Exploring a relative harvest rate strategy for moderately data-limited fisheries management

This repository (GA_MSE_HR) is a mirror of GA_MSE and GA_MSE_PA with the harvest_rate branch displayed as default branch.

Introduction

This repository contains the code for testing a harvest rate-based management procedure and optimising it with a genetic algorithm. The simulation is based on the Fisheries Library in R (FLR) and the Assessment for All (a4a) standard MSE framework (FLR/mse) developed during the Workshop on development of MSE algorithms with R/FLR/a4a (Jardim et al., 2017).

This is the harvest_rate branch which explores the use of harvest rates and contains the code for the publication:

Fischer, S. H., De Oliveira, J. A. A., Mumford, J. D., and Kell, L. T. (2022). Exploring a relative harvest rate strategy for moderately data-limited fisheries management. ICES Journal of Marine Science. 79: 1730-1741. https://doi.org/10.1093/icesjms/fsac103.

The master branch (GA_MSE) contains the code for the publication:

Fischer, S. H., De Oliveira, J. A. A., Mumford, J. D., and Kell, L. T. (2021). Using a genetic algorithm to optimise a data-limited catch rule. ICES Journal of Marine Science. 78: 1311-1323. https://doi.org/10.1093/icesjms/fsab018.

The PA branch (GA_MSE_PA) includes the optimisation with specific risk limits for the ICES precautionary approach (PA) and contains the code for the publication:

Fischer, S. H., De Oliveira, J. A. A., Mumford, J. D., and Kell, L. T. (2021). Application of explicit precautionary principles in data-limited fisheries management. ICES Journal of Marine Science. 78: 2931-2942. https://doi.org/10.1093/icesjms/fsab169.

The operating models provided as an input are those from the repository shfischer/wklifeVII as described in:

Fischer, S. H., De Oliveira, J. A. A., and Laurence T. Kell (2020). Linking the performance of a data-limited empirical catch rule to life-history traits. ICES Journal of Marine Science, 77: 1914-1926. https://doi.org/10.1093/icesjms/fsaa054.

Repository structure

The root folder contains the following R scripts:

• OM_hr.R: This script creates the operating models (OMs),
• funs.R contains functions and methods used for the creation of the operating models and for running the MSE,
• funs_GA.R contains the function used in the optimisation procedure,
• run_ms_hr.R is an R script for running MSE projections and is called from a job submission script
• run*.pbs are job submission scripts which are used on a high performance computing cluster and call run_ms.R
• analysis_hr.R is for analysing the results

The following input files are provided:

• input/stocks.csv contains the stock definitions and life-history parameters
• input/brps.rds contains the FLBRP objects which are the basis for the OMs
• input/catch_rates.rds examples catch rates

Summarised outputs are provided in:

• output/

R, R packages and version info

The MSE simulations were run on a high-performance computing cluster:

> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

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, FLash, FLBRP, ggplotFL, mse. See renv.lock for version details and sources.

The FLR package versions can also be installed manually with remotes (requires suitable tools to compile R packages):

remotes::install_github(repo = "flr/FLCore", ref = "3d694903b9e6717b86c3e8486fc14ebf92908786")
remotes::install_github(repo = "shfischer/FLash", ref = "d1fb86fa081aaa5b6980d74b07d9adb44ad19a7f", INSTALL_opts = "--no-multiarch") # silenced version of FLash
# INSTALL_opts = "--no-multiarch" to avoid issues in Windows
remotes::install_github(repo = "flr/FLBRP", ref = "3a4d6390abc56870575fbaba3637091036468217", INSTALL_opts = "--no-multiarch")
remotes::install_github(repo = "shfischer/mse", ref = "mseDL2.0", INSTALL_opts = "--no-multiarch")
# and the GA package for running the genetic algorithm
remotes::install_github(repo = "shfischer/GA")

For using MPI parallelisation, an MPI backend such as OpenMPI and the R packages Rmpi and doMPI are required.