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Catch.recons.Discard_TDGDLF.R
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Catch.recons.Discard_TDGDLF.R
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# SCRIPT FOR RECONSTRUCTING TOTAL DISCARDS IN TDGDLF #
#notes:
# observer data must have at least Min.obs.per.block & Min.shots.per.block
# Ratio estimator method used with all years combined.
# Model-based by species not used due to small sample sizes for most species
# Uncertainty determined using bootstrapping (as bootstrap resamples the shots,
# the procedure is self-weighting, i.e more weight is given to more
# commonly sampled blocks)
# Rarely discarded species were grouped as 'other' and then multiplied by proportion
# Longlines: Very few blocks (4 observed blocks only) so few observations and too
# much extrapolation...Hence, grouped commercial LL and GN and used observed GN ratio
rm(list=ls(all=TRUE))
options(stringsAsFactors = FALSE)
library(rlang)
library(tidyverse)
library(doParallel)
library(zoo)
library(abind)
library(stringr)
library(Hmisc)
library(ggrepel)
library(patchwork)
User="Matias"
if(!exists('handl_OneDrive')) source('C:/Users/myb/OneDrive - Department of Primary Industries and Regional Development/Matias/Analyses/SOURCE_SCRIPTS/Git_other/handl_OneDrive.R')
source(handl_OneDrive("Analyses/SOURCE_SCRIPTS/Git_other/Source_Shark_bio.R"))
source(handl_OneDrive("Analyses/SOURCE_SCRIPTS/Git_other/Smart_par.R"))
# 1. Data ---------------------------------------------------------
# Observers data
Res.ves=c("HAM","HOU","NAT","FLIN","RV BREAKSEA","RV Gannet","RV GANNET","RV SNIPE 2")
Dat_obs=DATA %>%
filter(Mid.Lat<=(-26) & !BOAT%in%Res.ves & Taxa%in%c('Elasmobranch','Teleost') & !COMMON_NAME=='WHALE') %>%
dplyr::select(c(SHEET_NO,date,Month,year,BOAT,zone,BLOCK,SOAK.TIME,MESH_SIZE,
MESH_DROP,NET_LENGTH,Mid.Lat,Mid.Long,Method,SPECIES,Taxa,
COMMON_NAME,SCIENTIFIC_NAME,CAES_Code,TL,FL,Disc.width,Number))
Dat_obs.LL=Dat_obs%>%
filter(Method=="LL")
Dat_obs=Dat_obs%>%
filter(Method=="GN")%>%
mutate(COMMON_NAME=ifelse(SPECIES=='SM' & is.na(Disc.width),'Stingrays',COMMON_NAME), #one smooth ray with no size info set to 'Dasyatidae'
SCIENTIFIC_NAME=ifelse(SPECIES=='SM' & is.na(Disc.width),'Family Dasyatidae',SCIENTIFIC_NAME),
CAES_Code=ifelse(SPECIES=='SM' & is.na(Disc.width),35000,CAES_Code),
SPECIES=ifelse(SPECIES=='SM' & is.na(Disc.width),'SR',SPECIES))
# Commercial catch data
Dat_total=read.csv(handl_OneDrive('Analyses\\Data_outs\\Data.monthly.csv'),stringsAsFactors = F)
# Species names
All.species.names=read.csv(handl_OneDrive("Data/Species_names_shark.only.csv")) #for catch
#List of discarded/retained species
Comm.disc.sp=read.csv(handl_OneDrive("Analyses/Ecosystem indices and multivariate/Shark-bycatch/SPECIES+PCS+FATE.csv"),stringsAsFactors = F)
#Length weight relationships
Len.wei=read.csv(handl_OneDrive("Data/Length_Weights/length.weights.csv"),stringsAsFactors = F)
#Weight ranges
Wei.range=read.csv(handl_OneDrive("Data/Length_Weights/Data.Ranges.csv"))
Wei.range.names=read.csv(handl_OneDrive("Data/Length_Weights/Species.names.csv"))
#Post capture mortality
PCM.north=read.csv(handl_OneDrive('Analyses/Reconstruction_catch_commercial/TableS1.PCM_North.csv'))
PCM.south=read.csv(handl_OneDrive('Analyses/Reconstruction_catch_commercial/TableS1.PCM_South.csv'))
#Spatial distribution of discarded species
elasmos_dist=read.csv(handl_OneDrive('Analyses/Reconstruction_total_bycatch_TDGDLF/elasmos_dist.csv'))
teleosts_dist=read.csv(handl_OneDrive('Analyses/Reconstruction_total_bycatch_TDGDLF/teleosts_dist.csv'))
# 2. Parameter ---------------------------------------------------------
Min.obs.per.block=10 #minimum number of observations per block for use in ratio estimator
Min.shots.per.block=5 #minimum number of shots per block for use in ratio estimator
do.explrtn="NO"
STRTA.obs.disc=c("BLOCK") #aggregating strata for observer data
STRTA.reported.ret=c("BLOCK") #aggregating strata for total landed retained catch
n.boot=1e3
Group_rare_criteria=0.02 #criteria for grouping rare species (proportion of catch)
Commercial.sp=subset(Comm.disc.sp,FATE=="C" & NATURE%in%c("S","R","T"))$SPECIES
Discarded.sp=subset(Comm.disc.sp,FATE=="D" & NATURE%in%c("S","R","T"))$SPECIES
do.sensitivity=TRUE
do.paper=FALSE
Stingrays=35000:40000
# Manipulate species names ---------------------------------------------------------
setwd(handl_OneDrive('Analyses/Reconstruction_total_bycatch_TDGDLF'))
All.species.names=All.species.names%>%
mutate(Name=capitalize(tolower(Name)),
Name=case_when(Name=='Port jackson shark'~'Port Jackson shark',
Name=='Eagle ray'~'Southern eagle ray',
TRUE~Name))
# Manipulate commercial catch ---------------------------------------------------------
Dat_total=Dat_total %>% filter(LAT<=(-26) & Estuary=="NO") %>%
mutate(YEAR=as.numeric(substr(FINYEAR,1,4)),
BLOCK=BLOCKX,
Catch=LIVEWT.c,
BLOCK=ifelse(BLOCK>=96000,paste(floor(abs(LAT)),(floor(LONG)-100)*10,sep=""),BLOCK),
BLOCK=substring(BLOCK,1,4)) %>%
dplyr::select(-c(ZnID,MonthlyID,ZoneID,YEAR.c,blockxFC,CONDITN,
Factor,RSCommonName,RSSpeciesId,LANDWT,LIVEWT,
FisheryZone,FisheryCode,Landing.Port,BDAYS,
Factor.c,LIVEWT.orgnl,LIVEWT.c,VesselID,BlockAveID,AnnualVesselAveID,
BlockID,Reporter,Sch.or.DogS,LIVEWT.reap,Tot.shk.livewt,
Shark.other.livewt,Reporter.old,Spec.old,Sname.old,NETLEN.c))%>%
left_join(All.species.names,by='SPECIES')
Dat_total.LL=Dat_total %>% filter(METHOD=="LL")
Percent.ktch.ll=100*sum(Dat_total.LL$Catch)/sum(Dat_total$Catch)
Dat_total=Dat_total %>% filter(METHOD=="GN") #other methods discarded are catch is negligible and not expected to have discards (e.g. handline)
#keep only elasmobranch species
DATA_total=list(GN=Dat_total%>%filter(SPECIES<50000),
LL=Dat_total.LL%>%filter(SPECIES<50000))
#Proportion of observed LL
prop.obs.LL=length(unique(Dat_obs.LL$SHEET_NO))/(length(unique(Dat_obs.LL$SHEET_NO))+length(unique(Dat_obs$SHEET_NO)))
# Manipulate observer data ---------------------------------------------------------
Dat_obs=Dat_obs%>%
mutate(COMMON_NAME=ifelse(SPECIES=='PD','Spurdogs',COMMON_NAME),
SCIENTIFIC_NAME=ifelse(SPECIES=='PD','Squalus spp.',SCIENTIFIC_NAME),
CAES_Code=ifelse(SPECIES=='PD',20000,CAES_Code),
SPECIES=ifelse(SPECIES=='PD','SD',SPECIES))%>%
left_join(All.species.names%>%rename(Species=SPECIES),by=c('SPECIES'='SP'))%>%
mutate(Name=ifelse(is.na(Name),COMMON_NAME,Name),
Scien.nm=ifelse(is.na(Scien.nm),SCIENTIFIC_NAME,Scien.nm),
Name=ifelse(Name=="Leatherjacket (general)","Leatherjackets",
ifelse(Name=="Triggerfish (general)","Triggerfishes",
ifelse(Name=="Scorpion fish (general)","Scorpion fish",
ifelse(Name=="Stripped marlin","Striped marlin",Name)))))
DATA_obs=list(GN=Dat_obs) #Use only observed GN as LL has very few observations
#define discarded/retained sp
for(i in 1:length(DATA_obs)) DATA_obs[[i]]$Discarded=with(DATA_obs[[i]],
ifelse(SPECIES%in%Commercial.sp,"Retained",
ifelse(SPECIES%in%Discarded.sp,"Discarded",
NA)))
# Remove unidentified sharks
for(i in 1:length(DATA_obs)) DATA_obs[[i]]=subset(DATA_obs[[i]],!SPECIES%in%c('XX','XX.T'))
# Group Angel sharks-------------------------------------------
for(i in 1:length(DATA_obs))
{
DATA_obs[[i]]=DATA_obs[[i]]%>%
mutate(COMMON_NAME=case_when(SPECIES%in%c("AO","AU")~"Angel Sharks (general)", TRUE~COMMON_NAME),
SCIENTIFIC_NAME=case_when(SPECIES%in%c("AO","AU")~"Family Squatinidae", TRUE~SCIENTIFIC_NAME),
CAES_Code=case_when(SPECIES%in%c("AO","AU")~24900, TRUE~CAES_Code),
Species=as.double(Species),
Species=case_when(SPECIES%in%c("AO","AU")~24900, TRUE~Species),
Name=case_when(SPECIES%in%c("AO","AU")~"Angel sharks", TRUE~Name),
Scien.nm=case_when(SPECIES%in%c("AO","AU")~"Squatinidae", TRUE~Scien.nm),
SPECIES=case_when(SPECIES%in%c("AO","AU")~"AA",TRUE~SPECIES))
}
# Extract data for Emity-------------------------------------------
do.this=FALSE
if(do.this)
{
write.csv(DATA_obs$GN%>%
filter(MESH_SIZE%in%c('6','6.5','7')),'C:/Users/myb/OneDrive - Department of Primary Industries and Regional Development/Desktop/For_Emily.csv',row.names = F)
}
# Show Decadal species composition for Commercial catch recons paper-------------------------------------------
do.this=FALSE
if(do.this)
{
#Observer data
these.common=c(23000,8001,10001,18001,18022,26999,13000,35000,24900,17006,
20000,39001,18023,19004,17003,17001,18007,7001,18003)
a=DATA_obs$GN%>%
mutate(Name=ifelse(Species%in%c(23001,23002),"Sawsharks",
ifelse(Species==18014,'Blacktips',
ifelse(Species%in%13001:13022,'Wobbegongs',Name))),
Species=ifelse(Species%in%c(23001,23002),23000,
ifelse(Species==18014,18014,
ifelse(Species%in%13001:13022,13000,Species))),
Name1=ifelse(Taxa=='Teleost',"Teleosts",
ifelse(Taxa== 'Elasmobranch'& !Species%in%these.common,'Other sharks',
Name)),
Decade=floor(year / 10) * 10)
a%>%
filter(Taxa== 'Elasmobranch')%>%
filter(!(Decade==2020 & zone=="Zone2"))%>%
filter(!(Decade==2020 & zone=="West"))%>%
group_by(Name1,Decade,zone)%>%
tally()%>%
ggplot(aes(x=Decade, y=n, fill=Name1)) +
geom_bar(position="fill", stat="identity")+
facet_wrap(~zone, scales = "free") +
theme(legend.position="top",
strip.text.x = element_text(size =11.5),
axis.title=element_text(size=16)) +
xlab("Decade") +
ylab("Proportion")+
labs(fill = "") +
facet_wrap(~zone)
ggsave(handl_OneDrive('Analyses/Reconstruction_catch_commercial/FigureS4.tiff'),width = 10,height = 6,compression = "lzw")
#Commercial catch
these.common=c(23000,10001,18001,18022,13000,35000,24900,17006,
20000,39001,18023,19004,17003,17001,18007,7001,18003)
a=DATA_total$GN%>%
mutate(Name=ifelse(SPECIES%in%c(23001,23002),"Sawsharks",
ifelse(SPECIES==18014,'Blacktips',
ifelse(SPECIES%in%13001:13022,'Wobbegongs',Name))),
SPECIES=ifelse(SPECIES%in%c(23001,23002),23000,
ifelse(SPECIES==18014,18014,
ifelse(SPECIES%in%13001:13022,13000,SPECIES))),
Name1=ifelse(!SPECIES%in%these.common,'Other sharks',Name),
Decade=floor(YEAR / 10) * 10)%>%
filter(SPECIES<50000)
a%>%
group_by(Name1,Decade,zone)%>%
tally()%>%
ggplot(aes(x=Decade, y=n, fill=Name1)) +
geom_bar(position="fill", stat="identity")+
facet_wrap(~zone, scales = "free") +
theme(legend.position="top",
strip.text.x = element_text(size =11.5),
axis.title=element_text(size=16)) +
xlab("Decade") +
ylab("Proportion")+
labs(fill = "") +
facet_wrap(~zone)
ggsave(handl_OneDrive('Analyses/Reconstruction_catch_commercial/FigureS5.tiff'),width = 10,height = 6,compression = "lzw")
}
# Show overall observed discarded and retained species-------------------------------------------
colfunc <- colorRampPalette(c("brown4","brown1","orange","yellow"))
#Elasmos
fun.horiz.bar=function(d,gear)
{
n=nrow(d%>%
filter(Taxa=="Elasmobranch" & Discarded=="Discarded")%>%
distinct(SPECIES))
d1=d%>%
mutate(taxa=ifelse(Discarded=="Retained","Elasmobranch",Taxa),
dummy=ifelse(Discarded=="Retained" & Taxa=="Elasmobranch","Retained elasmobranchs",
ifelse(Discarded=="Retained" & Taxa=="Teleost","Retained teleosts",
ifelse(Discarded=="Discarded" & Taxa=="Teleost","Discarded teleosts",
Name))),
dummy=ifelse(dummy=="Eagle ray","Southern eagle ray",dummy))%>%
group_by(dummy)%>%
tally%>%
mutate(Percent=100*n/sum(n),
Label=ifelse(Percent>=.5 & Percent<.1,'<1',
ifelse(Percent<.1,'<0.1',
round(Percent,1))),
Label=paste(Label,'%',sep=''),
Percent.original=Percent,
Percent=ifelse(Percent<0.1,0.1,Percent))%>%
data.frame%>%
arrange(-Percent)%>%
mutate(dummy=reorder(dummy, Percent),
grp=ifelse(dummy=='Retained elasmobranchs',"#228B22",
ifelse(dummy=='Retained teleosts',"#11BC11",
ifelse(!dummy%in%c('Retained elasmobranchs','Retained teleosts') & Percent.original>=5,"#8B2323",
ifelse(!dummy%in%c('Retained elasmobranchs','Retained teleosts') & Percent.original<5 & Percent.original>=1,"#D03434",
ifelse(!dummy%in%c('Retained elasmobranchs','Retained teleosts') & Percent.original<1 & Percent.original>0.1,"#FF4A39",
"#FF9A06"))))))
d1%>%
ggplot(aes(x = dummy,y = Percent)) +
geom_bar(fill = d1$grp,stat = "identity")+coord_flip()+
geom_text(aes(label=Label), position=position_dodge(width=0.9), hjust=-.1)+
theme_classic()+
theme(axis.title.y=element_blank(),
axis.text=element_text(size=14),
axis.title=element_text(size=16),
panel.border = element_rect(colour = "black", fill=NA, size=1))+
ylim(0, 10*round(max(d1$Percent)/10)+10)
ggsave(paste('Results/Figure2_observed.percentage_',gear,'.tiff',sep=''),
width = 8,height = 10,compression = "lzw")
d1=d1%>%mutate(Label=ifelse(Percent>1,round(Percent,2),'<1%'))
return(d1)
}
for(i in 1:length(DATA_obs)) Tabl.obs.GN=fun.horiz.bar(d=DATA_obs[[i]],gear=names(DATA_obs)[i])
#Teleosts
fun.horiz.bar.scalefish=function(d,gear)
{
n=nrow(d%>%
filter(Taxa=="Teleost" & Discarded=="Discarded")%>%
distinct(SPECIES))
d1=d%>%
mutate(taxa=ifelse(Discarded=="Retained","Elasmobranch",Taxa),
dummy=ifelse(Discarded=="Retained" & Taxa=="Elasmobranch","Retained elasmobranchs",
ifelse(Discarded=="Retained" & Taxa=="Teleost","Retained teleosts",
ifelse(Discarded=="Discarded" & Taxa=="Elasmobranch","Discarded elasmobranchs",
Name))),
dummy=capitalize(tolower(dummy)),
dummy=ifelse(dummy=='Sergeant baker','Sergeant Baker',dummy))%>%
group_by(dummy)%>%
tally%>%
mutate(Percent=100*n/sum(n),
Label=ifelse(Percent>=.5 & Percent<.1,'<1',
ifelse(Percent<.1,'<0.1',
round(Percent,1))),
Label=paste(Label,'%',sep=''),
Percent.original=Percent,
Percent=ifelse(Percent<0.1,0.1,Percent))%>%
data.frame%>%
arrange(-Percent)%>%
mutate(dummy=reorder(dummy, Percent),
grp=ifelse(dummy=='Retained elasmobranchs',"#228B22",
ifelse(dummy=='Retained teleosts',"#11BC11",
ifelse(!dummy%in%c('Retained elasmobranchs','Retained teleosts') & Percent.original>=5,"#8B2323",
ifelse(!dummy%in%c('Retained elasmobranchs','Retained teleosts') & Percent.original<5 & Percent.original>=1,"#D03434",
ifelse(!dummy%in%c('Retained elasmobranchs','Retained teleosts') & Percent.original<1 & Percent.original>0.1,"#FF4A39",
"#FF9A06"))))))
d1%>%
ggplot(aes(x = dummy,y = Percent)) +
geom_bar(fill = d1$grp,stat = "identity")+coord_flip()+
geom_text(aes(label=Label), position=position_dodge(width=0.9), hjust=-.1)+
theme_classic()+
theme(axis.title.y=element_blank(),
axis.text=element_text(size=14),
axis.title=element_text(size=16),
panel.border = element_rect(colour = "black", fill=NA, size=1))+
ylim(0, 10*round(max(d1$Percent)/10)+10)
ggsave(paste('Results/Recons.scalefish/Figure2_observed.percentage_',gear,'.tiff',sep=''),
width = 8,height = 10,compression = "lzw")
d1=d1%>%mutate(Label=ifelse(Percent>1,round(Percent,2),'<1%'))
return(d1)
}
for(i in 1:length(DATA_obs)) Tabl.obs.GN.scalefish=fun.horiz.bar.scalefish(d=DATA_obs[[i]],gear=names(DATA_obs)[i])
#Table of discarded and observed species
# Table.disc.obs=DATA_obs$GN%>%
# distinct(SPECIES,.keep_all = T)%>%
# dplyr::select(Name,Scien.nm,Discarded,Taxa)%>%
# rename(Fate=Discarded)%>%
# arrange(Taxa,Fate,Name)
Table.disc.obs=DATA_obs$GN%>%
group_by(COMMON_NAME,SCIENTIFIC_NAME,Taxa,Discarded)%>%
tally()%>%
arrange(Taxa,Discarded,COMMON_NAME)
#for teleosts, keep discarded teleosts and retained elasmos
DATA_obs_teleosts=DATA_obs
for(i in 1:length(DATA_obs_teleosts))
{
DATA_obs_teleosts[[i]]=subset(DATA_obs_teleosts[[i]],
(Taxa=='Elasmobranch' & Discarded=='Retained') |
(Taxa=='Teleost' & Discarded=='Discarded'))
}
#Keep only elasmobranchs from observed commercial boats
for(i in 1:length(DATA_obs)) DATA_obs[[i]]=subset(DATA_obs[[i]],Taxa=='Elasmobranch')
#Extract discarded species
Discarded.SP=DATA_obs$GN%>%
filter(Discarded=='Discarded')%>%
distinct(SPECIES,.keep_all = T)%>%
pull(SPECIES)
Discarded.SP_teleosts=DATA_obs_teleosts$GN%>%
filter(Discarded=='Discarded' & Taxa=='Teleost')%>%
distinct(SPECIES,.keep_all = T)%>%
pull(SPECIES)
# Set FL to disc width for stingrays for computational purposes----------------------------------------
Stingrays=35000:40000
for(i in 1:length(DATA_obs))
{
DATA_obs[[i]]=DATA_obs[[i]]%>%
mutate(FL=ifelse(CAES_Code%in%Stingrays,Disc.width,FL),
TL=ifelse(CAES_Code%in%Stingrays,NA,TL))
}
# Fill in missing FL info for elasmos ---------------------------------------------------------
#note: first use TL, then sample from species distribution, finally overall mean
UniK.sp=table(DATA_obs$GN$SPECIES)
UniK.sp=UniK.sp[UniK.sp>3]
UniK.sp=names(UniK.sp)
pdf("Results/Preliminary/size.frequency_observed.pdf")
for(u in 1:length(UniK.sp))
{
a=subset(DATA_obs$GN,SPECIES==UniK.sp[u] & !is.na(FL))
if(nrow(a)>2)hist(a$FL,main=a$Name[1],col=2, xlab="FL (cm)")
}
dev.off()
#1. Set to NA FL or TL records less than size at birth
size.birth=30 #FL, in cm
for(i in 1:length(DATA_obs))
{
DATA_obs[[i]]$FL=with(DATA_obs[[i]],ifelse(FL<size.birth,NA,FL))
DATA_obs[[i]]$TL=with(DATA_obs[[i]],ifelse(TL<(size.birth/.85),NA,TL))
DATA_obs_teleosts[[i]]$FL=with(DATA_obs_teleosts[[i]],ifelse(FL<size.birth & Taxa=='Elasmobranch',NA,FL))
DATA_obs_teleosts[[i]]$TL=with(DATA_obs_teleosts[[i]],ifelse(TL<(size.birth/.85) & Taxa=='Elasmobranch',NA,TL))
}
#2. Derive FL as a proportion of TL
for(i in 1:length(DATA_obs))
{
DATA_obs[[i]]$FL=with(DATA_obs[[i]],ifelse(is.na(FL),TL*.875,FL))
DATA_obs_teleosts[[i]]$FL=with(DATA_obs_teleosts[[i]],ifelse(is.na(FL) & Taxa=='Elasmobranch',TL*.875,FL))
}
#3. samples from species distribution
fn.samp.dist=function(d)
{
d=subset(d,FL>20)
his=hist(d$FL,breaks=seq(floor(min(d$FL,na.rm=T)),ceiling(max(d$FL,na.rm=T)),by=1),plot=F)
return(his)
}
for(i in 1:length(DATA_obs))
{
#Elasmos
all.sp=unique(DATA_obs[[i]]$SPECIES)
for(s in 1:length(all.sp))
{
d=subset(DATA_obs[[i]],SPECIES==all.sp[s])
if(sum(is.na(d$FL))>0 & sum(!is.na(d$FL))>0)
{
if(length(d$FL[!is.na(d$FL)])>2)Prob=fn.samp.dist(d)
if(length(Prob$density)>2)
{
id=which (is.na(DATA_obs[[i]]$FL) & DATA_obs[[i]]$SPECIES==all.sp[s])
DATA_obs[[i]]$FL[id]=sample(Prob$breaks[-1],
length(DATA_obs[[i]]$FL[id]),
replace=T,prob=Prob$density/sum(Prob$density))
}
}
if(all.sp[s]=="SR") #set stingrays equal to eagle ray
{
Prob=fn.samp.dist(d=subset(DATA_obs[[i]],SPECIES=='ER'))
id=which (is.na(DATA_obs[[i]]$FL) & DATA_obs[[i]]$SPECIES==all.sp[s])
DATA_obs[[i]]$FL[id]=sample(Prob$breaks[-1],
length(DATA_obs[[i]]$FL[id]),
replace=T,prob=Prob$density/sum(Prob$density))
}
}
#Elasmos in Teleost data frame
all.sp=DATA_obs_teleosts[[i]]%>%filter(Taxa=='Elasmobranch')%>%distinct(SPECIES)%>%pull(SPECIES)
for(s in 1:length(all.sp))
{
d=subset(DATA_obs_teleosts[[i]],SPECIES==all.sp[s])
if(sum(is.na(d$FL))>0 & sum(!is.na(d$FL))>0)
{
if(length(d$FL[!is.na(d$FL)])>2)Prob=fn.samp.dist(d)
if(length(Prob$density)>2)
{
id=which (is.na(DATA_obs_teleosts[[i]]$FL) & DATA_obs_teleosts[[i]]$SPECIES==all.sp[s])
DATA_obs_teleosts[[i]]$FL[id]=sample(Prob$breaks[-1],
length(DATA_obs_teleosts[[i]]$FL[id]),
replace=T,prob=Prob$density/sum(Prob$density))
}
}
}
}
#4. overall mean (only 2 records, Crocodile shark & Grey reef shark)
for(i in 1:length(DATA_obs))
{
Mn.whaler=mean(DATA_obs[[i]]%>%filter(CAES_Code%in%18001:18036)%>%pull(FL),na.rm=T)
DATA_obs[[i]]=DATA_obs[[i]]%>%
mutate(FL=ifelse(is.na(FL) & COMMON_NAME=="Crocodile shark",Mn.whaler,
ifelse(is.na(FL) & COMMON_NAME=="Grey reef shark",Mn.whaler,
FL)))
DATA_obs_teleosts[[i]]=DATA_obs_teleosts[[i]]%>%
mutate(FL=ifelse(is.na(FL) & COMMON_NAME=="Crocodile shark",Mn.whaler,
ifelse(is.na(FL) & COMMON_NAME=="Grey reef shark",Mn.whaler,
FL)))
}
pdf("Results/Preliminary/size.frequency_ammended.pdf")
for(u in 1:length(UniK.sp))
{
a=subset(DATA_obs$GN,SPECIES==UniK.sp[u] & !is.na(FL))
if(nrow(a)>2)hist(a$FL,main=a$Name[1],col=2, xlab="FL (cm)")
}
dev.off()
# Fix nonsense TL for teleost and fill in missing TL ---------------------------------------------------------
for(i in 1:length(DATA_obs_teleosts))
{
DATA_obs_teleosts[[i]]=DATA_obs_teleosts[[i]]%>%
mutate(TL=ifelse(TL<10,NA,TL))
}
fn.samp.dist_teleost=function(d)
{
his=hist(d$TL,breaks=seq(floor(min(d$TL,na.rm=T)),ceiling(max(d$TL,na.rm=T)),by=1),plot=F)
return(his)
}
for(i in 1:length(DATA_obs_teleosts))
{
for(s in 1:length(Discarded.SP_teleosts))
{
d=subset(DATA_obs_teleosts[[i]],SPECIES==Discarded.SP_teleosts[s])
if(sum(is.na(d$TL))>0 & sum(!is.na(d$TL))>0)
{
if(length(d$TL[!is.na(d$TL)])>2)Prob=fn.samp.dist_teleost(d)
if(length(Prob$density)>2)
{
id=which (is.na(DATA_obs_teleosts[[i]]$TL) & DATA_obs_teleosts[[i]]$SPECIES==Discarded.SP_teleosts[s])
DATA_obs_teleosts[[i]]$TL[id]=sample(Prob$breaks[-1],
length(DATA_obs_teleosts[[i]]$TL[id]),
replace=T,prob=Prob$density/sum(Prob$density))
}
}
}
}
for(i in 1:length(DATA_obs_teleosts)) #Marlin no TL data, only 1 record, set to tuna 90% percentile
{
Mean.tuna=quantile(Dat_obs%>%
filter(SPECIES=="SB.T" & TL>0)%>%
pull(TL),probs=.9)
DATA_obs_teleosts[[i]]=DATA_obs_teleosts[[i]]%>%
mutate(TL=ifelse(SPECIES=='SM.T',Mean.tuna,TL))
}
# Convert numbers to weight ---------------------------------------------------------
#note: calculate discard ratio using weight because total catch is in weight.
# Use FL and a, b parameters for fork length
#Elasmobranchs
Len.wei=Len.wei%>%
dplyr::select(c(SPECIES,a_w8t,b_w8t,Source,Comments))
for(i in 1:length(DATA_obs))
{
DATA_obs[[i]]=DATA_obs[[i]]%>%
left_join(Len.wei,by="SPECIES")%>%
mutate(Catch=a_w8t*(FL)^b_w8t)
}
#Teleosts
for(i in 1:length(DATA_obs_teleosts))
{
DATA_obs_teleosts[[i]]=DATA_obs_teleosts[[i]]%>%
left_join(Len.wei,by="SPECIES")%>%
mutate(TL=ifelse(SPECIES=="KF.T" & TL>25,25, #reset TL for some species (some too large observations)
ifelse(SPECIES=="SC.T" & TL>42,42,
TL)))
#Buff bream and North west blowfish length-weight is in FL, so convert TL to FL
DATA_obs_teleosts[[i]]=DATA_obs_teleosts[[i]]%>%
mutate(dummy.FL=ifelse(Taxa=='Elasmobranch',FL,
ifelse(Taxa=='Teleost',TL,
NA)),
dummy.FL=ifelse(SPECIES%in%c("BB.T",'NW.T'),TL*.85,
dummy.FL),
Catch=a_w8t*(dummy.FL)^b_w8t)%>%
dplyr::select(-dummy.FL)
}
# Remove white shark, greynurse and 'other shark' from observations ---------------------------------------------------------
#note: 2 very large white sharks acounts for a great proportion of discarded tonnage
for(i in 1:length(DATA_obs)) DATA_obs[[i]]=DATA_obs[[i]]%>%filter(!SPECIES%in%c('WP','GN','XX'))
#Reset min and maximum weights if nonsense
Wei.range=Wei.range%>%left_join(Wei.range.names,by=c("Sname"))
Wei.range=subset(Wei.range,!(is.na(SPECIES)|is.na(TW.min)|is.na(TW.max)))
for(i in 1:length(DATA_obs))
{
DATA_obs[[i]]=left_join(DATA_obs[[i]],Wei.range,by=c('Species'='SPECIES'))%>%
mutate(Sname=case_when(SPECIES%in%c("AO","AU")~"Angel Shark (general)", TRUE~Sname))
DATA_obs[[i]]=DATA_obs[[i]]%>%
mutate(Catch=case_when(!is.na(TW.max) & Catch> TW.max ~ TW.max,
!is.na(TW.min) & Catch< TW.min ~ TW.min,
TRUE~Catch))
}
pdf("Results/Recons.scalefish/Observed.Catch.frequency.pdf")
for(i in 1:length(DATA_obs_teleosts))
{
for(s in 1:length(Discarded.SP_teleosts))
{
d=subset(DATA_obs_teleosts[[i]],SPECIES==Discarded.SP_teleosts[s])
hist(d$Catch,xlab="Catch (kg)",main=d$Name[1],col=2)
}
}
dev.off()
# Group rare species ---------------------------------------------------------
#Elasmos
for(i in 1:length(DATA_obs))
{
Tab.sp=prop.table(with(subset(DATA_obs[[i]],Discarded=="Discarded"),table(SPECIES)))
Rare.sp=names(Tab.sp[Tab.sp<Group_rare_criteria])
DATA_obs[[i]]$SPECIES.ori=DATA_obs[[i]]$SPECIES
DATA_obs[[i]]$SPECIES=with(DATA_obs[[i]],ifelse(SPECIES%in%Rare.sp,"Grouped",SPECIES))
}
#Teleosts
for(i in 1:length(DATA_obs_teleosts))
{
Tab.sp=prop.table(with(subset(DATA_obs_teleosts[[i]],Discarded=="Discarded" & Taxa=='Teleost'),table(SPECIES)))
Rare.sp=names(Tab.sp[Tab.sp<Group_rare_criteria])
DATA_obs_teleosts[[i]]$SPECIES.ori=DATA_obs_teleosts[[i]]$SPECIES
DATA_obs_teleosts[[i]]$SPECIES=with(DATA_obs_teleosts[[i]],ifelse(SPECIES%in%Rare.sp,"Grouped",SPECIES))
}
# Define discarded_sp used to calculate discarded species proportions-------------------------------------------
#Elasmos
for(i in 1:length(DATA_obs))
{
DATA_obs[[i]]$Discarded_sp=with(DATA_obs[[i]],ifelse(Discarded=="Retained","Retained",SPECIES))
}
#Teleosts
for(i in 1:length(DATA_obs_teleosts))
{
DATA_obs_teleosts[[i]]$Discarded_sp=with(DATA_obs_teleosts[[i]],ifelse(Discarded=="Retained","Retained",SPECIES))
}
# Data exploration-------------------------------------------
if(do.explrtn=="YES")
{
A=with(Dat_obs[!duplicated(Dat_obs$SHEET_NO),],table(BLOCK))
Exp=5*(A/max(A))
Exp=ifelse(Exp<0.25,0.25,Exp)
Exp=data.frame(LAT=-(as.numeric(substr(names(Exp),1,2))+.5),
Long=100+as.numeric(substr(names(Exp),3,4))+.5,cex=Exp)
pdf("Results/Preliminary/shots_block_map.pdf")
with(Exp,plot(Long,LAT,cex=cex,pch=19,col='steelblue'))
legend('topright',paste(c(10,100,250)),pch=19,pt.cex=5*(c(10,100,250)/max(A)),
bty='n',col='steelblue',title="# shots")
dev.off()
YRs=sort(unique(Dat_obs$year))
pdf("Results/Preliminary/shots_block_year_map.pdf")
par(mfcol=c(5,4),mar=c(1,1.5,1,.5),oma=c(3,1.25,.5,.1),las=1,mgp=c(1.5,.5,0),cex.axis=.85)
for(y in 1:length(YRs))
{
x=subset(Dat_obs,year==YRs[y])
A=with(x[!duplicated(x$SHEET_NO),],table(BLOCK))
Exp=3.5*(A/max(A))
Exp=ifelse(Exp<0.25,0.25,Exp)
Exp=data.frame(LAT=-(as.numeric(substr(names(Exp),1,2))+.5),
Long=100+as.numeric(substr(names(Exp),3,4))+.5,cex=Exp)
with(Exp,plot(Long,LAT,cex=cex,pch=19,col='steelblue',ylim=c(-36,-26),
xlim=c(113,129),ann=F))
Quant=round(quantile(A,prob=c(.25,.5,.75)))
legend('topright',paste(Quant),pch=19,pt.cex=3.5*(Quant/max(A)),
bty='n',col='steelblue',title="# shots")
legend('topleft',paste(YRs[y]),bty='n')
}
dev.off()
#blocks by year
pdf("Results/Preliminary/shots_block_year_plot.pdf")
A=with(Dat_obs[!duplicated(Dat_obs$SHEET_NO),],table(year,BLOCK))
plot(1,1,col="transparent",ylim=c(1,ncol(A)),xlim=c(1,nrow(A)),ylab="block",xlab="year")
for(i in 1:nrow(A))
{
Exp=5*(A[i,]/max(A))
points(rep(i,ncol(A)),1:ncol(A),cex=Exp,pch=19,col='steelblue')
}
legend('bottomright',paste(c(10,30,80)),pch=19,pt.cex=5*(c(10,30,80)/max(A)),
bty='n',col='steelblue',title="# shots")
dev.off()
#Shark and ray species
plot(sort(table(Dat_obs$SPECIES)),type='h',ylab="# individuals")
}
# Get dataframe for infographic-------------------------------------------
enough.data.yrs=Dat_obs%>%
distinct(SHEET_NO,year)%>%
group_by(year)%>%
tally()%>%
filter(n>50)
n.shots=enough.data.yrs
enough.data.yrs=enough.data.yrs%>%pull(year)
size.titl=14
size.subtitl=12
a=Dat_obs%>%
left_join(Comm.disc.sp,by="SPECIES")%>%
filter(!SPECIES%in%c('XX','XX.T'))%>%
mutate(FATE=ifelse(FATE=="C","Retained",
ifelse(FATE=="D","Discarded",NA)),
FATE=factor(FATE))%>%
filter(year%in%enough.data.yrs)%>%
group_by(year,FATE)%>%
summarise(count=sum(Number))%>%
mutate(perc = count/sum(count))%>%
left_join(n.shots,by="year")
a$n[duplicated(a$n)] <- ""
Infographic1=a%>%
ggplot(aes(x = year, y = perc*100, fill = FATE)) +
geom_bar(stat="identity", width = .9) +
labs(x = "Year",y="Percentage", y = NULL) +
geom_text(aes(x = year,y = 100, label = n),
position = "identity",vjust = -0.5,
fontface='bold',color="grey30",size = 2.6)+
theme_minimal(base_size = 18) +
theme(legend.title=element_blank(),
legend.position="top",
legend.direction="horizontal",
legend.justification="right",
legend.margin=margin(0,5,0,0),
legend.box.margin=margin(-50,-10,-60,-10),
panel.grid.major.y = element_line( size=1, color="grey60" ),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size = 12),
axis.text.x = element_text(margin = margin(t = -10)),
plot.title = element_text(size =size.titl,face="bold"),
plot.subtitle = element_text(size =size.subtitl,face = "italic"),
plot.caption = element_text(size =8,face = "italic"),
plot.title.position = "plot")+
labs(title ="Observed percentage of total catch (by number)",
subtitle = "(the annual number of observed shots is shown above each bar)",
caption="(Only years with more than 50 observed shots are displayed)")
# remove stuff-------------------------------------------
rm(DATA,Dat_obs,Dat_obs.LL,Dat_total.LL,DATA.bio,DATA.ecosystems)
# Get discard ratio by block and species (D_i_h)-------------------------------------------
STRATA_obs=function(d,Strata,Min.obs.per.block,Min.shots.per.block)
{
#select minimum number of observations for use in analysis
tt <- table(d$BLOCK)
d <- d[d$BLOCK %in% names(tt[tt >= Min.obs.per.block]), ]
tt <- with(d[!duplicated(d$SHEET_NO),],table(BLOCK))
d <- d[d$BLOCK %in% names(tt[tt >= Min.shots.per.block]), ]
#get discard : retain ratios
a=d %>%
group_by(.dots=Strata) %>%
summarise(total = sum(Catch))%>%
spread(key = !! parse_expr(Strata[1]), value = total)%>%
as.data.frame
a[is.na(a)]=0
Vars=names(a)[-match(c(Strata[-1],"Retained"),names(a))]
a[,Vars]=a[,Vars]/a$Retained
names(a)[match(Vars,names(a))]=paste(names(a[,Vars]),".ratio",sep="")
#get bycatch proportional species composition
prop=subset(d,Discarded=="Discarded") %>%
group_by(.dots=c("SPECIES.ori",Strata[-1])) %>%
summarise(total = sum(Catch))%>%
spread(key = SPECIES.ori, value = total)%>%
as.data.frame
prop[is.na(prop)]=0
Vars.prop=names(prop)[-match(c(Strata[-1]),names(prop))]
prop[,Vars.prop]=prop[,Vars.prop]/rowSums(prop[,Vars.prop])
#keep only proportion for grouped species
id.prop=names(prop)[which(!names(prop)%in%Vars)]
if(length(id.prop)>1)
{
prop=prop[,id.prop]
#rescale to sum 'Grouped' to 1
prop[,-1]=prop[,-1]/rowSums(prop[,-1])
prop[is.na(rowSums(prop[,-1])),-1]=0
}
return(list(dat=a,prop=prop,vars=paste(Vars,".ratio",sep="")))
}
#Elasmos
Obs_ratio.strata=STRATA_obs(d=DATA_obs$GN,
Strata=c("Discarded_sp",STRTA.obs.disc),
Min.obs.per.block=Min.obs.per.block,
Min.shots.per.block=Min.shots.per.block)
write.csv(Obs_ratio.strata$dat,"Results/Table1_observed.ratios.csv",row.names = F)
#Teleosts
Obs_ratio.strata_teleosts=STRATA_obs(d=DATA_obs_teleosts$GN,
Strata=c("Discarded_sp",STRTA.obs.disc),
Min.obs.per.block=Min.obs.per.block,
Min.shots.per.block=Min.shots.per.block)
write.csv(Obs_ratio.strata_teleosts$dat,"Results/Recons.scalefish/Table1_observed.ratios.csv",row.names = F)
# Get stratified total reported retained catch (Y_h)-------------------------------------------
STRATA_total=function(d.gn,d.ll,Strata)
{
if(!"YEAR"%in%Strata) Strata=c("YEAR",Strata)
d=rbind(d.gn%>%dplyr::select(YEAR,BLOCK,Catch),d.ll%>%dplyr::select(YEAR,BLOCK,Catch))
a=d %>%
group_by(.dots=Strata) %>%
summarise(total.retained = sum(Catch))%>%
as.data.frame
return(a)
}
Total_strata=STRATA_total(d.gn=DATA_total$GN,d.ll=DATA_total$LL,Strata=STRTA.reported.ret)
# Add post capture mortality-------------------------------------------
#Elasmos
#from commercial catch reconstruction
PCM=rbind(PCM.north,PCM.south)%>%
distinct(Name,.keep_all = T)%>%
dplyr::select(-c(Trawl,LL))%>%
rename(PCM=GN)%>%
left_join(All.species.names%>%dplyr::select(-c(Scien.nm,Family,SPECIES)),by='Name')%>%
rename(SPECIES=SP)%>%
mutate(PCM=ifelse(SPECIES=='AA',0.405,PCM))%>%
filter(!Name=='Cobbler wobbegong')
#collated by Agustin from literature
this.PCM=Tabl.obs.GN%>%
filter(!dummy=='Retained')%>%
pull(dummy)
this.PCM=DATA_obs$GN%>%
filter(Name%in%this.PCM)%>%
distinct(Name,.keep_all = T)%>%
pull(SPECIES)
add.PCM=Comm.disc.sp%>%
filter(!is.na(PCS) & NATURE%in%c("R","S") & SPECIES%in%this.PCM)%>%
mutate(PCM=1-PCS)%>%
dplyr::select(SPECIES,COMMON_NAME,SCIENTIFIC_NAME,PCM)%>%
rename(Name=COMMON_NAME,
Scien.nm=SCIENTIFIC_NAME)%>%
dplyr::select(names(PCM))
PCM=rbind(PCM,add.PCM)%>%
filter(SPECIES%in%Discarded.SP)%>%
filter(!is.na(PCM))%>%
filter(!PCM==0)%>%
filter(!SPECIES%in%c('SE','SH'))
#add PCM for remaining discarded species
#Angel Shark, from Braccini et al 2012;
#Brown-banded catshark & catsharks, set at average of Rusty and Varied carpetshark from Braccini et al 2012
#Crocodile shark, no information available, set = to mako shark from Braccini et al 2012 as precaution
# Southern fiddler ray no available information, set at 10% due to hardiness
#Sliteye shark, set at 1, always dead when brought aboard
#Guitarfish & shovelnose ray, set equal to Whitespot shovelnose
#Spotted shovelnose & Western shovelnose ray, set equal to Whitespot shovelnose
#White shark, set = to mako shark from Braccini et al 2012
#Whitespot shovelnose, Braodhurst & Cullis 2020 (immediate mortality)+25%
#Cobbler wobbegong, set equal to Orectolobus parvimaculatus
#Dwarf spotted, western and floral wobbegongs, set equal to Cobbler Wobbegong from Comm.disc.sp
#Spikey dogfish, set equal to Squalus spp.
# Stingrays, set equal to Myliobatidae
PCM.remaining=data.frame(
Name=c("Eagle ray","Brown-banded catshark","Crocodile shark",
"Southern fiddler ray","Sliteye shark","Guitarfish & shovelnose ray",
"Spotted shovelnose","Dwarf spotted wobbegong","White shark",
"Whitespot shovelnose","Other shark",
'Cobbler wobbegong','Western Wobbegong','Floral banded wobbegong',
'Spikey dogfish','Stingrays',
'Catsharks','Western shovelnose ray'),
Scien.nm=c("Myliobatis australis","Chiloscyllium punctatum","Pseudocarcharias kamoharai",
"Trygonorrhina dumerilii","Loxodon macrorhinus","Families Rhinobatidae & Rhynchobatidae",
"Aptychotrema timorensis","Orectolobus parvimaculatus","Carcharodon Carcharias",
"Rhynchobatus australiae","",
"Sutorectus tentaculatus",'Orectolobus hutchinsi','Orectolobus floridus',
"Squalus megalops","Dasyatidae",
'Scyliorhinidae','Aptychotrema vincentiana'),
PCM=c(1-0.854,1-mean(c(0.771,0.687)),1-0.242,
1-.9,1,(0.29+0.29*.25),
(0.29+0.29*.25),0.057,1-0.242,
(0.29+0.29*.25),NA,
0.0570,0.0570,0.0570,
0.1729,0.1460,
0.2710,0.3625),
SPECIES=c("ER","BC","CR","FR","SE","SH","SS","WM","WP","WR","XX",
"PD",'WW',"WF",
"SR","WC",
"CA","AV"))
PCM=rbind(PCM,PCM.remaining)%>%
filter(!Name=='Spur Dog')%>%
mutate(PCM=ifelse(SPECIES=="PJ",2*PCM,PCM)) # bump up PCM for Port Jackson sharks
add.SM=subset(PCM,SPECIES=='WC')%>%
mutate(Name='Smooth stingray',
Scien.nm='Bathytoshia brevicaudata',
SPECIES='SM')
PCM=rbind(PCM,add.SM)
#Teleosts
PCM_teleosts=DATA_obs_teleosts$GN%>%
filter(SPECIES.ori%in%Discarded.SP_teleosts)%>%
distinct(SPECIES.ori,Name)%>%
mutate(SPECIES=substr(SPECIES.ori,1,2),
PCM=1)
# Annual discards by species (sum(D_i_h x P_i x Y_h)) -------------------------------------------
set.seed(666)
fn.total=function(disc.dat,tot.dat,pcm,Impute,Dist)
{
#combine observed ratios and total reported catch
Total.discard=merge(disc.dat$dat,tot.dat,by=STRTA.reported.ret,all.y=T)
#linear interpolate missing block discard ratio
Vars=disc.dat$vars
for(v in 1:length(Vars))
{
id=match(Vars[v],names(Total.discard))
if(Impute=='linear')
{
Total.discard[,id]=ifelse(is.na(Total.discard[,id]),
na.approx(zoo(Total.discard[,id])),
Total.discard[,id])
}
if(Impute=='random')
{
x=Total.discard[,id]
nn=length(x[is.na(x)])
x[is.na(x)] <- sample(x[!is.na(x)], nn, replace = TRUE)
Total.discard[,id]=x
}
}
#multiply ratio by total
Total.discard[,Vars]=Total.discard[,Vars]*Total.discard$total.retained
#rename
i.dvar=match(Vars,names(Total.discard))
names(Total.discard)[i.dvar]= paste("total",
sapply(strsplit(names(Total.discard)[i.dvar], ".", fixed = TRUE), "[", 1),sep=".")
#Split up 'Other'
if(!is.na(match("total.Grouped",names(Total.discard))))
{
dummy=Total.discard[,c("BLOCK","YEAR","total.Grouped")]
dummy=merge(disc.dat$prop,dummy,by="BLOCK",all.y=T)
#linear interpolate missing block discard ratio
Vars=subset(names(disc.dat$prop),!names(disc.dat$prop)=="BLOCK")