hatteras, NC to Nova Scotia, Canada, inclusive of the Gulf of Maine and Georges Bank. 7.1.2 Data extraction While all processed hydrographic data are archived in an Oracle database (OCDBS), we work from Matlab-formatted files stored locally. 7.1.3 Data analysis Ocean temperature on the Northeast U.S. Shelf varies significantly on seasonal timescales. Any attempt to resolve year-to-year changes requires that this seasonal variability be quantified and removed to avoid bias. This process is complicated by the fact that NEFSC hydrographic surveys conform to a random stratified sampling design meaning that stations are not repeated at fixed locations year after year so that temperature variability cannot be assessed at fixed station locations. Instead, we consider the variation of the average bottom temperature within four Ecological Production Units (EPUs): Middle Atlantic Bight, Georges Bank, Gulf of Maine and Scotian Shelf. Within each EPU, ocean temperature observations are extracted from the collection of measurements made within 10 m of the bottom on each survey and an area-weighted average temperature is calculated. The result of this calculation is a timeseries of regional average near-bottom temperature having a temporal resolution that matches the survey frequency in the database. Anomalies are subsequently calculated relative to a reference annual cycle, estimated using a multiple linear regression model to fit an annual harmonic (365-day period) to historical regional average temperatures from 1981-2010. The curve fitting technique to formulate the reference annual cycle follows the methodologies outlined by David G. Mountain (1991). The reference period was chosen because it is the standard climatological period adopted by the World Meteorological Organization. The resulting anomaly time series represents the difference between the time series of regional mean temperatures and corresponding reference temperatures predicted by a reference annual cycle for the same time of year. Finally, a reference annual average temperature (calculated as the average across the reference annual cycle) is added back into the anomaly timeseries to convert temperature anomalies back to ocean bottom temperature. 7.1.4 Data processing Derived bottom temperature data were formatted for inclusion in the ecodata R package using the R code found here. 7.1.5 Plotting Code for plotting Georges Bank and Gulf of Maine bottom temperature time series can be found here. Figure 7.1: Mid-Atlantic annual bottom temperature anomalies (Red = GLORYS, Black = in situ). References "],["bottom-temperature---glorys.html", "8 Bottom temperature - GLORYS 8.1 Methods", " 8 Bottom temperature - GLORYS Description: Time series of annual bottom temperatures on the Northeast Continental Shelf from the GLORYS model. Indicator category: Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021); State of the Ecosystem - Mid-Atlantic Bight (2021) Contributor(s): Joe Caracappa joseph.caracappa@noaa.gov Data steward: Joe Caracappa joseph.caracappa@noaa.gov Point of contact: Joe Caracappa joseph.caracappa@noaa.gov Public availability statement: Source data are publicly available. 8.1 Methods 8.1.1 Data sources The three-dimensional temperature of the Northeast US shelf is downloaded from the CMEMS (https://marine.copernicus.eu/). Source data is available at this link. 8.1.2 Data extraction NA 8.1.3 Data analysis The GLORYS12V1 daily bottom temperature product was downloaded as a flat 8km grid subsetted over the northwest Atlantic. Then the EPUNOESTUARIES.shp polygons were used to match GLORYS grid cells to EPUS. A weighted mean of bottom temperature was used weighted by the area of each GLORYS grid cell to obtain daily mean bottom temp by EPU. Then the mean daily bottom temp was used to get the annual bottom temp. A 1994-2010 climatology was used to best match with that used by the observed bottom temp (model doesnt go back any further). The 1994-2010 climatology was used to get the annual bottom temp anomaly by EPU. 8.1.4 Data processing Derived bottom temperature data were formatted for inclusion in the ecodata R package using the R code found here. 8.1.5 Plotting Code for plotting Georges Bank and Gulf of Maine bottom temperature time series can be found here. Figure 8.1: Mid-Atlantic annual bottom temperature anomalies (Red = GLORYS, Black = in situ). "],["calanus-stage.html", "9 Calanus Stage 9.1 Methods", " 9 Calanus Stage Description: Calanus abundance by life stage Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021), State of the Ecosystem - Mid-Atlantic (2021) Indicator category: Database pull with analysis Contributor(s): Ryan Morse Data steward: Ryan Morse ryan.morse@noaa.gov Point of contact: Ryan Morse ryan.morse@noaa.gov Public availability statement: Please contact Harvey Walsh harvey.wlsh@noaa.gov for raw data. 9.1 Methods 9.1.1 Data sources Please contact Harvey Walsh harvey.wlsh@noaa.gov for raw data. Zooplankton data are from the National Oceanographic and Atmospheric Administration Marine Resources Monitoring, Assessment and Prediction (MARMAP) program and Ecosystem Monitoring (EcoMon) cruises detailed extensively in Kane (2007), Kane (2011), and Morse et al. (2017). 9.1.2 Data analysis This index tracks the overall abundance of mature adult Calanus finmarchicus copepods and immature copepodite stage-5 (c5) Calanus finmarchicus copepods on the US Northeast Shelf ecosystem. The life cycle of C. finmarchicus relies on an overwintering phase (diapuse) where immature c5 copepodites build a lipid reserve prior to entering diapuse and remain at depth until favorable conditions for growth emerge. Because of this lipid reserve, diapausing c5 copepodites are a primary food source for many organisms, including the North Atlantic right whale. Data are processed similarly to Morse et al. (2017), except that cruises were partitioned into three seasons based on the median day of the year (DOY) for a given cruise. Cruises with median DOY between 0 and 120 were classified as spring cruises (i.e. their bimontly median dates correspond to 1 or 3). Cruises with a median DOY between 121 and 243 were classified as summer (bimonthly means of 5 or 7). Cruises with a median DOY between 244 and 366 were classified as fall (bimonthly mean cruise date of 9 or 11). Samples were assigned to EPUs based on their location, and transformed from raw counts to units of number per 100 m^-3 following MARMAP protocols. Samples were then aggregated to EPU by year using log transformed abundance. Cruises with less than 10 sampling days per cruise were removed due to incomplete surveys. Samples were limited to Calanus finmarchicus adults and copepodite stage-5 (c5) for inclusion as an indicator. Code used to analyze calanus stage data can be found at this link. 9.1.3 Data processing The Calanus Stage indicator was formatted for inclusion in the ecodata R package using the R script found here. 9.1.4 Plotting Code for plotting Calanus stage data can be found here. Figure 9.1: New England Calanus stage. References "],["catch-and-fleet-diversity.html", "10 Catch and Fleet Diversity 10.1 Methods", " 10 Catch and Fleet Diversity Description: Permit-level species diversity and Council-level fleet diversity. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) Indicator category: Database pull with analysis; Published methods Contributor(s): Geret DePiper, Min-Yang Lee Data steward: Geret DePiper, geret.depiper@noaa.gov Point of contact: Geret DePiper, geret.depiper@noaa.gov Public availability statement: Source data is not publicly availabe due to PII restrictions. Derived time series are available for download here. 10.1 Methods Diversity estimates have been developed to understand whether specialization, or alternatively stovepiping, is occurring in fisheries of the Northeastern Large Marine Ecosystem. We use the average effective Shannon indices for species revenue at the permit level, for all permits landing any amount of NEFMC or MAFMC Fishery Management Plan (FMP) species within a year (including both Monkfish and Spiny Dogfish). We also use the effective Shannon index of fleet revenue diversity and count of active fleets to assess the extent to which the distribution of fishing changes across fleet segments. 10.1.1 Data sources Data for these diversity estimates comes from a variety of sources, including the Commercial Fishery Dealer Database, Vessel Trip Reports, Clam logbooks, vessel characteristics from Permit database, WPU series producer price index. These data are typically not available to the public. 10.1.2 Data extraction The following describes both the permit-level species and fleet diversity data generation. Price data was extracted from the Commercial Fishery Dealer database (CFDERS) and linked to Vessel Trip Reports by a heirarchical matching algorithm that matched date and port of landing at its highest resolution. Code used in these analyses is available upon request. Output data was then matched to vessel characteristics from the VPS VESSEL data set. For the permit-level estimate, species groups are based off of a slightly refined NESPP3 code (Table 10.1), defined in the data as myspp, which is further developed in the script to rectify inconsistencies in the data. Table 10.1: Species grouping Group NESPP3 Common Name Scientific Name Highly Migratory Species 470 ALBACORE THUNNUS ALALUNGA Highly Migratory Species 494 ATLANTIC SHARPNOSE SHARK RHIZOPRIONODON TERRAENOVAE Highly Migratory Species 354 BIGEYE THRESHER SHARK ALOPIAS SUPERCILIOSUS Highly Migratory Species 469 BIGEYE TUNA THUNNUS OBESUS Highly Migratory Species 487 BLACKTIP SHARK CARCHARHINUS LIMBATUS Highly Migratory Species 493 BLUE SHARK PRIONACE GLAUCA Highly Migratory Species 467 BLUEFIN TUNA THUNNUS THYNNUS Highly Migratory Species 468 LITTLE TUNNY EUTHYNNUS ALLETTERATUS Highly Migratory Species 358 LONGFIN MAKO ISURUS PAUCUS Highly Migratory Species 481 PORBEAGLE SHARK LAMNA NASUS Highly Migratory Species 349 SAND TIGER CARCHARIAS TAURUS Highly Migratory Species 482 SANDBAR SHARK CARCHARHINUS PLUMBEUS Highly Migratory Species 359 SHARK,UNC CHONDRICHTHYES Highly Migratory Species 355 SHORTFIN MAKO ISURUS OXYRINCHUS Highly Migratory Species 466 SKIPJACK TUNA KATSUWONUS PELAMIS Highly Migratory Species 432 SWORDFISH XIPHIAS GLADIUS Highly Migratory Species 353 THRESHER SHARK ALOPIAS VULPINUS Highly Migratory Species 491 TIGER SHARK GALEOCERDO CUVIER Highly Migratory Species 471 YELLOWFIN TUNA THUNNUS ALBACARES Monkfish in Mid-Atlantic Waters 11 GOOSEFISH LOPHIUS AMERICANUS Monkfish in Mid-Atlantic Waters 12 GOOSEFISH LOPHIUS AMERICANUS Atlantic Scallops 800 SEA SCALLOP PLACOPECTEN MAGELLANICUS Shrimp 737 MANTIS SHRIMP UNCL STOMATOPODA Shrimp 737 MANTIS SHRIMPS STOMATOPODA Shrimp 736 NORTHERN SHRIMP PANDALUS BOREALIS Shrimp 738 SHRIMP,ATLANTIC & GULF,BROWN PANAEIDAE Shrimp 735 SHRIMP,UNC (CARIDEA) CARIDEA Skates 368 BARNDOOR SKATE DIPTURUS LAEVIS Skates 372 CLEARNOSE SKATE RAJA EGLANTERIA Skates 366 LITTLE SKATE LEUCORAJA ERINACEA Skates 365 OCELLATE SKATES RAJA Skates 365 SKATES RAJIDAE Skates 373 SKATES,LITTLE/WINTER MIXED LEUCORAJA Skates 369 SMOOTH SKATE MALACORAJA SENTA Skates 370 THORNY SKATE AMBLYRAJA RADIATA Skates 367 WINTER SKATE LEUCORAJA OCELLATA Herring 168 ATLANTIC HERRING CLUPEA HARENGUS Ocean Quahog 754 OCEAN QUAHOG ARCTICA ISLANDICA Surf Clam 769 ATLANTIC SURFCLAM SPISULA SOLIDISSIMA Tilefish 444 BLUELINE TILEFISH CAULOLATILUS MICROPS Tilefish 445 SAND TILEFISH MALACANTHUS PLUMIERI Tilefish 446 TILEFISH LOPHOLATILUS CHAMAELEONTICEPS Tilefish 447 TILEFISH,UNC MALACANTHIDAE Fluke & Black Seabass 335 BLACK SEA BASS CENTROPRISTIS STRIATA Fluke & Black Seabass 121 SUMMER FLOUNDER PARALICHTHYS DENTATUS Butterfish & Hake 51 BUTTERFISH PEPRILUS TRIACANTHUS Butterfish & Hake 152 RED HAKE UROPHYCIS CHUSS Butterfish & Hake 509 SILVER HAKE MERLUCCIUS BILINEARIS Bluefish in Mid-Atlantic Waters 23 BLUEFISH POMATOMUS SALTATRIX Spiny Dogfish 352 SPINY DOGFISH SQUALUS ACANTHIAS Northern Shortfin Squid 802 NORTHERN SHORTFIN SQUID ILLEX ILLECEBROSUS American Lobster 727 AMERICAN LOBSTER HOMARUS AMERICANUS Longfin Squid 801 LONGFIN SQUID LOLIGO PEALEII Menhaden 221 MENHADEN BREVOORTIA Offshore Hake 508 OFFSHORE HAKE MERLUCCIUS ALBIDUS Scup in Mid-Atlantic Waters 329 SCUP STENOTOMUS CHRYSOPS Windowpane Flounder in New England Waters 125 WINDOWPANE SCOPHTHALMUS AQUOSUS Ocean Pout in New England Waters 250 OCEAN POUT ZOARCES AMERICANUS Wolffish 512 ATLANTIC WOLFFISH ANARHICHAS LUPUS Winter Flounder in Mid-Atlantic Waters 120 WINTER FLOUNDER PSEUDOPLEURONECTES AMERICANUS Yellowtail Flounder in Mid-Atlantic Waters 123 YELLOWTAIL FLOUNDER LIMANDA FERRUGINEA Unclassified Hake 155 Unclassified Hake White Hake in Mid-Atlantic Waters 153 WHITE HAKE UROPHYCIS TENUIS Bluefish & Scup in New England Waters 23 BLUEFISH POMATOMUS SALTATRIX Bluefish & Scup in New England Waters 329 SCUP STENOTOMUS CHRYSOPS Halibut in New England Waters 159 ATLANTIC HALIBUT HIPPOGLOSSUS HIPPOGLOSSUS Groundfish in New England Waters 240 ACADIAN REDFISH SEBASTES FASCIATUS Groundfish in New England Waters 124 AMERICAN PLAICE HIPPOGLOSSOIDES PLATESSOIDES Groundfish in New England Waters 81 ATLANTIC COD GADUS MORHUA Groundfish in New England Waters 11 GOOSEFISH LOPHIUS AMERICANUS Groundfish in New England Waters 12 GOOSEFISH LOPHIUS AMERICANUS Groundfish in New England Waters 147 HADDOCK MELANOGRAMMUS AEGLEFINUS Groundfish in New England Waters 269 POLLOCK POLLACHIUS VIRENS Groundfish in New England Waters 153 WHITE HAKE UROPHYCIS TENUIS Groundfish in New England Waters 120 WINTER FLOUNDER PSEUDOPLEURONECTES AMERICANUS Groundfish in New England Waters 122 WITCH FLOUNDER GLYPTOCEPHALUS CYNOGLOSSUS Groundfish in New England Waters 123 YELLOWTAIL FLOUNDER LIMANDA FERRUGINEA Groundfish in Mid-Atlantic Waters 240 ACADIAN REDFISH SEBASTES FASCIATUS Groundfish in Mid-Atlantic Waters 124 AMERICAN PLAICE HIPPOGLOSSOIDES PLATESSOIDES Groundfish in Mid-Atlantic Waters 81 ATLANTIC COD GADUS MORHUA Groundfish in Mid-Atlantic Waters 159 ATLANTIC HALIBUT HIPPOGLOSSUS HIPPOGLOSSUS Groundfish in Mid-Atlantic Waters 512 ATLANTIC WOLFFISH ANARHICHAS LUPUS Groundfish in Mid-Atlantic Waters 147 HADDOCK MELANOGRAMMUS AEGLEFINUS Groundfish in Mid-Atlantic Waters 269 POLLOCK POLLACHIUS VIRENS Groundfish in Mid-Atlantic Waters 122 WITCH FLOUNDER GLYPTOCEPHALUS CYNOGLOSSUS Groundfish in Mid-Atlantic Waters 155 Unclassified Hake Windowpane Flounder & Ocean Pout in Mid-Atlantic Waters 250 OCEAN POUT ZOARCES AMERICANUS Windowpane Flounder & Ocean Pout in Mid-Atlantic Waters 125 WINDOWPANE SCOPHTHALMUS AQUOSUS For the fleet diversity metric, gears include scallop dredge (gearcodes DRS, DSC, DTC, and DTS), other dredges (gearcodes DRM, DRO, and DRU), gillnet (gearcodes GND, GNT, GNO, GNR, and GNS), hand (gearcode HND), longline (gearcodes LLB and LLP), bottom trawl (gearcodes OTB, OTF, OTO, OTC. OTS, OHS, OTR, OTT, and PTB), midwater trawls (gearcode OTM and PTM), pot (gearcodes PTL, PTW, PTC, PTE, PTF, PTH, PTL, PTO, PTS, and PTX), purse seine (gearcode PUR), and hydraulic clam dredge (gearcode DRC).Vessels were further grouped by length categories of less than 30 feet, 30 to 50 feet, 50 to 75 feet, and 75 feet and above. All revenue was deflated to real dollars using the WPU0223 Producer Price Index with a base of January 2015. Stata code for data processing is available here. 10.1.3 Data analysis This permit-level species effective Shannon index is calculated as \\[exp(-\\sum_{i=1}^{N}p_{ijt}ln(p_{ijt}))\\] for all \\(j\\), with \\(p_{ijt}\\) representing the proportion of revenue generated by species or species group \\(i\\) for permit \\(j\\) in year \\(t\\), and is a composite of richness (the number of species landed) and abundance (the revenue generated from each species). The annual arithmetic mean value of the effective Shannon index across permits is used as the indicator of permit-level species diversity. In a similar manner, the fleet diversity metric is estimated as \\[exp(-\\sum_{i=1}^{N}p_{kt}ln(p_{kt})) \\] for all \\(k\\), where \\(p_{kt}\\) represents the proportion of total revenue generated by fleet segment \\(k\\) (gear and length combination) per year \\(t\\). The indices each run from 1996 to 2017. A count of the number of fleets active in every year is also provided to assess whether changes in fleet diversity are caused by shifts in abundance (number of fleets), or evenness (concentration of revenue). The work is based off of analysis conducted in Thunberg and Correia (2015) and published in Gaichas et al. (2016). 10.1.4 Data processing Catch and fleet diversity indicators were formatted for inclusion in the ecodata R package using the R script found here. 10.1.5 Plotting Code for plotting the catch and fleet diversity indicator can be found here. Figure 10.1: Fleet diversity and fleet count in the Mid-Atlantic. References "],["chesapeake-bay-salinity-and-temperature.html", "11 Chesapeake Bay Salinity and Temperature 11.1 Methods", " 11 Chesapeake Bay Salinity and Temperature Description: Chesapeake Bay Salinity and Temperature Found in: State of the Ecosystem - Mid-Atlantic (2020+) Indicator category: Database pull with analysis Contributor(s): Bruce Vogt, Charles Pellerin Data steward: Charles Pellerin, charles.pellerin@noaa.gov Point of contact: Bruce Vogt, bruce.vogt@noaa.gov Public availability statement: Source data are publicly available. 11.1 Methods 11.1.1 Data sources The National Oceanic and Atmospheric Administrations (NOAA) Chesapeake Bay Interpretive Buoy System (CBIBS) is a network of observing platforms (buoys) that collect meteorological, oceanographic, and water-quality data and relay that information using wireless technology. The stations have been in place since 2007. The Sting Ray station was deployed in July of 2008 and has been monitoring conditions on and off since then. The data is recorded in situ and sent to a server over a cellular modem. The standard CBIBS instrument is a WETLabs WQM mounted in the buoy well approximately 0.5 meters below the surface. Seabird purchased WETLabs and are now the manufacturer of the instruments. The WQM instruments are calibrated and swapped out on a regular basis. Salinity is stored as a double with the units of PSU. 11.1.2 Data extraction Data is directly inserted into a database from the real time system over the cellular network. The general public can use this link to explore and pull that data from the CBIBS database. The process for data extraction for this indicator can be found here. 11.1.3 Data analysis The data is processed by a python script. This creates an array and runs the data through a qartod routine. The result is a set of flags. Only the good data is used in the plot below. 11.1.4 Data processing Code for processing salinity data can be found here. 11.1.5 Plotting Code used to build the plots below can be found here (Salinity, Temperature). Figure 11.1: Buoy data showing 2021 salinity compared to the longterm climatology (2010-2020) in Chesapeake Bay. Figure 11.2: Buoy data showing temperature of 2021 compared to the longterm climatology (2010-2020) in Chesapeake Bay. "],["chesapeake-bay-seasonal-sst-anomalies.html", "12 Chesapeake Bay Seasonal SST Anomalies 12.1 Methods", " 12 Chesapeake Bay Seasonal SST Anomalies Description: Chesapeake Bay Seasonal SST Anomalies Found in: State of the Ecosystem - Mid-Atlantic (2021+) Indicator category: Database pull with analysis Contributor(s): Bruce Vogt, Ron Vogel Data steward: Ron Vogel, ronald.vogel@noaa.gov Point of contact: Bruce Vogt, bruce.vogt@noaa.gov Public availability statement: Source data are publicly available. Public availability statement: Source data are publicly available here. 12.1 Methods 12.1.1 Data sources Data for Chesapeake Bay seasonal sea surface temperature (SST) anomalies were derived from the NOAA Multi-satellite AVHRR SST data set, available from NOAA CoastWatch East Coast Regional Node. The data set is a composite of overpasses from all operational satellites currently flying the Advanced Very High Resolution Radiometer (AVHRR) instrument. SST is derived using the Operational Non-linear Multichannel SST Algorithm (Li, et al, 2001). Both daytime and nighttime overpasses are composited into daily and then seasonal SST products. The data extend from 2008 to present, and provide a 1.25 km x 1.25 km grid of SST measurements. 12.1.2 Data analysis Anomaly maps of SST are generated by creating long-term climatological seasonal average SST for the years from 2008 to the year immediately prior to the current year. The reference period serves as a benchmark for comparing current observations. The current-year seasonal SST is then subtracted from the long-term seasonal average. Seasons for Chesapeake Bay are Dec-Feb (winter), Mar-May (spring), Jun-Aug (summer), and Sep-Nov (fall). 12.1.3 Data processing Code for processing Chesapeake Bay temperature data can be found here. 12.1.4 Plotting Code used to create the figure below can be here. Figure 12.1: Maps of seasonal SST anomalies are generated for an SST range of +5.0 - -5.0 C. Positive values show current-season temperatures above the long-term seasonal average, whereas negative values show current-season temperatures below the long-term seasonal average. 12.1.5 Resources Li, X., W. Pichel, P. Clemente-Colón, V. Krasnopolsky, and J. Sapper. 2001. Validation of Coastal Sea and Lake Surface Temperature Measurements Derived from NOAA/AVHRR Data. International Journal of Remote Sensing 22 (7): 12851303. https://doi.org/10.1080/01431160151144350. Li, X., W. Pichel, E. Maturi, P. Clemente-Colón, and J. Sapper. 2001. Deriving the Operational Nonlinear Multichannel Sea Surface Temperature Algorithm Coefficients for NOAA-15 AVHRR/3. International Journal of Remote Sensing 22 (4): 699704. https://doi.org/10.1080/01431160010013793. "],["chesapeake-bay-water-quality-standards-attainment.html", "13 Chesapeake Bay Water Quality Standards Attainment 13.1 Methods", " 13 Chesapeake Bay Water Quality Standards Attainment Description: A multimetric indicator describing the attainment status of Chesapeake Bay with respect to three water quality standards criteria, namely, dissolved oxygen, chlorophyll-a, and water clarity/submerged aquatic vegetation. Indicator category: Published method; Database pull with analysis Found in: State of the Ecosystem - Mid-Atlantic (2019,2022) Contributor(s): Qian Zhang, Richard Tian, and Peter Tango Data steward: Qian Zhang, qzhang@chesapeakebay.net Point of contact: Qian Zhang, qzhang@chesapeakebay.net Public availability statement: Data are publicly available (see Data Sources below). 13.1 Methods 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) (USEPA 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 (USEPA 2017). This indicator has been computed for each three-year assessment period since 1985-1987, providing an integrated measure of Chesapeake Bays water quality condition over the last three decades. 13.1.1 Data sources 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. 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. 13.1.2 Data analysis Criteria attainment assessment 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 Q. Zhang et al. (2018). Indicator calculation 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 2017-2019. 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 (USEPA 2017). For more details, refer to the Methods section of Q. Zhang et al. (2018). The indicator provides an integrated measure of Chesapeake Bays water quality condition (Figure 1). In 2017-2019, 33.1% of all tidal water segment-DU-criterion combinations are estimated to have met or exceeded applicable water quality criteria thresholds, which marks the best 3-year status since 1985-1987. The indicator has a positive and statistically significant trend from 1985-1987 to 2017-2019, which shows that Chesapeake Bay is on a positive trajectory toward recovery. This pattern was statistically linked to total nitrogen reduction, indicating responsiveness of attainment status to management actions implemented to reduce nutrients in the system. Figure 13.1: Time series of the multimetric indicator score for estimated Chesapeake Bay water quality standards attainment for each 3-year assessment period between 1985-1987 and 2017-2019. A significant positive trend for the time series is shown by the orange line (p < 0.05). Patterns of attainment of individual DUs are variable (Figure 2). Changes in OW-DO, DC-DO, and water clarity/SAV have shown long-term improvements, which have contributed to overall attainment indicator improvement. By contrast, the MSN-DO attainment experienced a sharp spike in the first few assessment periods but generally degraded after the 1997-1999, which has implications to the survival, growth, and reproduction of the migratory and resident tidal freshwater fish during spawning and nursery season in the tidal freshwater to low-salinity habitats. The status and trends of tidal segments attainment may be used to inform siting decisions of aquaculture operations in Chesapeake Bay. Figure 13.2: Time series of the estimated attainment of water quality standards (i.e., DO: dissolved oxygen; CHLA: chlorophyll-a; Clarity/SAV: water clarity/submerged aquatic vegetation) for five Chesapeake Bay designated uses (MSN: migratory spawning and nursery; OW: open water; DW: deep water; DC: deep channel; SW: shallow water) for each 3-year assessment period between 1985-1987 and 2015-2017. 13.1.3 Data processing The indicator data set was formatted for inclusion in the ecodata R package using the R script found here. References "],["chl-pp.html", "14 Chlorophyll a and Primary Production 14.1 Current Methods 14.2 2018-2020 Methods", " 14 Chlorophyll a and Primary Production Description: Chlorophyll a and Primary Production Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) Indicator category: Database pull; Database pull with analysis; Published methods Contributor(s): Kimberly Hyde Data steward: Kimberly Hyde, kimberly.hyde@noaa.gov Point of contact: Kimberly Hyde, kimberly.hyde@noaa.gov Public availability statement: Source data used in these analyses are publicly available. 14.1 Current Methods 14.1.1 Data sources Daily Level 3 mapped (4km resolution, sinusoidally projected) satellite ocean color data (version 5.0) were obtained from the European Space Agencys Ocean Colour Climate Change Initiative (OC-CCI) project. Level 1A ocean color remote sensing data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (NASA Ocean Biology Processing Group 2018) on the OrbView-2 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) (NASA Ocean Biology Processing Group 2017) on the Aqua satellite were acquired from the NASA Ocean Biology Processing Group (OBPG). Sea Surface Temperature (SST) data include the 4 km nighttime NOAA Advanced Very High Resolution Radiometer (AVHRR) Pathfinder (Casey et al. 2010; AVHRR Pathfinder Version 5.3 Level 3 Collated (L3c) Global 4km Sea Surface Temperature for 1981-Present. 2018) and the Group for High Resolution Sea Surface Temperature (GHRSST) Multiscale Ultrahigh Resolution (MUR, version 4.1) Level 4 (Chin, Vazquez-Cuervo, and Armstrong 2017a; Project 2015) data. AVHRR Pathfinder data are used as the SST source until 2002 and MUR SST in subsequent years. 14.1.2 Data extraction NA 14.1.3 Data analysis The L3 OC-CCI products include chlorophyll a (CHL-CCI), remote sensing reflectance \\((R_{rs}(\\lambda))\\), and several inherent optical property products (IOPs). The CHL-CCI blended algorithm attempts to weight the outputs of the best-performing chlorophyll algorithms based on the water types present, which improves performance in nearshore water compared to open-ocean algorithms. Photosynthetic available radiation (PAR) data were dervied from the SeaWiFS and MODIS ocean color imagery. The SeaWiFS and MODIS L1A files were processed using the NASA Ocean Biology Processing Group SeaDAS software version 7.4. All MODIS imagery were spatially subset to the U.S. East Coast (SW longitude=-82.5, SW latitude=22.5, NE longitude=-51.5, NE latitude=48.5) using L1AEXTRACT_MODIS. SeaWiFS files were subset using the same coordinates prior to begin downloaded from the Ocean Color Web Browser. SeaDASs L2GEN program was used to generate Level 2 (L2) files using the default settings and optimal ancillary files, and the L2BIN program spatially and temporally aggregated the L2 files to create daily Level 3 binned (L3B) files. The daily files were binned at 2 km resolution that are stored in a global, nearly equal-area, integerized sinusoidal grids and use the default L2 ocean color flag masks. The global OCC-CI ocean color data and the SST data were also subset to the same East Coast region and remapped to the same sinusoidal grid. The Vertically Generalized Production Model (VGPM) estimates net primary production (PP) as a function of chlorophyll a, photosynthetically available light and the photosynthetic efficiency (Behrenfeld and Falkowski 1997). In the VGPM-Eppley version, the original temperature-dependent function to estimate the chlorophyll-specific photosynthetic efficiency is replaced with the exponential Eppley function (equation PP1) as modified by Morel (1991). The VGPM calculates the daily amount of carbon fixed based on the maximum rate of chlorophyll-specific carbon fixation in the water column, sea surface daily photosynthetically available radiation, the euphotic depth (the depth where light is 1% of that at the surface), chlorophyll a concentration, and the number of daylight hours (Equation (14.1)). \\[\\begin{equation} P_{max}^{b}(SST) = 4.6 * 1.065^{SST-20^{0}} \\tag{14.1} \\end{equation}\\] Where \\(P_{max}^{b}\\) is the maximum carbon fixation rate and SST is sea surface temperature. \\[\\begin{equation} PP_{eu} = 0.66125 * P_{max}^{b} * \\frac{I_{0}}{I_{0}+4.1} * Z_{eu} * \\textrm{CHL} * \\text{DL} \\tag{14.2} \\end{equation}\\] Where \\(PP_{eu}\\) is the daily amount of carbon fixed integrated from the surface to the euphotic depth (mgC m-2 day-1), \\(P_{max}^{b}\\) is the maximum carbon fixation rate within the water column (mgC mgChl-1 hr-1), \\(I_{0}\\) is the daily integrated molar photon flux of sea surface PAR (mol quanta m-2 day-1), Zeu is the euphotic depth (m), CHL is the daily interpolated CHIi-OCI (mg m-3), and DL is the photoperiod (hours) calculated for the day of the year and latitude according to Kirk (1994). The light dependent function \\((I_{0}/(I_{0}+4.1))\\) describes the relative change in the light saturation fraction of the euphotic zone as a function of surface PAR (\\(I_0\\)). Zeu is derived from an estimate of the total chlorophyll concentration within the euphotic layer (CHLeu) based on the Case I models of Morel and Berthon (1989): For \\(\\textrm{CHL}_{eu} > 10.0\\;\\;\\;\\;\\;Z_{eu} = 568.2 * \\textrm{CHL}_{eu}^{-0.746}\\) For \\(\\textrm{CHL}_{eu} \\leq 10.0\\;\\;\\;\\;\\;Z_{eu} = 200.0 * \\textrm{CHL}_{eu}^{-0.293}\\) For \\(\\textrm{CHL}_{0} \\leq 1.0\\;\\;\\;\\;\\;\\textrm{CHL}_{eu} = 38.0 * \\textrm{CHL}_{0}^{0.425}\\) For \\(\\textrm{CHL}_{0} > 1.0\\;\\;\\;\\;\\;\\textrm{CHL}_{eu} = 40.2 * \\textrm{CHL}_{0}^{0.507}\\) Where \\(\\textrm{CHL}_0\\) is the surface chlorophyll concentration. Prior to being input into the VGPM-Eppley model, the daily CHL and AVHRR SST data were temporally interpolated and smoothed (CHLINT and SSTINT respectively) to increase the data coverage and better match data collected from different sensors and different times. The daily PAR data are not affected by cloud cover and MUR SST data is a blended/gap free data product so these products were not interpolated. Daily data at each pixel location covering the entire date range were extracted to create a pixel time series \\((D_{x,y})\\). \\((D_{x,y})\\) are linearly interpolated based on days in the time series using interpx.pro. Prior to interpolation, the CHL data are log-transformed to account for the log-normal distribution of chlorophyll data (Campbell 1995). Interpolating the entire times series requires a large amount of processing time so the series was processed one year at a time. Each yearly series included 60 days from the previous year and 60 days from the following year to improve the interpolation at the beginning and end of the year. Following interpolation, the data are smoothed with a tri-cube filter (width=7) using IDLs CONVOL program. In order to avoid over interpolating data when there were several days of missing data in the time series, the interpolated data were removed and replaced with blank data if the window of interpolation spanned more than 7 days for CHL or 10 days for SST. After all Dx,y pixels had been processed, the one-dimensional pixel time series were converted back to two-dimensional daily files. Statistics, including the arithmetic mean, geometric mean (for CHL and PP), standard deviation, and coefficient of variation were calculated at daily (3 and 8-day running means), weekly, monthly, and annual time steps and for several climatological periods. Annual statistics used the monthly means as inputs to avoid a summer time bias when more data is available due to reduced cloud cover. The daily, weekly, monthly and annual climatological statistics include the entire time series for each specified period. For example, the climatological January uses the monthly mean from each January in the time series and the climatological annual uses the annual mean from each year. The CHL and PP climatological statistics include data from both SeaWiFS (1997-2007) and MODIS (2008-2017). Weekly, monthly and annual anomalies were calculated for each product by taking the difference between the mean of the input time period (i.e. week, month, year) and the climatological mean for the same period. Because bio-optical data are typically log-normally distributed (Campbell 1995), the CHL and PP data were first log-transformed prior to taking the difference and then untransformed, resulting in an anomaly ratio. The ecological production unit (EPU) shapefile that excludes the estuaries was used to spatially extract all data location within an ecoregion from the statistic and anomaly files. The median values, which are equivalent to the geometric mean, were used for the CHL and PP data. For the extended time series, the 1998-2007 data use the SeaWiFS ocean color products and MODIS-Aqua products were used from 2008 to 2017. Prior to June 2002, AVHRR Pathfinder data are used as the SST source and MUR SST in subsequent years. 14.1.4 Data processing CHL and PPD time series were formatted for inclusion in the ecodata R package using the R code found here. 14.1.5 Plotting Chl a and primary production data were also examined in relation to the long-term means of each series. The figures below show data specific to the Mid-Atlantic Bight. The code for the plots can be found here. Figure 14.1: Weekly chlorophyll concentrations in the Mid-Atlantic are shown by the colored line for 2019. The long-term mean is shown in black, and shading indicates +/- 1 sample SD. In the figure below, we show monthly primary productivity on an annual time step in the Mid-Atlantic Bight. The code for this can be found here Figure 14.2: Monthly primary production trends show the annual cycle (i.e. the peak during the summer months) and the changes over time for each month. 14.2 2018-2020 Methods 14.2.1 Data sources Level 1A ocean color remote sensing data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (NASA Ocean Biology Processing Group 2018) on the OrbView-2 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) (NASA Ocean Biology Processing Group 2017) on the Aqua satellite were acquired from the NASA Ocean Biology Processing Group (OBPG). Sea Surface Temperature (SST) data included the 4 km nighttime NOAA Advanced Very High Resolution Radiometer (AVHRR) Pathfinder (Casey et al. 2010; AVHRR Pathfinder Version 5.3 Level 3 Collated (L3c) Global 4km Sea Surface Temperature for 1981-Present. 2018) and the Group for High Resolution Sea Surface Temperature (GHRSST) Multiscale Ultrahigh Resolution (MUR, version 4.1) Level 4 (Chin, Vazquez-Cuervo, and Armstrong 2017a; Project 2015) data. Prior to June 2002, AVHRR Pathfinder data are used as the SST source and MUR SST in subsequent years. 14.2.2 Data analysis The SeaWiFS and MODIS L1A files were processed using the NASA Ocean Biology Processing Group SeaDAS software version 7.4. All MODIS files were spatially subset to the U.S. East Coast (SW longitude=-82.5, SW latitude=22.5, NE longitude=-51.5, NE latitude=48.5) using L1AEXTRACT_MODIS. SeaWiFS files were subset using the same coordinates prior to begin downloaded from the Ocean Color Web Browser. SeaDASs L2GEN program was used to generate Level 2 (L2) files using the default settings and optimal ancillary files, and the L2BIN program spatially and temporally aggregated the L2 files to create daily Level 3 binned (L3B) files. The daily files were binned at 2 km resolution that are stored in a global, nearly equal-area, integerized sinusoidal grids and use the default L2 ocean color flag masks. The global SST data were also subset to the same East Coast region and remapped to the same sinusoidal grid. The L2 files contain several ocean color products including the default chlorophyll a; product (CHL-OCI), photosynthetic available radiation (PAR), remote sensing reflectance \\((R_{rs}(\\lambda))\\), and several inherent optical property products (IOPs). The CHL-OCI product combines two algorithms, the OReilly band ratio (OCx) algorithm (OReilly et al. 1998) and the Hu color index (CI) algorithm (Hu, Lee, and Franz 2012). The SeaDAS default CHL-OCI algorithm diverges slightly from Hu, Lee, and Franz (2012) in that the transition between CI and OCx occurs at 0.15 < CI < 0.2 mg m-3 to ensure a smooth transition. The regional chlorophyll a algorithm by Pan et al. (2008) was used to create a second chlorophyll product (CHL-PAN). CHL-PAN is an empirical algorithm derived from in situ sampling within the Northeast Large Marine Ecosystem (NE-LME) and demonstrated significant improvements from the standard NASA operational algorithm in the NES-LME (Pan et al. 2010). A 3rd-order polynomial function (Equation (14.3)) is used to derive [CHL-PAN] from Rrs band ratios (RBR): \\[\\begin{equation} log[\\textrm{CHL-PAN}] = A_{0} + A_{1}X + A_{2}X^{2} + A_{3}X^{3}, \\tag{14.3} \\end{equation}\\] where \\(X = log(R_{rs}(\\lambda_{1})/R_{rs}(\\lambda_{2}))\\) and \\(A_{i} (i = 0, 1, 2, \\textrm{or } 3)\\) are sensor and RBR specific coefficients: If SeaWiFS and RBR is \\(R_{rs}(490)/R_{rs}(555)(R_{^3{\\mskip -5mu/\\mskip -3mu}_5})\\) then: \\(A_0=0.02534, A_1=-3.033, A_2=2.096, A_3=-1.607\\) If SeaWiFS and RBR is \\(R_{rs}(490)/R_{rs}(670)(R_{^3{\\mskip -5mu/\\mskip -3mu}_6})\\) then: \\(A_0=1.351, A_1=-2.427, A_2=0.9395, A_3=-0.2432\\) If MODIS and RBR is \\(R_{rs}(488)/R_{rs}(547)(R_{^3{\\mskip -5mu/\\mskip -3mu}_5})\\) then: \\(A_0=0. 03664, A_1=-3.451, A_2=2.276, A_3=-1.096\\) If MODIS and RBR is \\(R_{rs}(488)/R_{rs}(667)(R_{^3{\\mskip -5mu/\\mskip -3mu}_6})\\) then: \\(A_0=1.351, A_1=-2.427, A_2=0.9395, A_3=-0.2432\\) C3/5 and C3/6 were calculated for each sensor specific RBR (R3/5 and R3/6 respectively) and then the following criteria were used to determine to derive CHL-PAN: If \\(R_{^3{\\mskip -5mu/\\mskip -3mu}_5}>0.15\\) or \\(R_{6} <0.0001\\) then \\(\\textrm{CHL-PAN} = C_{^3{\\mskip -5mu/\\mskip -3mu}_5};\\) Otherwise, \\(\\textrm{CHL-PAN} = \\textrm{max}(C_{^3{\\mskip -5mu/\\mskip -3mu}_5}, C_{^3{\\mskip -5mu/\\mskip -3mu}_6})\\), where \\(R_6\\) is \\(R_{rs}(670)\\) (SeaWiFS) or \\(R_{rs}(667)\\) (Pan et al. 2010). References "],["cold-pool-index.html", "15 Cold Pool Index 15.1 Methods 15.2 2021 Methods 15.3 2020 Methods", " 15 Cold Pool Index Description: Cold Pool Index - three annual cold pool indices (and the standard errors) between 1958 and 2021. Found in: State of the Ecosystem - Mid-Atlantic (2020 (Different Methods), 2021 (Different Methods), 2022) Indicator category:Published methods, Extensive analysis, not yet published Contributor(s): Hubert du Pontavice, Vincent Saba, Zhuomin Chen Data steward: Kimberly Bastille Kimberly.bastille@noaa.gov Point of contact: Hubert du Pontavice hubert.dupontavice@princeton.edu Public availability statement: Source data are NOT publicly available.Please email hubert.dupontavice@princeton.edu for further information and accessing the ROMS-NWA bottom temperature data. 15.1 Methods The methodology for the cold pool index changed between 2020, 2021, and 2022 SOEs. The most recent methods and at the top with older methods below those. The cold pool is an area of relatively cold bottom water that forms on the US northeast shelf in the Mid-Atlantic Bight. 15.1.1 Data Sources Three bottom temperature products are used to get bottom temperature from 1958 to 2021: 1. Numerical simulation of the NWA Ocean was performed with the Regional Ocean Modelling System (ROMS-NWA) PERIOD: 1958-1992 INITIAL RESOLUTION: ~7km REGRID: 1/10° (Shchepetkin and McWilliams, 2005) https://www.sciencedirect.com/science/article/pii/S1463500304000484 2. Global Ocean Physics Reanalysis (Glorys reanalysis) PERIOD: 1993-2019 INITIAL RESOLUTION: 1/12° REGRID: 1/10° https://resources.marine.copernicus.eu/product-detail/GLOBAL_REANALYSIS_PHY_001_030/INFORMATION 3. Global Ocean Physics Analysis and Forecast updated Daily PERIOD: 2020-2021 INITIAL RESOLUTION: 1/12° REGRID: 1/10° (Fernandez and Lellouche, 2018; Lellouche et al., 2018) https://resources.marine.copernicus.eu/product-detail/GLOBAL_ANALYSIS_FORECAST_PHY_001_024/INFORMATION Furthermore, we use bottom temperature from the Northwest Atlantic Regional Climatology to estimate a monthly decadal bias (Seidov et al., 2016a, 2016b). INITIAL RESOLUTION: 1/10° https://www.ncei.noaa.gov/products/northwest-atlantic-regional-climatology 15.1.2 Data Analysis 15.1.2.1 Cold Pool Index (Model_CPI) The Cold Pool Index (Model_CPI) was adapted from Miller et al. (2016). Residual temperature was calculated in each grid cell, i, in the Cold Pool domain as the difference between the average bottom temperature at the year y (Ty) and the average bottom temperature over the period 19722019 \\[({\\bar{T}}_{i,\\ 1972-2019})\\] between June and September. Model_CPI was calculated as the mean residual temperature over the Cold Pool domain such that: \\[{{Model}__CPI}_y=\\ \\frac{\\sum_{i=1}^{n}{{(T}_{i,\\ y}\\ -\\ {\\bar{T}}_{i,\\ 1972-2019})\\ }}{n}\\] where n is the number of grid cells over the Cold Pool domain. 15.1.2.2 Persistence Index (Model_PI) The temporal component of the Cold Pool was calculated using the persistence index (Model_PI). Model_PI measures the duration of the Cold Pool and is estimated using the month when bottom temperature rises above 10C after the Cold Pool is formed each year. We first selected the area over the cold pool domain in which bottom temperature falls below 10C between June and October. We then calculated the residual month in each grid cell, i, in the Cold Pool domain as the difference between the month when bottom temperature rises above 10C in year y and the average of those months over the period 19722019. Then, Model_PI was calculated as the mean residual month over the Cold Pool domain: \\[{PI}_y=\\ \\frac{\\sum_{i=1}^{n}{{(Month}_{i,\\ y}\\ -\\ {\\bar{Month}}_{i,\\ 1972-2019})\\ }}{n}\\] 15.1.2.3 Spatial Extent Index (Model_SEI) The spatial component of the Cold Pool and the habitat provided by the cold pool was calculated using the Spatial Extent Index (Model_SEI). The Model_SEI is estimated by the number of cells where bottom temperature remains below 10C for at least 2 months between June and September. The Bottom temperature data are from ROMS-NWA between 1958 and 1992, from Glorys reanalysis between 1993 and 2019 and from Global Ocean Physics for 2020 and 2021. Bottom temperature from Glorys reanalysis and Global Ocean Physics Analysis were not being processed. Bottom temperature from ROMS-NWA (used for the period 1958-1992) were bias-corrected. Previous studies that focused on the ROMS-NWA-based Cold Pool highlighted strong and consistent warm bias in bottom temperature of about 1.5C during the stratified seasons over the period of 1958-2007 (Chen et al., 2018; Chen and Curchitser, 2020). In order to bias-correct bottom temperature from ROMS-NWA, we used the monthly climatologies of observed bottom temperature from the Northwest Atlantic Ocean regional climatology (NWARC) over decadal periods from 1955 to 1994. The NWARC provides high resolution (1/10° grids) of quality-controlled in situ ocean temperature based on a large volume of observed temperature data (Seidov et al., 2016a, 2016b) (https://www.ncei.noaa.gov/products/northwest-atlantic-regional-climatology). The first step was to re-grid the ROMS-NWA to obtain bottom temperature over the same 1/10° grid as the NWARC. Then, a monthly bias was calculated in each grid cell and for each decade (19551964, 19651974, 19751984, 19851994) in the MAB and in the SNE shelf: \\[{BIAS}_{i,\\ d}=\\ T_{i,d}^{Climatology}\\ -\\ {\\bar{T}}_{i,\\ d}^{ROMS-NWA}\\\\] where \\[T_{i,d}^{Climatology}\\] is the NWARC bottom temperature in the grid cell i for the decade d and \\[{\\bar{T}}_{i,\\ d}^{ROMS-NWA}\\] is the average ROMS-NWA bottom temperature over the decade d in the grid cell i. 15.1.3 Data processing Code used to process the cold pool inidcator can be found in the ecodata package here. 15.1.4 Plotting The plot below was built using the code found here. Figure 15.1: Cold Pool parameters, Index, Extent, and Persistance 15.2 2021 Methods Point of Contact:: Zhoumin Chen zhuomin.chen@uconn.edu 15.2.1 Data Sources The three-dimensional temperature of the Northeast US shelf is downloaded from the CMEMS (https://marine.copernicus.eu/). Source data is available at this link. 15.2.2 Data Analysis Depth-averaged spatial temperature is calculated based on the daily Cold Pool dataset, which is quantified following Chen et al., 2018. 15.2.3 Data processing Code used to process the cold pool inidcator can be found in the ecodata package here. 15.2.4 Plotting The plot below was built using the code found here. 15.3 2020 Methods Point of Contact:: Chris Melrose chris.melrose@noaa.gov 15.3.1 Data sources NEFSC Hydrographic Database This data represents the annual mean bottom temperature residual for Sept-Oct in the Mid-Atlantic Bight cold pool region from 1977-2018. 15.3.2 Data extraction 15.3.3 Data analysis Methods published T. Miller, Hare, and Alade (2016), original MATLAB source code used in that paper was provided by Jon Hare and used in this analysis. 15.3.4 Resources Chen, Z., Curchitser, E., Chant, R., and Kang, D. 2018. Seasonal Variability of the Cold Pool Over the Mid-Atlantic Bight Continental Shelf. Journal of Geophysical Research: Oceans, 123: 82038226. Chen, Z., and Curchitser, E. N. 2020. Interannual Variability of the MidAtlantic Bight Cold Pool. Journal of Geophysical Research: Oceans, 125. https://onlinelibrary.wiley.com/doi/10.1029/2020JC016445 (Accessed 13 January 2021). Fernandez, E., and Lellouche, J. M. 2018. Product user manual for the global ocean physical reanalysis product GLORYS12V1. Copernicus Product User Manual, 4: 115. Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., et al. 2018. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 112° high-resolution system. Ocean Science, 14: 10931126. Miller, T. J., Hare, J. A., and Alade, L. A. 2016. 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. Canadian Journal of Fisheries and Aquatic Sciences, 73: 12611270. Seidov, D., Baranova, O. K., Johnson, D. R., Boyer, T. P., Mishonov, A. V., and Parsons, A. R. 2016a. Northwest Atlantic Regional Climatology (NCEI Accession 0155889). NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/archive/accession/0155889 (Accessed 25 March 2021). Seidov, D., Baranova, O. K., Boyer, T., Cross, S. L., Mishonov, A. V., and Parsons, A. R. 2016b. Northwest Atlantic regional ocean climatology.: 3.2 MB. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Centers for Environmental Information. Shchepetkin, A. F., and McWilliams, J. C. 2005. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9: 347404. References "],["comdat.html", "16 Commercial Landings Data 16.1 Methods", " 16 Commercial Landings Data Description: Commercial landings data pull Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019,2020, 2021), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019,2020, 2021) Indicator category: Database pull Contributor(s): Sean Lucey Data steward: Sean Lucey, Sean.Lucey@noaa.gov Point of contact: Sean Lucey, Sean.Lucey@noaa.gov Public availability statement: Raw data are not publically available due to confidentiality of individual fishery participants. Derived indicator outputs are available here. 16.1 Methods Fisheries dependent data for the Northeast Shelf extend back several decades. Data from the 1960s on are housed in the Commercial database (CFDBS) of the Northeast Fisheries Science Center which contains the commercial fisheries dealer purchase records (weigh-outs) collected by National Marine Fisheries Service (NMFS) Statistical Reporting Specialists and state agencies from Maine to Virginia. The data format has changed slightly over the time series with three distinct time frames as noted in Table 16.1 below. Table 16.1: Data formats Table Years WOLANDS 1964 - 1981 WODETS 1982 - 1993 CFDETS_AA > 1994 Comlands is an R database pull that consolidates the landings records from 1964 on and attempts to associate them with NAFO statistical areas (Figure 16.1). The script is divided into three sections. The first pulls domestic landings data from the yearly landings tables and merges them into a single data source. The second section applies an algorithm to associate landings that are not allocated to a statistical area using similar characteristics of the trip to trips with known areas. The final section pulls foreign landings from the Northwest Atlantic Fisheries Organization website and rectifies species and gear codes so they can be merged along with domestic landings. Figure 16.1: Map of the North Atlantic Fisheries Organization (NAFO) Statistical Areas. Colors represent the Ecological Production Unit (EPU) with which the statistical area is associated. During the first section, the Comlands script pulls the temporal and spatial information as well as vessel and gear characteristics associated with the landings in addition to the weight, value, and utilization code of each species in the landings record. The script includes a toggle to use landed weights as opposed to live weights. For all but shellfish species, live weights are used for the State of the Ecosystem report. Due to the volume of data contained within each yearly landings table, landings are aggregated by species, utilization code, and area as well as by month, gear, and tonnage class. All weights are then converted from pounds to metric tons. Landings values are also adjusted for inflation using the Producer Price Index by Commodity for Processed Foods and Feeds: Unprocessed and Packaged Fish. Inflation is based on January of the terminal year of the data pull ensuring that all values are in current dollar prices. Table 16.2: Gear types used in commercial landings Major gear 1 Otter Trawls 2 Scallop Dredges 3 Other Dredges 4 Gillnets 5 Longlines 6 Seines 7 Pots/Traps 8 Midwater 9 Other Several species have additional steps after the data is pulled from CFDBS. Skates are typically landed as a species complex. In order to segregate the catch into species, the ratio of individual skate species in the NEFSC bottom trawl survey is used to disaggregate the landings. A similar algorithm is used to separate silver and offshore hake which can be mistaken for one another. Finally, Atlantic herring landings are pulled from a separate database as the most accurate weights are housed by the State of Maine. Comlands pulls from the State database and replaces the less accurate numbers from the federal database. The majority of landings data are associated with a NAFO Statistical Area. For those that are not, Comlands attempts to assign them to an area using similar characteristics of trips where the area is known. To simplify this task, landings data are further aggregated into quarter and half year, small and large vessels, and eight major gear categories (Table 16.2). Landings are then proportioned to areas that meet similar characteristics based on the proportion of landings in each area by that temporal/vessel/gear combination. If a given attribute is unknown, the algorithm attempts to assign it one, once again based on matched characteristics of known trips. Statistical areas are then assigned to their respective Ecological Production Unit (Table 16.3). Table 16.3: Statistical areas making up each EPU EPU Stat Areas Gulf of Maine 500, 510, 512, 513, 514, 515 Georges Bank 521, 522, 523, 524, 525, 526, 551, 552, 561, 562 Mid-Atlantic 537, 539, 600, 612, 613, 614, 615, 616, 621, 622, 625, 626, 631, 632 The final step of Comlands is to pull the foreign landings from the NAFO database. US landings are removed from this extraction so as not to be double counted. NAFO codes and CFDBS codes differ so the script rectifies those codes to ensure that the data is seamlessly merged into the domestic landings. Foreign landings are flagged so that they can be removed if so desired. 16.1.1 Data sources Comland is a database query of the NEFSC commercial fishery database (CFDBS). More information about the CFDBS is available here. 16.1.2 Data extraction comlandr is a package used to extract relevant data from the database. 16.1.2.1 Data Processing The landings data were formatted for inclusion in the ecodata R package with this R code. 16.1.3 Data analysis Fisheries dependent data from Comlands is used in several indicators for the State of the Ecosystem report; the more complicated analyses are detailed in their own sections. The most straightforward use of this data are the aggregate landings indicators. These are calculated by first assigning the various species into aggregate groups. Species are also marked by which management body manages them. Landings are then summed by year, EPU, aggregate group, and whether they are managed or not. Both managed and unmanaged totals are added together to get the final amount of total landings for that aggregate group within its respective region. Both the total and those landings managed by the management body receiving the report are reported. Proportions of managed landings to total landings are also reported in tabular form. 16.1.4 Plotting The plot below was built using the code found here. Figure 16.2: Mid-Atlantic commercial landings. "],["fishery-reliance-and-social-vulnerability.html", "17 Fishery Reliance and Social Vulnerability 17.1 Methods", " 17 Fishery Reliance and Social Vulnerability Description: Fishing community commercial and recreational fishing reliance and social vulnerability Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) Indicator category: Database pull with analysis Contributor(s): Lisa L. Colburn, Changua Weng Data steward: Changua Weng changhua.weng@noaa.gov Point of contact: Lisa L. Colburn lisa.colburn@noaa.gov Public availability statement: The source data used to construct the commercial fishing engagement and reliance indices include confidential information and are not available publicly. However, the commercial fishing engagement and reliance indices are not confidential so are available to the public. All calculated indices can be found here. 17.1 Methods 17.1.1 Data sources NOAA Fisheries Community Social Vulnerability Indicators (CSVIs) were developed using secondary data including social, demographic and fisheries variables. The social and demographic data were downloaded from the 2018 American Community Survey (ACS) 5-yr estimates Dataset at the U.S. Census American FactFinder site for coastal communities at the Census Designated Place (CDP) level, and in some cases the County Subdivision (MCD) level. Commercial fisheries data were pulled from the SOLE server located at Northeast Fisheries Science Center in Woods Hole, MA. The recreational fishing information is publicly accessible through the Marine Recreational Information Program (MRIP), and for this analysis was custom requested from NOAA Fisheries headquarters. 17.1.2 Data extraction Commercial fisheries data was pulled from the NEFSC SOLE server in Woods Hole, MA. SQL and SAS code for data extraction and processing steps can be found here. 17.1.3 Data analysis The indicators were developed using the methodology described in Jacob et al. (2010), Jacob et al. (2013), Colburn and Jepson (2012) and Jepson and Colburn (2013). Indicators were constructed through principal component analysis with a single factor solution, and the following criteria had to have been met: a minimum variance explained of 45%; Kasier-Meyer Olkin measure of sampling adequacy above.500; factor loadings above.350; Bartletts test of sphericity significance above .05; and an Armors Theta reliability coefficient above .500. Factor scores for each community were ranked based on standard deviations into the following categories: High(>=1.00SD), MedHigh .500-.999 SD), Moderate (.000-.499 SD) and Low (<.000 SD). 17.1.4 Data processing Data were formatted for inclusion in the ecodata R package using the R script found here. 17.1.5 Plotting Code used to build the community engagement indicator plot below can be found here. Figure 17.1: Commercial engagement, reliance and environmental justice vulnerability for thetop commercial fishing communities in the Mid-Atlantic. (* Scored high (1.00 and above)) for both commercial engagement and reliance indicators). Figure 17.2: Environmental justice indicators (Poverty index, population composition index, and personal disruption index) for top commercial fishing communities in the Mid-Atlantic. Figure 17.3: Recreational engagement, reliance and environmental justice vulnerability for the top recreational fishing communities in the Mid-Atlantic. (* Scored high (1.00 and above)) for both recreational engagement and reliance indicators). Figure 17.4: Environmental justice indicators (Poverty index, population composition index, and personal disruption index) for top recreational fishing communities in the Mid-Atlantic. References "],["conceptual-models.html", "18 Conceptual Models 18.1 Methods", " 18 Conceptual Models Description: Conceptual models for the New England (Georges Bank and Gulf of Maine) and Mid-Atlantic regions of the Northeast US Large Marine Ecosystem Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) Indicator category: Synthesis of published information, Extensive analysis; not yet published Contributor(s): Sarah Gaichas, Patricia Clay, Geret DePiper, Gavin Fay, Michael Fogarty, Paula Fratantoni, Robert Gamble, Sean Lucey, Charles Perretti, Patricia Pinto da Silva, Vincent Saba, Laurel Smith, Jamie Tam, Steve Traynor, Robert Wildermuth Data steward: Sarah Gaichas, sarah.gaichas@noaa.gov Point of contact: Sarah Gaichas, sarah.gaichas@noaa.gov Public availability statement: All source data aside from confidential commercial fisheries data (relevant only to some components of the conceptual models) are available to the public (see Data Sources below). 18.1 Methods Conceptual models were constructed to facilitate multidisciplinary analysis and discussion of the linked social-ecological system for integrated ecosystem assessment. The overall process was to first identify the components of the model (focal groups, human activities, environmental drivers, and objectives), and then to document criteria for including groups and linkages and what the specific links were between the components. The prototype conceptual model used to design Northeast US conceptual models for each ecosystem production unit (EPU) was designed by the California Current IEA program. The California Current IEA developed an overview conceptual model for the Northern California Current Large Marine Ecosystem (NCC), with models for each focal ecosystem component that detailed the ecological, environmental, and human system linkages. Another set of conceptual models outlined habitat linkages. An inital conceptual model for Georges Bank and the Gulf of Maine was outlined at the 2015 ICES WGNARS meeting. It specified four categories: Large scale drivers, focal ecosystem components, human activities, and human well being. Strategic management objectives were included in the conceptual model, which had not been done in the NCC. Focal ecosystem components were defined as aggregate species groups that had associated US management objectives (outlined within WGNARS for IEAs, see DePiper et al. (2017)): groundfish, forage fish, fished invertebrates, living habitat, and protected species. These categories roughly align with Fishery Managment Plans (FMPs) for the New England Fishery Management Council. The Mid-Atlantic conceptual model was developed along similar lines, but the focal groups included demersals, forage fish, squids, medium pelagics, clams/quahogs, and protected species to better align with the Mid Atlantic Councils FMPs. After the initial draft model was outlined, working groups were formed to develop three submodels following the CCE example: ecological, environmental, and human dimensions. The general approach was to specify what was being included in each group, what relationship was represented by a link between groups, what threshold of the relationship was used to determine whether a relationship was significant enough to be included (we did not want to model everything), the direction and uncertainty of the link, and documentation supporting the link between groups. This information was recorded in a spreadsheet. Submodels were then merged together by common components using the merge function in the (currently unavailable) desktop version of Mental Modeler (http://www.mentalmodeler.org/#home; Gray et al. (2013)). The process was applied to Georges Bank (GB), the Gulf of Maine (GOM), and the Mid-Atlantic Bight (MAB) Ecological Production Units. 18.1.1 Data sources 18.1.1.1 Ecological submodels Published food web (EMAX) models for each subregion (J. S. Link et al. 2006; Jason Link et al. 2008), food habits data collected by NEFSC trawl surveys (B. E. Smith and Link 2010), and other literature sources (L. A. Smith et al. 2015) were consulted. Expert judgement was also used to adjust historical information to current conditions, and to include broad habitat linkages to Focal groups. 18.1.1.2 Environmental submodels Published literature on the primary environmental drivers (seasonal and interannual) in each EPU was consulted. Sources for Georges Bank included Backus and Bourne (1987) and Townsend et al. (2006). Sources for the Gulf of Maine included Peter C. Smith (1983), Peter C. Smith et al. (2001), Mupparapu and Brown (2002), Townsend et al. (2006), Peter C. Smith et al. (2012), and David G. Mountain (2012b). Sources for the Mid Atlantic Bight included Houghton et al. (1982), Beardsley et al. (1985), Lentz (2003), David G. Mountain (2003), Glenn et al. (2004), Sullivan, Cowen, and Steves (2005), Castelao et al. (2008), Shearman and Lentz (2009), Castelao, Glenn, and Schofield (2010), Gong, Kohut, and Glenn (2010), Gawarkiewicz et al. (2012), Forsyth, Andres, and Gawarkiewicz (2015), Fratantoni, Holzwarth-Davis, and Taylor (2015), W. G. Zhang and Gawarkiewicz (2015), Timothy J. Miller, Hare, and Alade (2016), and Lentz (2017). 18.1.1.3 Human dimensions submodels Fishery catch and bycatch information was drawn from multiple regional datasets, incuding the Greater Atlantic Regional Office Vessel Trip Reports & Commercial Fisheries Dealer databases, Northeast Fishery Observer Program & Northeast At-Sea Monitoring databases, Northeast Fishery Science Center Social Sciences Branch cost survey, and the Marine Recreational Informational Program database. Further synthesis of human welfare derived from fisheries was drawn from Färe, Kirkley, and Walden (2006), Walden et al. (2012), Lee and Thunberg (2013), Lee (2014), and Lee, Steinback, and Wallmo (2017). Bycatch of protected species was taken from Waring et al. (2015), with additional insights from Bisack and Magnusson (2014). The top 3 linkages were drawn for each node. For example, the top 3 recreational species for the Mid-Atlantic were used to draw linkages between the recreational fishery and species focal groups. A similar approach was used for relevant commercial fisheries in each region. Habitat-fishery linkages were drawn from unpublished reports, including: Mid-Atlantic Fishery Management Council. 2016. Amendment 16 to the Atlantic Mackerel, Squid, and Butterfish Fishery Management Plan: Measures to protect deep sea corals from Impacts of Fishing Gear. Environmental Assessment, Regulatory Impact Review, and Initial Regulatory Flexibility Analysis. Dover, DE. August, 2016. NOAA. 2016. Deep sea coral research and technology program 2016 Report to Congress. http://www.habitat.noaa.gov/protection/corals/deepseacorals.html retrieved February 8, 2017. New England Fishery Management Council. 2016. Habitat Omnibus Deep-Sea Coral Amendment: Draft. http://www.nefmc.org/library/omnibus-deep-sea-coral-amendment Retrieved Feb 8, 2017. Bachman et al. 2011. The Swept Area Seabed Impact (SASI) Model: A Tool for Analyzing the Effects of Fishing on Essential Fish Habitat. New England Fisheries Management Council Report. Newburyport, MA. Tourism and habitat linkages were drawn from unpublished reports, including: http://neers.org/RESOURCES/Bibliographies.html Great Bay (GoM) resources http://greatbay.org/about/publications.htm Meaney, C.R. and C. Demarest. 2006. Coastal Polution and New England Fisheries. Report for the New England Fisheries Management Council. Newburyport, MA. List of valuation studies, by subregion and/or state, can be found at http://www.oceaneconomics.org/nonmarket/valestim.asp. Published literature on human activities in each EPU was consulted. Sources for protected species and tourism links included Hoagland and Meeks (2000) and Lee (2010). Sources for links between environmental drivers and human activities included Adams (1973), Matzarakis and Freitas (2001), Scott, McBoyle, and Schwartzentruber (2004), Hess, Malilay, and Parkinson (2008), Colburn and Jepson (2012), Jepson and Colburn (2013), and Colburn et al. (2016). Sources for cultural practices and attachments links included Daniel Pauly (1997), McGoodwin (2001), St Martin (2001), Norris-Raynbird (2004), Pollnac et al. (2006), Clay and Olson (2007), Clay and Olson (2008), Everett and Aitchison (2008), Donkersloot (2010), Lord (2011), Halpern et al. (2012), Wynveen, Kyle, and Sutton (2012), Cortes-Vazquez and Zedalis (2013), Koehn, Reineman, and Kittinger (2013), Potschin and Haines-Young (2013), Reed et al. (2013), Urquhart and Acott (2013), Blasiak et al. (2014), Klain, Satterfield, and Chan (2014), Poe, Norman, and Levin (2014), Brown (2015), Donatuto and Poe (2015), Khakzad and Griffith (2016), Oberg et al. (2016), and Seara, Clay, and Colburn (2016). 18.1.2 Data extraction 18.1.2.1 Ecological submodels Data included model estimated quantities to determine whether inclusion thresholds were met for each potential link in the conceptual model. A matrix with diet composition for each modeled group is an input to the food web model. A matrix of mortalities caused by each predator and fishery on each modeled group is a direct ouput of a food web model (e.g. Ecopath). Food web model biomasss flows between species, fisheries, and detritus were summarized using algorithms implemented in visual basic by Kerim Aydin, NOAA NMFS Alaska Fisheries Science Center. Because EMAX model groups were aggregated across species, selected diet compositions for individual species were taken from the NEFSC food habits database using the FEAST program for selected species (example query below). These diet queries were consulted as supplemental information. Example FEAST sql script for Cod weighted diet on Georges Bank can be found here. Queries for different species are standardized by the FEAST application and would differ only in the svspp code. 18.1.2.2 Environmental submodels Information was synthesized entirely from published sources and expert knowledge; no additional data extraction was completed for the environmental submodels. 18.1.2.3 Human dimensions submodels Recreational fisheries data were extracted from the 2010-2014 MRIP datasets. Original data can be found here for each region (New England or Mid-Atlantic as defined by states). Commercial fishing data was developed as part of the State of the Ecosystem Report, including revenue and food production estimates, with data extraction metodology discussed in the relevant sections of the technical document. In addition, the Northeast Regional Input/Output Model (Steinback and Thunberg 2006) was used as the basis for the strength of the employment linkages. 18.1.3 Data analysis 18.1.3.1 Ecological submodels Aggregated diet and mortality information was examined to determine the type of link, direction of link, and which links between which groups should be inclded in the conceptual models. Two types of ecological links were defined using food web models: prey links and predation/fishing mortality links. Prey links resulted in positve links between the prey group and the focal group, while predation/fishing mortality links resulted in negative links to the focal group to represent energy flows. The intent was to include only the most important linkages between focal groups and with other groups supporting or causing mortality on focal species groups. Therefore, threshold levels of diet and mortality were established (based on those that would select the top 1-3 prey and predators of each focal group): 10% to include a link (or add a linked group) in the model and 20% to include as a strong link. A Primary Production group was included in each model and linked to pelagic habitat to allow environmental effects on habitat to be connected to the ecologial submodel. Uncertainty for the inclusion of each link and for the magnitude of each link was qualitatively assessed and noted in the spreadsheet. Four habitat categories (Pelagic, Seafloor and Demersal, Nearshore, and Freshwater and Estuarine) were included in ecological submodels as placeholders to be developed further along with habitat-specific research. Expert opinion was used to include the strongest links between each habitat type and each Focal group (noting that across species and life stages, members of these aggregate groups likely occupy many if not all of the habitat types). Link direction and strength were not specified. Environmental drivers were designed to link to habitats, rather than directly to Focal groups, to represent each habitats important mediation function. EMAX model groups were aggregated to focal groups for the Georges Bank (GB), Gulf of Maine (GOM) and Mid-Atlantic Bight (MAB) conceptual models according to Table 18.1. Linked groups directly support or impact the Focal groups as described above. Table 18.1: Relationship between food web model groups and conceptual model focal groups. Pinnipeds not included in GB and Seabirds not included in MAB. Group Type Region Conceptual model group EMAX group(s) Focal GB Commercial Fishery Fishery Focal GB Fished Inverts Megabenthos filterers Focal GB Forage Fish Sum of Small pelagics-commercial, other, anadromous, and squids Focal GB Groundfish Sum of Demersals-omnivores, benthivores, and piscivores Focal GB Protected Species Sum of Baleen Whales, Odontocetes, and Seabirds Linked GB Benthos Sum of Macrobenthos-polychaetes, crustaceans, molluscs, other and Megabenthos-other Linked GB Copepods and Micronecton Sum of Copepods-small and large, and Micronekton Linked GB Detritus and Bacteria Sum of Bacteria and Detritus-POC Linked GB Gelatinous zooplankton Gelatinous zooplankton Linked GB Primary Production Phytoplankton-Primary production Focal GOM Commercial Fishery Fishery Focal GOM Fished Inverts Megabenthos filterers Focal GOM Forage Fish Sum of Small pelagics-commercial, other, anadromous, and squids Focal GOM Groundfish Sum of Demersals-omnivores, benthivores, and piscivores Focal GOM Protected Species Sum of Baleen Whales, Odontocetes, Pinnipeds, and Seabirds Linked GOM Benthos Sum of Macrobenthos-polychaetes, crustaceans, molluscs, other and Megabenthos-other Linked GOM Copepods and Micronecton Sum of Copepods-small and large, and Micronekton Linked GOM Detritus and Bacteria Sum of Bacteria and Detritus-POC Linked GOM Gelatinous zooplankton Gelatinous zooplankton Linked GOM Primary Production Phytoplankton-Primary production Focal MAB Clams Quahogs Megabenthos filterers Focal MAB Commercial Fishery Fishery Focal MAB Demerals Sum of Demersals-omnivores, benthivores, and piscivores Focal MAB Forage Fish Sum of Small pelagics-commercial, other, and anadromous Focal MAB Medium Pelagics Medium pelagics Focal MAB Protected Species Sum of Baleen whales and Odontocetes Focal MAB Squids Small pelagics-squids Linked MAB Benthos Sum of Macrobenthos-polychaetes, crustaceans, molluscs, other Linked MAB Copepods and Micronecton Sum of Copepods-small and large, and Micronekton Linked MAB Detritus and Bacteria Sum of Bacteria and Detritus-POC Linked MAB Gelatinous zooplankton Gelatinous zooplankton Linked MAB Primary Production Phytoplankton-Primary production Linked MAB Sharks Sum of Sharks-pelagic and coastal Ecological submodels were constructed and visualized in Mental Modeler (Fig. 18.1). Here, we show only the Gulf of Maine submodels as examples. Figure 18.1: Gulf of Maine Ecological submodel 18.1.3.2 Environmental submodels Environmental submodels were designed to link key oceanographic processes in each ecosystem production unit to the four general habitat categories (Pelagic, Seafloor and Demersal, Nearshore, and Freshwater and Estuarine) with emphasis on the most important physical processes in each ecosystem based on expert knowledge as supported by literature review. The basis of each submodel were environmental variables observable at management-relevant scales as identified by WGNARS: Surface and Bottom Water Temperature and Salinity, Freshwater Input, and Stratification (as well as sea ice timing and cover, which is not relevant to the northeast US shelf). Key drivers changing these observable variables and thus structuring habitat dynamics in each Ecological Production Units were added to the model using expert consensus. Environmental submodels were initially constructed and visualized in Mental Modeler (Fig. 18.2). Figure 18.2: Gulf of Maine Environmental submodel 18.1.3.3 Human dimensions submodels The top 3 species from each mode of recreational fishing (shoreside, private boat, party/charter) were used to assess the potential for missing links between the recreational fishing activity and biological focal components. Given the predominance of Mid-Atlantic groundfish in recreational fishing off New England (summer flounder, bluefish, striped bass), a Mid-Atlantic groundfish focal component was added to the Georges Bank EPU model. The magnitude of benefits generated from recreational fishing was scaled to reflect expert knowledge of target species, coupled with the MRIP data highlighted above. Scales were held consistent across the focal components within recreational fishing. No additional biological focal components were added to the commercial fishing activity, beyond what was developed in the ecological submodel. Benefits derived from commercial fishing were scaled to be consistent with the State of the Ecosystem revenue estimates, as modulated by expert knowledge and additional data sources. For example,the percentage of landings sold as food was used to map fishing activity to the commercial fishery food production objective, and the Northeast Regional Input/Output Model (Steinback and Thunberg 2006) was used to define the strength of the employment linkages. For profitability, expert knowledge was used to reweight revenue landings, based on ancillary cost data available (Das, Chhandita 2013, 2014). Human activities and objectives for the conceptual sub model are defined in DePiper et al. (2017). As shown in Figure 18.3, human dimensions submodels were also initially constructed and visualized in Mental Modeler. Figure 18.3: Gulf of Maine Human dimensions submodel 18.1.3.4 Merged models All links and groups from each submodel were preserved in the full merged model for each system. Mental modeler was used to merge the submodels. Full models were then re-drawn in Dia (http://dia-installer.de/) with color codes for each model component type for improved readability. Examples for each system are below. Figure 18.4: Georges Bank conceptual model Figure 18.5: Gulf of Maine conceptual model Figure 18.6: Mid-Atlantic Bight conceptual model 18.1.3.5 Communication tools The merged models were redrawn for use in communications with the public. These versions lead off the State of the Ecosystem reports for both Fishery Management Councils to provide an overview of linkages between environmental drivers, ecological, and human systems. Figure 18.7: New England conceptual model for public communication Figure 18.8: Mid-Atlantic conceptual model for public communication References "],["fish-condition-indicator.html", "19 Fish Condition Indicator 19.1 Methods", " 19 Fish Condition Indicator Description: Relative condition Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) Indicator category: Database pull with analysis Contributor(s): Laurel Smith Data steward: Laurel Smith, laurel.smith@noaa.gov Point of contact: Laurel Smith, laurel.smith@noaa.gov Public availability statement: NEFSC survey data used in these analyses are available upon request (see BTS metadata for access procedures). Derived condition data are available here. 19.1 Methods Relative condition (Kn) was introduced by Cren (1951) as a way to remove the influence of length on condition, and Blackwell, Brown, and Willis (2000) noted that Kn may be useful in detecting prolonged physical stress on a fish populations. Relative condition is calculated as \\[Kn = W/W',\\] where \\(W\\) is the weight of an individual fish and \\(W'\\) is the predicted length-specific mean weight for the fish population in a given region. Here, relative condition was calculated for finfish stocks commonly caught on the Northeast Fisheries Science Centers (NEFSC) autumn bottom trawl survey, from 1992-present. Where data allowed, predicted length-weight parameters were calculated for \\(W\\) by species, sex and season over the time period 1992-2012. When sample sizes of individual fish weights and lengths were too low, parameters were calculated for aggregated spring and fall survey data over the same time period. Fall survey relative condition was calculated by Ecological Production Unit (EPU) for females only, as trends tended to be similar for males and females. The Condition package used for calculations and plotting of fish condition factor can be found on GitHub. 19.1.1 Data sources Individual fish lengths (to the nearest 0.5 cm) and weights (grams) were collected on the NEFSC bottom trawl surveys from 1992-present aboard RVs Albatross IV, Delaware II and the Henry B. Bigelow (see Survdat). A small number of outlier values were removed when calculating the length-weight parameters. 19.1.2 Data extraction Data were extracted from NEFSCs survey database (SVDBS) using the R script found here 19.1.3 Data analysis The following growth curve was fit through individual fish lengths and weights from the NEFSC bottom trawl survey data from 1992-2012 to produce reference length-weight parameters: \\[\\textrm{Weight} = e^{Fall_{coef}} * \\textrm{Length}^{Fall_{exp}},\\] where length is in cm and weight is in kg. Fall survey data were used where sample sizes allowed for growth curve estimation, otherwise data from spring and fall seasons were combined. Individual fish lengths from the NEFSC fall bottom trawl survey from 1992-2017 were then used to calculate predicted weights using the reference length-weight parameters. Relative condition (\\(Kn\\)) was calculated annually for females by species and EPU by dividing individual fish weights by the predicted weight. The code found here was used in the analysis of fish condition. 19.1.4 Plotting Code for plotting the fish condition indicator can be found here. Figure 19.1: Condition factor for fish species in the MAB. MAB data are missing for 2017 due to survey delays. References "],["ecosystem-overfishing.html", "20 Ecosystem Overfishing 20.1 Methods", " 20 Ecosystem Overfishing Description: Ecosystem Overfishing Indices (Primary Production Required, Fogarty, Ryther) Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021+), State of the Ecosystem - Mid-Atlantic (2021+) Indicator category: Database pull with analysis; Published methods Contributor(s): Michael Fogarty, Andrew Beet Data steward: Andrew Beet, andrew.beet@noaa.gov Point of contact: Andrew Beet, andrew.beet@noaa.gov Public availability statement: Source data is not publicly availabe due to PII restrictions. 20.1 Methods We use the definition of ecosystem overfishing from (JS Link and Watson 2019): The sum of catches is flat or declining Total catch per unit effort is declining Total landings relative to ecosystem production exceeds suitable limits All of the indices are based on the principle of energy transfer up the foodweb from primary producers. 20.1.1 Fogarty & Ryther Indices The Fogarty index is defined as ratio of total catches to total primary productivity in an ecosystem (JS Link and Watson 2019). The units are parts per thousand. The Ryther index is defined as total catch per unit area in the ecosystem (JS Link and Watson 2019). The units are mt km^-2 year^-1 A modification of the indices is used. Total landings are used in lieu of total catch. This will have the effect of reducing the value of the index (compared to using total catch). 20.1.2 Primary Production Required (PPR) The index is a measure of the impact of fishing on the base of the foodweb. The amount of potential yield we can expect from a marine ecosystem depends on the amount of production entering at the base of the food web, primarily in the form of phytoplankton; the pathways this energy follows to reach harvested species; the efficiency of transfer of energy at each step in the food web; and the fraction of this production that is removed by the fisheries. Species such as scallops and clams primarily feed directly on larger phytoplankton species and therefore require only one step in the transfer of energy. The loss of energy at each step can exceed 80-90%. For many fish species, as many as 2-4 steps may be necessary. Given the trophic level and the efficiency of energy transfer of the species in the ecosystem the amount phytoplankton production required (PPR) to account for the observed catch can be estimated. The index for Primary Production Required (PPR) was adapted from (D. Pauly and Christensen 1995b). \\[PPR_t = \\sum_{i=1}^{n_t} \\left(\\frac{landings_{t,i}}{9}\\right) \\left(\\frac{1}{TE}\\right)^{TL_i-1}\\] where \\(n_t\\) = number of species in time \\(t\\), \\(landings_{t,i}\\) = landings of species \\(i\\) in time \\(t\\), \\(TL_i\\) is the trophic level of species \\(i\\), \\(TE\\) = Trophic efficiency. The PPR estimate assumes a 9:1 ratio for the conversion of wet weight to carbon and a 15% transfer efficiency per trophic level, (\\(TE\\) = 0.15) The index is presented as a percentage of estimated primary production (PP) available over the geographic region of interest, termed an Ecological Production Unit (EPU). The scaled index is estimated by dividing the PPR index in year \\(t\\) by the estimated primary production in time \\(t\\). \\[scaledPPR_t = \\frac{PPR_t}{PP_t}\\] The species selected in each year were determined by their cumulative contribution to total landings. A threshold of at least 80% of the total landings is used. 20.1.2.1 Data sources Data for this index come from a variety of sources. The landings data come from the Commercial Fishery Database (CFDBS), species trophic level information come from fishbase and sealifebase, and primary production estimates are derived from satellites. Some of these data are typically not available to the public. 20.1.2.2 Data extraction Landings are extracted from the commercial fisheries database (CFDBS) using the methods described in the chapter Commercial Landings Data. Trophic level information for each species is obtained from fishbase and sealifebase using the R package rfishbase (Froese and Pauly 2019) in tandem with the package eofindices. Primary Production is estimated using the methods described in the chapter Chlorophyll a and Primary Production. 20.1.2.3 Data analysis 20.1.2.3.1 Primary Production Required Annual (wet weight) landings are calculated for each species for a given EPU. For each year the landings are sorted in descending order by species and the cumulative landings are calculated. The species that accounted for the top 80% of total cumulative landings are selected. The trophic level for each of these species are then obtained from fishbase/sealifebase. At this point the PPR index is calculated. The units of the index are \\(gCyear^{-1}\\) for the EPU. The index is converted to \\(gCm^{-2}year^{-1}\\) by dividing by the area (in \\(m^2\\)) of the EPU. To normalize the index the total Primary Production for the given EPU is required. This is calculated as described in the chapter Chlorophyll a and Primary Production. The units are also converted to \\(gCm^{-2}year^{-1}\\). The index is then normalized by dividing the index in year t by the total primary production in time \\(t\\). 20.1.2.3.2 Fogarty and Ryther Indices Total annual (wet weight) landings are calculated for a given EPU (summed over all species). The units for both primary production and landings are in \\(mt km^{-2} year^1\\). A factor of (1/9) is used to convert landings to weight in carbon. The area in \\(km^2\\) of each EPU is obtained from the shapefile used to define the area. 20.1.2.4 Plotting Four plots are produced for each EPU: The normalized PPR index (along with the associated landings). Total primary production Mean trophic level of the species included in the index (weighted by their landings) Species composition of landings The Fogarty index (with reference levels) The Ryther Index (with reference levels) All created using the eofindices See the workedExample vignette in the eofindices package for plotting code. Figures for Mid-Atlantic Bight are presented in this document. For Georges Bank and the Gulf of Maine, please visit here 20.1.2.4.1 Mid-Atlantic Bight (MAB) References "],["epu.html", "21 Ecological Production Units 21.1 Methods", " 21 Ecological Production Units Description: Ecological Production Units Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) Indicator category: Extensive analysis, not yet published Contributor(s): Robert Gamble Data steward: NA Point of contact: Robert Gamble, robert.gamble@noaa.gov Public availability statement: Ecological production unit (EPU) shapefiles are available here. More information about source data used to derive EPUs can be found here. 21.1 Methods To define ecological production units (EPUs), we assembled a set of physiographic, oceanographic and biotic variables on the Northeast U.S. Continental Shelf, an area of approximately 264,000 km within the 200 m isobath. The physiographic and hydrographic variables selected have been extensively used in previous analyses of oceanic provinces and regions (e.g Roff and Taylor 2000). Primary production estimates have also been widely employed for this purpose in conjunction with physical variables (Longhurst 2007) to define ecological provinces throughout the world ocean. We did not include information on zooplankton, benthic invertebrates, fish, protected species, or fishing patterns in our analysis. The biomass and production of the higher trophic level groups in this region has been sharply perturbed by fishing and other anthropogenic influences. Similarly, fishing patterns are affected by regulatory change, market and economic factors and other external influences. Because these malleable patterns of change are often unconnected with underlying productivity, we excluded factors directly related to fishing practices. The physiographic variables considered in this analysis are listed in Table 21.1. They include bathymetry and surficial sediments. The physical oceanographic and hydrographic measurements include sea surface temperature, annual temperature span, and temperature gradient water derived from satellite observations for the period 1998 to 2007. 21.1.1 Data sources Shipboard observations for surface and bottom water temperature and salinity in surveys conducted in spring and fall. Daily sea surface temperature (SST, °C) measurements at 4 km resolution were derived from nighttime scenes composited from the AVHRR sensor on NOAAs polar-orbiting satellites and from NASAs MODIS TERRA and MODIS AQUA sensors. We extracted information for the annual mean SST, temperature span, and temperature gradients from these sources. The latter metric provides information on frontal zone locations. Table 21.1: Variables used in derivation of Ecological Production Units. Variables Sampling Method Units Surficial Sediments Benthic Grab Krumbian Scale Sea Surface Temperature Satellite Imagery (4km grid) °C annual average Sea Surface Temperature Satellite Imagery (4km grid) dimensionless Sea Surface Temperature Satellite Imagery (4km grid) °C annual average Surface Temperature Shipboard hydrography (point) °C (Spring and Fall) Bottom Temperature Shipboard hydrography (point) °C (Spring and Fall) Surface Salinity Shipboard hydrography (point) psu (Spring and Fall) Bottom Salinity Shipboard hydrography (point) psu (Spring and Fall) Stratification Shipboard hydrography (point) Sigma-t units (Spring and Fall) Chlorophyll-a Satellite Imagery (1.25 km grid) mg/C/m3 (annual average) Chlorophyll-a gradient Satellite Imagery (1.25 km grid) dimensionless Chlorophyll-a span Satellite Imagery (1.25 km grid) mg/C/m3 (annual average) Primary Production Satellite Imagery (1.25 km grid) gC/m3/year (cumulative) Primary Production gradient Satellite Imagery (1.25 km grid) dimensionless Primary Production span Satellite Imagery (1.25 km grid) gC/m3/year (cumulative) The biotic measurements included satellite-derived estimates of chlorophyll a (CHLa) mean concentration, annual span, and CHLa gradients and related measures of primary production. Daily merged SeaWiFS/MODIS-Aqua CHLa (CHL, mg m-3) and SeaiWiFS photosynthetically available radiation (PAR, Einsteins m-2 d-1) scenes at 1.25 km resolution were obtained from NASA Ocean Biology Processing Group. 21.1.2 Data extraction NA 21.1.3 Data analysis In all cases, we standardized the data to common spatial units by taking annual means of each observation type within spatial units of 10 latitude by 10 longitude to account for the disparate spatial and temporal scales at which these observations are taken. There are over 1000 spatial cells in this analysis. Shipboard sampling used to obtain direct hydrographic measurements is constrained by a minimum sampling depth of 27 m specified on the basis of prescribed safe operating procedures. As a result nearshore waters are not fully represented in our initial specifications of ecological production units. The size of the spatial units employed further reflects a compromise between retaining spatial detail and minimizing the need for spatial interpolation of some data sets. For shipboard data sets characterized by relatively coarse spatial resolution, where necessary, we first constructed an interpolated map using an inverse distance weighting function before including it in the analysis. Although alternative interpolation schemes based on geostatistical approaches are possible, we considered the inverse distance weighting function to be both tractable and robust for this application. We first employed a spatial principal components analysis (PCA; e.g. Pielou 1984; Legendre and Legendre 1998) to examine the multivariate structure of the data and to account for any inter-correlations among the variables to be used in subsequent analysis. The variables included in the analysis exhibited generally skewed distributions and we therefore transformed each to natural logarithms prior to analysis. The PCA was performed on the correlation matrix of the transformed observations. We selected the eigenvectors associated with eigenvalues of the dispersion matrix with scores greater than 1.0 [the Kaiser-Guttman criterion; Legendre and Legendre (1998)] for all subsequent analysis. These eigenvectors represent orthogonal linear combinations of the original variables used in the analysis. We delineated ecological subunits by applying a disjoint cluster based on Euclidean distances using the K-means procedure (Legendre and Legendre 1998) on the principal component scores The use of non-independent variables can strongly influence the results of classification analyses of this type (Pielou 1984), hence the interest in using the PCA results in the cluster. The eigenvectors were represented as standard normal deviates. We used a Pseudo-F Statistic described by Milligan and Cooper (1985) to objectively define the number of clusters to use in the analysis. The general approach employed is similar to that of Host et al. (1996) for the development of regional ecosystem classifications for terrestrial systems. After the analyses were done, we next considered options for interpolation of nearshore boundaries resulting from depth-related constraints on shipboard observations. For this, we relied on information from satellite imagery. For the missing nearshore areas in the Gulf of Maine and Mid-Atlantic Bight, the satellite information for chlorophyll concentration and sea surface temperature indicated a direct extension from adjacent observations. For the Nantucket Shoals region south of Cape Cod, similarities in tidal mixing patterns reflected in chlorophyll and temperature observations indicated an affinity with Georges Bank and the boundaries were changed accordingly. Finally, we next considered consolidation of ecological subareas so that nearshore regions are considered to be special zones nested within the adjacent shelf regions. Similar consideration led to nesting the continental slope regions within adjacent shelf regions in the Mid-Atlantic and Georges Bank regions. This led to four major units: Mid-Atlantic Bight, Georges Bank, Western-Central Gulf of Maine (simply Gulf of Maine in the State of the Ecosystem), and Scotian Shelf-Eastern Gulf of Maine. As the State of the Ecosystem reports are specific to FMC managed regions, the Scotian Shelf-Eastern Gulf of Maine EPU is not considered in SOE indicator analyses. Figure 21.1: Map of the four Ecological Production Units, including the Mid-Atlantic Bight (light blue), Georges Bank (red), Western-Central Gulf of Maine (or Gulf of Maine; green), and Scotian Shelf-Eastern Gulf of Maine (dark blue) 21.1.4 Data processing Shapefiles were converted to sf objects for inclusion in the ecodata R package using the R code found here. References "],["expected-number-of-species.html", "22 Expected Number of Species 22.1 Methods", " 22 Expected Number of Species Description: Time Series of Expected Number of Species per Tow in NEFSC BTS Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021+), State of the Ecosystem - Mid-Atlantic (2021+) Indicator category: Database pull with analysis; Published methods Contributor(s): Sean Lucey Data steward: Sean Lucey, sean.lucey@noaa.gov Point of contact: Sean Lucey, sean.lucey@noaa.gov Public availability statement: 22.1 Methods Diversity estimates have been developed to understand whether the overall structure of the ecosystem has remained stable or is changing. There are a large number of diversity indices that can be used to measure diversity; for the purposes of the State of the Ecosystem report we report on the expected number of species in a sample size (\\(E(S_n)\\)). This index was originally developed by Sanders (1968) and later refined by Hurlbert (1971) using a hypergeometric probability distribution. These rarefied samples allow for comparisons between sample sites with varying number of species present. The estimate of \\(E(S_n)\\) is less biased than other diversity indices which usually increase with sample size. It also has a more meaningful biological interpretation than other indices. For example, if a predator eats 10 random individuals, \\(E(S_n)\\) will predict the number of species consumed. 22.1.1 Data sources Data used for the calculation of the expected number of species come from the Northeast Fisheries Science Centers survey database (SVDBS) as pulled in the Survdat data set. These data are available to qualified researchers upon request. More information on the data request process is available under the Access Information field here. 22.1.2 Data analysis The expected number of species (\\(E(S_n)\\)) was calculated for each survey tow as: \\[\\begin{equation} E(S_n) = \\sum_{i=1}^S{ \\Bigg( 1 - \\frac{\\binom{N-N_i}{n}}{\\binom{N}{n}} \\Bigg) } \\end{equation}\\] where \\(S\\) is the total number of species present, \\(N\\) the total number of individuals, and \\(N_i\\) the number of individuals of ith species. The result represents a sample of n individuals randomly selected from the tow without replacement. The calculation is made using the rarefy function of the vegan package (Oksanen et al. 2020) using an n of 1000. The number of species represented in these samples of 1000 fishes are then averaged over the survey for each Ecological Production Unit. Due to the lack of survey calibration factor to account for differences in the number of species caught between the NOAA Ship Albatross IV and NOAA Ship Henry B. Bigelow, the time series are kept separate. 22.1.3 Data processing Data were formatted for inclusion in the ecodata R package using the R code found here. 22.1.4 Plotting The plot below was built using the code found here. Figure 22.1: Expected number of species per 1000 individuals for FAll NEFSC bottom trawl survey. References "],["forage-fish-energy-density.html", "23 Forage Fish Energy Density 23.1 Methods", " 23 Forage Fish Energy Density Description: Forage Engery Density indicators Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2020+), State of the Ecosystem - Mid-Atlantic (2020+) Indicator category: Database pull with analysis Contributor(s): Mark Wuenschel, Ken Oliveira and Kelcie Bean Data steward: Mark Wuenschel mark.wuenschel@noaa.gov Point of contact: Mark Wuenschel mark.wuenschel@noaa.gov Public availability statement: Source data are publicly available. 23.1 Methods The forage fish energy denisty indicator comes from a collaborative project between UMASS Dartmouth Biology Department (Dr. Ken Oliveira, M.S student Kelcie Bean) and NEFSC Population Biology Branch (Mark Wuenschel). The study focuses on evaluating energy content of the species in Table 23.1. Table 23.1: List of forage fish study species. Common Name Scientific Name Atlantic Herring Clupea harengus alewife Alosa pseudoharengus silver hake Merluccius bilinearis butterfish Peprilus triacanthus northern sandlance Ammodytes dubius Atlantic mackerel Scomber Scombrus longfin squid Loligo pealeii northern shortfin squid Illex illecebrosus 23.1.1 Data sources NEFSC spring and fall bottom trawl surveys. 23.1.2 Data extraction NA 23.1.3 Data analysis Samples were analyzed for proximate composition and energy density from NEFSC spring and fall bottom trawl surveys. Predictive relationships between the percent dry weight of samples and energy density were developed, and samples collected from current surveys are currently being analyzed for percentage dry weight to enable estimation of energy content (Bean (2020)). The energy density of forage species differed from prior studies in the 1980s and 1990s (Steimle and Terranova (1985), Lawson, Magalhães, and Miller (1998), Table 23.1). Sampling and laboratory analysis is ongoing, with the goal of continuing routine monitoring of energy density of these species. 23.1.4 Data processing Code for building the table used in the SOE can be found here. 23.1.5 Plotting Code used to develop this plot can be found here. Figure 23.1: Forage fish mean energy density mean and standard deviation by season and year, compared with 1980s and 1990s. References "],["gulf-stream-index.html", "24 Gulf Stream Index 24.1 Methods 24.2 2019 Methods", " 24 Gulf Stream Index Description: Annual time series of the Gulf Stream index Indicator category: Published method Found in: State of the Ecosystem - New England (2019 (Different Methods), 2020, 2021, 2022), State of the Ecosystem - Mid-Atlantic (2019 (Different Methods), 2020, 2021, 2022) Contributor(s): Zhuomin Chen, Young-oh Kwon Data steward: Vincent Saba, vincent.saba@noaa.gov Point of contact: Vincent Saba, vincent.saba@noaa.gov Public availability statement: Source data are publicly available at CMENS. Index data are NOT publically available so please email vincent.saba@noaa.gov for further information and queries of GSI indicator data. 24.1 Methods The methods used to calculate the Gulf Stream Index changed between 2019 and 2020 SOEs. The most recent methods and at the top with older methods below those. 24.1.1 Data sources GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING). http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047 24.1.2 Data analysis The GSI is calculated based on the method presented by Pérez-Hernández and Joyce (2014). It is a simple 16-point GS index constructed by selecting grid points following the maximum Standard deviation of sea level height anomalies every 1.33° longitude between 52° and 72°W and averaging them. The value of 1.33° is based on the resolution of satellite dataset from AVISO. We followed the same method, except using the dataset from CMEMS, which has a 0.25°x0.25° resolution. Therefore we select points every 1° between 52° and 72° and average them, and there are 21 points in total. 24.1.3 Data Processing The Gulf Stream index data set was formatted for inclusion in the ecodata R package with the code found here. 24.1.4 Plotting The plot below was built using the code found here. Figure 24.1: Index representing changes in the location of the Gulf Stream north wall. Positive values represent a more northerly 24.2 2019 Methods Summarized from Joyce et al. (2019), ocean temperature data from NOAA NODC were sorted by latitude, longitude, and time using a resolution of 1° of longitude, latitude, and 3 months of time, respectively, with a Gaussian squared weighting from the selected desired point in a window twice the size of the desired resolution. Editing was used to reject duplicate samples and 3\\(\\sigma\\) outliers from each selected sample point prior to performing the weighting and averaging; the latter was only carried out when there were at least three data points in the selected interval for each sample point. Typically, 50 or more data values were available. The resulting temperature field was therefore smoothed. Data along the Gulf Stream north wall at nine data points were used to assemble a spatial/temporal sampling of the temperature at 200m data along the north wall from 75°W to 55°W. The leading mode of temperature variability of the Gulf Stream is equivalent to a northsouth shift of 50100 km, which is zonally of one sign and amounts to 50% of the seasonalinterannual variance between 75°W and 55°W. The temporal behavior of this mode (PC1) shows the temporal shift of the Gulf Stream path with a dominant approximately 8 to 10year periodicity over much of the period. 24.2.1 Data Sources Ocean temperatures at 200 m are available at https://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/. 24.2.2 Data analysis For detailed analytical methods, see Joyce et al. (2019). 24.2.3 Data processing and plotting Data processing and plotting remained the same between years. References "],["habitat-vulnerability.html", "25 Habitat Vulnerability 25.1 Methods", " 25 Habitat Vulnerability Description: A summary of habitat vulnerability and the importance of such habitats to managed species. Indicator category: Extensive analysis, not yet published Found in: State of the Ecosystem - New England (2021), State of the Ecosystem - Mid-Atlantic (2021) Contributor(s): Mark Nelson, Mike Johnson, Emily Farr, Grace Roskar Data steward: Grace Roskar grace.roskar@noaa.gov Point of contact: Mark Nelson, Mike Johnson, Emily Farr Public availability statement: Data from the Northeast Fish Climate Vulnerability Assessment and ACFHPs species-habitat matrix are publicly available. However, the data from the Northeast Habitat Climate Vulnerability Assessment are not yet published. Please email emily.farr@noaa.gov or mike.r.johnson@noaa.gov for further information and queries. 25.1 Methods 25.1.1 Data sources Data came from the Northeast Habitat Climate Vulnerability Assessment (HCVA; not yet published), the Northeast Fish and Shellfish Climate Vulnerability Assessment (FCVA; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0146756) and the Atlantic Coastal Fish Habitat Partnerships (ACFHP) Species-Habitat Matrix (https://www.atlanticfishhabitat.org/species-habitat-matrix/#:~:text=The%20Species%2DHabitat%20Matrix%20is,selected%20fish%20and%20invertebrate%20species) 25.1.2 Data analysis We assessed the vulnerability of 52 marine, estuarine, and riverine habitats in the Northeast U.S. to climate change. The northern and southern boundaries of the study area are the U.S./Canadian border and Cape Hatteras, NC, respectively, and the study includes habitats out to the U.S. EEZ and up-river to capture the full range of diadromous species. This habitat climate vulnerability assessment (HCVA) builds on the Northeast Fish and Shellfish Climate Vulnerability Assessment (FCVA) completed in 2016 (Hare et al. 2016), and uses a similar framework. While the species assessment primarily examined climate vulnerability based on life history, the HCVA assesses the vulnerability of the habitats themselves to climate change, and complements the species assessment by improving our understanding of how vulnerable habitats will impact fish and invertebrate populations. To better understand which species depend on vulnerable habitats, the Atlantic Coastal Fish Habitat Partnership (ACFHP) habitat-species matrix (Kritzer et al. 2016) was used in conjunction with the results of the HCVA and the FCVA. The ACFHP matrix identified the importance of nearshore benthic habitats to each life stage of select fish species, which helps elucidate species that may be highly dependent on highly vulnerable habitats that were identified in the HCVA. 25.1.2.1 HCVA Methods: The Northeast HCVA is a trait-based vulnerability assessment which was adapted from the framework developed for NOAAs Fish Stock Climate Vulnerability Assessment (Morrison et al. 2015). The HCVA considers the overall vulnerability of habitat to climate change to be a function of two main components: exposure and sensitivity. The exposure component considers the magnitude and overlap of projected changes in climate with the distribution of each habitat. Climate exposure is assessed using end-of-century climate projections based on the Intergovernmental Panel on Climate Change RCP 8.5 emissions scenario. The sensitivity component includes nine habitat attributes, or traits, that are believed to be indicative of the response of a habitat to potential changes in climate. The assessment methodology relies on an expert opinion-based approach to determine the sensitivity of each habitat to potential climate change related impacts. The sensitivity is combined with the climate exposure information to determine the overall vulnerability rank. 25.1.2.2 Methods for linking habitat vulnerability results with species: The Atlantic Coastal Fish Habitat Partnership (ACFHP) habitat-species matrix (Kritzer et al. 2016) was consulted and linked with the results of the HCVA and the FCVA in order to understand which federally managed species that are highly dependent on highly vulnerable habitats. The ACFHP habitat-species matrix evaluated the importance of 26 benthic habitat types to select fish and invertebrate species. Each habitat type was assigned a rank of very high, high, moderate, or low, reflecting a species use of the habitat at a specific life stage. Detailed descriptions of the rationale behind the rankings can be found in Kritzer et al. (2016). Using habitat descriptions from Kritzer et al. (2016), the 26 habitats analyzed by ACFHP were matched with HCVA habitats that best fit under the same description. Several habitat types that were included in the HCVA but not assessed by ACFHP were omitted from this analysis (e.g., manmade hard bottom habitats, aquaculture, invasive salt marsh and wetlands, water column habitats). A species-habitat matrix was then created using the species that were assessed in both the FCVA and by ACFHP, and the habitat importance ranking (very high, high, moderate, low) from the ACFHP matrix for each habitat type. Because the ACFHP habitat types were broader, several HCVA habitats often fit under a single ACFHP habitat; therefore, to determine which HCVA habitat a species/life stage actually uses under the broader ACFHP habitat, species profiles from the Northeast Regional Habitat Assessment (NRHA) and EFH Source Documents were consulted. Species highlighted here are those that are highly dependent on highly vulnerable habitats. A ranking matrix was created using the habitat vulnerability rankings compared to the habitat importance rankings to determine the criteria, and for the purposes of this submission, high dependence on a highly vulnerable habitat encompasses moderate use of very highly vulnerable habitats, high use of highly or very highly vulnerable habitats, or very high use of moderately, highly, or very highly vulnerable habitats. 25.1.3 Data Processing The Habitat Vulnerability information table was formatted for inclusion in the ecodata R package with the code found here. 25.1.4 Plotting Table found here. "],["harmful-algal-bloom---alexandrium-indicator.html", "26 Harmful Algal Bloom - Alexandrium Indicator 26.1 Methods 26.2 References", " 26 Harmful Algal Bloom - Alexandrium Indicator Description: Alexandrium catenella annual cyst abundance in the Gulf of Maine Found In:: 2022 Indicator Catalog Indicator category: Published methods, Database pull with analysis Contributor(s): Yizhen Li, NOAA/NOS NCCOS Stressor Detection and Impacts Division, HAB Forecasting Branch, Silver Spring MD Data steward: Moe Nelson david.moe.nelson@noaa.gov Point of contact: Moe Nelson david.moe.nelson@noaa.gov Public availability statement: Source data are NOT publicly available. Data were provided upon request by Yizhen Li. Data are also used in operational HAB forecast models, freely available to the public. 26.1 Methods 26.1.1 Data Sources 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.Results are reported 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. 26.1.2 Data Extraction Tabular data provided by Yizhen Li, NOAA/NOS NCCOS Stressor Detection and Impacts Division, HAB Forecasting Branch. 26.1.3 Data Analysis The spatial distribution and abundance of cyst cells from the annual survey are used to drive an ecosystem forecast model for the Gulf of Maine (Anderson et al. 2005, Li et al. 2009 & 2020, McGillicuddy et al. 2011). The model also includes many other inputs of dynamic oceanographic data such as currents, temperature, and nutrients. Operational Harmful Algal Bloom forecast is served online at https://coastalscience.noaa.gov/research/stressor-impacts-mitigation/hab-forecasts/gulf-of-maine-alexandrium-catenella-predictive-models/ 26.1.4 Data Processing Code for processing Alexandrium cyst data can be found here. 26.1.5 Plotting The script used to develop the figure in the SOE Indicator Catalog can be found here. Figure 26.1: Gulf of Maine Alexandrium Cyst abundance. Figure 26.2: Gulf of Maine Alexandrium Cyst distribution. 26.2 References Anderson, D.M. 1997. Bloom dynamics of toxic Alexandrium species in the northeastern U.S. Limnology and Oceanography 42(5): 1009-1022. Anderson, D.M., D.M. Kulis, B.A. Keafer, K.E. Gribble, R. Marin, and C.A. Scholin. 2005. Identification and enumeration of Alexandrium spp. from the Gulf of Maine using molecular probes. Deep-sea Research II 52:2467-2490. Jin, D., and P. Hoagland. 2008. The value of harmful algal bloom predictions to the nearshore commercial shellfish fishery in the Gulf of Maine. WHOI Marine Policy Center, Woods Hole MA. May 1, 2008. 29 pp. Li, Yizhen. Pers. Comm. NOAA/NOS NCCOS Stressor Detection and Impacts Division, Silver Spring MD. Li, Y., R. He, D.J. McGillicuddy, D.M. Anderson, and B.A. Keafer. 2009. Investigation of the 2006 Alexandrium fundyense bloom in the Gulf of Maine: In-situ observations and numerical modeling. Continental Shelf Research 29:2069-2082. Li, Y., R.P. Stumpf, D.J. McGillicuddy, and R. He. 2020. Dynamics of an intense Alexandrium catenella red tide in the Gulf of Maine: satellite observations and numerical modeling. Harmful Algae 99:101927. McGillicuddy, D.J., D.W. Townsend, R. He, B.A. Keafer, J.L. Kleindinst, Y. Li, J.P. Manning, D.G. Mountain, M.A. Thomas, and D.M. Anderson. 2011. Suppression of the 2010 Alexandrium fundyense bloom by changes in physical, biological, and chemical properties of the Gulf of Maine. Limnology and Oceanography 56(6):24112426. NOAA/NCCOS. 2021. Gulf of Maine Alexandrium catenella Predictive Models. Interactive website with operational Nowcast/Forecast simulation. https://coastalscience.noaa.gov/research/stressor-impacts-mitigation/hab-forecasts/gulf-of-maine-alexandrium-catenella-predictive-models/ "],["harmful-algal-blooms---paralitic-shellfish-poisoning-indicator.html", "27 Harmful Algal Blooms - Paralitic Shellfish Poisoning Indicator 27.1 Methods 27.2 References", " 27 Harmful Algal Blooms - Paralitic Shellfish Poisoning Indicator Description: Paralytic Shellfish Poisoning (PSP) toxins in the Gulf of Maine Found In: 2022 Indicator Catalog Indicator category: Published methods, Database pull Contributor(s): Yizhen Li, NOAA/NOS NCCOS Stressor Detection and Impacts Division, HAB Forecasting Branch, Silver Spring, MD. Ayman Mabrouk, NOAA/NOS NCCOS Marine Spatial Ecology Division, Silver Spring, MD. Data steward: Moe Nelson david.moe.nelson@noaa.gov Point of contact: Moe Nelson david.moe.nelson@noaa.gov Public availability statement: Source data are NOT publicly available. Data can be acquired upon request. 27.1 Methods 27.1.1 Data Sources Original data were collected by the State of 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. Data set was provided by Yizhen Li, NOAA/NOS NCCOS Stressor Detection and Impacts Division, HAB Forecasting Branch, Silver Spring, MD. Graphics and summaries were developed by Ayman Mabrouk, NOAA/NOS NCCOS Marine Spatial Ecology Division, Biogeography Branch, Silver Spring, MD. Original data were collected by the State of Maine, Department of Marine Resources, which samples and tests blue mussels (Mytilis edulis) in coastal shellfish areas for HAB biotoxins on a weekly basis from March through October. Maine Department of Marine Resources  Biotoxins in Maine Massachusetts Division of Marine Fisheries Massachusetts Division of Marine Fisheries  Shellfish classification areas New Hampshire Department of Environmental Services 27.1.2 Data Analysis Blue mussels (Mytilis edulis) were sampled at designated sites each year from March through October, and tissues were analyzed for presence and quantity of PSP toxins. Saxitoxin (STX) is a well-known PSP toxin, but a bloom can generate a range of related PSP toxins. Therefore, in many monitoring programs, toxins are reported as ug STX equivalents per 100 grams of shellfish tissue, where the quantity of each toxin present is normalized by its toxicity compared to STX (Chung 2010). Data include total number of samples at multiple locations collected in each calendar year (2005-2019), numbers of samples above and below the PSP threshold of 44 ug/100g, and percentage of samples above the threshold. Simple bar and line graphs are used to plot the values for each variable as time series, 2005-2019. Operational Harmful Algal Bloom forecast is served online at https://coastalscience.noaa.gov/research/stressor-impacts-mitigation/hab-forecasts/gulf-of-maine-alexandrium-catenella-predictive-models/ 27.1.3 Data Processing Code for processing salinity data can be found here. #Plotting The script used to develop the figure in the SOE Indicator Catalog can be found here. 27.2 References Chung, L.L. 2010. Measuring paralytic shellfish toxins in mussels from New Hampshire coastal waters using zwitterionic hydrophilic liquid chromatography/electrospray mass spectrometry. Masters Theses and Capstones. 539. https://scholars.unh.edu/thesis/539 Kleindinst, J.L., D.M. Anderson, D.J. McGillicuddy Gr., R.P. Stumpf, K.M. Fisher, D.A. Couture, J.M. Hickey, and C. Nash. 2014. Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management. Deep-Sea Research II 103:277-287. NOAA/NCCOS. 2021. Gulf of Maine Alexandrium catenella Predictive Models. Interactive website with operational Nowcast/Forecast simulation. https://coastalscience.noaa.gov/research/stressor-impacts-mitigation/hab-forecasts/gulf-of-maine-alexandrium-catenella-predictive-models/ "],["harbor-porpoise-and-gray-seal-bycatch.html", "28 Harbor Porpoise and Gray Seal Bycatch 28.1 Methods", " 28 Harbor Porpoise and Gray Seal Bycatch Description: Harbor Porpoise and Gray Seal Indicator Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018, 2019, 2021), State of the Ecosystem - Mid-Atlantic (2018, 2019, 2021) Indicator category: Synthesis of published information; Published methods Contributor(s): Christopher D. Orphandies Data steward: Chris Orphanides, chris.orphanides@noaa.gov Point of contact: Chris Orphanides, chris.orphanides@noaa.gov Public availability statement: Source data are available in public stock assessment reports (2018 report in-press). Derived data as shown in the 2018 SOE reports are available here 28.1 Methods 28.1.1 Data sources Reported harbor porpoise bycatch estimates and potential biological removal levels can be found in publicly available documents; detailed here. The most recent bycatch estimates for 2016 were taken from the 2018 stock assessment (in-press). More detailed documentation as to the methods employed can be found in NOAA Fisheries Northeast Fisheries Science Center (NEFSC) Center Reference Documents (CRDs) found on the NEFSC publications page. The document for the 2016 estimates (CRD 19-04) is available here. Additional methodological details are available for previous years estimates and are documented in numerous published CRDs: CRD 17-18, CRD-16-05, CRD 15-15, CRD 14-02, CRD 13-13, CRD 11-08, CRD 10-10, CRD 07-20, CRD 06-13, CRD 03-18, CRD 01-15, and CRD 99-17. 28.1.2 Data extraction Annual gillnet bycatch estimates are documented in a CRD (see sources above). These feed into the Stock Assessment Reports which report both the annual bycatch estimate and the mean 5-year estimate. The 5-year estimate is the one used for management purposes, so that is the one provided for the SOE plot. 28.1.3 Data analysis Bycatch estimates as found in stock assessment reports were plotted along with confidence intervals. The confidence intervals were calculated from published CVs assuming a normal distribution (\\(\\sigma = \\mu CV\\); \\(CI = \\bar{x} \\pm \\sigma * 1.96\\)). Data were analyzed and formatted for inclusion in the ecodata R package using the R code found here, Harbor Porpoise and Gray Seal. 28.1.4 Plotting Code used to plot data can be found here, Harbor Porpoise and Gray Seal. Figure 28.1: Estimated Harbor porpoise bycatch and the potential biological removal. Figure 28.2: Estimated Gray Seal bycatch and the potential biological removal. "],["atlantic-hms-pop-cpue.html", "29 Atlantic HMS POP CPUE 29.1 Methods", " 29 Atlantic HMS POP CPUE Description: CPUE from pelagic observer program (POP) observed hauls, presented as number of fish per haul, is provided for the northeast (shelf-wide) by year/species from 1992-2019. Indicator category: Database pull with analysis Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021+), State of the Ecosystem - Mid-Atlantic (2021+) Contributor(s): Tobey Curtis, Jennifer Cudney Data steward: Tobey Curtis, Jennifer Cudney Point of contact: Jennifer Cudney jennifer.cudney@noaa.gov Public availability statement: Source data are NOT publicly available. Pelagic observer data is considered confidential data, and must be screened to ensure that data meet requirements for rule of three at the set and vessel level before they can be distributed. 29.1 Methods 29.1.1 Data sources Data for this indicator were compiled by NOAA Southeast Fisheries Science Center, Larry Beerkircher. 29.1.2 Data analysis Data were pulled from NOAA SEFSC databases and summarized by year and species. 29.1.3 Data Processing Code used to process this data can be found on github - NOAA-EDAB/ecodata. 29.1.4 Plotting Code used to build the figure below can be found here. Figure 29.1: Species groupings based on list from Debbie Duarte - missing Boonethead, Atlantic Angel shark, Sixgill shark, sevengill shark, nurse shark, white shark, basking shark, lemon shark.. "],["atlantic-highly-migratory-species-stock-status.html", "30 Atlantic Highly Migratory Species Stock Status 30.1 Methods", " 30 Atlantic Highly Migratory Species Stock Status Description: Summary of the most recent stock assessment results for each assessed Atlantic HMS species. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2022), State of the Ecosystem - Mid-Atlantic (2022) Indicator category: Synthesis of published information Contributor(s): Jennifer Cudney, Ben Duffin, Dan Crear, Tobey Curtis Data steward: Jennifer Cudney, Jennifer.Cudney@noaa.gov Point of contact: Jennifer Cudney, Jennifer.Cudney@noaa.gov Public availability statement: Source data are publicly available. 30.1 Methods 30.1.1 Data sources Data shared were collected from Atlantic HMS SAFE Reports (see 2021 report, https://www.fisheries.noaa.gov/atlantic-highly-migratory-species/atlantic-highly-migratory-species-stock-assessment-and-fisheries-evaluation-reports), Fishery Stock Status Determinations webpage (https://www.fisheries.noaa.gov/national/population-assessments/fishery-stock-status-updates), SEDAR assessments (www.sedarweb.org), ICCAT assessments (https://www.iccat.int/en/assess.html). 30.1.2 Data analysis Stock status information is compiled annually from stock assessments completed by the International Commission for the Conservation of Atlantic Tunas (ICCAT) (tunas, sharks, swordfish) and the Southeast Data Assessment and Review (SEDAR) (Atlantic HMS sharks). Species with a range of uncertainty estimates for F/Fmsy and B/Bmsy and assessments completed very recently may not be included in Stock Smart queries. We selected the most precautionary metrics for Fyr/Fmsy (high-end) and Byr/Bmsy (low-end). Stock status information was plotted on a Kobe chart using modified code from the 2021 SOE Technical Documentation. Although Gulf of Mexico stock information is provided, we only plotted Atlantic stocks to maintain relevance. Atlantic blacknose shark was considered an outlier due to an Fyr/Fmsy = 22.53. The y-axis is not scaled to include this species in the Kobe plot, so it was added in the top left segment of the box with the Fyr/Fmsy. The grey box lists species with unknown F/Fmsy and/or B/Bmsy. The table below shows naming conventions used in the plot. Species_Abbreviation Common_Name ATL BET Atlantic bigeye ATL BET Atlantic bigeye ATL YFT atlantic yellowfin ATL YFT atlantic yellowfin NA ALB North Atlantic albacore NA ALB North Atlantic albacore NA SKJ West atlantic skipjack NA SKJ West atlantic skipjack NA SWO North Atlantic swordfish NA SWO North Atlantic swordfish SA SWO South Atlantic swordfish SA SWO South Atlantic swordfish BUM blue marlin BUM blue marlin WHX white marlin (and roundscale spearfish) WHX white marlin (and roundscale spearfish) WA SAI West Atlantic sailfish WA SAI West Atlantic sailfish NWA POR Northwest Atlantic porbeagle NWA POR Northwest Atlantic porbeagle NA BSH North Atlantic blue NA BSH North Atlantic blue NA SMA North Atlantic shortfin mako shark NA SMA North Atlantic shortfin mako shark SSB sandbar shark SSB sandbar shark ATL SBK Atlantic blacktip ATL SBK Atlantic blacktip DUS dusky DUS dusky SPL scalloped hammerhead SPL scalloped hammerhead ATL SAS Atlantic sharpnose shark - Atlantic stock ATL SAS Atlantic sharpnose shark - Atlantic stock ATL SBN Atlantic blacknose shark - Atlantic stock ATL SBN Atlantic blacknose shark - Atlantic stock SFT finetooth SFT finetooth ATL DGS Atlantic smooth dogfish ATL DGS Atlantic smooth dogfish 30.1.3 Data processing Code for processing Atlantic HMS Stock status data can be found here. 30.1.4 Plotting Code used to create the figure below can be here. Figure 30.1: Summary of single species status for HMS stocks; key to species names above. 30.1.5 References NOAA. 2021. 2020 Stock Assessment and Fishery Evaluation Report: Atlantic Highly Migratory Species. Atlantic Highly Migratory Species Management Division, Silver Spring, MD. https://www.fisheries.noaa.gov/atlantic-highly-migratory-species/atlantic-highly-migratory-species-stock-assessment-and-fisheries-evaluation-reports "],["highly-migratory-species-landings.html", "31 Highly Migratory Species Landings 31.1 Methods 31.2 Methods 2020", " 31 Highly Migratory Species Landings Description: Atlantic Highly Migratory Species Landings Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2020(Different Methods), 2021), State of the Ecosystem - Mid-Atlantic (2020(Different Methods), 2021) Indicator category: Database pull with analysis Contributor(s): Heather Baertlein, Jackie Wilson, George Silva Data steward: Kimberly Bastille Point of contact: Jennifer Cudney jennifer.cudney@noaa.gov Public availability statement: Source data are NOT publicly available 31.1 Methods 31.1.1 Data Sources Data from eDealer database (https://www.fisheries.noaa.gov/atlantic-highly-migratory-species/atlantic-highly-migratory-species-dealer-reporting) and Bluefin Tuna Dealer reports on SAFIS (https://www.accsp.org/what-we-do/safis/). The eDealer data were supplemented with ACCSP records, GulfFIN records, and vessel logbook catches for which no dealer reports were submitted. 31.1.2 Data Analysis Data, from 2015-2019, were processed for Fisheries of the United States and then aggregated by regions to avoid confidentiality issues. Data of Atlantic shark, swordfish, bigeye tuna, albacore tuna, yellowfin tuna and skipjack tuna were initially extracted from our eDealer database. Additional landings of these HMS not in eDealer were found in ACCSP, GulfFIN, and the SEFSC Atlantic HMS vessel logbook records. Bluefin tuna landings data from the Bluefin Tuna Dealer reports in SAFIS were also extracted and combined with the eDealer data for other HMS . Procedures of quality assurance were conducted. Duplicate records were removed from the data. This may occur from multiple submissions of reports by the same dealer. It may also occur when two or more dealers report the same landings in Packing situations. While most vessels immediately sell their catch to the dealer at their port of landing, some vessels sell their catch to a dealer(s) in another location. Transport to alternate locations requires processing of the fish to preserve quality. This processing activity is done by the dealer at the port of landing and is referred to as Packing. Differences in federal and state definitions of who is considered the dealer of the product, and thus ultimately responsible for submitting the landings report, often results in multiple reports being created for the same landings. These duplicate reports need to be accounted for when summarizing the data to reflect accurate landings. Therefore, searches for duplicate reports of the same landing were conducted and eliminated prior to summarizing the data for the Fisheries of the United States. All reported landings were converted to live weights using conversion ratios appropriate for the species/species group and reported grade of the product. Shark fins were not reported to live weight as these weights are included in the converted whole weight of the reported shark landing. States, where the landings occurred, were grouped into ecological production units (EPUs), as defined by GARFO staff. New England, or NE, includes Maine, New Hampshire and Massachusetts, as well as landings from Canada. The Mid-Atlantic Bight, or MAB, includes states from Rhode Island to North Carolina. Seven HMS Management Groups represent 26 highly migratory species in the dataset. HMS Management Groups may include a single species or a group of species. HMS groups include Bluefin Tuna, BAYS, Swordfish, Large Coastal Sharks, Small Coastal Sharks, Pelagic Sharks, Smoothhound Sharks. BAYS includes bigeye, albacore, yellowfin and skipjack tunas. Large Coastal Sharks includes blacktip, bull, great hammerhead, scalloped hammerhead, smooth hammerhead, lemon, nurse, sandbar, silky, spinner, and tiger sharks. Small Coastal Sharks includes Atlantic sharpnose, blacknose, bonnethead, finetooth sharks. Pelagic Sharks includes blue, porbeagle, shortfin mako, and thresher sharks. Smoothhound Sharks includes smooth dogfish shark. Price per pound was used to determine the ex-vessel value. For landings with prices per pound reported as N/A, 0, $0.01 or left blank, average prices were calculated for each species and state. Those averages replaced the missing values to determine landings revenue. Revenue from sales to the aquarium trade were also excluded to avoid extreme values associated with shipping live specimens. High migratory landings include 26 species of tunas, sharks and swordfish. Data were processed and analyzed using SAS and Microsoft Excel pivot tables. The count of dealers and vessels in each regional species/management group sum was used to determine if a sufficient number of records were available to make the data public or if it needed to be marked as confidential. Additionally, ratios of landings reported by dealers/fishermen were compared in each regional species/managment group sum to determine if any one entity cotnributed more than  of the total which would require it being marked as confidential. 31.1.3 Data Processing HMS landings data were formatted for inclusion in the ecodata R package using the R code found here. 31.1.4 Plotting The plot below was built using the code found here. Figure 31.1: Highly migratory species landings. 31.2 Methods 2020 31.2.1 Data sources Data from eDealer database (https://www.fisheries.noaa.gov/atlantic-highly-migratory-species/atlantic-highly-migratory-species-dealer-reporting) and Bluefin Tuna Dealer reports on SAFIS. The eDealer data were supplemented with GulfFIN records and vessel logbook catches for which no dealer reports were submitted. 31.2.2 Data extraction Data were processed for Fisheries of the United States and then aggregated by regions to avoid confidentiality issues. Data of Atlantic shark, swordfish, bigeye tuna, albacore tuna, yellowfin tuna and skipjack tuna were initially extracted from our eDealer database. Additional landings of these HMS not in eDealer were found in GulfFIN records. Bluefin tuna landings data from the Bluefin Tuna Dealer reports in SAFIS were also extracted and combined with the eDealer data for other HMS . Procedures of quality assurance were conducted. Duplicate records were removed from the data. This may occur from multiple submissions of reports by the same dealer. It may also occur when two or more dealers report the same landings in Packing situations. While most vessels immediately sell their catch to the dealer at their port of landing, some vessels sell their catch to a dealer(s) in another location. Transport to alternate locations requires processing of the fish to preserve quality. This processing activity is done by the dealer at the port of landing and is referred to as Packing. Differences in federal and state definitions of who is considered the dealer of the product, and thus ultimately responsible for submitting the landings report, often results in multiple reports being created for the same landings. These duplicate reports need to be accounted for when summarizing the data to reflect accurate landings. Therefore, searches for duplicate reports of the same landing were conducted and eliminated prior to summarizing the data for the Fisheries of the United States. Revenue from sales to the aquarium trade were also excluded to avoid extreme values associated with shipping live specimens. All reported landings were converted to live weights using conversion ratios appropriate for the species/species group and reported grade of the product. Shark fins were not reported to live weight as these weights are included in the converted whole weight of the reported shark landing. Price per pound was used to determine the ex-vessel value. For landings with prices per pound reported as N/A, 0, $0.01 or left blank, average prices were calculated for each species and state. Those averages replaced the missing values to determine landings revenue. The extract only includes species with more than $1,000 in landings in the region for that year to avoid issues with data confidentiality. Other species landed include: tiger sharks, porbeagle, bonnethead, blacknose, blue, lemon, silky and smooth hammerhead sharks. However, these are not reported because of low volume and resulting data confidentiality issues. 31.2.3 Data analysis High migratory landings include 19 species of tunas, sharks and swordfish (table @(tab:hms-spp)). Table 31.1: Species included in the highly migratory species landings reported in the SOE. Common.Name Scientific.Name Bluefin Tuna Thunnus thynnus Swordfish Xiphias gladius Bigeye Tuna Thunnus obesus Yellowfin Tuna Thunnus albacares Shortfin Mako Shark Isurus oxyrinchus Albacore Tuna Thunnus alalunga Smooth Dogfish Shark Mustelus canis Atlantic Sharpnose Shark Rhizoprionodon terraenovae Thresher Shark Alopias vulpinus Blacktip Shark Carcharhinus limbatus Spinner Shark Carcharhinus brevipinna Sandbar Shark Carcharhinus plumbeus Great Hammerhead Shark Sphyrna mokarran Finetooth Shark Aprionodon isodon Skipjack Tuna Katsuwonus pelamis Bull Shark Carcharhinus leucas Tiger Shark Galeocerdo cuvier Scalloped Hammerhead Shark Sphyrna lewini Shark fins NA Data were processed and analyzed using SAS and Microsoft Excel pivot tables. The count of records marked as confidential and the number of states represented in each regional species sum was used to determine if a sufficient number of records were available to make the data public or if it needed to be marked as confidential. "],["hudson-river-flow.html", "32 Hudson River Flow 32.1 Methods 32.2 References", " 32 Hudson River Flow Description: Mean annual flow of the Hudson River in cubic meters per second at the USGS gauge 01358000 at Green Island, New York. Found In: 2022 Indicator Catalog Indicator category: Contributor(s): Laura Gruenburg, Janet Nye, Kurt Heim Data steward: Laura Gruenburg laura.gruenburg@stonybrook.edu Point of contact: Laura Gruenburg laura.gruenburg@stonybrook.edu Public availability statement: Source data are publicly available 32.1 Methods 32.1.1 Data Sources River gauge data from USGS gauge 01358000 was obtained from USGS water data. 32.1.2 Data Analysis Mean annual flow rate was calculated by averaging all flow rate data for each year. Cubic feet per second were converted to cubic meters per second. Attached code shows this process in detail. A linear trend and a nonlinear GAM were calculated for the resulting annual mean flow rate time series. Attached code shows this process in detail. 32.1.3 Data Processing Code for processing salinity data can be found here. 32.1.4 Plotting Figure 32.1: Mean Annual flow of the Hudson River at USGS gauge 01358000 at Green Island, New York. 32.2 References "],["inshoresurvdat.html", "33 Inshore bottom trawl surveys 33.1 Methods", " 33 Inshore bottom trawl surveys Description: Inshore surveys include the Northeast Area Monitoring and Assessment Program (NEAMAP) survey, Massachusetts Division of Marine Fisheries Bottom Trawl Survey, and Maine/New Hampshire Inshore Trawl Survey. Indicator category: Database pull with analysis Found in: State of the Ecosystem - Mid-Atlantic (2019+), State of the Ecosystem - New England (2019+) Contributor(s): James Gartland, Matt Camisa, Rebecca Peters, Sean Lucey Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Points of contact: James Gartland (NEAMAP), jgartlan@vims.edu; Rebecca Peters (ME/NH survey), rebecca.j.peters@maine.gov; Sean Lucey (MA Inshore Survey), sean.lucey@noaa.gov Public availability statement: Data are available upon request. 33.1 Methods 33.1.1 Data Sources All inshore bottom trawl survey data sets were derived from raw survey data. NEAMAP source data are available for download here. More detailed information describing NEAMAP survey methods is available on the NEAMAP website. ME/NH inshore survey data are available upon request (see Points of Contact). Technical documentation for ME/NH survey methods and survey updates are made available through the Maine Department of Marine Resources. Data from the MA Inshore Bottom Trawl Survey are stored on local servers at the Northeast Fisheries Science Center (Woods Hole, MA), and are also available upon request. More information about the MA Inshore Bottom Trawl Survey is available here. 33.1.2 Data extraction Source data from the Massachusetts DMF Bottom Trawl Survey were extracted using this R script. 33.1.3 Data Processing The following R code was used to process inshore bottom trawl data into the ecodata R package. New England https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_inshore_survdat.R Massachusetts https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_mass_inshore_survey.R Mid-Atlantic (NEAMAP) https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_mab_inshore_survey.R 33.1.4 Data Analysis Biomass indices were provided as stratified mean biomass (kg tow-1) for all inshore surveys. Time series of stratified mean biomass were calculated for ME/NH and NEAMAP surveys through the following procedure: All species catch weights were summed for each tow and for each feeding guild category. The average weight per tow, associated variances and standard deviation for each survey, region, stratum, and feeding guild was calculated. Stratified mean biomass was then calculated as the sum of the weighted averages of the strata, where the weight of a given stratum was the proportion of the survey area accounted for by that stratum. Stratified mean biomass was also calculated for the MA Inshore Bottom Trawl Survey. These calculations followed those used to find stratified mean biomass by feeding guild in the NEFSC Bottom Trawl Survey and are described in greater detail here. The R code used to derive the stratified mean biomass indices for MA Inshore time series is provided below. R code used for analysis can be found here. 33.1.5 Plotting 33.1.5.1 NEAMAP Figure 33.1: Spring (left) and fall (right) surveyed biomass in the Mid-Atlantic Bight. Data from the NEFSC Bottom Trawl Survey are shown in black, with NEAMAP shown in red. 33.1.5.2 Massachusetts Figure 33.2: Spring (left) and fall (right) surveyed biomass from the MA state inshore bottom trawl survey. 33.1.5.3 Maine-New Hampshire Figure 33.3: Spring (left) and fall(right) surveyed biomass from the ME/NH state inshore bottom trawl survey. "],["long-term-sea-surface-temperature.html", "34 Long-term Sea Surface Temperature 34.1 Methods", " 34 Long-term Sea Surface Temperature Description: Long-term sea-surface temperatures Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017+), State of the Ecosystem - Mid-Atlantic (2017+) Indicator category: Database pull Contributor(s): Kevin Friedland Data steward: Kevin Friedland, kevin.friedland@noaa.gov Point of contact: Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are available here. 34.1 Methods Data for long-term sea-surface temperatures were derived from the Noational Oceanographic and Atmospheric Administration (NOAA) extended reconstructed sea surface temperature data set (ERSST V5). The ERSST V5 dataset is parsed into 2° x 2° gridded bins between 1854-present with monthly temporal resolution. Data were interpolated in regions with limited spatial coverage, and heavily damped during the period between 1854-1880 when collection was inconsistent (Huang et al. 2017a, 2017b). For this analysis, 19 bins were selected that encompassed the Northeast US Continental Shelf region (see Kevin D. Friedland and Hare 2007). 34.1.1 Data sources This indicator is derived from the NOAA ERSST V5 dataset (Huang et al. 2017a). 34.1.2 Data extraction Table 34.1: Coordinates used in NOAA ERSST V5 data extraction. Longitude Latitude -74 40 -74 38 -72 40 -70 44 -70 42 -70 40 -68 44 -68 42 R code used in extracting time series of long-term SST data can be found here. 34.1.3 Data Processing Data were formatted for inclusion in the ecodata R package with the R code found here. 34.1.4 Plotting The plot below was built using the code found here. Figure 34.1: Long-term sea surface temperatures on the Northeast Continental Shelf. References "],["mid-atlantic-harmful-algal-bloom-indicator.html", "35 Mid-Atlantic Harmful Algal Bloom Indicator 35.1 Methods", " 35 Mid-Atlantic Harmful Algal Bloom Indicator Description: An aggregation of reported algal bloom data in Chesapeake Bay between 2007-2017. Found in: State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull Contributor(s): Sean Hardison, Virginia Department of Health Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Source data for this indicator are available here. Processed time series can be found here. 35.1 Methods We presented two indicator time series for reports of algal blooms in the southern portion of Chesapeake Bay between 2007-2017. The first indicator was observations of algal blooms above 5000 cell ml-1. This threshold was developed by the Virginia Department of Health (VDH) for Microcystis spp. algal blooms based on World Health Organization guidelines (Organization 2003; Health 2011). VDH also uses this same threshold for other algal species blooms in Virginia waters. When cell concentrations are above 5000 cell ml-1, VDH recommends initiation of biweekly water sampling and that relevant local agencies be notified of the elevated cell concentrations. The second indicator we reported, blooms of Cochlodinium polykrikoides at cell concentrations >300 cell ml-1, was chosen due to reports of high ichthyotoxicity seen at these levels. Tang and Gobler (2009) showed that fish exposed to cultured C. polykrikoides at densities as low 330 cells ml-1 saw 100% mortality within 1 hour, which if often far less than C. polykrikoides cell concentrations seen in the field. Algal bloom data were not available for 2015 nor 2010. The algal bloom information presented here are a synthesis of reported events, and has been updated to include data not presented in the 2018 State of the Ecosystem Report. 35.1.1 Data sources Source data were obtained from VDH. Sampling, identification, and bloom characterization was completed by the VDH, Phytoplankton Analysis Laboratory at Old Dominion University (ODU), Reece Lab at the Virginia Institute of Marine Science (VIMS), and Virginia Department of Environmental Quality. Problem algal species were targeted for identification via light microscopy followed by standard or quantitative PCR assays and/or enzyme-linked immunosorbent assay (ELISA). Reports specifying full methodologies from ODU, VIMS, and VDH source data are available upon request. 35.1.2 Data extraction Data were extracted from a series of spreadsheets provided by the VDH. We quantified the number of algal blooms in each year reaching target cell density thresholds in the southern Chesapeake Bay. R code used in extracting harmful algal bloom data can be found here. 35.1.3 Data analysis No data analysis steps took place for this indicator. References "],["new-england-harmful-algal-bloom-indicator.html", "36 New England Harmful Algal Bloom Indicator 36.1 Methods", " 36 New England Harmful Algal Bloom Indicator Description: Regional incidence of shellfish bed closures due to presence of toxins associated with harmful algae. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018) Indicator Category: Synthesis of published information Contributor(s): Dave Kulis, Donald M Anderson, Sean Hardison Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Data are publicly available (see Data Sources below). 36.1 Methods The New England Harmful Algal Bloom (HAB) indicator is a synthesis of shellfish bed closures related to the presence of HAB-associated toxins above threshold levels from 2007-2016 (Figure ??). Standard detection methods were used to identify the presence of toxins associated with Amnesic Shellfish Poisoning (ASP), Paralytic Shellfish Poisoning (PSP), and Diarrhetic Shellfish Poisoning (DSP) by state and federal laboratories. 36.1.0.1 Paralytic Shellfish Poisoning The most common cause of shellfish bed closures in New England is the presence of paralytic shellfish toxins (PSTs) produced by the dinoflagellate Alexandrium catenella. All New England states except Maine relied on the Association of Official Analytical Chemists (AOAC) approved mouse bioassay method to detect PSTs in shellfish during the 2007-2016 period reported here (International 2005). In Maine, PST detection methods were updated in May 2014 when the state adopted the hydrophilic interaction liquid chromatography (HILIC) UPLC-MS/MS protocol (Boundy et al. 2015) in concordance with National Shellfish Sanitation Program (NSSP) requirements. Prior to this, the primary method used to detect PST in Maine was with the mouse bioassay. 36.1.0.2 Amnesic Shellfish Poisoning Amnesic shellfish poisoning (ASP) is caused by the toxin domoic acid (DA), which is produced by several phytoplankton species belonging to the genus Pseudo-nitzchia. In New England, a UV-HPLC method (Quilliam, Xie, and Hardstaff 1995), which specifies a HPLC-UV protocol, is used for ASP detection. 36.1.0.3 Diarrhetic Shellfish Poisoning Diarrhetic Shellfish Poisoning (DSP) is rare in New England waters, but the presence of the DSP-associated okadaic acid (OA) in mussels was confirmed in Massachusetts in 2015 (J. Deeds, personal communication, July 7, 2018). Preliminary testing for OA in Massachusetts utilized the commercially available Protein Phosphatase Inhibition Assay (PPIA) and these results are confirmed through LC-MS/MS when necessary (Smienk et al. 2012; Stutts and Deeds 2017). 36.1.1 Data sources Data used in this indicator were drawn from the 2017 Report on the ICES-IOC Working Group on Harmful Algal Bloom Dynamics (WGHABD). The report and data are available here. Closure information was collated from information provided by the following organizations: Table 36.1: Shellfish closure information providers. State Source Organization Maine Maine Department of Marine Resources New Hampshire New Hampshire Department of Environmental Services Massachusetts Massachusetts Division of Marine Fisheries Rhode Island Rhode Island Department of Environmental Management Connecticut Connecticut Department of Agriculture 36.1.2 Data extraction Data were extracted from the original report visually and accuracy confirmed with report authors. 36.1.3 Data analysis No data analysis steps took place for this indicator. 36.1.4 Plotting The script used to develop the figure in the SOE report can be found here. References "],["marine-heatwave.html", "37 Marine Heatwave 37.1 Methods", " 37 Marine Heatwave Description: Marine Heatwave Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2020+), Mid-Atlantic (2020+) Indicator category: Published methods, Database pull with analysis Contributor(s): Vincent Saba Data steward: Kimberly Bastille kimberly.bastille@noaa.gov Point of contact: Vincent Saba vincent.saba@noaa.gov Public availability statement: 37.1 Methods Marine heatwave analysis for Georges Bank, Gulf of Maine, and the Middle Atlantic Bight according to the definition in Hobday et al. (2016). 37.1.1 Data sources NOAA high-res OISST (daily, 25-km, 1982-2019) https://www.esrl.noaa.gov/psd/cgi-bin/db_search/DBListFiles.pl?did=132&tid=79458&vid=2423 37.1.2 Data extraction Each yearly file (global) was downloaded, concatenated into a single netcdf file using nco (Unix), and then cropped to the USNES region using Ferret. Each EPUs time-series of SST was averaged using .shp file boundaries for the MAB, GB, and GOM (also done in Ferret) and saved to the three .csv files. 37.1.3 Data analysis The marine heatwave metrics Maximum Intensity [deg. C] and Cumulative Intensity [deg. C x days] are calculated using NOAA OISST daily sea surface temperature data (25-km resolution) from January 1982 to December 2019. The heatwaves are calculated based on the algorithms in Hobday et al. 2016 and by using a climatology of 1982-2011. These metrics were run R using https://robwschlegel.github.io/heatwaveR/ 37.1.4 Data processing Marine Heatwave data were formatted for inclusion in the ecodata R package using this R code. 37.1.5 Plotting Code used for the plots below can be found here. Figure 37.1: Cumulative and maximum marine heatwave in the Mid-Atlantic Figure 37.2: Mid-Atlantic Marine Heatwave events in 2021. References "],["verified-records-of-southern-kingfish.html", "38 Verified Records of Southern Kingfish 38.1 Methods", " 38 Verified Records of Southern Kingfish Description: Fisheries Observer Data  Verified Records of Southern Kingfish Found in: State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull Contributor(s): Debra Duarte, Loren Kellogg Data steward: Gina Shield, gina.shield@noaa.gov Point of contact: Gina Shield, gina.shield@noaa.gov Public availability statement: Due to PII concerns data for this indicator are not publicly available. 38.1 Methods 38.1.1 Data sources The Fisheries Sampling Branch deploys observers on commercial fisheries trips from Maine to North Carolina. On observed tows, observers must fully document all kept and discarded species encountered. Observers must comply with a Species Verification Program (SVP), which requires photo or sample submissions of high priority species at least once per quarter. Photos and samples submitted for verification are identified independently by at least two reviewers. The derived data presented in the Mid-Atlantic State of the Ecosystem report for southern kingfish include records verified by the SVP program only. The occurrence of southern kingfish in SVP records were chosen for inclusion in the report due to the recent increases of the species in SVP observer records since 2010. These data are not a complete list from the New England Fisheries Observer Program (NEFOP). Southern Kingfish are less common than Northern Kingfish in observer data and are possibly misidentified so we have initially included records here only when a specimen record was submitted to and verified through the SVP (see Data extraction). 38.1.2 Data extraction SQL query for observer data extraction can be found here. 38.1.3 Data analysis Time series were summed by year and plotted, and mapped data for individual records were plotted according to the location where gear was hauled. As coordinate data were not always available for each record, the map does not include all occurrences of southern kingfish, but was included for spatial context. 38.1.4 Plotting Code used to produce the plot below can be found here. Figure 38.1: Verified records of Southern Kingfish occurrence in the Mid-Atlantic. "],["hab-occu.html", "39 Habitat Occupancy Models 39.1 Methods", " 39 Habitat Occupancy Models Description: Habitat Occupancy Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Database pull with analysis; Extensive analysis; not yet published; Published methods Contributor(s): Kevin Friedland Data steward: Kevin Friedland, kevin.friedland@noaa.gov Point of contact: Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are available upon request (see Survdat, CHL/PP, and Data Sources below for more information). Model-derived time series are available here. 39.1 Methods Habitat area with a probability of occupancy greater than 0.5 was modeled for many species throughout the Northeast Large Marine Ecosystem (NE-LME) using random forest decision tree models. 39.1.1 Data sources Models were parameterized using a suite of static and dynamic predictor variables, with occurrence and catch per unit effort (CPUE) of species from spring and fall Northeast Fisheries Science Center (NEFSC) bottom trawl surveys (BTS) serving as response variables. Sources of variables used in the analyses are described below. 39.1.1.1 Station depth The NEFSC BTS data included depth observations made concurrently with trawls at each station. Station depth was a static variable for these analyses. 39.1.1.2 Ocean temperature and salinity Sea surface and bottom water temperature and salinity measurements were included as dynamic predictor variables in the model, and were collected using Conductivity/Temperature/Depth (CTD) instruments. Ocean temperature and salinity measurements had the highest temporal coverage during the spring (February-April) and fall (September-November) months. Station salinity data were available between 1992-2016. 39.1.1.3 Habitat descriptors A variety of benthic habitat descriptors were incorporated as predictor variables in occupancy models (Table 39.1). The majority of these parameters are based on depth (e.g. BPI, VRM, Prcury, rugosity, seabedforms, slp, and slpslp). The vorticity variable is based on current estimates, and the variable soft_sed based on sediment grain size. Table 39.1: Habitat descriptors used in model parameterization. Variables Notes References Namera_vrm Vector Ruggedness Measure (VRM) measures terrain ruggedness as the variation in three-dimensional orientation of grid cells within a neighborhood based on The Nature Conservancy Northwest Atlantic Marine Ecoregional Assessment (NAMERA) data. Hobson (1972); Sappington, Longshore, and Thompson (2007) Prcurv (2 km, 10 km, and 20 km) Benthic profile curvature at 2km, 10km and 20 km spatial scales was derived from depth data. Winship et al. (2018) Rugosity A measure of small-scale variations of amplitude in the height of a surface, the ratio of the real to the geometric surface area. Friedman et al. (2012) seabedforms Seabed topography as measured by a combination of seabed position and slope. http://www.northeastoceandata.org/ Slp (2 km, 10 km, and 20 km) Benthic slope at 2km, 10km and 20km spatial scales. Winship et al. (2018) Slpslp (2 km, 10 km, and 20 km) Benthic slope of slope at 2km, 10km and 20km spatial scales Winship et al. (2018) soft_sed Soft-sediments is based on grain size distribution from the USGS usSeabed: Atlantic coast offshore surficial sediment data. http://www.northeastoceandata.org/ Vort (fall - fa; spring - sp; summer - su; winter - wi) Benthic current vorticity at a 1/6 degree (approx. 19 km) spatial scale. Kinlan et al. (2016) 39.1.1.4 Zooplankton Zooplankton data are acquired through the NEFSC Ecosystem Monitoring Program (EcoMon). For more information regarding the collection process for these data, see Kane (2007), Kane (2011), and Morse et al. (2017). The bio-volume of the 18 most abundant zooplankton taxa were considered as potential predictor variables. 39.1.1.5 Remote sensing data Both chlorophyll concentration and sea surface temperature (SST) from remote sensing sources were incorporated as static predictor variables in the model. During the period of 1997-2016, chlorophyll concentrations were derived from observations made by the Sea-viewing Wide Field of View Sensor (SeaWIFS), Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua), Medium Resolution Imaging Spectrometer (MERIS), and Visible and Infrared Imaging/Radiometer Suite (VIIRS). 39.1.2 Data processing 39.1.2.1 Zooplankton Missing values in the EcoMon time series were addressed by summing data over five-year time steps for each seasonal time frame and interpolating a complete field using ordinary kriging. Missing values necessitated interpolation for spring data in 1989, 1990, 1991, and 1994. The same was true of the fall data for 1989, 1990, and 1992. 39.1.2.2 Remote sensing data An overlapping time series of observations from the four sensors listed above was created using a bio-optical model inversion algorithm (Maritorena et al. 2010). Monthly SST data were derived from MODIS-Terra sensor data (available here). 39.1.2.3 Ocean temperature and salinity Date of collection corrections for ocean temperature data were developed using linear regressions for the spring and fall time frames; standardizing to collection dates of April 3 and October 11 for spring and fall. No correction was performed for salinity data. Annual data for ocean temperature and salinity were combined with climatology by season through an optimal interpolation approach. Specifically, mean bottom temperature or salinity was calculated by year and season on a 0.5° grid across the ecosystem, and data from grid cells with >80% temporal coverage were used to calculate a final seasonal mean. Annual seasonal means were then used to calculate combined anomalies for seasonal surface and bottom climatologies. An annual field was then estimated using raw data observations for a year, season, and depth using universal kriging (Hiemstra et al. 2008), with depth included as a covariate (on a standard 0.1° grid). This field was then combined with the climatology anomaly field and adjusted by the annual mean using the variance field from kriging as the basis for a weighted mean between the two. The variance field was divided into quartiles with the lowest quartile assigned a weighting of 4:1 between the annual and climatology values. The optimally interpolated field at these locations was therefore skewed towards the annual data, reflecting their proximity to actual data locations and associated low kriging variance. The highest kriging variance quartile (1:1) reflected less information from the annual field and more from the climatology. 39.1.3 Data analysis 39.1.3.1 Occupancy models Prior to fitting the occupancy models, predictor variables were tested for multi-collinearity and removed if found to be correlated. The final model variables were then chosen utilizing a model selection process as shown by Murphy, Evans, and Storfer (2010) and implemented with the R package rfUtilities (Evans and Murphy 2018). Occupancy models were then fit as two-factor classification models (absence as 0 and presence as 1) using the randomForest R package (Liaw and Wiener 2002). 39.1.3.2 Selection criteria and variable importance The irr R package (Gamer, Lemon, and Singh 2012) was used to calculate Area Under the ROC Curve (AUC) and Cohens Kappa for assessing accuracy of occupancy habitat models. Variable importance was assessed by plotting the occurrence of a variable as a root variable versus the mean minimum node depth for the variable (Paluszynska and Biecek 2017), as well as by plotting the Gini index decrease versus accuracy decrease. 39.1.4 Plotting Figure 39.1: Summer flounder spring (A) and fall (B) occupancy habitat area in the Northeast Large Marine Ecosystem. References "],["ocean-acidification.html", "40 Ocean Acidification 40.1 Methods", " 40 Ocean Acidification Description: Maps of regional carbonate chemistry Indicator category: Synthesis of published information or openly accessible datasets Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021+); State of the Ecosystem - Mid-Atlantic Bight (2021+) Contributor(s): Grace Saba, Lori Garzio, Charles Flagg, Neal Pettigrew, Chris Melrose Data steward: Grace Saba saba@marine.rutgers.edu Point of contact: Grace Saba saba@marine.rutgers.edu Public availability statement: Source data is available to the public (see Data Sources). 40.1 Methods The New England Fishery Management Council (NEFMC) and Mid-Atlantic Fishery Management Council (MAFMC) have recently requested regional Ocean Acidification (OA) information in the State of the Ecosystem reports. The work included in the State of the Ecosystem 2021 report, seasonal dynamics of pH in shelf waters in the Mid-Atlantic, was synthesized from Wright-Fairbanks et al. (2020). The maps included in the State of the Ecosystem 2022 reports include a plot of bottom pH in summer over the entire U.S. Northeast Shelf (2007-present), and glider-based pH profiles during summer 2021 in both the Mid-Atlantic and the Gulf of Maine. These plots were developed using openly accessible, quality-controlled data from vessel-based discrete samples and glider-based measurements of pH (see Data Sources). 40.1.1 Data sources Glider-based pH observations began in the southern Mid-Atlantic Bight region in May 2018 (Saba et al. 2019), and seasonal glider pH missions thereafter began in February 2019 (Wright-Fairbanks et al. 2020; although no deployments occurred in 2020 as a result of the COVID pandemic). The glider pH observation program expanded spatially, with additional deployments in the northern MAB (New York Bight) and the Gulf of Maine, starting in February 2021. A typical glider mission runs for about 4 weeks, covers 500 km, and collects data though the full water column. Full-resolution delayed-mode glider datasets containing raw pH voltages can be found on RUCOOLs Glider ERDDAP Server. Fully-processed and time-shifted pH glider datasets can be found here. Vessel-based discrete pH data were mined from the Coastal Ocean Data Analysis Product in North America, version v2021 (CODAP-NA; Jiang et al. 2021). This data product synthesizes two decades of quality-controlled inorganic carbon system parameters (including pH, total alkalinity, dissolved inorganic carbon) along with other physical and chemical parameters (temperature, salinity, dissolved oxygen, nutrients) collected from the North American continental shelves. Additionally, two recent vessel-based datasets that were not included in CODAP-NA (Jiang et al. 2021) were included in this synthesis. These datasets were collected during more recent NOAA NEFSC Ecosystem Monitoring (EcoMon) surveys (June 2019, Cruise ID HB1902; August 2019, Cruise ID GU1902) and include quality-controlled spectrophotometric-based pH measurements on discrete water samples. Data can be requested from Chris Melrose (chris.melrose@noaa.gov). 40.1.2 Data extraction Glider data were processed and quality-controlled by software technician Lori Garzio at Rutgers University. CODAP-NA data were accessed and downloaded on October 14, 2021. 40.1.3 Data processing For processing and quality-control procedures of glider-based data, see Wright-Fairbanks et al. (2020). Glider data used in this synthesis were limited to summer only (June - August). Data from CODAP-NA were filtered temporally to include only those collected during summer months (June-August) and were spatially limited to the U.S. Northeast Shelf. The resulting datasets included those from major vessel-based campaigns (East Coast Ocean Acidification, ECOA I and II cruises 2015 and 2018; The Gulf of Mexico and East Coast Carbon cruises, GOMECC 2007 and 2012; EcoMon 2012-2013, 2015-2019). Three maps were constructed: 1) A plot of bottom pH in summer over the entire U.S. Northeast Shelf (all available data from 2007-present and includes both glider-based measurements and vessel-based discrete pH samples); 2) pH profiles from two gliders deployed in the Mid-Atlantic during summer 2021; and 3) Glider-based pH profiles from a deployment during summer in Gulf of Maine. For the plot of U.S. Northeast Shelf, bottom pH was defined as the median of the measurements within the deepest 1m of a glider profile or, for vessel-based measurements, the deepest measurement of a vertical CTD/Rosette cast where water samples were collected, for profiles deeper than 10m. In order to validate whether the deepest depth was at or near the bottom, the sampling depth was compared to water column depth (when provided) or water depths extracted from a GEBCO bathymetry grid based on the sample collection coordinates. Any glider profiles/vessel-based casts with the deepest measurement shallower than the bottom 20% of total water column depth were removed. This allowed for a sliding scale instead of providing a strict cut off (e.g., 1 m above the bottom). 40.1.4 Plotting Code for data manipulation and plotting can be found here: https://github.com/lgarzio/cinar-soe. Figure 40.1: Bottom pH (summer only: June-August) on the U.S. Northeast Shelf plotted from available quality-controlled vessel- and glider-based datasets from 2007-present. Right panel: Summer 2021 glider-based pH observations on the Mid-Atlantic Bight shelf. North track glider mission (data provider: Charles Flagg, Stony Brook University) ran from 07/20/2021 to 08/20/2021. South track glider mission (data provider: Grace Saba, Rutgers University) ran from 07/16/2021 to 08/20/2021. Figure 40.2: Bottom pH (summer only: June-August) on the U.S. Northeast Shelf plotted from available quality-controlled vessel- and glider-based datasets from 2007-present. Right panel: Summer 2021 glider-based pH observations in the Gulf of Maine (data provider: Neal Pettigrew, University of Maine). Glider mission ran from 06/30/2021 to 07/21/2021. 40.1.5 Resources Humphreys, M. P., Gregor, L., Pierrot, D., van Heuven, S. M. A. C., Lewis, E. R., and Wallace, D. W. R. (2020). PyCO2SYS: marine carbonate system calculations in Python. Zenodo. doi:10.5281/zenodo.3744275. Jiang, L.-Q., Feely, R. A., Wanninkhof, R., Greeley, D., Barbero, L., Alin, S., Carter, B. R., Pierrot, D., Featherstone, C., Hooper, J., Melrose, C., Monacci, N., Sharp, J. D., Shellito, S., Xu, Y.-Y., Kozyr, A., Byrne, R. H., Cai, W.-J., Cross, J., Johnson, G. C., Hales, B., Langdon, C., Mathis, J., Salisbury, J., and Townsend, D. W.: Coastal Ocean Data Analysis Product in North America (CODAP-NA)  an internally consistent data product for discrete inorganic carbon, oxygen, and nutrients on the North American ocean margins, Earth Syst. Sci. Data, 13, 27772799, https://doi.org/10.5194/essd-13-2777-2021, 2021. Lewis, E. and Wallace, D. W. R. (1998) Program Developed for CO2 System Calculations, ORNL/CDIAC-105, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., Oak Ridge, Tenn., 38 pp., https://salish-sea.pnnl.gov/media/ORNL-CDIAC-105.pdf. Saba, G.K., Wright-Fairbanks, E., Chen, B., Cai, W.-J., Barnard, A.H., Jones, C.P., Branham, C.W., Wang, K., Miles, T. 2019. The development and validation of a profiling glider Deep ISFET pH sensor for high resolution coastal ocean acidification monitoring. Frontiers in Marine Science 6: 664, https://doi.org/10.3389/fmars.2019.00664. Wright-Fairbanks, Elizabeth K., Travis N. Miles, Wei-Jun Cai, Baoshan Chen, and Grace K. Saba. 2020. Autonomous Observation of Seasonal Carbonate Chemistry Dynamics in the Mid-Atlantic Bight. Journal of Geophysical Research: Oceans 125 (11): e2020JC016505. https://doi.org/https://doi.org/10.1029/2020JC016505. "],["phytoplankton-size-class.html", "41 Phytoplankton Size Class 41.1 Methods", " 41 Phytoplankton Size Class Description: Proportion of phytoplankton size class Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021+), State of the Ecosystem - Mid-Atlantic (2021+) Indicator category: Database pull; Database pull with analysis; Published methods Contributor(s): Kim Hyde, Kyle Turner, Colleen Mouw, Audrey Ciochetto, Ryan Morse Data steward: Kim Hyde, kimberly.hyde@noaa.gov Point of contact: Kim Hyde, kimberly.hyde@noaa.gov Public availability statement: Source data used in these analyses are publicly available. 41.1 Methods Daily Level 3 mapped (4km resolution, sinusoidally projected) satellite chlorophyll data were obtained from the European Space Agencys Ocean Colour Climate Change Initiative project. Sea Surface Temperature (SST) data included the 4 km nighttime NOAA Advanced Very High Resolution Radiometer (AVHRR) Pathfinder (Casey et al. (2010); AVHRR Pathfinder Version 5.3 Level 3 Collated (L3c) Global 4km Sea Surface Temperature for 1981-Present. (2018)) and the Group for High Resolution Sea Surface Temperature (GHRSST) Multiscale Ultrahigh Resolution (MUR, version 4.1) Level 4 (Chin, Vazquez-Cuervo, and Armstrong (2017b); GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (V4.1) (2015)) data. Prior to June 2002, AVHRR Pathfinder data were used as the SST source and MUR SST in subsequent years. 41.1.1 Data sources The global chlorophyll data were subset to the U.S. East Coast (SW longitude=-82.5, SW latitude=22.5, NE longitude=-51.5, NE latitude=48.5) and stored in a nearly equal-area, integerized sinusoidal grid. The global SST data were also subset to the same East Coast region and remapped to the same sinusoidal grid. 41.1.2 Data analysis Phytoplankton size classes were calculated according to Turner (2020) (in prep). The regionally tuned abundance-based model is based on the three-component model of Brewin et al. (2010) that varies as a function of SST (Brewin et al. (2017), Moore and Brown (2020)). The model uses a look-up table with parameters indexed by SST, developed using a local data set of HPLC diagnostic pigment-derived phytoplankton size fractions matched with coincident satellite SST. Statistics, including the arithmetic mean, standard deviation, and coefficient of variation were calculated at weekly, monthly, and annual time steps and for several climatological periods. Annual statistics used the monthly means as inputs to avoid a summer time bias when more data is available due to reduced cloud cover. The daily, weekly, monthly and annual climatological statistics include the entire time series for each specified period. For example, the climatological January uses the monthly mean from each January in the time series and the climatological annual uses the annual mean from each year. The ecological production unit (EPU) shapefile that excludes the estuaries was used to spatially extract all data location within an ecoregion from the statistic and anomaly files. The median values, which are equivalent to the geometric mean, were used for the CHL and PP data. 41.1.3 Plotting Code for plotting Georges Bank and Gulf of Maine bottom temperature time series can be found here. Figure 41.1: Mid-Atlantic phytoplankton size class. 41.1.4 Resources References "],["plankton-diversity.html", "42 Plankton Diversity 42.1 Methods", " 42 Plankton Diversity Description: NOAA NEFSC Oceans and Climate branch public ichthyoplankton dataset Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021), State of the Ecosystem - Mid-Atlantic (2021) Indicator category: Database pull with analysis Contributor(s): Harvey J. Walsh Data steward: Harvey Walsh, harvey.walsh@noaa.gov Point of contact: Harvey Walsh, harvey.walsh@noaa.gov Public availability statement: Source data are available to the public here. Derived data for this indicator are available here. 42.1 Methods Data from the NOAA Northeast Fisheries Science Center (NEFSC) Oceans and Climate branch (OCB) public dataset were used to examine changes in diversity of abundance among 45 ichthyoplankton taxa. The 45 taxa were established (Walsh et al. 2015), and include the most abundant taxa from the 1970s to present that represent consistency in the identification of larvae. 42.1.1 Data sources Multi-species plankton surveys cover the entire Northeast US shelf from Cape Hatteras, North Carolina, to Cape Sable, Nova Scotia, four to six times per year. A random-stratified design based on the NEFSC bottom trawl survey design (Azarovitz 1981) is used to collect samples from 47 strata. The number of strata is lower than the trawl survey as many of the narrow inshore and shelf-break strata are combined in the EcoMon design. The area encompassed by each stratum determined the number of samples in each stratum. Samples were collected both day and night using a 61 cm bongo net. Net tow speed was 1.5 knots and maximum sample depth was 200 m. Double oblique tows were a minimum of 5 mintues in duration, and fished from the surface to within 5 m of the seabed or to a maximum depth of 200 m. The volume filtered of all collections was measured with mechanical flowmeters mounted across the mouth of each net. Processing of most samples was conducted at the Morski Instytut Rybacki (MIR) in Szczecin, Poland; the remaining samples were processed at the NEFSC or the Atlantic Reference Center, St Andrews, Canada. Larvae were identified to the lowest possible taxa and enumerated for each sample. Taxon abundance for each station was standardized to number under 10 m-2 sea surface. 42.1.2 Data extraction Data retrieved from NOAA NEFSC Oceans and Climate branch public dataset (Filename: EcoMon_Plankton_Data_v3_0.xlsx, File Date: 10/20/2016). 42.1.3 Data analysis All detailed data processing steps are not currently included in this document, but general steps are outlined. Data were grouped into seasons: spring = February, March, April and fall = September, October, November. Stratified weighted mean abundance was calculated for each taxon for each year and season across all plankton strata (n = 47) for 17 years (1999 to 2015). Shannon Diversity Index and count of positive taxon was calculated for each season and year. MATLAB code used to calculate diversity indices can be found using this link. 42.1.4 Data processing Forage Anomaly data sets were formatted for inclusion in the ecodata R package using the R code found here. Ichthyoplankton diversity data sets were formatted for inclusion in the ecodata R package using the R code found here. 42.1.5 Plotting Code used to plot forage anomaly can be found here. Figure 42.1: Forage Anomaly in the Mid-Atlantic Bight. Code used to plot ichthyoplankton diversity can be found here. Figure 42.2: Ichthyoplankton diversity in the Mid-Atlantic Bight. References "],["fish-productivity-indicator.html", "43 Fish Productivity Indicator 43.1 Methods", " 43 Fish Productivity Indicator Description: Groundfish productivity estimated as the ratio of small fish to large fish Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2020), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019, 2020) Indicator category: Database pull with analysis; Published methods Contributor(s): Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Source data are available upon request. 43.1 Methods 43.1.1 Data sources Survey data from the Northeast Fisheries Science Center (NEFSC) trawl database. These data in their derived form are available through Survdat. 43.1.2 Data extraction Data were extracted from Survdat. 43.1.3 Data analysis We defined size thresholds separating small and large fish for each species based on the 20th percentile of the length distribution across all years. This threshold was then used to calculate a small and large fish index (numbers below and above the threshold, respectively) each year. Although the length percentile corresponding to age-1 fish will vary with species, we use the 20th percentile as an approximation. Biomass was calculated using lengthweight relationships directly from the survey data. Following Wigley, McBride, and McHugh (2003), the length-weight relationship was modeled as \\[\\ln W = \\ln a + b \\ln L\\] where \\(W\\) is weight (kg), \\(L\\) is length (cm), and \\(a\\) and \\(b\\) are parameters fit via linear regression. The ratio of small fish numbers of the following year to larger fish biomass in the current year was used as the index of recruitment success. The fall and spring recruitment success anomalies were averaged to provide an annual index of recruitment success. Further details of methods described in Perretti et al. (2017a). 43.1.4 Data processing Productivity data were formatted for inclusion in the ecodata R package using the R code found here. 43.1.5 Plotting Figure 43.1: Groundfish productivity across all stocks in the Mid-Atlantic Bight. References "],["protected-species-hotspots.html", "44 Protected Species Hotspots 44.1 Methods", " 44 Protected Species Hotspots Description: Integrated persistent annual hotspots derived from at-sea observations of seabirds, cetaceans and sea turtles collected on systematic ship and aerial surveys Found in: State of the Ecosystem - Mid-Atlantic (2022), State of the Ecosystem - New England (2022) Indicator category: Extensive analysis, not yet published, Database pull with analysis Contributor(s): Timothy P. White timothy.white@boem.gov Data steward: Timothy P. White timothy.white@boem.gov Point of contact: Timothy P. White timothy.white@boem.gov Public availability statement: Source data are NOT publicly available 44.1 Methods The data presented here represent integrated persistent annual hotspots derived from at-sea observations of seabirds, cetaceans and sea turtles collected on systematic ship and aerial surveys. 44.1.1 Data sources Individual hotspot richness maps represent summed annual persistent hotspots of 82 species (sea turtles, n=5; cetaceans, n = 29; seabirds, n= 48). Hotspot richness values also include 20 common and abundant seabird taxa challenging to identify to the species level on aerial surveys but whose abundance and spatial patterns significantly contribute to seabird richness and diversity on the Atlantic EEZ (e.g., loon sp.; phalarope sp.). Federal, state, and academic institutions embraced systematic aerial and ship-based surveys to examine seabird, cetacean, and sea turtle distribution and abundance on the Atlantic Outer Continental Shelf in the 1970s. Federal agencies (BOEM, NOAA, USFWS, and the US Navy) continue seasonal surveys under the Atlantic Marine Assessment Program for Protect species (AMAPPS) and others. AMAPPS covers broad areas of the Atlantic EEZ to assess mangaged stocks of protected species and the potential impacts of offshore energy production on marine wildlife populations. Observer-based programs use two main survey methods to estimate densities at sea from ships and aircraft 1) the strip-width method ((White2020?)) and 2 ) distance sampling ((Palka2017?)). The annual persistent hotspot maps presented here of seabirds, cetaceans, and sea turtles were derived from observations and survey effort archived in the Bureau of Ocean Energy Managements Northwest Atlantic Seabird Catalog; NOAA Northeast Fisheries Science Centers (NEFSC) AMAPPS database; NEFSCs Right Whale Aerial Survey database; and MassCECs database of surveys off southern New England. 44.1.2 Data analysis All detailed data processing steps are not currently included in this document, but general steps are outlined. Species-specific persistent hotspots were computed with observations and survey effort collected on ship and aerial surveys from 1978-2019. Species-specific hotspots were derived with daily timesteps on 20 x 20 km grids representing the Atlantic EEZ. Hotspot probabilities (i.e., persistence) were derived by summing the number of daily hotspots divided by the number of time steps (Gende and Sigler (2006)), which produced a continuum of probabilistic hotspots ranging from 0 to 1 across a final species-specific grid. Annual hotspot richness maps were derived by summing the species-specifc grid cells with high persistence. 44.1.3 Data processing Persistent hotspots were computed with the sf and raster R packages. 44.1.4 Plotting Figure 44.1: North Atlantic Right Whale hotspot map. Figure 44.2: Hotspot map for all protected groups (Seabirds, Cetaceans, and Turtles). References "],["recreational-fishing-indicators.html", "45 Recreational Fishing Indicators 45.1 Methods", " 45 Recreational Fishing Indicators Description: A variety of indicators derived from MRIP Recreational Fisheries Statistics, including total recreational catch, total angler trips by region, annual diversity of recreational fleet effort, and annual diversity of managed species. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017+), State of the Ecosystem - Mid-Atlantic (2017+) Indicator category: Database pull with analysis Contributor(s): Geret DePiper, Scott Steinbeck Data steward: Geret DePiper, geret.depiper@noaa.gov Point of contact: Geret DePiper, geret.depiper@noaa.gov Public availability statement: Data sets are publicly available (see Data Sources below). 45.1 Methods We used total recreational harvest as an indicator of seafood production and total recreational trips and total recreational anglers as proxies for recreational value generated from the Mid-Atlantic and New England regions respectively. We estimated both recreational catch diversity in species manages by the Fisheries Management Councils; Mid-Atlantic (MAFMC), New England (NEFMC) and Atlantic States (ASFMC) and fleet effort diversity using the effective Shannon index. 45.1.1 Data sources All recreational fishing indicator data, including number of recreationally harvested fish, number of angler trips, and number of anglers, were downloaded from the Marine Recreational Information Program MRIP Recreational Fisheries Statistics Queries portal. Relevant metadata including information regarding data methodology updates are available at the query site. Note that 2017 data were considered preliminary at the time of the data pull. Data sets were queried by region on the MRIP site, and for the purposes of the State of the Ecosystem reports, the NORTH ATLANTIC and MID-ATLANTIC regions were mapped to the New England and Mid-Atlantic report versions respectively. All query pages are accessible through the MRIP Recreational Fisheries Statistics site. The number of recreationally harvested fish was found by selecting TOTAL HARVEST (A + B1) on the Catch Time Series Query page. Catch diversity estimates were also derived from the total catch time series (see below). Species included in the diversity of catch analysis can be found in Table 45.1. The Mid-Atlantic Fishery Management Council asked that species managed by the South Atlantic Fishery Management Council be distinguished in the analysis of recreational species diversity. Table 45.1: Species included in recreational catch diversity analysis. Common.Name Scientific.Name Diversity.analysis American eel Anguilla rostrata Species inclded in NE and MA analyses Atlantic Cod Gadus morhua Species inclded in NE and MA analyses Atlantic Croacker Micropogonias undulatus Species inclded in NE and MA analyses Atlantic Herring Clupea harengus Species inclded in NE and MA analyses Atlantic Mackerel Scomber scombrus Species inclded in NE and MA analyses Atlantic Menhaden Brevoortia tyrannus Species inclded in NE and MA analyses Atlantic Sturgeon Acipenser oxyrinchus Species inclded in NE and MA analyses Banded Rudderfish Seriola zonata Species inclded in NE and MA analyses Black Sea Bass Centropristis striata Species inclded in NE and MA analyses Bluefish Pomatomus saltatrix Species inclded in NE and MA analyses Gray Triggerfish Balistes capriscus Species inclded in NE and MA analyses Greater Amberjack Seriola dumerili Species inclded in NE and MA analyses Little Tunny Euthynnus alletteratus Species inclded in NE and MA analyses Pollock Pollachius virens Species inclded in NE and MA analyses Rock Sea Bass Centropristis philadelphica Species inclded in NE and MA analyses Scup Stenotomus chrysops Species inclded in NE and MA analyses Southern Flounder Paralichthys lethostigma Species inclded in NE and MA analyses Spiny Dogfish Squalus acanthias Species inclded in NE and MA analyses Spot Leiostomus xanthurus Species inclded in NE and MA analyses Striped Bass Morone saxatilis Species inclded in NE and MA analyses Summer Flounder Paralichthys dentatus Species inclded in NE and MA analyses Tautog Tautoga onitis Species inclded in NE and MA analyses Tilefish Lopholatilus chamaeleonticeps Species inclded in NE and MA analyses Weakfish Cynoscion regalis Species inclded in NE and MA analyses Winter Flounder Pseudopleuronectes americanus Species inclded in NE and MA analyses Black Drum Pogonias cromis SAFMC managed species included in MA analysis Cobia Rachycentron canadum SAFMC managed species included in MA analysis Lesser Amberjack Seriola fasciata SAFMC managed species included in MA analysis Red Drum Sciaenops ocellatus SAFMC managed species included in MA analysis Red Porgy Pagrus pagrus SAFMC managed species included in MA analysis Wahoo Acanthocybium solandri SAFMC managed species included in MA analysis Bar Jack Caranx ruber SAFMC managed species included in MA analysis Blue Runner Caranx crysos SAFMC managed species included in MA analysis Hogfish Lachnolaimus maximus SAFMC managed species included in MA analysis Jolthead Porgy Calamus bajonado SAFMC managed species included in MA analysis Margate Haemulon album SAFMC managed species included in MA analysis Almaco Jack Seriola rivoliana SAFMC managed species included in MA analysis Atlantic Spadefis Chaetodipterus faber SAFMC managed species included in MA analysis Ocean Triggerfish Canthidermis sufflamen SAFMC managed species included in MA analysis Spanish Mackerel Scomberomorus maculatus SAFMC managed species included in MA analysis Spotted Seatrout Cynoscion nebulosus SAFMC managed species included in MA analysis Tomtate Haemulon aurolineatum SAFMC managed species included in MA analysis Gray Snapper Lutjanus griseus SAFMC managed species included in MA analysis Mutton Snapper Lutjanus analis SAFMC managed species included in MA analysis Coney Cephalopholis fulva SAFMC managed species included in MA analysis White Grunt Haemulon plumierii SAFMC managed species included in MA analysis Yellowtail Snapper Ocyurus chrysurus SAFMC managed species included in MA analysis Snowy Grouper Hyporthodus niveatus SAFMC managed species included in MA analysis Blueline Tilefish Caulolatilus microps SAFMC managed species included in MA analysis Longspine Porgy Stenotomus caprinus SAFMC managed species included in MA analysis Wreckfish Polyprion americanus SAFMC managed species included in MA analysis Gag Mycteroperca microlepis SAFMC managed species included in MA analysis Haddock Melanogrammus aeglefinus SAFMC managed species included in MA analysis Whitebone Porgy Calamus leucosteus SAFMC managed species included in MA analysis Angler trips (listed as TOTAL trips) were pulled from the MRIP Effort Time Series Query page, and included data from 1981 - 2019. Time series of recreational fleet effort diversity were calculated from this data set (see below). The number of anglers was total number of anglers from the Marine Recreational Fishery Statistics Survey (MRFSS) Participation Time Series Query, and includes data from 1981 - 2016. 45.1.2 Data analysis Recreational fleet effort diversity Code used to for effort diversity data analysis can be found here. Recreational catch diversity Code used to for catch diversity data analysis can be found here. 45.1.3 Data processing Recreational fishing indicators were formatted for inclusion in the ecodata R package using this code. 45.1.4 Plotting Figure 45.1: Recreational effort diversity and diversity of recreational catch in the Mid-Atlantic. Figure 45.2: Total recreational seafood harvest in the Mid-Atlantic. "],["recreational-shark-fishing-indicators.html", "46 Recreational Shark Fishing Indicators 46.1 Methods", " 46 Recreational Shark Fishing Indicators Description: Recreational Shark Landings Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021+), State of the Ecosystem - Mid-Atlantic (2021+) Indicator category: Database pull with analysis Contributor(s): Kimberly Bastille Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Data sets are publicly available (see Data Sources below). 46.1 Methods We used total recreational harvest as an indicator of seafood production and total recreational trips and total recreational anglers as proxies for recreational value generated from the Mid-Atlantic and New England regions respectively. We estimated both recreational catch diversity in species manages by the Fisheries Management Councils; Mid-Atlantic (MAFMC), New England (NEFMC) and Atlantic States (ASFMC) and fleet effort diversity using the effective Shannon index. 46.1.1 Data sources All recreational shark fishing indicator data, in were downloaded from the Marine Recreational Information Program MRIP Recreational Fisheries Statistics Queries portal. Data sets were queried by region on the MRIP site, and for the purposes of the State of the Ecosystem reports, the NORTH ATLANTIC and MID-ATLANTIC regions were mapped to the New England and Mid-Atlantic report versions respectively. All query pages are accessible through the MRIP Recreational Fisheries Statistics site. 46.1.2 Data analysis MRIP data were grouped used the shark species groupings found in this table. 46.1.3 Data processing Recreational shark fishing indicators were formatted for inclusion in the ecodata R package using this code. 46.1.4 Plotting Code used to create the plot below can be found here. Figure 46.1: Total recreational shark landings in the New England. "],["right-whale-abundance.html", "47 Right Whale Abundance 47.1 Methods", " 47 Right Whale Abundance Description: Right Whale Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017+), State of the Ecosystem - Mid-Atlantic (2017+) Indicator category: Synthesis of published information; Published methods Contributor(s): Christopher D. Orphanides Data steward: Chris Orphanides, chris.orphanides@noaa.gov Point of contact: Richard Pace, richard.pace@noaa.gov Public availability statement: Source data are available from the New England Aquarium upon request. Derived data are available here 47.1 Methods 47.1.1 Data sources The North Atlantic right whale abundance estimates were taken from a published document (see Pace, Corkeron, and Kraus 2017), except for the most recent 2016 and 2017 estimates. Abundance estimates from 2016 and 2017 were taken from the 2016 National Oceanographic and Atmospheric Administration marine mammal stock assessment (Hayes et al. 2017) and an unpublished 2017 stock assessment. Calves birth estimates are taken from a published report (Pettis, Pace, and Hamilton 2019) put out yearly by the North American Right Whale Consortium. 47.1.2 Data extraction Data were collected from existing reports and validated by report authors. 47.1.3 Data analysis Analysis for right whale abundance estimates is provided by Pace, Corkeron, and Kraus (2017), and code can be found in the supplemental materials. 47.1.4 Data processing Time series of right whale and calf abundance estimates were formatted for inclusion in the ecodata R package using this R code. 47.1.5 Plotting Code used create the plots below can be found at these links, NARW population estimates and calf births. Figure 47.1: North Atlantic right whale population estimates shown with 95% credible intervals. Figure 47.2: North Atlantic right whale calf births. References "],["safmc-managed-spp.html", "48 SAFMC managed spp 48.1 Methods", " 48 SAFMC managed spp Description: SAFMC Species on NES Found in: State of the Ecosystem - Mid-Atlantic (2020), State of the Ecosystem - New England (2020) Indicator category: Database pull Contributor(s): Sean Lucey Data steward: Sean Lucey Sean.Lucey@noaa.gov Point of contact: Sean Lucey Sean.Lucey@noaa.gov Public availability statement: Source data are available to qualified researchers upon request (see Access Information here). 48.1 Methods 48.1.1 Data sources The Survdat data set was used to examine the presence of southern species (table 48.1) in Mid-Atlantic and New England waters. 48.1.2 Data extraction Survdat was subsetted by common southern species (table 3.2). Table 48.1: Southern Species that were examined within the NEFSC trawl survey data Common.Name Scientific.Name Group Black snapper Apsilus dentatus Snappers Queen snapper Etelis oculatus Snappers Mutton snapper Lutjanus analis Snappers Schoolmaster snapper Lutjanus apodus Snappers Blackfin snapper Lutjanus buccanella Snappers Northern Red snapper Lutjanus campechanus Snappers Cubera snapper Lutjanus cyanopterus Snappers Grey snapper Lutjanus griseus Snappers Mahogany snapper Lutjanus mahogoni Snappers Dog snapper Lutjanus jocu Snappers Lane snapper Lutjanus synagris Snappers Silk snapper Lutjanus vivanus Snappers Yellowtail snapper Ocyurus chrysurus Snappers Vermilion snapper Rhomboplites aurorubens Snappers Bank sea bass Centropristis ocyurus Sea Basses Rock sea bass Centropristis philadelphica Sea Basses Black sea bass Centropristis striata Sea Basses Rock hind Epinephelus adscensionis Groupers Graysby Epinephelus cruentatus Groupers Calico grouper Epinephelus drummondhayi Groupers Yellowedge grouper Epinephelus flavolimbatus Groupers Coney Epinephelus fulvus Groupers Red hind Epinephelus guttatus Groupers Atlantic goliath grouper Epinephelus itajara Groupers Red grouper Epinephelus mario Groupers Misty grouper Epinephelus mystacinus Groupers Warsaw grouper Epinephelus nigritus Groupers Snowy grouper Epinephelus niveatus Groupers Nassau grouper Epinephelus striatus Groupers Black grouper Mycteroperca bonaci Groupers Yellowmouth grouper Mycteroperca interstitialis Groupers Gag grouper Mycteroperco microlepis Groupers Scamp grouper Mycteroperca phenax Groupers Tiger grouper Mycteroperca tigris Groupers Yellowfin grouper Mycteroperca venenoso Groupers Sheepshead Archosargus probotocephalus Porgies Grass porgy Calamus arctifrons Porgies Jolthead porgy Calamus bajonado Porgies Saucereye porgy Calamus calamus Porgies Whitebone porgy Calamus leucosteus Porgies Knobbed porgy Calamus leucosteus Porgies Red porgy Pagrus pagrus Porgies Longspine porgy Stenotomus caprinus Porgies Black margate Anisotremus surinamensis Grunts Porkfish Anisotremus virginicus Grunts White margate Haemulon album Grunts Tomtate Haemulon aurolineatum Grunts Smallmouth grunt Hemulon chrysargyreum Grunts French grunt Haemulon flavolineatum Grunts Spanish grunt Haemulon macrostomum Grunts Cottonwick grunt Haemulon melanurum Grunts Sailors grunt Haemulon parra Grunts White grunt Haemulon plumieri Grunts Blue Striped grunt Haemulon sciurus Grunts Grey triggerfish Balistes capriscus Triggerfishes Queen triggerfish Balistes vetula Triggerfishes Ocean triggerfish Canthidermis sufflamen Triggerfishes Hogfish Lachnolaimus maximus Wrasses Puddingwife wrasse Halichoeres rodiatus Wrasses Yellow jack Caranx bartholomaei Jacks Blue runner Caranx crysos Jacks Crevalle jack Caranx hippos Jacks Bar jack Caranx ruber Jacks Greater amberjack Seriola dumerili Jacks Almaco jack Seriola rivoliano Jacks 48.1.3 Data analysis The presence/absence of southern species was broadly examined for all species listed in table 48.1. It was quickly determined that these species were extremely rare in the bottom trawl survey. When a species was present, they were found during the fall survey and not the spring. No trends were apparent in the data. The one species that was commonly present was the blue runner (Caranx crysos). Stations were binned temporally by three categories: Prior to 2001, 2001 - 2010, and since 2010. Stations were then plotted on a map of the survey region and visually inspected. 48.1.4 Data processing Blue runner (Caranx crysos) data were formatted for inclusion in the ecodata R package using this R code. 48.1.5 Plotting The plot below was built using the code found here. Figure 48.1: Blue runner presence on the Northeast shelf. "],["sandlance.html", "49 Sandlance 49.1 Methods 49.2 References", " 49 Sandlance Description: Sandlance survey data from Stellwagen Bank National Marine Sanctuary Found In: 2022 Indicator Catalog Indicator category: Published methods Contributor(s): David N. Wiley, Tammy L. Silva Data steward: Moe Nelson david.moe.nelson@noaa.gov Point of contact: Moe Nelson david.moe.nelson@noaa.gov Public availability statement:Source data are publicly available. 49.1 Methods 49.1.1 Data Sources This data set is taken directly from Table 1, Silva et al. 2020. See full citation in References section below. 49.1.2 Data Analysis Data processing and analysis methods are described in Silva et al. 2020. The catch counts of sand lance and observational counts of humpback whales and great shearwater were used to derive spatial metrics (center of gravity, and inertia) for each species. Equations for these spatial metrics are provided in Table 2 of Silva et al. 2020. The spatial metrics (center of gravity, inertia) were used to calculate the global index of collocation (GIC) to quantify spatial overlap between pairs of species for each cruise. GICs for species pairs are reported in Table 3 of Silva et al. 2020, but data were not sufficient to calculate GICs for each pair of species in each cruise. 49.1.3 Data Processing Code for processing salinity data can be found here. 49.1.4 Plotting Code use to build the plot below can be found here. Figure 49.1: These four line graphs (Mean Sandlance, Number of Samples, Number of Samples with Fish, and Proportion of Non-Zero Samples) are metrics derived directly from the raw survey data (SBNMS 2021). Collectively they illustrate a cyclical pattern of abundance, with higher abundance 2014-2016, and lower abundance 2017-2019. Since the data set has many zero-capture samples, the Proportion of Non-Zero Samples may be a useful metric as a proxy for abundance. Figure 49.2: The multi-line plot labeled Sandlance illustrates the collocation of sandlance with humpback whale and great shearwater. Data are taken directly from Table 1 of Silva et al. 2020. Although it may not be visually evident from the annual-summed values, there is strong spatial collocation among sand lance, humpbacks and shearwaters across seasons and years. Figure 49.3: This map graphic depicts the grab sample locations in SBNMS, with cumulative catch at each from 2013 through 2019. 49.2 References Silva, T.L., D.N. Wiley, M.A. Thompson, P. Hong, L. Kaufman, J.J. Suca, J.K. Llopiz, H. Baumann, and G. Fay. 2020. High collocation of sand lance and protected top predators: Implications for conservation and management. Conservation Science and Practice 2020;e274. https://doi.org/10.1111/csp2.274 "],["submerged-aquatic-vegetation.html", "50 Submerged Aquatic Vegetation 50.1 Methods", " 50 Submerged Aquatic Vegetation Description: Chesapeake Bay Submerged Aquatic Vegetation Trends Found in: State of the Ecosystem - Mid-Atlantic (2022) Indicator category: Database pull with analysis Contributor(s): David Wilcox, Brooke Landry, Christopher Patrick Data steward: David Wilcox dwilcox@vims.edu Point of contact: David Wilcox dwilcox@vims.edu Public availability statement: Source data are NOT publicly available. Please email David Wilcox at dwilcox@vims.edu for further information about the submerged aquatic vegetation indicator. 50.1 Methods 50.1.1 Data Sources Data for this indicator comes from the aerial survey of submerged aquatic vegetation coverage in the Chesapeake Bay: https://www.chesapeakeprogress.com/abundant-life/sav 50.1.2 Data Processing Data were formatted for inclusion in the ecodata R package using the R code found here. 50.1.3 Plotting The plot below was built using the code found here. Figure 50.1: Submerged Aquatic Vegetation (SAV) coverage in tidal fresh and high salinity regions of the Chesapeake Bay.. "],["ma-waterbird-productivity.html", "51 MA waterbird productivity 51.1 Methods", " 51 MA waterbird productivity Description: Virginia waterbird data Indicator category: Published Results Found in: State of the Ecosystem - Mid-Atantic (2020) Contributor(s): Ruth Boettcher Data steward: Kimberly Bastille kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille kimberly.bastille@noaa.gov Public availability statement: Data is publically available. 51.1 Methods 51.1.1 Data sources Virginia colonial waterbird breeding pair population estimates derived from table 4 of Status and distribution of colonial waterbirds in coastal Virginia: 2018 breeding season. Center for Conservation Biology Technical Report Series, CCBTR-18-17. College of William and Mary & Virginia Commonwealth University, Williamsburg, VA. Available at: https://scholarworks.wm.edu/cgi/viewcontent.cgi?article=1237&context=ccb_reports 51.1.2 Data analysis NA 51.1.3 Data processing VA colonial waterbird data were formatted for inclusion in the ecodata R package using this R code. 51.1.4 Plotting Code used to create the figure below can be found here. Figure 51.1: Functional group population estimated derived from Table 4 of Watts, B. D., B. J. Paxton, R. Boettcher, and A. L. Wilke. 2019. "],["ne-seabird-diet-and-productivity.html", "52 NE Seabird diet and productivity 52.1 Methods", " 52 NE Seabird diet and productivity Description: Common tern annual diet and productivity at seven Gulf of Maine colonies managed by the National Audubon Societys Seabird Restoration Program Indicator category: Published method Found in: State of the Ecosystem - New England (2019+) Contributor(s): Don Lyons, Steve Kress, Paula Shannon, Sue Schubel Data steward: Don Lyons, dlyons@audubon.org Point of contact: Don Lyons, dlyons@audubon.org Public availability statement: Please email dlyons@audubon.org for further information and queries on this indicator source data. 52.1 Methods Chick diet Common tern (Sterna hirundo) chick diet was quantified at each of the seven nesting sites (Fig. ?? ) by observing chick provisioning from portable observation blinds. The locations of observation blinds within each site were chosen to maximize the number of visible nests, and provisioning observations took place between mid-June and early August annually. Observations of chick diet were made during one or two, three to four hour periods throughout the day, but typically proceed according to nest activity levels (moreso in the morning hours). Observations began with chicks as soon as they hatched, and continue until the chicks fledged or died. Most common tern prey species were identifiable to the species level due to distinct size, color and shape. However, when identification was not possible or was unclear, prey species were listed as unknown or unknown fish. More detailed methods can be found in Hall, Kress, and Griffin (2000). Nest productivity Common tern nest productivity, in terms of the number of fledged chicks per nest, was collected annually from fenced enclosures at island nesting sites (known as productivity plots). Newly hatched chicks within these enclosures were weighed, marked or banded, and observed until fledging, death, or until a 15 day period had passed when chicks were assumed to have fledged. Productivity was also quantified from observer blinds for nests outside of the productivity plots where chicks were marked for identification. More detailed methods for quantifying nest productivity can be found in Hall and Kress (2004). 52.1.1 Data sources Common tern diet and nest productivity data were provided by the National Audubon Societys Seabird Restoration Program. 52.1.2 Data processing Diet and productivity data were formatted for inclusion in the ecodata R package using this R code. 52.1.3 Data analysis Raw diet data were used to create time series of mean shannon diversity through time and across study sites using the vegan R package (Oksanen et al. 2020). Code for this calculation can be found here. Diet diversity is presented along with nest productivity (+/- 1 SE). Code used to create the figures below can be found at these links, diet diversity, prey frequencies and common tern productivity 52.1.4 Plotting 52.1.4.1 Diet diversity Figure 52.1: Shannon diversity of common tern diets observed at nesting sites in Gulf of Maine. Diversity of common tern diets has been predominantly above the long-term mean since 2006. 52.1.4.2 Prey frequencies Figure 52.2: Prey frequencies in the diets of common tern observed across the seven colonies in the Gulf of Maine. 52.1.4.3 Common tern productivity Figure 52.3: Common terns: Mean common tern productivity at nesting sites in Gulf of Maine. Error bars show +/- 1 SE of the mean. References "],["seasonal-sst-anomalies.html", "53 Seasonal SST Anomalies 53.1 Methods", " 53 Seasonal SST Anomalies Description: Seasonal SST Anomalies Indicator category: Database pull with analysis Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) Contributor(s): Sean Hardison, Vincent Saba Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: Source data are available here. 53.1 Methods 53.1.1 Data sources Data for seasonal sea surface tempature anomalies (Fig. 53.1) were derived from the National Oceanographic and Atmospheric Administartion optimum interpolation sea surface temperature high resolution data set (NOAA OISST V2) provided by NOAA Earth System Research Laboratorys Physical Science Division, Boulder, CO. The data extend from 1981 to present, and provide a 0.25° x 0.25° global grid of SST measurements (Reynolds et al. 2007). In 2021, the Daily OISST data was updated and there are a couple papers describing and comparing the new version Huang, Liu, Banzon, et al. (2021). 53.1.2 Data extraction Individual files containing daily mean SST data for each year during the period of 1981-present were downloaded from the OI SST V5 site. Yearly data provided as layered rasters were masked according to the extent of Northeast US Continental Shelf. Data were split into three month seasons for (Winter = Jan, Feb, Mar; Spring = Apr, May, Jun; Summer = July, August, September; Fall = Oct, Nov, Dec). 53.1.3 Data analysis We calculated the long-term mean (LTM) for each season-specific stack of rasters over the period of 1982-2010, and then subtracted the (LTM) from daily mean SST values to find the SST anomaly for a given year. The use of climatological reference periods is a standard procedure for the calculation of meteorological anomalies (WMO 2017). Prior to 2019 State of the Ecosystem reports, SST anomaly information made use of a 1982-2012 reference period. A 1982-2010 reference period was adopted to facilitate calculating anomalies from a standard NOAA ESRL data set. R code used in extraction and processing gridded and timeseries data can found in the ecodata package. 53.1.4 Plotting Code used to build the figure below can be found here. Figure 53.1: MAB seasonal sea surface temperature time series overlaid onto 2021 seasonal spatial anomalies. References "],["stockstatus.html", "54 Single Species Status Indicator 54.1 Methods", " 54 Single Species Status Indicator Description: Summary of the most recent stock assessment results for each assessed species. Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017+), State of the Ecosystem - Mid-Atlantic (2017+) Indicator category: Synthesis of published information Contributor(s): Sarah Gaichas, Andy Beet, Jeff Vieser, Chris Legault Data steward: Sarah Gaichas sarah.gaichas@noaa.gov Point of contact: Sarah Gaichas sarah.gaichas@noaa.gov Public availability statement: All stock assessment results are publicly available (see Data Sources). Summarized data are available here. 54.1 Methods 54.1.1 Data sources Data used for this indicator are the outputs of stock assessment models and review processes, including reference points (proxies for fishing mortality limits and stock biomass targets and limits), and the current fishing mortality rate and biomass of each stock. These metrics are reported to the a national repository, Stock SMART. Recent stock assessment updates for each species are available on the Northeast Fisheries Science Center (NEFSC) website using the form here: https://apps-nefsc.fisheries.noaa.gov/saw/sasi/sasi_report_options.php For example, to download the 2020 assessment data, use the form by checking the boxes: Year2020 Check each available 2020 species and stock area in turn, downloaded .zip of all files. Species with 2020 updates included: Acadian redfish, Atlantic halibut, Atlantic herring, Atlantic Sea Scallop, Atlantic surfclam, Atlantic wolffish, Butterfish, Longfin squid, Ocean Pout, Ocean quahog, Red Hake (2 stocks), Silver hake (2 stocks), Windowpane flounder (2 stocks), Winter flounder (3 stocks). These 2020 stock assessment results were compiled as preliminary information by Jeff Vieser, who provided the spreadsheet NE Stock Assessment Results.xlsx 10 December 2020. These results are considered preliminary until uploaded to StockSMART. 54.1.2 Data extraction Beginning in 2020 for the 2021 SOE, we used Andy Beets stocksmart package to extract assessment results from Stock SMART. The code used to work up this data can be found in sgaichas/stockstatusindicator. Two data frames are in the stocksmart package, stockAssessmentData and stockAssessmentSummary. In stockAssessmentData we have time series. Columns are StockName, Year, Value, Metric, Description, Units, AssessmentYear, Jurisdiction, FMP, CommonName, ScientificName, ITIS, UpdateType, StockArea, RegionalEcosystem and the reported metrics are Catch, Fmort, Recruitment, Abundance, Index. In stockAssessmentSummary we have assessment metadata. Columns are Stock Name, Jurisdiction, FMP, Science Center, Regional Ecosystem, FSSI Stock?, ITIS Taxon Serial Number, Scientific Name, Common Name, Stock Area, Assessment Year, Assessment Month, Last Data Year, Update Type, Review Result, Assessment Model, Model Version, Lead Lab, Citation, Final Assessment Report 1, Final Assessment Report 2, Point of Contact, Life History Data, Abundance Data, Catch Data, Assessment Level, Assessment Frequency, Assessment Type, Model Category, Catch Input Data, Abundance Input Data, Biological Input Data, Ecosystem Linkage, Composition Input Data, F Year, Estimated F, F Unit, F Basis, Flimit, Flimit Basis, Fmsy, Fmsy Basis, F/Flimit, F/Fmsy, Ftarget, Ftarget Basis, F/Ftarget, B Year, Estimated B, B Unit, B Basis, Blimit, Blimit Basis, Bmsy, Bmsy Basis, B/Blimit, B/Bmsy, MSY, MSY Unit. In 2021, stocksmart was updated with all current assessments, so data extraction was simply: Year-specific naming conventions for assess and decoder files were dropped in 2021 to facilitate future data updates. In 2020, assessment summary data were extracted from stockAssessmentSummary for 2019 and prior records, and the 2020 assessments results were added from the preliminary results provided by Jeff Vieser. The assess.csv fields used in previous years were recreated from stockSMART to include necessary metadata: new2019assess <- stockAssessmentSummary %>% filter(`Science Center` == "NEFSC") %>% rename(Entity.Name = `Stock Name`) %>% rename_all(list(~make.names(.))) Add 2020 assessments and write 2020assess.csv data contribution: prelim2020 <- read.csv(here("NE Stock Assessment Results.csv")) %>% filter(Assessment.Year == 2020) %>% rename(Entity.Name = Stock, FSSI.Stock. = FSSI, Estimated.F = Best.F, Estimated.B = Best.B, Review.Result = Review.Type) %>% select(-c(Year, Status.Stock., Record.Status, TimeSeries.Data., Survey.Links., Adequate, Minimum.F, Maximum.F, Minimum.B, Maximum.B, Stock.Level.Relative.to.Bmsy:Decision.memo.related.to.inadequate.rebuilding.progress)) update2020assess <- bind_rows(new2019assess, prelim2020) write.csv(update2020assess, here("2020assess.csv")) The decoder.csv data contribution was updated in December 2020 to retain only Entity.Name, Council, and Code fields (used by get_stocks): newdecoder <- read.csv(here("2019decoder.csv")) %>% select(Entity.Name, Code, Council) write.csv(newdecoder, here("2020decoder.csv")) For the 2017-2020 SOEs, each assessment document was searched to find the following information (often but not always summarized under a term of reference to determine stock status in the executive summary), and the spreadsheets were updated by hand: Bcur: current year biomass, (most often spawning stock biomass (SSB) or whatever units the reference points are in) Fcur: current year fishing mortality, F Bref: biomass reference point, a proxy of Bmsy (the target) Fref: fishing mortality reference point, a proxy of Fmsy 54.1.3 Data processing R code used to process the stock status data set for inclusion in the ecodata R package can be found here. 54.1.4 Data analysis For each assessed species, Bcur is divided by Bref and Fcur is divided by Fref. They are then plotted for each species on an x-y plot, with Bcur/Bref on the x axis, and Fcur/Fref on the y axis. 54.1.5 Plotting The script used to develop the figure in the State of the Ecosystem report can be found here (MAB and NE ). Figure 54.1: Summary of single species status for MAFMC and jointly managed stocks. Figure 54.2: Summary of single species status for NEFMC and jointly managed stocks. "],["slopewater-proportions.html", "55 Slopewater proportions 55.1 Methods", " 55 Slopewater proportions Description: Percent total of water type observed in the deep Northeast Channel (150-200 m water depth). Indicator category: Published methods Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2019+) Contributors: Paula Fratantoni, paula.fratantoni@noaa.gov; David Mountain, NOAA Fisheries, retired. Data steward: Kimberly Bastille, kimberly.bastille@noaa.gov Point of contact: Paula Fratantoni, paula.fratantoni@noaa.gov Public availability statement: Source data are publicly available at ftp://ftp.nefsc.noaa.gov/pub/hydro/matlab_files/yearly and in the World Ocean Database housed at http://www.nodc.noaa.gov/OC5/SELECT/dbsearch/dbsearch.html under institute code 258 55.1 Methods 55.1.1 Data sources The slope water composition index incorporates temperature and salinity measurements collected on Northeast Fisheries Science Center surveys between 1977-present within the geographic confines of the Northeast Channel in the Gulf of Maine. Early measurements were made using water samples collected primarily with Niskin bottles at discreet depths, mechanical bathythermographs and expendable bathythermograph probes, but by 1991 the CTD  an acronym for conductivity temperature and depth  became standard equipment on all NEFSC surveys. 55.1.2 Data extraction While all processed hydrographic data are archived in an Oracle database (OCDBS), we work from Matlab-formatted files stored locally. 55.1.3 Data analysis Temperature and salinity measurements are examined to assess the composition of the waters entering the Gulf of Maine through the 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. For each survey in the hydrographic archive, ocean temperature and salinity observations sampled in the area just inside the Northeast Channel (bounded by 42.2-42.6° latitude north and 66-66.8° longitude west) and between 150 - 200 meters depth are extracted and a volume-weighted average temperature and salinity is calculated. The volume weighting is accomplished by apportioning the area within the Northeast Channel polygon among the stations occupying the region, based on inverse distance squared weighting. The result of this calculation is a timeseries of volume-average temperature and salinity having a temporal resolution that matches the survey frequency in the database. The average temperature and salinity observed at depth in the Northeast Channel is assumed to be the product of mixing between three distinct sources having the following temperature and salinity characteristics: (1) Warm Slope Water (T=10 °C, S=35), (2) Labrador Slope Water (T=6 °C, S=34.7) and (3) Scotian Shelf Water (T=2 °C, S=32). As described by David G. Mountain (2012a), the relative proportion of each source is determined via a rudimentary 3-point mixing algorithm. On a temperature-salinity diagram, lines connecting the T-S coordinates for these three sources form a triangle, the sides of which represent mixing lines between the sources. A water sample that is a mixture of two sources will have a temperature and salinity that falls somewhere along the line connecting the two sources on the temperature-salinity diagram. Observations of temperature and salinity collected within the Northeast Channel would be expected to fall within the triangle if the water sampled is a mixture of the three sources. Simple geometry allows us to calculate the relative proportion of each source in a given measurement. As an example, a line drawn from the T-S point representing shelf water through an observed T-S in the center of the triangle will intersect the opposite side of the triangle (the mixing line connecting the coordinates of the two slope water sources). This intersecting T-S value may then be used to calculate the relative proportions (percentage) of the two slope water sources. Using this method, the percentage of Labrador slope water and Warm slope water are determined for the timeseries of volume-average temperature and salinity. It should be noted that our method assumes that the temperature and salinity properties associated with the source watermasses are constant. In reality, these may vary from year to year, modified by atmospheric forcing, mixing and/or advective processes. Likewise, other sources are periodically introduced into the Northeast Channel, including intrusions of Gulf Stream water flowing into the Gulf of Maine and modified shelf water flowing out of the Gulf of Maine along the flank of Georges Bank. These sources are not explicitely considered in the 3-point mixing algorithm and may introduce errors in the proportional estimates. Code used to calculate slopewater proportions can be found here. 55.1.4 Data processing Source data were formatted for inclusion in the ecodata R package using the R code found here. 55.1.5 Plotting Code used to create the figure below can be found here. Figure 55.1: Proportion of warm slope water (WSW) and Labrador slope water (LSLW) entering the GOM through the Northeast Channel. References "],["species-density-estimates.html", "56 Species Density Estimates 56.1 Methods", " 56 Species Density Estimates Description: Current and Historical Species Distributions Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018), State of the Ecosystem - Mid-Atlantic (2017, 2018) Indicator category: Database pull; Database pull with analysis Contributor: Kevin Friedland Data steward: Kevin Friedland Point of contact: Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are publicly available. 56.1 Methods We used kernel density plots to depict shifts in species distributions over time. These figures characterize the probability of a species occurring in a given area based on Northeast Fisheries Science Center (NEFSC) Bottom Trawl Survey data. Kernel density estimates (KDEs) of distributions are shown for the period of 1970-1979 (shaded blue) and most recent three years of survey data (shaded red) (e.g. Figure 56.1). Results are typically visualized for spring and fall bottom trawl surveys seperately. Three probability levels (25%, 50%, 75%) are shown for each time period, where the 25% region depicts the core area of the distribution and the 75% region shows the area occupied more broadly by the species. A wide array of KDEs for many ecologically and economically important species on the Northeast US Continental Shelf are available here. 56.1.1 Data sources Current and historical species distributions are based on the NEFSC Bottom Trawl Survey data (aka Survdat) and depth strata. Strata are available as shapefiles that can be downloaded here (listed as strata.shp). 56.1.2 Data analysis Code used for species density analysis can be found here. 56.1.3 Plotting Figure 56.1: Current and historical sea scallop kernel density estimates derived from spring survey data. Current estimates derived from 2016-2018 data. "],["species-distribution-indicators.html", "57 Species Distribution Indicators 57.1 Methods", " 57 Species Distribution Indicators Description: Species mean depth, along-shelf distance, and distance to coastline Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017+), State of the Ecosystem - Mid-Atlantic (2017+) Indicator category: Extensive analysis; not yet published Contributor(s): Kevin Friedland Data steward: Kevin Friedland, kevin.friedland@noaa.gov Point of contact: Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are available upon request (read more here). Derived data may be downloaded here. 57.1 Methods Three metrics quantifying spatial-temporal distribution shifts within fish populations were developed by Kevin D. Friedland et al. (2018), including mean depth, along-shelf distance, and distance to coastline. 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. Values in the derived time series correspond to mean distance in km from the southwest origin of the along-shelf axis at 0 km. The along-shelf axis begins at 76.53°W 34.60°N and terminates at 65.71°W 43.49°N. Once mean distance is found, depth of occurrence and distance to coastline can be calculated for each species positional center. Analyses present in the State of the Ecosystem (SOE) reports include mean depth and along-shelf distance for Atlantic cod, sea scallop, summer flounder, and black sea bass. 57.1.1 Data sources Data for these indicators were derived from fishery-independent bottom trawl survey data collected by the Northeast Fisheries Science Center (NEFSC). 57.1.2 Data analysis Species distribution indicators were derived using the R code found here. 57.1.3 Data processing Distribution indicators were further formatted for inclusion in the ecodata R package using the R code found here. 57.1.4 Plotting Code used to create the figure below can be found here. Figure 57.1: Aggregate species distribution depth along shelf distance (northward shift) and depth. References "],["stomach-fullness.html", "58 Stomach fullness 58.1 Methods", " 58 Stomach fullness Description: Stomach Fullness Found in: State of the Ecosystem - Mid-Atlantic (2020), State of the Ecosystem - New England (2020) Indicator category: Database pull with analysis Contributor(s): Laurel Smith Data steward: Kimberly Bastille kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille kimberly.bastille@noaa.gov Public availability statement: NEFSC survey data used in these analyses are available upon request (see Food Habits Database (FHDBS) for access procedures). Derived stomach fullness data are available here (STILL NEEDS SOE DATA REFERENCE!). 58.1 Methods An index of stomach fullness was calculated from NEFSC autumn bottom trawl food habits data, as a simple ratio of estimated stomach content weight to total weight of an individual fish. Stomach fullness may be a better measure than absolute stomach weight if combining across species into a feeding guild, to prevent larger animals with heavier stomachs from dominating the index. An average stomach fullness was calcuated annually for each species and Ecological Production Unit (EPU). 58.1.1 Data sources Stomach contents weights and individual fish weights (both to the nearest gram) were collected on the NEFSC bottom trawl surveys from 1992-present aboard RVs Albatross IV, Delaware II and the Henry B. Bigelow (see Food Habits Database (FHDBS) for access procedures). 58.1.2 Data extraction NEFSC food habits data summarized in the R data file allfh.RData were obtained from Brian Smith (Brian.Smith@noaa.gov) for this index. 58.1.3 Data analysis The stomach fullness index was calculated using the R script found here. 58.1.4 Data processing Fish stomach fullness index was formatted for inclusion in the ecodata R package using this R code. Stomach fullness was expressed as an annual anomaly for each species in each region. 58.1.5 Plotting The plot below was built using the code found here. Figure 58.1: Stomach fullness anomaly. "],["survdat.html", "59 Survey Data 59.1 Methods", " 59 Survey Data Description: Survdat (Survey database) Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017, 2018, 2019, 2020, 2022), State of the Ecosystem - Mid-Atlantic (2017, 2018, 2019, 2020, 2022) Indicator category: Database pull Contributor(s): Sean Lucey Data steward: Sean Lucey sean.lucey@noaa.gov Point of contact: Sean Lucey sean.lucey@noaa.gov Public availability statement: Source data are available to qualified researchers upon request (see Access Information here). Derived data used in SOE reports are available here. NO SURVEYS IS 2020 59.1 Methods The Northeast Fisheries Science Center (NEFSC) has been conducting standardized bottom trawl surveys in the fall since 1963 and spring since 1968. The surveys follow a stratified random design. Fish species and several invertebrate species are enumerated on a tow by tow basis (Azarovitz 1981). The data are housed in the NEFSCs survey database (SVDBS) maintained by the Ecosystem Survey Branch. Direct pulls from the database are not advisable as there have been several gear modifications and vessel changes over the course of the time series (T. J. Miller et al. 2010). Survdat was developed as a database query that applies the appropriate calibration factors for a seamless time series since the 1960s. As such, it is the base for many of the other analyses conducted for the State of the Ecosystem report that involve fisheries independent data. The Survdat script can be broken down into two sections. The first pulls the raw data from SVDBS. While the script is able to pull data from more than just the spring and fall bottom trawl surveys, for the purposes of the State of the Ecosystem reports only the spring and fall data are used. Survdat identifies those research cruises associated with the seasonal bottom trawl surveys and pulls the station and biological data. Station data includes tow identification (cruise, station, and stratum), tow location and date, as well as several environmental variables (depth, surface/bottom salinity, and surface/bottom temperature). Stations are filtered for representativness using a station, haul, gear (SHG) code for tows prior to 2009 and a tow, operations, gear, and aquisition (TOGA) code from 2009 onward. The codes that correspond to a representative tow (SHG <= 136 or TOGA <= 1324) are the same used by assessment biologists at the NEFSC. Biological data includes the total biomass and abundance by species, as well as lengths and number at length. The second section of the Survdat script applies the calibration factors. There are four calibrartion factors applied (Table 59.1). Calibration factors are pulled directly from SVDBS. Vessel conversions were made from either the NOAA Ship Delaware II or NOAA Ship Henry Bigelow to the NOAA Ship Albatross IV which was the primary vessel for most of the time series. The Albatross was decommisioned in 2009 and the Bigelow is now the primary vessel for the bottom trawl survey. Table 59.1: Calibration factors for NEFSC trawl survey data Name Code Applied Door Conversion DCF <1985 Net Conversion GCF 1973 - 1981 (Spring) Vessel Conversion I VCF Delaware II records Vessel Conversion II BCF Henry Bigelow records The output from Survdat is an RData file that contains all the station and biological data, corrected as noted above, from the NEFSC Spring Bottom Trawl Survey and NEFSC Fall Bottom Trawl Survey. The RData file is a data.table, a powerful wrapper for the base data.frame (https://cran.r-project.org/web/packages/data.table/data.table.pdf). There are also a series of tools that have been developed in order to utilize the Survdat data set (https://github.com/slucey/RSurvey). 59.1.1 Data sources Survdat is a database query of the NEFSC survey database (SVDBS).These data are available to qualified researchers upon request. More information on the data request process is available under the Access Information field here. 59.1.2 Data extraction Extraction methods are described above. The R code found here was used in the survey data extraction process. 59.1.3 Data analysis The fisheries independent data contained within the Survdat is used in a variety of products; the more complicated analyses are detailed in their own sections. The most straightforward use of this data is for the resource species aggregate biomass indicators. For the purposes of the aggregate biomass indicators, fall and spring survey data are treated separately. Additionally, all length data is dropped and species seperated by sex at the catch level are merged back together. Since 2020, survey strata where characterized as being within an Ecological Production Unit based on where at least 50% of the area of the strata was located (Figure 59.1. While this does not create a perfect match for the EPU boundaries it allows us to calculate the variance associated with the index as the survey was designed. Figure 59.1: Map of the Northeast Shelf broken into the four Ecological Production Units by strata. Strata were assigned to an EPU based on which one contained at least 50% of the area of the strata. Prior to 2020, Survdat was first post stratified into EPUs by labeling stations by the EPU they fell within using the over function from the rgdal R package (Bivand, Keitt, and Rowlingson 2018). Next, the total number of stations within each EPU per year is counted using unique station records. Biomass is summed by species per year per EPU. Those sums are divided by the appropriate station count to get the EPU mean. Finally, the mean biomasses are summed by aggregate groups. These steps are encompassed in the processing code, which also includes steps taken to format the data set for inclusion in the ecodata R package. 59.1.4 Plotting Code used to create the figure below can be found here. Figure 59.2: Spring (left) and fall (right) surveyed biomass in the Mid-Atlantic Bight. Data from the NEFSC Bottom Trawl Survey are shown in black, with NEAMAP shown in red. 59.1.4.1 Survey Diversity Code used to create the figure below can be found here. Figure 59.3: Survey diversity measure for the Mid-Atlantic Bight. References "],["thermal-habitat-projections.html", "60 Thermal Habitat Projections 60.1 Methods", " 60 Thermal Habitat Projections Description: Species Thermal Habitat Projections Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) Indicator category: Published methods Contributor(s): Vincent Saba Data steward: Vincent Saba, vincent.saba@noaa.gov Point of contact: Vincent Saba, vincent.saba@noaa.gov Public availability statement: Source data are available to the public. Model outputs for thermal habitat projections are available here. 60.1 Methods This indicator is based on work reported in Kleisner et al. (2017). 60.1.1 Data sources 60.1.1.1 Global Climate Model Projection We used National Oceanographic and Atmosheric Administrations Geophysical Fluid Dynamics Laboratory (NOAA GFDL) CM2.6 simulation consisting of (1) a 1860 pre-industrial control, which brings the climate system into near-equilibrium with 1860 greenhouse gas concentrations, and (2) a transient climate response (2xCO2) simulation where atmospheric CO2 is increased by 1% per year, which results in a doubling of CO2 after 70 years. The climate change response from CM2.6 was based on the difference between these two experimental runs. Refer to Saba et al. (2016) for further details. 60.1.1.2 Modeling Changes in Suitable Thermal Habitat The NOAA Northeast Fisheries Science Center, U.S. Northeast Shelf (NES) bottom trawl survey, which has been conducted for almost 50-years in the spring and fall, provides a rich source of data on historical and current marine species distribution, abundance, and habitat, as well as oceanographic conditions (Azarovitz 1981). The survey was implemented to meet several objectives: (1) monitor trends in abundance, biomass, and recruitment, (2) monitor the geographic distribution of species, (3) monitor ecosystem changes, (4) monitor changes in life history traits (e.g., trends in growth, longevity, mortality, and maturation, and food habits), and (5) collect baseline oceanographic and environmental data. These data can be leveraged for exploring future changes in the patterns of abundance and distribution of species in the region. 60.1.2 Data analysis 60.1.2.1 Global Climate Model Projection The CM2.6 80-year projections can be roughly assigned to a time period by using the International Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs), which describe four different 21st century pathways of anthropogenic greenhouse gas emissions, air pollutant emissions, and land use (IPCC 2014). There are four RCPs, ranging from a stringent mitigation scenario (RCP2.6), two intermediate scenarios (RCP4.5 and RCP6.0), and one scenario with very high greenhouse gas emissions (RCP8.5). For RCP8.5, the global average temperature at the surface warms by 2C by approximately 2060-2070 relative to the 1986-2005 climatology (see Figure SPM.7a in IPCC, 2013). For CM2.6, the global average temperature warms by 2C by approximately years 60-80 (see Fig. 1 in Winton et al. (2014)). Therefore, the last 20 years of the transient climate response simulation roughly corresponds to 2060-2080 of the RCP8.5 scenario. Here, the monthly differences in surface and bottom temperatures (deltas) for spring (February-April) and fall (September- November) are added to an average annual temperature climatology for spring and fall, respectively, derived from observed surface and bottom temperatures to produce an 80-year time series of future bottom and surface temperatures in both seasons. The observed temperatures come from the NEFSC spring and fall bottom trawl surveys conducted from 1968 to 2013 and represent approximately 30,000 observations over the time series. 60.1.2.2 Modeling Changes in Suitable Thermal Habitat We modeled individual species thermal habitat across the whole U.S. NES and not by sub-region because we did not want to assume that species would necessarily maintain these assemblages in the future. Indeed, the goal here is to determine future patterns of thermal habitat availability for species on the U.S. NES in more broad terms. We fit one generalizaed additive model (GAM) based on both spring and fall data (i.e., an annual model as opposed to separate spring and fall models) and use it to project potential changes in distribution and magnitude of biomass separately for each season for each species. By creating a single annual model based on temperature data from both spring and fall, we ensure that the full thermal envelope of each species is represented. For example, if a species with a wide thermal tolerance has historically been found in cooler waters in the spring, and in warmer waters in the fall, an annual model will ensure that if there are warmer waters in the spring in the future, that species will have the potential to inhabit those areas. Additionally, because the trawl survey data are subject to many zero observations, we use delta-lognormal GAMs (Wood 2011), which model presence-absence separately from logged positive observations. The response variables in each of the GAMs are presence/absence and logged positive biomass of each assemblage or individual species, respectively. A binomial link function is used in the presence/absence models and a Gaussian link function is used in the models with logged positive biomass. The predictor variables are surface and bottom temperature and depth (all measured by the survey at each station), fit with penalized regression splines, and survey stratum, which accounts for differences in regional habitat quality across the survey region. Stratum may be considered to account for additional information not explicitly measured by the survey (e.g., bottom rugosity). Predictions of species abundance are calculated as the product of the predictions from the presence-absence model, the exponentiated predictions from the logged positive biomass model, and a correction factor to account for the retransformation bias associated with the log transformation (Duan 1983; and see Pinsky et al. 2013). We calculated the suitable thermal habitat both in terms of changes in suitable thermal abundance, defined as the species density possible given appropriate temperature, depth and bathymetric conditions, and changes in suitable thermal area, defined as the size of the physical area potentially occupied by a species given appropriate temperature, depth and bathymetric conditions. Suitable thermal abundance is determined from the predictions from the GAMs (i.e., a prediction of biomass). However, this quantity should not be interpreted directly as a change in future abundance or biomass, but instead as the potential abundance of a species in the future given changes in temperature and holding all else (e.g., fishing effort, species interactions, productivity, etc.) constant. Suitable thermal area is determined as a change in the suitable area that a species distribution occupies in the future and is derived from the area of the kernel density of the distribution. To ensure that the estimates are conservative, we select all points with values greater than one standard deviation above the mean. We then compute the area of these kernels using the gArea function from the rgeos package in R (Bivand et al. 2011). 60.1.3 Plotting Figure 60.1: Current thermal habitat estimate (A), and 20-40 year thermal habitat projection (B) for summer flounder on the Northeast Continental Shelf. Note: The thermal habitat model output for all species presented in State of the Ecosystem reports is accessible through the NEFSC ERDDAP server. References "],["trend-analysis.html", "61 Trend Analysis 61.1 Methods", " 61 Trend Analysis Description: Time series trend analysis Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) Indicator category: Extensive analysis, not yet published Contributor(s): Sean Hardison, Charles Perretti, Geret DePiper Data steward: NA Point of contact: Kimberly Bastille, kimberly.bastille@noaa.gov Public availability statement: NA 61.1 Methods Summarizing trends for ecosystem indicators is desirable, but the power of statistical tests to detect a trend is hampered by low sample size and autocorrelated observations (see Nicholson and Jennings 2004; Wagner et al. 2013; Storch 1999). Prior to 2018, time series indicators in State of the Ecosystem reports were presented with trend lines based on a Mann-Kendall test for monotonic trends to test significance (p < 0.05) of both long term (full time series) and recent (20072016) trends, although not all time series were considered for trend analysis due to limited series lengths. There was also concern that a Mann-Kendall test would not account for any autocorrelation present in State of the Ecosystem (SOE) indicators. In a simulation study (Hardison et al. 2019), we explored the effect of time series length and autocorrelation strength on statistical power of three trend detection methods: a generalized least squares model selection approach, the Mann-Kendall test, and Mann-Kendall test with trend-free pre-whitening. Methods were applied to simulated time series of varying trend and autocorrelation strengths. Overall, when sample size was low (N = 10) there were high rates of false trend detection, and similarly, low rates of true trend detection. Both of these forms of error were further amplified by autocorrelation in the trend residuals. Based on these findings, we selected a minimum series length of N = 30 for indicator time series before assessing trend. We also chose to use a GLS model selection (GLS-MS) approach to evaluate indicator trends in the 2018 (and future) State of the Ecosystem reports, as this approach performed best overall in the simulation study. GLS-MS also allowed for both linear and quadratic model fits and quantification of uncertainty in trend estimates. The model selection procedure for the GLS approach fits four models to each time series and selects the best fitting model using AICc. The models are, 1) linear trend with uncorrelated residuals, 2) linear trend with correlated residuals, 3) quadratic trend with uncorrelated residuals, and 4) quadratic trend with correlated residuals. I.e., the models are of the form \\[ Y_t = \\alpha_0 + \\alpha_1X_t + \\alpha_2X_t^2 + \\epsilon_t\\] \\[\\epsilon_t = \\rho\\epsilon_{t-1} + \\omega_t\\] \\[w_t \\sim N(0, \\sigma^2)\\] Where \\(Y_t\\) is the observation in time \\(t\\), \\(X_t\\) is the time index, \\(\\epsilon_t\\) is the residual in time \\(t\\), and \\(\\omega_t\\) is a normally distributed random variable. Setting \\(\\alpha_2 = 0\\) yields the linear trend model, and \\(\\rho = 0\\) yields the uncorrelated residuals model. The best fit model was tested against the null hypothesis of no trend through a likelihood ratio test (p < 0.05). All models were fit using the R package nlme (Pinheiro et al. 2017) and AICc was calculated using the R package AICcmodavg (Mazerolle 2017). In SOE time series figures, significant positive trends were colored orange, and negative trends purple. 61.1.1 Data source(s) NA 61.1.2 Data extraction NA 61.1.3 Data analysis Code used for trend analysis can be found here. Example plot References "],["warm-core-rings.html", "62 Warm Core Rings 62.1 Methods", " 62 Warm Core Rings Description: Warm Core Rings Found in: State of the Ecosystem - Mid-Atlantic (2020+), State of the Ecosystem - New England (2020+) Indicator category: Published Results Contributor(s): Avijit Gangopadhyay avijit.gangopadhyay@umassd.edu Data steward: Avijit Gangopadhyay Point of contact: Avijit Gangopadhyay Public availability statement: Data is available upon request. 62.1 Methods The plot showing the number of warm core ring formations and regime shift replicates figure 3 in Gangopadhyay et al. (2019). Detailed methods on the warm core ring time series and regime shift analysis are described in the manuscript. 62.1.1 Data sources Gulf Stream charts from Jennifer Clark are the primary data source for the warm core ring analysis in Gangopadhyay et al. (2019). The Gulf Stream charts use infra-red (IR) imagery, satellite altimetry data, and surface in-situ temperature data in 3-day composite images are regularly produced by NOAA and/or the Johns Hopkins University Applied Physics Lab (fermi) group (see http://fermi.jhuapl.edu for more details). 62.1.2 Data extraction The data from Gangopadhyay et al. (2019) were provided by Avijit Gangopandhyay, School for Marine Science and Technology, University of Massachusetts Dartmouth, MA. 62.1.3 Data analysis A sequential regime shift detection algorithm was used to identify the regimes evident in the warm core ring formation time-series. See Gangopadhyay et al. (2019) for details. 62.1.4 Data processing Warm core ring data were formatted for inclusion in the ecodata R package using this R code. 62.1.5 Plotting The plot below was built using the code found here. Figure 62.1: Interannual Variability of the WCR formation. The regime shift (denoted by the split in the red solid line) is significant at the turn of the century. Figure reproduced with permission from Gangopadhyay, et al. (2019). References "],["wind-energy-delvelopment-timeline.html", "63 Wind Energy Delvelopment Timeline 63.1 Methods", " 63 Wind Energy Delvelopment Timeline Description: Wind Energy Lease Area Development Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2021+), State of the Ecosystem - Mid-Atlantic (2021+) Indicator category: Published methods, Synthesis of published information, Database pull, Database pull with analysis Contributor(s): Angela Silva, Andrew Lipsky, Doug Christel Data steward: Angela Silva Point of contact: Angela Silva angela.silva@noaa.gov Public availability statement: Source data are NOT publicly available. Please email angela.silva@noaa.gov for further information and queries of Speed and Extent of Offshore Wind Development indicator source data. 63.1 Methods 63.1.1 Data Sources BOEM lease area, Call Areas, Planning Area shapefiles: https://www.boem.gov/renewable-energy/mapping-and-data/renewable-energy-gis-data; Maine Area of Interest: Maine Department of Marine Resources, Central Atlantic Bight planning area draft (BOEM communication, INTERNAL ONLY private shapefile); Foundation and Cable data from South Fork Final Environemntal Impact Statement (SWFW FEIS) data tables E-4, E-4-1, E-2: https://www.boem.gov/sites/default/files/documents/renewable-energy/state-activities/SFWF%20FEIS.pdf 63.1.2 Data Analysis All data was updated for 2022 with South Fork Wind Farm FEIS and the following assumptions were made on future wind areas: * (1) There are no reported values for foundations, cable acres and miles and year of construction for NY WEA, Maine AOI, and Central Atlantic Bight draft planning area. * (2) To estimate the variables, the ratio of each (Cumul_FNDS, Cumul_Offsh_Cbl_Acres, Cumul_OffExp_Inter_Cab_Miles, TBNSinstall_no) was calculated by using reported values for existing lease area. All data is reported as 2030 Spatial Analysis for Project_Acres: Project Areas and Call Area acres were calculated using BOEM Project Area Shapefiles (Project_Areas_12_3_2019), BOEM NY Call Area Shapefiles (NY_Call_Areas), and NY Call Area Primary and Secondary Recommendation shapefiles (BOEM_NY_Draft_WEAs_11_1_2018) in ArcMap. Project_Areas_12_3_2019, NY_Call_Areas, and BOEM_NY_Draft_WEAs_11_1_2018 Acres were calculated using Add Field and Field Calculator tool. Python Expression = !shape.area@acres! Project_Name: Table E-4 of South Fork FEIS Project names were matched to shapefiles by name and lease number. FDNS: Number of foundations proposed or expected for each Project area taken directly from Table E-4 of South Fork DEIS. Offsh_Cbl_Acres: Values taken directly from Table A-4 in South Fork DEIS (Table A-4: Offshore Wind Leasing Activities in the U.S. East Coast: Projects and Assumptions [part 2], pg. E-3-4). Total values for MA/RI lease areas Bay State Wind, Liberty Wind, OCS-A 0522 Remainder, OCS-A 0500 Remainder, OCS-A 0521 Remainder, OCS-A 0520 were aggregated in the table (567 total acres). Values were evenly distributed across the 6 Project areas. As such, these values should be treated as estimates until more information is released specific to individual project areas and their landing sites. Dominion Energy was presented as 3 phases in Table E-4 for Project_Name (Dominion Energy Phase1, Dominion Energy Phase 2, Dominion Energy Phase 3). Only one Project shapefile area exists for this lease area OCS-A 0483. Therefore, the total shapefile acreage was evenly divided between 3 phases similar to how the foundations were treated in table E-4 (Future Offshore Wind Project Construction Schedule, pg. E-14). OffExpCab_Miles: Offshore Export Cable Length OCS-A 0482, OCS-A 0519 OCS-A 0490 had 360 offshore export cable miles reported in Table E-4. This number was divided by 3 and 120 were assigned to these three project areas. 63.1.3 Data Processing Data were formatted for inclusion in the ecodata R package using the R code found here. 63.1.4 Plotting The plot below was built using the code found here. 63.1.4.1 Proposed Wind Development Timeline Figure 63.1: Cumulative area of wind lease sites being developed on the Northeast Shelf. 63.1.4.2 Wind Development Map 63.1.4.3 Wind and Scientific Surveys Survey 1.Evaluate designs & Impacts 2.Design New Methods 3.Calibrate New/Existing Surveys 4.Bridge Solutions 5.Conduct New Surveys 6.Comms & Data Fall BTS Started Inital No No No Initial Spring BTS Started Initial No No No Initial EcoMon No No No No No No Scallop Started Initial No No No No Shellfish(Clams) No No No No No No Right Whale (Air) Inital Initial Initial No No No Marine Mammal/Turtle (Ship/Air) No No No No No No Altantic Shark (Bottom Long-Line No No No No No No GOM Bottom Long-Line No No No No No No GOM Shrimp Survey No No No No No No Atlantic Shark COASTPAN No No No No No No "],["wea-fishing-port-landings.html", "64 WEA Fishing Port Landings 64.1 Methods", " 64 WEA Fishing Port Landings Description: Port Landings from within Wind Lease Areas and Community Social Vulnerability Indicators Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2022+), State of the Ecosystem - Mid-Atlantic (2022+) Indicator category: Port Landings from within Wind Lease Areas and Community Social Vulnerability Indicators Contributor(s): Angela Silva, Doug Christel Data steward: Angela Silva Point of contact: Angela Silva angela.silva@noaa.gov Public availability statement: Source data are NOT publicly available. Please email angela.silva@noaa.gov for further information and queries of Speed and Extent of Offshore Wind Development indicator source data. 64.1 Methods 64.1.1 Data Sources Social Indicators Data: https://www.fisheries.noaa.gov/national/socioeconomics/social-indicators-coastal-communities https://www.st.nmfs.noaa.gov/data-and-tools/social-indicators/ Wind Data: https://www.greateratlantic.fisheries.noaa.gov/ro/fso/reports/WIND/ALL_WEA_BY_AREA_DATA.html https://www.fisheries.noaa.gov/resource/data/socioeconomic-impacts-atlantic-offshore-wind-development 64.1.2 Data Analysis Cumulative port landings(pounds) and revenue(dollars) from Wind Energy Areas (WEA) were pulled for communities along the Northeast US Shelf from 2010 to 2019. Percent of wind lease area landings were calculated compared to total landings for those communities. Environmental Justice and Gentrification Vulnerability were then matched to these communities. 64.1.3 Data Processing Data were formatted for inclusion in the ecodata R package using the R code found here. 64.1.4 Plotting The plot below was built using the code found here. 64.1.4.1 Mid-Atlantic Figure 64.1: Percent of Mid-Atlantic port revenue from Wind Energy Areas (WEA) in descending order from most to least port revenue from WEA. 64.1.4.2 New England Figure 64.2: Percent of New England port revenue from Wind Energy Areas (WEA) in descending order from most to least port revenue from WEA. "],["fisheries-revenue-in-wind-development-areas.html", "65 Fisheries Revenue in Wind Development Areas 65.1 Methods 65.2 Methods 2021", " 65 Fisheries Revenue in Wind Development Areas Description: Top Species Revenue from Wind Development Areas Found in: State of the Ecosystem - Gulf of Maine & Georges Bank 2022 (Different Methods 2021), State of the Ecosystem - Mid-Atlantic 2022 (Diferent Methods 2021) Indicator category: Database pull with analysis Contributor(s): Benjamin Galuardi, Douglas Christel Data steward: Doug Christel douglas.christel@noaa.gov Point of contact: Doug Christel douglas.christel@noaa.gov Public availability statement: Source data are NOT publicly available. Please email douglas.christel@noaa.gov for further information and queries of indicator source data. 65.1 Methods 65.1.1 Data Sources Modeled vessel trip report (VTR) data using the fishing footprint method (DePiper 2014 and Benjamin et al. 2017) linked with dealer reports for annual landings and revenue within wind lease areas and dealer report data for annual GARFO landings/revenue. 65.1.2 Data Analysis Using raster data of modeled VTR data using the Fishing Footprint method, we integrated dealer data and compared to existing/proposed wind lease areas to get landings/revenue in each area by year. 65.1.3 Data Processing Data were formatted for inclusion in the ecodata R package using the R code found here. 65.1.4 Plotting The plot below was built using the code found here. Figure 65.1: Top five MAFMC species revenue found in wind development areas. Table 65.1: Top ten species Landings and Revenue from Wind Energy Areas. GARFO and ASMFC Managed Species Maximum Percent Total Annual Regional Species Landings Minimum Percent Total Annual Regional Species Landings Maximum Percent Total Annual Regional Species Revenue Minimum Percent Total Annual Regional Species Revenue Atlantic surfclam 21 % 6 % 20 % 6 % American eel 13 % 2 % 18 % 0 % Atlantic menhaden 17 % 3 % 17 % 3 % Atlantic chub mackerel 15 % 0 % 16 % 0 % Yellowtail flounder 14 % 0 % 15 % 0 % Offshore hake 14 % 0 % 14 % 0 % Ocean quahog 14 % 5 % 13 % 5 % Atlantic sea scallops 12 % 1 % 10 % 1 % Skate wings 10 % 5 % 10 % 5 % Atlantic mackerel 9 % 0 % 10 % 0 % 65.2 Methods 2021 65.2.1 Data Sources This indicator is derived from the data underpinning the Sociceoeconomic Impacts of Atlantic Offshore Wind Development web site, which can be accessed at https://www.fisheries.noaa.gov/resource/data/socioeconomic-impacts-atlantic-offshore-wind-development. The underlying raster data is defined in Benjamin S, Lee MY, DePiper G. 2018. Visualizing fishing data as rasters. NEFSC Ref Doc 18-12; 24 p. This raster data was then linked to the Greater Atlantic Regional Offices Data Matching Imputation System (https://www.fisheries.noaa.gov/inport/item/17328) to derive revenue estimates from the Wind Energy Areas, defined as of December 11, 2020. Of note is that the version of DMIS utilized for this reporting includes the SFCLAM data missing from the traditional DMIS dataset. All revenue estimates are deflated to 2019 dollars using the St. Louis Federal Reserves Quarterly Implicit GDP Deflator, which can be accessed at https://fred.stlouisfed.org/data/GDPDEF.txt 65.2.2 Data Analysis Code used to analyze this data can be found here "],["wind-lease-areas-and-habitat-occupancy-overlap.html", "66 Wind lease areas and habitat occupancy overlap 66.1 Methods", " 66 Wind lease areas and habitat occupancy overlap Description: Wind lease areas and habitat occupancy Found in: State of the Ecosystem - Mid-Atlantic (2020) Indicator category: Database pull with analysis; Extensive analysis; not yet published; Published methods Contributor(s): Kevin Friedland Data steward: Kimberly Bastille kimberly.bastille@noaa.gov Point of contact: Kimberly Bastille kimberly.bastille@noaa.gov Public availability statement: Source data are publicly available. 66.1 Methods Habitat area with a probability of occupancy greater than 0.5 was modeled for many species throughout the Northeast Large Marine Ecosystem (NE-LME) (Kevin D. Friedland et al. 2020). Methodology for habitat occpancy models have been discussed in a seperate chapter. Bureau of Ocean Energy Management (BOEM) is the department responsible for the developement of offshore wind energy. Existing and proposed and lease areas were overlayed with habitat occupancy models to determine the species most likely to be found in the wind lease areas (Table 66.1). 66.1.1 Data extraction BOEM existing and proposed lease areas (as of Feb 2019) shape files were taken from the BOEM website (Figure ??). 66.1.2 Data analysis For the purposes of this indicator, the Northeast Shelf was broken into three general areas (North, Mid and South) (Figure ??). The species shown in the table below (Table 66.1)are those that have the highest average probablity of occupancy in the lease areas. 66.1.3 Data processing Code used to format wind lease area and habitat occupancy overlap for inclusion in the ecodata package can be found here. 66.1.4 Plotting Code used to build the table and figure below. Table 66.1: Species with highest probability of occupancy species each season and area, with observed trends Existing - North Proposed - North Existing - Mid Proposed - Mid Existing - South Season Species Trend Species Trend Species Trend Species Trend Species Trend Spring Little Skate \\(\\nearrow\\) Atlantic Herring Little Skate \\(\\nearrow\\) Spiny Dogfish \\(\\nearrow\\) Spiny Dogfish \\(\\nearrow\\) Spring Atlantic Herring \\(\\searrow\\) Little Skate \\(\\nearrow\\) Atlantic Herring \\(\\searrow\\) Atlantic Herring \\(\\searrow\\) Longfin Squid \\(\\nearrow\\) Spring Windowpane \\(\\nearrow\\) Longhorn Sculpin \\(\\nearrow\\) Spiny Dogfish \\(\\nearrow\\) Little Skate \\(\\nearrow\\) Summer Flounder \\(\\nearrow\\) Spring Winter Skate \\(\\nearrow\\) Windowpane \\(\\nearrow\\) Windowpane \\(\\nearrow\\) Alewife \\(\\searrow\\) Clearnose Skate \\(\\nearrow\\) Spring Longhorn Sculpin \\(\\nearrow\\) Alewife \\(\\searrow\\) Winter Skate \\(\\nearrow\\) Silver Hake \\(\\nearrow\\) Spotted Hake \\(\\nearrow\\) Fall Butterfish \\(\\nearrow\\) Butterfish \\(\\nearrow\\) Summer Flounder \\(\\nearrow\\) Longhorn Sculpin \\(\\nearrow\\) Longfin Squid \\(\\searrow\\) Fall Longfin Squid \\(\\nearrow\\) Fourspot Flounder Longfin Squid \\(\\nearrow\\) Little Skate \\(\\nearrow\\) Northern Searobin \\(\\nearrow\\) Fall Summer Flounder \\(\\nearrow\\) Longhorn Sculpin \\(\\searrow\\) Butterfish \\(\\nearrow\\) Butterfish \\(\\nearrow\\) Clearnose Skate \\(\\nearrow\\) Fall Winter Flounder \\(\\searrow\\) Summer Flounder \\(\\nearrow\\) Smooth Dogfish \\(\\nearrow\\) Sea Scallop \\(\\nearrow\\) Butterfish \\(\\nearrow\\) Fall Spiny Dogfish \\(\\searrow\\) Spiny Dogfish \\(\\searrow\\) Windowpane \\(\\nearrow\\) Fourspot Flounder \\(\\nearrow\\) Spiny Dogfish/Spotted Hake \\(\\nearrow\\) References "],["zooabund.html", "67 Zooplankton 67.1 Methods", " 67 Zooplankton Description: Annual time series of zooplankton abundance Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2017+), State of the Ecosystem - Mid-Atlantic (2017+) Indicator category: Database pull with analysis; Synthesis of published information; Extensive analysis, not yet published; Published methods Contributor(s): Ryan Morse, Kevin Friedland Data steward: Harvey Walsh, harvey.walsh@noaa.gov; Mike Jones, michael.jones@noaa.gov Point of contact: Ryan Morse, ryan.morse@noaa.gov; Harvey Walsh, harvey.walsh@noaa.gov; Kevin Friedland, kevin.friedland@noaa.gov Public availability statement: Source data are publicly available here. Derived data can be found here. 67.1 Methods 67.1.1 Data sources Zooplankton data are from the National Oceanographic and Atmospheric Administration Marine Resources Monitoring, Assessment and Prediction (MARMAP) program and Ecosystem Monitoring (EcoMon) cruises detailed extensively in Kane (2007), Kane (2011), and Morse et al. (2017). 67.1.2 Data extraction Data are from the publicly available zooplankton dataset on the NOAA File Transfer Protocol (FTP) server. The excel file has a list of excluded samples and cruises based on Kane (2007) and Kane (2011). R code used in extraction process. 67.1.3 Data analysis Annual abundance anomalies Data are processed similarly to Kane (2007) and Perretti et al. (2017b), where a mean annual abundance by date is computed by area for each species meeting inclusion metrics set in Morse et al. (2017). This is accomplished by binning all samples for a given species to bi-monthly collection dates based on median cruise date and taking the mean, then fitting a spline interpolation between mean bi-monthly abundance to give expected abundance on any given day of the year. Code used for zooplankton data analysis can be found here. Copepod Abundance anomalies (Figure 67.1) are computed from the expected abundance on the day of sample collection. Abundance anomaly time series are constructed for Centropages typicus, Pseudocalanus spp., Calanus finmarchicus, and total zooplankton biovolume. The small-large copepod size index is computed by averaging the individual abundance anomalies of Pseudocalanus spp., Centropages hamatus, Centropages typicus, and Temora longicornis, and subtracting the abundance anomaly of Calanus finmarchicus. This index tracks the overall dominance of the small bodied copepods relative to the largest copepod in the Northeast U.S. region, Calanus finmarchicus. Euphausiids and Cnidarians Stratified abundance of euphausiids and cnidarians were included in the 2020 State of the Ecosystem reports (Figure 67.2). These were calculated as the log of estimated absolute number of individuals. Seasonal abundance Time series of zooplankton abundance in the spring and fall months have been presented in the 2019 Mid-Atlantic State of the Ecosystem report. Raw abundance data were sourced from the EcoMon cruises referenced above, and ordinary kriging was used to estimate seasonal abundance over the Northeast Shelf. These data were then aggregated further into time series of mean abundance by Ecological Production Unit. Zooplankton Diversity Time series of zooplankton diveristy (effective shannon) (Figure 67.3) was calculated using 42 zooplankton classifications collected fromt the EcoMon cruises, referenced above. 67.1.4 Data processing Zooplankton abundances indicators were formatted for inclusion in the ecodata R package using the code at these links, abundance anomaly and seasonal abundance 67.1.5 Plotting Code used to create the figures below can be found linked here, copepod abundance (67.1), Euphausiid and Cnidarian abundance (67.2) and zooplankton diversity (67.3 ). Abundance anomaly Figure 67.1: Large (red) and small-bodied (blue) copepod abundance in the Mid-Atlantic Bight. Figure 67.2: Stratified abundance of cnidarians and euphausiids in Mid-Atlantic Bight. Zooplankton Diversity Figure 67.3: Zooplankton diversity in the Mid-Atlantic Bight. References "],["glossary.html", "68 Glossary", " 68 Glossary Apex Predator: Predators with no natural predators of their own, such as large sharks, toothed whales, seals, tunas, and billfish. Benthivore: Predator feeding on bottom-dwelling prey, such as lobster and haddock. Benthos: Organisms that live on or in the sea bottom (Madden and Grossman 2004), such as scallop and quahog. Bmsy: The weight (biomass) of a group of fish necessary to produce maximum sustainable yield (MSY) (Northwest Fisheries Science Center. Glossary, n.d.). 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). Climate Vulnerability: The degree to which the habitat/species are unable to cope with negative impacts of climate change. Climatology: Average conditions over a specific time period. Cold Pool: Area of relatively cold bottom water that forms on the US northeast shelf in the Mid-Atlantic Bight. Commercial Fishery: Large-scale industry selling fish, shellfish and other aquatic animals. Community Engagement: 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 (Madden and Grossman 2004). Condition: A mathematical measurement of the plumpness, or the general health of a fish or group of fishes (W. H. Wallace Richard K and Szedlmayer 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). Continental Slope: Part of the continental margin; the ocean floor from the continental shelf to the continental rise (Madden and Grossman 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). Effort: The amount of time and fishing power used to harvest fish; includes gear size, boat size, and horsepower (W. H. Wallace Richard K and Szedlmayer 1994). Elasmobranch: Describes a group of fish without a hard bony skeleton, including sharks, skates, and rays (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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 (United States and Administration 2005). Energy Density: A measurement of the amount of energy (calories) contained in a certain amount of food or prey organism. Estuary: Coastal body of brackish water which may be an important nursery habitat for many species of interest. Estuarine: 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 (Lemley and Adams 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 (Service 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 (Madden and Grossman 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 (W. H. Wallace Richard K and Szedlmayer 1994). Fishery-Independent Data: Stock/habitat/environmental data collected independently of the activity of the fishing sector usually on a research vessel (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). Fmsy: The rate of removal of fish from a population by fishing that, if applied constantly, would result in maximum sustainable yield (MSY) (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). GB: Georges Bank Ecological Production Unit (Technical Documentation: State of the Ecosystem, n.d.). GOM: Gulf of Maine Ecological Production Unit (Technical Documentation: State of the Ecosystem, n.d.). 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. Gulf Stream: 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). Harvest: The total number or weight of fish caught and kept from an area over a period of time (W. H. Wallace Richard K and Szedlmayer 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). Ichthyoplankton: Fish eggs and larvae belonging to the planktonic community (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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 (W. H. Wallace Richard K and Szedlmayer 1994). Large Marine Ecosystem (LME): A geographic area of an ocean that has distinct physical and oceanographic characteristics, productivity, and trophically dependent populations (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). MAB: Mid-Atlantic Bight Ecological Production Unit (Technical Documentation: State of the Ecosystem, n.d.). 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 (Hobday et al. 2016). Marine Mammals: Warm-blooded animals that live in marine waters and breathe air directly. These include porpoises, dolphins, whales, seals, and sea lions (R. K. Wallace and Fletcher 2000). Mortality Event: The death of one or more individuals of a species. Northeast Shelf: The Northeast U.S. Continental Shelf Large Marine Ecosystem (NES LME). The region spans from Cape Hatteras, NC to Nova Scotia and includes the waters between the eastern coastline of the U.S and the continental shelf break. Ocean Acidification (OA): 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 (Jewett et al. 2020). Overfished: When a stocks 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). Piscivore: Predator whose diet primarily consists of fish and squid, such as cod and striped bass. Planktivore: 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) (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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 (D. Pauly and Christensen 1995a). Primary Productivity: The rate at which food energy is generated, or fixed, by photosynthesis or chemosynthesis. Probability of Occupancy: 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) (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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. Recruitment: The number of young fish entering the population each year at the age first caught in fishing/survey gear. Revenue: 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) (Madden and Grossman 2004). Satellite Imagery: Imagery of the ocean surface gathered by earth-orbiting satellites (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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. (Technical Documentation: State of the Ecosystem, n.d.). 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). SS: Scotian Shelf Ecological Production Unit (Technical Documentation: State of the Ecosystem, n.d.). 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 (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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. (United Nations Food and Agricultural Organization. Fisheries Glossary, n.d.). 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 (United States and Administration 2005). Zooplankton: Plankton consisting of small animals and the immature stages of larger animals, ranging from microscopic organisms to large species, such as jellyfish. Figure 68.1: Map of Northeast U.S. Continental Shelf Large Marine Ecosystem from Hare et al. (2016). References "],["references-5.html", "References", " References "],["404.html", "Page not found", " Page not found The page you requested cannot be found (perhaps it was moved or renamed). You may want to try searching to find the page's new location, or use the table of contents to find the page you are looking for. "]]
diff --git a/bibliography/protected_species_hotspots.bib b/bibliography/protected_species_hotspots.bib
index f0e81c48..f68fa5b0 100644
--- a/bibliography/protected_species_hotspots.bib
+++ b/bibliography/protected_species_hotspots.bib
@@ -9,7 +9,7 @@ @article{Gende2006
   publisher={Elsevier}
 }
 
-@article{@White2020,
+@article{White2020,
   title={Spatial ecology of long-tailed ducks and white-winged scoters wintering on Nantucket Shoals},
   author={White, Timothy P and Veit, Richard R},
   journal={Ecosphere},
@@ -20,7 +20,7 @@ @article{
   publisher={Wiley Online Library}
 }
 
-@misc{@Palka2017,
+@misc{Palka2017,
   title={Atlantic Marine Assessment Program for Protected Species: 2010--2014 US Dept. of the Interior, Bureau of Ocean Energy Management, Atlantic OCS Region, Washington, DC. OCS Study BOEM 2017-071},
   author={Palka, DL and Chavez-Rosales, S and Josephson, E and Cholewiak, D and Haas, HL and Garrison, L and Orphanides, C},
   year={2017}
diff --git a/chapters/Catch_and_Fleet_Diversity_indicators.Rmd b/chapters/Catch_and_Fleet_Diversity_indicators.Rmd
index 758ad444..e0b8c75a 100644
--- a/chapters/Catch_and_Fleet_Diversity_indicators.Rmd
+++ b/chapters/Catch_and_Fleet_Diversity_indicators.Rmd
@@ -38,7 +38,7 @@ spp <- spp %>%
   dplyr::select(Group, NESPP3, 'Common Name', 'Scientific Name')
  
 knitr::kable(spp, caption="Species grouping", booktabs=T, longtable = T) %>%
-  kableExtra::kable_styling(full_width = T,  latex_options = c("repeat_header"), font_size = 8) %>%
+  kableExtra::kable_styling(latex_options = c("repeat_header"), font_size = 8) %>%
   kableExtra::collapse_rows(columns = 1)
 
 ```
diff --git a/chapters/Species_density_estimates.Rmd b/chapters/Species_density_estimates.Rmd
index f79b6a97..242f67ef 100644
--- a/chapters/Species_density_estimates.Rmd
+++ b/chapters/Species_density_estimates.Rmd
@@ -27,7 +27,7 @@ Current and historical species distributions are based on the NEFSC Bottom Trawl
 ### Data analysis
 
 Code used for species density analysis can be found [here](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/species_density_analysis.R). 
-```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/tech-doc/master/R/stored_scripts/species_density_analysis.R"), eval=F, echo=T}
+```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/tech-doc/master/R/stored_scripts/species_density_analysis.R"), eval=F, echo=F}
 
 ```
 
diff --git a/chapters/Zooplankton_indicators.Rmd b/chapters/Zooplankton_indicators.Rmd
index 54c109ee..c869d4cc 100644
--- a/chapters/Zooplankton_indicators.Rmd
+++ b/chapters/Zooplankton_indicators.Rmd
@@ -23,7 +23,7 @@ Zooplankton data are from the National Oceanographic and Atmospheric Administrat
 Data are from the publicly available zooplankton dataset on the NOAA File Transfer Protocol (FTP) server. The excel file has a list of excluded samples and cruises based on @Kane2007 and @Kane2011.
 
 R code used in extraction process.
-```{r, echo = T, eval = F}
+```{r, echo = F, eval = F}
 # load data
 URL='ftp://ftp.nefsc.noaa.gov/pub/hydro/zooplankton_data/EcoMon_Plankton_Data_v3_0.xlsx'
 ZPD=openxlsx::read.xlsx(URL, sheet='Data')
diff --git a/chapters/cold_pool_index.Rmd b/chapters/cold_pool_index.Rmd
index ad8e3be4..03aca3f2 100644
--- a/chapters/cold_pool_index.Rmd
+++ b/chapters/cold_pool_index.Rmd
@@ -53,7 +53,8 @@ The Cold Pool Index (Model_CPI) was adapted from Miller et al. (2016). Residual
 
 
 
-$${{Model}__CPI}_y=\ \frac{\sum_{i=1}^{n}{{(T}_{i,\ y}\ -\ {\bar{T}}_{i,\ 1972-2019})\ }}{n}$$
+$${{CPI}_y}=\ \frac{\sum_{i=1}^{n}{{(T}_{i,\ y}\ -\ {\bar{T}}_{i,\ 1972-2019})\ }}{n}$$
+
 
 
 
@@ -62,7 +63,7 @@ where n is the number of grid cells over the Cold Pool domain.
 
 #### Persistence Index (Model_PI)
 
-The temporal component of the Cold Pool was calculated using the persistence index (Model_PI). Model_PI measures the duration of the Cold Pool and is estimated using the month when bottom temperature rises above 10˚C after the Cold Pool is formed each year. We first selected the area over the cold pool domain in which bottom temperature falls below 10˚C between June and October. We then calculated the “residual month” in each grid cell, i, in the Cold Pool domain as the difference between the month when bottom temperature rises above 10˚C in year y and the average of those months over the period 1972–2019. Then, Model_PI was calculated as the mean “residual month” over the Cold Pool domain:
+The temporal component of the Cold Pool was calculated using the persistence index (Model_PI). Model_PI measures the duration of the Cold Pool and is estimated using the month when bottom temperature rises above 10C after the Cold Pool is formed each year. We first selected the area over the cold pool domain in which bottom temperature falls below 10C between June and October. We then calculated the “residual month” in each grid cell, i, in the Cold Pool domain as the difference between the month when bottom temperature rises above 10C in year y and the average of those months over the period 1972–2019. Then, Model_PI was calculated as the mean “residual month” over the Cold Pool domain:
 
 
 $${PI}_y=\ \frac{\sum_{i=1}^{n}{{(Month}_{i,\ y}\ -\ {\bar{Month}}_{i,\ 1972-2019})\ }}{n}$$
@@ -70,15 +71,15 @@ $${PI}_y=\ \frac{\sum_{i=1}^{n}{{(Month}_{i,\ y}\ -\ {\bar{Month}}_{i,\ 1972-201
 
 #### Spatial Extent Index (Model_SEI)
 
-The spatial component of the Cold Pool and the habitat provided by the cold pool was calculated using the Spatial Extent Index (Model_SEI). The Model_SEI is estimated by the number of cells where bottom temperature remains below 10˚C for at least 2 months between June and September. 
+The spatial component of the Cold Pool and the habitat provided by the cold pool was calculated using the Spatial Extent Index (Model_SEI). The Model_SEI is estimated by the number of cells where bottom temperature remains below 10C for at least 2 months between June and September. 
 
 The Bottom temperature data are from ROMS-NWA between 1958 and 1992, from Glorys reanalysis between 1993 and 2019 and from Global Ocean Physics for 2020 and 2021.
 
 Bottom temperature from Glorys reanalysis and Global Ocean Physics Analysis were not being processed. 
 
-Bottom temperature from ROMS-NWA (used for the period 1958-1992) were bias-corrected. Previous studies that focused on the ROMS-NWA-based Cold Pool highlighted strong and consistent warm bias in bottom temperature of about 1.5˚C during the stratified seasons over the period of 1958-2007 (Chen et al., 2018; Chen and Curchitser, 2020). In order to bias-correct bottom temperature from ROMS-NWA, we used the monthly climatologies of observed bottom temperature from the Northwest Atlantic Ocean regional climatology (NWARC) over decadal periods from 1955 to 1994. The NWARC provides high resolution (1/10° grids) of quality-controlled in situ ocean temperature based on a large volume of observed temperature data (Seidov et al., 2016a, 2016b) (https://www.ncei.noaa.gov/products/northwest-atlantic-regional-climatology). The first step was to re-grid the ROMS-NWA to obtain bottom temperature over the same 1/10° grid as the NWARC. Then, a monthly bias was calculated in each grid cell and for each decade (1955–1964, 1965–1974, 1975–1984, 1985–1994) in the MAB and in the SNE shelf:
+Bottom temperature from ROMS-NWA (used for the period 1958-1992) were bias-corrected. Previous studies that focused on the ROMS-NWA-based Cold Pool highlighted strong and consistent warm bias in bottom temperature of about 1.5C during the stratified seasons over the period of 1958-2007 (Chen et al., 2018; Chen and Curchitser, 2020). In order to bias-correct bottom temperature from ROMS-NWA, we used the monthly climatologies of observed bottom temperature from the Northwest Atlantic Ocean regional climatology (NWARC) over decadal periods from 1955 to 1994. The NWARC provides high resolution (1/10° grids) of quality-controlled in situ ocean temperature based on a large volume of observed temperature data (Seidov et al., 2016a, 2016b) (https://www.ncei.noaa.gov/products/northwest-atlantic-regional-climatology). The first step was to re-grid the ROMS-NWA to obtain bottom temperature over the same 1/10° grid as the NWARC. Then, a monthly bias was calculated in each grid cell and for each decade (1955–1964, 1965–1974, 1975–1984, 1985–1994) in the MAB and in the SNE shelf:
 
-$${BIAS}_{i,\ d}=\ T_{i,d}^{Climatology}\ -\ {\bar{T}}_{i,\ d}^{ROMS-NWA}\$$
+$${BIAS}_{i,\ d}=\ T_{i,d}^{Climatology}\ -\ {\bar{T}}_{i,\ d}^{ROMS-NWA}$$
 
 
 where $$T_{i,d}^{Climatology}$$ is the NWARC bottom temperature in the grid cell i for the decade d and $${\bar{T}}_{i,\ d}^{ROMS-NWA}$$ is the average ROMS-NWA bottom temperature over the decade d in the grid cell i. 
@@ -144,7 +145,7 @@ Chen, Z., and Curchitser, E. N. 2020. Interannual Variability of the Mid‐Atlan
 
 Fernandez, E., and Lellouche, J. M. 2018. Product user manual for the global ocean physical reanalysis product GLORYS12V1. Copernicus Product User Manual, 4: 1–15.
 
-Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., et al. 2018. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system. Ocean Science, 14: 1093–1126.
+Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., et al. 2018. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 112° high-resolution system. Ocean Science, 14: 1093–1126.
 
 Miller, T. J., Hare, J. A., and Alade, L. A. 2016. 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. Canadian Journal of Fisheries and Aquatic Sciences, 73: 1261–1270.
 
diff --git a/chapters/forage_energy_density.Rmd b/chapters/forage_energy_density.Rmd
index 50e48262..6a29842e 100644
--- a/chapters/forage_energy_density.Rmd
+++ b/chapters/forage_energy_density.Rmd
@@ -30,8 +30,8 @@ forage.tab <- data.frame('Common Name' = c('Atlantic Herring','alewife','silver
 names(forage.tab) <- c("Common Name","Scientific Name")
 
 
-knitr::kable(forage.tab, caption = "List of forage fish study species.",  booktabs=T) %>%
-  kableExtra::kable_styling(full_width = F)
+knitr::kable(forage.tab, caption = "List of forage fish study species.",  booktabs=T) #%>%
+ # kableExtra::kable_styling(full_width = F)
 ```
 
 
diff --git a/chapters/habs_psp.Rmd b/chapters/habs_psp.Rmd
index 601fca75..4e0f3e0a 100644
--- a/chapters/habs_psp.Rmd
+++ b/chapters/habs_psp.Rmd
@@ -1,4 +1,4 @@
-# Harmful Algal Blooms - Paralitic Shellfish Poisoning Indicator
+# Harmful Algal Blooms - Paralytic Shellfish Poisoning Indicator
 
 **Description**: Paralytic Shellfish Poisoning (PSP) toxins in the Gulf of Maine
 
diff --git a/chapters/hms_landings.Rmd b/chapters/hms_landings.Rmd
index 6b7e7c99..3ad856f9 100644
--- a/chapters/hms_landings.Rmd
+++ b/chapters/hms_landings.Rmd
@@ -36,7 +36,7 @@ Price per pound was used to determine the ex-vessel value. For landings with pri
 High migratory landings include 26 species of tunas, sharks and swordfish.
 
 Data were processed and analyzed using SAS and Microsoft Excel pivot tables. 
-The count of dealers and vessels in each regional species/management group sum was used to determine if a sufficient number of records were available to make the data public or if it needed to be marked as confidential. Additionally, ratios of landings reported by dealers/fishermen were compared in each regional species/managment group sum to determine if any one entity cotnributed more than ⅔ of the total which would require it being marked as confidential.
+The count of dealers and vessels in each regional species/management group sum was used to determine if a sufficient number of records were available to make the data public or if it needed to be marked as confidential. Additionally, ratios of landings reported by dealers/fishermen were compared in each regional species/managment group sum to determine if any one entity contributed more than 2/3 of the total which would require it being marked as confidential.
 
 ### Data Processing 
 
diff --git a/chapters/landings_data.Rmd b/chapters/landings_data.Rmd
index c45f3e8a..2c1515fb 100644
--- a/chapters/landings_data.Rmd
+++ b/chapters/landings_data.Rmd
@@ -24,8 +24,8 @@ Fisheries dependent data for the Northeast Shelf extend back several decades. Da
 ```{r calibration1, eval = T, echo = F}
 com.tables <- data.frame(Table = c('WOLANDS', 'WODETS', 'CFDETS_AA'),
                          Years = c('1964 - 1981', '1982 - 1993', '> 1994'))
-knitr::kable(com.tables, caption="Data formats", booktabs = T) %>% 
-  kableExtra::kable_styling(full_width = F)
+knitr::kable(com.tables, caption="Data formats", booktabs = T) #%>% 
+  #kableExtra::kable_styling(full_width = F)
 
 ```
 
@@ -50,8 +50,8 @@ gear.table <- data.frame('gear code' = c(1,2,3,4,5,6,7,8,9),
 names(gear.table) <- c("","Major gear")
 
 
-knitr::kable(gear.table, caption = "Gear types used in commercial landings",  booktabs=T) %>%
-  kableExtra::kable_styling(full_width = F)
+knitr::kable(gear.table, caption = "Gear types used in commercial landings",  booktabs=T)# %>%
+  #kableExtra::kable_styling(full_width = F)
 ```
 
 Several species have additional steps after the data is pulled from CFDBS.  Skates are typically landed as a species complex.  In order to segregate the catch into species, the ratio of individual skate species in the NEFSC bottom trawl survey is used to disaggregate the landings. A similar algorithm is used to separate silver and offshore hake which can be mistaken for one another.  Finally, Atlantic herring landings are pulled from a separate database as the most accurate weights are housed by the State of Maine.  Comlands pulls from the State database and replaces the less accurate numbers from the federal database.
diff --git a/chapters/long_term_sst_indicator.Rmd b/chapters/long_term_sst_indicator.Rmd
index fe6dea51..31f6159e 100644
--- a/chapters/long_term_sst_indicator.Rmd
+++ b/chapters/long_term_sst_indicator.Rmd
@@ -33,8 +33,8 @@ df <- data.frame(
 )
 
 knitr::kable(df,
-             caption="Coordinates used in NOAA ERSST V5 data extraction.",  booktabs=T) %>%
-  kableExtra::kable_styling(full_width = F) 
+             caption="Coordinates used in NOAA ERSST V5 data extraction.",  booktabs=T) #%>%
+  #kableExtra::kable_styling(full_width = F) 
 ```
 
 R code used in extracting time series of long-term SST data can be found [here](https://github.com/NOAA-EDAB/tech-doc/tree/master/R/stored_scripts/long-term-sst-extraction.R).
diff --git a/chapters/wind_habitat_occupancy.Rmd b/chapters/wind_habitat_occupancy.Rmd
index 02e90063..cdda9437 100644
--- a/chapters/wind_habitat_occupancy.Rmd
+++ b/chapters/wind_habitat_occupancy.Rmd
@@ -40,6 +40,20 @@ Code used to build the [table](https://github.com/NOAA-EDAB/ecodata/blob/master/
 
 ```
 
-```{r wind-map,code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/human_dimensions_MAB.Rmd-wind-map.R"), fig.cap="Map of BOEM existing (black) and proposed (red) lease areas as of February 2019.", message=FALSE, results=FALSE}
+<!-- # ```{r wind-map,code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/human_dimensions_MAB.Rmd-wind-map.R"), fig.cap="Map of BOEM existing (black) and proposed (red) lease areas as of February 2019.", message=FALSE, results=FALSE} -->
+<!-- #  -->
+<!-- # ``` -->
+
+
+```{r wind-map, fig.cap="Map of BOEM existing (black) and proposed (red) lease areas as of February 2019.", message=FALSE, results=FALSE}
+
+image.dir <- here::here("images")
+
+knitr::include_graphics(file.path(image.dir, "wind_hab_occupancy.png"))
+
+```
+
+
+
+
 
-```
\ No newline at end of file
diff --git a/packages.bib b/packages.bib
index ba5581ad..26b089b0 100644
--- a/packages.bib
+++ b/packages.bib
@@ -3,213 +3,161 @@ @Manual{R-base
   author = {{R Core Team}},
   organization = {R Foundation for Statistical Computing},
   address = {Vienna, Austria},
-  year = {2019},
+  year = {2021},
   url = {https://www.R-project.org/},
 }
+
 @Manual{R-bookdown,
   title = {bookdown: Authoring Books and Technical Documents with R Markdown},
   author = {Yihui Xie},
-  year = {2020},
-  note = {R package version 0.17},
+  year = {2021},
+  note = {R package version 0.24},
   url = {https://CRAN.R-project.org/package=bookdown},
 }
-@Manual{R-colorRamps,
-  title = {colorRamps: Builds color tables},
-  author = {Tim Keitt},
-  year = {2012},
-  note = {R package version 2.3},
-  url = {https://CRAN.R-project.org/package=colorRamps},
-}
+
 @Manual{R-cowplot,
-  title = {cowplot: Streamlined Plot Theme and Plot Annotations for 'ggplot2'},
+  title = {cowplot: Streamlined Plot Theme and Plot Annotations for ggplot2},
   author = {Claus O. Wilke},
-  year = {2019},
-  note = {R package version 1.0.0},
-  url = {https://CRAN.R-project.org/package=cowplot},
+  year = {2020},
+  note = {R package version 1.1.1},
+  url = {https://wilkelab.org/cowplot/},
 }
+
 @Manual{R-dplyr,
   title = {dplyr: A Grammar of Data Manipulation},
   author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller},
-  year = {2020},
-  note = {R package version 0.8.4},
+  year = {2022},
+  note = {R package version 1.0.8},
   url = {https://CRAN.R-project.org/package=dplyr},
 }
+
 @Manual{R-DT,
-  title = {DT: A Wrapper of the JavaScript Library 'DataTables'},
+  title = {DT: A Wrapper of the JavaScript Library DataTables},
   author = {Yihui Xie and Joe Cheng and Xianying Tan},
-  year = {2020},
-  note = {R package version 0.12},
-  url = {https://CRAN.R-project.org/package=DT},
+  year = {2021},
+  note = {R package version 0.20},
+  url = {https://github.com/rstudio/DT},
 }
+
 @Manual{R-ecodata,
   title = {ecodata: Documentation of Ecosystem Indicator Reporting},
-  author = {Sean Hardison},
+  author = {Kimberly Bastille and Sean Hardison},
   year = {2018},
   note = {R package version 0.1.0},
 }
-@Manual{R-forcats,
-  title = {forcats: Tools for Working with Categorical Variables (Factors)},
-  author = {Hadley Wickham},
-  year = {2020},
-  note = {R package version 0.5.0},
-  url = {https://CRAN.R-project.org/package=forcats},
-}
-@Manual{R-geosphere,
-  title = {geosphere: Spherical Trigonometry},
-  author = {Robert J. Hijmans},
-  year = {2019},
-  note = {R package version 1.5-10},
-  url = {https://CRAN.R-project.org/package=geosphere},
-}
+
 @Manual{R-ggplot2,
   title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics},
-  author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo and Hiroaki Yutani},
-  year = {2019},
-  note = {R package version 3.2.1},
+  author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo and Hiroaki Yutani and Dewey Dunnington},
+  year = {2021},
+  note = {R package version 3.3.5},
   url = {https://CRAN.R-project.org/package=ggplot2},
 }
+
 @Manual{R-ggrepel,
   title = {ggrepel: Automatically Position Non-Overlapping Text Labels with
-'ggplot2'},
+ggplot2},
   author = {Kamil Slowikowski},
-  year = {2019},
-  note = {R package version 0.8.1},
-  url = {https://CRAN.R-project.org/package=ggrepel},
+  year = {2021},
+  note = {R package version 0.9.1},
+  url = {https://github.com/slowkow/ggrepel},
 }
+
 @Manual{R-ggspatial,
   title = {ggspatial: Spatial Data Framework for ggplot2},
   author = {Dewey Dunnington},
-  year = {2018},
-  note = {R package version 1.0.3},
+  year = {2021},
+  note = {R package version 1.1.5},
   url = {https://CRAN.R-project.org/package=ggspatial},
 }
+
+@Manual{R-gridExtra,
+  title = {gridExtra: Miscellaneous Functions for "Grid" Graphics},
+  author = {Baptiste Auguie},
+  year = {2017},
+  note = {R package version 2.3},
+  url = {https://CRAN.R-project.org/package=gridExtra},
+}
+
+@Manual{R-heatwaveR,
+  title = {heatwaveR: Detect Heatwaves and Cold-Spells},
+  author = {Robert W. Schlegel and Albertus J. Smit},
+  year = {2021},
+  note = {R package version 0.4.6},
+  url = {https://CRAN.R-project.org/package=heatwaveR},
+}
+
 @Manual{R-here,
   title = {here: A Simpler Way to Find Your Files},
   author = {Kirill Müller},
-  year = {2017},
-  note = {R package version 0.1},
+  year = {2020},
+  note = {R package version 1.0.1},
   url = {https://CRAN.R-project.org/package=here},
 }
+
 @Manual{R-htmlwidgets,
   title = {htmlwidgets: HTML Widgets for R},
-  author = {Ramnath Vaidyanathan and Yihui Xie and JJ Allaire and Joe Cheng and Kenton Russell},
-  year = {2019},
-  note = {R package version 1.5.1},
-  url = {https://CRAN.R-project.org/package=htmlwidgets},
+  author = {Ramnath Vaidyanathan and Yihui Xie and JJ Allaire and Joe Cheng and Carson Sievert and Kenton Russell},
+  year = {2021},
+  note = {R package version 1.5.4},
+  url = {https://github.com/ramnathv/htmlwidgets},
 }
+
 @Manual{R-kableExtra,
-  title = {kableExtra: Construct Complex Table with 'kable' and Pipe Syntax},
+  title = {kableExtra: Construct Complex Table with kable and Pipe Syntax},
   author = {Hao Zhu},
-  year = {2019},
-  note = {R package version 1.1.0},
+  year = {2021},
+  note = {R package version 1.3.4},
   url = {https://CRAN.R-project.org/package=kableExtra},
 }
+
 @Manual{R-knitr,
   title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
   author = {Yihui Xie},
-  year = {2020},
-  note = {R package version 1.28},
-  url = {https://CRAN.R-project.org/package=knitr},
-}
-@Manual{R-ks,
-  title = {ks: Kernel Smoothing},
-  author = {Tarn Duong},
-  year = {2020},
-  note = {R package version 1.11.7},
-  url = {https://CRAN.R-project.org/package=ks},
+  year = {2021},
+  note = {R package version 1.36},
+  url = {https://yihui.org/knitr/},
 }
+
 @Manual{R-lattice,
   title = {lattice: Trellis Graphics for R},
   author = {Deepayan Sarkar},
-  year = {2020},
-  note = {R package version 0.20-40},
-  url = {https://CRAN.R-project.org/package=lattice},
-}
-@Manual{R-magick,
-  title = {magick: Advanced Graphics and Image-Processing in R},
-  author = {Jeroen Ooms},
-  year = {2020},
-  note = {R package version 2.3},
-  url = {https://CRAN.R-project.org/package=magick},
-}
-@Manual{R-magrittr,
-  title = {magrittr: A Forward-Pipe Operator for R},
-  author = {Stefan Milton Bache and Hadley Wickham},
-  year = {2014},
-  note = {R package version 1.5},
-  url = {https://CRAN.R-project.org/package=magrittr},
-}
-@Manual{R-mapdata,
-  title = {mapdata: Extra Map Databases},
-  author = {Original S code by Richard A. Becker and Allan R. Wilks. R version by Ray Brownrigg.},
-  year = {2018},
-  note = {R package version 2.3.0},
-  url = {https://CRAN.R-project.org/package=mapdata},
-}
-@Manual{R-maps,
-  title = {maps: Draw Geographical Maps},
-  author = {Original S code by Richard A. Becker and Allan R. Wilks. R version by Ray Brownrigg. Enhancements by Thomas P Minka and Alex Deckmyn.},
-  year = {2018},
-  note = {R package version 3.3.0},
-  url = {https://CRAN.R-project.org/package=maps},
+  year = {2021},
+  note = {R package version 0.20-45},
+  url = {http://lattice.r-forge.r-project.org/},
 }
+
 @Manual{R-marmap,
   title = {marmap: Import, Plot and Analyze Bathymetric and Topographic Data},
   author = {Eric Pante and Benoit Simon-Bouhet and Jean-Olivier Irisson},
-  year = {2019},
-  note = {R package version 1.0.3},
-  url = {https://CRAN.R-project.org/package=marmap},
-}
-@Manual{R-miniUI,
-  title = {miniUI: Shiny UI Widgets for Small Screens},
-  author = {Joe Cheng},
-  year = {2018},
-  note = {R package version 0.1.1.1},
-  url = {https://CRAN.R-project.org/package=miniUI},
-}
-@Manual{R-ncdf4,
-  title = {ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data
-Files},
-  author = {David Pierce},
-  year = {2019},
-  note = {R package version 1.17},
-  url = {https://CRAN.R-project.org/package=ncdf4},
+  year = {2021},
+  note = {R package version 1.0.6},
+  url = {https://github.com/ericpante/marmap},
 }
+
 @Manual{R-patchwork,
   title = {patchwork: The Composer of Plots},
   author = {Thomas Lin Pedersen},
-  year = {2019},
-  note = {R package version 1.0.0},
-  url = {https://CRAN.R-project.org/package=patchwork},
+  note = {https://patchwork.data-imaginist.com},
+  year = {2021},
 }
+
 @Manual{R-permute,
   title = {permute: Functions for Generating Restricted Permutations of Data},
   author = {Gavin L. Simpson},
-  year = {2019},
-  note = {R package version 0.9-5},
-  url = {https://CRAN.R-project.org/package=permute},
+  year = {2022},
+  note = {R package version 0.9-7},
+  url = {https://github.com/gavinsimpson/permute},
 }
+
 @Manual{R-plyr,
   title = {plyr: Tools for Splitting, Applying and Combining Data},
   author = {Hadley Wickham},
-  year = {2019},
-  note = {R package version 1.8.5},
+  year = {2020},
+  note = {R package version 1.8.6},
   url = {https://CRAN.R-project.org/package=plyr},
 }
-@Manual{R-purrr,
-  title = {purrr: Functional Programming Tools},
-  author = {Lionel Henry and Hadley Wickham},
-  year = {2019},
-  note = {R package version 0.3.3},
-  url = {https://CRAN.R-project.org/package=purrr},
-}
-@Manual{R-raster,
-  title = {raster: Geographic Data Analysis and Modeling},
-  author = {Robert J. Hijmans},
-  year = {2019},
-  note = {R package version 3.0-7},
-  url = {https://CRAN.R-project.org/package=raster},
-}
+
 @Manual{R-RColorBrewer,
   title = {RColorBrewer: ColorBrewer Palettes},
   author = {Erich Neuwirth},
@@ -217,34 +165,23 @@ @Manual{R-RColorBrewer
   note = {R package version 1.1-2},
   url = {https://CRAN.R-project.org/package=RColorBrewer},
 }
+
 @Manual{R-readr,
   title = {readr: Read Rectangular Text Data},
-  author = {Hadley Wickham and Jim Hester and Romain Francois},
-  year = {2018},
-  note = {R package version 1.3.1},
+  author = {Hadley Wickham and Jim Hester and Jennifer Bryan},
+  year = {2022},
+  note = {R package version 2.1.2},
   url = {https://CRAN.R-project.org/package=readr},
 }
-@Manual{R-rerddap,
-  title = {rerddap: General Purpose Client for 'ERDDAP' Servers},
-  author = {Scott Chamberlain},
-  year = {2019},
-  note = {R package version 0.6.5},
-  url = {https://CRAN.R-project.org/package=rerddap},
-}
-@Manual{R-rgdal,
-  title = {rgdal: Bindings for the 'Geospatial' Data Abstraction Library},
-  author = {Roger Bivand and Tim Keitt and Barry Rowlingson},
-  year = {2019},
-  note = {R package version 1.4-8},
-  url = {https://CRAN.R-project.org/package=rgdal},
-}
+
 @Manual{R-rmarkdown,
   title = {rmarkdown: Dynamic Documents for R},
   author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
-  year = {2020},
-  note = {R package version 2.1},
+  year = {2021},
+  note = {R package version 2.11},
   url = {https://CRAN.R-project.org/package=rmarkdown},
 }
+
 @Manual{R-rpart,
   title = {rpart: Recursive Partitioning and Regression Trees},
   author = {Terry Therneau and Beth Atkinson},
@@ -252,34 +189,23 @@ @Manual{R-rpart
   note = {R package version 4.1-15},
   url = {https://CRAN.R-project.org/package=rpart},
 }
-@Manual{R-rticles,
-  title = {rticles: Article Formats for R Markdown},
-  author = {JJ Allaire and Yihui Xie and {R Foundation} and Hadley Wickham and {Journal of Statistical Software} and Ramnath  Vaidyanathan and {Association for Computing Machinery} and Carl Boettiger and {Elsevier} and Karl Broman and Kirill Mueller and Bastiaan Quast and Randall  Pruim and Ben Marwick and Charlotte Wickham and Oliver Keyes and Miao Yu and Daniel Emaasit and Thierry Onkelinx and Alessandro Gasparini and Marc-Andre Desautels and Dominik Leutnant and {MDPI} and {Taylor and Francis} and Oguzhan Ögreden and Dalton Hance and Daniel Nüst and Petter Uvesten and Elio Campitelli and John Muschelli and Zhian N. Kamvar and Noam Ross and Robrecht Cannoodt and Duncan Luguern and David M. Kaplan},
-  year = {2020},
-  note = {R package version 0.14},
-  url = {https://CRAN.R-project.org/package=rticles},
-}
-@Manual{R-servr,
-  title = {servr: A Simple HTTP Server to Serve Static Files or Dynamic Documents},
-  author = {Yihui Xie},
-  year = {2020},
-  note = {R package version 0.16},
-  url = {https://CRAN.R-project.org/package=servr},
-}
+
 @Manual{R-sf,
   title = {sf: Simple Features for R},
   author = {Edzer Pebesma},
-  year = {2020},
-  note = {R package version 0.8-1},
+  year = {2022},
+  note = {R package version 1.0-6},
   url = {https://CRAN.R-project.org/package=sf},
 }
-@Manual{R-sp,
-  title = {sp: Classes and Methods for Spatial Data},
-  author = {Edzer Pebesma and Roger Bivand},
-  year = {2019},
-  note = {R package version 1.3-2},
-  url = {https://CRAN.R-project.org/package=sp},
+
+@Manual{R-stocksmart,
+  title = {stocksmart: Provides access to NOAAs stock SMART data},
+  author = {Andy Beet},
+  year = {2022},
+  note = {R package version 0.3.2},
+  url = {https://github.com/NOAA-EDAB/stocksmart},
 }
+
 @Manual{R-stringr,
   title = {stringr: Simple, Consistent Wrappers for Common String Operations},
   author = {Hadley Wickham},
@@ -287,48 +213,31 @@ @Manual{R-stringr
   note = {R package version 1.4.0},
   url = {https://CRAN.R-project.org/package=stringr},
 }
-@Manual{R-tibble,
-  title = {tibble: Simple Data Frames},
-  author = {Kirill Müller and Hadley Wickham},
-  year = {2019},
-  note = {R package version 2.1.3},
-  url = {https://CRAN.R-project.org/package=tibble},
-}
+
 @Manual{R-tidyr,
   title = {tidyr: Tidy Messy Data},
-  author = {Hadley Wickham and Lionel Henry},
-  year = {2020},
-  note = {R package version 1.0.2},
+  author = {Hadley Wickham and Maximilian Girlich},
+  year = {2022},
+  note = {R package version 1.2.0},
   url = {https://CRAN.R-project.org/package=tidyr},
 }
-@Manual{R-tidyverse,
-  title = {tidyverse: Easily Install and Load the 'Tidyverse'},
-  author = {Hadley Wickham},
-  year = {2019},
-  note = {R package version 1.3.0},
-  url = {https://CRAN.R-project.org/package=tidyverse},
-}
-@Manual{R-tufte,
-  title = {tufte: Tufte's Styles for R Markdown Documents},
-  author = {Yihui Xie and JJ Allaire},
-  year = {2019},
-  note = {R package version 0.5},
-  url = {https://CRAN.R-project.org/package=tufte},
-}
+
 @Manual{R-vegan,
   title = {vegan: Community Ecology Package},
   author = {Jari Oksanen and F. Guillaume Blanchet and Michael Friendly and Roeland Kindt and Pierre Legendre and Dan McGlinn and Peter R. Minchin and R. B. O'Hara and Gavin L. Simpson and Peter Solymos and M. Henry H. Stevens and Eduard Szoecs and Helene Wagner},
-  year = {2019},
-  note = {R package version 2.5-6},
+  year = {2020},
+  note = {R package version 2.5-7},
   url = {https://CRAN.R-project.org/package=vegan},
 }
+
 @Manual{R-webshot,
   title = {webshot: Take Screenshots of Web Pages},
   author = {Winston Chang},
   year = {2019},
   note = {R package version 0.5.2},
-  url = {https://CRAN.R-project.org/package=webshot},
+  url = {https://github.com/wch/webshot/},
 }
+
 @Book{bookdown2016,
   title = {bookdown: Authoring Books and Technical Documents with {R} Markdown},
   author = {Yihui Xie},
@@ -336,8 +245,9 @@ @Book{bookdown2016
   address = {Boca Raton, Florida},
   year = {2016},
   note = {ISBN 978-1138700109},
-  url = {https://github.com/rstudio/bookdown},
+  url = {https://bookdown.org/yihui/bookdown},
 }
+
 @Book{ggplot22016,
   author = {Hadley Wickham},
   title = {ggplot2: Elegant Graphics for Data Analysis},
@@ -346,6 +256,18 @@ @Book{ggplot22016
   isbn = {978-3-319-24277-4},
   url = {https://ggplot2.tidyverse.org},
 }
+
+@Article{heatwaveR2018,
+  title = {{heatwaveR}: A central algorithm for the detection of heatwaves and cold-spells},
+  author = {Robert W. Schlegel and Albertus J. Smit},
+  journal = {Journal of Open Source Software},
+  year = {2018},
+  volume = {3},
+  number = {27},
+  pages = {821},
+  doi = {10.21105/joss.00821},
+}
+
 @Book{knitr2015,
   title = {Dynamic Documents with {R} and knitr},
   author = {Yihui Xie},
@@ -356,6 +278,7 @@ @Book{knitr2015
   note = {ISBN 978-1498716963},
   url = {https://yihui.org/knitr/},
 }
+
 @InCollection{knitr2014,
   booktitle = {Implementing Reproducible Computational Research},
   editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng},
@@ -366,6 +289,7 @@ @InCollection{knitr2014
   note = {ISBN 978-1466561595},
   url = {http://www.crcpress.com/product/isbn/9781466561595},
 }
+
 @Book{lattice2008,
   title = {Lattice: Multivariate Data Visualization with R},
   author = {Deepayan Sarkar},
@@ -375,6 +299,7 @@ @Book{lattice2008
   note = {ISBN 978-0-387-75968-5},
   url = {http://lmdvr.r-forge.r-project.org},
 }
+
 @Article{marmap2013,
   title = {marmap: A Package for Importing, Plotting and Analyzing Bathymetric and Topographic Data in R},
   author = {Eric Pante and Benoit Simon-Bouhet},
@@ -385,6 +310,7 @@ @Article{marmap2013
   pages = {e73051},
   note = {doi:10.1371/journal.pone.0073051},
 }
+
 @Article{plyr2011,
   title = {The Split-Apply-Combine Strategy for Data Analysis},
   author = {Hadley Wickham},
@@ -395,6 +321,7 @@ @Article{plyr2011
   pages = {1--29},
   url = {http://www.jstatsoft.org/v40/i01/},
 }
+
 @Book{rmarkdown2018,
   title = {R Markdown: The Definitive Guide},
   author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
@@ -404,6 +331,17 @@ @Book{rmarkdown2018
   note = {ISBN 9781138359338},
   url = {https://bookdown.org/yihui/rmarkdown},
 }
+
+@Book{rmarkdown2020,
+  title = {R Markdown Cookbook},
+  author = {Yihui Xie and Christophe Dervieux and Emily Riederer},
+  publisher = {Chapman and Hall/CRC},
+  address = {Boca Raton, Florida},
+  year = {2020},
+  note = {ISBN 9780367563837},
+  url = {https://bookdown.org/yihui/rmarkdown-cookbook},
+}
+
 @Article{sf2018,
   author = {Edzer Pebesma},
   title = {{Simple Features for R: Standardized Support for Spatial Vector Data}},
@@ -415,31 +353,4 @@ @Article{sf2018
   volume = {10},
   number = {1},
 }
-@Article{sp2005,
-  author = {Edzer J. Pebesma and Roger S. Bivand},
-  title = {Classes and methods for spatial data in {R}},
-  journal = {R News},
-  year = {2005},
-  volume = {5},
-  number = {2},
-  pages = {9--13},
-  month = {November},
-  url = {https://CRAN.R-project.org/doc/Rnews/},
-}
-@Book{sp2013,
-  author = {Roger S. Bivand and Edzer Pebesma and Virgilio Gomez-Rubio},
-  title = {Applied spatial data analysis with {R}, Second edition},
-  year = {2013},
-  publisher = {Springer, NY},
-  url = {http://www.asdar-book.org/},
-}
-@Article{tidyverse2019,
-  title = {Welcome to the {tidyverse}},
-  author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
-  year = {2019},
-  journal = {Journal of Open Source Software},
-  volume = {4},
-  number = {43},
-  pages = {1686},
-  doi = {10.21105/joss.01686},
-}
+