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06_wining_losing_sp.R
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06_wining_losing_sp.R
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####################################################
# #### CALCULATE THE CHANGES IN SUITABILITY OF FOCAL SPECIES
# IN FUTURE SUITABLE AREAS FOR COFFEE AND COCOA
# This script generates the figure of change of suitability in area per species
#..............................................
#..............................................
# Packages ####
library("tidyverse")
library("svglite")
library("magrittr")
library("raster")
library("gridExtra")
library("scales")
library(rgdal)
#..............................................
#..............................................
# Read tree data ####
# Read species names
setwd("C:/Users/Pablo/OneDrive - Universidad de Córdoba/1_proyectos/2019_CocoAgroForecast/WP5 - suitability")
BD_calib <- read.csv("./BD/BD_calibrate.csv")
sp <- unique(BD_calib$species)
spnames <- sort(sp) # hacer acrónimos
# .........................................
# .........................................
# Prepare data and files to process maps ####
# names of RCP scenarios
RCP <- c("current","ssp126","ssp585")
scenario <- c("2021-2040","2041-2060")
#define extention of study area
ext <- raster::extent(-17,15,2,13)
# define projection
proj <- "+proj=longlat +datum=WGS84"
#..............................................
#..............................................
# Load mask
mascara <- "BD/Modified_grid/Cropland.shp"
mascara %<>%
readOGR(.) %>%
raster::crop(. , ext)
# plot(mascara)
# AREA SUITABLE WITHOUT MASK ####
# Read changes of focal species ###
# this loop calculate the frequencies of suitability of each
# focal species NOT using the mask generated above.
changes <- NULL
for (i in spnames) {
for (s in seq_along(scenario)){
for (j in RCP) {
if (j=="current") {
r <- raster(paste("processing/species_sets/", i, "/", i, "_presence.tif",sep="" ))
}else{
r <- raster(paste("processing/species_sets/", i, "/", i, "_", j ,"_",scenario[s],"_change.tif",sep="" ))
}
land <- r
frequencies <- data.frame(array(dim = c(1, 11)))
names(frequencies) <- c("acronym","system","never_suitable(0)", "remains_suitable(1)",
"no_longer_suitable(-1)", "new_habitat(2)","area_km2","remains_suitable_(1)_perc",
"no_longer_suitable(-1)_perc", "new_habitat_(2)_per","net_change_perc")
freq1 <- as.data.frame(raster::freq(land, useNA="no"))
freq1 <- freq1[is.na(freq1[, 1]) == F, ]
frequencies[1,1] <- i
frequencies[1,2] <- paste(scenario[s],j,sep="_")
if (length(freq1[freq1[, 1] == 0, 2]) > 0) {
frequencies[1, 3] <- freq1[freq1[, 1] == 0, 2]
}
if (length(freq1[freq1[, 1] == 1, 2]) > 0) {
frequencies[1, 4] <- freq1[freq1[, 1] == 1, 2]
}
if (length(freq1[freq1[, 1] == -1, 2]) > 0) {
frequencies[1, 5] <- freq1[freq1[, 1] == -1, 2]
}
if (length(freq1[freq1[, 1] == 2, 2]) > 0) {
frequencies[1, 6] <- freq1[freq1[, 1] == 2, 2]
}
frequencies[is.na(frequencies)] = 0
#percent of always suitable areas
frequencies[8] <- frequencies[,4] / sum(frequencies[,c(4,5)], na.rm = TRUE) * 100
#percent of no longer suitable areas
frequencies[9] <- frequencies[,5] / sum(frequencies[,c(4,5)], na.rm = TRUE) * 100
#percent of new suitable areas
frequencies[10] <- frequencies[,6] / sum(frequencies[,c(4,5)], na.rm = TRUE) * 100
#percent of net change
frequencies[11] <- frequencies[,10] - frequencies[,9]
#add to changes dataframe
changes <- rbind(changes, frequencies)
}
}
}
# export dataframe
write_csv(changes, paste0("processing/figures/wining_losing_sp.csv"))
#..............................................
#..............................................
# Calculate changes ####
changes <- read.csv("processing/figures/wining_losing_sp.csv")
changes <- changes[,-c(7:11)]
names(changes) <- c("acronym","System","never","remain","no_longer","new")
changes %<>%
mutate(area = rep("suitable", nrow(changes)),
area_raster = never + remain + no_longer + new, #number of cells (areas) the raster cover
cover = ((remain + no_longer) * 100) / area_raster, #proportion of current area
future_cover = ((remain + new) * 100) / area_raster, #proportion of future areas
change = future_cover - cover) %>% #change between current and future suitable areas
as_tibble()
# make labels of systems (land use)
changes$System_label <- as.factor(changes$System)
# levels(changes$System_label) <- RCP
# add names and labels of focal species
dic_species <- read.csv("BD/dic_species.csv",sep=";")
changes <- merge(changes,dic_species,by.x="acronym",by.y="Scientific.names",all.x=T)
changes$Uses <- as.factor(changes$Uses)
#..............................................
#..............................................
# Make charts ####
# colores de los nombres de las especies
miscolores <- c("#00AFBB", "#E7B800", "#FC4E07","light green")
# miscolores <- viridis(length(levels(changes$Uses)))
# miscolores <- "black"
# Loop por emission scenario
plots <- list()
it <- 1
for (s in seq_along(scenario)){
for(j in c(2:3)){
df <- subset(changes, grepl(scenario[s], changes$System))
df <- subset(df, grepl(RCP[j], df$System))
df$area <- " "
head(df)
df %<>% arrange(. , change)
df$id <- as.integer(factor(df$acronym, levels = unique(df$acronym)))
lab <- ""
p <- ggplot(data=df) +
geom_segment(data=subset(df, df$change <= 0),
mapping = aes(x=cover, xend=future_cover,
y=id, yend=id),
# arrow=arrow(length = unit(0.5, "cm")),
size=5,
color= "#ca0020") +
geom_segment(data=subset(df, df$change > 0),
mapping = aes(x=cover, xend=future_cover,
y=id, yend=id),
# arrow=arrow(length = unit(0.5, "cm")),
size=5,
color= "green4") +
geom_segment(aes(x=cover-0.5,xend=cover+0.5,y=id,yend=id), size=5, colour="black")+
scale_y_continuous(breaks = as.integer(df$id), labels = df$acronym) +
scale_x_continuous(breaks = seq(0,100, by=25), limits = c(0,100)) +
labs(x = paste0("Suitable area (%)"),
y = NULL) +
facet_grid(area ~ System_label) +
theme(axis.text.y = element_text(size = 10, angle = 0, hjust = 1,
vjust = 0.5, face="italic", colour=miscolores[df$Uses]),
axis.text.x = element_text(size = 12, angle = 0, hjust = 0.5,
vjust = 1, face="plain", colour = "black"),
axis.title = element_text(size = 12, face="bold"),
panel.border = element_rect(colour = "black", fill=NA, size=0.5),
plot.background = element_blank(),
panel.background = element_blank(),
strip.text.x = element_text(size = 12, colour = "black"),
strip.text.y = element_text(size=12, colour = "black"),
strip.background = element_rect(colour="black", fill="#FFFFFF"))
plots[[it]] <- p
it <- it+1
}
}
p20 <- grid.arrange(
plots[[1]],
plots[[2]],
nrow = 1)
p40 <- grid.arrange(
plots[[3]],
plots[[4]],
nrow = 1)
ggsave(paste0("processing/figures/changessuitability_ord_change21-40.png"),
plot = p20,
width = 30,
height = 15,
units = "cm")
ggsave(paste0("processing/figures/changessuitability_ord_change41-60.png"),
plot = p40,
width = 30,
height = 15,
units = "cm")
#################################################################
# AREA SUITABLE WITH MASK ####
# Read changes of focal species ###
# this loop calculate the frequencies of suitability of each
# focal species USING the mask generated above.
changes <- NULL
for (i in spnames) {
cat("/n######## /n Ensemble modelling for", i, "/n Time:", date())
for (s in seq_along(scenario)){
for (j in RCP) {
if (j=="current") {
r <- raster(paste("processing/species_sets/", i, "/", i, "_presence.tif",sep="" ))
r <- raster::mask(r , mascara, inverse = FALSE)
writeRaster(r,filename=paste("processing/species_sets/", i, "/", i, "_presence_mask.tif",sep = ""),overwrite=T)
}else{
r <- raster(paste("processing/species_sets/", i, "/", i, "_", j ,"_",scenario[s],"_change.tif",sep="" ))
r <- raster::mask(r , mascara, inverse = FALSE)
writeRaster(r,filename=paste("processing/species_sets/", i, "/", i, "_", j ,"_",scenario[s],"_change_mask.tif",sep="" ),overwrite=T)
}
land <- r
frequencies <- data.frame(array(dim = c(1, 11)))
names(frequencies) <- c("acronym","system","never_suitable(0)", "remains_suitable(1)",
"no_longer_suitable(-1)", "new_habitat(2)","area_km2","remains_suitable_(1)_perc",
"no_longer_suitable(-1)_perc", "new_habitat_(2)_per","net_change_perc")
freq1 <- as.data.frame(raster::freq(land, useNA="no"))
freq1 <- freq1[is.na(freq1[, 1]) == F, ]
frequencies[1,1] <- i
frequencies[1,2] <- paste(scenario[s],j,sep="_")
if (length(freq1[freq1[, 1] == 0, 2]) > 0) {
frequencies[1, 3] <- freq1[freq1[, 1] == 0, 2]
}
if (length(freq1[freq1[, 1] == 1, 2]) > 0) {
frequencies[1, 4] <- freq1[freq1[, 1] == 1, 2]
}
if (length(freq1[freq1[, 1] == -1, 2]) > 0) {
frequencies[1, 5] <- freq1[freq1[, 1] == -1, 2]
}
if (length(freq1[freq1[, 1] == 2, 2]) > 0) {
frequencies[1, 6] <- freq1[freq1[, 1] == 2, 2]
}
frequencies[is.na(frequencies)] = 0
#percent of always suitable areas
frequencies[8] <- frequencies[,4] / sum(frequencies[,c(4,5)], na.rm = TRUE) * 100
#percent of no longer suitable areas
frequencies[9] <- frequencies[,5] / sum(frequencies[,c(4,5)], na.rm = TRUE) * 100
#percent of new suitable areas
frequencies[10] <- frequencies[,6] / sum(frequencies[,c(4,5)], na.rm = TRUE) * 100
#percent of net change
frequencies[11] <- frequencies[,10] - frequencies[,9]
#add to changes dataframe
changes <- rbind(changes, frequencies)
cat("Step 2: model species distribution with selected ENM algorithms \n")
}
}
}
# export dataframe
write_csv(changes, paste0("processing/figures/wining_losing_sp_withmask.csv"))
# Calculate changes with mask####
changes <- read.csv("processing/figures/wining_losing_sp_withmask.csv")
changes <- changes[,-c(7:11)]
names(changes) <- c("acronym","System","never","remain","no_longer","new")
changes %<>%
mutate(area = rep("suitable", nrow(changes)),
area_raster = never + remain + no_longer + new, #number of cells (areas) the raster cover
cover = ((remain + no_longer) * 100) / area_raster, #proportion of current area
future_cover = ((remain + new) * 100) / area_raster, #proportion of future areas
change = future_cover - cover) %>% #change between current and future suitable areas
as_tibble()
# make labels of systems (land use)
changes$System_label <- as.factor(changes$System)
# levels(changes$System_label) <- RCP
dic_species <- read.csv("BD/dic_species.csv",sep=";")
changes <- merge(changes,dic_species,by.x="acronym",by.y="Scientific.names",all.x=T)
changes[changes$acronym=="Theobroma cacao","Uses"] <- "cocoa"
changes$Uses <- as.factor(changes$Uses)
# save to later calcualte differences
changes_withmask <- changes
# add names and labels of focal species
# uses <- sort(unique(changes$main_use))
#..............................................
#..............................................
# Make charts ####
# colores de los nombres de las especies
miscolores <- c("black","darkmagenta", "darkgreen", "darkblue","chocolate4")
yetiqueta <- "Suitable area not deforestation (%)"
# Loop por emission scenario
plots <- list()
it <- 1
for (s in seq_along(scenario)){
for(j in c(2:3)){
df <- subset(changes_withmask, grepl(scenario[s], changes_withmask$System))
df <- subset(df, grepl(RCP[j], df$System))
df$area <- " "
head(df)
df %<>% arrange(. , change)
df$id <- as.integer(factor(df$acronym, levels = unique(df$acronym)))
lab <- ""
p <- ggplot(data=df) +
geom_segment(data=subset(df, df$change <= 0),
mapping = aes(x=cover, xend=future_cover,
y=id, yend=id),
# arrow=arrow(length = unit(0.5, "cm")),
size=5,
color= "#ca0020") +
geom_segment(data=subset(df, df$change > 0),
mapping = aes(x=cover, xend=future_cover,
y=id, yend=id),
# arrow=arrow(length = unit(0.5, "cm")),
size=5,
color= "cornflowerblue") +
geom_segment(aes(x=cover-0.5,xend=cover+0.5,y=id,yend=id), size=5, colour="black")+
geom_segment(aes(x=0,xend=cover,y=id,yend=id), size=1, linetype="dotted" ,colour="grey")+
scale_y_continuous(breaks = as.integer(df$id), labels = df$acronym) +
scale_x_continuous(breaks = seq(0,100, by=25), limits = c(0,100)) +
labs(x = paste0(yetiqueta),
y = NULL) +
facet_grid(area ~ System_label) +
theme(axis.text.y = element_text(size = 10, angle = 0, hjust = 1,
vjust = 0.5, face="italic", colour = miscolores[df$Uses]),
axis.text.x = element_text(size = 12, angle = 0, hjust = 0.5,
vjust = 1, face="plain", colour = "black"),
axis.title = element_text(size = 12, face="bold"),
# panel.border = element_rect(colour = "black", fill=NA, size=0.5),
plot.background = element_blank(),
panel.background = element_blank(),
strip.text.x = element_text(size = 12, colour = "black"),
strip.text.y = element_text(size=12, colour = "black"),
strip.background = element_rect(colour="black", fill="#FFFFFF"))
plots[[it]] <- p
it <- it+1
}
}
p20 <- grid.arrange(
plots[[1]],
plots[[2]],
nrow = 1)
p40 <- grid.arrange(
plots[[3]],
plots[[4]],
nrow = 1)
ggsave(paste0("processing/figures/changessuitability_ord_change21-40_withmask.png"),
plot = p20,
width = 30,
height = 15,
units = "cm")
ggsave(paste0("processing/figures/changessuitability_ord_change41-60_withmask.png"),
plot = p40,
width = 30,
height = 15,
units = "cm")
# Calculate changes without mask####
changes <- read.csv("processing/figures/wining_losing_sp.csv")
changes <- changes[,-c(7:11)]
names(changes) <- c("acronym","System","never","remain","no_longer","new")
changes %<>%
mutate(area = rep("suitable", nrow(changes)),
area_raster = never + remain + no_longer + new, #number of cells (areas) the raster cover
cover = ((remain + no_longer) * 100) / area_raster, #proportion of current area
future_cover = ((remain + new) * 100) / area_raster, #proportion of future areas
change = future_cover - cover) %>% #change between current and future suitable areas
as_tibble()
# make labels of systems (land use)
changes$System_label <- as.factor(changes$System)
# levels(changes$System_label) <- RCP
changes <- merge(changes,dic_species,by.x="acronym",by.y="Scientific.names",all.x=T)
changes[changes$acronym=="Theobroma cacao","Uses"] <- "cocoa"
changes$Uses <- as.factor(changes$Uses)
# Combine with changes with deforestation (mask)
changes_all <- merge(changes_withmask,changes,by=c("acronym","System"),suffixes =c(".mask",""))
changes_all$difference <- changes_all$cover.mask-changes_all$cover
# add names and labels of focal species
# uses <- sort(unique(changes$main_use))
#..............................................
#..............................................
# Make charts ####
# colores de los nombres de las especies
yetiqueta <- "Suitable area (%)"
areapixel <- 20.25
# Loop por emission scenario
plots <- list()
it <- 1
for (s in seq_along(scenario)){
for(j in c(2:3)){
df <- subset(changes_all, grepl(scenario[s], changes_all$System))
df <- subset(df, grepl(RCP[j], df$System))
df$area <- " "
head(df)
df %<>% arrange(. , change)
df$id <- as.integer(factor(df$acronym, levels = unique(df$acronym)))
lab <- ""
p <- ggplot(data=df) +
geom_segment(data=subset(df, df$change <= 0),
mapping = aes(x=cover, xend=future_cover,
y=id, yend=id),
# arrow=arrow(length = unit(0.5, "cm")),
size=3,
color= "#ca0020") +
geom_segment(data=subset(df, df$change > 0),
mapping = aes(x=cover, xend=future_cover,
y=id, yend=id),
# arrow=arrow(length = unit(0.5, "cm")),
size=3,
color= "cornflowerblue") +
geom_segment(aes(x=cover-0.5,xend=cover+0.5,y=id,yend=id), size=3, colour="black")+
geom_segment(data=subset(df, df$change <= 0),
mapping = aes(x=cover.mask, xend=future_cover.mask,
y=id, yend=id),
# arrow=arrow(length = unit(0.5, "cm")),
size=3,
color= "#ca0020",alpha=0.6) +
geom_segment(data=subset(df, df$change > 0),
mapping = aes(x=cover.mask, xend=future_cover.mask,
y=id, yend=id),
# arrow=arrow(length = unit(0.5, "cm")),
size=3,
color= "cornflowerblue",alpha=0.6) +
geom_segment(aes(x=cover.mask-0.5,xend=cover.mask+0.5,y=id,yend=id), size=3, colour="grey")+
geom_segment(aes(x=0,xend=cover,y=id,yend=id), size=1, linetype="dotted" ,colour="grey")+
# annotate("text",label=round(df$difference,0),x=rep(100,nrow(df)),y=df$id,hjust = 1, vjust = 0.5)+
scale_y_continuous(breaks = as.integer(df$id), labels = df$acronym) +
scale_x_continuous(breaks = seq(0,100, by=25), limits = c(0,100)) +
labs(x = paste0(yetiqueta), y = NULL) +
facet_grid(area ~ System_label) +
theme(axis.text.y = element_text(size = 10, angle = 0, hjust = 1,
vjust = 0.5, face="italic", colour = miscolores[df$Uses]),
axis.text.x = element_text(size = 12, angle = 0, hjust = 0.5,
vjust = 1, face="plain", colour = "black"),
axis.title = element_text(size = 12, face="bold"),
# panel.border = element_rect(colour = "black", fill=NA, size=0.5),
plot.background = element_blank(),
panel.background = element_blank(),
strip.text.x = element_text(size = 12, colour = "black"),
strip.text.y = element_text(size=12, colour = "black"),
strip.background = element_rect(colour="black", fill="#FFFFFF"))
plots[[it]] <- p
it <- it+1
}
}
p20 <- grid.arrange(
plots[[1]],
plots[[2]],
nrow = 1)
p40 <- grid.arrange(
plots[[3]],
plots[[4]],
nrow = 1)
ggsave(paste0("processing/figures/changessuitability_ord_change21-40.png"),
plot = p20,
width = 30,
height = 15,
units = "cm")
ggsave(paste0("processing/figures/changessuitability_ord_change41-60.png"),
plot = p40,
width = 30,
height = 15,
units = "cm")
#####
differences <- unique(changes_all[,c("acronym","difference")])