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psmc_process.r
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setwd("/home/msupple/zoonomia/psmc")
#metafile="zoonomia_240spp.tsv"
metafile="/home/msupple/zoonomia/zoonomia_biodiversity/biodiversity_metadata.csv"
#when downloading from google doc, need to open and hit save
indir="/home/msupple/zoonomia/psmc/psmc_txt_out/"
# read in metadata
meta=read.table(metafile, header=T, sep=",")
#meta$sp=gsub(" ","_", meta$Species)
#meta$IUCN=meta$IUCN..new.
###############################################################################
# 5 larger order
#set IUCN colors
#palette(c("red","darkgrey","orange","blue","green","brown"))
iucn_cols=c("#377EB8","#4DAF4A","#A65628","#FF7F00","#E41A1C","gray") #LC, NT, VU, EN, CR, DD
#palette(c("#377EB8","#4DAF4A","#A65628","#FF7F00","#E41A1C")) #LC, NT, VU, EN, CR
palette(c("#E41A1C","gray","#FF7F00","#377EB8","#4DAF4A","#A65628"))
# BY ORDER
#pdf("psmc_order_IUCN.pdf")
png("psmc_order_IUCN.png", width=7.6, height=7, units="in", res=1000)
meta$Order2=meta$Order
levels(meta$Order2)=c(levels(meta$Order2),"OTHER")
meta$Order2[meta$Order2!="CARNIVORA" & meta$Order2!="CETARTIODACTYLA" &
meta$Order!="CHIROPTERA" & meta$Order!="PRIMATES" & meta$Order!="RODENTIA"]="OTHER"
meta$Order2=droplevels(meta$Order2)
ymaxs=c(10,20,30,15,60,30)
orders=levels(meta$Order2)
#orders=c(levels(meta$Order)[2],levels(meta$Order)[7])
par(mfrow=c(3,2), mar=c(4,4,2,0.5))
# loop over IUCN categories
for (i in 1:length(orders))
{
order=orders[i]
print(order)
#subset
metasub=subset(meta, Order2==order)
#set up plot
plot(c(10000,10000000), c(0,ymaxs[i]),pch="",
log='x', ylab="",xlab="", xaxt="n",
main=order)
axis(1, tcl=-0.7,
at=c(10^(4:10)),
labels=sapply(c(4:10),function(i) as.expression(bquote(10^.(i)))))
axis(1, tcl=-0.4, labels=NA,
at=c(2*10^(4:10),3*10^(4:10),4*10^(4:10),5*10^(4:10),
6*10^(4:10),7*10^(4:10),8*10^(4:10),9*10^(4:10)))
# mtext("Years", side=1, line=2.5)
# mtext(expression(paste("Effective Population Size (x10"^"4",")")),
# side=2, line=2)
if (order=="CETARTIODACTYLA")
{legend("topright",
legend=c("CR","EN","VU","NT","LC","DD"),
#legend=c("Critically Endangered","Endangered","Vulnerable","Near threatened","Least Concern","Data deficient"),
col=iucn_cols,
#col=c("red","orange","brown","green","blue","darkgrey"),
#legend=levels(metasub$IUCN),
#col=as.numeric(as.factor(levels(metasub$IUCN))),
pch=20)}
#loop over individuals in subset
for (j in 1:dim(metasub)[1])
{
print(as.character(metasub$species[j]))
#print(as.character(metasub$Order[j]))
#print(as.character(metasub$IUCN[j]))
#read in psmc
if(file.exists(paste(indir,"/",as.character(metasub$genus_species[j]),".0.txt", sep="")))
{
psmc=read.table(paste(indir,"/",as.character(metasub$genus_species[j]),".0.txt", sep=""))
lines(psmc$V1,psmc$V2, col=metasub$IUCN[j])
}
}
}
mtext("Years", side=1, line=2.5, at=3000)
mtext(expression(paste("Effective Population Size (x10"^"4",")")),
side=2, line=31, at=65)
dev.off()
#########################################################################
# BY IUCN CATEGORY (6 panels) with captive/wild/dom
pdf("psmc_iucn_wild.pdf")
par(mfrow=c(3,2), mar=c(4,4,2,0.5))
#iucncats=levels(meta$IUCN)
iucncats=c("CR","EN","VU","NT","LC","DD")
#iucncats=c(levels(meta$IUCN)[4],levels(meta$IUCN)[6])
#set wild/cap colors
palette(c("blue","green","red","black"))
#reorder
meta$wild_status_reseq=factor(meta$wild_status_reseq, level=c("wild","captive","domesticated","unknown"))
# loop over IUCN categories
for (i in 1:length(iucncats))
{
iucncat=iucncats[i]
print(iucncat)
#subset
metasub=subset(meta, IUCN==iucncat)
#set up plot
plot(c(10000,10000000), c(0,50),pch="",
log='x', ylab="",xlab="", xaxt="n",
main=iucncat
)
axis(1, tcl=-0.7,
at=c(10^(4:10)),
labels=sapply(c(4:10),function(i) as.expression(bquote(10^.(i)))))
axis(1, tcl=-0.4, labels=NA,
at=c(2*10^(4:10),3*10^(4:10),4*10^(4:10),5*10^(4:10),
6*10^(4:10),7*10^(4:10),8*10^(4:10),9*10^(4:10)))
# mtext("Years", side=1, line=2.5)
# mtext(expression(paste("Effective Population Size (x10"^"4",")")),
# side=2, line=2)
if (iucncat=="EN")
{legend("topright",
legend=levels(meta$wild_status_reseq),
col=c(1:4),
# col=as.numeric(as.factor(levels(meta$wild_status_reseq))),
#legend=levels(metasub$IUCN),
#col=as.numeric(as.factor(levels(metasub$IUCN))),
pch=20)}
#loop over individuals in subset
for (j in 1:dim(metasub)[1])
{
print(as.character(metasub$species[j]))
#print(as.character(metasub$Order[j]))
#print(as.character(metasub$IUCN[j]))
#read in psmc
if(file.exists(paste(indir,"/",as.character(metasub$genus_species[j]),".0.txt", sep="")))
{
psmc=read.table(paste(indir,"/",as.character(metasub$genus_species[j]),".0.txt", sep=""))
lines(psmc$V1,psmc$V2, col=as.numeric(metasub$wild_status_reseq[j]))
}
}
}
mtext("Years", side=1, line=2.5, at=3000)
mtext(expression(paste("Effective Population Size (x10"^"4",")")),
side=2, line=28.5, at=110)
dev.off()
#########################################################################
# INDIVIDUAL SAMPLES
pdf("psmc_indiv.pdf")
# loop over individuals
for (i in 1:length(meta$species))
{
print(as.character(meta$species[i]))
#print(as.character(meta$Order[i]))
#print(as.character(meta$IUCN[i]))
#check if file exists
if(file.exists(paste(indir,"/",as.character(meta$genus_species[i]),".0.txt", sep="")))
{
#read in psmc
psmc=read.table(paste(indir,"/",as.character(meta$genus_species[i]),".0.txt", sep=""))
psmc[1,1]=psmc[1,1]+0.000001 #fix log 0 problem
#plot
plot(psmc$V1,psmc$V2,
xlim=c(10000,10000000),
ylim=c(0,30),
log='x', type="l",
main=paste(as.character(meta$species[i]),as.character(meta$Common.Name[i]),
as.character(meta$Order[i]),as.character(meta$IUCN[i]), sep=", "),
ylab="",xlab="", xaxt="n")
axis(1, tcl=-0.7,
at=c(10^(4:10)),
labels=sapply(c(4:10),function(i) as.expression(bquote(10^.(i)))))
axis(1, tcl=-0.4, labels=NA,
at=c(2*10^(4:10),3*10^(4:10),4*10^(4:10),5*10^(4:10),
6*10^(4:10),7*10^(4:10),8*10^(4:10),9*10^(4:10)))
mtext("Years", side=1, line=2.5)
mtext(expression(paste("Effective Population Size (x10"^"4",")")),
side=2, line=2)
}
}
dev.off()
# BY IUCN CATEGORY
pdf("psmc_iucn.pdf")
iucncats=levels(meta$IUCN)
#iucncats=c(levels(meta$IUCN)[4],levels(meta$IUCN)[6])
# loop over IUCN categories
for (i in 1:length(iucncats))
{
iucncat=iucncats[i]
print(iucncat)
#subset
metasub=subset(meta, IUCN==iucncat)
#set up plot
plot(c(10000,10000000), c(0,30),pch="",
log='x', ylab="",xlab="", xaxt="n",
main=iucncat
)
axis(1, tcl=-0.7,
at=c(10^(4:10)),
labels=sapply(c(4:10),function(i) as.expression(bquote(10^.(i)))))
axis(1, tcl=-0.4, labels=NA,
at=c(2*10^(4:10),3*10^(4:10),4*10^(4:10),5*10^(4:10),
6*10^(4:10),7*10^(4:10),8*10^(4:10),9*10^(4:10)))
mtext("Years", side=1, line=2.5)
mtext(expression(paste("Effective Population Size (x10"^"4",")")),
side=2, line=2)
#loop over individuals in subset
for (j in 1:dim(metasub)[1])
{
print(as.character(metasub$species[j]))
#print(as.character(metasub$Order[j]))
#print(as.character(metasub$IUCN[j]))
#read in psmc
if(file.exists(paste(indir,"/",as.character(metasub$genus_species[j]),".0.txt", sep="")))
{
psmc=read.table(paste(indir,"/",as.character(metasub$genus_species[j]),".0.txt", sep=""))
lines(psmc$V1,psmc$V2)
}
}
}
dev.off()
# CALC METRICS and plot all
palette(c("#E41A1C","gray","#FF7F00","#377EB8","#4DAF4A","#A65628"))
meta$psmc_min=NA
meta$harm_mean_wt2=NA
pdf("psmc_all.pdf")
#set up plot
plot(c(10000,10000000), c(0,30),pch="",
log='x', ylab="",xlab="", xaxt="n",
main="PSMC")
axis(1, tcl=-0.7,
at=c(10^(4:10)),
labels=sapply(c(4:10),function(i) as.expression(bquote(10^.(i)))))
axis(1, tcl=-0.4, labels=NA,
at=c(2*10^(4:10),3*10^(4:10),4*10^(4:10),5*10^(4:10),
6*10^(4:10),7*10^(4:10),8*10^(4:10),9*10^(4:10)))
mtext("Years", side=1, line=2.5)
mtext(expression(paste("Effective Population Size (x10"^"4",")")),
side=2, line=2)
#loop over individuals in subset
for (j in 1:dim(meta)[1])
{
print(as.character(meta$species[j]))
#print(as.character(meta$Order[j]))
#print(as.character(meta$IUCN[j]))
#read in psmc
if(file.exists(paste(indir,"/",as.character(meta$genus_species[j]),".0.txt", sep="")))
{
psmc=read.table(paste(indir,"/",as.character(meta$genus_species[j]),".0.txt", sep=""))
lines(psmc$V1,psmc$V2, col=meta$IUCN[j])
#calc metrics
#psmc min
meta$psmc_min[j]=min(subset(psmc, V1>10000 & V1<1000000)$V2)
#harmonic mean
temp_data=subset(psmc, V1>10000 & V1<1000000)[,1:2]
meta$harm_mean[j]=length(temp_data$V2)/sum(1/temp_data$V2)
#harmonic mean weighted by generation length of interval
#method 1: based on older interval
t2=psmc$V1[-1]
t2[length(psmc$V1)]=NA
psmc=cbind(psmc,t2)
temp_data=psmc[-(length(psmc$V1)),]
temp_data=subset(temp_data, V1>10000)
temp_data$wt1=temp_data$t2-temp_data$V1
# meta$harm_mean_wt1[j]=sum(temp_data$wt1)/sum(temp_data$wt1/temp_data$V2)
#method 2: based on center of interval
t1=psmc$V1[-(length(psmc$V1))]
t1=append(t1,NA, after=0)
psmc=cbind(psmc,t1)
temp_data=psmc[-(length(psmc$V1)),]
temp_data=temp_data[-1,]
temp_data=subset(temp_data, V1>10000)
temp_data$wt2=temp_data$t2-temp_data$t1
meta$harm_mean_wt2[j]=sum(temp_data$wt2)/sum(temp_data$wt2/temp_data$V2)
}
}
legend("topright",
legend=c("Critically Endangered","Endangered","Vulnerable","Near threatened","Least Concern","Data deficient"),
col=c("red","orange","brown","green","blue","darkgrey"),
#legend=levels(metasub$IUCN),
#col=as.numeric(as.factor(levels(metasub$IUCN))),
pch=20)
dev.off()
write.csv(meta, "zoonomia_psmc_metrics.csv")