inst/Frameworks/LFA2733Framework/1a.IndicatorsSensitivitytoWeekofSeasonFSRSCommercial.r
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
require(bio.lobster)
require(bio.utilities)
require(PBSmapping)
la()
sensitivity.to.seasonality = T
if(sensitivity.to.seasonality){
lobster.db('fsrs.commercial.samples')
fs = subset(fsrs.comm,SYEAR>2002)
p = lobster.db('seasonal.landings')
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
dt = DTs[grep('33',names(DTs))]
ad = 2004:2016
cH = lobster.db('community.to.grid.contemporary')
cH = subset(cH, LFA==33)
cG = lobster.db('community.to.grid.historic')
cG = subset(cG, LFA==33)
outN = list()
outSall = list()
grps = list(WOS = 1:4, WOS2=5:8,WOS3=9:12,WOS4 = 13:16,WOS5=17:20, WOS6=21:24, WOS7=25:28,WOS8 = 1:28)
oAll = list()
for(j in 1:length(grps)) {
io = grps[j]
names(io) = 'WOS'
outS = out = list()
for(i in 1:length(ad)) {
print(ad[i])
po = 33
yo = ad[i]
mm = 82.5
da = atSeaWeightings(atSea = fs, fsrs.commercial.samples=T, comGridHist =subset(cG,LFA==33),comGridCont = subset(cH,LFA==33 & SYEAR==ad[i]), year=ad[i],lfa=33,females.only=F,mls=10)
op = weightedCLF(x=da,returnLF=T,grouping = io,fsrs.commercial.samples=T)
os = op
os$Grouping = os$vec<-NULL
outS[[i]] <- unlist(os)
ll = 'LFA33'
lle = 'LFA33'
yo = ad[i]
mls=mm = 10.5
lp = p[,c('SYEAR',names(p)[grep(ll,names(p))])]
lp = rename.df(lp,'SYEAR','YR')
dt = DTs[[grep('33W',names(DTs))]]
dt = dt[which(names(dt)==85):which(names(dt)==130)]
dt = c(dt[1],mean(dt[2:3]),mean(dt[4:5]),mean(dt[6:7]),mean(dt[8:9]))
Tc = 0.3
if(!is.null(op)){
vec = c(10.5,11,12,13,14)
oo = op$vec[op$vec>=mm & op$vec<15]
v0 = table(oo)
ho = as.numeric(names(v0))
wts = lobLW(ho,fsrs=T)
bwts = v0 * wts
le = subset(lp,substr(YR,6,9) == yo)[,2]
acWt = bwts / sum(bwts) * le
N = acWt / wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = N, Len = names(v0),Year = yo,MLS=mm)
outS[[i]] = c(outS[[i]], new.rec = as.numeric(v0[1] / sum(v0)))
ca = cohortAnalysis(lens = as.numeric(names(N)), N = as.numeric(N), dt = c(dt[1],dt[2:length(dt)]*2)/365) #annual
out[[i]] = c(YEAR=yo, MLS=mm, N = sum(N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
out = as.data.frame(do.call(rbind,out))
out = toNums(out,2:ncol(out))
out$W = j
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
outS$W = j
oAll[[j]] = out
outSall[[j]] = outS
}
a = do.call(rbind,oAll)
h = do.call(rbind,outSall)
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Exploitation",ylim=c(0.2,00.9))
w = unique(a$W)
for(i in w){
with(subset(a,W==i),lines(YEAR,expl,col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'CohortAnalysisPlots','LFA33WOS.png'),type='png')
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Proportion Berried",ylim=c(0,0.04))
w = unique(h$W)
for(i in w){
with(subset(h,W==i),lines(Year,prop.berried,col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33BerriedWOS.png'),type='png')
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Proportion Female",ylim=c(0.35,0.55))
w = unique(h$W)
for(i in w){
with(subset(h,W==i),lines(Year,prop.female,col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33FemaleWOS.png'),type='png')
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Maximum Size",ylim=c(10,15))
w = unique(h$W)
h = rename.df(h , 'quants.97.5%','Max')
for(i in w){
with(subset(h,W==i),lines(jitter(as.numeric(Year)),jitter(Max),col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33MaxSizejitter.png'),type='png')
plot(1:7,rep(1,7),type='n',xlab='Weeks of Season',ylab="Maximum Size",ylim=c(10,16),xaxt='n')
axis(side=1,at=1:7,labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28'))
w = unique(h$Year)
h = subset(h,W<8)
h = rename.df(h , 'quants.97.5%','Max')
for(i in 1:length(w)){
with(subset(h,Year==w[i]),lines(jitter(as.numeric(W)),jitter(Max),col=cols[i],lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',w,col=cols,lty=1:16,title='Years',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33MaxSizejitter.png'),type='png')
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Median Size",ylim=c(8,12))
w = unique(h$W)
h = rename.df(h , 'quants.50%','Med')
for(i in w){
with(subset(h,W==i),lines(jitter(as.numeric(Year)),jitter(Med),col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33MaxSizejitter.png'),type='png')
####year lines not week lines
jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
cols = jet.colors(16)
plot(1:8,rep(1,8),type='n',xlab='Weeks of Season',ylab="Exploitation",ylim=c(0.35,0.9),xaxt='n')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
w = unique(a$YEAR)
#a = subset(a,W<8)
for(i in 1:length(w)){
with(subset(a,YEAR==w[i] & W %in% 1:7),lines(W,expl,col=cols[i],lty=i,lwd=2,type='b',pch=16))
with(subset(a,YEAR==w[i] & W %in% 8),points(8,expl,col=cols[i],lty=i,lwd=2,type='b',pch=16,cex=1.2))
}
legend('bottomleft',legend=w,col=cols,lty=1:16,title='Years',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'CohortAnalysisPlots','LFA33YearLines.png'),type='png')
plot(1:8,rep(1,8),type='n',xlab='Weeks of Season',ylab="Proportion Female",ylim=c(0.38,0.54),xaxt='n')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
w = unique(h$Year)
#a = subset(a,W<8)
for(i in 1:length(w)){
with(subset(h,Year==w[i] & W %in% 1:7),lines(W,prop.female,col=cols[i],lty=i,lwd=2,type='b',pch=16))
with(subset(h,Year==w[i] & W %in% 8),points(8,prop.female,col=cols[i],lty=i,lwd=2,type='b',pch=16,cex=1.2))
}
legend('bottomleft',legend=w,col=cols,lty=1:16,title='Years',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33ProportionFemaleYearLines.png'),type='png')
plot(1:8,rep(1,8),type='n',xlab='Weeks of Season',ylab="Proportion Berried",ylim=c(0.0,0.05),xaxt='n')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
w = unique(h$Year)
#a = subset(a,W<8)
for(i in 1:length(w)){
with(subset(h,Year==w[i] & W %in% 1:7),lines(W,prop.berried,col=cols[i],lty=i,lwd=2,type='b',pch=16))
with(subset(h,Year==w[i] & W %in% 8),points(8,prop.berried,col=cols[i],lty=i,lwd=2,type='b',pch=16,cex=1.2))
}
legend('bottomleft',legend=w,col=cols,lty=1:16,title='Years',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33ProportionBerriedYearLines.png'),type='png')
#boxplots by weeks of season
boxplot(prop.female~W,data=h,ylab='Sex Ratio',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensPropFemale.png'),type='png')
boxplot(prop.berried~W,data=h,ylab='Proportion Berried',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensPropBerried.png'),type='png')
boxplot(expl~W,data=a,ylab='Exploitation',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'CohortAnalysisPlots','FSRSCommSensExploitation.png'),type='png')
h = rename.df(h,'quants.50%','Median')
boxplot(Median~W,data=h,ylab='Median Size',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensMedianSize.png'),type='png')
h = rename.df(h,'quants.97.5%','Max')
boxplot(Max~W,data=h,ylab='Maximum Size',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensMaxSize.png'),type='png')
boxplot(new.rec~W,data=h,ylab='Proportion New Recruits',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensNewRec.png'),type='png')
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
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require(bio.lobster)
require(bio.utilities)
require(PBSmapping)
la()
sensitivity.to.seasonality = T
if(sensitivity.to.seasonality){
lobster.db('fsrs.commercial.samples')
fs = subset(fsrs.comm,SYEAR>2002)
p = lobster.db('seasonal.landings')
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
dt = DTs[grep('33',names(DTs))]
ad = 2004:2016
cH = lobster.db('community.to.grid.contemporary')
cH = subset(cH, LFA==33)
cG = lobster.db('community.to.grid.historic')
cG = subset(cG, LFA==33)
outN = list()
outSall = list()
grps = list(WOS = 1:4, WOS2=5:8,WOS3=9:12,WOS4 = 13:16,WOS5=17:20, WOS6=21:24, WOS7=25:28,WOS8 = 1:28)
oAll = list()
for(j in 1:length(grps)) {
io = grps[j]
names(io) = 'WOS'
outS = out = list()
for(i in 1:length(ad)) {
print(ad[i])
po = 33
yo = ad[i]
mm = 82.5
da = atSeaWeightings(atSea = fs, fsrs.commercial.samples=T, comGridHist =subset(cG,LFA==33),comGridCont = subset(cH,LFA==33 & SYEAR==ad[i]), year=ad[i],lfa=33,females.only=F,mls=10)
op = weightedCLF(x=da,returnLF=T,grouping = io,fsrs.commercial.samples=T)
os = op
os$Grouping = os$vec<-NULL
outS[[i]] <- unlist(os)
ll = 'LFA33'
lle = 'LFA33'
yo = ad[i]
mls=mm = 10.5
lp = p[,c('SYEAR',names(p)[grep(ll,names(p))])]
lp = rename.df(lp,'SYEAR','YR')
dt = DTs[[grep('33W',names(DTs))]]
dt = dt[which(names(dt)==85):which(names(dt)==130)]
dt = c(dt[1],mean(dt[2:3]),mean(dt[4:5]),mean(dt[6:7]),mean(dt[8:9]))
Tc = 0.3
if(!is.null(op)){
vec = c(10.5,11,12,13,14)
oo = op$vec[op$vec>=mm & op$vec<15]
v0 = table(oo)
ho = as.numeric(names(v0))
wts = lobLW(ho,fsrs=T)
bwts = v0 * wts
le = subset(lp,substr(YR,6,9) == yo)[,2]
acWt = bwts / sum(bwts) * le
N = acWt / wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = N, Len = names(v0),Year = yo,MLS=mm)
outS[[i]] = c(outS[[i]], new.rec = as.numeric(v0[1] / sum(v0)))
ca = cohortAnalysis(lens = as.numeric(names(N)), N = as.numeric(N), dt = c(dt[1],dt[2:length(dt)]*2)/365) #annual
out[[i]] = c(YEAR=yo, MLS=mm, N = sum(N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
out = as.data.frame(do.call(rbind,out))
out = toNums(out,2:ncol(out))
out$W = j
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
outS$W = j
oAll[[j]] = out
outSall[[j]] = outS
}
a = do.call(rbind,oAll)
h = do.call(rbind,outSall)
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Exploitation",ylim=c(0.2,00.9))
w = unique(a$W)
for(i in w){
with(subset(a,W==i),lines(YEAR,expl,col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'CohortAnalysisPlots','LFA33WOS.png'),type='png')
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Proportion Berried",ylim=c(0,0.04))
w = unique(h$W)
for(i in w){
with(subset(h,W==i),lines(Year,prop.berried,col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33BerriedWOS.png'),type='png')
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Proportion Female",ylim=c(0.35,0.55))
w = unique(h$W)
for(i in w){
with(subset(h,W==i),lines(Year,prop.female,col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33FemaleWOS.png'),type='png')
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Maximum Size",ylim=c(10,15))
w = unique(h$W)
h = rename.df(h , 'quants.97.5%','Max')
for(i in w){
with(subset(h,W==i),lines(jitter(as.numeric(Year)),jitter(Max),col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33MaxSizejitter.png'),type='png')
plot(1:7,rep(1,7),type='n',xlab='Weeks of Season',ylab="Maximum Size",ylim=c(10,16),xaxt='n')
axis(side=1,at=1:7,labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28'))
w = unique(h$Year)
h = subset(h,W<8)
h = rename.df(h , 'quants.97.5%','Max')
for(i in 1:length(w)){
with(subset(h,Year==w[i]),lines(jitter(as.numeric(W)),jitter(Max),col=cols[i],lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',w,col=cols,lty=1:16,title='Years',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33MaxSizejitter.png'),type='png')
plot(2004:2016,rep(1,13),type='n',xlab='Year',ylab="Median Size",ylim=c(8,12))
w = unique(h$W)
h = rename.df(h , 'quants.50%','Med')
for(i in w){
with(subset(h,W==i),lines(jitter(as.numeric(Year)),jitter(Med),col=i,lty=i,lwd=2,type='b',pch=16))
}
legend('bottomleft',c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'),col=1:8,lty=1:8,title='Weeks of Season',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33MaxSizejitter.png'),type='png')
####year lines not week lines
jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
cols = jet.colors(16)
plot(1:8,rep(1,8),type='n',xlab='Weeks of Season',ylab="Exploitation",ylim=c(0.35,0.9),xaxt='n')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
w = unique(a$YEAR)
#a = subset(a,W<8)
for(i in 1:length(w)){
with(subset(a,YEAR==w[i] & W %in% 1:7),lines(W,expl,col=cols[i],lty=i,lwd=2,type='b',pch=16))
with(subset(a,YEAR==w[i] & W %in% 8),points(8,expl,col=cols[i],lty=i,lwd=2,type='b',pch=16,cex=1.2))
}
legend('bottomleft',legend=w,col=cols,lty=1:16,title='Years',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'CohortAnalysisPlots','LFA33YearLines.png'),type='png')
plot(1:8,rep(1,8),type='n',xlab='Weeks of Season',ylab="Proportion Female",ylim=c(0.38,0.54),xaxt='n')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
w = unique(h$Year)
#a = subset(a,W<8)
for(i in 1:length(w)){
with(subset(h,Year==w[i] & W %in% 1:7),lines(W,prop.female,col=cols[i],lty=i,lwd=2,type='b',pch=16))
with(subset(h,Year==w[i] & W %in% 8),points(8,prop.female,col=cols[i],lty=i,lwd=2,type='b',pch=16,cex=1.2))
}
legend('bottomleft',legend=w,col=cols,lty=1:16,title='Years',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33ProportionFemaleYearLines.png'),type='png')
plot(1:8,rep(1,8),type='n',xlab='Weeks of Season',ylab="Proportion Berried",ylim=c(0.0,0.05),xaxt='n')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
w = unique(h$Year)
#a = subset(a,W<8)
for(i in 1:length(w)){
with(subset(h,Year==w[i] & W %in% 1:7),lines(W,prop.berried,col=cols[i],lty=i,lwd=2,type='b',pch=16))
with(subset(h,Year==w[i] & W %in% 8),points(8,prop.berried,col=cols[i],lty=i,lwd=2,type='b',pch=16,cex=1.2))
}
legend('bottomleft',legend=w,col=cols,lty=1:16,title='Years',bty='n',cex=0.8,ncol=2,pch=rep(16,4))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','LFA33ProportionBerriedYearLines.png'),type='png')
#boxplots by weeks of season
boxplot(prop.female~W,data=h,ylab='Sex Ratio',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensPropFemale.png'),type='png')
boxplot(prop.berried~W,data=h,ylab='Proportion Berried',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensPropBerried.png'),type='png')
boxplot(expl~W,data=a,ylab='Exploitation',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'CohortAnalysisPlots','FSRSCommSensExploitation.png'),type='png')
h = rename.df(h,'quants.50%','Median')
boxplot(Median~W,data=h,ylab='Median Size',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensMedianSize.png'),type='png')
h = rename.df(h,'quants.97.5%','Max')
boxplot(Max~W,data=h,ylab='Maximum Size',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensMaxSize.png'),type='png')
boxplot(new.rec~W,data=h,ylab='Proportion New Recruits',xaxt='n',xlab='Weeks of Season')
axis(side=1,at=c(1:8),labels = c('1-4','5-8','9-12','13-16','17-20','21-24','25-28','All'))
savePlot(file = file.path(project.figuredirectory('bio.lobster'),'AtSeaIndictors','FSRSCommSensNewRec.png'),type='png')
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