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20240826_EcoCaseStudy_Gaichas.Rmd
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---
title: "Operationalizing the use of ecosystem <br /> information in Mid-Atlantic science<br /> and management decisions"
subtitle: "SCS8 Case Study <br /> 26 August 2024"
author: Sarah Gaichas <br /> Mid-Atlantic SSC and NOAA NMFS Northeast Fisheries Science Center <br /> <br />Thanks to Geret DePiper (MAFMC SSC, NEFSC) and Brandon Muffley (MAFMC)
output:
xaringan::moon_reader:
css: ["default", "libs/EDAB_theme3_508_SOE_16by9.css", "libs/cols.css"]
lib_dir: libs
nature:
titleSlideClass: ["right", "middle", "my-title"]
ratio: "16:9"
highlightStyle: githubp
highlightLines: true
countIncrementalSlides: false
beforeInit: ["libs/macros.js", "libs/cols_macro.js"]
revealjs::revealjs_presentation:
self_contained: false
reveal_plugins: ["notes", "search"]
---
class: top, left
background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/MAFMC+Jurisdiction+Map+v3-01.jpg")
background-size: 200px
background-position: right
<style>
p.caption {
font-size: 0.6em;
}
</style>
<style>
.reduced_opacity {
opacity: 0.5;
}
</style>
```{r setup, include=FALSE}
options(htmltools.dir.version = FALSE)
knitr::opts_chunk$set(echo = F,
fig.retina = 3,
#fig.width = 4,
#fig.height = 2,
fig.asp = 0.45,
warning = F,
message = F)
#Plotting and data libraries
library(tidyverse)
library(tidyr)
library(here)
library(kableExtra)
library(ecodata)
library(readxl); library(data.table)
library(DT)
library(widgetframe)
library(patchwork)
# if figure output defaults to docs/imaages/ replace with this before moving to
# presentations/docs in both the html output (search and replace)
# and folder name
# 20220316_MAFMCSSC_Gaichas_files/figure-html/
```
```{r, load_refs, include=FALSE, cache=FALSE}
library(RefManageR)
BibOptions(check.entries = FALSE,
bib.style = "authoryear",
cite.style = "authoryear",
longnamesfirst = FALSE,
max.names = 1,
style = "markdown")
myBib <- ReadBib("./MidSOE.bib", check = FALSE)
#,
# hyperlink = FALSE,
# dashed = FALSE
```
# Operational: **in use or ready for use**
Components of an operational process:
* Clear articulation of management objectives (use)
* Standardized information updates on a regular reporting schedule
* Feedbacks from the management system and adjustments to achieve management objectives
"Operationalizing" is an iterative process within a specific context
.contrib[*Mid Atlantic fishery management plans and species*]
.footnote[Source: http://www.mafmc.org/fishery-management-plans]
???
(we spend a lot of time on this, understand that operational processes require a lot of time and resources and are *not* research)
---
background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/Ecov_1_North_BT.png")
background-size: 500px
background-position: right
# Mid Atlantic operational use of ecosystem information
.pull-left-60[
## Stock assessments
## ABC setting
## EAFM: risk assessment → conceptual model → management strategy evaluation → operational management tool
## Co-evolution of ecosystem reporting and operational use
Right: Bottom temperature time series covariate on recruitment in the 2024 operational black sea bass assessment
]
.pull-right-40[
.footnote[
Image courtesy Emily Liljestrand, NEFSC
]
]
???
* Stock assessments (ESPs-->assessment structure; ecosystem covariates-->OFL projections)
* ABC setting (data quality, model performance, and ecosystem information-->OFL CV-->ABC)
* EAFM risk assessment (ecosystem information-->prioritize scoping and MSE)
In development, research coordinated with SSC
* Ecosystem overfishing thresholds
Focus on the risk assessment and its operational use, process to bring new science in
---
## Ecosystem and Socioeconomic Profiles (ESPs) in Assessments, and MAFMC ABC process
.pull-left[
.footnote[
Images courtesy Abigail Tyrell, NEFSC
]
]
.pull-right[
### ABC proportion of OFL given OFL CV
```{r OFLCVtoABC, fig.asp = 0.7}
ABCprop <- data.frame(Bfrac = seq(0, 2.5, 0.01)) %>%
dplyr::mutate(pstar = case_when(Bfrac <= 0.1 ~ 0,
Bfrac > 0.1 & Bfrac <= 1.0 ~ (Bfrac-0.1)*((0.45-0)/(1-0.1)) ,
Bfrac > 1.0 & Bfrac <= 1.5 ~ (Bfrac-1.0)*((0.49-0.45)/(1.5-1))+0.45,
Bfrac > 1.5 ~ 0.49)) %>%
dplyr::mutate("CV=60%" = qlnorm(pstar, log(1), log(0.60 + 1)),
"CV=100%" = qlnorm(pstar, log(1), log(1.00 + 1)),
"CV=150%" = qlnorm(pstar, log(1), log(1.50 + 1))) %>%
pivot_longer(-c(Bfrac, pstar), names_to = "CVlevel")
p2 <- ggplot2::ggplot(ABCprop, aes(x=Bfrac, y=value, colour=CVlevel)) +
geom_line()+
theme_minimal() +
xlab("B/Bmsy") +
ylab("ABC/OFL proportion") +
labs(colour = "OFL CV") #+
#theme(legend.position="bottom")
print(p2, vp=grid::viewport(gp=grid::gpar(cex=1.5)))
```
.footnote[
See [MAFMC ABC control rule](http://www.mafmc.org/s/MAFMC-ABC-Control-Rule-White-Paper.pdf) and [update for 2020 MAFMC risk policy](https://www.mafmc.org/s/b_Implications-of-MAFMC-Risk-Policy-for-Multi.pdf)
]
]
---
## MAFMC Ecosystem Approach to Fisheries Management
.pull-left[
* 2016 Ecosystem Approach to Fishery Management (EAFM) [Policy Guidance document](
http://www.mafmc.org/s/EAFM-Doc-Revised-2019-02-08.pdf)
.center[
]
]
.pull-right[
* Mid-Atlantic EAFM framework:
.center[
]
]
???
The Council’s EAFM framework has similarities to the IEA loop on slide 2. It uses risk assessment as a first step to prioritize combinations of managed species, fleets, and ecosystem interactions for consideration. Second, a conceptual model is developed identifying key environmental, ecological, social, economic, and management linkages for a high-priority fishery. Third, quantitative modeling addressing Council-specified questions and based on interactions identified in the conceptual model is applied to evaluate alternative management strategies that best balance management objectives. As strategies are implemented, outcomes are monitored and the process is adjusted, and/or another priority identified in risk assessment can be addressed.
`r Cite(myBib, "depiper_operationalizing_2017")`
`r Cite(myBib, "bastille_improving_2021")`
`r Cite(myBib, "muffley_there_2021")`
`r Cite(myBib,"gaichas_implementing_2018")`
`r Cite(myBib,"depiper_learning_2021")`
`r Cite(myBib, "gaichas_framework_2016")`
---
## EAFM Risk Assessment: 2024 Update with new elements
.pull-left[
*Species level risk elements*
```{r sptable}
#tab.cap="Species level risk analysis results; l=low risk (green), lm= low-moderate risk (yellow), mh=moderate to high risk (orange), h=high risk (red)\\label{sptable}",
# spplist oc, sc, flk, scp, bsb, mack, but, lsq, ssq, gtile, btile, blu, dog, monk
risk.species<-data.frame(
Species = c("Ocean Quahog", "Surfclam", "Summer flounder", "Scup", "Black sea bass", "Atl. mackerel", "Chub mackerel", "Butterfish", "Longfin squid", "Shortfin squid", "Golden tilefish", "Blueline tilefish", "Bluefish", "Spiny dogfish", "Monkfish", "Unmanaged forage", "Deepsea corals"),
Assess = c("l", "l", "l", "l", "l", "l", "h", "l", "lm", "h", "l", "h", "l", "l", "h", "na", "na"),
Fstatus = c("l", "l", "h", "l", "l", "l", "lm", "l", "lm", "lm", "l", "h", "l", "h", "lm", "na", "na"),
Bstatus = c("l", "l", "lm", "l", "l", "h", "lm", "lm", "lm", "lm", "lm", "mh", "lm", "l", "lm", "na", "na"),
#FW1Pred = c("l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l"),
#FW1Prey = c("l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "l", "lm", "l"),
PreyA = c("tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd"),
PredP = c("tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd"),
FW2Prey = c("l", "l", "l", "l", "l", "l", "l", "l", "lm", "lm", "l", "l", "l", "l", "l", "lm", "l"),
Climate = c("h", "mh", "lm", "lm", "mh", "lm", "na", "l", "l", "l", "mh", "mh","l", "l", "l", "na", "na"),
DistShift = c("mh", "mh", "mh", "mh", "mh", "mh", "na", "h", "mh", "h", "l", "l", "mh", "h", "mh", "na", "na"),
EstHabitat = c("l", "l", "h", "h", "h", "l", "l", "l", "l", "l", "l", "l", "h", "l", "l", "na", "na"),
OffHab = c("tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd", "tbd")#,
)
# these elements were removed by the council
# PopDiv = c("na", "na", "na", "na", "na", "na", "na", "na", "na", "na", "na", "na", "na", "na"),
# FoodSafe = c(),
# one column test
# risk.species %>%
# mutate(Fstatus =
# cell_spec(Fstatus, format="latex", color = "black", align = "c", background =factor(Fstatus, c("na", "l", "lm", "mh", "h"),c("white", "green", "yellow", "orange", "red")))) %>%
# kable(risk.species, format="latex", escape = F, booktabs = T, linesep = "")
#convert to longer text for consistency and display in html table
risk.species <- risk.species %>%
mutate_at(vars(-Species), function(x){
recode(x,'tbd' = "tbd", 'l'="lowest",'lm'="lowmod",'mh'="modhigh",'h'="highest")}) %>%
as.data.frame()
#generalize to all
risk.species %>%
mutate_at(vars(-Species), function(x){
cell_spec(x, format="html", color = factor(x, c("tbd", "na", "lowest", "lowmod", "modhigh", "highest"),c("grey","black", "black", "black", "black", "white")), align = "c", background =factor(x, c("tbd","na", "lowest", "lowmod", "modhigh", "highest"),c("grey","white", "lightgreen", "yellow", "orange", "red")), background_as_tile=F)}) %>%
kable(format = "html", escape = F, table.attr='class="risktable"') %>%
kable_styling(font_size = 9)
```
.contrib[
* Mackerel and dogfish **Fstatus** risk reduced to low, Summer flounder risk increased to high. Spiny dogfish **Bstatus** risk decreased to low
* Indicators in development for new Prey Availability, Predation Pressure, and Offshore Habitat elements
]
*Ecosystem level risk elements*
```{r ecotable}
#tab.cap="Ecosystem level risk analysis results; l=low risk (green), lm= low-moderate risk (yellow), mh=moderate to high risk (orange), h=high risk (red)\\label{sptable}",
risk.eco<-data.frame(
System = c("Mid-Atlantic"),
EcoProd = c("lm"),
#EcoDiv = c("lm"),
CommVal = c("mh"),
RecVal = c("lm"),
FishRes1 = c("l"),
FishRes2 = c("mh"),
#CommJobs = c("mh"),
#RecJobs = c("l"),
ComDiv = c("l"),
RecDiv = c("tbd"),
Social = c("lm"),
ComFood = c("mh"),
RecFood = c("mh")
)
#convert to longer text for consistency and display in html table
risk.eco <- risk.eco %>%
mutate_at(vars(-System), function(x){
recode(x,'tbd' = "tbd", 'l'="lowest",'lm'="lowmod",'mh'="modhigh",'h'="highest")}) %>%
as.data.frame()
#make table
risk.eco %>%
mutate_at(vars(-System), function(x){
cell_spec(x, format="html", color = factor(x, c("tbd", "na", "lowest", "lowmod", "modhigh", "highest"),c("grey","black", "black", "black", "black", "white")), align = "c", background =factor(x, c("tbd","na", "lowest", "lowmod", "modhigh", "highest"),c("grey","white", "lightgreen", "yellow", "orange", "red")), background_as_tile=F)}) %>%
kable(format = "html", escape = F, table.attr='class="risktable"') %>%
kable_styling(font_size = 9)
```
.contrib[
* Recreational value risk increased from low to low-moderate
* Recreational diversity added, risk criteria in development
]
]
.pull-right[
*Species and Sector level risk elements*
```{r mgttable, echo=FALSE, message=FALSE, warnings=FALSE}
#tab.cap="Species and sector level risk analysis results; l=low risk (green), lm= low-moderate risk (yellow), mh=moderate to high risk (orange), h=high risk (red)\\label{sptable}",
risk.sppsector<-data.frame(
Species = c("Ocean Quahog-C", "Surfclam-C", "Summer flounder-R", "Summer flounder-C","Scup-R", "Scup-C","Black sea bass-R", "Black sea bass-C","Atl. mackerel-R", "Atl. mackerel-C","Butterfish-C", "Longfin squid-C", "Shortfin squid-C", "Golden tilefish-R", "Golden tilefish-C","Blueline tilefish-R","Blueline tilefish-C", "Bluefish-R", "Bluefish-C","Spiny dogfish-R", "Spiny dogfish-C", "Chub mackerel-C", "Unmanaged forage", "Deepsea corals"),
FControl = c(1,1,2,2,4,1,4,2,2,1,1,1,2,9,1,1,2,2,1,1,1,1,1,9),
Interact = c(1,1,1,2,1,2,1,2,1,2,2,3,2,1,1,1,1,1,1,1,3,2,1,9),
#OceanUse = c(2,2,2,2,2,3,3,4,1,3,3,4,2,1,1,1,1,1,2,1,3,2,3,3),
OSW1 = c(6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6),
OSW2 = c(6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6),
OtherUse = c(6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6),
RegComplex = c(1,1,4,2,4,2,4,2,2,4,3,3,3,1,1,2,1,3,2,1,4,1,1,9),
Discards = c(3,3,3,3,3,3,3,4,2,2,3,3,1,1,1,1,1,2,2,2,2,1,1,9),
Allocation = c(1,1,4,1,4,1,4,1,1,1,1,1,1,1,1,1,1,4,1,1,1,1,1,9)
)
#convert to text for consistency
risk.sppsector <- risk.sppsector %>%
mutate_at(vars(-Species), function(x){
recode(x,'1'="lowest",'2'="lowmod",'3'="modhigh",'4'="highest",'9'="na",
'6' = 'tbd')}) %>%
as.data.frame()
#make table
risk.sppsector %>%
mutate_at(vars(-Species), function(x){
cell_spec(x, format="html", color = factor(x, c("tbd", "na", "lowest", "lowmod", "modhigh", "highest"),c("grey", "black", "black", "black", "black", "white")), align = "c", background =factor(x, c("tbd","na", "lowest", "lowmod", "modhigh", "highest"),c("grey","white", "lightgreen", "yellow", "orange", "red")), background_as_tile=F)}) %>%
kable(format = "html", escape = F, table.attr='class="risktable"') %>%
kable_styling(font_size = 10)
```
.contrib[
* Management fully updated for existing elements
* Offshore wind (OSW) risks split into 2 new elements in development, non-OSW uses added
]
]
---
.pull-left[
```{r sfconceptmod, echo = F, fig.align = "center"}
# setup for conceptual model
PKG <- c(#"foreign","foodweb","sna", "DiagrammeR","circlize", "kableExtra", "googledrive", "readxl"
"RColorBrewer","QPress",
"chorddiag",
"widgetframe")
for (p in PKG) {
if(!require(p,character.only = TRUE)) {
install.packages(p)
require(p,character.only = TRUE)}
}
data.dir <- here::here("data")
#assumes this is a project and .dia file is in data directory
edges <- model.dia(file.path(data.dir, "Summer_Flounder_July22_2019.dia"))
source("R/interactive_chord_sfconsmod.R")
conmod <- interactive_chord_sfconsmod(edges, width=710, height=710, margin=130)
frameWidget(conmod)
```
]
.pull-right[
## How is MAFMC using the risk assessment?
* Risk based prioritization: the Council selected summer flounder for conceptual modeling
* Council completed management strategy evaluation (MSE) addressing [recreational fishery discards](https://www.mafmc.org/actions/summer-flounder-mse) based on conceptual modeling
* Stakeholder driven MSE coupled population and recreational demand models
* Results: potential improvement in angler welfare with low risk to stock status
]
???
In this interactive circular graph visualization, model elements identified as important by the Council (through risk assessment) and by the working group (through a range of experience and expertise) are at the perimeter of the circle. Elements are defined in detail in the last section of [this page](https://gdepiper.github.io/Summer_Flounder_Conceptual_Models/sfconsmod_riskfactors_subplots.html). Relationships between elements are represented as links across the center of the circle to other elements on the perimeter. Links from a model element that affect another element start wide at the base and are color coded to match the category of the element they affect.Hover over a perimeter section (an element) to see all relationships for that element, including links from other elements. Hover over a link to see what it connects. Links by default show text for the two elements and the direction of the relationship (1 for relationship, 0 for no relationship--most links are one direction).For example, hovering over the element "Total Landings" in the full model shows that the working group identified the elements affected by landings as Seafood Production, Recreational Value, and Commercial Profits (three links leading out from landings), and the elements affecting landings as Fluke SSB, Fluke Distributional Shift, Risk Buffering, Management Control, Total Discards, and Shoreside Support (6 links leading into Total Landings).
---
background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/RecFishDecisionSupportTool.png")
background-size: 1050px
background-position: bottom
## Operational multispecies recreational demand model sets specifications
---
## Updating the risk assessment: add recreational and cross-sectoral risks, static → dynamic indicators
Example: Evaluate risks posed by prey availability to achieving OY for Council managed species
.pull-left[
```{r}
fallmacro <- readRDS(url("https://github.com/NOAA-EDAB/foodweb-risk/raw/main/preyindices/fallmacrobenthosindex.rds"))
fixmacro<- fallmacro |>
dplyr::filter(Var %in% c("Fall Macrobenthos Biomass Index Estimate"),
EPU %in% c("MAB")) |>
dplyr::group_by(EPU) |>
dplyr::summarise(max = max(Value))
p <- fallmacro |>
dplyr::filter(Var %in% c("Fall Macrobenthos Biomass Index Estimate",
"Fall Macrobenthos Biomass Index Estimate SE"),
EPU %in% c("MAB")) |>
dplyr::group_by(EPU) |>
tidyr::separate(Var, into = c("Season", "A", "B", "C", "D", "Var")) |>
dplyr::mutate(Var = tidyr::replace_na(Var, "Mean")) |> #,
#max = as.numeric(Value)) |>
tidyr::pivot_wider(names_from = Var, values_from = Value) |>
dplyr::left_join(fixmacro) |>
dplyr::mutate(#Value = Value/resca,
Mean = as.numeric(Mean),
#max = as.numeric(Value),
Mean = Mean/max,
SE = SE/max,
Upper = Mean + SE,
Lower = Mean - SE) |>
ggplot2::ggplot(ggplot2::aes(x = Time, y = Mean, group = Season))+
#ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
# xmin = setup$x.shade.min , xmax = setup$x.shade.max,
# ymin = -Inf, ymax = Inf) +
ggplot2::geom_ribbon(ggplot2::aes(ymin = Lower, ymax = Upper, fill = Season), alpha = 0.5)+
ggplot2::geom_point()+
ggplot2::geom_line()+
ggplot2::ggtitle("Benthic invertebrate prey index")+
ggplot2::ylab(expression("Relative macrofauna biomass"))+
ggplot2::xlab(ggplot2::element_blank())+
ggplot2::facet_wrap(.~EPU)+
ecodata::geom_gls()+
ecodata::theme_ts()+
ecodata::theme_facet()+
ecodata::theme_title()
p
```
```{r}
p <- ecodata::forage_index |>
dplyr::filter(Var %in% c("Fall Forage Fish Biomass Estimate",
"Fall Forage Fish Biomass Estimate SE"),
EPU %in% c("MAB")) |>
dplyr::group_by(EPU) |>
tidyr::separate(Var, into = c("Season", "A", "B", "C", "D", "Var")) |>
dplyr::mutate(Var = tidyr::replace_na(Var, "Mean")) |> #,
#max = as.numeric(Value)) |>
tidyr::pivot_wider(names_from = Var, values_from = Value) |>
dplyr::left_join(fixmacro) |>
dplyr::mutate(#Value = Value/resca,
Mean = as.numeric(Mean),
#max = as.numeric(Value),
Mean = Mean/max,
SE = SE/max,
Upper = Mean + SE,
Lower = Mean - SE) |>
ggplot2::ggplot(ggplot2::aes(x = Time, y = Mean, group = Season))+
#ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
# xmin = setup$x.shade.min , xmax = setup$x.shade.max,
# ymin = -Inf, ymax = Inf) +
ggplot2::geom_ribbon(ggplot2::aes(ymin = Lower, ymax = Upper, fill = Season), alpha = 0.5)+
ggplot2::geom_point()+
ggplot2::geom_line()+
ggplot2::ggtitle("Forage fish prey index")+
ggplot2::ylab(expression("Relative forage biomass"))+
ggplot2::xlab(ggplot2::element_blank())+
ggplot2::facet_wrap(.~EPU)+
ecodata::geom_gls()+
ecodata::theme_ts()+
ecodata::theme_facet()+
ecodata::theme_title()
p
```
]
.pull-right[
```{r}
mafmc_cond <- c("Atlantic mackerel",
"Black sea bass",
"Bluefish",
"Butterfish",
"Goosefish",
"Illex", #not in condition
"Loligo", #not in condition
"Scup",
"Spiny dogfish",
"Summer flounder",
"Surf clam", #not in condition
"Ocean quahog") #not in condition
#"Clams", # Atlantic surfclam and ocean quahog not separate groups
#"Tilefish") # Blueline and golden tilefish not separate groups
AnnualRelCond2023_Fall <- readr::read_csv("https://raw.githubusercontent.com/NOAA-EDAB/foodweb-risk/main/condition/AnnualRelCond2023_Fall.csv")
survEPUcond <- AnnualRelCond2023_Fall |>
dplyr::select(Time = YEAR,
Var = Species,
EPU,
Value = MeanCond,
nCond) |>
dplyr::group_by(EPU, Var) |>
dplyr::mutate(scaleCond = scale(Value,scale =T,center=T))
xs <- quantile(survEPUcond$scaleCond, seq(0,1, length.out = 6), na.rm = TRUE)
survEPUcond <- survEPUcond |>
dplyr::mutate(category = cut(scaleCond,
breaks = xs,
labels = c( "Poor Condition",
"Below Average",
"Neutral",
"Above Average",
"Good Condition"),
include.lowest = TRUE))
condquants <- data.frame(ymin = xs[1:5],
ymax = xs[2:6],
category = sort(unique(survEPUcond$category))
)
vir <- viridis::viridis_pal()(5)
mafmcsurvEPUcond <- survEPUcond |>
dplyr::filter(Var %in% mafmc_cond[2], #BSB
EPU %in% c("MAB"))
p <- ggplot2::ggplot() +
ggplot2::theme_bw() +
ggplot2::geom_rect(data = condquants,
aes(ymin = ymin, ymax = ymax, fill = category, xmin = -Inf, xmax = Inf),
alpha = .3) +
ggplot2::scale_fill_manual(values=vir) +
ggplot2::geom_point(data= mafmcsurvEPUcond, ggplot2::aes(x=Time, y=scaleCond[,1])) +
#ggplot2::geom_hline(yintercept = xs[2:5]) +
#ggplot2::geom_line() +
ggplot2::facet_grid(Var~factor(EPU, levels = c("MAB", "GB", "GOM", "SS", "NA")), ) +
ggplot2::ylab("Scaled condition") +
ggplot2::ggtitle(paste(unique(mafmcsurvEPUcond$Var), "fall body condition")) +
ggplot2::guides(fill = ggplot2::guide_legend(reverse = TRUE))
p
```
```{r}
mafmcsurvEPUcond <- survEPUcond |>
dplyr::filter(Var %in% mafmc_cond[3], #Bluefish
EPU %in% c("MAB"))
p <- ggplot2::ggplot() +
ggplot2::theme_bw() +
ggplot2::geom_rect(data = condquants,
aes(ymin = ymin, ymax = ymax, fill = category, xmin = -Inf, xmax = Inf),
alpha = .3) +
ggplot2::scale_fill_manual(values=vir) +
ggplot2::geom_point(data= mafmcsurvEPUcond, ggplot2::aes(x=Time, y=scaleCond[,1])) +
#ggplot2::geom_hline(yintercept = xs[2:5]) +
#ggplot2::geom_line() +
ggplot2::facet_grid(Var~factor(EPU, levels = c("MAB", "GB", "GOM", "SS", "NA")), ) +
ggplot2::ylab("Scaled condition") +
ggplot2::ggtitle(paste(unique(mafmcsurvEPUcond$Var), "fall body condition")) +
ggplot2::guides(fill = ggplot2::guide_legend(reverse = TRUE))
p
```
]
???
Council and Advisory Panel members recommended new elements addressing human dimensions (recreational access equity), new elements addressing cross-sectoral impacts (offshore wind impacts on biology and ecosystem as well as fishery access and scientific sampling), and transitions from static ecosystem indicators to time series indicators (prey availability, predation pressure, and fishing community vulnerability). New ecosystem science was required to support these requests. The process included development of new indicators of prey availability based on spatio-temporal modeling using ecological datasets (stomach contents, zooplankton), and new spatial analyses of habitat, revenue, and surveys relative to wind energy development areas. Development of potential risk criteria is ongoing; thresholds between low, moderate, and high risk that are essential to operational use are developed collaboratively with Council and Advisory Panel members.
The slide shows a higher risk example (black sea bass, low recent condition correlated with recently declining prey) and a lower risk example (bluefish, despite a long term decline in forage fish prey. recent condition has been good)
`r Cite(myBib, "gaichas_assessing_2023")`
---
## Evolution of ecosystem reporting: from physical time series to specific management risks
.pull-left[
*2016-2023 Reports: Climate Section*
```{r}
ecodata::plot_bottom_temp_comp()
```
*2024 Report: Climate/Ecosystem Risks*
**Risks to Spatial Management/Allocation**
.contrib[
* Indicators and potential drivers of distribution shifts
]
**Risks to Seasonal Management/Timed Closures**
.contrib[
* Indicators and potential drivers of changing timing (phenology)
]
**Risks to Quota Management/Rebuilding**
.contrib[
* Indicators and potential drivers of changing productivity
]
]
.pull-right[
*2024 Report: Days at stressful scallop temperature*
.right[
]
.footnote[
Image courtesy Joseph Caracappa, NEFSC
]
]
???
---
background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/SOE-MA-030424_Page_2.png")
background-size: 500px
background-position: right
# Conclusions
.pull-left-60[
* Ecosystem information is being used operationally to support decisions in multiple processes
+ Assessment, ABC setting, EAFM
+ Contextual information is valued--used--by the Councils
+ Align information with objectives
* Risk assessment frameworks are one useful basis for operational use of ecosystem information
+ Alaska assessment risk tables
+ New England developing indicator-based risk policy
* Management Strategy Evaluation is needed to identify robust operational approaches
* Collaborative iteration is recommended into the future
]
.pull-right-40[]
???
---
background-image: url("https://www.fisheries.noaa.gov/s3/styles/original/s3/2022-08/640x427-Black-Sea-Bass-NOAAFisheries.png"),url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/Framework.png")
background-size: 290px, 430px
background-position: left bottom, right bottom
## THANK YOU! Assessment and Ecosystem Working Groups
.table[
![:col_row
*Black Sea Bass Assessment*<br>
Emily Liljestrand (NEFSC) - MTA lead<br>
Anna Mercer (NEFSC) - RTA chair<br>
Kiersten Curti (NEFSC) - RTA lead<br>
Julia Beaty (MAFMC)<br>
Gavin Fay (UMassD)<br>
Marissa McMahan (Manomet)<br>
Jason McNamee (RIDEM)<br>
Tim Miller (NEFSC)<br>
Sam Truesdell (MADMF/NEFSC)<br>
Ricky Tabendera (NEFSC/HCRI)<br>
Alex Hansell (NEFSC)<br>
Andy Jones (NEFSC)<br>
Jeff Brust (NJDEP)<br>
Lisa Chong (MSU)<br>
Scott Large (NEFSC)<br>
Abby Tyrell (NEFSC)<br>
Andie Painten (UMassD)<br>
Maria Cristina Perez (UMassD)<br>
Hannah Verkamp (CFRF)<br>
John Wiedenmann (Rutgers)<br>
Paula Fratantoni (NEFSC)<br>
Gary Shephard (NEFSC - retired),
*EAFM Products*<br>
Greg Ardini<br>
Fred Akers<br>
Leah Barton<br>
Kimberly Bastille<br>
Bob Beal<br>
Rick Bellavance<br>
Mark Binsted<br>
Eleanor Bochenek<br>
Bonnie Brady<br>
Jeff Brust<br>
Andrew Carr-Harris<br>
Paul Caruso<br>
Jessica Coakley<br>
Dustin Colson-Leaning<br>
Jonathan Cummings<br>
Kiley Dancy<br>
Maureen Davidson<br>
Jeff Deem<br>
Peter deFur<br>
Neil Delanoy<br>
John DePerseniaire<br>
Jonathan Deroba<br>
Greg DiDomenico<br>
Michelle Duval<br>
G. Warren Elliott<br>
Gavin Fay<br>
Jeremy Firestone<br>
Emily Gilbert<br>
Willy Goldsmith,
*EAFM Products*<br>
Zachary Greenberg<br>
Kaili Gregory<br>
Joseph Grist<br>
Pam Lyons Gromen<br>
Jeremy Hancher<br>
Jay Hermsen<br>
Peter Himchak<br>
Rich Hittinger <br>
Jorge Holzer<br>
Fiona Hogan<br>
Jeff Kaelin<br>
LCDR Matt Kahley<br>
Emily Keiley<br>
Jeff Kipp<br>
Meghan Lapp<br>
Scott Lenox<br>
Douglas Lipton<br>
Carl LoBue<br>
Sean Lucey<br>
Jason McNamee<br>
Adam Nowalsky<br>
Mike Oppegaard<br>
Robert O’Reilly<br>
Danielle Palmer<br>
Charles Perretti<br>
Michael Plaia<br>
Kirby Rootes-Murdy<br>
Scott Rubow,
*EAFM Products*<br>
Robert Ruhle<br>
Tom Schlichter<br>
Robin Scott<br>
Rich Seagraves<br>
Philip Simon<br>
Annabelle Stanley<br>
Mark Terceiro<br>
George Topping<br>
Michael Waine<br>
Judith Weis<br>
Michael Wilberg<br>
Sara Winslow<br>
Greg Wojcik<br>
Harvey Yenkinson]
]
<br>
???
<br>
*EAFM: Conceptual Model*<br>
Greg Ardini NEFSC<br>
Mark Terceiro NEFSC<br>
Michael Wilberg UMD<br>
Douglas Lipton NMFS<br>
Kiley Dancy MAFMC<br>
Jessica Coakley MAFMC<br>
Kirby Rootes-Murdy ASMFC,
*EAFM: Conceptual Model Cont'd*<br>
Jason McNamee RI DEM<br>
Jeff Brust NJ DEP<br>
Danielle Palmer GARFO<br>
Emily Gilbert GARFO<br>
Robert O’Reilly EOP & VMRC<br>
G. Warren Elliott EOP Chair<br>
Charles Perretti NEFSC<br>
Geret DePiper NEFSC<br>
Sarah Gaichas NEFSC<br>
Brandon Muffley MAFMC<br>
<br>
*EAFM: MSE Technical Group*<br>
Andrew Carr-Harris/NEFSC<br>
Dustin Colson-Leaning/ASMFC<br>
Jonathan Cummings/USFWS<br>
Kiley Dancy/MAFMC<br>
Geret DePiper/NEFSC<br>
Jon Deroba/NEFSC<br>
Gavin Fay/UMass Dartmouth<br>
Sarah Gaichas/NEFSC<br>
Kaili Gregory/Cornell<br>
Jorge Holzer/U. Maryland<br>
Emily Keiley/GARFO<br>
Jeff Kipp/ASMFC<br>
Doug Lipton/NOAA Fisheries<br>
Annabelle Stanley/Cornell<br>
Mark Terceiro/NEFSC<br>
Mike Wilberg/U. Maryland<br>
Greg Wojcik/CT DEEP,
*EAFM: MSE Stakeholder Group*<br>
Leah Barton/Shore<br>
Rick Bellavance/Charter Boat<br>
Eleanor Bochenek/Academic<br>
Neil Delanoy/Party Boat<br>
John DePersenaire/National Recreational Org.<br>
Greg DiDomenico/Commercial<br>
Paul Haertel/Private Boat<br>
Rich Hittinger/Private Boat<br>
Mike Oppegaard/Charter Boat<br>
Michael Plaia/Charter Boat<br>
Harvey Yenkinson/Private Boat<br>
Mike Waine/Rec. Secondary Market<br>
<br>
*EAFM: MSE Council Advisory Group*<br>
Adam Nowalsky<br>
Justin Davis<br>
Tony DeLernia<br>
Peter deFur<br>
<br>
*Recreational Fishery Decision Support Tool*<br>
Kim Bastille (NEFSC)<br>
Andrew Carr-Harris (NEFSC)<br>
Geret DePiper (NEFSC)<br>
Scott Steinback (NEFSC)]
]
---
background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images//noaa-iea.png")
background-size: 350px
background-position: right bottom
## THANK YOU! Ecosystem reporting made possible by (at least) 80 contributors from 20+ institutions
.table[
![:col_row
Kimberly Bastille<br>
Aaron Beaver (Anchor QEA)<br>
Andy Beet<br>
Brandon Beltz<br>
Ruth Boettcher (Virginia Department of Game and Inland Fisheries)<br>
Mandy Bromilow (NOAA Chesapeake Bay Office)<br>
Baoshan Chen (Stony Brook University)<br>
Zhuomin Chen (U Connecticut)<br>
Joseph Caracappa<br>
Doug Christel (GARFO)<br>
Patricia Clay<br>
Lisa Colburn<br>
Jennifer Cudney (NMFS Atlantic HMS Management Division)<br>
Tobey Curtis (NMFS Atlantic HMS Management Division)<br>
Art Degaetano (Cornell U)<br>
Geret DePiper<br>
Dan Dorfman (NOAA-NOS-NCCOS)<br>
Hubert du Pontavice<br>
Emily Farr (NMFS Office of Habitat Conservation)<br>
Michael Fogarty<br>
Paula Fratantoni<br>
Kevin Friedland<br>
Marjy Friedrichs (Virginia Institute of Marine Science)<br>
Sarah Gaichas<br>
Ben Galuardi (GARFO)<br>
Avijit Gangopadhyay (School for Marine Science and Technology UMass Dartmouth)<br>
James Gartland (Virginia Institute of Marine Science)<br>
Lori Garzio (Rutgers University),
Glen Gawarkiewicz (Woods Hole Oceanographic Institution)<br>
Sean Hardison<br>
Dvora Hart<br>
Kimberly Hyde<br>
John Kocik<br>
Steve Kress (National Audubon Society’s Seabird Restoration Program)<br>
Young-Oh Kwon (Woods Hole Oceanographic Institution)<br>
Scott Large<br>
Gabe Larouche (Cornell U)<br>
Daniel Linden<br>
Andrew Lipsky<br>
Sean Lucey<br>
Don Lyons (National Audubon Society’s Seabird Restoration Program)<br>
Chris Melrose<br>
Shannon Meseck<br>
Ryan Morse<br>
Ray Mroch (SEFSC)<br>
Brandon Muffley (MAFMC)<br>
Kimberly Murray<br>
David Moe Nelson (NCCOS)<br>
Janet Nye (University of North Carolina at Chapel Hill)<br>
Chris Orphanides<br>
Richard Pace<br>
Debi Palka<br>
Tom Parham (Maryland DNR)<br>
Charles Perretti<br>
CJ Pellerin (NOAA Chesapeake Bay Office)<br>
Kristin Precoda,
Grace Roskar (NMFS Office of Habitat Conservation)<br>
Jeffrey Runge (U Maine)<br>
Grace Saba (Rutgers)<br>
Vincent Saba<br>
Sarah Salois<br>
Chris Schillaci (GARFO)<br>
Amy Schueller (SEFSC)<br>
Teresa Schwemmer (Stony Brook University)<br>
Dave Secor (CBL)<br>
Angela Silva<br>
Adrienne Silver (UMass/SMAST)<br>
Emily Slesinger (Rutgers University)<br>
Laurel Smith<br>
Talya tenBrink (GARFO)<br>
Bruce Vogt (NOAA Chesapeake Bay Office)<br>
Ron Vogel (UMD Cooperative Institute for Satellite Earth System Studies and NOAA/NESDIS Center for Satellite Applications and Research)<br>
John Walden<br>
Harvey Walsh<br>
Changhua Weng<br>
Dave Wilcox (VIMS)<br>
Timothy White (Environmental Studies Program BOEM)<br>
Sarah Wilkin (NMFS Office of Protected Resources)<br>
Mark Wuenschel<br>
Qian Zhang (U Maryland)]
]
???
---
## References
.contrib[
```{r refs, echo=FALSE, results="asis"}
PrintBibliography(myBib)
```
]
## Resources
.contrib[
* [MAFMC SSC](https://www.mafmc.org/ssc)
* [MAFMC EAFM](https://www.mafmc.org/eafm)
* [MAFMC EAFM Summer Flounder MSE](https://www.mafmc.org/actions/summer-flounder-mse)
* Slides available at https://noaa-edab.github.io/presentations
* Contact: <Sarah.Gaichas@noaa.gov>
]
---
.center[
# Extra
]
---
## How to get the OFL CV? Characterizing scientific uncertainty