In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
setwd("C:/Users/HowseVJ/Documents/GitHub/bio.lobster/inst/Updates/LFA35-38/LFA35_Update Markdown")
require(rgdal)
require(devtools)
require(roxygen2)
require(geosphere)
require(SpatialHub)
require(lubridate)
require(bio.utilities)
require(bio.lobster)
require(rstanarm)
require(rlang)
require(glue)
require(PBSmapping)
p = bio.lobster::load.environment()
la()
assessment.year = 2022 ##Check Year
p$syr = 1989
p$yrs = p$syr:assessment.year
figdir = file.path("LFA35_Update Markdown")
p$lfas = c("35") # specify lfa
logs=lobster.db("process.logs")
land =lobster.db("annual.landings")
Sland = lobster.db('seasonal.landings')
Analysis and Response
Indicators of Stock Status
The stock status of the Lobster in LFA 35 is assessed using primary, secondary, and contextual
indicators. This update includes the primary indicator that is used to define stock status in
relation to reference points defined in Cook et al. (In press.1) and secondary indicators that
display time-series trends but do not have reference points. The data sources available for
establishing indicators for LFA 35 come from both fishery-dependent and fishery-independent
data sources. Fishery-dependent data consist of commercial logbooks that report information on
date, location (grid), effort, and estimated catch. The fishery-independent data sources are from
the DFO Maritimes Region Summer Research Vessel Survey (herein RV survey).
Something about the challenges with Survey?
Primary Indicator
In LFA 35, there is one primary indicator for stock status that describes the time-series trends
relative to reference points. The primary indicator for describing stock status is standardized
commercial Catch Per Unit Effort (CPUE). There is currently no primary indicator of fishing
pressure or exploitation.
Catch Per Unit Effort
Commercial catch rates are a preferred indicator over landings, as they are standardized to
account for the level of fishing effort. This is especially important in effort-controlled fisheries.
The commercial fishing data used in the estimation of catch rates were obtained from mandatory logbooks that were put in place in the mid-2000s.
It has been well documented that trap-based catch rates vary throughout a fishing season due to factors other than available
biomass. These factors include fishing behavior, localized depletion, and environmental
conditions (Drinkwater et al. 2006). In an effort to account for these factors, CPUE data were
standardized through generalized linear modelling with explanatory variables of Year, Day of
Season, Temperature, and the interaction between Day of Season and Temperature. Year was
treated as a factor rather than a continuous variable to reduce any constraints from inter-annual
variability.
Model predictions were made for day 1 of the fishing season at the median day 1 temperature
across all years. The available time series covers the current high-productivity period and a
lower-productivity period from 2006–2010 (Figure 2). The median of the high-productivity period
(2011–2018) was used as the proxy for the biomass at carrying capacity (K). Following the
recommendations of DFO (2009), the Upper Stock Reference (USR) and Limit Reference Point
(LRP) were set to 40% and 20% of the K proxy. A 3-year running median is used to compare
the standardized CPUE to the USR and LRP. This value will dampen the impact of any
anomalous years that may occur due to factors outside of changes in abundance.
logs=lobster.db("process.logs")
TempModelling = TempModel( annual.by.area=F, redo.data=F)
CPUE.data<-CPUEModelData(p,redo=T,TempModelling)
mf1 = formula(logWEIGHT ~ fYEAR + DOS + TEMP + DOS * TEMP)
CPUE.data<- CPUEModelData(p,redo=F)
t=with(subset(CPUE.data,DOS==1),tapply(TEMP,LFA,mean))
pData=list()
CPUEModelResults = list()
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$YEAR %in% 2011:2018)])
usr = .4*k
lrp = .2*k
plot(l35$YEAR,l35$mu,xlab='Year',ylab=' Standardized CPUE (kg/TH)',type='p',pch=16,ylim=c(0,6),xlim=c(2005,2022))
running.median = with(rmed(l35$YEAR,l35$mu),data.frame(YEAR=yr,running.median=x))
l35=merge(l35,running.median,all=T)
lines(l35$YEAR,l35$running.median,col='blue',lty=1,lwd=3)
points(l35$YEAR[nrow(l35)],l35$mu[nrow(l35)], type='b', pch=24,bg='blue')
abline(h=usr,col='green',lwd=2,lty=2)
abline(h=lrp,col='red',lwd=2,lty=3)
dev.off()
The trend in CPUE indicates that an increase in stock biomass occurred between 2005 and
2012 (Figure ##). The CPUE has remained high (more than twice the USR) since 2011. The
3-year running median for CPUE for the 2022 season is ## kg per Trap Haul (kg/TH), which is
above the USR (1.97 kg/TH) and LRP (0.98 kg/TH).
The CPUE for ### is preliminary due to outstanding logs; as of ### the monthly
reporting rate was between ##% to ##% by month.
Secondary Indicators
Secondary indicators represent time-series trends that are tracked individually, without defined
reference points. The secondary indicators for LFA 35 include the LFA-specific landings and
total effort, as well as recruit abundance, commercial biomass, and relative fishing mortality
estimates from the RV survey of the Bay of Fundy region (Strata 484, 490–495). Scallop survey
recruit abundance is not included in this update as the survey was not conducted in 2020, and it
was not yet completed for the 2021 season at the time of this update.
Landings and Effort
Commercial landings are related to population biomass, as fishery controls are input- (effort
controls) rather than output-based (total allowable catch). There are many factors that can affect
this relationship, including changes in fishing effort, catchability (including the effects of
environment, gear efficiency), Lobster size distribution, and the spatial overlap between
distribution of Lobster and effort.
Fishing effort, recorded as the number of Trap Hauls (THs), in the Lobster fishery is controlled
by fishing season length, trap limits, and limited number of fishing licences. Consequently, there
is a maximum fishing effort that can be deployed; however, this maximum is never met because
factors such as weather conditions, seasonally variable catch rates, and fishing partnerships
limit the total number of THs. Total fishing effort is calculated from mandatory logbooks.
Figure ## has been updated to include the preliminary data for the 2020–2021 fishing season
and effort has been corrected to address an error in the 2020 stock status update (DFO 2021b).
Effort in the corresponding figure was calculated incorrectly due to the use of the wrong
calibration.
a=lobster.db('process.logs')
a = subset(a,LFA==35)
b=aggregate(cbind(NUM_OF_TRAPS,WEIGHT_KG)~SYEAR,data=a,FUN=sum)
b$CPUE=b$WEIGHT_KG/b$NUM_OF_TRAPS
h=lobster.db('seasonal.landings.redo')
h=lobster.db('seasonal.landings')
h
h=h[,c('SYEAR','LFA35')]
h
h$SYEAR=1976:2022
merge(b,h)
g=merge(b,h)
g$EFF = g$LFA35/g$CPUE
par(mar=c(5.1, 4.1, 4.1, 5.1),las=1)
plot(g$SYEAR,g$LFA35,xlab='Year',ylab='Landings(t)',type='h',ylim=c(0,max(g$LFA35)*1.2),pch=15,col='grey',lwd=10,lend=3)
lines(g$SYEAR[nrow(g)],g$LFA35[nrow(g)],type='h',pch=21,col='steelblue4',lwd=10,lend=3)
par(new=T)
plot(g$SYEAR,g$EFF,ylab='',xlab='', type='b', pch=16, axes=F,ylim=c(0,max(g$EFF,na.rm=T)))
points(g$SYEAR[nrow(g)],g$EFF[nrow(g)], type='b', pch=24,bg='black')
axis(4)
mtext("Effort ('000s Trap Hauls)", 4, 3.5, outer = F,las=0)
dev.off()
subsamp<-g[g$SYEAR >2009 & g$SYEAR <2023,]
range(subsamp$LFA35)
median(subsamp$LFA35)
DFO RV Survey Commercial Biomass and Recruit Abundance
Despite strata boundaries having significant overlap with LFA 35–38, there were few (< 20 per year) sets within each LFA, suggesting that the value of indicators derived from these data was limited. Extending the commercial survey biomass index to years prior to 1999, when size information was not collected, was performed using the ratio of commercial to total biomass estimated between 1999 and 2018 (0.746). The time series of commercial biomass showed a pulsed increase from 2000 to 2004, with a variable, but increasing, trend from 2010 to 2018; however, survey catch rates in the last two years were the lowest in the last 10 years (Figure ???). The size-at-maturity for Lobster in the Bay of Fundy is substantially greater than the MLS, and, as such, the commercial biomass available post-fishery will constitute those individuals entering the spawning population in the upcoming year.
Data from the 2021 RV survey were not available, Figures 6, 7 and 8 only include results up to the end of the 2020 survey.
RV survey recruit abundance (70–82 mm CL) exhibits a similar pattern to the total abundance, with increases from 2010 to 2013, followed by variable catch rates at a substantially higher level than has been observed in the time series (Figure 7).
Relative Fishing Mortality
Relative fishing mortality (relF) uses both the RV survey commercial biomass estimates and landings to show the changes in removals (Ct) relative to the survey indices (It). As the RV survey occurs after the fishery is complete, the estimation of relF was adjusted by the landings as:
(ADD EQUATION)
Assuming that survey catchabilities were constant and the index of commercial biomass was proportional to true commercial biomass, relF is an index of fishing mortality (F).
The estimates of relF reflect the variation in the commercial biomass index, with decreases between the late 1990s and early 2000s ,
(ADD Mention of the decrease in the 1980s)
increases to 2010, then decreases to 2013 with variable, but low, estimates of relF since 2013 (Figure 6). Tracking the relF for the Bay of Fundy provides a depiction of the patterns observed across the larger area.
require(bio.survey)
require(bio.lobster)
p = bio.lobster::load.environment()
p$libs = NULL
ff = "LFA35-38Assessment"
fp1 = file.path(project.datadirectory('bio.lobster'),"analysis",ff)
fpf1 = file.path(project.figuredirectory('bio.lobster'),ff)
dir.create(fpf1,showWarnings=F)
dir.create(fp1,showWarnings=F)
p$yrs = 1970:2022
p1 = p
stratifiedAnalysesCommercial = function( p=p1, survey,lfa, fpf = fpf1, fp = fp1,f=ff,wd=10,ht=8){
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$area = lfa
p$reweight.strata = T #this subsets
aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
write.csv(aout,file=file.path(fpf, paste(lfa,'DFOCommB.csv')))
p$add.reference.lines = F
p$time.series.start.year = p$years.to.estimate[1]
p$time.series.end.year = p$years.to.estimate[length(p$years.to.estimate)]
p$metric = 'weights' #weights
p$measure = 'stratified.total' #'stratified.total'
p$figure.title = ""
p$reference.measure = 'median' # mean, geomean
p$file.name = file.path(f,paste(lfa,'DFOrestratifiedtotalweightscommercial.png',sep=""))
p$y.maximum = NULL # NULL # if ymax is too high for one year
p$show.truncated.weights = F #if using ymax and want to show the weights that are cut off as values on figure
p$legend = FALSE
p$running.median = T
p$running.length = 3
p$running.mean = F #can only have rmedian or rmean
p$error.polygon=F
p$error.bars=T
require(bio.lobster)
p$ylim2 = NULL
xx = aggregate(ObsLobs~yr,data=aout,FUN=sum)
names(xx) =c('x','y')
p$ylim=NULL
ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht)
return(aout)
}
aout = stratifiedAnalysesCommercial(survey='DFO',lfa='LFA35-38')
write.csv(aout,file.path(fp1,'LFA3538CommercialB.csv'))
require(bio.survey)
require(bio.lobster)
require(lubridate)
require(devtools)
require(bio.utilities)
require(PBSmapping)
la()
p = bio.lobster::load.environment()
p$libs = NULL
ff = "LFA35-38Assessment"
fp1 = file.path(project.datadirectory('bio.lobster'),"analysis",ff)
fpf1 = file.path(project.figuredirectory('bio.lobster'),ff)
dir.create(fpf1,showWarnings=F)
dir.create(fp1,showWarnings=F)
p1 = p
p1$yrs = 1970:2022
stratifiedAnalyses = function(p=p1, survey,lfa, fpf = fpf1, fp = fp1,f=ff,wd=10,ht=8){
p$series =c('summer')
p$define.by.polygons = T
p$lobster.subunits=F
p$area = lfa
p$years.to.estimate = p$yrs[-1]
p$length.based = F
p$by.sex = F
p$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$reweight.strata = T #this subsets
aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
write.csv(aout,file=file.path(fpf, paste(lfa,'DFOtotalabund.csv')))
p$add.reference.lines = F
p$time.series.start.year = p$years.to.estimate[1]
p$time.series.end.year = p$years.to.estimate[length(p$years.to.estimate)]
p$metric = 'numbers' #weights
p$measure = 'stratified.mean' #'stratified.total'
p$figure.title = ""
p$reference.measure = 'median' # mean, geomean
p$file.name = file.path(f,paste(lfa,'DFOrestratifiednumbers.png',sep=""))
p$y.maximum = NULL # NULL # if ymax is too high for one year
p$show.truncated.numbers = F #if using ymax and want to show the numbers that are cut off as values on figure
p$legend = FALSE
p$running.median = T
p$running.length = 3
p$running.mean = F #can only have rmedian or rmean
p$error.polygon=F
p$error.bars=T
p$ylim=NULL
if(lfa == 'LFA35-38') p$ylim=c(0,150)
p$box=T
p$file.name = file.path(f,paste(lfa,'DFOrestratifiednumbersNOY.png', sep=""))
ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht)
p$box=T
p$ylim=NULL
p$metric = 'weights'
p$file.name = file.path(f,paste(lfa,'DFOrestratifiedweightsNOY.png',sep=""))
ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht)
p$box=NULL
p$ylim=NULL
p$file.name = file.path(f,paste(lfa,'DFOrestratifiedDWAO.png',sep=""))
p$metric = 'dwao'
ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht)
p$file.name = file.path(f,paste(lfa,'DFOrestratifiedgini.png',sep=""))
p$metric = 'gini'
p$ylim =c(0,1)
ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht)
p$ylim = NULL
return(aout)
}
a = stratifiedAnalyses(survey='DFO',lfa='LFA35-38')
require(bio.survey)
require(bio.lobster)
require(lubridate)
require(devtools)
require(bio.utilities)
require(PBSmapping)
la()
p = bio.lobster::load.environment()
p$libs = NULL
ff = "LFA35-38Assessment"
fp1 = file.path(project.datadirectory('bio.lobster'),"analysis",ff)
fpf1 = file.path(project.figuredirectory('bio.lobster'),ff)
dir.create(fpf1,showWarnings=F)
dir.create(fp1,showWarnings=F)
p1 = p
p1$yrs = 1970:2022
stratifiedAnalysesRecruits = function(p=p1, survey,lfa, fpf = fpf1, fp = fp1,f=ff,wd=10,ht=8){
p$series =c('summer')# p$series =c('georges');p$series =c('fall')
p$area = lfa
p$years.to.estimate = p$yrs
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$lobster.subunits=F
p$reweight.strata = T #this subsets
aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
write.csv(aout,file=file.path(fpf, paste(lfa,'DFOrecruits.csv')))
p$add.reference.lines = F
p$time.series.start.year = p$years.to.estimate[1]
p$time.series.end.year = p$years.to.estimate[length(p$years.to.estimate)]
p$metric = 'numbers' #numbers
p$measure = 'stratified.mean' #'stratified.total'
p$figure.title = ""
p$reference.measure = 'median' # mean, geomean
p$file.name = file.path(f,paste(lfa,'DFOrestratifiednumbersrecruits.png',sep=""))
p$y.maximum = NULL # NULL # if ymax is too high for one year
p$show.truncated.numbers = F #if using ymax and want to show the numbers that are cut off as values on figure
p$legend = FALSE
p$running.median = T
p$running.length = 3
p$running.mean = F #can only have rmedian or rmean
p$error.polygon=F
p$error.bars=T
if(lfa == 'LFA35-38') p$ylim = c(0,80)
p$file.name = file.path(f,paste(lfa,'NOYDFOrestratifiednumbersrecruits.png',sep=""))
ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht)
}
stratifiedAnalysesRecruits(survey='DFO',lfa='LFA35-38')
require(bio.lobster)
require(PBSmapping)
a = lobster.db('annual.landings')
b = lobster.db('seasonal.landings')
ff = "LFA35-38Assessment"
b$YR = substr(b$SYEAR,6,9)
a = subset(a,YR<1976)
b = subset(b,YR>1975 & YR<=2022)
fpf1 = file.path(project.figuredirectory('bio.lobster'),ff)
fp1 = file.path(project.datadirectory('bio.lobster'),"analysis",ff)
LFA = c('LFA35','LFA36','LFA38')
for(i in LFA){
aa = a[,c('YR',i)]
bb = b[,c('YR',i)]
aa = (rbind(aa,bb))
aa = aa[order(aa$YR),]
file.name = paste('Landings',i,'.png',sep="")
png(file=file.path(fpf1,'LandingsL3538.png'),units='in',width=15,height=12,pointsize=18, res=300,type='cairo')
plot(aa$YR,aa[,i],type='h',lwd=4,col='black',xlab='Year',ylab='Landings (t)')
lines(aa$YR,runmed(aa[,i],3),col='salmon',lwd=3)
dev.off()
}
df2 = read.csv(file.path(fpf1,'LFA35-38 DFOCommB.csv'))
df = read.csv(file.path(fpf1,'LFA35-38 DFOtotalabund.csv'))
df = subset(df,yr<1999)
df2 = subset(df2,yr>1998)
df = as.data.frame(rbind(df,df2))
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
with(df,plot(yr,w.Yst,pch=1,xlab='Year',ylab='Commerical Biomass (t)',ylim=c(0,9500)))
with(df,arrows(yr,y0=w.ci.Yst.u,y1=w.ci.Yst.l, length=0))
with(subset(df,yr>1998),points(yr,w.Yst,pch=16))
xx = rmed(df$yr,df$w.Yst)
xx = as.data.frame(do.call(cbind,xx))
with(subset(xx,yr<1999),lines(yr,x,col='salmon',lwd=1))
with(subset(xx,yr>1998),lines(yr,x,col='salmon',lwd=3))
dev.off()
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)
png(file=file.path(fpf1,'LFA3538RelFDFO.png'),units='in',width=10,height=8,pointsize=18, res=300,type='cairo')
plot(df$yr,df$rM,type='p',pch=16,col='black',xlab='Year',ylab='Relative F')
arrows(df$yr,y0 = df$rL,y1 = df$rU,length=0)
with(rmed(df$yr,df$rM),lines(yr,x,col='salmon',lwd=3))
dev.off()
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
R Package Documentation
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setwd("C:/Users/HowseVJ/Documents/GitHub/bio.lobster/inst/Updates/LFA35-38/LFA35_Update Markdown") require(rgdal) require(devtools) require(roxygen2) require(geosphere) require(SpatialHub) require(lubridate) require(bio.utilities) require(bio.lobster) require(rstanarm) require(rlang) require(glue) require(PBSmapping) p = bio.lobster::load.environment() la() assessment.year = 2022 ##Check Year p$syr = 1989 p$yrs = p$syr:assessment.year figdir = file.path("LFA35_Update Markdown") p$lfas = c("35") # specify lfa logs=lobster.db("process.logs") land =lobster.db("annual.landings") Sland = lobster.db('seasonal.landings')
Analysis and Response
Indicators of Stock Status
The stock status of the Lobster in LFA 35 is assessed using primary, secondary, and contextual indicators. This update includes the primary indicator that is used to define stock status in relation to reference points defined in Cook et al. (In press.1) and secondary indicators that display time-series trends but do not have reference points. The data sources available for establishing indicators for LFA 35 come from both fishery-dependent and fishery-independent data sources. Fishery-dependent data consist of commercial logbooks that report information on date, location (grid), effort, and estimated catch. The fishery-independent data sources are from the DFO Maritimes Region Summer Research Vessel Survey (herein RV survey).
Something about the challenges with Survey?
Primary Indicator
In LFA 35, there is one primary indicator for stock status that describes the time-series trends relative to reference points. The primary indicator for describing stock status is standardized commercial Catch Per Unit Effort (CPUE). There is currently no primary indicator of fishing pressure or exploitation.
Catch Per Unit Effort
Commercial catch rates are a preferred indicator over landings, as they are standardized to account for the level of fishing effort. This is especially important in effort-controlled fisheries. The commercial fishing data used in the estimation of catch rates were obtained from mandatory logbooks that were put in place in the mid-2000s. It has been well documented that trap-based catch rates vary throughout a fishing season due to factors other than available biomass. These factors include fishing behavior, localized depletion, and environmental conditions (Drinkwater et al. 2006). In an effort to account for these factors, CPUE data were standardized through generalized linear modelling with explanatory variables of Year, Day of Season, Temperature, and the interaction between Day of Season and Temperature. Year was treated as a factor rather than a continuous variable to reduce any constraints from inter-annual variability.
Model predictions were made for day 1 of the fishing season at the median day 1 temperature across all years. The available time series covers the current high-productivity period and a lower-productivity period from 2006–2010 (Figure 2). The median of the high-productivity period (2011–2018) was used as the proxy for the biomass at carrying capacity (K). Following the recommendations of DFO (2009), the Upper Stock Reference (USR) and Limit Reference Point (LRP) were set to 40% and 20% of the K proxy. A 3-year running median is used to compare the standardized CPUE to the USR and LRP. This value will dampen the impact of any anomalous years that may occur due to factors outside of changes in abundance.
logs=lobster.db("process.logs") TempModelling = TempModel( annual.by.area=F, redo.data=F) CPUE.data<-CPUEModelData(p,redo=T,TempModelling) mf1 = formula(logWEIGHT ~ fYEAR + DOS + TEMP + DOS * TEMP) CPUE.data<- CPUEModelData(p,redo=F) t=with(subset(CPUE.data,DOS==1),tapply(TEMP,LFA,mean)) pData=list() CPUEModelResults = list() 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 plot(l35$YEAR,l35$mu,xlab='Year',ylab=' Standardized CPUE (kg/TH)',type='p',pch=16,ylim=c(0,6),xlim=c(2005,2022)) running.median = with(rmed(l35$YEAR,l35$mu),data.frame(YEAR=yr,running.median=x)) l35=merge(l35,running.median,all=T) lines(l35$YEAR,l35$running.median,col='blue',lty=1,lwd=3) points(l35$YEAR[nrow(l35)],l35$mu[nrow(l35)], type='b', pch=24,bg='blue') abline(h=usr,col='green',lwd=2,lty=2) abline(h=lrp,col='red',lwd=2,lty=3) dev.off()
The trend in CPUE indicates that an increase in stock biomass occurred between 2005 and 2012 (Figure ##). The CPUE has remained high (more than twice the USR) since 2011. The 3-year running median for CPUE for the 2022 season is ## kg per Trap Haul (kg/TH), which is above the USR (1.97 kg/TH) and LRP (0.98 kg/TH).
The CPUE for ### is preliminary due to outstanding logs; as of ### the monthly reporting rate was between ##% to ##% by month.
Secondary Indicators
Secondary indicators represent time-series trends that are tracked individually, without defined reference points. The secondary indicators for LFA 35 include the LFA-specific landings and total effort, as well as recruit abundance, commercial biomass, and relative fishing mortality estimates from the RV survey of the Bay of Fundy region (Strata 484, 490–495). Scallop survey recruit abundance is not included in this update as the survey was not conducted in 2020, and it was not yet completed for the 2021 season at the time of this update.
Landings and Effort
Commercial landings are related to population biomass, as fishery controls are input- (effort controls) rather than output-based (total allowable catch). There are many factors that can affect this relationship, including changes in fishing effort, catchability (including the effects of environment, gear efficiency), Lobster size distribution, and the spatial overlap between distribution of Lobster and effort.
Fishing effort, recorded as the number of Trap Hauls (THs), in the Lobster fishery is controlled by fishing season length, trap limits, and limited number of fishing licences. Consequently, there is a maximum fishing effort that can be deployed; however, this maximum is never met because factors such as weather conditions, seasonally variable catch rates, and fishing partnerships limit the total number of THs. Total fishing effort is calculated from mandatory logbooks. Figure ## has been updated to include the preliminary data for the 2020–2021 fishing season and effort has been corrected to address an error in the 2020 stock status update (DFO 2021b). Effort in the corresponding figure was calculated incorrectly due to the use of the wrong calibration.
a=lobster.db('process.logs') a = subset(a,LFA==35) b=aggregate(cbind(NUM_OF_TRAPS,WEIGHT_KG)~SYEAR,data=a,FUN=sum) b$CPUE=b$WEIGHT_KG/b$NUM_OF_TRAPS h=lobster.db('seasonal.landings.redo') h=lobster.db('seasonal.landings') h h=h[,c('SYEAR','LFA35')] h h$SYEAR=1976:2022 merge(b,h) g=merge(b,h) g$EFF = g$LFA35/g$CPUE par(mar=c(5.1, 4.1, 4.1, 5.1),las=1) plot(g$SYEAR,g$LFA35,xlab='Year',ylab='Landings(t)',type='h',ylim=c(0,max(g$LFA35)*1.2),pch=15,col='grey',lwd=10,lend=3) lines(g$SYEAR[nrow(g)],g$LFA35[nrow(g)],type='h',pch=21,col='steelblue4',lwd=10,lend=3) par(new=T) plot(g$SYEAR,g$EFF,ylab='',xlab='', type='b', pch=16, axes=F,ylim=c(0,max(g$EFF,na.rm=T))) points(g$SYEAR[nrow(g)],g$EFF[nrow(g)], type='b', pch=24,bg='black') axis(4) mtext("Effort ('000s Trap Hauls)", 4, 3.5, outer = F,las=0) dev.off() subsamp<-g[g$SYEAR >2009 & g$SYEAR <2023,] range(subsamp$LFA35) median(subsamp$LFA35)
DFO RV Survey Commercial Biomass and Recruit Abundance
Despite strata boundaries having significant overlap with LFA 35–38, there were few (< 20 per year) sets within each LFA, suggesting that the value of indicators derived from these data was limited. Extending the commercial survey biomass index to years prior to 1999, when size information was not collected, was performed using the ratio of commercial to total biomass estimated between 1999 and 2018 (0.746). The time series of commercial biomass showed a pulsed increase from 2000 to 2004, with a variable, but increasing, trend from 2010 to 2018; however, survey catch rates in the last two years were the lowest in the last 10 years (Figure ???). The size-at-maturity for Lobster in the Bay of Fundy is substantially greater than the MLS, and, as such, the commercial biomass available post-fishery will constitute those individuals entering the spawning population in the upcoming year. Data from the 2021 RV survey were not available, Figures 6, 7 and 8 only include results up to the end of the 2020 survey.
RV survey recruit abundance (70–82 mm CL) exhibits a similar pattern to the total abundance, with increases from 2010 to 2013, followed by variable catch rates at a substantially higher level than has been observed in the time series (Figure 7).
Relative Fishing Mortality
Relative fishing mortality (relF) uses both the RV survey commercial biomass estimates and landings to show the changes in removals (Ct) relative to the survey indices (It). As the RV survey occurs after the fishery is complete, the estimation of relF was adjusted by the landings as:
(ADD EQUATION)
Assuming that survey catchabilities were constant and the index of commercial biomass was proportional to true commercial biomass, relF is an index of fishing mortality (F). The estimates of relF reflect the variation in the commercial biomass index, with decreases between the late 1990s and early 2000s ,
(ADD Mention of the decrease in the 1980s)
increases to 2010, then decreases to 2013 with variable, but low, estimates of relF since 2013 (Figure 6). Tracking the relF for the Bay of Fundy provides a depiction of the patterns observed across the larger area.
require(bio.survey) require(bio.lobster) p = bio.lobster::load.environment() p$libs = NULL ff = "LFA35-38Assessment" fp1 = file.path(project.datadirectory('bio.lobster'),"analysis",ff) fpf1 = file.path(project.figuredirectory('bio.lobster'),ff) dir.create(fpf1,showWarnings=F) dir.create(fp1,showWarnings=F) p$yrs = 1970:2022 p1 = p stratifiedAnalysesCommercial = function( p=p1, survey,lfa, fpf = fpf1, fp = fp1,f=ff,wd=10,ht=8){ p$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 = lfa p$reweight.strata = T #this subsets aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p) write.csv(aout,file=file.path(fpf, paste(lfa,'DFOCommB.csv'))) p$add.reference.lines = F p$time.series.start.year = p$years.to.estimate[1] p$time.series.end.year = p$years.to.estimate[length(p$years.to.estimate)] p$metric = 'weights' #weights p$measure = 'stratified.total' #'stratified.total' p$figure.title = "" p$reference.measure = 'median' # mean, geomean p$file.name = file.path(f,paste(lfa,'DFOrestratifiedtotalweightscommercial.png',sep="")) p$y.maximum = NULL # NULL # if ymax is too high for one year p$show.truncated.weights = F #if using ymax and want to show the weights that are cut off as values on figure p$legend = FALSE p$running.median = T p$running.length = 3 p$running.mean = F #can only have rmedian or rmean p$error.polygon=F p$error.bars=T require(bio.lobster) p$ylim2 = NULL xx = aggregate(ObsLobs~yr,data=aout,FUN=sum) names(xx) =c('x','y') p$ylim=NULL ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht) return(aout) } aout = stratifiedAnalysesCommercial(survey='DFO',lfa='LFA35-38') write.csv(aout,file.path(fp1,'LFA3538CommercialB.csv'))
require(bio.survey) require(bio.lobster) require(lubridate) require(devtools) require(bio.utilities) require(PBSmapping) la() p = bio.lobster::load.environment() p$libs = NULL ff = "LFA35-38Assessment" fp1 = file.path(project.datadirectory('bio.lobster'),"analysis",ff) fpf1 = file.path(project.figuredirectory('bio.lobster'),ff) dir.create(fpf1,showWarnings=F) dir.create(fp1,showWarnings=F) p1 = p p1$yrs = 1970:2022 stratifiedAnalyses = function(p=p1, survey,lfa, fpf = fpf1, fp = fp1,f=ff,wd=10,ht=8){ p$series =c('summer') p$define.by.polygons = T p$lobster.subunits=F p$area = lfa p$years.to.estimate = p$yrs[-1] p$length.based = F p$by.sex = F 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$years.to.estimate),Y=p) p$reweight.strata = T #this subsets aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p) write.csv(aout,file=file.path(fpf, paste(lfa,'DFOtotalabund.csv'))) p$add.reference.lines = F p$time.series.start.year = p$years.to.estimate[1] p$time.series.end.year = p$years.to.estimate[length(p$years.to.estimate)] p$metric = 'numbers' #weights p$measure = 'stratified.mean' #'stratified.total' p$figure.title = "" p$reference.measure = 'median' # mean, geomean p$file.name = file.path(f,paste(lfa,'DFOrestratifiednumbers.png',sep="")) p$y.maximum = NULL # NULL # if ymax is too high for one year p$show.truncated.numbers = F #if using ymax and want to show the numbers that are cut off as values on figure p$legend = FALSE p$running.median = T p$running.length = 3 p$running.mean = F #can only have rmedian or rmean p$error.polygon=F p$error.bars=T p$ylim=NULL if(lfa == 'LFA35-38') p$ylim=c(0,150) p$box=T p$file.name = file.path(f,paste(lfa,'DFOrestratifiednumbersNOY.png', sep="")) ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht) p$box=T p$ylim=NULL p$metric = 'weights' p$file.name = file.path(f,paste(lfa,'DFOrestratifiedweightsNOY.png',sep="")) ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht) p$box=NULL p$ylim=NULL p$file.name = file.path(f,paste(lfa,'DFOrestratifiedDWAO.png',sep="")) p$metric = 'dwao' ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht) p$file.name = file.path(f,paste(lfa,'DFOrestratifiedgini.png',sep="")) p$metric = 'gini' p$ylim =c(0,1) ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht) p$ylim = NULL return(aout) } a = stratifiedAnalyses(survey='DFO',lfa='LFA35-38')
require(bio.survey) require(bio.lobster) require(lubridate) require(devtools) require(bio.utilities) require(PBSmapping) la() p = bio.lobster::load.environment() p$libs = NULL ff = "LFA35-38Assessment" fp1 = file.path(project.datadirectory('bio.lobster'),"analysis",ff) fpf1 = file.path(project.figuredirectory('bio.lobster'),ff) dir.create(fpf1,showWarnings=F) dir.create(fp1,showWarnings=F) p1 = p p1$yrs = 1970:2022 stratifiedAnalysesRecruits = function(p=p1, survey,lfa, fpf = fpf1, fp = fp1,f=ff,wd=10,ht=8){ p$series =c('summer')# p$series =c('georges');p$series =c('fall') p$area = lfa p$years.to.estimate = p$yrs p$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$reweight.strata = T #this subsets aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p) write.csv(aout,file=file.path(fpf, paste(lfa,'DFOrecruits.csv'))) p$add.reference.lines = F p$time.series.start.year = p$years.to.estimate[1] p$time.series.end.year = p$years.to.estimate[length(p$years.to.estimate)] p$metric = 'numbers' #numbers p$measure = 'stratified.mean' #'stratified.total' p$figure.title = "" p$reference.measure = 'median' # mean, geomean p$file.name = file.path(f,paste(lfa,'DFOrestratifiednumbersrecruits.png',sep="")) p$y.maximum = NULL # NULL # if ymax is too high for one year p$show.truncated.numbers = F #if using ymax and want to show the numbers that are cut off as values on figure p$legend = FALSE p$running.median = T p$running.length = 3 p$running.mean = F #can only have rmedian or rmean p$error.polygon=F p$error.bars=T if(lfa == 'LFA35-38') p$ylim = c(0,80) p$file.name = file.path(f,paste(lfa,'NOYDFOrestratifiednumbersrecruits.png',sep="")) ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p,wd=wd,ht=ht) } stratifiedAnalysesRecruits(survey='DFO',lfa='LFA35-38')
require(bio.lobster) require(PBSmapping) a = lobster.db('annual.landings') b = lobster.db('seasonal.landings') ff = "LFA35-38Assessment" b$YR = substr(b$SYEAR,6,9) a = subset(a,YR<1976) b = subset(b,YR>1975 & YR<=2022) fpf1 = file.path(project.figuredirectory('bio.lobster'),ff) fp1 = file.path(project.datadirectory('bio.lobster'),"analysis",ff) LFA = c('LFA35','LFA36','LFA38') for(i in LFA){ aa = a[,c('YR',i)] bb = b[,c('YR',i)] aa = (rbind(aa,bb)) aa = aa[order(aa$YR),] file.name = paste('Landings',i,'.png',sep="") png(file=file.path(fpf1,'LandingsL3538.png'),units='in',width=15,height=12,pointsize=18, res=300,type='cairo') plot(aa$YR,aa[,i],type='h',lwd=4,col='black',xlab='Year',ylab='Landings (t)') lines(aa$YR,runmed(aa[,i],3),col='salmon',lwd=3) dev.off() } df2 = read.csv(file.path(fpf1,'LFA35-38 DFOCommB.csv')) df = read.csv(file.path(fpf1,'LFA35-38 DFOtotalabund.csv')) df = subset(df,yr<1999) df2 = subset(df2,yr>1998) df = as.data.frame(rbind(df,df2)) df = df[,c('yr','w.Yst','w.ci.Yst.l','w.ci.Yst.u')] #proportion of total weight that is commercial df$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$yr<1999)]*0.746 with(df,plot(yr,w.Yst,pch=1,xlab='Year',ylab='Commerical Biomass (t)',ylim=c(0,9500))) with(df,arrows(yr,y0=w.ci.Yst.u,y1=w.ci.Yst.l, length=0)) with(subset(df,yr>1998),points(yr,w.Yst,pch=16)) xx = rmed(df$yr,df$w.Yst) xx = as.data.frame(do.call(cbind,xx)) with(subset(xx,yr<1999),lines(yr,x,col='salmon',lwd=1)) with(subset(xx,yr>1998),lines(yr,x,col='salmon',lwd=3)) dev.off() a$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$L3538) png(file=file.path(fpf1,'LFA3538RelFDFO.png'),units='in',width=10,height=8,pointsize=18, res=300,type='cairo') plot(df$yr,df$rM,type='p',pch=16,col='black',xlab='Year',ylab='Relative F') arrows(df$yr,y0 = df$rL,y1 = df$rU,length=0) with(rmed(df$yr,df$rM),lines(yr,x,col='salmon',lwd=3)) dev.off()
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