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

Power of proxy reference points and indicators to detect overfishing

• Question: What assumptions are the most important in i) determining stock dynamics and ii) the power of proxies reference points to detect stock status? In particular
• Which potential indicators and proxy reference points are the most robust? i.e. have high power to detect overfishing; and

• What are the benefits of reducing uncertainty?

• Methods Simulate a range of stock dynamics

• Project for different levels of constant F relative to $F_{MSY}$ and summarise scenarios by clustering on the
  • Expected dynamics i.e. reference points and proxies; and
  • Time series, i.e. frequency spectrum
• Run scenarios with an increasing trend in F that leads to overfishing, then implement a recovery plan that brings fishing to the $F_{MSY}$ level then screen potential empirical MPs by
• Generating length, catch and CPUE indicators using an OEM
• Compare indicators to OM, both trends and status relative to reference points using Reciever Operating Characteristics (ROCs)
• Compare to model based indicators by fitting indices using LBSPR, SRA and a biomass dynamic model then evaluate using ROC and compare with empirical indicators

Prediction Skill

• Screen model based MPs
• Perform 1,2,3, step ahead projections using LBSPR, SRA and a biomass dynamic model
• Use MASE to identify assessment models with prediction skill

knitr::opts_chunk$set(echo = FALSE)

library(knitr)

opts_chunk$set(comment   =NA, 
               warning   =FALSE, 
               message   =FALSE, 
               error     =FALSE, 
               echo      =FALSE,
               fig.width =10, 
               fig.height=10,
               cache     =TRUE, 
               fig.path  ="tex/proxies-",
               cache.path="cache/proxies/")

iFig=0
iTab=0

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

library(ggplotFL)
library(scales)
library(plyr)
library(dplyr)
library(reshape)
library(grid)
library(reshape)
library(popbio)
library(magrittr)
library(broom)
library(GGally)

library(doParallel)
library(foreach)

cl=makeCluster(3)
registerDoParallel(cl)

par=lhPar(FLPar(c(linf= 59.1,  k=0.28, t0=-0.4, s=0.9,
           a=0.01111,b=3.15,a50=4.0, l50=43.25),units="NA"))

par=propagate(par,16)
dat=expand.grid(bg=c(3,3.1),sel3=c(5000,5),s=c(0.75,0.9),k=0.28*c(1,0.5))

par["bg"]  =dat$bg
par["sel3"]=dat$sel3
par["s" ]  =dat$s
par["k" ]  =dat$k

smry=popdyn(par)

Figures

MSY reference points and proxies

ggpairs(model.frame(smry[c("msy","fmsy","bmsy","r","rc")][,-6]),
        #mapping = ggplot2::aes(color=as.character(sel3)),
        lower = list(continuous = wrap(mydas:::my_smooth)),
        diag=list(continuous=wrap(mydas:::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))+
  theme_bw(16)

Figure r iFig=iFig+1; iFig. Relationship between MSY reference points and population growth rate.

ggpairs(model.frame(smry[c("lopt","clmsy","slmsy","mk","fm","lfm","spr0","msy","fmsy","bmsy")])[,-11],
        lower = list(continuous = wrap(mydas:::my_smooth)),
        diag=list(continuous=wrap(mydas:::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))+
  theme_bw(16)

Figure r iFig=iFig+1; iFig. Relationship between MSY reference points and their potential proxies.

idx=transform(merge(ind,refs[,c("slmsy","clmsy","lopt","lfm","iter")]),
              sln=sln/slmsy,cln=cln/clmsy,
              sl50=sln/l50,cl50=cln/l50,
              slopt=sln/lopt,clopt=cln/lopt,
              slfm =sln/lfm,clfm=cln/lfm,
              fmsy=fbar/msy_harvest)[,c("f","CV","AR","M","s","k","bg","sel3","fmsy",
            "sln","cln","sl50","cl50","slopt","clopt","slfm","clfm")]

dat=melt(idx,id=c("f","CV","AR","M","s","k","bg","sel3","fmsy"))

ggplot(dat)+
  geom_boxplot(aes(ac(f),value))+
  geom_hline(aes(yintercept=1),col="red")+
  facet_wrap(~variable,ncol=2)+
  xlab("F times FMSY")+ylab("Ratio with FMSY")

References {#References}

\newpage

Session Info

sessionInfo()

laurieKell/mydas-pkg documentation built on Nov. 8, 2019, 10:58 p.m.