Beta version FLSRTMB: fitting stock-recruitment with TMB in FLR
Installing FLSRTMB requires the librabry(devtools), which can be install by ‘install.packages(’devtools’)’ and a R version >= 3.5. FLSRTMB also requires the latest version of library(FLCore) and suggests using library(ggplotFL) for plotting. All can be installed from github.
devtools::install_github("flr/FLCore")
devtools::install_github("flr/ggplotFL")
devtools::install_github("flr/FLSRTMB")
Compiling C++ in windows can be troublesome. As an alternative to installing from github, a windows package binary zip file can be downloaded here.
Example data are loaded for North Sea from FLCore
data(ple4)
The Beverton and Holt model is parameterized as a function of steepness (s) and unfished spawning potential ratio SPR0. To create the FLSR input object, the model=bevholtSV is selected.
sr <- as.FLSR(ple4,model=bevholtSV)
The function spr0y computes annual spr0. A good starting point can be the average
spr0 <- yearMeans(spr0y(ple4))
Fit a bevholt model without constraints
bh = srrTMB(sr,s.est=T, spr0=spr0)
plot(bh)
Note the output params are a and b by default but can be changed to R0 and s.
params(srrTMB(sr,s.est=T, spr0=spr0,report.sR0 = TRUE))
For many stock spr0 time-varying due to variations in weight-at-age, M-at-age or maturity-at-age plot(spr0y(ple4))+ylab(“spr0”)
To account for reduced or increased unfished stock sizes given these variation, spr0y() can be directly inputted
bh.y = srrTMB(sr,s.est=T, spr0=spr0y(ple4))
Note that by default a,b are computed from the average spr0 across all years. However, if the analyst suspects a non reversable systematic change, the spr0 reference can also be taken from the most recent years
bh.y3 = srrTMB(sr,s.est=T, spr0=spr0y(ple4),nyears=3)
Compare fits
plot(FLSRs(spr0=bh,spr0y=bh.y,spr0y3 = bh.y3))+theme(legend.position="right")
Option to estimate steepness with prior, e.g. from meta-analysis, such as FishLife (Thorson, 2020) This requires to specify s with the prior mean and s.logitsd is the sd on logit scale
bh.prior = srrTMB(sr,s.est=T,s=0.7,s.logitsd=0.4, spr0=spr0y(ple4))
plot(FLSRs(spr0=bh,spr0y=bh.y,s.prior = bh.prior))+theme(legend.position="right")
It is also possible to fix steepness
s = c(0.75,0.8,0.85,0.9,0.95)
bhs <- FLSRs(sapply(s, function(x) { return( srrTMB(sr,s=x,s.est=F, spr0=spr0y(ple4)))}))
bhs@names = c(paste("s =",round(s,3)))
plot(bhs)+theme(legend.position="right")
First fit a simple hockey-stick without constraints
hs = srrTMB(as.FLSR(ple4,model=segreg),s.est=T, spr0=spr0y(ple4))
Now add the constraint that plim = Blim/B0 > 0.1, which can ensure that Blim is within plausible risk adverse biological limits relative B0
hs.b01 = srrTMB(as.FLSR(ple4,model=segreg),s.est=T, spr0=spr0y(ple4),plim=0.1)
Compare
plot(FLSRs(hs=hs,hs.b01=hs.b01))