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5.0_analyses_and_figures.R
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################################################################################
## Will Current Protected Areas Harbour Refugia for ##
## Threatened Arctic Vegetation Types until 2050? ##
## A First Assessment ##
## Merin Reji Chacko ##
## Analysis Figures 1-3 ##
## Last edited: 21.12.2022 ##
## chacko.merin@gmail.com ##
################################################################################
## load libraries and datasets
rm(list=ls())
library(data.table)
library(ggplot2)
library(tidyverse)
"%ni%" <- Negate("%in%")
#this code runs based on the dataset: "zonal_histogram_results.csv"
#this dataset is freely available at the following DOI:
# www.doi.org/10.5281/zenodo.6902764
full <- fread("zonal_histogram_results.csv")
################################################################################
###
### Figure 1: Present distribution of vegetation within and outside of protected
### areas in the Arctic
#drop non-vegetated areas and subset for the present
present <- subset(full, full$Model == "Present" & !is.na(full$Area) &
full$Pixels %ni% c("91", "92", "93", "99"))
#total tundra area:
sum(subset(present, Status == "All")$Area)
#total protected tundra
sum(subset(present, Status == "Protected")$Area)
#fraction protected:
sum(subset(present, Status == "Protected")$Area)/
sum(subset(present, Status == "All")$Area)*100
# Fraction of protected areas
# for unprotected, NA
present$Total <- NA
# for total, we want to know relative abundance of VT in comparison with
# present arctic
present$Total <- ifelse(present$Status=="All",
sum(subset(present, Status == "All")$Area),
present$Total)
#for protected, we want to know relative to total protected area
present$Total <- ifelse(present$Status=="Protected",
sum(subset(present, Status == "Protected")$Area),
present$Total)
present$Relative_Abundance <- round(present$Area/present$Total*100, digit=2)
present$Relative_Abundance <- paste(present$Relative_Abundance, "%", sep="")
present$Relative_Abundance <- gsub("3.8%", "3.80%", present$Relative_Abundance)
present$Relative_Abundance <- gsub("10.3%", "10.30%", present$Relative_Abundance)
present$Relative_Abundance <- gsub("0.5%", "0.50%", present$Relative_Abundance)
present$Relative_Abundance <- gsub("2.3%", "2.30%", present$Relative_Abundance)
present$Relative_Abundance <- gsub("2.5%", "2.50%", present$Relative_Abundance)
# divide area by 1000 as our unit is 10^2 km^2
present$Area <- present$Area/1000
present$Total <- present$Total/1000 #also for the total
protected <- subset(present, present$Status == "Protected")
all <- subset(present, present$Status == "All")
ggplot(mapping=aes(Vegetation_Type, Area, fill=Group, alpha=0.1))+
geom_bar(data=all, stat = "identity")+
geom_bar(data=protected,stat="identity", alpha=3)+
geom_text(data=all, aes(label = Relative_Abundance),
vjust = -0.2, fontface="bold", color="black")+
geom_text(data=protected, aes(label = Relative_Abundance),
vjust = -0.2, fontface="bold", color="black")+
labs(x="Vegetation type",
y=expression(paste("Area (",10^3," ",km^2,"",sep = ")")))+
scale_fill_manual(values=c("#9f8c5d", "#e8c970", "#eabebf",
"#94b042", "#40aa9b"))+
theme(legend.position = "none")+
theme(axis.ticks.x = element_blank(),panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA),
text = element_text(size=20))
################################################################################
###
### Figure 2: Future predictions, using the "realistic scenario"
# the realistic scenario is IPSL, SSP585, 20km
# the Model column has the combination of scenarios which form each prediction
# in the following pattern:
# treedispersalrate_climatemodel_emissionsscenario
# tree disperals: unlimited (unlim), 5km, 20km
# climate models: EC-Earth-3-Veg (e3v), IPSL-CM5 (ipsl), and MRI-CGCM3 (mri)
# emissions scenarios:SSP126 (126) and SSP585 (585)
# the following models lack predictions for some parts of the arctic due to a
# data gap, and were removed for this analysis:
full <- subset(full, full$Model %ni% c("unlim_e3v_585",
"5km_e3v_585",
"20km_e3v_585"))
# the realistic model:
real_mod <- "20km_ipsl_585"
future <- subset(full, full$Group %in% c("G", "P", "S") & full$Model!="Present")
real <- subset(future, future$Model %in% c(sprintf("%s", real_mod)))
real$Area <- real$Area/1000
# now calculate the maximum and minimum envelopes from other models
large <- list()
small <- list()
veg <- unique(real$Vegetation_Type)
type <- unique(real$Status)
for (i in 1:length(veg)){
a <- subset(future, future$Vegetation_Type==veg[i])
for (j in 1:length(type)){
b <- subset(a, a$Status==type[j])
small[[j]] <- data.frame(Vegetation_Type = veg[i],
Group = unique(b$Group),
Maximum = max(b$Area),
Minimum = min(b$Area),
Status = unique(b$Status)
)
}
large[[i]] <- do.call(rbind, small)
}
envelopes <- do.call(rbind, large)
rm(large, small, a, b,i,j,type, veg)
#also we need realistic in here
dd <- merge(real,envelopes, by=c("Vegetation_Type", "Group", "Status"))
dd$Time <- "Future"
#we need present info as well
present <- subset(present, present$Group %in% c("G", "P", "S"))
names(dd)[names(dd) %ni% names(present)]
present$Time <- "Present"
present$Maximum <- NA
present$Minimum <- NA
names(present)[names(present) %ni% names(dd)]
present <- present[,-c(8:9)]
dd <- rbind(dd, present)
#dd <- subset(dd, dd$Status!="All")
dd$Maximum <- dd$Maximum/1000
dd$Minimum <- dd$Minimum/1000
dd$Status <- factor(dd$Status, levels = c("Unprotected", "Protected", "All"))
dd$Time <- factor(dd$Time, levels = c("Present", "Future"))
#you need time and protected to be factors
# max/min for unprotected needs to be the ones for all
# but the area for unprotected needs to stay the same
protected <- subset(dd, dd$Status=="Protected")
unprotected <- subset(dd, dd$Status=="Unprotected")
unprotected <- unprotected[,-c(8:9)]
all <- subset(dd, dd$Status=="All")
unprotected <- cbind(unprotected, all[,c(8:9)])
dd <- rbind(protected, unprotected)
ggplot(dd, aes(x = Time, y = Area, fill = Status,alpha=Time),lty="blank") +
geom_bar(position = "stack", stat = "identity", size=1.1) +
scale_alpha_manual("Time",values=c(1, 0.5),labels=c("Present","2050"))+
facet_grid(~Vegetation_Type, switch = "x")+
geom_errorbar(aes(ymin=Minimum, ymax=Maximum), size=1,
width=.4, position=position_dodge(width=0.5),
color="black", alpha=1) +
labs(x="Vegetation type",
y=expression(paste("Area (",10^3," ",km^2,"",sep = ")")))+
scale_fill_manual("Protection",
values=c("#c4d398","#95af44"),
labels=c("Not Protected","Protected"))+ #S
#scale_fill_manual("Protection",values=c("#f1e1b0","#e8c970"),
#labels=c("Not Protected","Protected")) + #G
#scale_fill_manual("Protection",values=c("#f3dbdb","#ebbdbf"),l
#abels=c("Not Protected","Protected")) + #P
theme(legend.position = "none", axis.ticks.x = element_blank(),
axis.text.y = element_text(colour="black"),
axis.text.x=element_blank(),strip.background=element_rect(fill="white"),
panel.background = element_blank(),panel.spacing = unit(-0.5, "lines"),
text = element_text(size=20))+
guides(alpha = guide_colorbar(order = 1),fill = guide_legend(order = 1))
# colours were manually changed in inkscape
################################################################################
###
### Figure 3: the red list classification
# restricted distribution:
# 5000 = vulnerable, 2000 is endangered and 200 is critically endangered
# none of then are below 5000 so all vegetation types are not threatened
# according to this classification
#decline in distribution:
#calculate for all models, how they change from now to future
now <- present[, c(3,6,7)]
names(now)[3] <- "Past_Area"
future <- merge(future, now)
future$Past_Area <- future$Past_Area*1000
future$Change_Area <- ((1-future$Area/future$Past_Area)*-1)*100
#1-fraction remaining area *-1 = fraction of lost area
redlist <- subset(future, future$Status=="All")
#let's round and classify levels
redlist$Change_Area <- round(redlist$Change_Area, digits=2)
redlist$Red_List <- NA
redlist$Red_List <- ifelse(redlist$Change_Area > (-30),
"Not threatened",
redlist$Red_List)
redlist$Red_List <- ifelse(redlist$Change_Area <= (-30),
"Vulnerable",
redlist$Red_List)
redlist$Red_List <- ifelse(redlist$Change_Area <= (-50),
"Endangered",
redlist$Red_List)
redlist$Red_List <- ifelse(redlist$Change_Area <= (-80),
"Critically endangered",
redlist$Red_List)
#realistic: rpred_20km_e3v_585
real <- subset(redlist, redlist$Model==sprintf("%s", real_mod))
#calc envelopes
#so we take each vegetation, minus present, and calculate which is max and min
large <- list()
veg <- unique(real$Vegetation_Type)
for (i in 1:length(veg)){
a <- subset(redlist, redlist$Vegetation_Type==veg[i])
large[[i]] <- data.frame(Vegetation_Type = veg[i],
Maximum = max(a$Change_Area),
Minimum = min(a$Change_Area))
}
envelopes <- do.call(rbind, large)
real <- merge(real, envelopes, by="Vegetation_Type")
ggplot(real, aes(x=reorder(Vegetation_Type, Change_Area), y=Change_Area)) +
geom_rect(aes(xmin=-Inf, xmax=Inf,ymin = -30, ymax = Inf),
fill="#00426B", alpha = 0.08)+
geom_rect(aes(xmin=-Inf, xmax=Inf,ymin = -50, ymax = -30),
fill="#BA832B", alpha = 0.08)+
geom_rect(aes(xmin=-Inf, xmax=Inf,ymin = -50, ymax = -80),
fill="#BA622B", alpha = 0.08)+
geom_rect(aes(xmin=-Inf, xmax=Inf,ymin = -80, ymax = -100),
fill="#681300", alpha = 0.08)+
geom_linerange(aes(ymin = Minimum, ymax = Maximum))+
geom_point(aes(col = factor(Red_List)), size=5) +
geom_point(shape = 1,size = 5,colour = "black")+
labs(x= "Vegetation type", y="Change in total distribution (%)") +
scale_color_manual("Risk status",
values=c("#681300","#BA622B","#00426B" ,"#BA832B"))+
theme_bw() +
geom_hline(yintercept=0) +
theme(legend.position = c(0.11, 0.91),
legend.background = element_rect(fill="transparent"),
axis.title=element_text(size=20),
axis.text=element_text(size=18),
legend.text=element_text(size=18),
panel.grid.major.x = element_blank(),
#panel.grid.major.y = element_blank(),
#panel.grid.minor.y = element_blank(),
legend.title=element_text(size=18)) +
scale_x_discrete(limits=c("G1","P1","G2","P2","G3","S1", "G4", "S2"))+
scale_y_continuous(breaks=c(-100,-50,0,50,80))
################################################################################
###
### Figure 4: refugia map was built in ArcMap