NOAA-EDAB · BBeltz1 · Nov 10, 2025 · Oct 16, 2025 · Oct 16, 2025 · Oct 16, 2025
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: placeholder
 Type: Book
 Title: Technical Documentation, State of the Ecosystem Report
 Description: Technical documentation for State of the Ecosystem Reporting.
-Version: 2.0.0
+Version: 2026.0.0
 Depends: 
   bookdown, 
   rmarkdown,
@@ -16,7 +16,7 @@ Depends:
   stocksmart,
   DT
 Remotes:
-  NOAA-EDAB/ecodata,
+  NOAA-EDAB/ecodata@dev,
   NOAA-EDAB/stocksmart
 
 
diff --git a/_bookdown.yml b/_bookdown.yml
@@ -31,7 +31,8 @@ rmd_files:
 #
 # #################### MEGAFAUNA ##################
     -   "chapters/sectionHeaders/megafauna.rmd"
-    #-   "chapters/aggregate_biomass.Rmd" *Missing tech doc page
+    -   "chapters/aggregate_biomass.rmd" 
+    -   "chapters/survey_shannon.rmd" 
     -   "chapters/mab_inshore_survey.Rmd"
     #-   "chapters/mass_inshore_survey.Rmd" *Shares tech doc page with mab_inshore_survey
     #-   "chapters/ne_inshore_survey.Rmd" *Shares tech doc page with mab_inshore_survey

diff --git a/chapters/Quota_Catch_NE.Rmd b/chapters/Quota_Catch_NE.Rmd
@@ -16,13 +16,13 @@
 
 ## Methods
 
-### Data Sources
+### Data sources
 
 Data found in NFMS [Species Information System (SIS)](https://apps-st.fisheries.noaa.gov/sis/#no-back-button). 
 
 SIS Annual Catch Limit reports were used to collate data for each Fisheries Management Plan (FMP). The Allowable Biological Catch and Grand Total Catch (Commercial + Recreational) were recorded. 
 
-### Data Analysis
+### Data analysis
 
 Each stock has a threshold and catch value assigned to it from the sources above. The table below outlines the data pull for each FMP. 
 
@@ -38,7 +38,7 @@ Each stock has a threshold and catch value assigned to it from the sources above
 
 
 
-### Data Processing 
+### Data processing 
 
 Data were formatted for inclusion in the `ecodata` R package using the R code found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_abc.acl.R).
 

diff --git a/chapters/SAV.Rmd b/chapters/SAV.Rmd
@@ -16,16 +16,16 @@
 
 ## Methods
 
-### Data Sources
+### 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.
 
-### Data Extraction
+### Data extraction
 The data is available in excel spreadsheet form using the Downloads `Data (.xlsx)` link. The data used is in the “Salinity zone totals” tab and the hectares column can be extracted for each salinity zone.
 
-### Data Analysis 
+### Data analysis 
 The [analysis and methods](https://d18lev1ok5leia.cloudfront.net/chesapeakeprogress/chart-assets/submerged-aquatic-vegetation-sav-abundance-1984-2019/Analysis-and-Methods_2020-Submerged-Aquatic-Vegetation_Prelim_070621_final.pdf) are described at the Chesapeake progress page.  
 
-### Data Processing 
+### Data processing 
 
 Data were formatted for inclusion in the `ecodata` R package using the R code found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_SAV.R).
 

diff --git a/chapters/Wind_dev_speed.Rmd b/chapters/Wind_dev_speed.Rmd
@@ -15,13 +15,13 @@
 **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.
 
 ## Methods
-### Data Sources
+### 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
 
 
-### Data Analysis
+### 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""
@@ -50,7 +50,7 @@ Dominion Energy was presented as 3 phases in Table E-4 for Project_Name (Dominio
 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. 
 
 
-### Data Processing 
+### Data processing 
 
 Data were formatted for inclusion in the `ecodata` R package using the R code found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_wind_dev_speed.R).
 

diff --git a/chapters/abc_acl.Rmd b/chapters/abc_acl.Rmd
@@ -16,11 +16,11 @@
 
 ## Methods
 
-### Data Sources
+### Data sources
 
 These data were compiled from MAFMC [Fishery Information Documents](https://www.mafmc.org/fishery-performance-reports), Stock Assessment reports, [SSC reports](https://www.mafmc.org/ssc), GARFO catch/landings database, and MRIP queries. 
 
-### Data Analysis
+### Data analysis
 
 Each stock has a threshold and catch value assigned to it from the sources above. The table below shows where the information comes from for each stock. 
 
@@ -52,7 +52,7 @@ Allowable Biological Catch (ABC) for each managed stock is set by the MAFMC Scie
 
 Each species, depending upon data availability, sectors, fleets etc., goes through a different data processing process. 
 
-### Data Processing 
+### Data processing 
 
 Data were formatted for inclusion in the `ecodata` R package using the R code found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_abc.acl.R).
 

diff --git a/chapters/aggregate_biomass.rmd b/chapters/aggregate_biomass.rmd
@@ -0,0 +1,39 @@
+# Aggregate Survey Biomass {#aggregate_biomass}
+
+**Description**: Bottom Trawl Survey data biomass for season and region aaggregated over species groupings
+
+**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)**: Andy Beet
+
+**Data steward**: Andy Beet <andrew.beet@noaa.gov>
+
+**Point of contact**: Andy Beet <andrew.beet@noaa.gov>
+
+**Public availability statement**: Source data are available to qualified researchers upon request (see "Access Information" [here](https://inport.nmfs.noaa.gov/inport/item/22560)). 
+
+## 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 and are explained in detail the [Survey Data](survdat) section.
+
+The R package [`survdat`](https://noaa-edab.github.io/survdat/) is used to pull and process the survey data.
+
+
+### Data sources
+
+[`survdat`](https://noaa-edab.github.io/survdat/) is an R package that allows for queries 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](https://inport.nmfs.noaa.gov/inport/item/22560).
+
+### Data extraction 
+
+The R package  [`survdat`](https://noaa-edab.github.io/survdat/) was used in the survey data extraction process.
+
+
+### Data analysis
+
+For the purposes of the aggregate biomass indicators, fall and spring survey data are treated separately.  Additionally, all length data is dropped and species separated by sex at the catch level are merged back together.
+
+Biomass was summed by species per year per Ecological Production Unit (EPU).  Those sums were divided by the appropriate station count to get the EPU mean. Finally, the mean biomasses were summed by [aggregate groups](#species_groupings). 
+
+**catalog link**
+<https://noaa-edab.github.io/catalog/aggregate_biomass.html>
diff --git a/chapters/aquaculture.Rmd b/chapters/aquaculture.Rmd
@@ -18,7 +18,7 @@
 
 ## Methods
 
-### Data Sources 
+### Data sources 
 Data was synthesized from state specific sources, listed below. 
 
 * [State of Maine, Department of Marine Resources.](https://www.maine.gov/dmr/aquaculture/data/index.html)                
@@ -31,7 +31,7 @@ Data was synthesized from state specific sources, listed below.
 
 * [State of Maryland, Aquaculture Coordinating Council](https://calendarmedia.blob.core.windows.net/assets/1495a281-9eab-422a-9f90-a16ac9686db8.pdf) 
 
-### Data Extraction/Analysis
+### Data extraction/analysis
 Production described as the number of oysters harvested is collected by individual states. This means that time series maybe vary by state. A table of start dates are shown below. Individual state information is available at the above links.  
 
 Only the New England State of the Ecosystem includes aquaculture information as there are reporting issues and many states are do not have available data in the Mid-Atlantic States.  

diff --git a/chapters/benthos_index.Rmd b/chapters/benthos_index.Rmd
@@ -16,11 +16,11 @@
 
 ## Methods
 
-### Data Sources
+### 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
+### Data analysis
 
 VAST spatio-temporal modeling [@thorson_comparing_2017; @thorson_guidance_2019] is described here.
 
@@ -38,7 +38,7 @@ Model selection results are reported at [this link](https://noaa-edab.github.io/
 
 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 
+### 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.
 

diff --git a/chapters/calanus_variation.Rmd b/chapters/calanus_variation.Rmd
@@ -16,19 +16,19 @@
 
 ## Methods
 
-### Data Sources
+### 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
+### 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 
+### 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.
 

diff --git a/chapters/cetacean_acoustic.Rmd b/chapters/cetacean_acoustic.Rmd
@@ -16,15 +16,15 @@
 
 ## Methods
 
-### Data Sources
+### 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
+### 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 @vanparijs_establishing_2023.
 
-### Data Processing 
+### Data processing 
 
 Species-specific vocalizations of eight cetacean were identified in the acoustic data using multiple automated detectors following the methodology described in @vanparijs_establishing_2023.
 

diff --git a/chapters/chl_pp.Rmd b/chapters/chl_pp.Rmd
@@ -25,7 +25,7 @@ Daily Level 3 mapped (4km resolution, sinusoidally projected) satellite ocean co
 
 The global OC-CCI, GlobColour, and the SST data are mapped to the same sinusoidal map projection and subset to the east coast region (SW longitude=82.5$^\circ$W, SW latitude=22.5$^\circ$N, NE longitude=51.5$^\circ$W, NE latitude=48.5$^\circ$N).
 
-#### Data Interpolation
+#### Data interpolation
 
 Daily CHL and AVHRR SST data are temporally interpolated and smoothed (CHLINT and SSTINT respectively) for use in the primary production model.  The interpolation increases the data coverage and is necessary to 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 product so these parameters were not interpolated. Daily data at each pixel location are linearly interpolated based on days in the time series using [interpx.pro](https://github.com/callumenator/idl/blob/master/external/JHUAPL/INTERPX.PRO). Prior to interpolation, the CHL data are log-transformed to account for the log-normal distribution of chlorophyll data (@campbell_lognormal_1995). The time series are processed in one-year chunks, with each yearly series including 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](https://www.harrisgeospatial.com/docs/CONVOL.html) 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. 
 
@@ -63,9 +63,9 @@ Where $\textrm{CHL}_0$ is the surface chlorophyll concentration.
 
 Phytoplankton size classes (PSC) are calculated according to @turner_optimization_2021. The regionally tuned abundance-based model is based on the three-component model of @brewin_three-component_2010 that varies as a function of SST (@brewin_uncertainty_2017, @moore_incorporating_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.
 
-### Data Analysis
+### Data analysis
 
-#### Statistics and Anomalies
+#### Statistics and anomalies
 
 Gridded statistics, including the arithmetic mean, geometric mean, median, standard deviation, and coefficient of variation are 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 are 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. Prior to 2024, the climatological date range was from 1998 to the current year. Starting in 2024, the climatological period was fixed to 1998 to 2020 in order to be consistent with the climatological date range of other products. 
 

diff --git a/chapters/cold_pool.Rmd b/chapters/cold_pool.Rmd
@@ -21,11 +21,11 @@ The methodology for the cold pool index changed between 2020, 2021, and 2022 SOE
 The cold pool is an area of relatively cold bottom water that forms on the US northeast shelf in the Mid-Atlantic Bight.
 
 
-### Data Sources
+### Data sources
 The three cold pool indices were calculated using a high-resolution long-term bottom temperature product. All the details on the bottom temperature dataset are available in the [Bottom Temperature - High Resolution](https://noaa-edab.github.io/tech-doc/bottom-temperature---high-resolution.html) chapter and in @noauthor_high-resolution_2023.
 
 
-### Data Analysis 
+### Data analysis 
 
 #### Cold Pool Domain
 
@@ -75,10 +75,10 @@ Code used to process the cold pool inidcator can be found in the `ecodata` packa
 ## 2021 Methods
 **Point of Contact:**: Zhoumin Chen <zhuomin.chen@uconn.edu>
 
-### Data Sources
+### 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](https://resources.marine.copernicus.eu/?option=com_csw&task=results?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_030).
 
-### Data Analysis
+### Data analysis
 Depth-averaged spatial temperature is calculated based on the daily Cold Pool dataset, which is quantified following @chen_seasonal_2018.
 
 ### Data processing

diff --git a/chapters/comdat.Rmd b/chapters/comdat.Rmd
@@ -74,7 +74,7 @@ A database query of the NEFSC commercial fishery database (CFDBS). More informat
 
 [`comlandr`](https://noaa-edab.github.io/comlandr/) is an R package used to extract relevant data from the database.
 
-#### Data Processing
+#### Data processing
 
 The landings data were formatted for inclusion in the [`ecodata`](https://noaa-edab.github.io/ecodata/) R package