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---
title: "Technical Documentation, State of the Ecosystem Report"
author: "Northeast Fisheries Science Center"
date: "`r format(Sys.Date(), '%e %B %Y')`"
site: bookdown::bookdown_site
knit: "bookdown::render_book"
always_allow_html: true
documentclass: book
bibliography: ["bibliography/introduction.bib","bibliography/aggregate_groups.bib","bibliography/seasonal_sst_anomaly_maps.bib","bibliography/Aquaculture.bib","bibliography/Bennet_indicator.bib","bibliography/bottom_temperature.bib","bibliography/bottom_temp_highres.bib","bibliography/Revenue_Diversity.bib","bibliography/ches_bay_water_quality.bib","bibliography/phytoplankton.bib","bibliography/ecosystem_overfishing.bib","bibliography/comm_eng.bib","bibliography/calanus_stage.bib","bibliography/ches_bay_temp.bib","bibliography/conceptmods.bib","bibliography/Condition.bib","bibliography/EPU.bib","bibliography/Expected_Number.bib","bibliography/cold_pool_index.bib","bibliography/sandlance.bib","bibliography/gulf_stream_index.bib","bibliography/habitat_diversity.bib","bibliography/habitat_vulnerability.bib","bibliography/Ich_div.bib","bibliography/long_term_sst.bib","bibliography/MAB_HAB.bib","bibliography/NE_HAB.bib","bibliography/habs.bib","bibliography/occupancy.bib","bibliography/productivity_tech_memo.bib","bibliography/RW.bib","bibliography/seabird_ne.bib","bibliography/seal_pup.bib","bibliography/slopewater_proportions.bib","bibliography/Species_dist.bib","bibliography/survey_data.bib","bibliography/thermal_hab_proj.bib","bibliography/trans_dates.bib","bibliography/trend_analysis.bib","bibliography/zooplankton.bib","bibliography/cold_pool_index.bib","bibliography/forage_energy_density.bib","bibliography/Forage_Fish_Biomass_Index.bib","bibliography/marine_heatwave.bib","bibliography/protected_species_hotspots.bib","bibliography/ocean_acidification.bib","bibliography/wind_habitat_occupancy.bib","bibliography/warm_core_rings.bib", "bibliography/glossary.bib","packages.bib"]
geometry: "left=1.0in, right=1.0in, top=1.0in, bottom=1.0in, includefoot"
biblio-style: apalike
link-citations: true
github-repo: NOAA-EDAB/tech-doc
description: "This book documents each indicator and analysis used in State of the Ecosystem reporting"
---
# 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](https://www.nefsc.noaa.gov/) for the [New England Fisheries Management Council](https://www.nefmc.org/) and the [Mid-Atlantic Fisheries Management Council](http://www.mafmc.org/). The metadata for each indicator (in accordance with the [Public Access to Research Results (PARR) directive](http://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf)) 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](https://noaa-edab.github.io/tech-doc/). The PDF version of this document is for archiving only. The [PDF version](https://repository.library.noaa.gov/welcome) from previous years is archived in NOAA's Institutional Repository.
Indicators included in this document were selected to clearly align with management objectives, which is required for integrated ecosystem assessment [@levin_integrated_2009], and has been advised many times in the literature [@degnbol_review_2004; @jennings_indicators_2005; @rice_framework_2005; @link_translating_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_definitions_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](https://www.fisheries.noaa.gov/resource/document/magnuson-stevens-fishery-conservation-and-management-act)) and regional sources [@depiper_operationalizing_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.
```{r setup, echo=FALSE, message = FALSE, warning = FALSE, results='hide'}
knitr::opts_chunk$set(echo = F,
message = F,
warning = F,
#dev = "cairo_pdf",
fig.path = here::here("images/"))
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE)
#update.packages(ask = FALSE, checkBuilt = TRUE) # update R packages
#tinytex::tlmgr_update()
#source directories
image.dir <- here::here("images")
r.dir <- here::here("R")
gis.dir <- here::here("gis")
data.dir <- here::here("data")
#Plotting and data libraries
remotes::install_github("noaa-edab/ecodata")
remotes::install_github("noaa-edab/stocksmart")
remotes::install_github("thomasp85/patchwork")
#remotes::install_github("andybeet/arfit")
library(ggplot2)
#library(formatR)
#library(magrittr)
library(dplyr)
library(tidyr)
library(ecodata)
library(here)
library(kableExtra)
library(ggrepel)
#library(stringr)
library(patchwork)
library(heatwaveR)
library(gridExtra)
library(vegan)
library(grid)
library(rpart)
library(knitr)
library(rmarkdown)
library(readr)
library(RColorBrewer)
library(DT)
library(AICcmodavg)
library(plyr)
library(cowplot)
#library(plotly)
#GIS libraries
library(sf)
#library(rgdal)
#library(raster)
library(marmap)
library(ggspatial)
#Time series constants
shade.alpha <- 0.3
shade.fill <- "lightgrey"
lwd <- 1
pcex <- 2
trend.alpha <- 0.5
trend.size <- 2
hline.size <- 1
hline.alpha <- 0.35
hline.lty <- "dashed"
label.size <- 5
hjust.label <- 1.5
letter_size <- 4
feeding.guilds1 <- c("Piscivore","Planktivore","Benthivore","Benthos")
feeding.guilds <- c("Apex Predator","Piscivore","Planktivore","Benthivore","Benthos")
x.shade.min <- 2012
x.shade.max <- 2022
#Function for custom ggplot facet labels
label <- function(variable,value){
return(facet_names[value])
}
#Map line parameters
map.lwd <- 0.4
#CRS
crs <- "+proj=longlat +lat_1=35 +lat_2=45 +lat_0=40 +lon_0=-77 +x_0=0 +y_0=0 +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0"
#Coastline shapefile
# coast <- ne_countries(scale = 10,
# continent = "North America",
# returnclass = "sf") %>%
# sf::st_transform(crs = crs)
# # #State polygons
# ne_states <- ne_states(country = "united states of america",
# returnclass = "sf") %>%
# sf::st_transform(crs = crs)
# #high-res polygon of Maine
# new_england <- read_sf(gis.dir,"new_england")
#EPU shapefile
#epu_sf <- ecodata::epu_sf %>%
# filter(EPU %in% c("MAB","GB","GOM"))
#identifiers
council <- "Mid-Atlantic Fishery Management Council"
council_abbr <- "MAFMC"
epu <- "Mid-Atlantic Bight"
epu_abbr <- "MAB"
region <- "Mid-Atlantic"
region_abbr <- "MA"
```
(ref:neusmap) Map of Northeast U.S. Continental Shelf Large Marine Ecosystem from @Hare2016.
```{r neusmap, message = FALSE, warning=FALSE, fig.align='center', fig.height=6, echo = F, fig.cap='(ref:neusmap)'}
knitr::include_graphics("images/journal.pone.0146756.g002.PNG")
```
The table below shows which versions of all related products correspond to a specific State of the Ecosystem report cycle. The reports and supporting products including the technical documentation are developed annually. The DOI links will be included once they are available so may lag.
**DOIs**
* [MAFMC SOE 2020](https://doi.org/10.25923/1f8j-d564)
* [NEFMC SOE 2020](https://doi.org/10.25923/4tdk-eg57)
* [Technical Documentation SOE 2020](https://doi.org/10.25923/64pf-sc70)
* [MAFMC SOE 2021](https://repository.library.noaa.gov/view/noaa/29525)
* [NEFMC SOE 2021](https://repository.library.noaa.gov/view/noaa/29524)
* [Technical Documentation SOE 2021](https://repository.library.noaa.gov/view/noaa/29277)
* [MAFMC SOE 2022](https://doi.org/10.25923/5s5y-0h81)
* [NEFMC SOE 2022](https://doi.org/10.25923/ypv2-mw79)
* [Technical Documentation SOE 2022](https://doi.org/10.25923/xq8b-dn10)
<!--chapter:end:index.Rmd-->
# Data and Code Access {#erddap}
### About
The Technical Documentation for the State of the Ecosystem (SOE) reports is a [bookdown](https://bookdown.org) document; hosted on the NOAA Northeast Fisheries Science Center (NEFSC) Ecosystems Dynamics and Assessment Branch [Github page](https://github.com/NOAA-EDAB), and developed in R. Derived data used to populate figures in this document are queried directly from the [ecodata](https://github.com/NOAA-EDAB/ecodata) R package or the NEFSC [ERDDAP server](https://comet.nefsc.noaa.gov/erddap/info/index.html?page=1&itemsPerPage=1000). ERDDAP queries are made using the R package [rerddap](https://cran.r-project.org/web/packages/rerddap/vignettes/Using_rerddap.html).
```{r global-opts1, echo = FALSE}
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE)
```
### 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 included[^1].
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](https://github.com/NOAA-EDAB/ecodata) R package.
### Building the document
Start a local build of the SOE bookdown document by first cloning the project's associated [git repository](https://github.com/NOAA-EDAB/tech-doc). 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](https://github.com/NOAA-EDAB/tech-doc/tree/master/R/stored_scripts/erddap_query_and_build_code.R). 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.
#### 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]((https://github.com/NOAA-EDAB/ecodata/tree/master/R)).
[^1]: There are multiple R scripts sourced throughout this document in an attempt to keep code concise. These scripts include [BasePlot_source.R](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/BasePlot_source.R), [GIS_source.R](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/GIS_source.R), and [get_erddap.R](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/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.
<!--chapter:end:chapters/erddap_query_and_build.Rmd-->
# Aggregate Groups {#species_groupings}
**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).
```{r global-opts2, echo = FALSE}
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE)
```
## 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 @garrison2000dietary and @link2006EMAX. 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 \@ref(tab:soe2017class)). These seven were the same guilds used in @garrison2000dietary, 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 \@ref(tab:soe2018class)) along the lines of @link2006EMAX. 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.
```{r soe2017class, eval = T, echo = F}
soe.17.class <- data.frame('Feeding Guild' = c('Apex Predator', 'Piscivore',
'Macrozoo-piscivore', 'Macroplanktivore',
'Mesoplanktivore', 'Benthivore',
'Benthos', 'Other'),
Description = c('Top of the food chain', 'Fish eaters',
'Shrimp and small fish eaters', 'Amphipod and shrimp eaters',
'Zooplankton eaters', 'Bottom eaters',
'Things that live on the bottom',
'Things not classified above'))
kable(soe.17.class, booktabs = TRUE,
caption = "Aggregate groups use in 2017 SOE. Classifications are based on Garrison and Link (2000). \\label{}")
```
```{r soe2018class, eval = T, echo = F}
soe.18.class <- data.frame('Feeding Guild' = c('Apex Predator', 'Piscivore',
'Planktivore', 'Benthivore',
'Benthos', 'Other'),
Description = c('Top of the food chain', 'Fish eaters',
'Zooplankton eaters', 'Bottom eaters',
'Things that live on the bottom',
'Things not classified above'))
kable(soe.18.class, booktabs = TRUE,
caption = "Aggregate groups use since 2018 SOE. Classifications are based on Link et al. (2006).")
```
### 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](https://www.integratedecosystemassessment.noaa.gov/regions/northeast)) website. These groupings are based on EMAX groupings [@link2006EMAX], 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.
#### 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](http://integratedecosystemassessment.noaa.gov).
More information about the New Engalnd Fisheries Management Council is available [here](https://www.nefmc.org/).
More information about the Mid-Atlantic Fisheries Management Council is available [here](http://www.mafmc.org/).
### Data extraction
Species lists are pulled from SVDBS and CFDBS. They are merged using the ITIS code. Classifications from Garrison and Link [@garrison2000dietary] and Link et al. [@link2006EMAX] are added manually. The R code used in the extraction process can be found [here](https://github.com/slucey/RSurvey/blob/master/Species_list.R).
<!--chapter:end:chapters/aggregate_groups.rmd-->
# 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](https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html).
## Methods
### 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](https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html)) 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 [@Reynolds2007]. 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](https://github.com/NOAA-EDAB/tech-doc/tree/master/gis)).
### 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](https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html) 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 [@WMO2017]. 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](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/annual_sst_cycles_extraction_and_processing.R).
<!--chapter:end:chapters/Annual_SST_cycle_indicator.Rmd-->
# Aquaculture {#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
## Methods
### 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)
* [State of New Hampshire, Marine Aquaculture Compendium](https://drive.google.com/file/d/1eCg0cP2rsjZ0AAloPuxIyDiA01urOcjR/view?usp=sharing)
* [State of Massachusetts, Division of Marine Fisheries](https://www.mass.gov/service-details/dmf-annual-reports)
* [State of Rhode Island, Coastal Resource Management Council](http://www.crmc.ri.gov/aquaculture.html)
* [State of Maryland, Aquaculture Coordinating Council](https://calendarmedia.blob.core.windows.net/assets/1495a281-9eab-422a-9f90-a16ac9686db8.pdf)
### 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.
| State | Timeseries Start Year |
|---------------|-----------------------|
| Maine | 2009 |
| New Hampshire | 2013 |
| Massachusetts | 2009 |
| Rhode Island | 2009 |
| New Jersey | 2012* |
| Maryland | 2012 |
| Virginia | 2009 |
\* only includes data through 2016.
No further analysis was conducted on these.
### Data processing
Aquaculture data were formatted for inclusion in the `ecodata` R package using the code found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_aquaculture.R).
## 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.
### 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 [@Hudson2017a]. 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 [@ACC2017], 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 [@Calvo2017].
### 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](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_aquaculture.R).
### Data analysis
No data analyses occurred for this indicator.
<!--chapter:end:chapters/Aquaculture_indicators.Rmd-->
# Bennet Indicator {#bennet}
**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](#comdat)).
## Methods
### 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](https://comet.nefsc.noaa.gov/erddap/tabledap/group_landings_soe_v1.html).
### Data extraction
For information regarding processing of CFDBS, please see [Comland](#comdat) 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](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/bennet_extraction.R).
### 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 [@Cross2009]. 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 [@Balk2010], and has been used to examine revenue changes in Swedish pharmacies, productivity change in U.S. railroads [@lim2009], and dividend changes in banking operations [@Grifell-Tatje2004]. An attractive feature of the BI is that the overall indicator is equal to the sum of its subcomponents [@Balk2010]. 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 [@Georgianna2017]:
$$VI = \frac{1}{2}(\sum_{j=1}^{J}p_{j}^{t+1}y_{j}^{t+1} - \sum_{j=1}^{J}p_{j}^{t+1}y_{j}^{t} + \sum_{j=1}^{J}p_{j}^{t}y_{j}^{t+1} - \sum_{j=1}^{J}p_{j}^{t}y_{j}^{t})$$
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.
### Data processing
Bennet indicator time series were formatted for inclusion in the `ecodata` R package using the R code found [here](https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/data-raw/get_bennet.R).
<!--chapter:end:chapters/Bennet_indicator.Rmd-->
# 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.
## Methods
### 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 extraction
NA
### 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.
### Data processing
Derived bottom temperature 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_bottom_temp.R).
<!--chapter:end:chapters/bottom_temperature_GLORYS.Rmd-->
# Bottom temperature - High Resolution {#bottom_temp_seasonal_gridded}
**Description**: Seasonal bottom temperatures on the Northeast Continental Shelf between 1959 and 2022 in a 1/12° grid.
**Indicator category**: Published Methods, Synthesis of published information
**Found in**: State of the Ecosystem - Gulf of Maine & Georges Bank (2023); State of the Ecosystem - Mid-Atlantic Bight (2023)
**Contributor(s)**: Hubert du Pontavice, Vincent Saba, Zhuomin Chen
**Data steward**: Hubert du Pontavice, hubert.dupontavice@noaa.gov
**Point of contact**: Hubert du Pontavice, hubert.dupontavice@noaa.gov
**Public availability statement**: Source data are NOT publicly available. Please email hubert.dupontavice@noaa.gov for further information and queries of bottom temperature source data.
## Methods
### Data sources
#### Study area
The bottom temperature product covered the northeast U.S. shelf marine ecosystem (NEUS) and specifically an area of four Ecological Production Units (EPUs) defined by NOAA's Northeast Fisheries Science Center (https://noaa-edab.github.io/tech-doc/epu.html).
#### Design of the gridded bottom temperature time series
The bottom temperature product is in a horizontal 1/12 degree grid between 1959 and 2022 and is made of daily bottom temperature estimates from:
*Bias-corrected ROMS-NWA (ROMScor) between 1959 and 1992 which was regridded
in the same 1/12degree grid as GLORYS using bilinear interpolation;
*GLORYS12v1 in its original 1/12 degree grid between 1993 and 2020;
*GLO12v3 (called PSY4V3R1 in @duPontavice2023 and @Lellouche2018) in its original 1/12 degree grid for 2021.
*GLO12v4 in its original 1/12 degree grid for 2022.
#### Ocean model data
Four ocean models were used to get high-resolution daily bottom temperature on the NEUS between 1959 and 2022.
For the period between 1959 and 1992, we used daily ocean bottom temperature from the long-term (1958–2007) high-resolution numerical simulation of the Northwest Atlantic Ocean in the Regional Ocean Modelling System (ROMS), a split-explicit, free-surface, terrain-following, hydrostatic, primitive equation model (@Shchepetkin2005). The model domain covers the Northwest Atlantic Ocean with ~7km horizontal resolution and 40 vertical terrain- following layers. A detailed description of ROMS-NWA can be found in @Chen2018.
For the period between 1992 and 2020, the daily bottom temperature outputs from the GLORYS12v1 ocean reanalysis product were used. GLORYS12v1 is a global ocean, eddy-resolving, and data assimilated hindcast from Mercator Ocean (European Union-Copernicus Marine Service, 2018; Fernandez and Lellouche2018; @Lellouche2021) with 1/12 degree horizontal resolution and 50 vertical levels. The base ocean model is the Nucleus for European Modelling of the Ocean 3.1 (NEMO 3.1; Madec, 2016) driven at the surface by the European Centre for the Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (@Dee2011). Remotely sensed and in situ observations are jointly assimilated by means of a reduced-order Kalman filter.
For the year 2021, we used daily bottom temperature from the Operational Mercator global ocean analysis and forecast system (GLO12v3 called PSY4V3R1 in @duPontavice2023 and @Lellouche2018). GLO12v3 is a global ocean, eddy-resolving, monitoring forecasting system (@Lellouche2018) with the same ocean model grid (1/12 degree horizontal resolution and 50 vertical levels) and has many similarities with GLORYS12v1. Remotely sensed and in situ observations are also jointly assimilated by means of a reduced-order Kalman filter.
For the year 2022, we used GLO12v4 which is a revised and updated version of GLO12v3 (European Union-Copernicus Marine Service, 2016). The general model structure is similar to GLO12v3 with some changes in model configuration, parameterizations, relaxations to avoid spurious drifts, river inputs, atmospheric fluxes and data assimilation (more detail in https://data.marine.copernicus.eu/product/GLOBAL_ANALYSISFORECAST_PHY_001_024/description)
#### Bias-correction process of NWA-ROMS
We used the methodology presented in du Pontavice et al. (2023) based on the Northwest Atlantic Regional Ocean Climatology (NWARC). The first step was to regrid ROMS-NWA bottom temperature over the same 1/10 degree horizontal grid as the NWARC using bilinear interpolation. Then, we conducted the bottom temperature bias-correction in the 1/10 degree NWARC grid using monthly climatologies from NWARC over four decadal periods from 1955 to 1994. A monthly bias was calculated in each 1/10 degree grid cell and for each decade (1955–1964, 1965–1974, 1975–1984, 1985–1994). Based on this monthly bias, we estimated a daily bias for each decade in each grid cell. Lastly, for each ROMS-NWA grid cell we identified the bias from the closest 1/10 degree NWARC grid cell and subtracted the daily bias to the daily ROMS-NWA bottom temperature for all years and days of each decade.
### Data processing
Derived bottom temperature 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_bottom_temp_comp.R).
<!--chapter:end:chapters/bottom_temperature_highres.Rmd-->
# Bottom temperature - in situ {#bottom_temp}
**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.
## Methods
### 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.
### Data extraction
While all processed hydrographic data are archived in an Oracle database (OCDBS), we work from Matlab-formatted files stored locally.
### 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](#epu) (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 @mountain1991. 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.
### Data processing
Derived bottom temperature 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_bottom_temp.R).
<!--chapter:end:chapters/bottom_temperature_insitu.Rmd-->
# 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.walsh@noaa.gov>) for raw data.
## Methods
### 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 @Kane2007, @Kane2011, and @Morse2017.
### 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 @Morse2017, 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](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/CalanusStage_SOE.R).
### Data processing
The Calanus Stage indicator was 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_calanus_stage.R).
<!--chapter:end:chapters/calanus_stage.Rmd-->
# Catch and Fleet Diversity {#commercial_div}
**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](https://comet.nefsc.noaa.gov/erddap/tabledap/comm_data_soe_v1.html).
## 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](https://www.nefmc.org/) or [MAFMC](http://www.mafmc.org/) 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.
### 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.
### 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.
<!-- For NOAA personnel: Code currently archived in the \\\\net\\home2\\mlee\\diversity\\code folder, while data is currently archived in \\\\net\\home2\\mlee\\diversity folder. -->
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 \@ref(tab:spp-groupings)), defined in the data as "myspp", which is further developed in the script to rectify inconsistencies in the data.
```{r spp-groupings, eval = T}
raw.dir <- here::here("data")
spp <- read.csv(file.path(raw.dir,"spp_groupings.csv"),stringsAsFactors = F)
spp <- spp %>%
dplyr::rename( 'Common Name' = COMNAME,
'Scientific Name' = SCINAME) %>%
dplyr::select(Group, NESPP3, 'Common Name', 'Scientific Name')
knitr::kable(spp, caption="Species grouping", booktabs=T, longtable = T) %>%
kableExtra::kable_styling(
latex_options = c("repeat_header","scale_down"), font_size = 5) %>%
kableExtra::collapse_rows(columns = 1)
```
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](https://github.com/NOAA-EDAB/tech-doc/tree/master/data/Human_Dimensions_code).
### 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 @eric_m_thunberg_measures_2015 and published in @gaichas_framework_2016.
### Data processing
Catch and fleet diversity indicators 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_commercial_div.R).
<!--chapter:end:chapters/Catch_and_Fleet_Diversity_indicators.Rmd-->
# Chesapeake Bay Salinity and Temperature {#ches_bay_sal}
**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.
## Methods
### Data sources
The National Oceanic and Atmospheric Administration’s (NOAA) Chesapeake Bay Interpretive Buoy System ([CBIBS](https://buoybay.noaa.gov/data)) 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 (water quality monitor) 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.
### 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](https://buoybay.noaa.gov/observations/data-download) to explore and pull that data from the CBIBS database. The process for data extraction for this indicator can be found [here](https://github.com/NOAA-EDAB/tech-doc/tree/master/R/stored_scripts/ches_bay_sal_extraction.txt).
### Data analysis
The data is processed with a [python script](https://github.com/NOAA-EDAB/tech-doc/tree/master/R/stored_scripts/ches_bay_sal_analysis.py). This creates an array and runs the data through a [QARTOD](https://ioos.noaa.gov/project/qartod/) (Quality Assurance/Quality Control of Real-Time Oceanographic Data) routine. The result is a set of flags. Only the good data is used in the plot below.
The stations include annapolis ([AN](https://buoybay.noaa.gov/locations/annapolis)), goose reef ([GR](https://buoybay.noaa.gov/locations/gooses-reef)), potomac ([PL](https://buoybay.noaa.gov/locations/potomac)), and york-split ([YS](https://buoybay.noaa.gov/locations/york-spit)).
### Data processing
Code for processing salinity data can be found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_ch_bay_sal.R).
<!--chapter:end:chapters/ches_bay_sal.Rmd-->
# Chesapeake Bay Seasonal SST Anomalies {#ches_bay_sst}
**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](https://eastcoast.coastwatch.noaa.gov/cw_avhrr.php).
## Methods
### 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](https://eastcoast.coastwatch.noaa.gov). 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 (@Li2001a, @Li2001b). 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.
### 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 `(max(Year) - 1)`. 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).
### Data processing
Code for processing Chesapeake Bay temperature data can be found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_ch_bay_temp.R).
<!--chapter:end:chapters/ches_bay_sst.Rmd-->
# Chesapeake Bay Water Quality Standards Attainment {#ches_bay_wq}
**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).
## Methods
To protect the aquatic living resources of Chesapeake Bay, the [Chesapeake Bay Program](https://www.chesapeakebay.net/) (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) [@usepa2003]. 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 [@usepa2017]. 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.
### 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](http://www.chesapeakebay.net/data/downloads/cbp_water_quality_database_1984_present). These data have been routinely reported to the CBP by the Maryland Department of Natural Resources, Virginia Department of Environmental Quality, Old Dominion University, Virginia Institute of Marine Science, and citizen/volunteer monitoring initiatives.
### 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 @zhang2018.
**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 [@usepa2017]. For more details, refer to the Methods section of @zhang2018.
The indicator provides an integrated measure of Chesapeake Bay’s 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.
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.### Data processing
The indicator data set was 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_ches_bay_wq.R).
<!--chapter:end:chapters/ches_bay_water_quality.Rmd-->
# Cold Pool Index {#cold_pool}
**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@noaa.gov>
**Public availability statement**: Source data are NOT publicly available. Please email hubert.dupontavice@noaa.gov for further information and accessing the ROMS-NWA bottom temperature data.
## 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.
### 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 @duPontavice2023.
### Data Analysis
#### Cold Pool Domain
The first step was to define the Cold Pool domain, which is typically located within the MAB and the southern flank of Georges Bank (@Chen2018; @Houghton1982; @Lentz2017). Here, we delineated a spatial domain covering the management area of the SNEMA yellowtail flounder (since this method was initially developed to study the Cold Pool impact on yellowtail flounder recruitment) comprising the MAB and in the SNE shelf between the 20 and 200 m isobaths (@Chen2018; @Chen2020). We restricted the time period from June (to match the start of the settlement period; @Sullivan2005) to September (which is the average end date of the Cold Pool (calendar day 269) estimated by @Chen2020. The Cold Pool domain was defined as the area, wherein average bottom temperature was cooler than 10°C between June and September from 1959 to 2022. We then developed the three Cold Pool indices using bottom temperature from ocean models.
#### Cold Pool Index (Model_CPI)
The Cold Pool Index (Model_CPI) was adapted from @miller2016 based on the method developed in @dupontavice2022. 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 1959–2022 $$({\bar{T}}_{i,\ 1958-2022})$$ between June and September. Model_CPI was calculated as the mean residual temperature over the Cold Pool domain such that:
$${{CPI}_y}=\ \frac{\sum_{i=1}^{n}{{(T}_{i,\ y}\ -\ {\bar{T}}_{i,\ 1958-2022})\ }}{n}$$
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 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 1959–2022. Then, Model_PI was calculated as the mean “residual month” over the Cold Pool domain:
$${PI}_y=\ \frac{\sum_{i=1}^{n}{{(Month}_{i,\ y}\ -\ {\bar{Month}}_{i,\ 1958-2022})\ }}{n}$$
#### 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 is the average ROMS-NWA bottom temperature over the decade $$d$$ in the grid cell $$i$$. All above methods @dupontavice2022.
Bottom temperature from Glorys reanalysis and Global Ocean Physics Analysis were not being processed.
Bottom temperature from ROMS-NWA (used for the period 1959-1992) 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 (@Chen2018; @Chen2020). 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 (@Seidov2016a, @Seidov2016b) (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:
$${BIAS}_{i,\ d}=\ T_{i,d}^{Climatology}\ -\ {\bar{T}}_{i,\ d}^{ROMS-NWA}$$
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. All above methods @dupontavice2022.
### Data processing
Code used to process the cold pool inidcator can be found in the `ecodata` package [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_cold_pool.R).
## 2021 Methods
**Point of Contact:**: Zhoumin Chen <zhuomin.chen@uconn.edu>
### 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
Depth-averaged spatial temperature is calculated based on the daily Cold Pool dataset, which is quantified following @Chen2018.
### Data processing
Code used to process the cold pool inidcator can be found in the `ecodata` package [here](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_cold_pool.R).
## 2020 Methods
**Point of Contact:**: Chris Melrose <chris.melrose@noaa.gov>
### 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.
### Data extraction
### Data analysis
Methods published @miller2016, [original MATLAB source code](https://github.com/NOAA-EDAB/tech-doc/tree/master/R/stored_scripts/cold_pool_analysis.txt) used in that paper was provided by Jon Hare and used in this analysis.
<!--chapter:end:chapters/cold_pool_index.Rmd-->
# Commercial Landings Data {#comdat}
**Description**: Commercial landings data pull
**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)**: 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 publicly available due to confidentiality of individual fishery participants. Derived indicator outputs are
available [here](https://comet.nefsc.noaa.gov/erddap/tabledap/group_landings_soe_v1.html).
## 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 \@ref(tab:calibration1) below.
```{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)
```
Comlands is an R database pull that consolidates the landings records from 1964 on and attempts to associate them with NAFO statistical areas (Figure \@ref(fig:StatAreaMap)). 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.
```{r StatAreaMap, fig.cap="Map of the North Atlantic Fisheries Organization (NAFO) Statistical Areas. Colors represent the Ecological Production Unit (EPU) with which the statistical area is associated.", echo=F, eval=T, out.width = "50%", fig.align = "center"}
image.dir <- here::here('images')
knitr::include_graphics(file.path(image.dir, 'Stat_Area_Map.jpg'))
```
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.
```{r geartypes, eval = T, echo = F}
gear.table <- data.frame('gear code' = c(1,2,3,4,5,6,7,8,9),
'Major gear' = c('Otter Trawls', 'Scallop Dredges',
'Other Dredges', 'Gillnets', 'Longlines',
'Seines', 'Pots/Traps', 'Midwater', 'Other'))
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)
```
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 \@ref(tab:geartypes)). 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](#epu) (Table \@ref(tab:statareas)).
```{r statareas, eval = T, echo = F}
area.table <- data.frame(EPU = c('Gulf of Maine', 'Georges Bank', 'Mid-Atlantic'),
'Stat Areas' = c('500, 510, 512, 513, 514, 515',
'521, 522, 523, 524, 525, 526, 551, 552, 561, 562',
'537, 539, 600, 612, 613, 614, 615, 616, 621, 622, 625, 626, 631, 632'))
names(area.table) [2]<- "Stat Areas"
kable(area.table, caption = "Statistical areas making up each EPU") %>%
kable_styling(latex_options = "HOLD_position")
```
The final step of Comlands is to pull the foreign landings from the [NAFO database](https://www.nafo.int/Data/frames). 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.
### Data sources
Comland is a database query of the NEFSC commercial fishery database (CFDBS). More information about the CFDBS is available [here](https://inport.nmfs.noaa.gov/inport/item/27401).
### Data extraction
[`comlandr`](https://github.com/NOAA-EDAB/comlandr) is a package used to extract relevant data from the database.
#### Data Processing
The landings data were formatted for inclusion in the `ecodata` R package with this [R code](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_comdat.R).
### 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 (ie. [bennet index](#bennet)). The most straightforward use of this data are the region total and aggregate landings indicators. Regional totals sum landings three ways: 1) All landings regardless of management authority and eventual use (i.e. food or bait), 2) All landings used for seafood but regardless of management authority, and 3) All landings used for seafood and managed by the regional fisheries management council for whom the report is presented.
Landings are also calculated by aggregate groups per region. These are calculated by first assigning the various species into [aggregate groups](#aggroups). Landings are then summed by year, [EPU](#epu), aggregate group, and whether they are managed by the regional fisheries management council or not. Proportions of managed landings to total landings are also calculated and have been reported in some reports.
These are calculated by first assigning the various species into [aggregate groups](#aggroups). Landings are then summed by year, [EPU](#epu), aggregate group, and whether they are managed by the regional fisheries management council or not. Proportions of managed landings to total landings are also calculated and have been reported in some reports.
<!--chapter:end:chapters/landings_data.Rmd-->
# 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).
## 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)](https://www.integratedecosystemassessment.noaa.gov/regions/california-current/cc-ecosystem-components), with models for each [focal ecosystem component](https://www.integratedecosystemassessment.noaa.gov/regions/california-current/cc-coastalpelagicspecies#overview) that detailed the [ecological](https://www.integratedecosystemassessment.noaa.gov/regions/california-current/cc-coastalpelagicspecies#ecologicalinteractions), [environmental](https://www.integratedecosystemassessment.noaa.gov/regions/california-current/cc-coastalpelagicspecies#environmentalDrivers), and [human system](https://www.integratedecosystemassessment.noaa.gov/regions/california-current/cc-coastalpelagicspecies#humanActivities) linkages. Another set of conceptual models outlined [habitat](https://www.integratedecosystemassessment.noaa.gov/regions/california-current/cc-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_operationalizing_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.
```{r draftmod, echo = F, eval = T, out.width='80%'}
knitr::include_graphics(file.path(image.dir, 'GBGOMconceptual1.png'))
```