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Main script including data prep and modeling.R
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Main script including data prep and modeling.R
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#### jags function for SOCB model
library(ggplot2)
library(ggrepel)
library(ggforce)
library(rjags)
library(jagsUI)
library(stringr)
library(MCMCvis)
library(mgcv)
ind = "index"
lci = "lci"
uci = "uci"
year = "year"
base.yr = 1970
popsource = "Pop.source"
set.seed(2019)
popest = read.csv("Rosenberg et al species list.csv",
stringsAsFactors = F)
popest$popse = ((popest$popestuci-popest$popestlci)/(1.96*2))
indicesraw = read.csv("Rosenberg et al annual indices of abundance.csv",
stringsAsFactors = F)
indicesraw = indicesraw[order(indicesraw$species,indicesraw$year),]
# ###################### GAM smoothing of indices
sps = popest$species
for(ss in sps){
wss = which(indicesraw$species == ss)
tmp = indicesraw[wss,]
fyr = unique(tmp$firstyear)
lyr = unique(tmp$lastyear)
torep = which(indicesraw$species == ss & (indicesraw$year >= fyr & indicesraw$year <= lyr))
if(any(!is.na(tmp$lci.raw))){
wprec = T
}else{
wprec = F
}
tmpd = tmp[which(!is.na(tmp$index.raw)),]
nknots = min(c(11,max(floor(nrow(tmpd)/3),3)))
if(ss %in% c("Cackling Goose","Greater White???fronted Goose")){nknots = 3}
form = as.formula(paste("lindex","~",
"s(year,k =",nknots,")"))
ncounts = nrow(tmpd)
###### building the GAM basis function
# gam basis functions created using function jagam from mgcv package
yminy = min(tmpd$year,na.rm = T)
ymaxy = max(tmpd$year,na.rm = T)
yearvec = tmpd$year-(yminy-1)
yrs = seq(yminy,ymaxy,by = 1)
nyears = length(yrs)
ymin = min(yearvec)
ymax = max(yearvec)
tmpd$lindex = log(tmpd$index.raw)
lindex = tmpd$lindex
preddat = data.frame(lindex = 1,
year = yrs)
if(wprec){
### if true then jags model runs
#### setting of the number of knots
preci = 1/(((log(tmpd[,"uci.raw"])-log(tmpd[,"lci.raw"]))/(1.96*2))^2)
gamprep = jagam(formula = form,
data = tmpd,
file = "tempgam.txt",
centred = T)
gamprep.pred = jagam(formula = form,
data = preddat,
file = "tempgampred.txt",
centred = T)
dat = list(X = gamprep$jags.data$X,
S1 = gamprep$jags.data$S1,
ncounts = nrow(tmpd),
lindex = lindex,
nknots = nknots,
preci = preci,
X.pred = gamprep.pred$jags.data$X,
zero = gamprep$jags.data$zero)
mg <- jags.model(data = dat,
file = paste0("GAM model smoothing indices jagam.txt"),
n.chains = 3)
adaptest <- adapt(object = mg,
n.iter = 10)
while(adaptest == F){
adaptest <- adapt(object = mg,
n.iter = 1000)
}
nburn = 10000
mgo = coda.samples(mg,
c("ind.pred"
#"rho"
#"mu",
#"b"
),
n.iter = 10000,
n.burnin = nburn,
thin = 10)
mgosum = summary(mgo)
predg = mgosum$quantiles
predgs = mgosum$statistics
indicesraw[torep,"index"] <- exp(predg[grep(row.names(predg),pattern = "ind.pred"),"50%"])
indicesraw[torep,"lci"] <- exp(predg[grep(row.names(predg),pattern = "ind.pred"),"2.5%"])
indicesraw[torep,"uci"] <- exp(predg[grep(row.names(predg),pattern = "ind.pred"),"97.5%"])
}else{ ### else if wprec
m1 = gam(formula = form,
data = tmpd)
pred = predict(m1,newdata = preddat,
type = "link",
se.fit = T)
indicesraw[torep,"index"] <- exp(pred$fit)
indicesraw[torep,"lci"] <- exp(pred$fit-(1.96*pred$se.fit))
indicesraw[torep,"uci"] <- exp(pred$fit+(1.96*pred$se.fit))
} ### end if wprec
}
save(indicesraw,file = "output/post GAM indices.RDATA")
######################### end GAM smoothing of annual indices
load("output/post GAM indices.RDATA")
indices = indicesraw
yrs = sort(unique(indices$year))
indices[,"se"] <- ((indices[,uci]-indices[,lci])/(1.96*2))
indices <- indices[order(indices$firstyear,indices$species,indices$year),]
splist <- unique(indices[,c("species","firstyear","lastyear")])
splist3 = merge(splist,popest,by.x = "species",by.y = "species")
splist3$spfactor <- factor(splist3$species,
levels = splist3$species,
ordered = T)
splist3$spfact <- as.integer(splist3$spfactor)
indices$spfactor = factor(indices$species,
levels = splist3$species,
ordered = T)
splist = splist3
base.i <- rep(NA, length = length(unique(indices$species)))
names(base.i) <- unique(indices$species)
base.se.sp <- rep(NA, length = length(unique(indices$species)))
names(base.se.sp) <- unique(indices$species)
se = "se"
for (sn in 1:nrow(splist)) {
s = as.character(splist[sn,"species"])
r <- which(indices[,"species"] == s)
rmpre = which(indices[,"species"] == s &
is.na(indices[,ind]) &
indices[,"year"] < splist[sn,"firstyear"])
rmpost = which(indices[,"species"] == s &
is.na(indices[,"se"]) &
indices[,"year"] > splist[sn,"lastyear"])
base.s <- indices[which(indices$species == s & indices$year == base.yr),ind] #stores the base index value
base.se <- indices[which(indices$species == s & indices$year == base.yr),se] #stores the base se value
if(is.na(base.s)){
byr <- min(indices[which(indices$species == s & !is.na(indices[,ind])),"year"],na.rm = T)
base.s <- indices[which(indices$species == s & indices$year == byr),ind] #stores the base index value
base.se <- indices[which(indices$species == s & indices$year == byr),se] #stores the base se value
}
for (y in r) {
indices[y,"index.s"] <- (indices[y,ind])/base.s # standardized index WRT base year
indices[y,"cvar.s"] <- (((indices[y,se]^2)/(indices[y,ind]^2))+((base.se^2)/(base.s^2)))
indices[y,"logthetahat"] <- log(indices[y,"index.s"])
indices[y,"prec.logthetahat"] <- 1/log(1+(indices[y,"cvar.s"]))
}
print(s)
}
indices = merge(indices,splist[,c("species","spfactor","spfact")],by = "species")
indices$year_i = (indices$year - base.yr)+1
indices = indices[order(indices$spfact,indices$year_i),]
splist = splist[order(splist$spfact),]
nspecies <- nrow(splist)
nyears = max(indices$year_i)
splist$g1 = as.integer(factor(splist$Winter.Biome))
splist$g2 = as.integer(factor(splist$Breeding.Biome))
grps = as.matrix(splist[,c("g1","g2")])
ngroups1 = max(grps[,1])
ngroups2 = max(grps[,2])
ident = function(x){
return(x)
}
logthetahat <- as.matrix(tapply(indices[,"logthetahat"],indices[,c("spfact","year_i")],ident))
prec.logthetahat <- as.matrix(tapply(indices[,"prec.logthetahat"],indices[,c("spfact","year_i")],ident))
ne = splist$popest #population estimate
tau.ne = 1/splist$popse^2 #precision of population estimate
#
#
yest = (splist$year_est1-base.yr)+1 # first year over which species population should be averaged
yavg = 1+(splist$year_est2-splist$year_est1) ## number of years over which to average the species population estimate
##### species group indexing
subgrps = unique(grps)
subgrps = subgrps[order(subgrps[,1],subgrps[,2]),]
nsubgrps = table(subgrps[,1])
subgrpsl = matrix(NA,ncol = ngroups1,nrow = max(nsubgrps))
nsppsubbiomesmat = table(grps[,1],grps[,2])
spsubgrpmat = array(NA,dim = c(ngroups1,ngroups2,max(nsppsubbiomesmat)))
for(i in 1:ngroups1){
subs = subgrps[which(subgrps[,1] == i),2]
subgrpsl[1:nsubgrps[i],i] = subs
for(j in subs){
spsubgrpmat[i,j,1:nsppsubbiomesmat[i,j]] = which(grps[,1] == i & grps[,2] == j)
}
}#i
#### breeding biome indexing
nsppbiomes = table(grps[,2])
spinbiomes = matrix(NA,nrow = max(nsppbiomes),ncol = max(ngroups2))
for(g in 1:ngroups2){
spinbiomes[1:nsppbiomes[g],g] <- which(grps[,2] == g)
}
### wintering biome indexing
nsppwinters = table(grps[,1])
spinwinters = matrix(NA,nrow = max(nsppwinters),ncol = max(ngroups1))
for(g in 1:ngroups1){
spinwinters[1:nsppwinters[g],g] <- which(grps[,1] == g)
}
### indexing for family summaries
splist$famfact = (factor(splist$Family))
splist$famfactn = as.integer(factor(splist$Family))
nsppfams = table(splist$famfact)
spinfams = matrix(NA,nrow = max(nsppfams),ncol = length(nsppfams))
for(f in 1:length(nsppfams)){
fn = names(nsppfams)[f]
spinfams[1:nsppfams[f],f] <- which(splist$Family == fn)
}
nfams = length(nsppfams)
fams = unique(splist[,c("Family","famfact","famfactn")])
fams = fams[order(fams$famfactn),]
#indexing for bird.group summaries
splist$birdgroupfact = (factor(splist$bird.group))
splist$birdgroupfactn = as.integer(factor(splist$bird.group))
nsppbirdgroups = table(splist$birdgroupfact)
spinbirdgroups = matrix(NA,nrow = max(nsppbirdgroups),ncol = length(nsppbirdgroups))
for(f in 1:length(nsppbirdgroups)){
fn = names(nsppbirdgroups)[f]
spinbirdgroups[1:nsppbirdgroups[f],f] <- which(splist$bird.group == fn)
}
nbirdgroups = length(nsppbirdgroups)
birdgroups = unique(splist[,c("bird.group","birdgroupfact","birdgroupfactn")])
birdgroups = birdgroups[order(birdgroups$birdgroupfactn),]
#indexing for migration summaries
splist$migratefact = (factor(splist$Migrate))
splist$migratefactn = as.integer(factor(splist$Migrate))
nsppmigrates = table(splist$migratefact)
spinmigrates = matrix(NA,nrow = max(nsppmigrates),ncol = length(nsppmigrates))
for(f in 1:length(nsppmigrates)){
fn = names(nsppmigrates)[f]
spinmigrates[1:nsppmigrates[f],f] <- which(splist$Migrate == fn)
}
nmigrates = length(nsppmigrates)
migrates = unique(splist[,c("Migrate","migratefact","migratefactn")])
migrates = migrates[order(migrates$migratefactn),]
#indexing for ai summaries
splist$aifact = (factor(splist$AI))
splist$aifactn = as.integer(factor(splist$AI))
nsppais = table(splist$aifact)
spinais = matrix(NA,nrow = max(nsppais),ncol = length(nsppais))
for(f in 1:length(nsppais)){
fn = names(nsppais)[f]
spinais[1:nsppais[f],f] <- which(splist$AI == fn)
}
nais = length(nsppais)
ais = unique(splist[,c("AI","aifact","aifactn")])
ais = ais[order(ais$aifactn),]
#indexing for native summaries
splist$nativefact = (factor(splist$native))
splist$nativefactn = as.integer(factor(splist$native))
nsppnatives = table(splist$nativefact)
spinnatives = matrix(NA,nrow = max(nsppnatives),ncol = length(nsppnatives))
for(f in 1:length(nsppnatives)){
fn = names(nsppnatives)[f]
spinnatives[1:nsppnatives[f],f] <- which(splist$native == fn)
}
nnatives = length(nsppnatives)
natives = unique(splist[,c("native","nativefact","nativefactn")])
natives = natives[order(natives$nativefactn),]
#imputing the missing data with assumptions of no-change from most recent year with data and gradually decreasing precision
wspecieslate = as.integer(splist[which(splist$firstyear > 1970),"spfact"])
yearswo1late = rep(1,nspecies)
yearswo2late = (splist[,"firstyear"])-1970
wspeciesearly = as.integer(splist[which(splist$lastyear < 2017),"spfact"])
yearswo1early = (splist[,"lastyear"]+1)-1969
yearswo2early = rep(nyears,nspecies)
yearsw1 = (splist[,"firstyear"])-1969
yearsw2 = (splist[,"lastyear"])-1969
prec.powdrop = 2 #exponential function for decreasing precision with years (i.e., precision decreases with square of the years since real data)
for( s in wspecieslate) { # wspecieslate = vector of species that don't have data in year-1
for(y in yearswo1late[s]:yearswo2late[s]){
logthetahat[s,y] <- logthetahat[s,yearsw1[s]]
prec.logthetahat[s,y] <- prec.logthetahat[s,yearsw1[s]]/((yearsw1[s]-y)^prec.powdrop)#
}}
### imputing missing data for species without data at the end of the time series
## currently assumes that the precision decreases with the square of the number of years since the last data
for( s in wspeciesearly) { # wspecieslate = vector of species that don't have data in year-1
for(y in yearswo1early[s]:yearswo2early[s]){
logthetahat[s,y] <- logthetahat[s,yearsw2[s]]
prec.logthetahat[s,y] <- prec.logthetahat[s,yearsw2[s]]/((y-yearsw2[s])^prec.powdrop)#
}}
data.jags = list(nspecies = nspecies,
nyears = nyears,
logthetahat = logthetahat,
prec.logthetahat = prec.logthetahat,
ne = ne,
tau.ne = tau.ne,
yest = yest,
ngroups1 = ngroups1,
ngroups2 = ngroups2,
grps = grps,
yavg = yavg,
spinbiomes = spinbiomes,
nsppbiomes = nsppbiomes,
spinfams = spinfams,
nsppfams = nsppfams,
nfams = nfams,
spinbirdgroups = spinbirdgroups,
nsppbirdgroups = nsppbirdgroups,
nbirdgroups = nbirdgroups,
spinmigrates = spinmigrates,
nsppmigrates = nsppmigrates,
nmigrates = nmigrates,
spinais = spinais,
nsppais = nsppais,
nais = nais,
spinwinters = spinwinters,
nsppwinters = nsppwinters,
spinnatives = spinnatives,
nsppnatives = nsppnatives,
nnatives = nnatives,
subgrpsl = subgrpsl,
nsubgrps = nsubgrps,
nsppsubbiomesmat = nsppsubbiomesmat,
spsubgrpmat = spsubgrpmat)
params = c("expmu1",
#"expm2",
"expmu2",
"Psi",
"tau",
"NEst",
"Nsum",
"N",
"Nlost",
"Nalost",
"Nlost.S",
"Nlost.fam",
"Nlost.biome",
"Nalost.fam",
"Nlost.migrate",
"Nalost.migrate",
"plost.migrate",
"Nlost.birdgroup",
"Nalost.birdgroup",
"plost.birdgroup",
"Nlost.ai",
"Nalost.ai",
"plost.ai",
"plost.native",
"Nlost.native",
"Nalost.native",
"plost.winter",
"Nlost.winter",
"Nalost.winter",
"Nalost.biome",
"plost.fam",
"plost.biome",
"plost",
"plost.S",
"Nsum.subgrp")
mod = "Rosenberg et al model.txt"
adaptSteps = 500 # Number of steps to "tune" the samplers.
burnInSteps = 30000 # Number of steps to "burn-in" the samplers.
nChains = 3 # Number of chains to run.
numSavedSteps=10000 # Total number of steps to save per chain.
thinSteps=50 # Number of steps to "thin" (1=keep every step).
nIter = ceiling( ( (numSavedSteps * thinSteps ) + burnInSteps) / nChains ) # Steps per chain.
t1 = Sys.time()
jagsMod = jags(data = data.jags,
model.file = mod,
n.chains = nChains,
n.adapt = adaptSteps,
n.burnin = burnInSteps,
n.thin = thinSteps,
n.iter = nIter+burnInSteps,
parameters.to.save = params,
parallel = T)
#sumqalt = as.data.frame(jagsMod$summary)
q90 = function(x){
quantile(x,probs = c(0.025,0.25,0.75,0.975))
}
sumq = MCMCsummary(jagsMod$samples,func = q90,Rhat = F,n.eff = F,func_name = c("lci","lqrt","uqrt","uci"))
sumq = data.frame(sumq)
names(sumq) <- c("mean","sd","lci95","med","uci95","lci","lqrt","uqrt","uci")
write.csv(sumq,paste0("output/population change parameters NA loss.csv"))
#write.csv(sumqalt,paste0("population change parameters NA loss w neff.csv"))
faml = as.data.frame(sumq[paste0("Nlost.fam[",1:nfams,"]"),])
famp = as.data.frame(sumq[paste0("plost.fam[",1:nfams,"]"),])
names(famp) = paste0("plost.fam.",names(famp))
faml = cbind(faml,famp)
faml = cbind(faml,fams)
nativel = as.data.frame(sumq[paste0("Nlost.native[",1:nnatives,"]"),])
nativep = as.data.frame(sumq[paste0("plost.native[",1:nnatives,"]"),])
names(nativep) = paste0("plost.native.",names(nativep))
nativel = cbind(nativel,nativep)
nativel = cbind(nativel,natives)
migratel = as.data.frame(sumq[paste0("Nlost.migrate[",1:nmigrates,"]"),])
migratep = as.data.frame(sumq[paste0("plost.migrate[",1:nmigrates,"]"),])
names(migratep) = paste0("plost.migrate.",names(migratep))
migratel = cbind(migratel,migratep)
migratel = cbind(migratel,migrates)
ail = as.data.frame(sumq[paste0("Nlost.ai[",1:nais,"]"),])
aip = as.data.frame(sumq[paste0("plost.ai[",1:nais,"]"),])
names(aip) = paste0("plost.ai.",names(aip))
ail = cbind(ail,aip)
ail = cbind(ail,ais)
birdgroupl = as.data.frame(sumq[paste0("Nlost.birdgroup[",1:nbirdgroups,"]"),])
birdgroupp = as.data.frame(sumq[paste0("plost.birdgroup[",1:nbirdgroups,"]"),])
names(birdgroupp) = paste0("plost.birdgroup.",names(birdgroupp))
birdgroupl = cbind(birdgroupl,birdgroupp)
birdgroupl = cbind(birdgroupl,birdgroups)
biomes = unique(splist[,c("g2","Breeding.Biome")])
biomes = biomes[order(biomes$g2),]
biomel = as.data.frame(sumq[paste0("Nlost.biome[",1:ngroups2,"]"),])
biomep = as.data.frame(sumq[paste0("plost.biome[",1:ngroups2,"]"),])
names(biomep) = paste0("plost.biome.",names(biomep))
biomel = cbind(biomel,biomep)
biomel = cbind(biomel,biomes)
winters = unique(splist[,c("g1","Winter.Biome")])
winters = winters[order(winters$g1),]
winterl = as.data.frame(sumq[paste0("Nlost.winter[",1:ngroups1,"]"),])
winterp = as.data.frame(sumq[paste0("plost.winter[",1:ngroups1,"]"),])
names(winterp) = paste0("plost.winter.",names(winterp))
winterl = cbind(winterl,winterp)
winterl = cbind(winterl,winters)
alll = data.frame(sumq[c("Nlost","plost"),])
allp = data.frame(sumq[c("plost","Nlost"),])
names(allp) = paste0("plost",names(allp))
alll = cbind(alll,allp)
alll = alll[1,]
alll[,c(19:21)] <- NA
biomel[,21] <- NA
winterl[,21] <- NA
names(alll)[20] = "Group"
alll$nspecies = nspecies
nativel$nspecies = nsppnatives
migratel$nspecies = nsppmigrates
birdgroupl$nspecies = nsppbirdgroups
ail$nspecies = nsppais
biomel$nspecies = nsppbiomes
winterl$nspecies = nsppwinters
faml$nspecies = nsppfams
nms = names(alll)
names(nativel) = nms
names(migratel) = nms
names(birdgroupl) = nms
names(ail) = nms
names(biomel) = nms
names(winterl) = nms
names(faml) = nms
allsums = rbind(alll,
nativel,
migratel,
birdgroupl,
ail,
biomel,
winterl,
faml, stringsAsFactors = FALSE)
allsout = allsums[,c("Group",
"nspecies",
"med",
"lci",
"uci",
"plostmed",
"plostlci",
"plostuci")]
for(j in c("med","lci","uci")){
allsout[,j] <- signif(allsout[,j]/1e6,5)
}
#### biome summaries
biomepop = expand.grid(biome = 1:ngroups2,
yrs = 1:nyears)
biomepop$param = paste0("Nalost.biome[",biomepop$biome,",",biomepop$yrs,"]")
biomepopt = as.data.frame(sumq[biomepop$param,])
biomepop = cbind(biomepop,biomepopt)
biomepop = merge(biomepop,biomes,by.x = "biome",by.y = "g2")
biomepop$year = biomepop$yrs + (base.yr-1)
biomepop = biomepop[order(biomepop$Breeding.Biome,biomepop$year),]
biomlab = biomepop[which(biomepop$year == 2017),]
pmain = ggplot(data = biomepop,aes(x = year,y = med))+
geom_ribbon(aes(x = year,ymin = lci,ymax = uci,group = Breeding.Biome,fill = Breeding.Biome),alpha = 0.2)+
geom_line(aes(colour = Breeding.Biome))+
geom_label_repel(data = biomlab,aes(label = Breeding.Biome,colour = Breeding.Biome),xlim = c(2017,2040))+
labs(x = "",y = "Change in number of birds in North America")+
xlim(1970,2035)+
theme_minimal()+
theme(legend.position = "none")
pdf(paste0("Biome population population change trajectory loss.pdf"))
print(pmain)
dev.off()
pdf(paste0("Biome population population change trajectory facet loss.pdf"))
for(jj in 1:ceiling(ngroups2/4)){
pmain = ggplot(data = biomepop,aes(x = year,y = med))+
geom_ribbon(aes(x = year,ymin = lci,ymax = uci),fill = grey(0.5),alpha = 0.2)+
geom_line(aes(colour = "Breeding.Biome"))+
facet_wrap_paginate(~Breeding.Biome,ncol = 2,nrow = 2,scales = "free",page = jj)
print(pmain)
}
dev.off()
write.csv(biomepop[,c("Breeding.Biome",
"year",
"med",
"lci",
"uci")],"Breeding Biome population change trajectories.csv")
#### wintering summaries
winterpop = expand.grid(winter = 1:ngroups2,
yrs = 1:nyears)
winterpop$param = paste0("Nalost.winter[",winterpop$winter,",",winterpop$yrs,"]")
winterpopt = as.data.frame(sumq[winterpop$param,])
winterpop = cbind(winterpop,winterpopt)
winterpop = merge(winterpop,winters,by.x = "winter",by.y = "g1")
winterpop$year = winterpop$yrs + (base.yr-1)
winterpop = winterpop[order(winterpop$Winter.Biome,winterpop$year),]
biomlab = winterpop[which(winterpop$year == 2017),]
pmain = ggplot(data = winterpop,aes(x = year,y = med))+
geom_ribbon(aes(x = year,ymin = lci,ymax = uci,group = Winter.Biome,fill = Winter.Biome),alpha = 0.2)+
geom_line(aes(colour = Winter.Biome))+
geom_label_repel(data = biomlab,aes(label = Winter.Biome,colour = Winter.Biome),xlim = c(2017,2040))+
labs(x = "",y = "Change in number of birds in North America")+
xlim(1970,2035)+
theme_minimal()+
theme(legend.position = "none")
pdf(paste0("winter population population change trajectory loss.pdf"))
print(pmain)
dev.off()
pdf(paste0("winter population population change trajectory facet loss.pdf"))
for(jj in 1:ceiling(ngroups2/4)){
pmain = ggplot(data = winterpop,aes(x = year,y = med))+
geom_ribbon(aes(x = year,ymin = lci,ymax = uci),fill = grey(0.5),alpha = 0.2)+
geom_line(aes(colour = "Winter.Biome"))+
facet_wrap_paginate(~Winter.Biome,ncol = 2,nrow = 2,scales = "free",page = jj)
print(pmain)
}
dev.off()
write.csv(winterpop[,c("Winter.Biome",
"year",
"med",
"lci",
"uci")],"wintering group population change trajectories.csv")
#### fam summaries
fampop = expand.grid(fam = 1:nfams,
yrs = 1:nyears)
fampop$param = paste0("Nalost.fam[",fampop$fam,",",fampop$yrs,"]")
fampopt = as.data.frame(sumq[fampop$param,])
fampop = cbind(fampop,fampopt)
fampop = merge(fampop,fams,by.x = "fam",by.y = "famfactn")
fampop$year = fampop$yrs + (base.yr-1)
biomlab = fampop[which(fampop$year == 2017),]
pmain = ggplot(data = fampop,aes(x = year,y = med))+
geom_ribbon(aes(x = year,ymin = lci,ymax = uci,fill = Family),alpha = 0.2)+
geom_line(aes(colour = Family))+
labs(x = "",y = "Change in number of birds in North America")+
geom_label_repel(data = biomlab,aes(label = Family,colour = Family),xlim = c(2017,2050))+
xlim(1970,2045)+
theme_minimal()+
theme(legend.position = "none")
pdf(paste0("Family population population change trajectory loss.pdf"),
width = 14,
height = 10)
print(pmain)
dev.off()
pdf(paste0("Family population population change trajectory facet loss.pdf"))
for(jj in 1:ceiling(ngroups2/4)){
pmain = ggplot(data = fampop,aes(x = year,y = med))+
geom_ribbon(aes(x = year,ymin = lci,ymax = uci,fill = Family),alpha = 0.2)+
geom_line(aes(colour = Family))+
theme_minimal()+
theme(legend.position = "none")+
facet_wrap_paginate(~Family,ncol = 2,nrow = 2,scales = "fixed",page = jj)
print(pmain)
}
dev.off()
#### overall summaries
totp = as.data.frame(sumq[paste0("Nsum[",1:nyears,"]"),c("med","lci","uci")])
names(totp) = paste0("N_",names(totp))
totp$year_i = 1:nyears
totp$year = totp$year_i + (base.yr-1)
lossa = sumq[paste0("Nalost[",1:nyears,"]"),c("med","lci","uci")]
names(lossa) = paste0("Loss_",names(lossa))
lossa = cbind(totp,lossa)
write.csv(lossa,row.names = F,"overall avifauna trajectories N and loss.csv")
lost = as.data.frame(sumq["Nlost",])
lost.s = as.data.frame(sumq[paste0("Nlost.S[",1:nspecies,"]"),c("med","lci","uci","lqrt","uqrt")])
names(lost.s) = paste0("Loss_",names(lost.s))
for(i in 1:ncol(lost.s)){
lost.s[,i] <- signif(lost.s[,i],3)
}
lost.s = cbind(splist,lost.s)
# Psi = as.data.frame(sumq[paste0("Psi[",1:nspecies,"]"),c("med","lci","uci")])
# names(Psi) = paste0("Psi_",names(Psi))
# lost.s = cbind(lost.s,Psi)
# NEst = as.data.frame(sumq[paste0("NEst[",1:nspecies,"]"),c("med","lci","uci")])
# names(NEst) = paste0("NEst_",names(NEst))
# lost.s = cbind(lost.s,NEst)
# June 2021 - adding species level proportional loss ----------------------
plost.s = as.data.frame(sumq[paste0("plost.S[",1:nspecies,"]"),c("med","lci","uci","lqrt","uqrt")])
names(plost.s) = paste0("Proportional_loss_",names(plost.s))
for(i in 1:ncol(plost.s)){
plost.s[,i] <- signif(plost.s[,i],3)
}
lost.s = cbind(lost.s,plost.s)
# June 2021 - adding starting and ending population sizes -----------------
n1.S = as.data.frame(sumq[paste0("N[",1:nspecies,",1]"),c("med","lci","uci","lqrt","uqrt")])
names(n1.S) = paste0("Estimated_population_size_1970_",names(n1.S))
for(i in 1:ncol(n1.S)){
n1.S[,i] <- signif(n1.S[,i],3)
}
n2.S = as.data.frame(sumq[paste0("N[",1:nspecies,",",nyears,"]"),c("med","lci","uci","lqrt","uqrt")])
names(n2.S) = paste0("Estimated_population_size_2017_",names(n2.S))
for(i in 1:ncol(n2.S)){
n2.S[,i] <- signif(n2.S[,i],3)
}
lost.s = cbind(lost.s,n1.S)
lost.s = cbind(lost.s,n2.S)
write.csv(lost.s,paste0("population change by species NA loss.csv"))
#summarize th eproportion of declining species for tables 1 and S2
lost.s$decline = F
lost.s[which(lost.s$Loss_med > 0),"decline"] = T
allsout$node = row.names(allsout)
for(j in 1:nrow(allsout)){
if(j == 1){
S = lost.s[,"decline"]
tf = table(S)/length(S)
}else{
nn = gsub(gsub(allsout[j,"node"],pattern = "Nlost.",fixed = T,replacement = ""),pattern = "\\[.*",replacement = "")
nn = paste0(nn,"fact")
if(grepl(nn,pattern = "biome")){
nn = "Breeding.Biome"
}
if(grepl(nn,pattern = "winter")){
nn = "Winter.Biome"
}
S = lost.s[which(lost.s[,nn] == allsout[j,"Group"]),"decline"]
tf = table(S)/length(S)
}
allsout[j,"proportion.species.decline"] = as.numeric(tf["TRUE"])
allsout[j,"n.species.decline"] = sum(S)
}
write.csv(allsout,"table 1 and table S2.csv")
lostpy = 0.4*max(totp$N_med)
if(lost$med > 0){
lostp = paste0(signif(lost$med/1e9,4)," Billion birds lost [",signif(lost$lci/1e9,4),"-",signif(lost$uci/1e9,4),"]")
}else{
lostp = paste0(signif(abs(lost$med/1e9),4)," Billion birds gained [",signif(abs(lost$lci)/1e9,4),"-",signif(abs(lost$uci)/1e9,4),"]")
}
sppop = sumq[paste0("N[",rep(1:nspecies,each = nyears),",",rep(1:nyears,times = nspecies),"]"),]
sppop = as.data.frame(sppop)
sppop$spfact = rep(1:nspecies,each = nyears)
sppop$year_i = rep(1:nyears,times = nspecies)
sppop$year = sppop$year_i + (base.yr-1)
sppop = merge(sppop,splist,
by= "spfact")
spstartpop = sppop[which(sppop$year == base.yr),]
spstartpop$meanpopstart = spstartpop$mean
indices2 = merge(indices,spstartpop[,c("species","meanpopstart")],by = "species")
for(s in unique(indices2$species)){
wsp = which(indices2$species == s)
by = unique(indices2[wsp,"firstyear"])
wspb = which(indices2$species == s & indices2$year == by)
indices2[wsp,"index.s.raw"] <- indices2[wsp,"index.raw"]/indices2[wspb,"index.raw"]
}
indices2$rescindex = indices2$index.s*indices2$meanpopstart
indices2$rescindex.raw = indices2$index.s.raw*indices2$meanpopstart
sppop2 = merge(sppop,indices2[,c("species","rescindex","rescindex.raw","year")],