inst/FSAR/LFA35vh.r
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
### FSAR for LFA 35 figures
require(bio.lobster)
require(bio.utilities)
require(devtools)
require(ggplot2)
require(sf)
p = bio.lobster::load.environment()
la()
assessment.year = 2023 ##Check Year
p$syr = 1989
p$yrs = p$syr:assessment.year
readRDS("E:/Nova Scotia/Lobster Job/Stock Assessment/LFA 34-38/35 Update/LFA FSAR/LFA35FSARindices.rds")
#landings
a=lobster.db('seasonal.landings')
a$yr= as.numeric(substr(a$SYEAR,6,9))
aaa = a
#raw cpue
#lobster.db('logs.redo')
b = lobster.db('process.logs')
b = subset(b,SYEAR %in% 2005:2023 & LFA =='35')
aa = split(b,f=list(b$LFA,b$SYEAR))
cpue.lst<-list()
for(i in 1:length(aa)){
tmp<-aa[[i]]
tmp = tmp[,c('DATE_FISHED','WEIGHT_KG','NUM_OF_TRAPS')]
names(tmp)<-c('time','catch','effort')
tmp$date<-as.Date(tmp$time)
first.day<-min(tmp$date)
tmp$time<-julian(tmp$date,origin=first.day-1)
tmp$time = ceiling(tmp$time/7) #convert to week of season
g<-as.data.frame(biasCorrCPUE(tmp,by.time=F))
g$lfa=unique(aa[[i]]$LFA)
g$yr = unique(aa[[i]]$SYEAR)
g = t(g)[,1]
cpue.lst[[i]] <- g
}
cc =as.data.frame(do.call(rbind,cpue.lst))
cc$CPUE = as.numeric(cc$`biasCorrCPUE(tmp, by.time = F)`)
cc$yr = as.numeric(cc$yr)
cp = as.data.frame(do.call(cbind,rmed(cc$yr,cc$CPUE)))
#effort
ef = merge(cc,aaa)
ef$Effort = ef$LFA35/(ef$CPUE)
#standardized cpue
# lobster.db('logs.redo')
#lobster.db('process.logs.redo')
#lobster.db('temperature.data.redo')
#v = TempModelData(redo=F)
#TempModelling = TempModel(areas='lfa', annual.by.area=F, redo.data=F)
#saveRDS(TempModelling,file=file.path(project.datadirectory('bio.lobster'),'tempmodelling.rds'))
# step through this function CPUE.data<-CPUEModelData(p,redo=T,TempModelling)
CPUE.data<-CPUEModelData(p,redo=F,TempModelling2023)
mf1 = formula(logWEIGHT ~ fYEAR + DOS + TEMP + DOS * TEMP)
#CPUE.data<- cpue.data
# t=with(subset(CPUE.data,DOS==1),tapply(TEMP,LFA,mean))
# CPUE.data$fYEAR=as.factor(CPUE.data$SYEAR)
# CPUEModelResults = CPUEmodel(mf1,CPUE.data,t=t,d=1)
# outs =CPUEModelResults$pData
#oo = as.data.frame(do.call(cbind,rmed(outs$YEAR,outs$mu)))
CPUE.data<- CPUEModelData(p,redo=F)
t=with(subset(CPUE.data,DOS==1),tapply(TEMP,LFA,mean))
pData=list()
CPUEModelResults = list()
p$lfas =c("35","36","38")
for(i in 1:length( p$lfas)){
mdata = subset(CPUE.data,LFA==p$lfas[i]&SYEAR%in%p$yrs)
if(nrow(mdata)>10){
CPUEModelResults[[i]] = CPUEmodel(mf1,mdata,t=t[i],d=1)
pData[[i]]=CPUEModelResults[[i]]$pData
pData[[i]]$LFA=p$lfas[i]
}
}
names(CPUEModelResults) = p$lfas
CPUEindex=do.call("rbind",pData)
l35 = subset(CPUEindex,LFA==35,c("YEAR","mu"))
k = median(l35$mu[which(l35$YEAR %in% 2011:2018)])
usr = .4*k
lrp = .2*k
outs = subset(CPUEindex,LFA==35,c("YEAR","mu"))
oo = as.data.frame(do.call(cbind,with(rmed(l35$YEAR,l35$mu),data.frame(YEAR=yr,running.median=x))))
#commB
require(bio.survey)
require(bio.lobster)
p = bio.lobster::load.environment()
p$libs = NULL
p$yrs = 1970:2023
p1 = p
p$series =c('summer')# p$series =c('georges');p$series =c('fall')
p$years.to.estimate = p$yrs
p$length.based = T
p$by.sex = T
p$size.class = c(83,300)
p$sex = c(1,2)
p$bootstrapped.ci=T
p$strata.files.return=F
p$vessel.correction.fixed=1.2
p$strat = NULL
p$clusters = c( rep( "localhost", 7) )
p$strata.efficiencies = F
p = make.list(list(yrs=p$years.to.estimate),Y=p)
p$define.by.polygons = T
p$lobster.subunits=F
p$area = 'LFA35-38'
p$reweight.strata = T #this subsets
aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
p$by.sex = p$length.based = F
aoutf= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
aoutf = subset(aoutf,yr<1999)
aout = subset(aout,yr>1998)
df = as.data.frame(rbind(aoutf,aout))
df = df[,c('yr','w.Yst','w.ci.Yst.l','w.ci.Yst.u')] #proportion of total weight that is commercial
df$w.Yst[which(df$yr<1999)] <- df$w.Yst[which(df$yr<1999)]*0.746
df$w.ci.Yst.l[which(df$yr<1999)] <- df$w.ci.Yst.l[which(df$yr<1999)]*0.746
df$w.ci.Yst.u[which(df$yr<1999)] <- df$w.ci.Yst.u[which(df$yr<1999)]*0.746
xx = as.data.frame(do.call(cbind,rmed(df$yr,df$w.Yst)))
#relf
a = lobster.db('annual.landings')
b = lobster.db('seasonal.landings')
b$YR = substr(b$SYEAR,6,9)
a = subset(a,YR<1976)
b = subset(b,YR>1975 & YR<=2022)
a$L3538 = rowSums(a[,12:14])
b$L3538 = rowSums(b[,4:6])
c358 = as.data.frame(rbind(a[,c('YR','L3538')],b[,c('YR','L3538')]))
names(c358)[1] = 'yr'
df =merge(df,c358)
df$rL = df$L3538/(df$w.ci.Yst.l+df$L3538)
df$rU =df$L3538/ (df$w.ci.Yst.u+df$L3538)
df$rM = df$L3538/(df$w.Yst+df$L3538)
pp = as.data.frame(do.call(cbind,rmed(df$yr,df$rM)))
#recN
p$length.based = T
p$by.sex = T
p$size.class = c(70,82)
p$sex = c(1,2)
p$bootstrapped.ci=T
p$strata.files.return=F
p$vessel.correction.fixed=1.2
p$strat = NULL
p$clusters = c( rep( "localhost", 7) )
p$strata.efficiencies = F
p = make.list(list(yrs=p$years.to.estimate),Y=p)
p$define.by.polygons = T
p$lobster.subunits=F
p$area = 'LFA35-38'
p$reweight.strata = T #this subsets
recout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
recxx = as.data.frame(do.call(cbind,rmed(recout$yr,recout$n.yst)))
#scallop survey reworked in 2023
lobster.db('scallop')
sc = scallopSurveyIndex(redo=T,size_range = c(70,82), lfa=35)
sc = sc[[1]]
#plotting as per csasdown 4 panel plot
#add in the theme_csas
theme_csas <- function(base_size = 14, base_family = "", text_col = "grey20",
panel_border_col = "grey70") {
half_line <- base_size / 2
theme_light(base_size = base_size, base_family = "") +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.ticks.length = unit(half_line / 2.2, "pt"),
strip.background = element_rect(fill = NA, colour = NA),
strip.text.x = element_text(colour = text_col),
strip.text.y = element_text(colour = text_col),
axis.text = element_text(colour = text_col),
axis.title = element_text(colour = text_col),
legend.title = element_text(colour = text_col, size = rel(0.9)),
panel.border = element_rect(fill = NA, colour = panel_border_col, size = 1),
legend.key.size = unit(0.9, "lines"),
legend.text = element_text(size = rel(0.9), colour = text_col),
legend.key = element_rect(colour = NA, fill = NA),
legend.background = element_rect(colour = NA, fill = NA),
plot.title = element_text(colour = text_col, size = rel(1)),
plot.subtitle = element_text(colour = text_col, size = rel(.85))
)
}
#format from FSAR branch of CSASdown
# Catch and eff
aap <- aaa[nrow(aaa),]
aaa = aaa[1:(nrow(aaa)-1),]
efp = ef[nrow(ef),]
ef = ef[-nrow(ef),]
ymax=5000
scaleright = max(ef$Effort)/ymax
g1 <- ggplot(data = subset(aaa,yr>1990), aes(x = yr,y=LFA35)) +
geom_bar(stat='identity',fill='black') +
geom_bar(data=aap,aes(x=yr,y=LFA35),stat='identity',fill='gray66') +
geom_point(data=efp,aes(x=yr,y=Effort/scaleright),colour='grey66',shape=17,size=2.5)+
geom_line(data=ef,aes(x=yr,y=Effort/scaleright),colour='black',lwd=0.75,linetype='dashed')+
geom_point(data=ef,aes(x=yr,y=Effort/scaleright),colour='black',shape=16,size=2)+
scale_y_continuous(name='Catch (t)', sec.axis= sec_axis(~.*scaleright, name= 'Effort',breaks = seq(0,2000,by=250)))+
labs(x = "Year") +
theme_csas()
# standarzized cpue
#change last point
outs23 = outs[nrow(outs),]
outs22 =outs[1:(nrow(outs)-1),]
g2 <- ggplot(data = outs, aes(x = YEAR)) +
geom_point(data=outs22,aes(y = mu),size=2) +
geom_point(data=outs23,aes(y=mu),shape =17, size = 2)+
geom_line(data=oo,aes(x=YEAR,y=running.median),colour='grey45',lwd=1.25)+
labs(x = "Year", y = "Standardized CPUE") +
geom_hline(yintercept=mean(outs$mu[7:14]) * .4,colour='grey',lwd=1.25,linetype='dashed')+
geom_hline(yintercept=mean(outs$mu[7:14]) * .2,colour='grey',lwd=1.25,linetype='dotted')+
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
###FISHING MORT --- not calculated
g3 <- ggplot(data = outs, aes(x = YEAR)) +
geom_point(aes(y = mu),size=0,colour='white') +
labs(x = "Year", y = "Fishing Mortality") +
annotate("text",x =2014, y = 4.5,label= "Data Not Available", size =5, colour= 'grey')+
theme_csas() +
theme(axis.text.y = element_blank(),axis.text.x = element_blank(),axis.title.y = ggtext::element_markdown())
#g3 = ggplot() + # Draw ggplot2 plot with text only
# annotate("text",
# x = 1,
# y = 1,
## size = 4,
# label = "") +
# theme_void()
g4 <- ggplot(data = recout, aes(x = yr)) +
geom_point(aes(y = n.yst),size=2) +
geom_line(data=recxx,aes(x=yr,y=x),colour='grey45',lwd=1.25)+
labs(x = "Year", y = "Recruit Abundance") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
plot1<-cowplot::plot_grid(g1, g2, g3, g4, ncol = 2, labels = "AUTO", align = "hv")
ggsave("plot1.png")
#second plot
# cpue
ccp = cc[nrow(cc),]
ccf = cc[-nrow(cc),]
g1a <- ggplot(data = ccf, aes(x = yr,y = CPUE)) +
geom_point(size=2)+
geom_point(data=ccp,aes(x=yr,y=CPUE),colour='grey45',shape=17,size=3)+
geom_line(data=cp,aes(x=yr,y=x),colour='grey45',lwd=1.25)+
labs(x = "Year", y = "Raw CPUE") +
theme_csas()
#
g2a <- ggplot(data = sc, aes(x = YEAR,y = as.numeric(Index))) +
geom_point(size=2)+
geom_line(data=sc,aes(x=YEAR,y=Rmed),colour='grey45',lwd=1.25)+
labs(x = "Year", y = "Scallop Survey Recruit Abundance") +
theme_csas()
# relative F
g3a <- ggplot(data = df, aes(x = yr)) +
geom_point(aes(y = rM)) +
geom_line(data=pp,aes(x=yr,y = x), colour = "grey45",lwd=1.25) +
scale_y_continuous(expand = expansion(mult = c(0, 0.02)), limits = c(0, NA)) +
labs(x = "Year", y = "Relative Fishing Mortality") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
g4a <- ggplot(data = df, aes(x = yr)) +
geom_point(aes(y = w.Yst)) +
geom_line(data=xx,aes(x=yr,y = x), colour = "grey45",lwd=1.25) +
scale_y_continuous(expand = expansion(mult = c(0, 0.02)), limits = c(0, NA)) +
labs(x = "Year", y = "Commercial Biomass Index") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
cowplot::plot_grid(g1a, g2a, g3a, g4a, ncol = 2, labels = "AUTO", align = "hv")
saveRDS(list(aaa,aap,ef,efp,outs,recout,cc,sc,df),file=file.path(project.datadirectory('bio.lobster'),'data','LFA35FSARindices.rds'))
################FRENCH ##################################
# Catch and eff
aap <- aaa[nrow(aaa),]
aaa = aaa[1:(nrow(aaa)-1),]
efp = ef[nrow(ef),]
ef = ef[-nrow(ef),]
ymax=5000
scaleright = max(ef$Effort)/ymax
g1 <- ggplot(data = subset(aaa,yr>1990), aes(x = yr,y=LFA35)) +
geom_bar(stat='identity',fill='black') +
geom_bar(data=aap,aes(x=yr,y=LFA35),stat='identity',fill='gray66') +
geom_point(data=efp,aes(x=yr,y=Effort/scaleright),colour='grey66',shape=17,size=2.5)+
geom_line(data=ef,aes(x=yr,y=Effort/scaleright),colour='black',lwd=0.75,linetype='dashed')+
geom_point(data=ef,aes(x=yr,y=Effort/scaleright),colour='black',shape=16,size=2)+
scale_y_continuous(name='Débarquements (t)', sec.axis= sec_axis(~.*scaleright, name= 'Effort (milliers de casiers levés)',breaks = seq(0,2000,by=250)))+
labs(x = "Année") +
theme_csas()
# standarzized cpue
#change last point
outs23 = outs[nrow(outs),]
outs22 =outs[1:(nrow(outs)-1),]
g2 <- ggplot(data = outs, aes(x = YEAR)) +
geom_point(data=outs22,aes(y = mu),size=2) +
geom_point(data=outs23,aes(y=mu),shape =17, size = 2)+
geom_line(data=oo,aes(x=YEAR,y=running.median),colour='grey45',lwd=1.25)+
labs(x = "Année", y = "CPUE Standardisé (kg/casier levé)") +
geom_hline(yintercept=mean(outs$mu[7:14]) * .4,colour='grey',lwd=1.25,linetype='dashed')+
geom_hline(yintercept=mean(outs$mu[7:14]) * .2,colour='grey',lwd=1.25,linetype='dotted')+
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
###FISHING MORT --- not calculated
g3 <- ggplot(data = outs, aes(x = YEAR)) +
geom_point(aes(y = mu),size=0,colour='white') +
labs(x = "Année", y = "Mortalité par pêche") +
annotate("text",x =2014, y = 4.5,label= "Données Non Disponibles", size =5, colour= 'grey')+
theme_csas() +
theme(axis.text.y = element_blank(),axis.text.x = element_blank(),axis.title.y = ggtext::element_markdown())
g4 <- ggplot(data = recout, aes(x = yr)) +
geom_point(aes(y = n.yst),size=2) +
geom_line(data=recxx,aes(x=yr,y=x),colour='grey45',lwd=1.25)+
labs(x = "Année", y = "Abondance des recrues") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
plot1<-cowplot::plot_grid(g1, g2, g3, g4, ncol = 2, labels = "AUTO", align = "hv")
#second plot
# cpue
ccp = cc[nrow(cc),]
ccf = cc[-nrow(cc),]
g1a <- ggplot(data = ccf, aes(x = yr,y = CPUE)) +
geom_point(size=2)+
geom_point(data=ccp,aes(x=yr,y=CPUE),colour='grey66',shape=17,size=3)+
geom_line(data=cp,aes(x=yr,y=x),colour='gray45',lwd=1.25)+
labs(x = "Année", y = "CPUE brut") +
theme_csas()
#
g2a <- ggplot(data = sc, aes(x = YEAR,y = as.numeric(Index))) +
geom_point(size=2)+
geom_line(data=sc,aes(x=YEAR,y=Rmed),colour='gray45',lwd=1.25)+
labs(x = "Année", y = "Abondance des recrutés du relevé des pétoncles") +
theme_csas()
# relative F
g3a <- ggplot(data = df, aes(x = yr)) +
geom_point(aes(y = rM)) +
geom_line(data=pp,aes(x=yr,y = x), colour = "grey45",lwd=1.25) +
scale_y_continuous(expand = expansion(mult = c(0, 0.02)), limits = c(0, NA)) +
labs(x = "Année", y = "Mortalité par pêche relative") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
g4a <- ggplot(data = df, aes(x = yr)) +
geom_point(aes(y = w.Yst)) +
geom_line(data=xx,aes(x=yr,y = x), colour = "grey45",lwd=1.25) +
scale_y_continuous(expand = expansion(mult = c(0, 0.02)), limits = c(0, NA)) +
labs(x = "Année", y = "Biomasse commerciale") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
cowplot::plot_grid(g1a, g2a, g3a, g4a, ncol = 2, labels = "AUTO", align = "hv")
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
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### FSAR for LFA 35 figures
require(bio.lobster)
require(bio.utilities)
require(devtools)
require(ggplot2)
require(sf)
p = bio.lobster::load.environment()
la()
assessment.year = 2023 ##Check Year
p$syr = 1989
p$yrs = p$syr:assessment.year
readRDS("E:/Nova Scotia/Lobster Job/Stock Assessment/LFA 34-38/35 Update/LFA FSAR/LFA35FSARindices.rds")
#landings
a=lobster.db('seasonal.landings')
a$yr= as.numeric(substr(a$SYEAR,6,9))
aaa = a
#raw cpue
#lobster.db('logs.redo')
b = lobster.db('process.logs')
b = subset(b,SYEAR %in% 2005:2023 & LFA =='35')
aa = split(b,f=list(b$LFA,b$SYEAR))
cpue.lst<-list()
for(i in 1:length(aa)){
tmp<-aa[[i]]
tmp = tmp[,c('DATE_FISHED','WEIGHT_KG','NUM_OF_TRAPS')]
names(tmp)<-c('time','catch','effort')
tmp$date<-as.Date(tmp$time)
first.day<-min(tmp$date)
tmp$time<-julian(tmp$date,origin=first.day-1)
tmp$time = ceiling(tmp$time/7) #convert to week of season
g<-as.data.frame(biasCorrCPUE(tmp,by.time=F))
g$lfa=unique(aa[[i]]$LFA)
g$yr = unique(aa[[i]]$SYEAR)
g = t(g)[,1]
cpue.lst[[i]] <- g
}
cc =as.data.frame(do.call(rbind,cpue.lst))
cc$CPUE = as.numeric(cc$`biasCorrCPUE(tmp, by.time = F)`)
cc$yr = as.numeric(cc$yr)
cp = as.data.frame(do.call(cbind,rmed(cc$yr,cc$CPUE)))
#effort
ef = merge(cc,aaa)
ef$Effort = ef$LFA35/(ef$CPUE)
#standardized cpue
# lobster.db('logs.redo')
#lobster.db('process.logs.redo')
#lobster.db('temperature.data.redo')
#v = TempModelData(redo=F)
#TempModelling = TempModel(areas='lfa', annual.by.area=F, redo.data=F)
#saveRDS(TempModelling,file=file.path(project.datadirectory('bio.lobster'),'tempmodelling.rds'))
# step through this function CPUE.data<-CPUEModelData(p,redo=T,TempModelling)
CPUE.data<-CPUEModelData(p,redo=F,TempModelling2023)
mf1 = formula(logWEIGHT ~ fYEAR + DOS + TEMP + DOS * TEMP)
#CPUE.data<- cpue.data
# t=with(subset(CPUE.data,DOS==1),tapply(TEMP,LFA,mean))
# CPUE.data$fYEAR=as.factor(CPUE.data$SYEAR)
# CPUEModelResults = CPUEmodel(mf1,CPUE.data,t=t,d=1)
# outs =CPUEModelResults$pData
#oo = as.data.frame(do.call(cbind,rmed(outs$YEAR,outs$mu)))
CPUE.data<- CPUEModelData(p,redo=F)
t=with(subset(CPUE.data,DOS==1),tapply(TEMP,LFA,mean))
pData=list()
CPUEModelResults = list()
p$lfas =c("35","36","38")
for(i in 1:length( p$lfas)){
mdata = subset(CPUE.data,LFA==p$lfas[i]&SYEAR%in%p$yrs)
if(nrow(mdata)>10){
CPUEModelResults[[i]] = CPUEmodel(mf1,mdata,t=t[i],d=1)
pData[[i]]=CPUEModelResults[[i]]$pData
pData[[i]]$LFA=p$lfas[i]
}
}
names(CPUEModelResults) = p$lfas
CPUEindex=do.call("rbind",pData)
l35 = subset(CPUEindex,LFA==35,c("YEAR","mu"))
k = median(l35$mu[which(l35$YEAR %in% 2011:2018)])
usr = .4*k
lrp = .2*k
outs = subset(CPUEindex,LFA==35,c("YEAR","mu"))
oo = as.data.frame(do.call(cbind,with(rmed(l35$YEAR,l35$mu),data.frame(YEAR=yr,running.median=x))))
#commB
require(bio.survey)
require(bio.lobster)
p = bio.lobster::load.environment()
p$libs = NULL
p$yrs = 1970:2023
p1 = p
p$series =c('summer')# p$series =c('georges');p$series =c('fall')
p$years.to.estimate = p$yrs
p$length.based = T
p$by.sex = T
p$size.class = c(83,300)
p$sex = c(1,2)
p$bootstrapped.ci=T
p$strata.files.return=F
p$vessel.correction.fixed=1.2
p$strat = NULL
p$clusters = c( rep( "localhost", 7) )
p$strata.efficiencies = F
p = make.list(list(yrs=p$years.to.estimate),Y=p)
p$define.by.polygons = T
p$lobster.subunits=F
p$area = 'LFA35-38'
p$reweight.strata = T #this subsets
aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
p$by.sex = p$length.based = F
aoutf= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
aoutf = subset(aoutf,yr<1999)
aout = subset(aout,yr>1998)
df = as.data.frame(rbind(aoutf,aout))
df = df[,c('yr','w.Yst','w.ci.Yst.l','w.ci.Yst.u')] #proportion of total weight that is commercial
df$w.Yst[which(df$yr<1999)] <- df$w.Yst[which(df$yr<1999)]*0.746
df$w.ci.Yst.l[which(df$yr<1999)] <- df$w.ci.Yst.l[which(df$yr<1999)]*0.746
df$w.ci.Yst.u[which(df$yr<1999)] <- df$w.ci.Yst.u[which(df$yr<1999)]*0.746
xx = as.data.frame(do.call(cbind,rmed(df$yr,df$w.Yst)))
#relf
a = lobster.db('annual.landings')
b = lobster.db('seasonal.landings')
b$YR = substr(b$SYEAR,6,9)
a = subset(a,YR<1976)
b = subset(b,YR>1975 & YR<=2022)
a$L3538 = rowSums(a[,12:14])
b$L3538 = rowSums(b[,4:6])
c358 = as.data.frame(rbind(a[,c('YR','L3538')],b[,c('YR','L3538')]))
names(c358)[1] = 'yr'
df =merge(df,c358)
df$rL = df$L3538/(df$w.ci.Yst.l+df$L3538)
df$rU =df$L3538/ (df$w.ci.Yst.u+df$L3538)
df$rM = df$L3538/(df$w.Yst+df$L3538)
pp = as.data.frame(do.call(cbind,rmed(df$yr,df$rM)))
#recN
p$length.based = T
p$by.sex = T
p$size.class = c(70,82)
p$sex = c(1,2)
p$bootstrapped.ci=T
p$strata.files.return=F
p$vessel.correction.fixed=1.2
p$strat = NULL
p$clusters = c( rep( "localhost", 7) )
p$strata.efficiencies = F
p = make.list(list(yrs=p$years.to.estimate),Y=p)
p$define.by.polygons = T
p$lobster.subunits=F
p$area = 'LFA35-38'
p$reweight.strata = T #this subsets
recout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
recxx = as.data.frame(do.call(cbind,rmed(recout$yr,recout$n.yst)))
#scallop survey reworked in 2023
lobster.db('scallop')
sc = scallopSurveyIndex(redo=T,size_range = c(70,82), lfa=35)
sc = sc[[1]]
#plotting as per csasdown 4 panel plot
#add in the theme_csas
theme_csas <- function(base_size = 14, base_family = "", text_col = "grey20",
panel_border_col = "grey70") {
half_line <- base_size / 2
theme_light(base_size = base_size, base_family = "") +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.ticks.length = unit(half_line / 2.2, "pt"),
strip.background = element_rect(fill = NA, colour = NA),
strip.text.x = element_text(colour = text_col),
strip.text.y = element_text(colour = text_col),
axis.text = element_text(colour = text_col),
axis.title = element_text(colour = text_col),
legend.title = element_text(colour = text_col, size = rel(0.9)),
panel.border = element_rect(fill = NA, colour = panel_border_col, size = 1),
legend.key.size = unit(0.9, "lines"),
legend.text = element_text(size = rel(0.9), colour = text_col),
legend.key = element_rect(colour = NA, fill = NA),
legend.background = element_rect(colour = NA, fill = NA),
plot.title = element_text(colour = text_col, size = rel(1)),
plot.subtitle = element_text(colour = text_col, size = rel(.85))
)
}
#format from FSAR branch of CSASdown
# Catch and eff
aap <- aaa[nrow(aaa),]
aaa = aaa[1:(nrow(aaa)-1),]
efp = ef[nrow(ef),]
ef = ef[-nrow(ef),]
ymax=5000
scaleright = max(ef$Effort)/ymax
g1 <- ggplot(data = subset(aaa,yr>1990), aes(x = yr,y=LFA35)) +
geom_bar(stat='identity',fill='black') +
geom_bar(data=aap,aes(x=yr,y=LFA35),stat='identity',fill='gray66') +
geom_point(data=efp,aes(x=yr,y=Effort/scaleright),colour='grey66',shape=17,size=2.5)+
geom_line(data=ef,aes(x=yr,y=Effort/scaleright),colour='black',lwd=0.75,linetype='dashed')+
geom_point(data=ef,aes(x=yr,y=Effort/scaleright),colour='black',shape=16,size=2)+
scale_y_continuous(name='Catch (t)', sec.axis= sec_axis(~.*scaleright, name= 'Effort',breaks = seq(0,2000,by=250)))+
labs(x = "Year") +
theme_csas()
# standarzized cpue
#change last point
outs23 = outs[nrow(outs),]
outs22 =outs[1:(nrow(outs)-1),]
g2 <- ggplot(data = outs, aes(x = YEAR)) +
geom_point(data=outs22,aes(y = mu),size=2) +
geom_point(data=outs23,aes(y=mu),shape =17, size = 2)+
geom_line(data=oo,aes(x=YEAR,y=running.median),colour='grey45',lwd=1.25)+
labs(x = "Year", y = "Standardized CPUE") +
geom_hline(yintercept=mean(outs$mu[7:14]) * .4,colour='grey',lwd=1.25,linetype='dashed')+
geom_hline(yintercept=mean(outs$mu[7:14]) * .2,colour='grey',lwd=1.25,linetype='dotted')+
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
###FISHING MORT --- not calculated
g3 <- ggplot(data = outs, aes(x = YEAR)) +
geom_point(aes(y = mu),size=0,colour='white') +
labs(x = "Year", y = "Fishing Mortality") +
annotate("text",x =2014, y = 4.5,label= "Data Not Available", size =5, colour= 'grey')+
theme_csas() +
theme(axis.text.y = element_blank(),axis.text.x = element_blank(),axis.title.y = ggtext::element_markdown())
#g3 = ggplot() + # Draw ggplot2 plot with text only
# annotate("text",
# x = 1,
# y = 1,
## size = 4,
# label = "") +
# theme_void()
g4 <- ggplot(data = recout, aes(x = yr)) +
geom_point(aes(y = n.yst),size=2) +
geom_line(data=recxx,aes(x=yr,y=x),colour='grey45',lwd=1.25)+
labs(x = "Year", y = "Recruit Abundance") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
plot1<-cowplot::plot_grid(g1, g2, g3, g4, ncol = 2, labels = "AUTO", align = "hv")
ggsave("plot1.png")
#second plot
# cpue
ccp = cc[nrow(cc),]
ccf = cc[-nrow(cc),]
g1a <- ggplot(data = ccf, aes(x = yr,y = CPUE)) +
geom_point(size=2)+
geom_point(data=ccp,aes(x=yr,y=CPUE),colour='grey45',shape=17,size=3)+
geom_line(data=cp,aes(x=yr,y=x),colour='grey45',lwd=1.25)+
labs(x = "Year", y = "Raw CPUE") +
theme_csas()
#
g2a <- ggplot(data = sc, aes(x = YEAR,y = as.numeric(Index))) +
geom_point(size=2)+
geom_line(data=sc,aes(x=YEAR,y=Rmed),colour='grey45',lwd=1.25)+
labs(x = "Year", y = "Scallop Survey Recruit Abundance") +
theme_csas()
# relative F
g3a <- ggplot(data = df, aes(x = yr)) +
geom_point(aes(y = rM)) +
geom_line(data=pp,aes(x=yr,y = x), colour = "grey45",lwd=1.25) +
scale_y_continuous(expand = expansion(mult = c(0, 0.02)), limits = c(0, NA)) +
labs(x = "Year", y = "Relative Fishing Mortality") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
g4a <- ggplot(data = df, aes(x = yr)) +
geom_point(aes(y = w.Yst)) +
geom_line(data=xx,aes(x=yr,y = x), colour = "grey45",lwd=1.25) +
scale_y_continuous(expand = expansion(mult = c(0, 0.02)), limits = c(0, NA)) +
labs(x = "Year", y = "Commercial Biomass Index") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
cowplot::plot_grid(g1a, g2a, g3a, g4a, ncol = 2, labels = "AUTO", align = "hv")
saveRDS(list(aaa,aap,ef,efp,outs,recout,cc,sc,df),file=file.path(project.datadirectory('bio.lobster'),'data','LFA35FSARindices.rds'))
################FRENCH ##################################
# Catch and eff
aap <- aaa[nrow(aaa),]
aaa = aaa[1:(nrow(aaa)-1),]
efp = ef[nrow(ef),]
ef = ef[-nrow(ef),]
ymax=5000
scaleright = max(ef$Effort)/ymax
g1 <- ggplot(data = subset(aaa,yr>1990), aes(x = yr,y=LFA35)) +
geom_bar(stat='identity',fill='black') +
geom_bar(data=aap,aes(x=yr,y=LFA35),stat='identity',fill='gray66') +
geom_point(data=efp,aes(x=yr,y=Effort/scaleright),colour='grey66',shape=17,size=2.5)+
geom_line(data=ef,aes(x=yr,y=Effort/scaleright),colour='black',lwd=0.75,linetype='dashed')+
geom_point(data=ef,aes(x=yr,y=Effort/scaleright),colour='black',shape=16,size=2)+
scale_y_continuous(name='Débarquements (t)', sec.axis= sec_axis(~.*scaleright, name= 'Effort (milliers de casiers levés)',breaks = seq(0,2000,by=250)))+
labs(x = "Année") +
theme_csas()
# standarzized cpue
#change last point
outs23 = outs[nrow(outs),]
outs22 =outs[1:(nrow(outs)-1),]
g2 <- ggplot(data = outs, aes(x = YEAR)) +
geom_point(data=outs22,aes(y = mu),size=2) +
geom_point(data=outs23,aes(y=mu),shape =17, size = 2)+
geom_line(data=oo,aes(x=YEAR,y=running.median),colour='grey45',lwd=1.25)+
labs(x = "Année", y = "CPUE Standardisé (kg/casier levé)") +
geom_hline(yintercept=mean(outs$mu[7:14]) * .4,colour='grey',lwd=1.25,linetype='dashed')+
geom_hline(yintercept=mean(outs$mu[7:14]) * .2,colour='grey',lwd=1.25,linetype='dotted')+
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
###FISHING MORT --- not calculated
g3 <- ggplot(data = outs, aes(x = YEAR)) +
geom_point(aes(y = mu),size=0,colour='white') +
labs(x = "Année", y = "Mortalité par pêche") +
annotate("text",x =2014, y = 4.5,label= "Données Non Disponibles", size =5, colour= 'grey')+
theme_csas() +
theme(axis.text.y = element_blank(),axis.text.x = element_blank(),axis.title.y = ggtext::element_markdown())
g4 <- ggplot(data = recout, aes(x = yr)) +
geom_point(aes(y = n.yst),size=2) +
geom_line(data=recxx,aes(x=yr,y=x),colour='grey45',lwd=1.25)+
labs(x = "Année", y = "Abondance des recrues") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
plot1<-cowplot::plot_grid(g1, g2, g3, g4, ncol = 2, labels = "AUTO", align = "hv")
#second plot
# cpue
ccp = cc[nrow(cc),]
ccf = cc[-nrow(cc),]
g1a <- ggplot(data = ccf, aes(x = yr,y = CPUE)) +
geom_point(size=2)+
geom_point(data=ccp,aes(x=yr,y=CPUE),colour='grey66',shape=17,size=3)+
geom_line(data=cp,aes(x=yr,y=x),colour='gray45',lwd=1.25)+
labs(x = "Année", y = "CPUE brut") +
theme_csas()
#
g2a <- ggplot(data = sc, aes(x = YEAR,y = as.numeric(Index))) +
geom_point(size=2)+
geom_line(data=sc,aes(x=YEAR,y=Rmed),colour='gray45',lwd=1.25)+
labs(x = "Année", y = "Abondance des recrutés du relevé des pétoncles") +
theme_csas()
# relative F
g3a <- ggplot(data = df, aes(x = yr)) +
geom_point(aes(y = rM)) +
geom_line(data=pp,aes(x=yr,y = x), colour = "grey45",lwd=1.25) +
scale_y_continuous(expand = expansion(mult = c(0, 0.02)), limits = c(0, NA)) +
labs(x = "Année", y = "Mortalité par pêche relative") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
g4a <- ggplot(data = df, aes(x = yr)) +
geom_point(aes(y = w.Yst)) +
geom_line(data=xx,aes(x=yr,y = x), colour = "grey45",lwd=1.25) +
scale_y_continuous(expand = expansion(mult = c(0, 0.02)), limits = c(0, NA)) +
labs(x = "Année", y = "Biomasse commerciale") +
theme_csas() +
theme(axis.title.y = ggtext::element_markdown())
cowplot::plot_grid(g1a, g2a, g3a, g4a, ncol = 2, labels = "AUTO", align = "hv")
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