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CCISS_Step4f_Figure_Richness.R
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##======================================================================================
## CCISS Publication Scripts
## Step 4f - Figures - Manuscript plot of Suitability Richness
##======================================================================================
# Colin Mahony
# 778-288-4008
# July 21, 2019
source("./_CCISS_Packages.R") ## packages required
source("./_CCISS_Functions.R") ## common functions
source("./_CCISS_Parameters.R") ## settings used through all scripts
#===============================================================================
# create a dem from the climateBC input data
#===============================================================================
## create a dem from the climateBC input data
points <- read.csv(paste("inputs\\",grid,".csv", sep=""))
dim(points)
# points <- points[order(points$lon, points$lat),]
# head(points)
# transform latlon points to albers (this only works because the points were originally in albers)
P4S.latlon <- CRS("+proj=longlat +datum=WGS84")
P4S.albers <- CRS("+proj=aea +lat_1=50 +lat_2=58.5 +lat_0=45 +lon_0=-126 +x_0=1000000 +y_0=0 +datum=NAD83 +units=m +no_defs")
pts <- points[,4:3]
coordinates(pts) <- pts
projection(pts) <- P4S.latlon
pts.albers <- spTransform(pts, P4S.albers) #reproject the gridpoints
## assemble a simple spatial data frame of the points (seems awkward, but this is how i got the raster to work properly)
pts.df <- as.data.frame(pts.albers)
pts.df<- round(pts.df[,3:4],1)
df = data.frame(z = 1:dim(points)[1],
xc = pts.df[,1],
yc = pts.df[,2])
coordinates(df) = ~xc+yc
gridded(df) = TRUE
df = as(df, "SpatialGridDataFrame") # to full grid
plotOrder <- df$z #plotting order relative to input data
## rasterize
dem <- raster(df)
projection(dem) <- P4S.albers
par(mfrow=c(1,1))
plot(dem)
sum(!is.na(values(dem)))
X <- dem
values(X) <- points$el[df$z]
plot(X)
#===============================================================================
# generate the vector of predicted reference BGCs
#===============================================================================
BGC <- as.character(read.csv(paste("outputs\\BGC.pred", grid, "ref", model, "csv", sep="."), header=F)[,1])
#BGC zones
zone <- rep(NA, length(BGC))
for(i in BGCcolors$classification){ zone[grep(i,BGC)] <- i }
table(zone)
zone <- factor(zone, levels=c("CDF", "CWH", "MH", "ESSF", "MS", "IDF", "PP", "BG", "ICH", "SBPS", "SBS", "BWBS", "SWB", "CMA", "IMA", "BAFA"))
#===============================================================================
# generic spatial data
#===============================================================================
### admin boundaries
bdy.bc <- readOGR("inputs\\shapes\\ProvincialOutline.shp")
#===============================================================================
# Import suitability tables
#===============================================================================
S1 <- treesuit
S1 <- unique(S1[,1:4])
# select the species to run the analysis on
spps <- unique(S1$Spp)
spps <- spps[-which(spps=="X")]
spps.candidate <- spps.lookup$TreeCode[-which(spps.lookup$Exclude=="x")]
spps <- spps[which(spps%in%spps.candidate)]
#===============================================================================
# calculate mean optionality and turnover for each edatope/year/rcp
#===============================================================================
# read in metrics for reference period and historical decades
for(edatope in edatopes){
assign(paste("SuitRichness.ref", edatope, sep="."), read.csv(paste("outputs\\SuitRichness.ref", grid, edatope, "csv", sep="."))[,1])
assign(paste("SppRichness.ref", edatope, sep="."), read.csv(paste("outputs\\SppRichness.ref", grid, edatope, "csv", sep="."))[,1])
print(edatope)
}
for(edatope in edatopes){
for(rcp in rcps){
for(proj.year in proj.years){
assign(paste("SuitRichness", rcp, proj.year, edatope, sep="."), read.csv(paste("outputs\\SuitRichness", grid, rcp, proj.year, edatope, "csv", sep="."))[,1])
assign(paste("SuitRichnessChange", rcp, proj.year, edatope, sep="."), read.csv(paste("outputs\\SuitRichnessChange", grid, rcp, proj.year, edatope, "csv", sep="."))[,1])
assign(paste("SppRichness", rcp, proj.year, edatope, sep="."), read.csv(paste("outputs\\SppRichness", grid, rcp, proj.year, edatope, "csv", sep="."))[,1])
assign(paste("SppRichnessChange", rcp, proj.year, edatope, sep="."), read.csv(paste("outputs\\SppRichnessChange", grid, rcp, proj.year, edatope, "csv", sep="."))[,1])
assign(paste("SuitTurnover", rcp, proj.year, edatope, sep="."), read.csv(paste("outputs\\SuitTurnover", grid, rcp, proj.year, edatope, "csv", sep="."))[,1])
assign(paste("SppTurnover", rcp, proj.year, edatope, sep="."), read.csv(paste("outputs\\SppTurnover", grid, rcp, proj.year, edatope, "csv", sep="."))[,1])
assign(paste("SuitPersistence", rcp, proj.year, edatope, sep="."), read.csv(paste("outputs\\SuitPersistence", grid, rcp, proj.year, edatope, "csv", sep="."))[,1])
assign(paste("SppPersistence", rcp, proj.year, edatope, sep="."), read.csv(paste("outputs\\SppPersistence", grid, rcp, proj.year, edatope, "csv", sep="."))[,1])
print(proj.year)
}
print(rcp)
}
print(edatope)
}
#===============================================================================
# BGC projections
#===============================================================================
# Future BGC
PredSum <- data.frame()
for(rcp in rcps){
for(proj.year in proj.years){
for(GCM in GCMs){
BGC.pred <- as.character(read.csv(paste("outputs\\BGC.pred",grid, GCM, rcp, proj.year,model,"csv", sep="."), header=F)[,1])
assign(paste("BGC.pred", GCM, rcp, proj.year, sep="."), BGC.pred) #bgc projection
PredSum <- rbind(PredSum, as.data.frame(table(BGC.pred)))
# print(GCM)
}
print(proj.year)
}
print(rcp)
}
################################
## Manuscript figure
################################
edatope=edatopes[2]
rcp=rcps[1]
proj.year=proj.years[2]
for(proj.year in proj.years){
for(edatope in edatopes){
#===============================================================================
# assemble the species mix for the reference period
#===============================================================================
# for(edatope in edatopes){
comm.ref <- as.data.frame(matrix(rep(NA, dim(points)[1]*length(spps)), dim(points)[1], length(spps)))
for(spp in spps){
Suit <- read.csv(paste("outputs\\Suit.ref", grid, spp, edatope, "csv", sep="."))[,1]
Suit[is.na(Suit)] <- 5 #XXX note this is different from the equivalent line for the other time periods.
Suit <- 1-(Suit-1)/4
comm.ref[,which(spps==spp)] <- Suit
}
names(comm.ref) <- spps
assign(paste("comm.ref", edatope, sep="."), comm.ref)
# Optionality <- apply(comm.ref, 1, sum)
# assign(paste("Optionality.ref", edatope, sep=""), Optionality)
# write.csv(Optionality, paste("outputs\\Optionality.ref", grid, edatope, "csv", sep="."), row.names = F)
# print(edatope)
# }
# ===============================================================================
# assemble the projected community
# ===============================================================================
for(GCM in GCMs){
# for(edatope in edatopes){
# for(rcp in rcps){
# for(proj.year in proj.years){
comm.proj <- as.data.frame(matrix(rep(NA, dim(points)[1]*length(spps)), dim(points)[1], length(spps)))
for(spp in spps){
Suit <- read.csv(paste("outputs\\Suit", grid, GCM, rcp, proj.year, spp, edatope, "csv", sep="."))[,1]
Suit[Suit==5] <- 5
Suit <- 1-(Suit-1)/4
comm.proj[,which(spps==spp)] <- Suit
}
names(comm.proj) <- spps
assign(paste("comm.proj", GCM, sep="."), comm.proj)
# # print(proj.year)
# }
# # print(rcp)
# }
# print(edatope)
# }
# print(GCM)
}
# ===============================================================================
# get metrics for the rcp, time period and edatope
# ===============================================================================
SuitRichness.ref <- get(paste("SuitRichness.ref", edatope, sep="."))
SuitRichness.proj <- get(paste("SuitRichness", rcp, proj.year, edatope, sep="."))
SuitRichnessChange <- get(paste("SuitRichnessChange", rcp, proj.year, edatope, sep="."))
SuitRichnessChangePct <- SuitRichness.proj/SuitRichness.ref
SuitRichnessChangePct[!is.finite(SuitRichnessChangePct)] <- NA
SppRichness.ref <- get(paste("SppRichness.ref", edatope, sep="."))
SppRichness.proj <- get(paste("SppRichness", rcp, proj.year, edatope, sep="."))
SppRichnessChange <- get(paste("SppRichnessChange", rcp, proj.year, edatope, sep="."))
SppRichnessChangePct <- SppRichness.proj/SppRichness.ref
SppRichnessChangePct[!is.finite(SppRichnessChangePct)] <- NA
SuitTurnover <- get(paste("SuitTurnover", rcp, proj.year, edatope, sep="."))
SppTurnover <- get(paste("SppTurnover", rcp, proj.year, edatope, sep="."))
SuitTurnover[which(SuitTurnover==99)] <- NA
SppTurnover[which(SppTurnover==99)] <- NA
SuitPersistence <- get(paste("SuitPersistence", rcp, proj.year, edatope, sep="."))
SppPersistence <- get(paste("SppPersistence", rcp, proj.year, edatope, sep="."))
# ===============================================================================
# calculate richness with truncation of ensemble mean suitability at 0.375 (treat these as unsuitable)
# ===============================================================================
comm.proj.ensMean <- as.data.frame(matrix(rep(0, dim(points)[1]*length(spps)), dim(points)[1], length(spps)))
for(GCM in GCMs){
comm.proj.ensMean <- comm.proj.ensMean+get(paste("comm.proj", GCM, sep="."))
# print(GCM)
}
comm.proj.ensMean <- comm.proj.ensMean/length(GCMs)
names(comm.proj.ensMean) <- spps
head(comm.proj.ensMean)
comm.proj.ensMean.trunc <- as.matrix(comm.proj.ensMean)
comm.proj.ensMean.trunc[which(comm.proj.ensMean.trunc<0.375)] <- 0
comm.proj.ensMean.trunc <- as.data.frame(comm.proj.ensMean.trunc)
head(comm.proj.ensMean.trunc)
SuitRichness.proj.truncate <- apply(comm.proj.ensMean.trunc, 1, sum)
SuitRichnessChangePct.truncate <- SuitRichness.proj.truncate/SuitRichness.ref
SuitRichnessChangePct.truncate[!is.finite(SuitRichnessChangePct.truncate)] <- NA
# metric <- "SuitRichnessChangePct"
metric <- "SuitRichnessChangePct.truncate"
# x11(width=6.5, height=5, pointsize=8)
png(filename=paste("results\\Manu_Richness\\CCISS_manu_", metric, edatope, rcp, proj.year,"png",sep="."), type="cairo", units="in", width=6.5, height=5, pointsize=8, res=400)
# pdf(file=paste("Results\\CCISS.Fig8", metric, edatope, rcp, proj.year,"pdf",sep="."), width=6.5, height=5, pointsize=8)
par(mar=c(0,0,0,0))
plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
# box()
#map
par(mar=c(0,0,0,0), plt = c(0.1, 0.9, 0.125, 0.999), new = TRUE)
# SuitTurnover <- get(paste("SuitTurnover", rcp, proj.year, edatope, sep="."))
# SppTurnover <- get(paste("SppTurnover", rcp, proj.year, edatope, sep="."))
ylim <- c(-3,3)
y <- get(metric)
y[y<2^(ylim[1])] <- 2^(ylim[1])
y <- log2(y)
values(X) <- y[plotOrder]
breakpoints <- c(-99,seq(-2,2,0.5),99); length(breakpoints)
# breakpoints <- c(-99,-55,seq(-2,2,0.25),55,99); length(breakpoints)
labels <- c(paste(round(2^(breakpoints[c(2,4)])*100),"%", sep=""), "no change", paste(round(2^(breakpoints[length(breakpoints)-c(3,1)])*100),"%", sep=""))
ColScheme <- brewer.pal(11,"RdBu")[-6]; length(ColScheme)
# ColScheme <- c(rep(brewer.pal(11,"RdBu")[1:4], each=2), brewer.pal(11,"RdBu")[5],rep("gray90", 2),brewer.pal(11,"RdBu")[7], rep(brewer.pal(11,"RdBu")[8:11], each=2)); length(ColScheme)
plot(bdy.bc, border="black", lwd=0.4)
image(X, add=T, xaxt="n", yaxt="n", col=ColScheme, breaks=breakpoints, maxpixels= ncell(X))
# mtext(paste("Edatope:", edatope), side=1, line=-1.5, adj=0.02, cex=1.1, font=2)
par(xpd=T)
xl <- 150000; yb <- 1100000; xr <- 250000; yt <- 1550000
xl <- 2025000; yb <- 500000; xr <- 2100000; yt <- 950000
rect(xl, head(seq(yb,yt,(yt-yb)/length(ColScheme)),-1), xr, tail(seq(yb,yt,(yt-yb)/length(ColScheme)),-1), col=ColScheme)
rect(xl, head(seq(yb,yt,(yt-yb)/length(ColScheme)),-1), xr, tail(seq(yb,yt,(yt-yb)/length(ColScheme)),-1), col=ColScheme)
text(rep(xl,length(labels)),seq(yb,yt,(yt-yb)/(length(breakpoints)-1))[c(2,4,6,8,10)],labels,pos=2,cex=1,font=1)
# text(rep(xl,2),c(yb,yt)+c(-10000, 10000),c("No change", "Full turnover"),pos=c(1,3),cex=1,font=1)
text(xr+40000, mean(c(yb,yt))-30000, paste("Relative feasibility richness"), srt=90, pos=3, cex=1, font=2)
# rect(xl, yt+20000, xr, yt+60000, col=ColScheme[length(ColScheme)])
# text(xr, yt+40000, bquote(">"*.(breakseq[3])*sigma),pos=4,cex=1,font=1)
par(xpd=F)
mtext("(a)", side=3, line=-3.5, adj=0.025, cex=1, font=2)
mtext(paste(edatope.name[which(edatopes==edatope)], " sites (", edatope, ")", sep=""), side=3, line=-1.5, adj=0.35, cex=1, font=2)
#select a single gridpoint within a specified BGC
focal.bgcs <- c("ICHxw", "SBSwk1", "ESSFmv4", "SBSmc2")
q.selects <- c(0.955, 0.96, 0.96, 0.97) #quantile of turnover to select the example grid point from
pts.select <- c(106160, 129584, 202644, 50260) # use this if you have already selected the points
line.x <- c(0, 300000, 500000, -400000)
line.y <- c(-200000, 300000, 400000, 300000)
# pts.select <- NA
for(i in 1:4){
focal.bgc <- which(BGC==focal.bgcs[i])
# pt <- focal.bgc[which(SuitTurnover[focal.bgc]==min(SuitTurnover[focal.bgc][SuitTurnover[focal.bgc]>quantile(SuitTurnover[focal.bgc], q.selects[i], na.rm=T)], na.rm=T))[1]] #select the grid point
# pt <- pt[1]
pt <- pts.select[i]
# pts.select[i] <- pt
lines(c(pts.df[pt,1], pts.df[pt,1]+line.x[i]), c(pts.df[pt,2], pts.df[pt,2]+line.y[i]), lwd=1.5)
points(pts.df[pt,], pch=21, cex=1.75, lwd=1.5, bg=ColScheme[cut(y[pt],breaks=breakpoints)])
# print(y[pt])
# print(pt)
}
## Callout box for Location 1
i=1
bgc.select <- focal.bgcs[i]
focal.bgc <- which(BGC==focal.bgcs[i])
pt <- pts.select[i]
#convert fractional suitability to standard suitability
comm.ref.pt <- comm.ref[pt,]
comm.ref.pt <- round((1-comm.ref.pt)*4+1)
comm.ref.pt[comm.ref.pt==4] <- 5
#calculate ensemble mean fractional suitability and convert to standard suitability
for(GCM in GCMs){
if(GCM==GCMs[1]) comm.proj.pt <- get(paste("comm.proj", GCM, sep="."))[pt,] else comm.proj.pt <- rbind(comm.proj.pt,get(paste("comm.proj", GCM, sep="."))[pt,])
}
comm.proj.pt.ensMean <- apply(comm.proj.pt, 2, FUN=mean, na.rm=T)
comm.proj.pt.ensMean <- round((1-comm.proj.pt.ensMean)*4+1)
comm.proj.pt.ensMean[comm.proj.pt.ensMean==4] <- 5
std.ref <- paste(paste(names(comm.ref.pt)[which(comm.ref.pt==1)],collapse=""), "(",
paste(names(comm.ref.pt)[which(comm.ref.pt==2)],collapse=""), ")((",
paste(names(comm.ref.pt)[which(comm.ref.pt==3)],collapse=""), "))", sep="")
std.proj <- paste(paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==1)],collapse=""), "(",
paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==2)],collapse=""), ")((",
paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==3)],collapse=""), "))", sep="")
BGC.list <- vector()
for(GCM in GCMs){
BGC.list[which(GCMs==GCM)] <- get(paste("BGC.pred", GCM, rcp, proj.year, sep="."))[pt]
# print(BGC.pred[pt])
# print(GCM)
}
BGC.list
par(mar=c(0,0,0,0), plt = c(0.6, 0.99, 0.01, 0.195), new = TRUE, mgp=c(2,0.1,0))
plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
rect(-9,-9,9,9, col="white", lwd=0)
mtext("(e)", side=3, line=-1, adj=-0.08, cex=1, font=2)
box()
par(mar=c(0,0,0,0), plt = c(0.6, 0.99, 0.01, 0.195), new = TRUE, mgp=c(2,0.1,0))
plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
text(-0.025, .95, paste("Historical bioclimate"), pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.825, bgc.select, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.65, "Historical feasibilities", pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.525, std.ref, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.35, paste("Mean projected feasibilities", sep=""), pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.225, std.proj, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.05, paste("Mean rel. feas. richness: ", round(get(metric)[pt]*100), "%", sep=""), pos=4, cex=0.8, offset=0.1, font=2)
text(0.605, .95, paste("Projected bioclimates"), pos=4, cex=0.8, font=2, offset=0.1)
par(mar=c(3,0,1,0), plt = c(0.85, 0.985, 0.10, 0.165), new = TRUE, mgp=c(0.5,0.1,0))
bar.bgc <- names(rev(sort(table(BGC.list))))
bar.zone <- rep(NA, length(bar.bgc))
for(i in BGCcolors$classification){ bar.zone[grep(i,bar.bgc)] <- i }
barplot(rev(sort(table(BGC.list))), col=as.character(BGCcolors$colour[match(bar.zone, BGCcolors$classification)]), horiz=F, las=2, ylab=list("# GCMs", cex=0.7), yaxt="n", cex.names=0.7)
par(mgp=c(1,0,0))
axis(2, at=seq(0,12,4), labels=seq(0,12,4), las=1, tck=0, cex.axis=0.8, lwd=0)
## Callout box for Location 2
i=2
bgc.select <- focal.bgcs[i]
focal.bgc <- which(BGC==focal.bgcs[i])
pt <- pts.select[i]
#convert fractional suitability to standard suitability
comm.ref.pt <- comm.ref[pt,]
comm.ref.pt <- round((1-comm.ref.pt)*4+1)
comm.ref.pt[comm.ref.pt==4] <- 5
#calculate ensemble mean fractional suitability and convert to standard suitability
for(GCM in GCMs){
if(GCM==GCMs[1]) comm.proj.pt <- get(paste("comm.proj", GCM, sep="."))[pt,] else comm.proj.pt <- rbind(comm.proj.pt,get(paste("comm.proj", GCM, sep="."))[pt,])
}
comm.proj.pt.ensMean <- apply(comm.proj.pt, 2, FUN=mean, na.rm=T)
comm.proj.pt.ensMean <- round((1-comm.proj.pt.ensMean)*4+1)
comm.proj.pt.ensMean[comm.proj.pt.ensMean==4] <- 5
std.ref <- paste(paste(names(comm.ref.pt)[which(comm.ref.pt==1)],collapse=""), "(",
paste(names(comm.ref.pt)[which(comm.ref.pt==2)],collapse=""), ")((",
paste(names(comm.ref.pt)[which(comm.ref.pt==3)],collapse=""), "))", sep="")
std.proj <- paste(paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==1)],collapse=""), "(",
paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==2)],collapse=""), ")((",
paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==3)],collapse=""), "))", sep="")
BGC.list <- vector()
for(GCM in GCMs){
BGC.list[which(GCMs==GCM)] <- get(paste("BGC.pred", GCM, rcp, proj.year, sep="."))[pt]
# print(BGC.pred[pt])
# print(GCM)
}
BGC.list
plt.callout <- c(0.65, 0.99, 0.575, 0.755)
par(mar=c(0,0,0,0), plt = plt.callout, new = TRUE, mgp=c(2,0.1,0))
plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
rect(-9,-9,9,9, col="white", lwd=0)
mtext("(d)", side=3, line=0.25, adj=0.01, cex=1, font=2)
box()
par(mar=c(0,0,0,0), plt = plt.callout, new = TRUE, mgp=c(2,0.1,0))
plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
text(-0.025, .95, paste("Historical bioclimate"), pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.825, bgc.select, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.65, "Historical feasibilities", pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.525, std.ref, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.35, paste("Mean projected feasibilities", sep=""), pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.225, std.proj, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.05, paste("Mean rel. feas. richness: ", round(get(metric)[pt]*100), "%", sep=""), pos=4, cex=0.8, offset=0.1, font=2)
text(0.57, .95, paste("Projected bioclimates"), pos=4, cex=0.8, font=2, offset=0.1)
par(mar=c(3,0,1,0), plt = plt.callout+c(0.225, -0.005, 0.08, -0.04), new = TRUE, mgp=c(0.5,0.1,0))
bar.bgc <- names(rev(sort(table(BGC.list))))
bar.zone <- rep(NA, length(bar.bgc))
for(i in BGCcolors$classification){ bar.zone[grep(i,bar.bgc)] <- i }
barplot(rev(sort(table(BGC.list))), col=as.character(BGCcolors$colour[match(bar.zone, BGCcolors$classification)]), horiz=F, las=2, ylab=list("# GCMs", cex=0.7), yaxt="n", cex.names=0.7)
par(mgp=c(1,0,0))
axis(2, at=seq(0,12,2), labels=seq(0,12,2), las=1, tck=0, cex.axis=0.8, lwd=0)
## Callout box for Location 3
i=3
bgc.select <- focal.bgcs[i]
focal.bgc <- which(BGC==focal.bgcs[i])
pt <- pts.select[i]
#convert fractional suitability to standard suitability
comm.ref.pt <- comm.ref[pt,]
comm.ref.pt <- round((1-comm.ref.pt)*4+1)
comm.ref.pt[comm.ref.pt==4] <- 5
#calculate ensemble mean fractional suitability and convert to standard suitability
for(GCM in GCMs){
if(GCM==GCMs[1]) comm.proj.pt <- get(paste("comm.proj", GCM, sep="."))[pt,] else comm.proj.pt <- rbind(comm.proj.pt,get(paste("comm.proj", GCM, sep="."))[pt,])
}
comm.proj.pt.ensMean <- apply(comm.proj.pt, 2, FUN=mean, na.rm=T)
comm.proj.pt.ensMean <- round((1-comm.proj.pt.ensMean)*4+1)
comm.proj.pt.ensMean[comm.proj.pt.ensMean==4] <- 5
std.ref <- paste(paste(names(comm.ref.pt)[which(comm.ref.pt==1)],collapse=""), "(",
paste(names(comm.ref.pt)[which(comm.ref.pt==2)],collapse=""), ")((",
paste(names(comm.ref.pt)[which(comm.ref.pt==3)],collapse=""), "))", sep="")
std.proj <- paste(paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==1)],collapse=""), "(",
paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==2)],collapse=""), ")((",
paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==3)],collapse=""), "))", sep="")
BGC.list <- vector()
for(GCM in GCMs){
BGC.list[which(GCMs==GCM)] <- get(paste("BGC.pred", GCM, rcp, proj.year, sep="."))[pt]
# print(BGC.pred[pt])
# print(GCM)
}
BGC.list
plt.callout <- c(0.65, 0.99, 0.81, 0.99)
par(mar=c(0,0,0,0), plt = plt.callout, new = TRUE, mgp=c(2,0.1,0))
plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
rect(-9,-9,9,9, col="white", lwd=0)
mtext("(c)", side=3, line=-1, adj=-0.08, cex=1, font=2)
box()
par(mar=c(0,0,0,0), plt = plt.callout, new = TRUE, mgp=c(2,0.1,0))
plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
text(-0.025, .95, paste("Historical bioclimate"), pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.825, bgc.select, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.65, "Historical feasibilities", pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.525, std.ref, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.35, paste("Mean projected feasibilities", sep=""), pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.225, std.proj, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.05, paste("Mean rel. feas. richness: ", round(get(metric)[pt]*100), "%", sep=""), pos=4, cex=0.8, offset=0.1, font=2)
text(0.57, .95, paste("Projected bioclimates"), pos=4, cex=0.8, font=2, offset=0.1)
par(mar=c(3,0,1,0), plt = plt.callout+c(0.225, -0.005, 0.08, -0.04), new = TRUE, mgp=c(0.5,0.1,0))
bar.bgc <- names(rev(sort(table(BGC.list))))
bar.zone <- rep(NA, length(bar.bgc))
for(i in BGCcolors$classification){ bar.zone[grep(i,bar.bgc)] <- i }
barplot(rev(sort(table(BGC.list))), col=as.character(BGCcolors$colour[match(bar.zone, BGCcolors$classification)]), horiz=F, las=2, ylab=list("# GCMs", cex=0.7), yaxt="n", cex.names=0.7)
par(mgp=c(1,0,0))
axis(2, at=seq(0,12,2), labels=seq(0,12,2), las=1, tck=0, cex.axis=0.8, lwd=0)
## Callout box for Location 4
i=4
bgc.select <- focal.bgcs[i]
focal.bgc <- which(BGC==focal.bgcs[i])
pt <- pts.select[i]
#convert fractional suitability to standard suitability
comm.ref.pt <- comm.ref[pt,]
comm.ref.pt <- round((1-comm.ref.pt)*4+1)
comm.ref.pt[comm.ref.pt==4] <- 5
#calculate ensemble mean fractional suitability and convert to standard suitability
for(GCM in GCMs){
if(GCM==GCMs[1]) comm.proj.pt <- get(paste("comm.proj", GCM, sep="."))[pt,] else comm.proj.pt <- rbind(comm.proj.pt,get(paste("comm.proj", GCM, sep="."))[pt,])
}
comm.proj.pt.ensMean <- apply(comm.proj.pt, 2, FUN=mean, na.rm=T)
comm.proj.pt.ensMean <- round((1-comm.proj.pt.ensMean)*4+1)
comm.proj.pt.ensMean[comm.proj.pt.ensMean==4] <- 5
std.ref <- paste(paste(names(comm.ref.pt)[which(comm.ref.pt==1)],collapse=""), "(",
paste(names(comm.ref.pt)[which(comm.ref.pt==2)],collapse=""), ")((",
paste(names(comm.ref.pt)[which(comm.ref.pt==3)],collapse=""), "))", sep="")
std.proj <- paste(paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==1)],collapse=""), "(",
paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==2)],collapse=""), ")((",
paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==3)],collapse=""), "))", sep="")
BGC.list <- vector()
for(GCM in GCMs){
BGC.list[which(GCMs==GCM)] <- get(paste("BGC.pred", GCM, rcp, proj.year, sep="."))[pt]
# print(BGC.pred[pt])
# print(GCM)
}
BGC.list
plt.callout <- c(0.01, 0.32, 0.57, 0.75)
par(mar=c(0,0,0,0), plt = plt.callout, new = TRUE, mgp=c(2,0.1,0))
plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
rect(-9,-9,9,9, col="white", lwd=0)
mtext("(b)", side=3, line=0.25, adj=0.025, cex=1, font=2)
box()
par(mar=c(0,0,0,0), plt = plt.callout, new = TRUE, mgp=c(2,0.1,0))
plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
text(-0.025, .95, paste("Historical bioclimate"), pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.825, bgc.select, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.65, "Historical feasibilities", pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.525, std.ref, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.35, paste("Mean projected feasibilities", sep=""), pos=4, cex=0.8, font=2, offset=0.1)
text(0, 0.225, std.proj, pos=4, cex=0.8, offset=0.1)
text(-.025, 0.05, paste("Mean rel. feas. richness: ", round(get(metric)[pt]*100), "%", sep=""), pos=4, cex=0.8, offset=0.1, font=2)
text(0.525, .95, paste("Projected bioclimates"), pos=4, cex=0.8, font=2, offset=0.1)
par(mar=c(3,0,1,0), plt = plt.callout+c(0.2, -0.005, 0.08, -0.04), new = TRUE, mgp=c(0.5,0.1,0))
bar.bgc <- names(rev(sort(table(BGC.list))))
bar.zone <- rep(NA, length(bar.bgc))
for(i in BGCcolors$classification){ bar.zone[grep(i,bar.bgc)] <- i }
barplot(rev(sort(table(BGC.list))), col=as.character(BGCcolors$colour[match(bar.zone, BGCcolors$classification)]), horiz=F, las=2, ylab=list("# GCMs", cex=0.7), yaxt="n", cex.names=0.7)
par(mgp=c(1,0,0))
axis(2, at=seq(0,12,2), labels=seq(0,12,2), las=1, tck=0, cex.axis=0.8, lwd=0)
## Summary by zone
par(mar=c(4.5,2,0.1,0.1), plt = c(0.075, 0.365, 0.125, 0.385), new = TRUE, mgp=c(2.5,0.25,0))
xlim=c(1, length(levels(droplevels(zone[-grep("BAFA|CMA|IMA",zone)]))))
z <- boxplot(y~zone, ylim=ylim, ylab="", vertical = TRUE, plot=F)
for(i in 1:length(levels(zone))){
temp <- y[which(zone==levels(zone)[i])]
z$stats[c(1,5), i] <- quantile(temp[!is.na(temp)],c(0.05, 0.95))
}
bxp(z, ylim=ylim, xlim=xlim, xaxt="n", yaxt="n", xaxs="i", ylab="", pch=0,outline=FALSE)
lines(c(-99,99), c(0,0), lwd=2, col="darkgrey")
bxp(z, add=T, boxfill = as.character(BGCcolors$colour[match(levels(droplevels(zone[-grep("BAFA|CMA|IMA",zone)])), BGCcolors$classification)]), xaxt="n", yaxt="n", xaxs="i", ylab="Relative feasibility richness", pch=0,outline=FALSE)
axis(1, at=1:length(levels(zone)), levels(zone), tick=F, las=2, cex=0.8)
axis(2,at=seq(ylim[1], ylim[2]), labels=paste(round(2^(seq(ylim[1], ylim[2]))*100),"%", sep=""), las=2, tck=0)
# lines(range(grep("CWH|MH|CDF", levels(zone)))+c(-0.6,0.6), if(scenario=="6variable") rep(2.7,2) else rep(3.2,2), col=alpha("red", 0.5), lwd=3, lty=1)
# lines(range(grep("ESSF|MS|IDF|PP|BG|ICH|SBPS|SBS|BWBS|SWB", levels(zone)))+c(-0.6,0.6), if(scenario=="6variable") rep(1.5,2) else rep(1.6,2), col=alpha("red", 0.5), lwd=3, lty=1)
axis(1, at=c(mean(grep("CWH|MH|CDF", levels(zone))), mean(grep("ESSF|MS|IDF|PP|BG|ICH|SBPS|SBS|BWBS|SWB", levels(zone))),mean(grep("CMA|IMA|BAFA", levels(zone)))),
labels = c("Coast", "Interior", "Alpine"), tick=F, line=2.95 )
par(xpd=NA)
x1 <- range(grep("CWH|MH|CDF", levels(zone)))+c(-0.4,0.4)
x2 <- range(grep("ESSF|MS|IDF|PP|BG|ICH|SBPS|SBS|BWBS|SWB", levels(zone)))+c(-0.4,0.4)
# x3 <- range(grep("CMA|IMA|BAFA", levels(zone)))+c(-0.4,0.4)
bracketpos=-5.3
lines(rep(x1, each=2), bracketpos*c(0.95, 1,1,0.95))
lines(rep(x2, each=2), bracketpos*c(0.95, 1,1,0.95))
# lines(rep(x3, each=2), bracketpos*c(0.95, 1,1,0.95))
par(xpd=F)
mtext("(f)", side=3, line=0.5, adj=0.025, cex=1, font=2)
dev.off()
print(edatope)
}
print(proj.year)
}
################################
## Supplemental figure of edatopes and time periods
###################################
rcp=rcps[1]
# metric <- "SuitRichnessChangePct"
metric <- "SuitRichnessChangePct.truncate"
# x11(width=6.5, height=5, pointsize=8)
png(filename=paste("results\\Manu_Richness\\CCISS_manu_", metric,".png",sep=""), type="cairo", units="in", width=6.5, height=8.5, pointsize=11, res=400)
# pdf(file=paste("results\\CCISS_SummaryByBGC_", metric,".pdf",sep=""), width=7.5, height=5.625, pointsize=15)
par(mar=c(0.1,0.1, 0.1,0.1), mgp=c(2,0.25,0), mfrow=c(3,2))
for(edatope in edatopes){
for(proj.year in proj.years[1:2]){
#===============================================================================
# assemble the species mix for the reference period
#===============================================================================
# for(edatope in edatopes){
comm.ref <- as.data.frame(matrix(rep(NA, dim(points)[1]*length(spps)), dim(points)[1], length(spps)))
for(spp in spps){
Suit <- read.csv(paste("outputs\\Suit.ref", grid, spp, edatope, "csv", sep="."))[,1]
Suit[is.na(Suit)] <- 5 #XXX note this is different from the equivalent line for the other time periods.
Suit <- 1-(Suit-1)/4
comm.ref[,which(spps==spp)] <- Suit
}
names(comm.ref) <- spps
assign(paste("comm.ref", edatope, sep="."), comm.ref)
# Optionality <- apply(comm.ref, 1, sum)
# assign(paste("Optionality.ref", edatope, sep=""), Optionality)
# write.csv(Optionality, paste("outputs\\Optionality.ref", grid, edatope, "csv", sep="."), row.names = F)
# print(edatope)
# }
# ===============================================================================
# assemble the projected community
# ===============================================================================
for(GCM in GCMs){
# for(edatope in edatopes){
# for(rcp in rcps){
# for(proj.year in proj.years){
comm.proj <- as.data.frame(matrix(rep(NA, dim(points)[1]*length(spps)), dim(points)[1], length(spps)))
for(spp in spps){
Suit <- read.csv(paste("outputs\\Suit", grid, GCM, rcp, proj.year, spp, edatope, "csv", sep="."))[,1]
Suit[Suit==5] <- 5
Suit <- 1-(Suit-1)/4
comm.proj[,which(spps==spp)] <- Suit
}
names(comm.proj) <- spps
assign(paste("comm.proj", GCM, sep="."), comm.proj)
# # print(proj.year)
# }
# # print(rcp)
# }
# print(edatope)
# }
# print(GCM)
}
SuitRichness.ref <- get(paste("SuitRichness.ref", edatope, sep="."))
SuitRichness.proj <- get(paste("SuitRichness", rcp, proj.year, edatope, sep="."))
SuitRichnessChange <- get(paste("SuitRichnessChange", rcp, proj.year, edatope, sep="."))
SuitRichnessChangePct <- SuitRichness.proj/SuitRichness.ref
SuitRichnessChangePct[!is.finite(SuitRichnessChangePct)] <- NA
comm.proj.ensMean <- as.data.frame(matrix(rep(0, dim(points)[1]*length(spps)), dim(points)[1], length(spps)))
for(GCM in GCMs){
comm.proj.ensMean <- comm.proj.ensMean+get(paste("comm.proj", GCM, sep="."))
# print(GCM)
}
comm.proj.ensMean <- comm.proj.ensMean/length(GCMs)
names(comm.proj.ensMean) <- spps
head(comm.proj.ensMean)
comm.proj.ensMean.trunc <- as.matrix(comm.proj.ensMean)
comm.proj.ensMean.trunc[which(comm.proj.ensMean.trunc<0.375)] <- 0
comm.proj.ensMean.trunc <- as.data.frame(comm.proj.ensMean.trunc)
head(comm.proj.ensMean.trunc)
SuitRichness.proj.truncate <- apply(comm.proj.ensMean.trunc, 1, sum)
SuitRichnessChangePct.truncate <- SuitRichness.proj.truncate/SuitRichness.ref
SuitRichnessChangePct.truncate[!is.finite(SuitRichnessChangePct.truncate)] <- NA
ylim <- c(-3,3)
y <- get(metric)
y[y<2^(ylim[1])] <- 2^(ylim[1])
y <- log2(y)
values(X) <- y[plotOrder]
breakpoints <- c(-99,seq(-2,2,0.5),99); length(breakpoints)
# breakpoints <- c(-99,-55,seq(-2,2,0.25),55,99); length(breakpoints)
labels <- c(paste(round(2^(breakpoints[c(2,4)])*100),"%", sep=""), "no change", paste(round(2^(breakpoints[length(breakpoints)-c(3,1)])*100),"%", sep=""))
ColScheme <- brewer.pal(11,"RdBu")[-6]; length(ColScheme)
# ColScheme <- c(rep(brewer.pal(11,"RdBu")[1:4], each=2), brewer.pal(11,"RdBu")[5],rep("gray90", 2),brewer.pal(11,"RdBu")[7], rep(brewer.pal(11,"RdBu")[8:11], each=2)); length(ColScheme)
plot(bdy.bc, border="black", lwd=0.4)
image(X, add=T, xaxt="n", yaxt="n", col=ColScheme, breaks=breakpoints, maxpixels= ncell(X))
par(xpd=T)
xl <- 1550000; yb <- 1000000; xr <- 1650000; yt <- 1700000
rect(xl, head(seq(yb,yt,(yt-yb)/length(ColScheme)),-1), xr, tail(seq(yb,yt,(yt-yb)/length(ColScheme)),-1), col=ColScheme)
rect(xl, head(seq(yb,yt,(yt-yb)/length(ColScheme)),-1), xr, tail(seq(yb,yt,(yt-yb)/length(ColScheme)),-1), col=ColScheme)
text(rep(xr,length(labels)),seq(yb,yt,(yt-yb)/(length(breakpoints)-1))[c(2,4,6,8,10)],labels,pos=4,cex=1,font=1)
# text(rep(xl,2),c(yb,yt)+c(-10000, 10000),c("No change", "Full turnover"),pos=c(1,3),cex=1,font=1)
text(xl-40000, mean(c(yb,yt))-30000, paste("Relative feasibility richness"), srt=90, pos=3, cex=1, font=2)
# rect(xl, yt+20000, xr, yt+60000, col=ColScheme[length(ColScheme)])
# text(xr, yt+40000, bquote(">"*.(breakseq[3])*sigma),pos=4,cex=1,font=1)
par(xpd=F)
mtext(paste("(", if(proj.year==proj.years[1]) letters[c(1,3,5)[which(edatopes==edatope)]] else letters[c(2,4,6)[which(edatopes==edatope)]], ")\n", proj.year.name[which(proj.years==proj.year)], "\n", edatope.name[which(edatopes==edatope)], " (", edatope, ")", sep=""), side=1, line=-1.5, adj=0.01, cex=1, font=2)
print(proj.year)
}
print(edatope)
}
dev.off()
hist(y)
# # #####################
# # #### exploratory Suitability profile for individual locations
# # #####################
#
# edatope=edatopes[2]
# rcp=rcps[1]
# proj.year=proj.years[2]
#
# focal.bgcs <- c("ICHxw", "SBSwk1", "ESSFmv4", "SBSmc2")
# q.selects <- c(0.955, 0.96, 0.96, 0.97) #quantile of turnover to select the example grid point from
# pts.select <- c(106160, 129584, 202644, 50260) # use this if you have already selected the points
#
# # for(j in 1:4){
# #select a single gridpoint within a specified BGC
# bgc.select <- focal.bgcs[j]
#
# bgc.select <- "SBSmc2"
# focal.bgc <- which(BGC==bgc.select)
# par(mar=c(4,4,1,1))
# hist(SuitTurnover[focal.bgc])
# # hist(SuitPersistence[focal.bgc])
# i=0.97
# # i=q.selects[j]
# for(i in seq(0.5, 99, 0.01)){
# q.select <- i #quantile of turnover to select the example grid point from
# pt <- focal.bgc[which(SuitTurnover[focal.bgc]==min(SuitTurnover[focal.bgc][SuitTurnover[focal.bgc]>quantile(SuitTurnover[focal.bgc], q.select, na.rm=T)], na.rm=T))[1]] #select the grid point
# pt <- pt[1]
# # pt <- plotOrder[!is.na(plotOrder)][161020]
# #convert fractional suitability to standard suitability
# comm.ref.pt <- comm.ref[pt,]
# comm.ref.pt <- round((1-comm.ref.pt)*4+1)
# comm.ref.pt[comm.ref.pt==4] <- 5
#
# #calculate ensemble mean fractional suitability and convert to standard suitability
# for(GCM in GCMs){
# if(GCM==GCMs[1]) comm.proj.pt <- get(paste("comm.proj", GCM, sep="."))[pt,] else comm.proj.pt <- rbind(comm.proj.pt,get(paste("comm.proj", GCM, sep="."))[pt,])
# }
# comm.proj.pt.ensMean <- apply(comm.proj.pt, 2, FUN=mean, na.rm=T)
# comm.proj.pt.ensMean <- round((1-comm.proj.pt.ensMean)*4+1)
# comm.proj.pt.ensMean[comm.proj.pt.ensMean==4] <- 5
#
# std.ref <- paste(paste(names(comm.ref.pt)[which(comm.ref.pt==1)],collapse=""), "(",
# paste(names(comm.ref.pt)[which(comm.ref.pt==2)],collapse=""), ")((",
# paste(names(comm.ref.pt)[which(comm.ref.pt==3)],collapse=""), "))", sep="")
# std.proj <- paste(paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==1)],collapse=""), "(",
# paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==2)],collapse=""), ")((",
# paste(names(comm.proj.pt.ensMean)[which(comm.proj.pt.ensMean==3)],collapse=""), "))", sep="")
#
# GCM <- GCMs[2]
# pt.bgc <- get(paste("BGC.pred", GCM, rcp, proj.year, sep="."))[pt]
#
# #map
# par(mar=c(0,0,0,0))
#
# BGC.list <- vector()
# for(GCM in GCMs){
# BGC.list[which(GCMs==GCM)] <- get(paste("BGC.pred", GCM, rcp, proj.year, sep="."))[pt]
# # print(BGC.pred[pt])
# # print(GCM)
# }
# BGC.list
#
# ylim <- c(-3,3)
# y <- get(metric)
# y[y<2^(ylim[1])] <- 2^(ylim[1])
# y <- log2(y)
# values(X) <- y[plotOrder]
#
# par(mfrow=c(1,1), mar=c(0,0,0,0))
# image(X, xaxt="n", yaxt="n", col=ColScheme, breaks=breakpoints, maxpixels= ncell(X))
# # plot(bdy.bc, add=T, border="black", lwd=0.4)
# # points(pts.df[plotOrder[!is.na(plotOrder)][161025],], cex=2, lwd=2)
# points(pts.df[pt,], cex=2, lwd=2)
# legend("topright", legend=i, cex=2)
#
# par(mar=c(0,0,0,0), plt = c(0.01, 0.335, 0.01, 0.38), new = TRUE, mgp=c(2,0.1,0))
# plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
# box()
#
# par(mar=c(0,0,0,0), plt = c(0.01, 0.335, 0.01, 0.38), new = TRUE, mgp=c(2,0.1,0))
# plot(0, xlim=c(0,1), ylim=c(0,1), col="white", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
# text(-0.025, .95, paste("Historical bioclimate"), pos=4, cex=0.8, font=2, offset=0.1)
# text(0, 0.85, bgc.select, pos=4, cex=0.8, offset=0.1)
# text(-.025, 0.75, "Historical feasibilities", pos=4, cex=0.8, font=2, offset=0.1)
# text(0, 0.65, std.ref, pos=4, cex=0.8, offset=0.1)
# text(-.025, 0.55, paste("Mean projected feasibilities", sep=""), pos=4, cex=0.8, font=2, offset=0.1)
# text(0, 0.45, std.proj, pos=4, cex=0.8, offset=0.1)
# text(-.025, 0.35, paste("Mean turnover: ", round(SuitTurnover[pt]*100), "%", sep=""), pos=4, cex=0.8, offset=0.1, font=2)
# text(-.025, 0.25, paste("Mean persistence: ", round(SuitPersistence[pt]*100), "%", sep=""), pos=4, cex=0.8, offset=0.1, font=2)
# text(-.025, 0.15, paste("Mean richness change: ", round(SuitRichnessChangePct[pt]*100), "%", sep=""), pos=4, cex=0.8, offset=0.1, font=2)
#
# par(mar=c(3,0,1,0), plt = c(0.8, 0.99, 0.09, 0.15), new = TRUE, mgp=c(0.5,0.1,0))
# barplot(rev(sort(table(BGC.list))), horiz=F, las=2, ylab=list("# of GCMs", cex=0.7), yaxt="n", cex.names=0.6)
# par(mgp=c(1,0,0))
# axis(2, las=1, tck=0, cex.axis=0.7)
# title(main=list("Projected bioclimates", cex=0.7))
# }
# # SuitPersistence examples
# # decline: IDFxh4, (CWHmm2), CWHws1
# # stable:
# # increase: ESSFdkp
#
#
# # SuitTurnover examples
# # Low: SBSmk1
# # High: ESSFwc4 (includes some CWHws1/2 and MHmm2 units);
# # High: ICHxw;
#
# levels(bymedian)