Page updates from 2025 submissions

Author: BBeltz1

chapters/benthos_index.Rmd

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+# Benthic Invertebrate Indices {#benthos_index}
+
+**Description**: Aggregate macrobenthos and megabenthos invertebrate indices from fish stomach contents
+
+**Found in**: State of the Ecosystem - Indicator Catalog (2025) 
+
+**Indicator category**: Extensive analysis, not yet published
+
+**Contributor(s)**: Sarah Gaichas, James Gartland, Brian E. Smith, Sarah Weisberg, Sean Lucey
+  
+**Data steward**: Sarah Gaichas <[email protected]>
+  
+**Point of contact**: Sarah Gaichas <[email protected]>
+  
+**Public availability statement**: Source data are publicly available. All data and code available on GitHub at <https://github.com/NOAA-EDAB/benthosindex>
+
+## Methods
+
+### Data Sources
+
+Data used to develop these indicators comes from multispecies diet data collected on the Northeast Fisheries Science Center (NEFSC) and NorthEast Area Monitoring and Assessment Program (NEAMAP) bottom trawl surveys. Bottom temperature data is described in [Bottom temperature - High Resolution](https://noaa-edab.github.io/tech-doc/bottom_temp_seasonal_gridded.html). 
+
+### Data Analysis
+
+VAST spatio-temporal modeling [@thorson_comparing_2017; @thorson_guidance_2019] is described here.
+
+The approach follows that used for the forage fish index [@gaichas_assessing_2023], which was in turn based on @ng_predator_2021.
+
+Two stages of model selection determined whether to include:
+
+1. spatial and spatio-temporal random effects, and
+2. vessel effects, and "catchability" covariates affecting the observation process: mean predator size, number of predators, and bottom temperature.
+Using REML in stage 1, models including spatial and spatio-temporal random effects as well as anisotropy were best supported by the data. This was true for the spring dataset and the fall dataset for both macrobenthos and megabenthos.
+
+In stage 2, combinations of catchability covariates were better supported by the data than vessel effects. Model comparisons led us to the best model fit using mean predator length, number of predator species, and bottom temperature at a survey station as catchability covariates.
+
+Model selection results are reported at [this link](https://noaa-edab.github.io/benthosindex/WorkflowDecisions.html).
+
+Scripts used to run the model selection and to produce the final bias corrected models are posted at <https://github.com/NOAA-EDAB/benthosindex/tree/main/VASTscripts>
+
+### Data Processing 
+
+The basic workflow is to develop a dataset of stomach contents data where fish predators act as samplers of the prey field, then fit a vector autoregressive spatio-temporal (VAST) model to this dataset to generate an index. Dataset development is described here.
+
+NEFSC survey food habits data were extracted and provided by Brian Smith (NEFSC). NEAMAP survey food habits data were extracted and processed by James Gartland (VIMS). Macrobenthos and Megabenthos categories were those used in Northeast US food web models. The Macrobenthos Rpath category has 833 food habits database species codes. The Megabenthos Rpath category has 105 food habits database species codes. All are listed at [this link](https://noaa-edab.github.io/benthosindex/WorkflowDecisions.html).
+
+Benthic predator/size combinations were selected using a cluster analysis of a diet similarity matrix provided by Brian Smith. Species categorized as pelagic or piscivorous feeders were eliminated, and all other species were retained as general benthivores. This resulted in 88 predator/size combinations used to "sample" benthic invertebrates. The predator/size list is available at [this link](https://noaa-edab.github.io/benthosindex/WorkflowDecisions.html).
+
+These input datasets were processed, aggregated, and combined with bottom temperature data to become VAST model input datasets using the script at [this link](https://github.com/NOAA-EDAB/benthosindex/blob/main/fhdata/VASTbenthos_ProcessInputDat.R).
+
+**catalog link**
+<https://noaa-edab.github.io/catalog/benthos_index.html>

chapters/calanus_variation.Rmd

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+# Seasonal and multiannual variation in abundance of Calanus finmarchicus in the western Gulf of Maine {#calanus_variation}
+
+**Description**: The data presented here are abundance estimates (no./m2) of the planktonic copepod, Calanus finmarchicus, collected at the NERACOOS-MBON Wilkinson Basin Time Series (WBTS) station between 2005-2023.
+
+**Found in**: State of the Ecosystem - Indicator Catalog (2024+) 
+
+**Indicator category**: Published methods
+
+**Contributor(s)**: Jeffrey A. Runge, Cameron R.S. Thompson, Shawn Shellito, Emma C. Dullaert, Isabel A. Honda, Douglas Vandemark, Dylan Pugh, Riley Young-Morse, Jackie Motyka, Rebecca J. Jones, Lee Karp Boss, Rubao Ji
+  
+**Data steward**: Jeffrey Runge <[email protected]>
+  
+**Point of contact**: Jeffrey Runge <[email protected]>
+  
+**Public availability statement**: Source data are publicly available.
+
+## Methods
+
+### Data Sources
+
+Observations from 2004 - 2017: https://data.neracoos.org/erddap/tabledap/WBTS_CFIN_2004_2017.html
+
+Observations beginning in 2020: https://data.neracoos.org/erddap/tabledap/WBTS_CFIN_start_2020.html
+
+### Data Analysis
+
+All analyses were conducted using the R programming software (R Core Team, 2023), utilizing the mgcv package for GAMs (Wood, 2023). Model estimation was conducted using Restricted Maximum Likelihood (REML) and to ensure the robustness of the model, we utilized DHARMa (Hartig, 2023) residual diagnostics to perform a Kolmogorov-Smirnov test for scaled residuals, assess dispersion, and detect outliers. The GAMs that passed the significance and diagnostic tests were then visualized using ggplot2 in R to graphically display the model outputs and trends. The GAMs of seasonal trends in a time series measurement were only depicted if the day of year smoother was significant, while the GAMs of annual climatologies were only depicted if the year smoother was significant. 
+
+Time series indices of C. finmarchicus  abundance of copepodid stages and total mesozooplankton biomassare presented in the following format: the accepted GAM of each time series variable is used to estimate the expected average value and confidence interval  to depict climatology over an annual time period, or to depict the  trend in each season over multiple years. These estimated expected average values are the indices for each variable and can be calculated for any combination of year and day of year. For depicting the annual climatology , the year is set to 2012 while days range from 1 to 365. For depicting interannual trends, a single day is set within each season (see figure captions for which day) while years vary from 2023 to 2005. A transformation by the square root was applied  to achieve a normal distribution for the analysis, and in the depicted figure these values are displayed by their untransformed number.
+
+### Data Processing 
+
+The Wilkinson Basin Time Series (WBTS) Station (42°51.7ʹN, -69°51.8ʹW, previously Station WB-7) is located 60 km from Portsmouth, New Hampshire, in the northwest corner of Wilkinson Basin at an average station depth of 257 m.  Since the start of the time series in December, 2004, it has been accessed by day trips using the University of New Hampshire research vessel, R/V Gulf Challenger. Contingent on funding support, the station was sampled at approximately monthly intervals between January, 2005- August, 2008, April, 2012-May, 2013, October, 2015-July, 2017, January, 2021- March, 2024 and at less frequent intervals in other years, for a total of 142 visits for CTD casts, 116 of which also include net tows. It continues to be sampled at approximately monthly intervals since 2020 as part of the U.S. MBON.
+
+Sampling at the WBTS and CMTS stations generally follow guidelines established by the Atlantic Zone Monitoring Program (AZMP) operated in Canadian Maritime waters by Fisheries and Oceans Canada (Mitchell et al., 2002). All samples were taken during daytime hours, typically mid-morning to mid-afternoon. 
+
+To measure zooplankton abundance and biomass, two net two casts were made using a 0.75 meter diameter single ring (CMTS and WBTS stations) or a SEA-GEAR Model 9600 twin-ring 200 µm mesh net (WBTS station). The nets were towed vertically at approx. 40 m/min with the ring starting at  5-7 meters from the bottom. The samples were preserved in 4% buffered formaldehyde. In the laboratory, samples were split with a Folsom Splitter with half of the sample designated for measurement of total zooplankton biomass and the other for taxonomic enumeration. The biomass sample split was filtered onto one or more 47 mm diameter glass fiber filters or 100-200 µm mesh nitex screens preweighed in a plastic petri dish. The split sample was poured through the filter or screen mounted in a filter holder assisted with gentle vacuum pumping, rinsed with 100 ml of tap water, dried in an oven 65°C for 24-48 h and then collectively weighed on a Mettler Toledo PG403-S microbalance (1 mg precision). The half sample for enumeration was drained of formaldehyde solution on a fine mesh screen, the contents of which were then placed in a 4 l beaker containing a known quantity of filtered seawater (typically 2500-3000 ml).  Subsamples were taken while randomly stirring with either a 25 ml Hensen-Stempel pipette or a large-mouth pipette (we used a modified turkey baster)  emptied into graduated cylinder to measure subsample volume. Multiple subsamples were taken to ensure at least 50 Calanus copepodid stages were enumerated (typically about 3% of the total half-sample) under a Leica MZ 12.5 Zoom stereo microscope. While all copepodid stages in the subsamples were enumerated, an index of C. finmarchicus abundance is calculated as the total abundance of stage C3 to adult stage C6. Water column (i.e. to net depth) biomass (g dry weight m-2) and abundance (number of individuals m-2) were calculated by dividing the measurement in the total sample (taking into account split and aliquot of subsamples) by the area of the ring net (0.4418 m2).  Net volume filtered was also determined by a General Oceanics flowmeter installed in the mouth of the net, but the geometrically determined volumes were chosen as the standard because reliable flowmeter data was not available for every cast.
+
+**catalog link**
+<https://noaa-edab.github.io/catalog/calanus_variation.html>

chapters/cetacean_acoustic.Rmd

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+# Cetacean Weekly Acoustic Presence {#cetacean_acoustic}
+
+**Description**: This figure shows a summary of weekly acoustic presence of eight cetacean species summarized across two years at four recording sites in southern New England.
+
+**Found in**: State of the Ecosystem - Indicator Catalog (2025) 
+
+**Indicator category**: Published methods
+
+**Contributor(s)**: Sofie Van Parijs, Annamaria DeAngelis, Tyler Aldrich, Rochelle Gordon, Amanda Holdman, Jessica McCordic, Xavier Mouy, Tim Rowell, Sara Tennant, Annabel Westell, and Genevieve Davis
+  
+**Data steward**: Rebecca Van Hoeck <[email protected]>
+  
+**Point of contact**: Rebecca Van Hoeck <[email protected]>; Genevieve Davis <[email protected]>
+  
+**Public availability statement**: Source data are publicly available.
+
+## Methods
+
+### Data Sources
+
+Data collected by the NEFSC Passive Acoustic Branch from four recording sites in and around the southern New England Wind Energy areas. 
+
+### Data Analysis
+
+Manual verification of the automated detections was conducted to confirm daily acoustic presence for each species. Weekly acoustic presence was summarized as the median number of days of acoustic presence per calendar week across all data. Horizontal lines within the boxes indicate the median, box boundaries indicate the 25th (lower quartile) and 75th (upper quartile) percentiles, vertical lines indicate the largest (upper whisker) and smallest (lower whisker) values no further than 1.5 times the interquartile range, and black dots represent outliers. Further details of the analysis can be found in Van Parijs et al (2023).
+
+### Data Processing 
+
+Species-specific vocalizations of eight cetacean were identified in the acoustic data using multiple automated detectors following the methodology described in Van Parijs et al (2023).
+
+**catalog link**
+<https://noaa-edab.github.io/catalog/cetacean_acoustic.html>

chapters/engagement.Rmd

@@ -1,38 +1,36 @@
-# Fishery Reliance and Social Vulnerability {#engagement}
+# Community Social Vulnerability Indicators (CSVI) {#engagement}
 
-**Description**: Fishing community commercial and recreational fishing reliance and social vulnerability
+**Description**: The data presented here are 2022 environmental justice indicators in top commercial and top recreational communities in Mid-Atlantic and New England regions, respectively.
 
-**Found in**: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+) 
+**Found in**: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+), State of the Ecosystem - Indicator Catalog (2024+)
 
 **Indicator category**: Database pull with analysis
 
-**Contributor(s)**: Lisa L. Colburn, Changhua Weng
+**Contributor(s)**: Robert Murphy, Changhua Weng, Tanya Noteva
 
-**Data steward**: Changhua Weng <[email protected]>
+**Data steward**: Robert Murphy <[email protected]>; Changhua Weng <[email protected]>
 
-**Point of contact**: Lisa L. Colburn <lisa.colburn@noaa.gov>
+**Point of contact**: Robert Murphy <[email protected]>; Changhua Weng <changhua.weng@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](https://www.fisheries.noaa.gov/national/socioeconomics/social-indicators-coastal-communities).
+**Public availability statement**: Source data are NOT publicly available. Please email Robert Murphy <[email protected]> for further information and queries of fishing and vulnerability indicator source data.
 
-
 ## Methods
 
-
 ### 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 Community Survey (ACS)](https://www.census.gov/programs-surveys/acs/) 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)](https://www.st.nmfs.noaa.gov/recreational-fisheries/MRIP/), and for this analysis was custom requested from NOAA Fisheries headquarters.
-
+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 2022 American Community Survey (ACS) 5-yr estimates Dataset in the U.S. Census American Community Survey (ACS) 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 CAMS 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.
 
 ### 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](https://github.com/NOAA-EDAB/tech-doc/tree/master/R/stored_scripts/comm_rel_vuln_extraction.sql). 
 
-
-
 ### Data analysis
-The indicators were developed using the methodology described in @Jacob2010, @Jacob2013, @colburn_social_2012 and @Jepson2013. 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; Bartlett's test of sphericity significance above .05; and an Armor's 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).
+The indicators were developed using the methodology described in Jacob et al. (2010), Jacob et al. (2013), Colburn and Jepson (2012) and M. 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 0.500; factor loadings above 0.350; Bartlett’s test of sphericity significance above 0.05; and an Armor’s Theta reliability coefficient above 0.500. Factor scores for each community were ranked based on standard deviations into the following categories: High (>=1.00SD), Medium-High 0.500-0.999 SD), Medium (0.000-0.499 SD) and Low (<0.000 SD).
+
+Note, commercial and recreational reliance indicators have been renamed as ‘population relative engagement’ indicators given that they are a proxy for how engaged each community is in fishing relative to its total population size. The calculation of these indicators remains the same.
 
 ### Data processing
+Commercial fisheries data was pulled from the NEFSC CAMS server in Woods Hole, MA.
 
 Data were formatted for inclusion in the ecodata R package using the R script found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_engagement.R).