Conditioning simulation models on life history relationships

Length Based Indicators and Methods

Introduction

Installation

Quick Start

Life History Relationships

Operating Model

Length Based Indicators

Length Based Methods

Reciever Operating Characteristics

Management Strategy Evaluation

More information

References

Introduction

• Data
  • Life history parameters
• Simulation
  • Condition Operating Models on life history theory
  • Use a sawtooth exploitation history, i.e. gradual increase in \(F\) until the stock is overexploited after which a recover and long-term management plan is implemented.
• Methods
  • Length based indicators
  • Length based methods
  • Evaluate ability of length based indicators (LBIs) to detect overexploited and recovery.
• Management Strategy Evaluation

Installation

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To run the the code in this vignette a number of packages need to be installed, from CRAN and the FLR website, where tutorials are also available.

Libraries

FLR

The FLR packages can be installed from www.flr-project.org

install.packages(c("FLCore", "ggplotFL", "FLFishery", "FLasher", "FLBRP", "mpb", 
    "FLife"), repos = "http://flr-project.org/R")

Then loaded

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

library(FLife)
library(mydas)

library(lengthMehods)

FLCandy includes various prototypes under developedment, these are being tested before migrating them to FLCore. These can be installed from GitHub

Packages

The examples make extensive use of the packages of Hadley Wickham. For example plotting is done using ggplot2 based on the Grammar of Graphics ¹. Grammar is to specifies the individual building blocks and allows them to be combined to create the graphic desired².

While ‘dplyr’ is a grammar of data manipulation, providing a consistent set of verbs that help solve the most common data manipulation challenges, while ‘plyr’ is a set of tools to split up a big data structure into homogeneous pieces, apply a function to each piece and then combine all the results back together.

library(ggplot2)
library(plyr)
library(dplyr)

library(reshape)

Quick Start

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Life History Relationships

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Figure 1 Relationships between life history traits.

Create a set of life history parameters if \(L_{\infty}\) is known

par = lhPar(FLPar(linf = 100))

par

An object of class "FLPar"
params
     linf         k        t0         a         b       l50       a50     ato95 
 100.0000    0.1653   -1.4347    0.0003    3.0000   52.1594    3.0252    1.0000 
     asym        bg        m1        m2        m3         s         v      sel1 
   1.0000    3.0000    0.5500   -1.6100    1.4400    0.9000 1000.0000    4.0252 
     sel2      sel3 
   1.0000 5000.0000 
units:  NA 

Figure 2 Vectors of natural mortality, mass, maturity and selectivity-at-age.

Figure 3 Equilibrium dynamics with \(MSY\) reference points.

Condition Oprerating Model

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An object of class "FLQuant"
, , unit = unique, season = all, area = unique

     year
age   1     
  all 8.5659

units:  NA 

An object of class "FLPar"
params
     fmsy       msy      bmsy         v      spr0       l50        lc      lopt 
 9.02e-01  1.48e+02  1.43e+02  1.00e+03  5.88e-02  4.32e+01  3.63e+01  4.19e+01 
       gt         r        rc        fm        mk      linf         k        t0 
 8.57e+00  4.85e-01  2.18e-01  3.28e+00  1.86e+00  5.91e+01  2.80e-01 -4.00e-01 
      a50     ato95         a         b         s     alpha      beta       sln 
 4.00e+00  1.00e+00  1.11e-02  3.15e+00  9.00e-01  1.75e+04  2.86e+01  6.27e+00 
    alpha      beta       lfm   avirgin   wvirgin      amsy      wmsy 
 1.75e+04  2.86e+01  4.20e+01  2.54e-04  3.64e+00  2.54e-04  3.64e+00 
units:  NA 

Figure 4 Time series relative to MSY benchmarks.

Stochastic simulations

Condition Operating Model

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Figure 5 Time series relative to MSY benchmarks.

Length based indicators

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Figure 6. Simulated length frequencies distributions with indicators.

Figure 7. Time series of indicators compared to \(F:F_{MSY}\), vertical lines indicate 1 (green), 1.5 (orange) and 2 (red) times \(F_{MSY}\).

Length Based Methods

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Catch curve analysis

data(ple4)
ctc = as.data.frame(catch.n(ple4))
ctc = ddply(ctc, .(year), with, cc(age = age, n = data))
ctc = ddply(transform(ctc, decade = factor(10 * (year%/%10))), .(decade, age), with, 
    data.frame(sel = mean(sel)))
ggplot(subset(ctc, age < 10)) + geom_line(aes(age, sel, colour = decade))

Figure 8. Z.

Figure 9. Z v F, 60 to 100

Figure 10. Z v F, 100 to 120

Reciever Operating Characteristics

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Figure 11. Z v F, 60 to 100

Figure 12. Z v F, 100 to 120

LBSPR

is a R package for simulation and estimation using life-history ratios and length composition data

Figure 13 Observation error model.

Figure 14 Estimates of F/M.

Figure 15 Estimates of SPR.

Management Strategy Evaluation

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References

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More information

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• You can submit bug reports, questions or suggestions on this tutorial at https://github.com/flr/doc/issues.
• Or send a pull request to https://github.com/flr/doc/
• For more information on the FLR Project for Quantitative Fisheries Science in R, visit the FLR webpage, http://flr-project.org.

Software Versions

• R version 4.0.3 (2020-10-10)
• FLCore: 2.6.18.9010
• FLasher: 0.6.8.9003
• Compiled: Thu Jan 20 14:50:56 2022

License

This document is licensed under the Creative Commons Attribution-ShareAlike 4.0 International license.

Author information

Laurence KELL. laurie@seaplusplus.co.uk

Acknowledgements

Software Versions

• R version 4.0.3 (2020-10-10)
• FLCore: 2.6.18.9010
• FLPKG:
• Compiled: Thu Jan 20 14:50:56 2022
• Git Hash: dd74519

References

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1. Wilkinson, L. 1999. The Grammar of Graphics, Springer. doi 10.1007/978-3-642-21551-3_13.↩

2. http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html↩