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Merge pull request #149 from NOAA-EDAB/doc_format_changes
Formatting/content fixes
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_bookdown.yml

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#
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# #################### MEGAFAUNA ##################
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- "chapters/sectionHeaders/megafauna.rmd"
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#- "chapters/aggregate_biomass.Rmd" *Missing tech doc page
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- "chapters/aggregate_biomass.rmd"
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- "chapters/survey_shannon.rmd"
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- "chapters/mab_inshore_survey.Rmd"
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#- "chapters/mass_inshore_survey.Rmd" *Shares tech doc page with mab_inshore_survey
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#- "chapters/ne_inshore_survey.Rmd" *Shares tech doc page with mab_inshore_survey

chapters/Quota_Catch_NE.Rmd

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## Methods
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### Data Sources
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### Data sources
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Data found in NFMS [Species Information System (SIS)](https://apps-st.fisheries.noaa.gov/sis/#no-back-button).
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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.
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### Data Analysis
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### Data analysis
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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.
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### Data Processing
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### Data processing
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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).
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chapters/SAV.Rmd

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## Methods
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### Data Sources
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### Data sources
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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.
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### Data Extraction
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### Data extraction
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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.
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### Data Analysis
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### Data analysis
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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.
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### Data processing
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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).
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chapters/Wind_dev_speed.Rmd

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**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.
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## Methods
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### Data Sources
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### Data sources
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BOEM lease area, Call Areas, Planning Area shapefiles: https://www.boem.gov/renewable-energy/mapping-and-data/renewable-energy-gis-data;
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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
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### Data Analysis
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### Data analysis
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All data was updated for 2022 with South Fork Wind Farm FEIS and the following assumptions were made on future wind areas:
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* (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.
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* (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""
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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.
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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).
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chapters/abc_acl.Rmd

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## Methods
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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.
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### Data analysis
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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.
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Each species, depending upon data availability, sectors, fleets etc., goes through a different data processing process.
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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).
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chapters/aggregate_biomass.rmd

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# Aggregate Survey Biomass {#aggregate_biomass}
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**Description**: Bottom Trawl Survey data biomass for season and region aaggregated over species groupings
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**Found in**: State of the Ecosystem - Gulf of Maine & Georges Bank (2017+), State of the Ecosystem - Mid-Atlantic (2017+)
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**Indicator category**: Database pull with analysis
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**Contributor(s)**: Andy Beet
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**Data steward**: Andy Beet <andrew.beet@noaa.gov>
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**Point of contact**: Andy Beet <andrew.beet@noaa.gov>
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**Public availability statement**: Source data are available to qualified researchers upon request (see "Access Information" [here](https://inport.nmfs.noaa.gov/inport/item/22560)).
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## Methods
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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.
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The R package [`survdat`](https://noaa-edab.github.io/survdat/) is used to pull and process the survey data.
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### Data sources
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[`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).
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### Data extraction
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The R package [`survdat`](https://noaa-edab.github.io/survdat/) was used in the survey data extraction process.
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### Data analysis
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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.
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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).
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**catalog link**
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<https://noaa-edab.github.io/catalog/aggregate_biomass.html>

chapters/aquaculture.Rmd

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## Methods
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Data was synthesized from state specific sources, listed below.
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* [State of Maine, Department of Marine Resources.](https://www.maine.gov/dmr/aquaculture/data/index.html)
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* [State of Maryland, Aquaculture Coordinating Council](https://calendarmedia.blob.core.windows.net/assets/1495a281-9eab-422a-9f90-a16ac9686db8.pdf)
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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.
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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.

chapters/benthos_index.Rmd

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## Methods
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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).
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VAST spatio-temporal modeling [@thorson_comparing_2017; @thorson_guidance_2019] is described here.
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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>
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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.
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chapters/calanus_variation.Rmd

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## Methods
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Observations from 2004 - 2017: https://data.neracoos.org/erddap/tabledap/WBTS_CFIN_2004_2017.html
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Observations beginning in 2020: https://data.neracoos.org/erddap/tabledap/WBTS_CFIN_start_2020.html
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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.
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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.
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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.
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chapters/cetacean_acoustic.Rmd

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## Methods
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Data collected by the NEFSC Passive Acoustic Branch from four recording sites in and around the southern New England Wind Energy areas.
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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.
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Species-specific vocalizations of eight cetacean were identified in the acoustic data using multiple automated detectors following the methodology described in @vanparijs_establishing_2023.
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