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figure_2.R
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#===================================================================
#==ANALYSIS OF THE COLLAPSE OF SNOW CRAB IN THE EASTERN BERING SEA==
#===================================================================
library(dplyr)
library(reshape2)
library(tidyr)
library(ggplot2)
library(autoimage)
library(akima) #install.packages("akima")
library(viridis)
library(broom)
library(maps)
library("rnaturalearth")
library(interp)
library(RColorBrewer)
library(reshape2)
library(mgcv)
library(PBSmapping)
library(mapdata)
library(maptools)
library(mapproj)
library(metR)
library(ggplot2)
library(reshape)
library(dplyr)
library(ggplot2)
library(ggridges)
library(gridExtra)
library(png)
library(grid)
library(PBSmodelling)
library(patchwork)
library(GGally)
library(itsadug)
library(gratia)
library(scam)
annotation_custom2 <- function (grob, xmin = -Inf, xmax = Inf, ymin = -Inf,ymax = Inf, data){ layer(data = data,
stat = StatIdentity,
position = PositionIdentity,
geom = ggplot2:::GeomCustomAnn,
inherit.aes = TRUE,
params = list(grob = grob,xmin = xmin, xmax = xmax,
ymin = ymin, ymax = ymax))}
#===================================================================
# READ IN POP DY MODEL OUTPUT
#===================================================================
#===================================================================
# PopDy Input
#===================================================================
input<-"C:/Users/cody.szuwalski/Work/snow_down_pub/models/model_base/snow_down.DAT"
styr <- scan(input,skip=2,n=1,quiet=T)
endyr <- scan(input,skip=4,n=1,quiet=T)
year_n <- scan(input,skip=6,n=1,quiet=T)
years <- scan(input,skip=8,n=year_n,quiet=T)
size_n <- scan(input,skip=10,n=1,quiet=T)
sizes <- scan(input,skip=12,n=size_n,quiet=T)
imm_n_at_size_obs <- matrix(scan(input,skip=18,n=year_n*size_n,quiet=T),ncol=size_n,byrow=T)
mat_n_at_size_obs <- matrix(scan(input,skip=52,n=year_n*size_n,quiet=T),ncol=size_n,byrow=T)
prob_term_molt <- matrix(scan(input,skip=86,n=year_n*size_n,quiet=T),ncol=size_n,byrow=T)
size_trans <- (matrix(scan(input,skip=120,n=size_n*size_n,quiet=T),ncol=size_n,byrow=T))
survey_select<-scan(input,skip=142,n=size_n,quiet=T)
imm_cv <- scan(input,skip=134,n=year_n,quiet=T)
mat_cv <- scan(input,skip=136,n=year_n,quiet=T)
#===================================================================
# Output
#===================================================================
rep_files<-c("models/model_base/snow_down.rep",
"models/model_vary_m/snow_down.rep",
"models/model_vary_q/snow_down.rep",
"models/model_vary_q_m/snow_down.rep")
take_ind<-2
outs<-list(list())
for(x in 1:length(rep_files))
outs[[x]]<-readList(rep_files[x])
obs_imm_comp<-imm_n_at_size_obs
obs_mat_comp<-mat_n_at_size_obs
for(x in 1:nrow(imm_n_at_size_obs))
{
obs_imm_comp[x,]<-imm_n_at_size_obs[x,]/sum(imm_n_at_size_obs[x,])
obs_mat_comp[x,]<-mat_n_at_size_obs[x,]/sum(mat_n_at_size_obs[x,])
}
out_like<-NULL
for(x in 1:length(outs))
out_like<-c(out_like,sum(outs[[x]]$likelihoods))
used_par<-c(94,160,162,230)
AIC<-2*used_par+2*out_like
#===plot outputs building complexity================================
plot_build_up<-data.frame(values=c(outs[[1]]$`natural mortality`[,1],
outs[[1]]$`mature natural mortality`[,1],
outs[[1]]$`survey selectivity`[,1],
outs[[1]]$`mature survey selectivity`[,1]),
process=c(rep("Immature mortality",length(outs[[1]]$`natural mortality`[,1])),
rep("Mature mortality",length(outs[[1]]$`natural mortality`[,1])),
rep("Immature catchability",length(outs[[1]]$`natural mortality`[,1])),
rep("Mature catchability",length(outs[[1]]$`natural mortality`[,1]))),
model="Time-invariant",year=seq(1989,2021))
in_mod<-c("Invariant","Vary M","Vary q","Vary both")
for(x in 2:4)
{ temp<-data.frame(values=c(outs[[x]]$`natural mortality`[,1],
outs[[x]]$`mature natural mortality`[,1],
outs[[x]]$`survey selectivity`[,1],
outs[[x]]$`mature survey selectivity`[,1]),
process=c(rep("Immature mortality",length(outs[[x]]$`natural mortality`[,1])),
rep("Mature mortality",length(outs[[x]]$`natural mortality`[,1])),
rep("Immature catchability",length(outs[[x]]$`natural mortality`[,1])),
rep("Mature catchability",length(outs[[x]]$`natural mortality`[,1]))),
model=in_mod[x],year=seq(1989,2021))
plot_build_up<-rbind(plot_build_up,temp)
}
png("plots/mod_comp.png",height=8,width=8,res=400,units='in')
ggplot(filter(plot_build_up,year<2020))+
geom_line(aes(x=year,y=values,col=model),lwd=2)+
facet_wrap(~process,scales='free_y')+
theme_bw()
dev.off()
#==uncertainty in estimates of imm and mat N
ret <- list()
parfile <- as.numeric(scan("models/model_vary_m/snow_down.par", what = '', n=16, quiet=TRUE)[c(6,11,16)])
ret$nopar <- as.integer(parfile[1])
ret$nlogl <- parfile[2]
ret$maxgrad <- parfile[3]
file <- "models/model_vary_m/snow_down.cor"
if (file.exists(file))
{
lin <- readLines(file)
ret$npar <- length(lin)-2
ret$logDetHess <- as.numeric(strsplit(lin[1], '=')[[1]][2])
sublin <- lapply(strsplit(lin[1:ret$npar+2], ' '),function(x)x[x!=''])
ret$names <- unlist(lapply(sublin,function(x)x[2]))
ret$est <- as.numeric(unlist(lapply(sublin,function(x)x[3])))
ret$std <- as.numeric(unlist(lapply(sublin,function(x)x[4])))
ret$cor <- matrix(NA, ret$npar, ret$npar)
corvec <- unlist(sapply(1:length(sublin), function(i)sublin[[i]][5:(4+i)]))
ret$cor[upper.tri(ret$cor, diag=TRUE)] <- as.numeric(corvec)
ret$cor[lower.tri(ret$cor)] <- t(ret$cor)[lower.tri(ret$cor)]
ret$cov <- ret$cor*(ret$std%o%ret$std)
}
imm_sd<-ret$std[which(ret$names=="imm_n_sd")]
mat_sd<-ret$std[which(ret$names=="mat_n_sd")]
#==immature size comps
rownames(obs_imm_comp)<-years
colnames(obs_imm_comp)<-sizes
df_1<-melt(obs_imm_comp)
colnames(df_1)<-c("Year","Size","Proportion")
df_1$quant<-'Observed'
df2<-data.frame(pred=apply(outs[[take_ind]]$'immature numbers at size',2,median),
Size=(sizes))
allLevels <- levels(factor(c(df_1$Size,df2$Size)))
df_1$Size <- factor(df_1$Size,levels=(allLevels))
df2$Size <- factor(df2$Size,levels=(allLevels))
imm_size<-ggplot(data=df2,aes(x = factor(Size), y =pred)) +
geom_boxplot(data=df_1,aes(x = Size, y =Proportion ),fill='grey') +
stat_summary(fun.y=mean, geom="line", aes(group=1),lwd=1.5,col='blue',alpha=.8) +
ylab("Size composition")+theme(axis.title=element_text(size=11))+
theme_void()
#==mature size comps
rownames(obs_mat_comp)<-years
colnames(obs_mat_comp)<-sizes
df_1<-melt(obs_mat_comp)
colnames(df_1)<-c("Year","Size","Proportion")
df_1$quant<-'Observed'
df2<-data.frame(pred=apply(outs[[take_ind]]$'mature numbers at size',2,median),
Size=(sizes))
allLevels <- levels(factor(c(df_1$Size,df2$Size)))
df_1$Size <- factor(df_1$Size,levels=(allLevels))
df2$Size <- factor(df2$Size,levels=(allLevels))
mat_size<-ggplot(data=df2,aes(x = factor(Size), y =pred)) +
geom_boxplot(data=df_1,aes(x = Size, y =Proportion ),fill='grey') +
stat_summary(fun=mean, geom="line", aes(group=1),lwd=1.5,col='blue',alpha=.8) +
ylab("")+
xlab("Carapace width (mm)")+
theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
panel.background=element_blank(),
panel.grid=element_blank(),
axis.line.x=element_line(colour="black"))
#==fits index
# imm
div_n<-1000000000
df_1<-data.frame(pred=outs[[take_ind]]$imm_numbers_pred/div_n,
obs=outs[[1]]$imm_n_obs/div_n,
year=years,
ci_dn=(outs[[1]]$imm_n_obs/div_n) / exp(1.96*sqrt(log(1+imm_cv^2))),
ci_up=(outs[[1]]$imm_n_obs/div_n) * exp(1.96*sqrt(log(1+imm_cv^2))),
est_ci_up=outs[[take_ind]]$imm_numbers_pred/div_n + 1.96*imm_sd/div_n,
est_ci_dn=outs[[take_ind]]$imm_numbers_pred/div_n - 1.96*imm_sd/div_n,
recruits=(outs[[take_ind]]$recruits)/div_n)
df_1<-df_1[-32,]
imm_abnd<-ggplot(data=df_1)+
geom_segment(aes(x=year,xend=year,y=ci_dn,yend=ci_up))+
geom_point(aes(x=year,y=obs))+
geom_line(aes(x=year,y=pred),lwd=1,col='blue',alpha=.8)+
geom_ribbon(aes(x=year,ymin=est_ci_dn,ymax=est_ci_up),fill='blue',alpha=0.2)+
scale_y_continuous(breaks=c(0,2.5,5.0),limits=c(-0.1,6))+
ylab("IMMATURE
Abundance (billions)")+
theme(axis.line.y = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),)
# mature
df_1<-data.frame(pred=outs[[take_ind]]$mat_numbers_pred/div_n,
obs=outs[[1]]$mat_n_obs/div_n,
year=years,
ci_dn=(outs[[1]]$mat_n_obs/div_n) / exp(1.96*sqrt(log(1+mat_cv^2))),
ci_up=(outs[[1]]$mat_n_obs/div_n) * exp(1.96*sqrt(log(1+mat_cv^2))),
est_ci_up=outs[[take_ind]]$mat_numbers_pred/div_n + 1.96*mat_sd/div_n,
est_ci_dn=outs[[take_ind]]$mat_numbers_pred/div_n - 1.96*mat_sd/div_n)
df_1<-df_1[-32,]
mat_abnd<-ggplot(data=df_1)+
geom_segment(aes(x=year,xend=year,y=ci_dn,yend=ci_up))+
geom_point(aes(x=year,y=obs))+
geom_line(aes(x=year,y=pred),lwd=1,col='blue',alpha=.8)+
geom_ribbon(aes(x=year,ymin=est_ci_dn,ymax=est_ci_up),fill='blue',alpha=0.2)+
theme_bw()+
scale_y_continuous(position = "left",limits=c(0,2.1))+
ylab("MATURE
Abundance (billions)")+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
#===================================================================
# READ IN COVARIATES
#===================================================================
#survDAT<-read.csv("data/EBSCrab_Haul/EBSCrab_Haul.csv",header=T,skip=5)
load("survey_data.RData")
drvYear<-as.numeric(substr(survDAT$CRUISE,1,4))
SurvYR<-unique(drvYear)
drvYear<-as.numeric(substr(survDAT$CRUISE,1,4))
SurvYR<-unique(drvYear)
AllStation<-unique(survDAT$GIS_STATION)
#==plot GIS stations
AllStnLoc<-matrix(ncol=2,nrow=length(AllStation))
for(w in 1:length(AllStation))
{
temp<-survDAT[survDAT$GIS_STATION==AllStation[w],]
AllStnLoc[w,1]<-temp$MID_LATITUDE[1]
AllStnLoc[w,2]<-temp$MID_LONGITUDE[1]
}
unq_ebs_station<-unique(AllStation)
#===================================================================
#==set up for map making
#===================================================================
nmiSurv<-140350
DensityM_45_55<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
DensityF_45_55<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
DensityM_55_65<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
DensityM_65_75<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
DensityM_45_85<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
DensityM_78_100<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
DensityM101<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
bcd_density<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
num_M_45_85<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
num_M_101<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
StationYr<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
station_bot_temp<-matrix(nrow=length(SurvYR),ncol=length(AllStation))
for(y in 1:length(SurvYR))
{
yrDAT<-survDAT[drvYear==SurvYR[y],]
fileyr<-SurvYR[y]
stationsUNQ<-(unique(yrDAT$GIS_STATION))
#==density at station
for(j in 1:length(stationsUNQ))
{
stationALL<-yrDAT[yrDAT$GIS_STATION==stationsUNQ[j],]
StationYr[y,j]<-as.character(stationsUNQ[j])
Hauls<-(unique(stationALL$HAUL))
female_45_55<-stationALL[stationALL$SEX==2 & stationALL$WIDTH>44 & stationALL$WIDTH<56,]
male_45_55<-stationALL[stationALL$SEX==1 & stationALL$WIDTH>44 & stationALL$WIDTH<56,]
male_55_65<-stationALL[stationALL$SEX==1 & stationALL$WIDTH>55 & stationALL$WIDTH<66,]
male_65_75<-stationALL[stationALL$SEX==1 & stationALL$WIDTH>65 & stationALL$WIDTH<76,]
male_45_85<-stationALL[stationALL$SEX==1 & stationALL$WIDTH>44 & stationALL$WIDTH<86,]
male_78_100<-stationALL[stationALL$SEX==1 & stationALL$WIDTH>78 & stationALL$WIDTH<101,]
Males101<-stationALL[stationALL$SEX==1& stationALL$WIDTH>101,]
#==densities across hauls in crabs per km^2
tempDensM45<-NULL
tempDensF45<-NULL
tempDensM55<-NULL
tempDensM65<-NULL
tempDensM85<-NULL
tempDensM75101<-NULL
tempDensM101<-NULL
tempDensF<-NULL
temp_num_45<-0
temp_num_101<-0
for(k in 1:length(Hauls))
{
SampFactM<-female_45_55$SAMPLING_FACTOR[which(female_45_55$HAUL==Hauls[k])[1]]
AreaSweptM<-female_45_55$AREA_SWEPT[which(female_45_55$HAUL==Hauls[k])[1]]
tempDensF45<-length(female_45_55$HAUL==Hauls[k])*SampFactM/AreaSweptM
SampFactM<-male_45_55$SAMPLING_FACTOR[which(male_45_55$HAUL==Hauls[k])[1]]
AreaSweptM<-male_45_55$AREA_SWEPT[which(male_45_55$HAUL==Hauls[k])[1]]
tempDensM45<-length(male_45_55$HAUL==Hauls[k])*SampFactM/AreaSweptM
SampFactM<-male_55_65$SAMPLING_FACTOR[which(male_55_65$HAUL==Hauls[k])[1]]
AreaSweptM<-male_55_65$AREA_SWEPT[which(male_55_65$HAUL==Hauls[k])[1]]
tempDensM55<-length(male_55_65$HAUL==Hauls[k])*SampFactM/AreaSweptM
SampFactM<-male_65_75$SAMPLING_FACTOR[which(male_65_75$HAUL==Hauls[k])[1]]
AreaSweptM<-male_65_75$AREA_SWEPT[which(male_65_75$HAUL==Hauls[k])[1]]
tempDensM65<-length(male_65_75$HAUL==Hauls[k])*SampFactM/AreaSweptM
SampFactM<-male_45_85$SAMPLING_FACTOR[which(male_45_85$HAUL==Hauls[k])[1]]
AreaSweptM<-male_45_85$AREA_SWEPT[which(male_45_85$HAUL==Hauls[k])[1]]
tempDensM85<-length(male_45_85$HAUL==Hauls[k])*SampFactM/AreaSweptM
temp_num_45<-temp_num_45+length(Males101$HAUL==Hauls[k])*SampFactM
SampFactM<-male_78_100$SAMPLING_FACTOR[which(male_78_100$HAUL==Hauls[k])[1]]
AreaSweptM<-male_78_100$AREA_SWEPT[which(male_78_100$HAUL==Hauls[k])[1]]
tempDensM78101<-length(male_78_100$HAUL==Hauls[k])*SampFactM/AreaSweptM
SampFactM<-Males101$SAMPLING_FACTOR[which(Males101$HAUL==Hauls[k])[1]]
AreaSweptM<-Males101$AREA_SWEPT[which(Males101$HAUL==Hauls[k])[1]]
tempDensM101<-length(Males101$HAUL==Hauls[k])*SampFactM/AreaSweptM
temp_num_101<-temp_num_101+length(Males101$HAUL==Hauls[k])*SampFactM
}
DensityF_45_55[y,j]<-mean(tempDensF45)
DensityM_45_55[y,j]<-mean(tempDensM45)
DensityM_55_65[y,j]<-mean(tempDensM55)
DensityM_65_75[y,j]<-mean(tempDensM65)
DensityM_45_85[y,j]<-mean(tempDensM85)
DensityM_78_100[y,j]<-mean(tempDensM78101)
DensityM101[y,j]<-mean(tempDensM101)
num_M_45_85[y,j]<-temp_num_45
num_M_101[y,j]<-temp_num_101
station_bot_temp[y,j]<-stationALL$GEAR_TEMPERATURE[1]
bcd_density[y,j]<-sum(stationALL$SAMPLING_FACTOR[which(stationALL$DISEASE_CODE==2)])/sum(stationALL$SAMPLING_FACTOR)
}
}
ebs_dat<-data.frame(log_abund_101=log(c(t(DensityM101[-1,]))),
recruits=c(t(DensityM_78_100[-nrow(DensityM_78_100),])),
bot_temp=c(t(station_bot_temp[-nrow(station_bot_temp),])),
station=c(t(StationYr[-1,])),
lat=rep(AllStnLoc[,1],(nrow(DensityM_78_100)-1)),
lon=rep(AllStnLoc[,2],(nrow(DensityM_78_100)-1)),
year=rep(SurvYR[-1],each=ncol(DensityM101)),
log_abund_101=log(c(t(DensityM101[-1,]))),
log_abund_45=log(c(t(DensityM_45_55[-1,]))),
log_abund_45_f=log(c(t(DensityF_45_55[-1,]))),
log_abund_55=log(c(t(DensityM_55_65[-1,]))),
log_abund_65=log(c(t(DensityM_65_75[-1,]))),
log_abund_85=log(c(t(DensityM_45_85[-1,]))),
loc="EBS",
obs_num_101=c(t(num_M_101[-1,])),
obs_num_45_85=c(t(num_M_45_85[-1,])),
bitter_crab=(c(t(bcd_density[-1,]))))
for(x in 1:length(AllStation))
{
ebs_dat$lat[ebs_dat$station==AllStation[x]]<-AllStnLoc[x,1]
ebs_dat$lon[ebs_dat$station==AllStation[x]]<-AllStnLoc[x,2]
}
size_covar_1<-30
size_covar_2<-95
#==make another survDAT that makes uses a cutoff for mature and immature
pred_n_size_imm<-sweep(outs[[take_ind]]$'immature numbers at size', 1, outs[[take_ind]]$imm_numbers_pred, FUN="*")
rownames(pred_n_size_imm)<-years
colnames(pred_n_size_imm)<-sizes
pred_n_size_mat<-sweep(outs[[take_ind]]$'mature numbers at size', 1, outs[[take_ind]]$mat_numbers_pred, FUN="*")
rownames(pred_n_size_mat)<-years
colnames(pred_n_size_mat)<-sizes
ugh<-pred_n_size_imm/(pred_n_size_imm+pred_n_size_mat)
mid_size<-rep(0,nrow(ugh))
for(x in 1:nrow(ugh))
mid_size[x]<-as.numeric(names(which(abs(ugh[x,]-0.5)==min(abs(ugh[x,]-0.5)))))
larp<-data.frame(Size=mid_size[-length(mid_size)],Year=c(seq(1988,2019)))
png("plots/mat_midpt.png",height=5,width=8,res=400,units='in')
ggplot(data=larp,aes(x=Year,y=Size))+
geom_line()+theme_bw()+geom_point()
dev.off()
survDAT_mat<-NULL
survDAT_imm<-NULL
for(x in 1:length(years))
{
if(years[x]>1988)
{
temp_d<-survDAT[survDAT$AKFIN_SURVEY_YEAR==years[x],]
survDAT_mat<-rbind(survDAT_mat,temp_d[nchar(temp_d$GIS_STATION)==4 & temp_d$WIDTH>mid_size[x],])
survDAT_imm<-rbind(survDAT_imm,temp_d[nchar(temp_d$GIS_STATION)==4 & temp_d$WIDTH<=mid_size[x],])
}
}
#===================================================================
# Disease
#===================================================================
in_break<-seq(0, max(survDAT$WIDTH,na.rm=T), 5)
mid_pts<-rep(0,length(in_break)-1)
for(x in 1:length(mid_pts))
mid_pts[x] <- (in_break[x]+in_break[x+1])/2
#==mature disease
tmp_tot_mat<-survDAT_mat %>%
group_by(AKFIN_SURVEY_YEAR ,group = cut(WIDTH, breaks =in_break),SEX) %>%
summarise(bin_n1 = sum(SAMPLING_FACTOR ))
tmp_tot_mat$mid_pt<-mid_pts[tmp_tot_mat$group]
tmp_dis_mat<-survDAT_mat %>%
group_by(AKFIN_SURVEY_YEAR ,group = cut(WIDTH, breaks =in_break),SEX) %>%
summarise(bin_n1 = sum(DISEASE_CODE==2 * SAMPLING_FACTOR,na.rm=T ))
tmp_dis_mat$mid_pt<-mid_pts[tmp_dis_mat$group]
tot_dis_mat<-tmp_tot_mat
tot_dis_mat$disease<-tmp_dis_mat$bin_n1
tot_dis_mat$prop<-tot_dis_mat$disease/tot_dis_mat$bin_n1
tot_dis_mat$mid_pt<-mid_pts[tot_dis_mat$group]
use_disease_mat<-filter(tot_dis_mat,SEX==1 & AKFIN_SURVEY_YEAR>1988 & mid_pt>size_covar_1 & mid_pt<size_covar_2 & bin_n1>1)%>%
group_by(AKFIN_SURVEY_YEAR)%>%
summarize(med_dis=median(prop))
#==immature disease
tmp_tot_imm<-survDAT_imm %>%
group_by(AKFIN_SURVEY_YEAR ,group = cut(WIDTH, breaks =in_break),SEX) %>%
summarise(bin_n1 = sum(SAMPLING_FACTOR ))
tmp_tot_imm$mid_pt<-mid_pts[tmp_tot_imm$group]
tmp_dis_imm<-survDAT_imm %>%
group_by(AKFIN_SURVEY_YEAR ,group = cut(WIDTH, breaks =in_break),SEX) %>%
summarise(bin_n1 = sum(DISEASE_CODE==2 * SAMPLING_FACTOR,na.rm=T ))
tmp_dis_imm$mid_pt<-mid_pts[tmp_dis_imm$group]
tot_dis_imm<-tmp_tot_imm
tot_dis_imm$disease<-tmp_dis_imm$bin_n1
tot_dis_imm$prop<-tot_dis_imm$disease/tot_dis_imm$bin_n1
tot_dis_imm$mid_pt<-mid_pts[tot_dis_imm$group]
use_disease_imm<-filter(tot_dis_imm,SEX==1 & AKFIN_SURVEY_YEAR>1988 & mid_pt>size_covar_1 & mid_pt<size_covar_2 & bin_n1>1)%>%
group_by(AKFIN_SURVEY_YEAR)%>%
summarize(med_dis=median(prop))
#===================================================================
# Temperature occupied
#===================================================================
in_break<-seq(0, max(survDAT$WIDTH,na.rm=T), 5)
mid_pts<-rep(0,length(in_break)-1)
for(x in 1:length(mid_pts))
mid_pts[x] <- (in_break[x]+in_break[x+1])/2
#==temperature index by 'maturity state'
temp_tot_ind_imm<-filter(survDAT_imm, SEX<2) %>%
group_by(AKFIN_SURVEY_YEAR ,SEX) %>%
summarise(temp_occ = weighted.mean(x=GEAR_TEMPERATURE,w=SAMPLING_FACTOR,na.rm=T))
temp_tot_ind_imm$Maturity<-"Immature"
temp_tot_ind_mat<-filter(survDAT_mat, SEX<2) %>%
group_by(AKFIN_SURVEY_YEAR ,SEX) %>%
summarise(temp_occ = weighted.mean(x=GEAR_TEMPERATURE,w=SAMPLING_FACTOR,na.rm=T))
temp_tot_ind_mat$Maturity<-"Mature"
#==to make size composition
temp_tot<-survDAT %>%
group_by(AKFIN_SURVEY_YEAR ,group = cut(WIDTH, breaks =in_break),SEX) %>%
summarise(temp_occ = weighted.mean(x=GEAR_TEMPERATURE,w=SAMPLING_FACTOR,na.rm=T))
temp_tot$mid_pt<-mid_pts[temp_tot$group]
wide_tmp_occ<-dcast(filter(temp_tot,SEX==1), AKFIN_SURVEY_YEAR~mid_pt,value.var='temp_occ')
rownames(wide_tmp_occ)<-wide_tmp_occ[,1]
wide_tmp_occ<-wide_tmp_occ[,which(as.numeric(colnames(wide_tmp_occ))>size_covar_1&as.numeric(colnames(wide_tmp_occ))<size_covar_2)]
wide_tmp_occ<-wide_tmp_occ[rownames(wide_tmp_occ)>1988,]
wtd_avg_temp_occ_mat<-rep(0,nrow(wide_tmp_occ))
wtd_avg_temp_occ_imm<-rep(0,nrow(wide_tmp_occ))
for(x in 1:(nrow(wide_tmp_occ)))
{
wtd_avg_temp_occ_imm[x]<-weighted.mean(x=wide_tmp_occ[x,],w=pred_n_size_imm[x,])
wtd_avg_temp_occ_mat[x]<-weighted.mean(x=wide_tmp_occ[x,],w=pred_n_size_mat[x,])
}
names(wtd_avg_temp_occ_imm)<-rownames(wide_tmp_occ)
names(wtd_avg_temp_occ_mat)<-rownames(wide_tmp_occ)
#===================================================================
# cannibalism index
#===================================================================
cannibals<-read.csv('data/cannibal_index.csv',header=T)
cannibal_ind_imm<-filter(cannibals,X2<52)%>%
group_by(X1)%>%
summarize(med_cannib=mean(value))
colnames(cannibal_ind_imm)[1]<-"year"
#===================================================================
# fishery data
#===================================================================
fish_dat2<-read.csv("data/fisheries_dat.csv")
#===================================================================
# make covariate dataframe
#===================================================================
covars_imm<-data.frame(year=seq(1989,2020),
temperature=NA,
disease=NA,
cannibalism=NA,
bycatch=NA,
discard=NA,
predation=NA,
imm_pop = NA,
mat_pop = NA)
covars_mat<-data.frame(year=seq(1989,2020),
temperature=NA,
disease=NA,
cannibalism=NA,
bycatch=NA,
discard=NA,
predation=NA,
imm_pop = NA,
mat_pop = NA)
#===================================================================
# Total population numbers
#===================================================================
use_mat_sel<-outs[[take_ind]]$pred_mat_pop_num
use_imm_sel<-outs[[take_ind]]$pred_imm_pop_num
tot_imm_pop<-apply(use_imm_sel,1,sum)
tot_mat_pop<-apply(use_mat_sel,1,sum)
covars_imm$temperature[which(!is.na(match(covars_imm$year,names(wtd_avg_temp_occ_imm))))]<-scale(wtd_avg_temp_occ_imm[which(!is.na(match(names(wtd_avg_temp_occ_imm),covars_imm$year)))])
covars_imm$disease[which(!is.na(match(covars_imm$year,use_disease_imm$AKFIN_SURVEY_YEAR)))]<-scale(use_disease_imm$med_dis[which(!is.na(match(use_disease_imm$AKFIN_SURVEY_YEAR,covars_imm$year)))])
covars_imm$cannibalism[which(!is.na(match(covars_imm$year,cannibal_ind_imm$year)))]<-scale(cannibal_ind_imm$med_cannib[which(!is.na(match(cannibal_ind_imm$year,covars_imm$year)))])
covars_imm$imm_pop[which(!is.na(match(covars_imm$year,years)))]<-scale(tot_imm_pop[which(!is.na(match(years,covars_imm$year)))])
covars_imm$mat_pop[which(!is.na(match(covars_imm$year,years)))]<-scale(tot_mat_pop[which(!is.na(match(years,covars_imm$year)))])
covars_mat$temperature[which(!is.na(match(covars_mat$year,names(wtd_avg_temp_occ_mat))))]<-scale(wtd_avg_temp_occ_mat[which(!is.na(match(names(wtd_avg_temp_occ_mat),covars_mat$year)))])
covars_mat$disease[which(!is.na(match(covars_mat$year,use_disease_mat$AKFIN_SURVEY_YEAR)))]<-scale(use_disease_mat$med_dis[which(!is.na(match(use_disease_mat$AKFIN_SURVEY_YEAR,covars_mat$year)))])
covars_mat$imm_pop[which(!is.na(match(covars_mat$year,years)))]<-scale(tot_imm_pop[which(!is.na(match(years,covars_mat$year)))])
covars_mat$mat_pop[which(!is.na(match(covars_mat$year,years)))]<-scale(tot_mat_pop[which(!is.na(match(years,covars_mat$year)))])
common_yrs<-intersect(fish_dat2$Year,rownames(pred_n_size_imm))
use_bycatch<-fish_dat2$bycatch[which(!is.na(match(fish_dat2$Year,common_yrs)))]
bycatch_mort<-scale(use_bycatch/(tot_mat_pop[-length(tot_mat_pop)]+tot_imm_pop[-length(tot_mat_pop)]))
covars_imm$bycatch<-bycatch_mort
covars_mat$bycatch<-bycatch_mort
common_yrs<-intersect(fish_dat2$Year,rownames(pred_n_size_imm))
use_discard<-fish_dat2$discard[which(!is.na(match(fish_dat2$Year,common_yrs)))]
discard_mort<-scale(use_discard/(tot_mat_pop[-length(tot_mat_pop)]))
covars_imm$discard<-NA
covars_mat$discard<-discard_mort
#===================================================================
#==cod predation
#===================================================================
alt_pred<-read.csv("data/cod_consump.csv")
wt_size<-read.csv("data/wt_at_size.csv")
use_wts<-filter(wt_size,Size%in%sizes)
common_yrs<-intersect(alt_pred$Year,rownames(pred_n_size_imm))
last_bin<-ncol(use_imm_sel)
tot_pred<-alt_pred$use_this[match(common_yrs,alt_pred$Year)]
cod_yrs<-alt_pred$Year[match(common_yrs,alt_pred$Year)]
#==uncomment this line of code and rerun analysis to explore impacts of different indices of predation
# tot_pred<-alt_pred$steve_50cm[match(common_yrs,alt_pred$Year)]
# tot_pred<-alt_pred$kir_nbs_ebs[match(common_yrs,alt_pred$Year)]
tot_crab_imm<-apply(outs[[1]]$`pred_imm_pop_num`[,1:13],1,sum)[-c(32,33)]
tot_crab_mat<-apply(outs[[1]]$`pred_mat_pop_num`[,1:13],1,sum)[-c(32,33)]
tot_crab_all<-(tot_crab_imm+tot_crab_mat)
covars_imm$predation[which(!is.na(match(covars_imm$year,alt_pred$Year)))]<-scale(tot_pred/tot_crab_all)
covars_mat$predation[which(!is.na(match(covars_mat$year,alt_pred$Year)))]<-scale(tot_pred/tot_crab_all)
#==show the comparison of different potential predation indices
all_cod<-alt_pred
colnames(all_cod)[2:3]<-c("Stomach contents","Abundance >50 cm")
tmp_pred<-all_cod$"Stomach contents"[match(common_yrs,all_cod$Year)]
tmp1<-data.frame(value=scale(tmp_pred/tot_crab_all),
Year=seq(1989,2019),
variable="Consumption ratio")
tmp_pred<-all_cod$"Abundance >50 cm"[match(common_yrs,all_cod$Year)]
tmp2<-data.frame(value=scale(tmp_pred/tot_crab_all),
Year=seq(1989,2019),
variable="Abundance ratio")
cod_fig<-melt(all_cod,id.vars="Year")
cod_fig_dat<-rbind(cod_fig,tmp1,tmp2)
raw_cod<-ggplot(filter(cod_fig_dat,variable=="Stomach contents" | variable =="Abundance >50 cm"))+
geom_line(aes(x=Year,y=value))+
facet_wrap(~variable,scale='free_y',ncol=1)+
theme_bw()+
expand_limits(y=0)+
ylab('')
ratio_cod<-ggplot(filter(cod_fig_dat,variable=="Consumption ratio" | variable =="Abundance ratio"))+
geom_line(aes(x=Year,y=value))+
facet_wrap(~variable,scale='free_y',ncol=1)+
theme_bw()+
expand_limits(y=0)+
ylab('') +
scale_y_continuous(position='right')+
geom_hline(data=data.frame(yint=c(0,0)),aes(yintercept=yint),lty=2)
png(paste("plots/cod_sensitivity.png",sep=''),height=6,width=8,res=350,units='in')
raw_cod | ratio_cod
dev.off()
#===================================================================
# plot all covars
#===================================================================
plot_imm<-melt(covars_imm,id=c("year"))
plot_mat<-melt(covars_mat,id=c("year"))
#====pairs plots
png(paste("plots/pairs_plot_mat.png",sep=''),height=8,width=8,res=350,units='in')
in_pairs<- covars_mat[which(complete.cases(covars_mat[,c(2,3,5,6,7,8,9)])),c(2,3,5,6,7,8,9)]
in_pairs$bycatch<-as.numeric(in_pairs$bycatch)
in_pairs$discard<-as.numeric(in_pairs$discard)
ggpairs(data=(in_pairs))
dev.off()
png(paste("plots/pairs_plot_imm.png",sep=''),height=8,width=8,res=350,units='in')
bleh<- covars_imm[which(complete.cases(covars_imm[,c(2:5,7,8,9)])),c(2:5,7,8,9)]
bleh$bycatch<-as.numeric(bleh$bycatch)
ggpairs(bleh)
dev.off()
#==time series plots
plot_mat$maturity<-"Mature covariates"
plot_imm$maturity<-"Immature covariates"
all_covars<-rbind(plot_mat,plot_imm)
var_on_var_all<-ggplot()+
geom_line(data=all_covars,aes(x=year,y=value,color=variable),lwd=1.25)+
#geom_line(data=plt_1,aes(x=Year,y=value),lwd=2)+
theme_bw()+
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank())+
#, legend.position=c(.1,.85))+
geom_hline(yintercept=0,lty=2)+facet_wrap(~maturity)
var_on_var_all2<-ggplot()+
geom_line(data=all_covars,aes(x=year,y=value,color=variable),lwd=1.25)+
#geom_line(data=plt_1,aes(x=Year,y=value),lwd=2)+
theme_bw()+
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank())+
#, legend.position=c(.1,.85))+
geom_hline(yintercept=0,lty=2)+facet_grid(vars(variable),vars(maturity))+
xlim(1992,2019)
png("plots/grid_covariates.png",height=8,width=5,res=350,units='in')
print(var_on_var_all2)
dev.off()
#===================================================================
# simulate mortality trajectories using covariance matrix
#===================================================================
getADMBHessian <- function(direc){
## Thank you, Cole Monnahan
filename <- file(paste(direc,"admodel.hes",sep=""), "rb")
on.exit(close(filename))
num.pars <- readBin(filename, "integer", 1)
hes.vec <- readBin(filename, "numeric", num.pars^2)
hes <- matrix(hes.vec, ncol=num.pars, nrow=num.pars)
hybrid_bounded_flag <- readBin(filename, "integer", 1)
scale <- readBin(filename, "numeric", num.pars)
result <- list(num.pars=num.pars, hes=hes,
hybrid_bounded_flag=hybrid_bounded_flag, scale=scale)
return(result)
}
admb_outs<-getADMBHessian(direc="C:/Users/cody.szuwalski/Work/snow_down/models/model_vary_m/")
cov <- solve(admb_outs$hes)
#==pull the rows of interest for covariance matrix
imm_m_ind<-seq(27,59)
mat_m_ind<-seq(60,92)
imm_m_cov<-cov[imm_m_ind,imm_m_ind]
mat_m_cov<-cov[mat_m_ind,mat_m_ind]
#==pull the estimated parameters
styr<-1989
endyr<-2021
years<-seq(styr,endyr)
par_file<-"models/model_vary_m/snow_down.par"
imm_devs <- scan(par_file,skip=6,n=length(years),quiet=T)
mat_devs <- scan(par_file,skip=8,n=length(years),quiet=T)
imm_m_mu <- scan(par_file,skip=18,n=2,quiet=T)[1]
mat_m_mu <- scan(par_file,skip=18,n=2,quiet=T)[2]
#==little matrix multiplication for simulated
library(MASS)
sim_imm_m<-mvrnorm(n=100,mu=imm_devs,Sigma=imm_m_cov)
sim_mat_m<-mvrnorm(n=100,mu=mat_devs,Sigma=mat_m_cov)
trans_sim_imm <- exp(imm_m_mu + sim_imm_m)
trans_sim_mat <- exp(mat_m_mu + sim_mat_m)
mle_imm<-exp(imm_m_mu + imm_devs)
mle_mat<- exp(mat_m_mu + mat_devs)
colnames(trans_sim_imm)<-years
colnames(trans_sim_mat)<-years
melted<-melt(t(trans_sim_imm))
melted2<-melt(t(trans_sim_mat))
melted$state<-"Immature"
melted2$state<-"Mature"
in_plot<-rbind(melted,melted2)
mle_est<-data.frame(X1=seq(1989,2021),value=c(mle_imm,mle_mat),state=c(rep("Immature",33),rep("Mature",33)))
png("plots/covariance_sims.png",height=8,width=5,res=400,units='in')
ggplot(filter(in_plot,X1<2020))+
geom_line(aes(x=X1,y=value,group=X2),alpha=0.1,color='grey')+
geom_line(data=filter(mle_est,X1<2020),aes(x=X1,y=value))+
theme_bw()+
facet_wrap(~state,scales='free_y',ncol=1)+
expand_limits(y=0)+
ylab("Estimated mortality")+
xlab("Year")
dev.off()
#===================================================================
# build models for mortality
#===================================================================
est_vars<-data.frame(year=years,
est_mat_m=(outs[[take_ind]]$'mature natural mortality')[,1],
est_imm_m=(outs[[take_ind]]$'natural mortality')[,1])
#===================================================================
#==mature mortality
#===================================================================
#==GAM==============
input_dat<-merge(est_vars[,c(1,2)],covars_mat)[,-c(5)]
input_dat<-input_dat[complete.cases(input_dat),]
colnames(input_dat)[2]<-"dep_var"
input_dat<-input_dat[-c(1),] #==chop first year of data because of initialization conditions
write.csv(input_dat,"mature_df_opilio.csv")
#==check a few models...mod_base is presented in text
mod_base<-gam(data=input_dat,(dep_var)~s(temperature,k=4)+s(disease,k=3)+s(discard,k=3)+s(bycatch,k=4)+s(mat_pop,k=3)+s(predation,k=3))
mod_base1<-gam(data=input_dat,(dep_var)~s(temperature,k=4)+s(disease,k=3)+s(discard,k=3)+s(bycatch,k=4)+s(mat_pop,k=3))
summary(mod_base)
plot(mod_base,page=1)
summary(mod_base1)
plot(mod_base1,page=1)
mod_base_temp<-gam(data=input_dat,(dep_var)~s(temperature,k=4))
summary(mod_base_temp)
plot(mod_base_temp,page=1)
mod_base_temp_mmb<-gam(data=input_dat,(dep_var)~s(temperature,k=4)+s(mat_pop,k=4))
summary(mod_base_temp_mmb)
plot(mod_base_temp,page=1)
AIC(mod_base1)
AIC(mod_base)
mod_base_t<-gam(data=input_dat,(dep_var)~s(temperature,k=4)+s(mat_pop,k=3) )
summary(mod_base_t)
plot(mod_base_t,page=1)
dev_expl_trim<-summary(mod_base_t)$dev.expl
#==try shape constrained additive models for mature population--monotonic increasing
b <- scam(data=input_dat,dep_var~s(temperature,k=3)+s(mat_pop,bs='mpi')+s(discard,k=3)+s(predation,k=3)+s(bycatch,k=3) )
b <- scam(data=input_dat,dep_var~s(disease,k=3)+s(temperature,k=3)+s(mat_pop,bs='mpi')+s(discard,k=3)+s(predation,k=3)+s(bycatch,k=3) )
b <- scam(data=input_dat,dep_var~disease+s(temperature,k=3)+s(mat_pop,bs='mpi')+discard+predation+bycatch )
summary(b)
plot(b,pages=1)
#==try betar to see if assumptions about distribution impact the
beta_dat<-input_dat
beta_dat$dep_var<-1-exp(-beta_dat$dep_var)
bm <- gam((dep_var)~s(temperature,k=4)+s(disease,k=3)+s(discard,k=3)+s(bycatch,k=4)+s(mat_pop,k=3)+s(predation,k=3),family=betar(link="logit"),data=beta_dat)
summary(bm)
plot(bm,pages=1)
#==predict mortality with averages vars and high temp
new_data<-data.frame(temperature=max(input_dat$temperature),
disease=mean(input_dat$disease),
discard=min(input_dat$discard),
bycatch=mean(input_dat$bycatch),
mat_pop=min(input_dat$mat_pop),
predation=mean(input_dat$predation))
predict(mod_base,new_data)
in_temp <-seq(min(input_dat$temperature),max(input_dat$temperature),length.out=20)
in_mmb <-seq(min(input_dat$mat_pop),max(input_dat$mat_pop),length.out=20)
new_dats<-expand.grid(in_temp,in_mmb)
new_data<-data.frame(temperature=new_dats[,1],
mat_pop=new_dats[,2])
outs<-matrix(predict(mod_base_temp_mmb,new_data),ncol=length(in_temp))
colnames(outs)<-in_temp
rownames(outs)<-in_mmb
mod_2d<-gam(data=input_dat,(dep_var)~s(temperature,mat_pop,k=15))
summary(mod_2d)
plot(mod_2d,too.far=1,scheme=2,cex=3,main='',ylab='Mature population',
xlab='Temperature occupied')
#==prep GAM tables and diagnostics for supplementary materials
gamtabs(mod_base,caption=paste("GAM output for full model predicting mature mortality. Deviance explained = ",round(100*summary(mod_base)$dev,2),"%"))
gamtabs(mod_base_t,caption=paste("GAM output for trimmed model predicting mature mortality. Deviance explained = ",round(100*summary(mod_base_t)$dev,2),"%"))
png("plots/gam_smooths_mat.png",height=5,width=8,res=350,units='in')
draw(mod_base,scales='fixed')&theme_bw()
dev.off()
png("plots/gam_check_full_mat.png",height=8,width=8,res=350,units='in')
par(mfrow=c(2,2))
gam.check(mod_base)
dev.off()
png("plots/gam_check_trim_mat.png",height=8,width=8,res=350,units='in')
par(mfrow=c(2,2))
gam.check(mod_base_t)
dev.off()
preds <- predict(mod_base, se.fit = TRUE)
my_data <- data.frame(input_dat,
mu = (preds$fit),
low = (preds$fit - 1.96 * preds$se.fit),
high = (preds$fit + 1.96 * preds$se.fit))
dev_expl<-summary(mod_base)$dev
#==cross validation============
cv_plot_mat_m<-list()
use_dat<-input_dat
dev_expl_cv<-rep(0,nrow(use_dat))
predicted_cv<-matrix(nrow=nrow(use_dat),ncol=nrow(use_dat)-1)
cv_yrs<-matrix(nrow=nrow(use_dat),ncol=nrow(use_dat)-1)
smooth_table<-list()
p_table<-list()
for(x in 1:nrow(use_dat))
{
cv_dat<-use_dat[-c(x),]
#mod_cv<-gam(data=cv_dat,dep_var~s(temperature,k=4)+s(disease,k=3)+s(discard,k=3)+s(bycatch,k=4)+s(mat_pop,k=3) )
mod_cv<-gam(data=cv_dat,(dep_var)~s(temperature,k=4)+s(disease,k=3)+s(discard,k=3)+s(bycatch,k=3)+s(mat_pop,k=3)+s(predation,k=3))
#summary(mod_cv)
#plot(mod_cv,pages=1)
dev_expl_cv[x]<-summary(mod_cv)$dev.expl
cv_plot_mat_m[[x]]<-plot(mod_cv,page=1)
predicted_cv[x,]<-predict(mod_cv)
cv_yrs[x,]<-cv_dat$year
smooth_table[[x]]<-summary(mod_cv)$s.table
p_table[[x]]<-summary(mod_cv)$p.table
print(x)
}
#==massage data for ggplots
mat_cv_M_outs<-data.frame(pred=c(predicted_cv),
year=c(cv_yrs),
replic=as.factor(rep(1:nrow(predicted_cv),times=nrow(predicted_cv)-1)),
fits="Mature mortality")
mat_cv_M_dev<-data.frame(deviance_explained=dev_expl_cv,
fits="Mature mortality")
in_names<-c("Temperature","Disease","Discard","Bycatch","Mature population","Predation")
smooth_cv_outs<-matrix(ncol=length(smooth_table),nrow=nrow(smooth_table[[1]]))
for(x in 1:length(smooth_table))
try(smooth_cv_outs[,x]<-smooth_table[[x]][,4],silent=TRUE)
rownames(smooth_cv_outs)<-in_names
colnames(smooth_cv_outs)<-seq(1,ncol(smooth_cv_outs))
mat_cv_M_pval<-melt(smooth_cv_outs)
colnames(mat_cv_M_pval)<-c("Process","Rep","Value")
mat_cv_M_pval$fits<-"Mature mortality"
plot_pval_mat<-ggplot(data=mat_cv_M_pval)+
geom_histogram(aes(x=Value,group=Process,fill=Process),bins=50,alpha=.8)+
theme_bw()+ geom_vline(aes(xintercept=0.05),lty=2)
#==test impact of uncertainty in estimates============
unc_plot_mat_m<-list()
use_dat<-input_dat
dev_expl_unc<-rep(0,nrow(trans_sim_mat))
predicted_unc<-matrix(nrow=nrow(trans_sim_mat),ncol=nrow(input_dat))
unc_yrs<-matrix(nrow=nrow(trans_sim_mat),ncol=nrow(input_dat))
smooth_table_unc<-list()
for(x in 1:nrow(trans_sim_mat))
{
unc_dat<-use_dat
use_dat$dep_var<-trans_sim_mat[x, which(!is.na(match(colnames(trans_sim_mat),use_dat$year)))]
mod_unc<-gam(data=use_dat,dep_var~s(temperature,k=4)+s(disease,k=3)+s(discard,k=3)+s(bycatch,k=4)+s(mat_pop,k=3)+s(predation,k=3) )
dev_expl_unc[x]<-summary(mod_unc)$dev.expl
predicted_unc[x,]<-predict(mod_unc)
unc_yrs[x,]<-use_dat$year
smooth_table_unc[[x]]<-summary(mod_unc)$s.table
}
#==massage data for ggplots
colnames(predicted_unc)<-unc_yrs[1,]
rownames(predicted_unc)<-seq(1,nrow(predicted_unc))
mat_unc<-melt(predicted_unc)
mat_unc$fits<-"Mature mortality"
mat_unc_M_dev<-data.frame(deviance_explained=dev_expl_unc,
fits="Mature mortality")
in_names<-c("Temperature","Disease","Discard","Bycatch","Mature population","Predation")
smooth_unc_outs<-matrix(ncol=length(smooth_table_unc),nrow=nrow(smooth_table_unc[[1]]))
for(x in 1:length(smooth_table_unc))
smooth_unc_outs[,x]<-smooth_table_unc[[x]][,4]
rownames(smooth_unc_outs)<-in_names
colnames(smooth_unc_outs)<-seq(1,ncol(smooth_unc_outs))
mat_unc_M_pval<-melt(smooth_unc_outs)
colnames(mat_unc_M_pval)<-c("Process","Rep","Value")
mat_unc_M_pval$fits<-"Mature mortality"
plot_pval_mat<-ggplot(data=mat_unc_M_pval)+
geom_histogram(aes(x=Value,group=Process,fill=Process),bins=50,alpha=.8)+
theme_bw()
#==prediction==================
my_data_mat_m_pred<-NULL
for(y in 0:2)
{
train_dat<-input_dat[-seq(nrow(input_dat)-y,nrow(input_dat)),]
mod_pred<-gam(data=train_dat,dep_var~s(temperature,k=4)+s(mat_pop,k=3))
preds<-predict(mod_pred, se.fit = TRUE,newdata=input_dat)
my_data_mat_m_pred <- rbind(my_data_mat_m_pred,data.frame(input_dat,
mu = (preds$fit),
low = (preds$fit - 1.96 * preds$se.fit),
high = (preds$fit + 1.96 * preds$se.fit),
peel= rep(y+1,length(preds$fit))))
}