Introduction

The Operating Model is based on the ICES stock assessment. All coding is done using R (R Core Team 2021) and FLR (Kell et al. 2007) in Rmarkdown (Xie, Allaire, and Grolemund 2018), and summarised in as vignettes. The information (data, files, etc.) required to re-do the analysis, is available at ???, including the ICES assessment data set, and the inputs and outputs from the ecosystem models.

Code for cleaning the input data, and R libraries used for the analysis, are contained in the ‘Rmd’ files such as this one. Only Key steps in the analysis are shown in the documents generated, code used for plotting are not echoed in the output but can be found in the ‘Rmd’ file.

This vignette is divided into sections for Installation of the required libraries, sourcing bespoke functions, and loading the data, before the Operating Condition is conducted. Hypotheses about Natural Mortality and Stock Rcruitment are then fitted.

Details on Funding, Software Versions andReferences can be found at the end of the document.

Installation

Libraries (i.e. packages) can be installed from gihtub using the remotes package:

install.packages("remotes")

library(remotes)

install.packages("ggplot2")
install.packages("plyr")
install.packages("dplyr")
install.packages("reshape")

remotes::install_github("flr/FLCore")
remotes::install_github("flr/ggplotFL")
remotes::install_github("flr/FLBRP")
remotes::install_github("flr/FLasher")
remotes::install_github("flr/FLAssess")
remotes::install_github("flr/FLife")
remotes::install_github("flr/mydas")
remotes::install_github("flr/FLCandy")

remotes::install_github("flr/FLSRTMB")

install.packages("patchwork")

remotes::install_github("james-thorson/FishLife")

remotes::install_github('fishfollower/SAM/stockassessment', ref='components')
remotes::install_github("flr/FLfse/FLfse")
library(FLCore) 
library(ggplotFL)
library(FLBRP)
library(FLasher)
library(FLAssess)

library(plyr)
library(dplyr)
library(reshape)

library(FLife)
library(FLBRP)
library(FLCandy)

library(FLSRTMB)
library(patchwork)

library(stockassessment)
library(FLfse)

library(FishLife)

theme_set(theme_bw(16))

Figure 1 Harvest Control Rule

Data

The data are the ICES inputs and historical estimates (ICES 2023) generated by “10_ICES” which runs the ICES assessment

load("../data/inputs/ices/icesRefs.RData")

par=as(model.frame(mcf(FLQuants(ftar=refs[["fmsy"]],
                                btrig=refs[["msybtrigger"]],
                                fmin=refs[["fmsy"]]%=%0.005,
                                bmin=refs[["blim"]]%=%0,
                                blim=refs[["blim"]])),drop=T)[,-1],"FLPar")
par["ftar"][ is.na(par["ftar"])] =min(par["ftar"], na.rm=T)
par["btrig"][is.na(par["btrig"])]=min(par["btrig"],na.rm=T)
par["blim"][ is.na(par["blim"])] =min(par["blim"], na.rm=T)

ref2021=FLPar(laply(refs,window, end=2021, start=2021))
dimnames(ref2021)$params=c("Flim","Fpa","Fmsy","Fcap","Blim","Bpa","MSYBtrigger")

Reference Case

Figure 2 MSE

Robustness Set

SRR

  • Steepness prior
  • Steepness = 0.9
  • Depensation

Natural Mortality

  • Trend
  • High
  0:     35.973680:  15.3876 -0.916291 0.510959
  1:     29.225133:  15.7514 -0.623011 0.592835
  2:     28.700658:  15.7089 -0.678998 0.560722
  3:     28.638368:  15.6370 -0.698069 0.539666
  4:     28.578389:  15.6685 -0.712532 0.548767
  5:     28.577976:  15.6704 -0.716610 0.547163
  6:     28.577667:  15.6713 -0.714185 0.543177
  7:     28.577617:  15.6731 -0.715863 0.539109
  8:     28.577589:  15.6728 -0.715362 0.539157
  9:     28.577579:  15.6726 -0.715074 0.539587
 10:     28.577577:  15.6722 -0.715119 0.540031
 11:     28.577577:  15.6725 -0.715068 0.540165
 12:     28.577576:  15.6723 -0.715071 0.540368
 13:     28.577576:  15.6723 -0.715100 0.540237
 14:     28.577576:  15.6723 -0.715089 0.540295
 15:     28.577576:  15.6723 -0.715089 0.540295
  0:     39.374447:  15.5488 -0.916291 0.510959
  1:     38.095193:  16.2918 -0.275592 0.704385
  2:     33.783807:  16.4059 -0.636587 0.377767
  3:     31.700316:  15.9923 -0.543698 0.112467
  4:     30.072045:  15.7799 -0.613141 0.559709
  5:     29.484364:  15.8570 -0.672751 0.580482
  6:     29.439253:  15.8806 -0.693798 0.580702
  7:     29.426027:  15.8961 -0.697177 0.553345
  8:     29.422824:  15.9020 -0.695097 0.531525
  9:     29.422723:  15.9032 -0.694400 0.532169
 10:     29.422723:  15.9031 -0.694470 0.532498
 11:     29.422722:  15.9031 -0.694417 0.532421
 12:     29.422722:  15.9031 -0.694422 0.532413
  0:     48.996588:  15.4068 -0.916291 0.510959
  1:     38.182769:  16.0848 -0.217375 0.738777
  2:     33.995388:  16.0396 -0.414074 0.684156
  3:     32.247514:  15.9040 -0.780724 0.535730
  4:     31.400201:  15.9285 -0.658757 0.543214
  5:     31.391126:  16.0119 -0.657886 0.450580
  6:     31.372662:  15.9709 -0.645180 0.486445
  7:     31.371230:  15.9794 -0.648485 0.494866
  8:     31.370599:  15.9741 -0.645797 0.495457
  9:     31.370538:  15.9745 -0.647327 0.493442
 10:     31.370525:  15.9750 -0.646817 0.493459
 11:     31.370525:  15.9749 -0.646877 0.493745
 12:     31.370525:  15.9749 -0.646893 0.493731
 13:     31.370525:  15.9749 -0.646892 0.493732
  0:     37.519724:  15.8440 -0.916291 0.510959
  1:     31.134508:  16.1755 -0.584999 0.562270
  2:     30.607971:  16.1189 -0.631992 0.538100
  3:     30.498232:  16.0954 -0.691931 0.495161
  4:     30.473554:  16.0906 -0.667779 0.489843
  5:     30.473423:  16.1014 -0.675628 0.468473
  6:     30.472204:  16.0952 -0.664860 0.466184
  7:     30.471224:  16.0971 -0.667664 0.467096
  8:     30.471101:  16.0977 -0.668198 0.470473
  9:     30.471060:  16.0971 -0.669011 0.470283
 10:     30.471059:  16.0971 -0.669035 0.470147
 11:     30.471059:  16.0971 -0.669003 0.470112
 12:     30.471059:  16.0971 -0.669015 0.470133
 13:     30.471059:  16.0971 -0.669016 0.470141

[1] "Reference Prior"
[1] "Reference h=0.9"
[1] "Reference Depensation"
[1] "Environmental\nDriver Prior"
[1] "High M Prior"

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Software Version

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Funding

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References

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ICES. 2023. “Mackerel (Scomber scombrus) in subareas 1–8 and 14, and in Division 9.a (Northeast Atlantic and adjacent waters),” September. https://doi.org/10.17895/ices.advice.21856533.v1.
Kell, L.T., I. Mosqueira, P. Grosjean, J.M. Fromentin, D. Garcia, R. Hillary, E. Jardim, et al. 2007. “FLR: An Open-Source Framework for the Evaluation and Development of Management Strategies.” ICES J. Mar. Sci. 64 (4): 640.
R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown.