In flr/mydas: Tools for conducting Management Strategy Evaluation for data limited stocks

library(knitr)
## Global options
opts_chunk$set(cache     =!TRUE,
               echo      =TRUE,
               eval      =TRUE,
               prompt    =FALSE,
               comment   =NA,
               message   =FALSE,
               warning   =FALSE,
               tidy      =FALSE,
               fig.height=6,
               fig.width =8)

options(digits=3)
iFig=0

Installation

FLR packages

To follow this tutorial a number of packages need to be installed, either from CRAN or from www.flr-project.org where variety of packages and tutorials are available.

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

Load Libraries

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

library(FLife)
library(mydas)

Life history parameters

For more details on how to loading your data into FLR see the (vignette)[http://www.flr-project.org/doc/Loading_your_data_into_FLR.html]

Here life history parameters are loaded from the fishnets github repository, a library of multivariate priors for fish population dynamics parameters

load(url("https://github.com//fishnets//fishnets//blob//master//data//fishbase-web//fishbase-web.RData?raw=True"))

Select species of interest

spp=data.frame(
   species=c("Psetta maxima","Scophthalmus rhombus","Raja clavata",
              "Pollachius pollachius","Sprattus sprattus sprattus"),
   name=   c("Turbot",       "Brill",               "Ray",        
             "Pollack",      "Sprat"))

lh=subset(fb,as.character(species)%in%as.character(spp$species))

names(lh)[c(14,17)]=c("l50","a50")

Add common names and order by size, using name as a factor

lh=merge(lh,spp,by="species")
lh$name=factor(lh$name,levels=c("Sprat","Brill","Turbot","Pollack","Ray"))

head(lh)

Parameters include von Bertanffy growth parameters ($L_{\infty}$, $k$, $b$ and $l_{50}$) length at maturity ($l_{50}$) and the length weight exponent ($b$)

library(GGally)

my_smooth <- function(data,mapping,...){
  ggplot(data=data,mapping=mapping)+
  geom_point(...,size=.5)+
  geom_smooth(...,method="lm",se=FALSE)}

my_density <- function(data,mapping,...){
  ggplot(data=data,mapping=mapping)+
  geom_density(...,lwd=1)}

#ggpairs(transmute(lh,linf=log(linf),k=log(k),l50=log(l50)),
ggpairs(transform(lh[,c("name","linf","k","l50","b")],linf=log(linf),k=log(k),l50=log(l50)),
  mapping = ggplot2::aes(color=name),
  lower = list(continuous = wrap(my_smooth)),
  diag=list(continuous=wrap(my_density,alpha=0.2)),
  title = "")+
  theme(legend.position ="none",
  panel.grid.major =element_blank(),
  axis.ticks       =element_blank(),
  axis.text.x      =element_blank(),
  axis.text.y      =element_blank(),
  panel.border     =element_rect(linetype = 1, colour="black", fill=NA))

Figure r iFig=iFig+1; iFig Pairwise scatter plots of life history parameters.

Create an FLPar object

par=as(lh[,c("linf","k","t0","a","b","l50")],"FLPar")

Replace NAs

par["a",is.na(par["a"])]=0.001
par["b",is.na(par["b"])]=3
par["t0"]=-0.1

Use life history relationships to estimate missing values

par=lhPar(par)
attributes(par)$name=lh$name

Derive vectors for biological processses for pollack, by creating an object of class FLBRP

pollack=iter(par,attributes(par)$name=="Pollack")

eql=lhEql(pollack)

ggplot(FLQuants(eql,"m","mat","catch.wt"))+
  geom_line(aes(age,data,group=iter))+
  facet_grid(qname~.,scale="free")+
  coord_cartesian(xlim=c(0,15))+
  xlab("Age")+ylab("")

Figure r iFig=iFig+1; iFig Vectors of m, maturity and weight-at-age.

plot(eql,refpts="msy")

Figure r iFig=iFig+1; iFig Equilibrium curves.

Software Versions

r version$version.string
FLCore: r packageVersion('FLCore')
FLBRP: r packageVersion('FLBRP')
FLife: r packageVersion('FLife')
mydas: r packageVersion('mydas')
Compiled: r date()

Author information

Laurence KELL. laurie.kell.es

Acknowledgements

This vignette and many of the methods documented in it were developed under the MyDas project funded by the Irish exchequer and EMFF 2014-2020. The overall aim of MyDas is to develop and test a range of assessment models and methods to establish Maximum Sustainable Yield (MSY) reference points (or proxy MSY reference points) across the spectrum of data-limited stocks.