Risk equivalence in data-limited and data-rich fisheries management: an example based on the ICES advice framework

Introduction

This repository contains the data and code for a comparison of the data-rich ICES MSY rule (category 1) to the ICES category 3 data-limited empirical methods (rfb rule, hr rule, https://doi.org/10.17895/ices.advice.19801564) and is the basis for the publication:

Fischer, S. H., De Oliveira, J. A. A., Mumford, J. D., and Kell, L. T. (2023). Risk equivalence in data-limited and data-rich fisheries management: an example based on the ICES advice framework. Fish and Fisheries 24(2): 231-247. https://doi.org/10.1111/faf.12722

Three case study stocks are included:

1. Plaice (Pleuronectes platessa) in Division 7.e (western English Channel) (https://doi.org/10.17895/ices.advice.7822)

2. Cod (Gadus morhua) in Subarea 4, Division 7.d, and Subdivision 20 (North Sea, eastern English Channel, Skagerrak) (https://doi.org/10.17895/ices.advice.7746)

3. Herring (Clupea harengus) in Subarea 4 and divisions 3.a and 7.d, autumn spawners (North Sea, Skagerrak and Kattegat, eastern English Channel) (https://doi.org/10.17895/ices.advice.7770)

The operating models (OMs) are created using the SAM stockassessment R package and follows the principles developed during the ICES Workshop on North Sea stocks management strategy evaluation (WKNSMSE).

The simulation is based on the Fisheries Library in R (FLR) and its mse package.

The data-limited MSE framework is an adaptation of Fischer et al. (2021), see the shfischer/GA_MSE_PA GitHub repository for the original source code and documentation.

For exact reproducibility, R packages versions are recorded with renv in a renv.lock file.

Repository structure

• funs_*: Function libraries, defining the functions used in the other scripts

  • funs.R: generic function library, including definition of data-limited management procedures (MPs)

  • funs_analysis.R: for analysis of results

  • funs_GA.R: functions used in the optimisation with the genetic algorithm (GA)

  • funs_OM.R: functions for generating the operating models

  • funs_WKNSMSE.R: functions required for the ICES MSY rule

• OM_*: Scripts for operating models (OMs, including alternative OMs)

  • OM_ple.27.7e.R for plaice

  • OM_cod.27.47d20.R for cod

  • OM_her.27.3a47d.R for herring

  • OM_MSY.R: script for estimating MSY

  • OM_MSY.pbs: job script, calling OM_MSY.R

• MP_*: Script for running and analysing the MSE

  • MP_analysis.R: script for analysing MSE results (summarising, exporting, visualisation, …)

  • MP_run.R: script for running any MP in the MSE and optimising MPs

  • MP_*.pbs: job submission scripts, used for running MP_run.R on a high-performance computing cluster, e.g. MP_run_rfb_mult.pbs for optimising the rfb rule with a multiplier

• MP_check_SPiCT.R: script for checking the SPiCT model

input/: This directory contains all files required for generating the OMs for the three stocks (OM_*.R)

• input/ple.27.7e/preparation/: for plaice

• input/cod.27.47d20/preparation/: for cod

• input/her.27.3a47d/preparation/: for herring

• input/OM_refpts.csv: summarised OM reference points

output/: This directory contains some summarised results

• output/MPs_baseline.csv: summary statistics and parameterisations of default and optimised MPs for all stocks

• output/plots/wormplots/: Projections (wormplots) for all default and optimised MPs, for all stocks and OMs

R, R packages and version info

The MSE simulations were run with R:

> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)

The package versions and their dependencies are recorded with the R package renv and stored in the file renv.lock. The exact package version can be restored by cloning this repository, navigating into this folder in R (or setting up a project), installing the renv package

install.packages("renv")

and calling

renv::restore()

See renv and the package documentation for details.

The framework is based on the Fisheries Library in R (FLR) framework and uses the FLR packages FLCore, FLasher, FLBRP, FLAssess, FLXSA, ggplotFL, mse, and FLfse. See renv.lock for version details and sources.

Also, the R package stockassessmentis used.

For running the optimisations on a high-performance computing cluster, a suitable MPI back-end and the R package Rmpi are required.