inst/Updates/LFA35-38/Previous Updates/Update35_2016.b.R
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
####### Update 35: May 2015
require('bio.lobster')
## Landings
AnnualSlip<-lobster.db( DS="annual.landings.redo")
SeasonalSlip<-lobster.db( DS="seasonal.landings.redo")
#Historic<-lobster.db( DS="historical.landings.redo")
AnnualSlip<-lobster.db( DS="annual.landings")
SeasonalSlip<-lobster.db( DS="seasonal.landings")
#Historic<-lobster.db( DS="historical.landings")
Annual35<-subset(AnnualSlip,select=c("YR","LFA35"))
Annual35$YEAR = Annual35$YR
SeasonalSlip$YEAR<-as.numeric(substr(SeasonalSlip$SYEAR,6,9))
Seasonal35<-subset(SeasonalSlip,select=c("LFA35","SYEAR"))
require(ggplot2)
# Plot Landings (1892-present) Figure 2
pdf(file.path( project.datadirectory("lobster"), "R","LFA35Landings.pdf"),8,6)
ggplot(Annual35,aes(YEAR,LFA35)) + geom_bar(data=Annual35,fill=rgb(0,0,1,0.5),stat='identity') +
geom_line(data=Seasonal35,colour='red',size=1) +
scale_y_continuous(breaks=seq(0, 25000, 2000)) + scale_x_continuous(breaks=seq(1890, 2015, 5)) +
theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5,size=10, colour='black'),panel.background=element_rect(fill="white", colour='black'),panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15)) + xlab('') + ylab('Landings (t)')
dev.off()
## Commercial catch rate index
LFA35logData<-read.delim(file.path(project.datadirectory('lobster'),"data","Commercial","LFA35_CPUE_Data_2015.05.12.txt")) # from Cheryl
logDat <- lobster.db('process.logs')
logDat <- subset(logDat,SYEAR %in% 2003:(p$current.assessment.year-1) & LFA %in% c(35,36,38))
catch.new <- with(logDat,tapply(WEIGHT_KG,SYEAR,sum))
effort.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,sum))
n.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA3538.dat <- data.frame(year=2004:(p$current.assessment.year-1),n=c(n,n.new),catch=c(catch,catch.new),effort=c(effort,effort.new),cpue=c(catch/effort,catch.new/effort.new))
LFA35logData<-subset(LFA35logData,SYEAR!="OOS"&SYEAR<2015)
LFA35logData$WEIGHT_KG<-LFA35logData$WEIGHT_LBS*0.4536
catch<-with(LFA35logData,tapply(WEIGHT_KG,SYEAR,sum))
effort<-with(LFA35logData,tapply(NUM_OF_TRAPS,SYEAR,sum))
n<-with(LFA35logData,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA35.dat<-data.frame(year=sort(unique(LFA35logData$SYEAR)),n=n,catch=catch,effort=effort,cpue=catch/effort)
write.csv(cpueLFA35.dat,file.path(project.datadirectory('lobster'),'data',"LFA35CPUE.csv"),row.names=F)
KgPTH<- cpueLFA35.dat$cpue
yrs<-sort(as.numeric(cpueLFA35.dat$year))
rmKgPTH<-ma(KgPTH)
# Plot Commercial CPUE Figure 3
pdf(file.path( project.datadirectory("lobster"), "R","LFA35CommercialCPUE.pdf"),8,6)
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 35 - Commercial Log CPUE",xaxt='n')
#axis(1)
#axis(1,yrs,lab=F,tck=-0.01)
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
#arrows(yrs, KgPTH+KgPTHse, yrs, KgPTH-KgPTHse ,code=3,angle=90,length=0.1)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[1:11]*0.8),col=rgb(0,0,1,0.5))
text(max(yrs)+.5,median(KgPTH[2:15]*0.8)*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
dev.off()
# Scallop Survey
SCALSURV35.dat<-ScallopSurveyProcess(SPA=c("1","29"))
CLFsurvey$ScallopSurvey<-t(sapply(2005:2014,function(y){colMeans(subset(SCALSURV35.dat,YEAR==y,paste0("CL",seq(5,220,5))),na.rm=T)}))
BubblePlotCLF(CLFsurvey,inch=0.2,bg=rgb(0,1,0,0.1),yrs=2005:2014,bins=seq(0,220,5),filen="SurveyLFA35",prop=lT)
BarPlotCLF(CLFsurvey,yrs=2005:2014,bins=seq(0,220,5),col='grey',filen="SurveyLFA35",rel=F,ymax=c(25,1))
# at sea sampling
lobster.db('atSea')
atSea.LFA35.dat<-addSYEAR(subset(atSea,LFA==35))
atSea.LFA35.dat$YEAR<-year(atSea.LFA35.dat$SDATE)
#CLF(subset(atSea.LFA35.dat,!is.na(SYEAR),c("SYEAR","CARLENGTH")))
LFA35lf<-CLF(subset(atSea.LFA35.dat,!is.na(YEAR),c("YEAR","CARLENGTH")),yrs=1982:2014)
BubblePlotCLF(LFA35lf,inch=0.2,bg=rgb(0,1,0,0.1),prop=T,filen="SeaSamplingLFA35",yrs=1982:2014)
LFA35lf<-CLF(subset(atSea.LFA35.dat,!is.na(YEAR),c("YEAR","CARLENGTH")),yrs=2005:2014)
BarPlotCLF(LFA35lf,yrs=2005:2014,bins=seq(0,220,5),col='grey',filen="SeaSamplingLFA35",rel=T)
# port sampling
lobster.db('port')
port35<-subset(port,LFA==35)
######## Distribution plots ######################################
#
# lobster distribution by year in LFA 35 from survey
#
pdf(file.path( project.datadirectory("lobster"), "R","LFA35LobsterDensity.pdf"),8,11)
for(i in 2005:2013){
# interpolate abundance
interp.data<-na.omit(subset(surveyLobsters35,YEAR==i,c('SET_ID','SET_LONG','SET_LAT','NUM_STANDARDIZED')))
lob.contours<-interpolation(interp.data,ticks='define',place=3,nstrata=5,str.min=0,interp.method='gstat',blank=T,res=0.005,smooth=F,idp=3.5,blank.dist=0.2)
# define contour lines
print(lob.contours$str.def)
lvls=c(1, 5, 10, 20, 50, 100, 200, 500)
# generate contour lines
cont.lst<-contour.gen(lob.contours$image.dat,lvls,subset(LFAs,LFA==35),col="YlGn",colorAdj=1)
# plot Map
LobsterMap('35',mapRes="UR",contours=cont.lst,title=paste("LFA 35 Lobster Density",i),isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(SET_LAT~SET_LONG,surveyLobsters,subset=YEAR==i,pch=16,cex=0.5)#,col=rgb(0,0,0,0.5))
ContLegend("bottomright",lvls=lvls,Cont.data=cont.lst$Cont.data,title="#/standard tow",inset=0.02,cex=0.8,bg='white')
}
#dev.off()
######## seperate July and Sept for 2014
surveyLobsters$MONTH<-month(surveyLobsters$BOARD_DATE,T)
#pdf(file.path( project.datadirectory("lobster"), "R","LFA35LobsterDensity2014.pdf"),8,11)
for(i in c("Jul","Sep")){
# interpolate abundance
interp.data<-na.omit(subset(surveyLobsters,LFA==35&HAULCCD_ID==1&YEAR==2014&MONTH==i,c('SET_ID','SET_LONG','SET_LAT','NUM_STANDARDIZED')))
lob.contours<-interpolation(interp.data,ticks='define',place=3,nstrata=5,str.min=0,interp.method='gstat',blank=T,blank.type=1,res=0.01,smooth=F,idp=3.5,blank.dist=0.2)
# define contour lines
print(lob.contours$str.def)
lvls=c(1, 5, 10, 20, 50, 100, 200, 500)
# generate contour lines
cont.lst<-contour.gen(lob.contours$image.dat,lvls,col="YlGn",colorAdj=1)
# plot Map
LobsterMap('35',mapRes="UR",contours=cont.lst,title=paste("LFA 35 Lobster Density",i,"2014"),isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(SET_LAT~SET_LONG,interp.data,pch=16,cex=0.5)#,col=rgb(0,0,0,0.5))
ContLegend("bottomright",lvls=lvls,Cont.data=cont.lst$Cont.data,title="#/standard tow",inset=0.02,cex=0.8,bg='white')
}
dev.off()
## berried females
pdf(file.path( project.datadirectory("lobster"), "R","LFA35BerriedFemaleDensity.pdf"),8,11)
for(i in 2005:2013){
# interpolate abundance
interp.data<-na.omit(subset(surveyLobsters,LFA==35&YEAR==i,c('SET_ID','SET_LONG','SET_LAT','BERRIED_FEMALES')))
lob.contours<-interpolation(interp.data,ticks='define',place=3,nstrata=5,str.min=0,interp.method='gstat',blank=T,res=0.005,smooth=F,idp=3.5,blank.dist=0.2)
# define contour lines
print(lob.contours$str.def)
lvls=c(0.5, 1, 2, 3, 4, 5)
# generate contour lines
cont.lst<-contour.gen(lob.contours$image.dat,lvls,subset(LFAs,LFA==35),col="YlGn",colorAdj=1)
# plot Map
LobsterMap('35',mapRes="UR",contours=cont.lst,title=paste("LFA 35 Lobster Density",i),isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(SET_LAT~SET_LONG,surveyLobsters,subset=YEAR==i,pch=16,cex=0.5)#,col=rgb(0,0,0,0.5))
ContLegend("bottomright",lvls=lvls,Cont.data=cont.lst$Cont.data,title="#/standard tow",inset=0.02,cex=0.8,bg='white')
}
#dev.off()
######## seperate July and Sept for 2014
surveyLobsters$MONTH<-month(surveyLobsters$BOARD_DATE,T)
#pdf(file.path( project.datadirectory("lobster"), "R","LFA35LobsterDensity2014.pdf"),8,11)
for(i in c("Jul","Sep")){
# interpolate abundance
interp.data<-na.omit(subset(surveyLobsters,LFA==35&YEAR==2014&MONTH==i,c('SET_ID','SET_LONG','SET_LAT','BERRIED_FEMALES')))
lob.contours<-interpolation(interp.data,ticks='define',place=3,nstrata=5,str.min=0,interp.method='gstat',blank=T,blank.type=1,res=0.01,smooth=F,idp=3.5,blank.dist=0.2)
# define contour lines
print(lob.contours$str.def)
lvls=c(0.5, 1, 2, 3, 4, 5)
# generate contour lines
cont.lst<-contour.gen(lob.contours$image.dat,lvls,col="YlGn",colorAdj=1)
# plot Map
LobsterMap('35',mapRes="UR",contours=cont.lst,title=paste("LFA 35 Lobster Density",i,"2014"),isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(SET_LAT~SET_LONG,interp.data,pch=16,cex=0.5)#,col=rgb(0,0,0,0.5))
ContLegend("bottomright",lvls=lvls,Cont.data=cont.lst$Cont.data,title="#/standard tow",inset=0.02,cex=0.8,bg='white')
}
dev.off()
# FSRS CPUE
FSRScpue.dat<-read.csv(file.path( project.datadirectory("lobster"), "data","FSRScpue.csv"))
FSRScpue.dat$subarea[FSRScpue.dat$LFA=='35']<-FSRScpue.dat$LFA[FSRScpue.dat$LFA=='35']
FSRScpue35.dat<-reshape( subset(FSRScpue.dat,LFA=='35',c("SYEAR","pred.s.cpue","pred.l.cpue")),varying=list(c("pred.s.cpue","pred.l.cpue")),direction='long',timevar='Size',times=c("sublegal","legal"))
xyplot(pred.s.cpue~SYEAR|Size, data=FSRScpue35.dat, ylab="CPUE (No. Lobsters / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,7))
pdf(file.path( project.datadirectory("lobster"), "R","LFA35sublegalFSRS.pdf"),8,5)
xyplot(pred.s.cpue~SYEAR|LFA, data=subset(FSRScpue.dat,subarea%in%c('33 West','35')), ylab="CPUE (No. Lobsters / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,7))
dev.off()
##-- Update 35-38
## Commercial catch rate index
LFA3538logData<-read.delim(file.path(project.datadirectory('lobster'),"data","Commercial","LFA35-38CPUE_2015.05.19.txt"))
LFA3538logData<-subset(LFA3538logData,SYEAR!="OOS"&SYEAR%in%2006:2016)
LFA3538logData$WEIGHT_KG<-LFA3538logData$WEIGHT_LBS*0.4536
catch<-with(LFA3538logData,tapply(WEIGHT_KG,SYEAR,sum))
effort<-with(LFA3538logData,tapply(NUM_OF_TRAPS,SYEAR,sum))
n<-with(LFA3538logData,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA3538.dat<-data.frame(year=sort(unique(LFA3538logData$SYEAR)),n=n,catch=catch,effort=effort,cpue=catch/effort)
write.csv(cpueLFA3538.dat,file.path(project.datadirectory('lobster'),'data',"LFA3538CPUE.csv"),row.names=F)
KgPTH<- cpueLFA3538.dat$cpue
yrs<-sort(as.numeric(cpueLFA3538.dat$year))
rmKgPTH<-mavg(KgPTH)
#pdf(file.path( project.datadirectory("lobster"), "R","LFA3538CommercialCPUE.pdf"),8,6)
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 35 to 38 - Commercial Log CPUE",xaxt='n')
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[1:4]*0.5),col=rgb(0,0,1,0.5))
text(max(yrs),median(KgPTH[1:4]*0.5)*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
dev.off()
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####### Update 35: May 2015
require('bio.lobster')
## Landings
AnnualSlip<-lobster.db( DS="annual.landings.redo")
SeasonalSlip<-lobster.db( DS="seasonal.landings.redo")
#Historic<-lobster.db( DS="historical.landings.redo")
AnnualSlip<-lobster.db( DS="annual.landings")
SeasonalSlip<-lobster.db( DS="seasonal.landings")
#Historic<-lobster.db( DS="historical.landings")
Annual35<-subset(AnnualSlip,select=c("YR","LFA35"))
Annual35$YEAR = Annual35$YR
SeasonalSlip$YEAR<-as.numeric(substr(SeasonalSlip$SYEAR,6,9))
Seasonal35<-subset(SeasonalSlip,select=c("LFA35","SYEAR"))
require(ggplot2)
# Plot Landings (1892-present) Figure 2
pdf(file.path( project.datadirectory("lobster"), "R","LFA35Landings.pdf"),8,6)
ggplot(Annual35,aes(YEAR,LFA35)) + geom_bar(data=Annual35,fill=rgb(0,0,1,0.5),stat='identity') +
geom_line(data=Seasonal35,colour='red',size=1) +
scale_y_continuous(breaks=seq(0, 25000, 2000)) + scale_x_continuous(breaks=seq(1890, 2015, 5)) +
theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5,size=10, colour='black'),panel.background=element_rect(fill="white", colour='black'),panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15)) + xlab('') + ylab('Landings (t)')
dev.off()
## Commercial catch rate index
LFA35logData<-read.delim(file.path(project.datadirectory('lobster'),"data","Commercial","LFA35_CPUE_Data_2015.05.12.txt")) # from Cheryl
logDat <- lobster.db('process.logs')
logDat <- subset(logDat,SYEAR %in% 2003:(p$current.assessment.year-1) & LFA %in% c(35,36,38))
catch.new <- with(logDat,tapply(WEIGHT_KG,SYEAR,sum))
effort.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,sum))
n.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA3538.dat <- data.frame(year=2004:(p$current.assessment.year-1),n=c(n,n.new),catch=c(catch,catch.new),effort=c(effort,effort.new),cpue=c(catch/effort,catch.new/effort.new))
LFA35logData<-subset(LFA35logData,SYEAR!="OOS"&SYEAR<2015)
LFA35logData$WEIGHT_KG<-LFA35logData$WEIGHT_LBS*0.4536
catch<-with(LFA35logData,tapply(WEIGHT_KG,SYEAR,sum))
effort<-with(LFA35logData,tapply(NUM_OF_TRAPS,SYEAR,sum))
n<-with(LFA35logData,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA35.dat<-data.frame(year=sort(unique(LFA35logData$SYEAR)),n=n,catch=catch,effort=effort,cpue=catch/effort)
write.csv(cpueLFA35.dat,file.path(project.datadirectory('lobster'),'data',"LFA35CPUE.csv"),row.names=F)
KgPTH<- cpueLFA35.dat$cpue
yrs<-sort(as.numeric(cpueLFA35.dat$year))
rmKgPTH<-ma(KgPTH)
# Plot Commercial CPUE Figure 3
pdf(file.path( project.datadirectory("lobster"), "R","LFA35CommercialCPUE.pdf"),8,6)
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 35 - Commercial Log CPUE",xaxt='n')
#axis(1)
#axis(1,yrs,lab=F,tck=-0.01)
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
#arrows(yrs, KgPTH+KgPTHse, yrs, KgPTH-KgPTHse ,code=3,angle=90,length=0.1)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[1:11]*0.8),col=rgb(0,0,1,0.5))
text(max(yrs)+.5,median(KgPTH[2:15]*0.8)*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
dev.off()
# Scallop Survey
SCALSURV35.dat<-ScallopSurveyProcess(SPA=c("1","29"))
CLFsurvey$ScallopSurvey<-t(sapply(2005:2014,function(y){colMeans(subset(SCALSURV35.dat,YEAR==y,paste0("CL",seq(5,220,5))),na.rm=T)}))
BubblePlotCLF(CLFsurvey,inch=0.2,bg=rgb(0,1,0,0.1),yrs=2005:2014,bins=seq(0,220,5),filen="SurveyLFA35",prop=lT)
BarPlotCLF(CLFsurvey,yrs=2005:2014,bins=seq(0,220,5),col='grey',filen="SurveyLFA35",rel=F,ymax=c(25,1))
# at sea sampling
lobster.db('atSea')
atSea.LFA35.dat<-addSYEAR(subset(atSea,LFA==35))
atSea.LFA35.dat$YEAR<-year(atSea.LFA35.dat$SDATE)
#CLF(subset(atSea.LFA35.dat,!is.na(SYEAR),c("SYEAR","CARLENGTH")))
LFA35lf<-CLF(subset(atSea.LFA35.dat,!is.na(YEAR),c("YEAR","CARLENGTH")),yrs=1982:2014)
BubblePlotCLF(LFA35lf,inch=0.2,bg=rgb(0,1,0,0.1),prop=T,filen="SeaSamplingLFA35",yrs=1982:2014)
LFA35lf<-CLF(subset(atSea.LFA35.dat,!is.na(YEAR),c("YEAR","CARLENGTH")),yrs=2005:2014)
BarPlotCLF(LFA35lf,yrs=2005:2014,bins=seq(0,220,5),col='grey',filen="SeaSamplingLFA35",rel=T)
# port sampling
lobster.db('port')
port35<-subset(port,LFA==35)
######## Distribution plots ######################################
#
# lobster distribution by year in LFA 35 from survey
#
pdf(file.path( project.datadirectory("lobster"), "R","LFA35LobsterDensity.pdf"),8,11)
for(i in 2005:2013){
# interpolate abundance
interp.data<-na.omit(subset(surveyLobsters35,YEAR==i,c('SET_ID','SET_LONG','SET_LAT','NUM_STANDARDIZED')))
lob.contours<-interpolation(interp.data,ticks='define',place=3,nstrata=5,str.min=0,interp.method='gstat',blank=T,res=0.005,smooth=F,idp=3.5,blank.dist=0.2)
# define contour lines
print(lob.contours$str.def)
lvls=c(1, 5, 10, 20, 50, 100, 200, 500)
# generate contour lines
cont.lst<-contour.gen(lob.contours$image.dat,lvls,subset(LFAs,LFA==35),col="YlGn",colorAdj=1)
# plot Map
LobsterMap('35',mapRes="UR",contours=cont.lst,title=paste("LFA 35 Lobster Density",i),isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(SET_LAT~SET_LONG,surveyLobsters,subset=YEAR==i,pch=16,cex=0.5)#,col=rgb(0,0,0,0.5))
ContLegend("bottomright",lvls=lvls,Cont.data=cont.lst$Cont.data,title="#/standard tow",inset=0.02,cex=0.8,bg='white')
}
#dev.off()
######## seperate July and Sept for 2014
surveyLobsters$MONTH<-month(surveyLobsters$BOARD_DATE,T)
#pdf(file.path( project.datadirectory("lobster"), "R","LFA35LobsterDensity2014.pdf"),8,11)
for(i in c("Jul","Sep")){
# interpolate abundance
interp.data<-na.omit(subset(surveyLobsters,LFA==35&HAULCCD_ID==1&YEAR==2014&MONTH==i,c('SET_ID','SET_LONG','SET_LAT','NUM_STANDARDIZED')))
lob.contours<-interpolation(interp.data,ticks='define',place=3,nstrata=5,str.min=0,interp.method='gstat',blank=T,blank.type=1,res=0.01,smooth=F,idp=3.5,blank.dist=0.2)
# define contour lines
print(lob.contours$str.def)
lvls=c(1, 5, 10, 20, 50, 100, 200, 500)
# generate contour lines
cont.lst<-contour.gen(lob.contours$image.dat,lvls,col="YlGn",colorAdj=1)
# plot Map
LobsterMap('35',mapRes="UR",contours=cont.lst,title=paste("LFA 35 Lobster Density",i,"2014"),isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(SET_LAT~SET_LONG,interp.data,pch=16,cex=0.5)#,col=rgb(0,0,0,0.5))
ContLegend("bottomright",lvls=lvls,Cont.data=cont.lst$Cont.data,title="#/standard tow",inset=0.02,cex=0.8,bg='white')
}
dev.off()
## berried females
pdf(file.path( project.datadirectory("lobster"), "R","LFA35BerriedFemaleDensity.pdf"),8,11)
for(i in 2005:2013){
# interpolate abundance
interp.data<-na.omit(subset(surveyLobsters,LFA==35&YEAR==i,c('SET_ID','SET_LONG','SET_LAT','BERRIED_FEMALES')))
lob.contours<-interpolation(interp.data,ticks='define',place=3,nstrata=5,str.min=0,interp.method='gstat',blank=T,res=0.005,smooth=F,idp=3.5,blank.dist=0.2)
# define contour lines
print(lob.contours$str.def)
lvls=c(0.5, 1, 2, 3, 4, 5)
# generate contour lines
cont.lst<-contour.gen(lob.contours$image.dat,lvls,subset(LFAs,LFA==35),col="YlGn",colorAdj=1)
# plot Map
LobsterMap('35',mapRes="UR",contours=cont.lst,title=paste("LFA 35 Lobster Density",i),isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(SET_LAT~SET_LONG,surveyLobsters,subset=YEAR==i,pch=16,cex=0.5)#,col=rgb(0,0,0,0.5))
ContLegend("bottomright",lvls=lvls,Cont.data=cont.lst$Cont.data,title="#/standard tow",inset=0.02,cex=0.8,bg='white')
}
#dev.off()
######## seperate July and Sept for 2014
surveyLobsters$MONTH<-month(surveyLobsters$BOARD_DATE,T)
#pdf(file.path( project.datadirectory("lobster"), "R","LFA35LobsterDensity2014.pdf"),8,11)
for(i in c("Jul","Sep")){
# interpolate abundance
interp.data<-na.omit(subset(surveyLobsters,LFA==35&YEAR==2014&MONTH==i,c('SET_ID','SET_LONG','SET_LAT','BERRIED_FEMALES')))
lob.contours<-interpolation(interp.data,ticks='define',place=3,nstrata=5,str.min=0,interp.method='gstat',blank=T,blank.type=1,res=0.01,smooth=F,idp=3.5,blank.dist=0.2)
# define contour lines
print(lob.contours$str.def)
lvls=c(0.5, 1, 2, 3, 4, 5)
# generate contour lines
cont.lst<-contour.gen(lob.contours$image.dat,lvls,col="YlGn",colorAdj=1)
# plot Map
LobsterMap('35',mapRes="UR",contours=cont.lst,title=paste("LFA 35 Lobster Density",i,"2014"),isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(SET_LAT~SET_LONG,interp.data,pch=16,cex=0.5)#,col=rgb(0,0,0,0.5))
ContLegend("bottomright",lvls=lvls,Cont.data=cont.lst$Cont.data,title="#/standard tow",inset=0.02,cex=0.8,bg='white')
}
dev.off()
# FSRS CPUE
FSRScpue.dat<-read.csv(file.path( project.datadirectory("lobster"), "data","FSRScpue.csv"))
FSRScpue.dat$subarea[FSRScpue.dat$LFA=='35']<-FSRScpue.dat$LFA[FSRScpue.dat$LFA=='35']
FSRScpue35.dat<-reshape( subset(FSRScpue.dat,LFA=='35',c("SYEAR","pred.s.cpue","pred.l.cpue")),varying=list(c("pred.s.cpue","pred.l.cpue")),direction='long',timevar='Size',times=c("sublegal","legal"))
xyplot(pred.s.cpue~SYEAR|Size, data=FSRScpue35.dat, ylab="CPUE (No. Lobsters / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,7))
pdf(file.path( project.datadirectory("lobster"), "R","LFA35sublegalFSRS.pdf"),8,5)
xyplot(pred.s.cpue~SYEAR|LFA, data=subset(FSRScpue.dat,subarea%in%c('33 West','35')), ylab="CPUE (No. Lobsters / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,7))
dev.off()
##-- Update 35-38
## Commercial catch rate index
LFA3538logData<-read.delim(file.path(project.datadirectory('lobster'),"data","Commercial","LFA35-38CPUE_2015.05.19.txt"))
LFA3538logData<-subset(LFA3538logData,SYEAR!="OOS"&SYEAR%in%2006:2016)
LFA3538logData$WEIGHT_KG<-LFA3538logData$WEIGHT_LBS*0.4536
catch<-with(LFA3538logData,tapply(WEIGHT_KG,SYEAR,sum))
effort<-with(LFA3538logData,tapply(NUM_OF_TRAPS,SYEAR,sum))
n<-with(LFA3538logData,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA3538.dat<-data.frame(year=sort(unique(LFA3538logData$SYEAR)),n=n,catch=catch,effort=effort,cpue=catch/effort)
write.csv(cpueLFA3538.dat,file.path(project.datadirectory('lobster'),'data',"LFA3538CPUE.csv"),row.names=F)
KgPTH<- cpueLFA3538.dat$cpue
yrs<-sort(as.numeric(cpueLFA3538.dat$year))
rmKgPTH<-mavg(KgPTH)
#pdf(file.path( project.datadirectory("lobster"), "R","LFA3538CommercialCPUE.pdf"),8,6)
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 35 to 38 - Commercial Log CPUE",xaxt='n')
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[1:4]*0.5),col=rgb(0,0,1,0.5))
text(max(yrs),median(KgPTH[1:4]*0.5)*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
dev.off()
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