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output_Figures.R
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require(corrplot)
require(raster)
require(data.table)
require(ggplot2)
require(ggmap)
chDir = getwd()
prefix = 'samples_pt33_seed'
validPrefix = 'samples_pt34_seed'
shinyDir = "Plectranthus_barbatus_SA_maps"
burnin = 15000
thin = 450
setwd(chDir)
seeds = c(1:5,7:10)
load(file="toAnalyse.Rdata")
load(file="data_for_model")
load(file="data_full")
load(file="validation_kit")
xm = range(ctab$x)[1];xM = range(ctab$x)[2]
xRg = c(xm-(xM-xm)/10,xM+(xM-xm)/10)
ym = range(ctab$y)[1];yM = range(ctab$y)[2]
yRg = c(ym-(yM-ym)/10,yM+(yM-ym)/10)
spatial_ext = extent(c(xRg,yRg))
mapo = get_stamenmap( bbox = c(left=spatial_ext[1],
bottom=spatial_ext[3],
right=spatial_ext[2],
top = spatial_ext[4]),
zoom = 8, maptype = "toner-lite")
params = c('M',"theta","matur",'d_s','d_l','phi',
'Beta','rho','pdetec','iniPop')
allParams = c('lM','theta','matur',
'd_s','d_l','lphi',
'Beta','rho',
'pdetec','popIni','avgAgeRatioIni')
nDS = dim(Y)[3]
nCell = dim(x)[1]
nYear = dim(x)[2]
#####
# Functions
#####
panel.hist <- function(x, ...){
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE,breaks="fd")
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts;y=y/max(y)
rect(breaks[-nB], 0, breaks[-1], y,...)
}
minRho=1-(1/8.4e6)^(1/maxAge)
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
# Priors
modelPriors=list(
prior_lM=function(val)log(dnorm(val,mean = log(2e5),sd=.5)),
prior_theta=function(val)log(dunif(val,1e-5,100)),
prior_matur=function(val)log(dbinom(val-1,prob=.3,6)),
prior_d_s=function(val)log(dunif(val,0,1)),
prior_d_l=function(val)log(dunif(val,0,1)),
prior_lphi=function(val)log(dnorm(val,mean = log(8.4e6),sd=.7)),
# CUSTOMIZED FOR PLECTRANTHUS CASE
prior_Beta=function(vals){
sum(log(dnorm(vals[1:3],mean = 0,sd = 30)))+ # linear bioclim terms
sum(log(2*dnorm(vals[4:5],mean = 0,sd = 30)*vals[3:4]<0))+ # quadratic bioclim terms must be negative
sum(log(dnorm(vals[6:8],mean = 0,sd = 30)))}, # land cover rate terms
prior_rho=function(val)log(dunif(val,minRho,1)),
prior_pdetec=function(vals)sum(log(sapply(vals,function(val)dunif(val,0,1)))),
prior_popIni=function(vals)sum(log(sapply(1:length(vals),function(i)if(initN[i]>0){dunif(vals[i],0,1000)}else if(vals[i]==0){1}else{0}))),
prior_avgAgeRatioIni=function(vals)sum(log(dunif(vals,0,1)))
)
# Priors
sepPriors=list(
prior_lM=function(val)log(dnorm(val,mean = log(2e5),sd=.5)),
prior_theta=function(val)log(dunif(val,1e-5,100)),
prior_matur=function(val)log(dbinom(val-1,prob=.3,6)),
prior_d_s=function(val)log(dunif(val,0,1)),
prior_d_l=function(val)log(dunif(val,0,1)),
prior_lphi=function(val)log(dnorm(val,mean = log(8.4e6),sd=.7)),
prior_Beta=function(vals){
c(log(dnorm(vals[1:3],mean = 0,sd = 30)),
log(2*dnorm(vals[4:5],mean = 0,sd = 30)*vals[3:4]<0),
log(dnorm(vals[6:8],mean = 0,sd = 30))) },
prior_rho=function(val)log(dunif(val,minRho,1)),
prior_pdetec=function(vals)c(log(sapply(vals,function(val)dunif(val,0,1)))),
prior_popIni=function(vals)sum(log(sapply(1:length(vals),function(i)if(initN[i]>0){dunif(vals[i],0,1000)}else if(vals[i]==0){1}else{0}))),
prior_avgAgeRatioIni=function(vals)sum(log(dunif(vals,0,1)))
)
prior.sample = list(
prior_popIni=function(n)sapply(1:dim(x)[1],function(i)if(initN[i]>0){runif(n,0,1000)}else{rep(0,n)}),
prior_avgAgeRatioIni=function(n)matrix(runif(n*dim(x)[1],0,1),n,dim(x)[1])
)
getIniPop=function(popIni,avgAgeRatioIni,maxAge){
t(sapply(1:length(popIni),function(i){
if(popIni[i]>0){
tmp = popIni[i]*
dbinom(0:(maxAge-1),
size=maxAge-1,
prob = avgAgeRatioIni[i])
tmp[which.max(tmp)]=ceiling(tmp[which.max(tmp)]) # At least one
return(round(tmp))
}else{return(rep(0,maxAge))}
}))
}
calculate.logProb=function(momo,params){
for(par in params){
eval(parse(text=paste('momo$logProb_',par,'=momo$prior_',par,'(momo$',par,')',sep="")))
}
return(momo)
}
calculate.model = function(mod){
theta = mod$theta
M = exp(mod$lM)
matur = mod$matur
d_s = mod$d_s
d_l = mod$d_l
phi = exp(mod$lphi)
Beta = mod$Beta
pdetec = mod$pdetec
rho = mod$rho
mod$iniPop = getIniPop(mod$popIni,mod$avgAgeRatioIni,maxAge)
nCell = dim(mod$iniPop)[1]
mod$fecund = sapply(1:maxAge,function(k) floor( M*k^theta / ((M*(matur-1)^theta)+k^theta)) )
mod$n = array(NA,dim = c(nCell,nYear,dim(mod$iniPop)[2]))
mod$n[1:nCell,1,1:maxAge] <- mod$iniPop[1:nCell,1:maxAge]
mod$dispI = 1+ (d_s-d_l*urban) * as.vector(Adj %*% matrix(1,nCell,1)) + d_l*urban*(nCell-1)
mod$disp_to_from = t( (diag(nCell) + d_s*Adj + d_l* urban* (1-Adj)*(1-diag(nCell)))/mod$dispI )
mod$s_prod =matrix(NA,nCell,dim(x)[2]);mod$s=mod$s_prod;mod$p=mod$s_prod;mod$COMP=mod$s_prod
mod$E_y = array(NA,dim=c(nCell,dim(x)[2],nDS))
# Following years
for(t in 2:nYear){
mod$s_prod[,t-1] = mod$n[,t-1,]%*%matrix(mod$fecund,maxAge,1)
mod$s[,t-1] = mod$disp_to_from%*%mod$s_prod[,t-1,drop=F]
pre_p = cbind(rep(1,dim(x)[1]),x[,1,])%*%matrix(Beta,dim(x)[3]+1,1)
mod$p[,t] = exp(pre_p)/(1+exp(pre_p))
survive = round((1-rho)*mod$n[,t-1,1:(maxAge-1)])
mod$COMP[,t-1] = sapply(1:nCell,function(i){
nOccupied = max(mod$p[i,t]*area[i]*phi-sum(survive[i,]),0)
nSlots = max(mod$p[i,t]*area[i]*phi,mod$s[i,t-1])
return(nOccupied/nSlots)
})
mod$n[,t,1] = round(mod$s[,t-1]*mod$COMP[,t-1]*mod$p[,t])
mod$n[,t,2:maxAge] = survive
}
for(t in 1:nYear){
for(i in 1:nCell){
for(d in 1:nDS){
mod$E_y[i,t,d] <- pdetec[d]*TG[i,t,d]*sum(mod$n[i,t,])
}
}
}
return(mod)
}
calculate.logLik = function(mod,params){
pred = mod$E_y[]+1e-50
mod$logLikY = sum( Y[]*log(pred) - pred - log(factorial(Y[])) ,na.rm=T)
logL = mod$logLikY
for(i in 1:length(params)){
logProbPar = mod[[paste('logProb_',params[i],sep="")]]
logL = logL + logProbPar
}
mod$logLikFull = logL
saturated_logLikY = sum(Y[]*log(Y[])-Y[]-log(factorial(Y[])),na.rm=T)
mod$relativeLogLik = (mod$logLikFull - saturated_logLikY)
mod$relativeLogLikY = (mod$logLikY - saturated_logLikY)
return(mod)
}
multiplot <- function(plots=NULL, file, cols=1, layout=NULL) {
library(grid)
numPlots = length(plots)
print(numPlots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
get.model=function(proto,params){
model = modelPriors
model$theta = proto$theta
model$lM = proto$lM
model$matur = proto$matur
model$d_s = proto$d_s
model$d_l = proto$d_l
model$lphi = proto$lphi
model$Beta = proto$Beta
model$pdetec = proto$pdetec
model$rho = proto$rho
model$iniPop = proto$iniPop
model$popIni=proto$popIni
model$avgAgeRatioIni=proto$avgAgeRatioIni
model = calculate.model(model)
model = calculate.logProb(model,params)
model = calculate.logLik(model,params)
return(model)
}
get.dynamic = function(mod){
nYear = dim(mod$n)[2]
ss = colSums(mod$s)
nn = sapply(1:nYear,function(yearo)sum(mod$n[,yearo,]))
nt = colSums(mod$n[,,1])
nCell = sapply(1:nYear,function(yearo)sum(rowSums(mod$n[,yearo,])>0) )
sl = rep(NA,nYear)
ssh = rep(NA,nYear)
ntl = rep(NA,nYear)
ntsh = rep(NA,nYear)
Shanon = rep(NA,nYear)
Disp = mod$disp_to_from
diag(Disp) = 0
Displ = (1 - Adj)*Disp
Dispsh = Adj*Disp
DisplFrom = colSums(Displ)
DispshFrom = colSums(Dispsh)
for(year in 1:nYear){
sl[year] = sum(mod$s_prod[,year] * DisplFrom)
ssh[year] = sum(mod$s_prod[,year] * DispshFrom)
if(year>1){
newTreesPerCellLD = mod$p[,year]*
mod$COMP[,year-1]*
colSums(mod$s_prod[,year-1] * t(Displ))
ntl[year] = sum(newTreesPerCellLD)
newTreesPerCellSD = mod$p[,year]*
mod$COMP[,year-1]*
colSums(mod$s_prod[,year-1] * t(Dispsh))
ntsh[year] = sum(newTreesPerCellSD)
}
ny = rowSums(mod$n[,year,]) + 1e-20
ny = ny/sum(ny)
Shanon[year] = -sum(ny*log(ny))
}
res=data.frame(year=1979+(1:nYear),
pop=nn,
seeds=ss,
nCell=nCell,
seedsSD=ssh,
seedsLD=sl,
newTree=nt,
newTreeSD=ntsh,
newTreeLD=ntl,
sha=Shanon)
return(res)
}
refineSample = function(df){
nNeg = min(2*sum(df$y>0),sum(df$y==0))
# We chose the minimum sampling effort threshold so that there is at least nNeg negative samples
tgThres = max(df$tg[df$y==0])
nSamp = sum(df$tg[df$y==0]>=tgThres)
while(nSamp<nNeg){
tgThres = tgThres - 1
nSamp = sum(df$tg[df$y==0]>=tgThres)
}
return(df[df$y>0 | df$tg>=tgThres,])
}
auc_wmw <- function(presences, scores){
pos <- scores[presences]
neg <- scores[!presences]
U <- as.numeric(wilcox.test(pos, neg)$statistic)
U/(length(pos) * length(neg))
}
######
# Figure S4.1 - 3 Parameter sample vs prior density
######
setwd(chDir)
load(file = 'toAnalyse.Rdata')
pars = c('matur','theta','rho','lM',
'd_s','d_l','pdetec','lphi','Beta')
setwd(chDir)
seqLen = 50
itNames=paste0('iteraction_',post$iteration)
k = 1
pList=list()
for(par in pars){
print(par)
if(par%in%c('pdetec','Beta')){
sample = post[,regexpr(par,colnames(post))>0]
priorX = sapply(1:dim(sample)[2],
function(i)seq(min(sample[,i])-sd(sample[,i])/3,
max(sample[,i])+sd(sample[,i])/3,length.out=seqLen))
colnames(priorX) = colnames(sample)
prior=t(sapply(1:seqLen,function(j)exp(sepPriors[paste0('prior_',par)][[1]](priorX[j,]))))
colnames(prior)=colnames(sample)
if(par=="Beta"){
sample=sample[,!colnames(sample)%in%paste0('Beta_',4:5)]
prior = prior[,!colnames(prior)%in%paste0('Beta_',4:5)]
priorX = priorX[,!colnames(priorX)%in%paste0('Beta_',4:5)]
}
}else{
vec=sapply(1:dim(post)[1],function(i)chs[[as.numeric(post$chain[i])]][itNames[i]][[1]][par][[1]])
sample = matrix(vec,dim(post)[1],1)
colnames(sample)=par
if(par=="matur"){
custom = seq(0,10,1)
priorX = matrix(custom,length(custom),1)
}else{
priorX = matrix(seq(min(sample[,1])-sd(sample[,1])/3,
max(sample[,1])+sd(sample[,1])/3,length.out=seqLen),seqLen,1)
}
colnames(priorX)=par
prior = matrix(exp(modelPriors[paste('prior_',par,sep="")][[1]](priorX[,1])),
dim(priorX)[1],1)
colnames(prior)=par
}
for(col in colnames(sample)){
breaks = hist(sample[,col],plot=F,breaks="fd")$breaks
if(length(breaks)>2*dim(sample)[1]){breaks=hist(sample[,col],plot=F)$breaks}
forPrior = data.frame(x=priorX[,col],y=prior[,col])
forPost = data.frame(x=sample[,col])
pList[[k]] = ggplot()+
geom_line(data=forPrior,aes(x=x,y=y),col="blue",size=1)+
geom_histogram(data=forPost,breaks=breaks,aes(x=x,y=..density..),fill="red",alpha=.5)+
scale_x_continuous(limits=c(min(priorX[,col]),max(priorX[,col])))+ggtitle(col)+theme_bw()
k=k+1
}
}
png('Z_post_priors.png',height=1000,width=666)
multiplot(pList,cols=4)
dev.off()
par=c('popIni')
k = 1
pList=list()
print(par)
sample = t(sapply(1:dim(post)[1],function(i)chs[[as.numeric(post$chain[i])]][itNames[i]][[1]][par][[1]][initN>0]))
colnames(sample)=paste0(par,'_cell_',which(initN>0))
forPrior=data.frame(x=c(.01,999.99),y=dunif(c(.01,999.99),min = 0,max = 1000))
for(col in colnames(sample)){
breaks = hist(sample[,col],plot=F,breaks="fd")$breaks
if(length(breaks)>2*dim(sample)[1]){breaks=hist(sample[,col],plot=F)$breaks}
forPost = data.frame(x=sample[,col],type='posterior')
pList[[k]] = ggplot()+
geom_histogram(data=forPost,breaks=breaks,aes(x=x,y=..density..),alpha=.5,fill='red')+
geom_line(data=forPrior,aes(x=x,y=y),color='blue')+
scale_x_continuous(limits=c(0,1000))+
ggtitle(col)+theme_bw()+theme(legend.position = 'None')
k=k+1
}
setwd(chDir)
png('Z_post_priors_popIni.png',height=1000,width=666)
multiplot(pList,cols=4)
dev.off()
par='avgAgeRatioIni'
k = 1
pList=list()
print(par)
sample = t(sapply(1:dim(post)[1],function(i)chs[[as.numeric(post$chain[i])]][itNames[i]][[1]][par][[1]][initN>0]))
colnames(sample)=paste0(par,'_cell_',which(initN>0))
forPrior=data.frame(x=c(.01,.99),y=dunif(c(.01,.99),min = 0,max = 1))
for(col in colnames(sample)){
breaks = hist(sample[,col],plot=F,breaks="fd")$breaks
if(length(breaks)>2*dim(sample)[1]){breaks=hist(sample[,col],plot=F)$breaks}
forPost = data.frame(x=sample[,col],type='posterior')
pList[[k]] = ggplot()+
geom_histogram(data=forPost,breaks=breaks,aes(x=x,y=..density..),alpha=.5,fill='red')+
geom_line(data=forPrior,aes(x=x,y=y),color='blue')+
scale_x_continuous(limits=c(0,1))+
ggtitle(col)+theme_bw()+theme(legend.position = 'None')
k=k+1
}
setwd(chDir)
png('Z_post_priors_avgAgeRatioIni.png',height=1000,width=666)
multiplot(pList,cols=4)
dev.off()
######
# Figure S4.4 Parameters correlation matrix (Identifiability)
######
setwd(chDir)
load(file = 'toAnalyse.Rdata')
#reOrd = c('matur','theta','rho','M','d_s','d_l','pdetec_1','pdetec_2','pdetec_3',
# 'phi','nTot','Beta_1','Beta_2','Beta_5','Beta_6','Beta_7')
reOrd = c('matur','theta','rho','M','d_s','d_l','pdetec_1','pdetec_2','pdetec_3',
'phi','Beta_1','Beta_2','Beta_3','Beta_6','Beta_7','Beta_8')
parVals = post
for(col in c('theta','M','d_s','d_l','phi','rho',
'pdetec_1','pdetec_2','pdetec_3')){
eval(parse(text=paste('parVals$',col,'=','log10(post$',col,')',sep="")))
}
corMat=matrix(NA,length(reOrd),length(reOrd))
for(i in 1:length(reOrd)){
for(j in 1:length(reOrd)){
cd = complete.cases(parVals[,c(reOrd[c(i,j)])])
corMat[i,j] = cor(parVals[cd,reOrd[i]],parVals[cd,reOrd[j]])
}
}
rownames(corMat)=reOrd;colnames(corMat)=reOrd
corMat[is.na(corMat)]=0
png('parameter_corrplot.png',height=1000,width=1200)
print(corrplot(corMat[reOrd,reOrd],type="upper", tl.col="black", tl.srt=45, diag=F,cl.cex=1.5,cl.lim=c(-1,1),addCoef.col = "grey80",number.cex=1))
dev.off()
formu = as.formula(paste("~",paste(reOrd,collapse="+")))
png("parameter_pairs.png",height=1100,width=1100)
pairs( formu ,data=post ,
diag.panel = panel.hist,
upper.panel = NULL ,
labels= reOrd,
pch=3 ,
col= rep('black',dim(post)[1]) ,
cex.labels = 1.4 ,
cex.axis = 1)
dev.off()
SVD=svd(corMat)
axes=SVD$d[1:2]*t(SVD$v)[1:2,,drop=F]
print(sum(SVD$d[1:2])/sum(SVD$d))
colnames(axes)=reOrd
print(axes,digits=1)
######
# Figure 7. Fecundity curves
######
setwd(chDir)
load(file = 'toAnalyse.Rdata')
for(id in 1:dim(post)[1]){
it = post$iteration[id]
ich = post$chain[id]
mod = chs[[ich]][paste0('iteraction_',it)][[1]]
M = exp(mod$lM)
theta = mod$theta
matur = mod$matur
tmp = data.frame(age=1:maxAge,iteration=it,chain=ich,label=paste('ch',ich,'_it',it,sep=""),chain=as.character(ich))
tmp$relFecundity = sapply(1:maxAge,function(k) floor( M*k^theta / ((M*(matur-1)^theta)+k^theta))/M )
if(id==1){toPlot=tmp}else{toPlot=rbind(toPlot,tmp)}
cat('\r Processed ',round(1000*id/dim(post)[1])/10,'%')
}
toPlot$label=factor(toPlot$label)
toPlot$chain = factor(toPlot$chain)
toPlot2 = aggregate(list(relFecundity=toPlot$relFecundity),
by=list(age=toPlot$age),mean)
tmp = aggregate(list(minF=toPlot$relFecundity),
by=list(age=toPlot$age),function(x)quantile(x,prob=.025,na.rm=T))
toPlot2 = cbind(toPlot2,tmp[,c('minF'),drop=F])
tmp = aggregate(list(maxF=toPlot$relFecundity),
by=list(age=toPlot$age),function(x)quantile(x,prob=.975,na.rm=T))
toPlot2 = cbind(toPlot2,tmp[,c('maxF'),drop=F])
pFec = ggplot()+geom_line(data=toPlot2,aes(x=age,y=relFecundity))+
geom_point(data=toPlot2,aes(x=age,y=relFecundity))+
geom_ribbon(data=toPlot2,aes(x=age,ymin=minF,ymax=maxF),fill="grey50",alpha=.4)+
ylab('Fecundity / M')+
theme_bw()
if(F){pFec = ggplot(toPlot,aes(x=age,y=relFecundity,group=label,colour=chain))+geom_line(alpha=.5)+geom_point(alpha=.5)+ylab('Fecundity / M')+theme_bw()}
png('Fecundity_vs_age_relative.png')
print(pFec)
dev.off()
#####
# Figure 4. Population, cells, seeds vs time
#####
setwd(chDir)
load(file = 'toAnalyse.Rdata')
for(id in 1:dim(post)[1]){
flush.console()
cat('\r sample ',id,' over ',dim(post)[1])
it = post$iteration[id]
ich = post$chain[id]
modo = get.model(chs[[ich]][paste0('iteraction_',it)][[1]],allParams)
tmp=get.dynamic(modo)
tmp$iteration=post$iteration[id]
tmp$chain = post$chain[id]
tmp$label = paste('ch',ich,'_it',it,sep="")
if(id==1){toPlot=tmp}else{toPlot=rbind(toPlot,tmp)}
}
toPlot$label=factor(toPlot$label)
toPlot$chain=factor(toPlot$chain)
setwd(chDir)
saveRDS(toPlot,file = 'pop_and_seed_vs_year.Rdata')
toPlot2 = readRDS(file='pop_and_seed_vs_year.Rdata')
toPlot = aggregate(list(pop=log10(toPlot2$pop),sha=toPlot2$sha,seeds=log10(toPlot2$seeds)),
by=list(year=toPlot2$year),mean)
tmp = aggregate(list(minPop=log10(toPlot2$pop),minSha=toPlot2$sha,minSeeds=log10(toPlot2$seeds)),
by=list(year=toPlot2$year),function(x)quantile(x,prob=.025,na.rm=T))
toPlot = cbind(toPlot,tmp[,c('minPop','minSha','minSeeds')])
tmp = aggregate(list(maxPop=log10(toPlot2$pop),maxSha=toPlot2$sha,maxSeeds=log10(toPlot2$seeds)),
by=list(year=toPlot2$year),function(x)quantile(x,prob=.975,na.rm=T))
toPlot = cbind(toPlot,tmp[,c('maxPop','maxSha','maxSeeds')])
pPop = ggplot(toPlot)+
geom_line(aes(x=year,y=pop),size=2)+
geom_ribbon(aes(x=year,ymin=minPop,ymax=maxPop),col='grey50',alpha=.4)+
ylab('Log10-Total population')+
theme_bw()+theme(text=element_text(size=20))
pSha = ggplot(toPlot)+
geom_line(aes(x=year,y=sha),size=2)+
geom_ribbon(aes(x=year,ymin=minSha,ymax=maxSha),col='grey50',alpha=.4)+
ylab('Shanon entropy across cells')+
theme_bw()+theme(text=element_text(size=20))
pSee = ggplot(toPlot)+
geom_line(aes(x=year,y=seeds),size=2)+
geom_ribbon(aes(x=year,ymin=minSeeds,ymax=maxSeeds),col='grey50',alpha=.4)+
ylab('Log10-Number of seeds produced')+
theme_bw()+theme(text=element_text(size=20))
png('pop_and_seed_vs_year.png',height=1000,width=666)
multiplot(list(pPop,pSha,pSee),cols=1)
dev.off()
#####
# Figure XX. Invasion syndromes Legend
#####
tp = as.data.frame(
expand.grid(b=seq(0,1,.05),
r=seq(0,1,.05)))
tp$color= sapply(1:dim(tp)[1],
function(i)rgb(tp$r[i],0,tp$b[i]))
pts = data.frame(b=c(1,0,1,.3,0),r=c(0,0,1,1,.6))
pts$col = sapply(1:dim(pts)[1],
function(i)rgb(pts$r[i],0,pts$b[i]))
tp$color = factor(tp$color)
pts$col=factor(pts$col)
sizo = 1000
p=ggplot()+
geom_tile(data=tp,aes(x=r,y=b,fill=color),alpha=.6)+
scale_fill_manual(values=levels(tp$color))+
geom_point(data=pts,aes(x=r,y=b,colour=col),size=round(.05*sizo))+
scale_colour_manual(values=levels(pts$col))+
scale_x_continuous(limits=c(-.3,1.3))+
scale_y_continuous(limits=c(-.3,1.3))+
theme_void()+theme(legend.position = "None")
setwd('C:/Users/user/pCloud local/boulot/data/Invasions SA et FR/pt33/')
jpeg('Figure_invasionSyndrome_legend.jpeg',height=sizo,width=sizo,quality=75)
print(p)
dev.off()
#######
# Figure 2 & 3. Main historical maps (MS)
#######
setwd(chDir)
load(file = 'toAnalyse.Rdata')
thre = .65
years = c(1980,1984,1988,1996,2008,2020)
### Plot pop growth
statusLevels = c('uncertain',
'no disp & decline',
'no disp but growth',
'disp & growth',
'disp',
'disp & decline')
colors = c('gray50',
rgb(0,0,0),# black
rgb(0,0,1),# blue
rgb(1,0,1),# lila
rgb(1,0,.2),# Red
rgb(.6,0,0))# rust
#write.table(ctab,'ctab.csv',sep=";",row.names=F,col.names=T)
pList=list()
k=1
subo=500
sampo=sort(sample(1:dim(post)[1],subo,replace = F))
for(year in years-1979){
cat('\n year ',year+1979,'\n \n')
tmp=as.data.frame(
expand.grid(cell=ctab$cell[order(ctab$arrayID)],
model=1:dim(post)[1]))
### Plot pop growth
tmp$decline = NA
tmp$growth = NA
for(i in sampo){
flush.console()
cat('\r Processed ',round(100*i/dim(post)[1]),'% iterations')
it = post$iteration[i];ich = post$chain[i]
mod = get.model(chs[[ich]][paste0('iteraction_',it)][[1]],allParams)
nn = rowSums(mod$n[,year,])
nn_ = rowSums(mod$n[,year+1,])
phi = exp(mod$lphi)
tmp$growth[tmp$model==i] = nn_>nn
tmp$decline[tmp$model==i] = nn_<nn
Disp = mod$disp_to_from
ndie = nn[maxAge] + round(sum(mod$rho*mod$n[,year,1:(maxAge-1)]))
diag(Disp) = 0
to_from = mod$p[,year+1]*mod$COMP[,year]*t(mod$s_prod[,year] * t(Disp))
tmp$spread[tmp$model==i] = colSums(to_from)>0
}
if(sum(is.na(tmp$growth))>0){tmp=tmp[!is.na(tmp$growth),]}
toPlot = aggregate(list(
decline=tmp$decline,
growth=tmp$growth,
locGrowth=tmp$locGrowth,
spread=tmp$spread),by=list(cell=tmp$cell),
FUN=function(bol)sum(bol)/subo)
toPlot = merge(toPlot,ctab,by="cell",all.x=T)
toPlot$status = NA
toPlot$status[toPlot$decline>thre & toPlot$spread<=thre] = 'no disp & decline'
toPlot$status[toPlot$growth>thre & toPlot$spread<=thre] = 'no disp but growth'
toPlot$status[toPlot$growth>thre & toPlot$spread>thre] = 'disp & growth'
toPlot$status[toPlot$growth<=thre & toPlot$decline<=thre & toPlot$spread>thre] = "disp"
toPlot$status[toPlot$decline>thre & toPlot$spread>thre] = 'disp & decline'
toPlot$status[is.na(toPlot$status)]="uncertain"
cd = statusLevels%in%toPlot$status
toPlot$status = factor(toPlot$status,levels=statusLevels[cd])
pList[[k]]=ggmap(mapo)+geom_tile(data=toPlot,aes(x=x,y=y,fill=status),alpha=.6)+
scale_fill_manual(values=colors[cd])+ggtitle(as.character(1979+year))+
xlab('Longitude')+ylab('Latitude')+
scale_x_continuous(limits=c(spatial_ext@xmin,spatial_ext@xmax))+
scale_y_continuous(limits=c(spatial_ext@ymin,spatial_ext@ymax))+
theme_bw()+
theme(legend.position="None",text=element_text(size=30))
if(year==years[max(1,round(length(years)/2))]-1979){
pLeg = ggmap(mapo)+geom_tile(data=toPlot,aes(x=x,y=y,fill=status),alpha=.6)+
scale_fill_manual(values=colors[cd])+ggtitle(as.character(1979+year))+
xlab('Longitude')+ylab('Latitude')+theme(text=element_text(size=30))
}
k=k+1
}
setwd(chDir)
png('map_growth_Main.png',width=2.5*1200,height=2.5*700)
multiplot(pList,cols=2)
dev.off()
setwd(chDir)
png('map_growth_Main_Legend.png',width=2.5*1200,height=2.5*700)
print(pLeg)
dev.off()
### Plot pop state
bLabels = c(']0,q40]',']q40,q70]',']q70,q90]',']q90,q100]')
qtLevels = c(bLabels,'uncertain')
popCol = colorRampPalette(colors = c('goldenrod','darkorchid4'))(length(bLabels))
colors = c(popCol,'gray50')
Obs = sapply(1:nYear,function(yea)rowSums(Y[,yea,])>0);Obs[!Obs]=NA;Obs[!is.na(Obs)]="record"
tgObs = sapply(1:nYear,function(yea)rowSums(TG[,yea,])>0)
obsLev = c('no record but TG records','record','not any record');obsCol = c('firebrick2','deepskyblue2','gray50')
k = 1
pList = list()
for(year in years-1979){
tmp=as.data.frame(
expand.grid(cell=ctab$cell[order(ctab$arrayID)],
model=1:dim(post)[1]))
tmp$qt = NA
for(i in sampo){
it = post$iteration[i]
ich = post$chain[i]
mod = get.model(chs[[ich]][paste0('iteraction_',it)][[1]],allParams)
nn = sapply(1:nYear,function(yea) rowSums(mod$n[,yea,]) )
nn = nn/max(nn,na.rm=T)
breakos = c(-.1,quantile(as.vector(nn),prob=c(.4,.7,.9)),1)
nf = data.frame(num=cut(nn[,year],breaks=breakos))
leg = data.frame(num=levels(nf$num),qt=as.character(c(bLabels)))
nf$qt = sapply(1:dim(nf)[1],function(ii)leg$qt[leg$num==nf$num[ii]])
nf$qt=factor(nf$qt,levels=leg$qt[leg$qt%in%unique(nf$qt)])
tmp$qt[tmp$model==i] = as.character(nf$qt)
}
toPlot = aggregate(list(qt=tmp$qt),
by=list(cell=tmp$cell),
FUN=function(fac){
prop=table(fac)/subo
if(max(as.numeric(prop))>=thre){
return(names(prop)[which.max(as.numeric(prop))[1]])
}else{return('uncertain')}})
toPlot = merge(toPlot,ctab,by="cell",all.x=T)
cd = qtLevels%in%as.character(toPlot$qt)
toPlot$qt = factor(toPlot$qt,levels=qtLevels[cd])
toPlot$obs = Obs[,year];toPlot$obs[is.na(toPlot$obs) & tgObs[,year]>0]='no record but TG records';toPlot$obs[is.na(toPlot$obs)]="not any record"
cdCol=obsLev%in%toPlot$obs
toPlot$obs=factor(toPlot$obs,levels=obsLev[cdCol])
pList[[k]]=ggmap(mapo)+geom_tile(data=toPlot,aes(x=x,y=y,fill=qt,colour=obs),alpha=.6)+
scale_fill_manual(values=colors[cd])+
scale_x_continuous(limits=c(spatial_ext@xmin,spatial_ext@xmax))+
scale_y_continuous(limits=c(spatial_ext@ymin,spatial_ext@ymax))+
scale_colour_manual(values=obsCol[cdCol])+
ggtitle(as.character(1979+year))+
xlab('Longitude')+ylab('Latitude')+theme_bw()+
theme(legend.position="None",text=element_text(size=30))
k=k+1
}
setwd(chDir)
png('map_pop_Main.png',width=2.5*1200,height=2.5*700)
multiplot(pList,cols=2)
dev.off()
#####
# Images for Shiny app
#####
thre = .65
setwd(chDir)
if(!shinyDir%in%list.files()){
dir.create(shinyDir)
}
# no disp & decline (marine = half blue)
# no disp but growth (blue)
# disp & growth (lila = red + blue)
# disp (magenta = red + half blue)
# disp & decline (rusty = half red)
### Plot pop growth
statusLevels = c('uncertain',
'no disp & decline',
'no disp but growth',
'disp & growth',
'disp',
'disp & decline')
colorsGro = c('gray50',
rgb(0,0,0),# black
rgb(0,0,1),# blue
rgb(1,0,1),# lila
rgb(1,0,.25),# red
rgb(.6,0,0))# rust
bLabels = c('[0,q40]',']q40,q70]',']q70,q90]',']q90,q100]')
qtLevels = c(bLabels,'uncertain')
popCol = colorRampPalette(colors = c('goldenrod','darkorchid4'))(length(bLabels))
colors = c(popCol,'gray50')
Obs = sapply(1:nYear,function(yea)rowSums(Y[,yea,])>0);Obs[!Obs]=NA;Obs[!is.na(Obs)]="record"
tgObs = sapply(1:nYear,function(yea)rowSums(TG[,yea,])>0)
obsLev = c('no record but TG records','record','not any record');obsCol = c('firebrick2','deepskyblue2','gray50')
#write.table(ctab,'ctab.csv',sep=";",row.names=F,col.names=T)
subo=200
sampo=sort(sample(1:dim(post)[1],subo,replace = F))
for(year in 1:nYear){
cat('\n year ',year,' \n')
print(year)
tmp=as.data.frame(
expand.grid(cell=ctab$cell[order(ctab$arrayID)],
model=sampo,
growth=NA,decline=NA,locGrowth=NA,spread=NA))
if(year<nYear){
### Plot pop growth
for(i in sampo){
flush.console()
cat('\r Processed ',round(100*i/dim(post)[1]),'% iterations')
it = post$iteration[i];ich = post$chain[i]
mod = get.model(chs[[ich]][paste0('iteraction_',it)][[1]],allParams)
nn = rowSums(mod$n[,year,])
nn_ = rowSums(mod$n[,year+1,])
phi = exp(mod$lphi)
tmp$growth[tmp$model==i] = nn_>nn
tmp$decline[tmp$model==i] = nn_<nn
Disp = mod$disp_to_from
seeds_local = diag(Disp) * mod$s_prod[,year]
nnew = round(seeds_local * mod$COMP[,year] * mod$p[,year+1])
ndie = nn[maxAge] + round(sum(mod$rho*mod$n[,year,1:(maxAge-1)]))
tmp$locGrowth[tmp$model==i] = nnew>ndie
diag(Disp) = 0
to_from = mod$p[,year+1]*mod$COMP[,year]*t(mod$s_prod[,year] * t(Disp))
tmp$spread[tmp$model==i] = colSums(to_from)>0
}
toPlotGro = aggregate(list(
decline=tmp$decline,
growth=tmp$growth,
locGrowth=tmp$locGrowth,
spread=tmp$spread),by=list(cell=tmp$cell),
FUN=function(bol)sum(bol)/subo)
toPlotGro = merge(toPlotGro,ctab,by="cell",all.x=T)
toPlotGro$status = NA
toPlotGro$status[toPlotGro$decline>thre & toPlotGro$spread<=thre] = 'no disp & decline'
toPlotGro$status[toPlotGro$growth>thre & toPlotGro$spread<=thre] = 'no disp but growth'
toPlotGro$status[toPlotGro$growth>thre & toPlotGro$spread>thre] = 'disp & growth'
toPlotGro$status[toPlotGro$growth<=thre & toPlotGro$decline<=thre & toPlotGro$spread>thre] = "disp"
toPlotGro$status[toPlotGro$decline>thre & toPlotGro$spread>thre] = 'disp & decline'
toPlotGro$status[is.na(toPlotGro$status)]="uncertain"
cd = statusLevels%in%toPlotGro$status
toPlotGro$status = factor(toPlotGro$status,levels=statusLevels[cd])
pGro= ggmap(mapo)+geom_tile(data=toPlotGro,aes(x=x,y=y,fill=status),alpha=.7)+
scale_fill_manual('growth syndrome',values=colorsGro[cd])+
ggtitle(paste('Map of population growth syndrome in',year+1979))+
xlab('Longitude')+ylab('Latitude')+
theme(text=element_text(size=18))
}else{
pGro=list()
}
tmp=as.data.frame(
expand.grid(cell=ctab$cell[order(ctab$arrayID)],
model=sampo))
# Plot pop
tmp$qt = NA
for(i in sampo){
it = post$iteration[i]
ich = post$chain[i]
mod = get.model(chs[[ich]][paste0('iteraction_',it)][[1]],allParams)
nn = sapply(1:nYear,function(yea) rowSums(mod$n[,yea,]) )
nn = nn/max(nn,na.rm=T)
breakos = c(-.1,quantile(as.vector(nn),prob=c(.4,.7,.9)),1)
nf = data.frame(num=cut(nn[,year],breaks=breakos))
leg = data.frame(num=levels(nf$num),qt=as.character(bLabels))
nf$qt = sapply(1:dim(nf)[1],function(ii)leg$qt[leg$num==nf$num[ii]])
nf$qt=factor(nf$qt,levels=leg$qt[leg$qt%in%unique(nf$qt)])
tmp$qt[tmp$model==i] = as.character(nf$qt)
}
toPlotPop = aggregate(list(qt=tmp$qt),
by=list(cell=tmp$cell),
FUN=function(fac){
prop=table(fac)/subo
if(max(as.numeric(prop))>=thre){
return(names(prop)[which.max(as.numeric(prop))[1]])
}else{return('uncertain')}})
toPlotPop = merge(toPlotPop,ctab,by="cell",all.x=T)
cd = qtLevels%in%as.character(toPlotPop$qt)
toPlotPop$qt = factor(toPlotPop$qt,levels=qtLevels[cd])
toPlotPop$obs = Obs[,year];toPlotPop$obs[is.na(toPlotPop$obs) & tgObs[,year]>0]='no record but TG records';toPlotPop$obs[is.na(toPlotPop$obs)]="not any record"
cdCol=obsLev%in%toPlotPop$obs
toPlotPop$obs=factor(toPlotPop$obs,levels=obsLev[cdCol])
pPop = ggmap(mapo)+geom_tile(data=toPlotPop,aes(x=x,y=y,fill=qt,colour=obs),alpha=.6,size=.3)+
ggtitle(paste('Map of relative population in',year+1979))+
scale_fill_manual('pop. percentile ',values=colors[cd])+
scale_colour_manual('data',values=obsCol[cdCol])+
xlab('Longitude')+ylab('Latitude')+
theme(text=element_text(size=18))
setwd(paste(chDir,'/',shinyDir,'/',sep=""))
png(paste('double_map_',year+1979,'.png',sep=""),width=1200,height=800)
multiplot(list(pGro,pPop))
dev.off()
gc(reset=T)
}
#####
# Figure 6. Relative pop and cell decrease under dispersal ablation
#####
setwd(chDir)
load(file = 'toAnalyse.Rdata')
post$label = paste0(post$chain,'_',post$iteration)
subo = dim(post)[1]
sampo = sample(1:dim(post)[1],subo)
metricos = as.data.frame(expand.grid(
year=1:nYear,
label=post$label[sampo],
dispMode=c('all','onlyShort','onlyLong'),
sha=NA,pop=NA))
metricos = merge(metricos,post[,c('label','iteration','chain')],by="label",all.x=T)
metricos$chain=factor(metricos$chain)
for(lab in unique(metricos$label)){
it = unique(metricos$iteration[metricos$label==lab])
ich = unique(metricos$chain[metricos$label==lab])
mod = get.model(chs[[ich]][paste0('iteraction_',it)][[1]],allParams)
dlo=mod$d_l
dso=mod$d_s
for(dispersal in c('all','onlyShort','onlyLong')){
if(dispersal=='onlyLong'){
mod$d_s=0
mod$d_l=dlo
mod = calculate.model(mod)
}else if(dispersal=='onlyShort'){
mod$d_l=0
mod$d_s=dso
mod = calculate.model(mod)
}
for(year in 1:nYear){
cd = metricos$year==year & metricos$label==lab & metricos$dispMode==dispersal
metricos$pop[cd] = sum(mod$n[,year,])
ny = rowSums(mod$n[,year,]) + 1e-20
ny = ny/sum(ny)
metricos$sha[cd] = -sum(ny*log(ny))
}
}
}
tmpo = aggregate(list(pop=metricos$pop,sha=metricos$sha),by=list(year=metricos$year,dispMode=metricos$dispMode),mean)
popRatio = as.data.frame(expand.grid(year=1:nYear,label=unique(metricos$label),dispMode=c('onlyShort','onlyLong')))
popRatio$popDiff = NA
popRatio$shaDiff = NA
for(i in 1:dim(popRatio)[1]){
yearo = popRatio$year[i]
labo = popRatio$label[i]
dispo = as.character(popRatio$dispMode[i])
cdNow = metricos$year==yearo & metricos$label==labo
#cdBef = metricos$year==(yearo-1) & metricos$label==labo
popRatio$popDiff[i] = metricos$pop[cdNow & metricos$dispMode==dispo] - metricos$pop[cdNow & metricos$dispMode=="all"]
popRatio$popDiff[i] = popRatio$popDiff[i] / metricos$pop[cdNow & metricos$dispMode=="all"]
popRatio$shaDiff[i] = metricos$sha[cdNow & metricos$dispMode==dispo] - metricos$sha[cdNow & metricos$dispMode=="all"]
popRatio$shaDiff[i] = popRatio$sha[i] / metricos$sha[cdNow & metricos$dispMode=="all"]
if(i/100==round(i/100)){
flush.console()
cat('\r Processed...',round(1000*i/dim(popRatio)[1])/10,'%')
}
}
popRatio$grouping = paste(popRatio$dispMode,"_",popRatio$label,sep="")
tmp= as.data.frame(expand.grid(year=1:nYear,
label="MEAN",
dispMode=c('onlyShort','onlyLong')))
Alpha=.1
for(i in 1:dim(tmp)[1]){
vals = popRatio$popDiff[popRatio$year==tmp$year[i] & popRatio$dispMode==tmp$dispMode[i]]
tmp$popDiff[i] = mean(vals)
tmp$lowerPopDiff[i] = quantile(vals,probs=Alpha/2)
tmp$upperPopDiff[i] = quantile(vals,probs=1-Alpha/2)
vals = popRatio$shaDiff[popRatio$year==tmp$year[i] & popRatio$dispMode==tmp$dispMode[i]]
tmp$shaDiff[i] = mean(vals)
tmp$lowerShaDiff[i] = quantile(vals,probs=Alpha/2)
tmp$upperShaDiff[i] = quantile(vals,probs=1-Alpha/2)
}
tmp$year = tmp$year+1979
pPopDiff = ggplot(tmp,aes(x=year,y=popDiff,group=dispMode,colour=dispMode,fill=dispMode),size=1)+
geom_line(size=1)+
geom_ribbon(aes(ymin=lowerPopDiff,ymax=upperPopDiff),alpha=0.3,size=.3)+
geom_point(size=2)+
scale_y_continuous(limits=c(-1,max(tmp$upperPopDiff)))+ylab('Relative population difference (vs full model)')+
theme_bw()+theme(text=element_text(size=20))
pShaDiff = ggplot(tmp,aes(x=year,y=shaDiff,colour=dispMode,fill=dispMode))+
geom_line(size=1)+
geom_ribbon(aes(ymin=lowerShaDiff,ymax=upperShaDiff),alpha=0.3,size=.3)+
geom_point(size=2)+
scale_y_continuous(limits=c(-1,max(tmp$upperShaDiff)))+ylab('Relative Shanon entropy difference (vs full model)')+
theme_bw()+theme(text=element_text(size=20))
png('relative_decrease_ablation.png',width=666,height=1000)
multiplot(list(pPopDiff,pShaDiff),cols=1)
dev.off()
#####
# Figure 5. Number of new plants from long-distance dispersal vs year
#####
setwd(chDir)
load(file = 'toAnalyse.Rdata')
post$label = paste0(post$chain,'_',post$iteration)
seedSpread = as.data.frame(expand.grid(year=1:(nYear-1),label=post$label))
seedSpread$nSeeds = NA