In laurieKell/mydas-pkg: Tools for conducting Management Strategy Evaluation for data limited stocks

mydas package

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)

iFig=0

library(mydas)

theme_set(theme_bw())
options(digits=3)

Install 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","mpb","FLife"), 
             repos="http://flr-project.org/R")

devtools also needs to be installed and loaded so that the mydas package can be installed from the GitHub repository.

install.packages("devtools",dependencies=TRUE)

library(devtools)

devtools::install_github("lauriekell/mydas-pkg")

Load Libraries

library(FLCore)
library(FLasher)
library(FLBRP)
library(FLife)
library(mydas)

Life history parameters

Get fishbase data

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

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

finfish=subset(fb,as.character(species)%in%as.character(stock$species))

names(lh)[c(14,17)] = c("l50","a50")
lh=lh[,c("linf","k","t0","a","b","a50","l50")]

head(lh)

Life history parameters

data(mydas)

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(transform(wklife[,-c(1:5)],linf=log(linf),k=log(k),l50=log(l50)),
  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.

Equilibrium Dynamics

Create an FLPar

wkpar=as(wklife[,6:13],"FLPar")
attributes(wkpar)[names(wklife)[1:5]]=wklife[,1:5]

Then use life history relationships to estimate missing values

par <- lhPar(wkpar)
units(par) <- c("", "", "cm", "", "cm", "cm", "cm", "cm")

and then to derive vectors for processses such as natural mortality

eql=lhEql(par)

sel<-function(x) 
  catch.sel(x)%/%fapex(catch.sel(x))

ggplot(FLQuants(eql,"m","catch.sel"=sel,"mat","catch.wt"))+
  geom_line(aes(age,data,col=attributes(wkpar)$name[iter]))+
  facet_wrap(~qname,scale="free")+
  scale_x_continuous(limits=c(0,15))+ 
  guides(colour=guide_legend(title="Species",title.position="top"))

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

and estimate equilibrium dynamics and reference points, e.g. for lemon sole

plot(iter(eql,3))

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

Simulation

Create a forward projection, i.e. an FLStock from an equilibrium object

lmsl=as(iter(eql,7),"FLStock")

units(mat(lmsl))="NA"
units(harvest(lmsl))="f"
plot(lmsl)

Figure r iFig=iFig+1; iFig Simulate a stock with increasing F

Software Versions

r version$version.string
FLCore: r packageVersion('FLCore')
FLPKG: r # packageVersion('FLPKG')
Compiled: r date()
Git Hash: r system("git log --pretty=format:'%h' -n 1", intern=TRUE)

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.