Introduction

In fisheries management, indicators are used to monitor stock trends in biomass or exploitation level, and as part of Harvest Control Rules (HCR) to set catches. Indicators should be able to track trends in stock status, and potentially accurately classify a stock’s status. This vignette provides an introduction to conducting Management Strategy Evaluation (MSE) using FLR to select for use in HCRs.

For data-limited stocks, where there is no an existing stock assessment, life history theory can be used to develop simulations by conditioning an Operating Model. Life history traits include individual growth, age at maturity, fecundity, and natural mortality, and help in constructing biologically based population dynamics models and to build an understanding of the response of stocks to fishing pressures.

This document outlines software developed for conducting MSE in FLR for data-limited and capacity-limited fisheries. These vignettes extending the FLR package

The software, including a set of vignettes, is currently available at GitHub, and may later move to the FLR-Project.

Management Strategy Evaluation

The steps when conducting a Management Strategy are i) Identify and prioritise objectives, and trade-offs ii) Selection of Hypotheses for Conditioning the Operating Model iii) Conditioning the Operating Models based on data and knowledge iv) Identifying candidate management strategies v) Running the Management Procedure as a feedback controller to simulate the long-term impact of management; and then vi) Selecting the Management Procedures that robustly meet management objectives ## Vignettes Overview

The vignettes cover various tasks in developing a management plan including the steps required to install and run the code (HTML Version, RMarkdown Source)

Operating Model Conditioning: using life history theory and expert local knowledge (HTML Version, R Markdown Source)
Observation Error Model: simulate the type of data that can be collected (HTML Version, R Markdown Source)
Indicators: to monitor stock trends (HTML Version, R Markdown Source)
Management Procedures: running management procedures (HTML Version, R Markdown Source)
Seasonality: covering spawning, growth, movement, and fishing. (HTML Version, R Markdown Source)
Management Strategy Evaluation: running (HTML Version, R Markdown Source)
Implementation Review: reviewing the performance of management (HTML Version, R Markdown Source)

Quick Start

The packages FLife is required for modelling life-histories and estimation. The simplest way to obtain these to install them from the FLR repository

See help(install.packages) for more details.

Libraries (i.e. packages) can be installed from cran or via gihtub using the remotes package for the FLR, FishBase and FishLife packages

install.packages("remotes")

library(remotes)

remotes::install_github("flr/FLCore")

FLR

library(FLCore)
library(FLBRP)
library(FLasher)
library(FLife)
library(ggplotFL)

library(FLCandy)

FishLife

There are compatability problems with FishBase, so install an earlier version. These have been fixed now. see https://james-thorson-noaa.github.io/FishLife/

remotes::install_github("ropensci/rfishbase",ref="d150f2e0f5")

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

library(rfishbase)
library(SPMpriors)
library(FishLife)

Plotting and Data wrangling

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

library(ggplot2)
theme_set(theme_bw())

library(ggpubr)
library(ggcorrplot)
library(GGally)

Examples

The examples here use FLR, tutorials on the various methods and packages can be found at https://flr-project.org/doc/

The Fisheries Library in R (FLR) is a collection of tools for quantitative fisheries science, developed in the R language, that facilitates the construction of bio-economic simulation models of fisheries systems. FLR builds on the powerful R environment and syntax to create a domain-specific language for the quantitative analysis of the expected risks and effects of fisheries management decision. The classes and methods in FLR consider uncertainty an integral part of our knowledge of fisheries system.

Life history traits include growth rate; age and size at sexual maturity; the temporal pattern or schedule of reproduction; the number, size, and sex ratio of offspring; the distribution of intrinsic or extrinsic mortality rates (e.g., patterns of senescence); and patterns of dormancy and dispersal. These traits contribute directly to age-specific survival and reproductive functions. The FLife package has a variety of methods for modelling life history traits and functional forms for processes for use in fish stock assessment and for conducting Management Strategy Evaluation.

These relationships have many uses, for example in age-structured population models, functional relationships for these processes allow the calculation of the population growth rate and have been used to to develop priors in stock assesments and to parameterise ecological models. The FLife package has methods for modelling functional forms, for simulating equilibrium FLBRP and dynamic stock objects FLStock.

Create an FLPar object For a fish of length 45 cm.

par=FLPar(linf=45)

par

An object of class "FLPar"
params
linf 
  45 
units:  NA

FLPar() creates an FLR object of class FLPar

is(par)

[1] "FLPar"     "array"     "structure" "vector"

?FLPar

Life history theory means that traits like length at maturity (\(l_{50}\)) can be derived from the expected size of a species, the lhPar method therefore uses life history theory to derive missing parameters.

par=lhPar(par)
par

An object of class "FLPar"
params
     linf         k        t0         a         b       l50       a50     ato95 
  45.0000    0.2756   -1.3327    0.0003    3.0000   24.8210    1.5775    1.0000 
     asym        bg        m1        m2        m3         s         v      sel1 
   1.0000    3.0000    0.5500   -1.6100    1.4400    0.9000 1000.0000    2.5775 
     sel2      sel3 
   1.0000 5000.0000 
units:  NA

?lhPar

An FLBRP object can then be created using lhEql

eql=lhEql(par)

?lhEql

Help on topic 'lhEql' was found in the following packages:

  Package               Library
  FLCandy               /home/laurie/R/x86_64-pc-linux-gnu-library/4.2
  FLife                 /home/laurie/R/x86_64-pc-linux-gnu-library/4.2


Using the first match ...

This creates an object of class FLBRP

is(eql)

[1] "FLBRP"  "FLComp"

FLBRP is used to estimate reference points, and model equilibrium dynamics

refpts(eql)

An object of class "FLPar"
        quant
refpt    harvest  yield    rec      ssb      biomass  revenue  cost    
  virgin 0.00e+00 0.00e+00 3.53e+02 1.00e+03 1.48e+03       NA       NA
  msy    1.03e+00 1.54e+02 2.97e+02 1.28e+02 5.14e+02       NA       NA
  crash  6.17e+00 3.16e-05 4.47e-05 3.52e-06 5.43e-05       NA       NA
  f0.1   4.43e-01 1.39e+02 3.30e+02 2.82e+02 7.23e+02       NA       NA
  fmax   1.39e+17     -Inf     -Inf      NaN     -Inf       NA       NA
        quant
refpt    profit  
  virgin       NA
  msy          NA
  crash        NA
  f0.1         NA
  fmax         NA
units:  NA

ggplotFL is used for plotting FLR objects with ggplot2

plot(eql)

plot(eql,refpts="msy")

More Information

For more information on the FLR Project for Quantitative Fisheries Science in R, visit the FLR webpage ¹.

Author information

Laurence Kell. laurie@seaplusplus.co.uk

Acknowledgements

Software Versions

R version 4.2.1 (2022-06-23)

knitr: 1.43
FLCore: 2.6.19.9105
FLBRP: 2.5.9.9022
FLasher: 0.7.1.9221
FLife: 3.4.0
ggplotFL: 2.7.0.9132
FLCandy: 0.1.0
rfishbase: 3.1.9.99
SPMpriors: 1.0.4
FishLife: 3.0.0
plyr: 1.8.9
dplyr: 1.1.4
reshape: 0.8.9
ggplot2: 3.4.4
ggpubr: 0.6.0
ggcorrplot: 0.1.3
GGally: 2.1.2

Compiled: Sun Jan 21 12:33:46 2024

References

http://flr-project.org ↩︎

00: Introduction

FAO: Small Pelagics Fisheries

L Kell

21 January, 2024