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RSCRIPTBATCH_OVERSEE_ensemble_community_diversity_mappe.R
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# --------------------------------------------------------------------------------------------------------------------------------
library("raster")
library("sp")
library("stringr")
library("reshape2")
library("tidyverse")
library("biomod2")
library("viridis")
library("scales")
library("maps")
library("betapart")
library("cmocean")
library("ncdf4")
world2 <- map_data("world2")
# --------------------------------------------------------------------------------------------------------------------------------
### Set the working directories, vectors etc.
WD <- getwd()
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85")
months <- c("jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec")
### 1°) First, map all monthly richness and shannon-weiver indices values (baseline and future)
# files1 <- dir()[grep("_compo_baseline_", dir())]; files1 # baseline
# files2 <- dir()[grep("_compo_2100-2000", dir())]; files2 # baseline
# Examine some txt files and check
# Test
# f <- "table_phyto_mean_compo_baseline_apr.txt"
# f <- "table_phyto_mean_compo_2100-2000_MRI-NEMURO_rcp85_apr.txt"
# diversity.mapper <- function(f = files1) {
#
# if( f %in% files1 ) {
#
# # Useless message
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
# group <- do.call(cbind,strsplit(as.character(f), split = "_"))[2,1]
# if(group == "zoo") { group2 <- "Zooplankton" }
# if(group == "phyto") { group2 <- "Phytoplankton" }
# m <- do.call(cbind,strsplit(as.character(f), split = "_"))[6,1]
# m <- str_replace(m,".txt","") # m
# message(paste("Mapping baseline SR and H' for ",m, sep = ""))
# table <- read.table(f, sep = "\t")
# # summary(table[,c("x","y")])
# # Maps!
# map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = rich), data = na.omit(table)) +
# scale_fill_viridis(name = paste(group2,"\n diversity (SR)",sep="")) +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
#
# map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = H), data = na.omit(table)) +
# scale_fill_viridis(name = paste(group2,"\n diversity (H)",sep="")) +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ### Save
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/maps")
# ggsave(plot = map1, filename = paste("map_rich_",group,"_base_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# ggsave(plot = map2, filename = paste("map_shannon_",group,"_base_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
#
# } else if ( f %in% files2 ) {
#
# # Useless message
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
# group <- do.call(cbind,strsplit(as.character(f), split = "_"))[2,1]
# if(group == "zoo") { group2 <- "Zooplankton" }
# if(group == "phyto") { group2 <- "Phytoplankton" }
# esm <- do.call(cbind,strsplit(as.character(f), split = "_"))[6,1]
# m <- do.call(cbind,strsplit(as.character(f), split = "_"))[8,1]
# m <- str_replace(m,".txt","") # m
# message(paste("Mapping future SR and H' for ",m," under ",esm, sep = ""))
# table <- read.table(f, sep = "\t")
#
# # Maps!
# map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = rich), data = na.omit(table)) +
# scale_fill_viridis(name = paste(group2,"\n diversity (SR)",sep="")) +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
#
# map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = H), data = na.omit(table)) +
# scale_fill_viridis(name = paste(group2,"\n diversity (H)",sep="")) +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ### Save
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/maps")
# ggsave(plot = map1, filename = paste("map_rich_",group,"_2100-2000_",esm,"_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# ggsave(plot = map2, filename = paste("map_shannon_",group,"_2100-2000_",esm,"_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
#
# } # eo if else loop
#
# }
#
# require("parallel")
# mclapply(files2, FUN = diversity.mapper, mc.cores = 25)
# ---------------------------------------------------------------
### 09/03/2020: 2°) Same but on the annual scale: compute mean annual diversity (rich and H') and map
### First, for the baseline period
# With lapply, extract each monthly output and compute mean
# m <- "apr"
# require("parallel")
# res <- mclapply(months, function(m) {
#
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
# # Extract monthly files
# message(paste("Extracting baseline monthly diversity indices for ",m, sep = ""))
# table.phyto <- read.table(paste("table_phyto_mean_compo_baseline_",m,".txt",sep=""), sep = "\t")
# table.zoo <- read.table(paste("table_zoo_mean_compo_baseline_",m,".txt",sep=""), sep = "\t")
# # dim(table.phyto) ; dim(table.zoo)
# ddf <- data.frame(id = table.phyto$cell_id, x = table.phyto$x, y = table.phyto$y, month = m,
# rich_phyto = table.phyto$rich, rich_zoo = table.zoo$rich, Hphyto = table.phyto$H, Hzoo = table.zoo$H)
# # summary(ddf)
# return(ddf)
#
# }, mc.cores = 20
#
# ) # eo lapply
# # Rbind
# table <- dplyr::bind_rows(res)
# dim(table) ; head(table)
# ddf <- data.frame(table %>% group_by(id) %>% summarize(x = unique(x), y = unique(y),
# rich_phyto = mean(rich_phyto, na.rm=T), rich_zoo = mean(rich_zoo, na.rm=T),
# Hphyto = mean(Hphyto, na.rm=T), Hzoo = mean(Hzoo, na.rm=T) )
# ) # eo ddf
# summary(ddf)
#
# # Maps!
# map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = Hphyto), data = na.omit(ddf)) +
# scale_fill_viridis(name = "Annual\nPhytoplankton\ndiversity (H')") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
#
# map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = Hzoo), data = na.omit(ddf)) +
# scale_fill_viridis(name = "Annual\nZooplankton\ndiversity (H')") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ### Save
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/maps")
# ggsave(plot = map1, filename = "map_annual_shannon_phyto_baseline.jpg", dpi = 300, width = 7, height = 5)
# ggsave(plot = map2, filename = "map_annual_shannon_zoo_baseline.jpg", dpi = 300, width = 7, height = 5)
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
### Also, compute the species' mean ANNUAL HSI for baeline and future (per ESM) and save tables
# require("parallel")
# res <- mclapply(months, function(m) {
#
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
# # Extract monthly files
# message(paste("Extracting plankton baseline monthly composition for ",m, sep = ""))
# table.phyto <- read.table(paste("table_phyto_mean_compo_baseline_",m,".txt",sep=""), sep = "\t")
# table.zoo <- read.table(paste("table_zoo_mean_compo_baseline_",m,".txt",sep=""), sep = "\t")
# # Melt after removing the div metrics from the columns
# m.table.phyto <- reshape2::melt(data = table.phyto[,c(1:341)], id.vars = colnames(table.phyto)[c(1:3)])
# colnames(m.table.phyto)[c(4:5)] <- c("species","HSI")
# m.table.zoo <- reshape2::melt(data = table.zoo[,c(1:527)], id.vars = colnames(table.zoo)[c(1:3)])
# colnames(m.table.zoo)[c(4:5)] <- c("species","HSI")
# m.table.phyto$group <- "Phytoplankton"
# m.table.zoo$group <- "Zooplankton"
# # rbind them, add month, and return
# m.table <- rbind(m.table.phyto, m.table.zoo)
# m.table$month <- m
# return(m.table)
#
# }, mc.cores = 20
#
# ) # eo lapply
# table <- dplyr::bind_rows(res)
# dim(table) ; head(table)
# rm(res); gc()
# # Compute mean annual species' level HSI
# ddf <- data.frame(table %>% group_by(cell_id,group,species) %>% summarize(x = unique(x), y = unique(y), mean = mean(HSI, na.rm = T)))
# head(ddf); dim(ddf)
# # Dcast and save
# d_ddf <- dcast(ddf[,c(1,3:6)], cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T, value.var = "mean")
# head(d_ddf); dim(d_ddf)
#
# # And map annual total diversity metrics
# require("vegan")
# d_ddf$rich <- rowSums(as.matrix(d_ddf[,c(4:length(d_ddf))]))
# d_ddf$H <- diversity(d_ddf[,c(4:(length(d_ddf)-1))], index = "shannon")
# summary(d_ddf)
#
# # Maps!
# map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = rich), data = na.omit(d_ddf)) +
# scale_fill_viridis(name = "Annual\nPlankton\ndiversity (H')") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
#
# map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = H), data = na.omit(d_ddf)) +
# scale_fill_viridis(name = "Annual\nPlankton\ndiversity (H')") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ### Save
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/maps")
# ggsave(plot = map1, filename = "map_annual_rich_plankton_baseline.jpg", dpi = 300, width = 7, height = 5)
# ggsave(plot = map2, filename = "map_annual_shannon_plankton_baseline.jpg", dpi = 300, width = 7, height = 5)
# # Save
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
# write.table(d_ddf, file = "table_annual_mean_compo_plankton_baseline.txt", sep = "\t")
### Same for ESMs (future annual mean HSI)
require("parallel")
# Vector of earth system models
ESMs <- c("CESM-BEC","CNRM-PISCES","GFDL-TOPAZ","IPSL-PISCES","MRI-NEMURO")
rcp <- "rcp85"
# res <- mclapply(ESMs, function(esm) {
#
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
# res2 <- lapply(months, function(m) {
# # Extract monthly files
# message(paste("Extracting phytoplankton baseline monthly composition for ",esm,", for ",m, sep = ""))
# table.phyto <- read.table(paste("table_phyto_mon_composition_2100-2000_",esm,"_rcp85_",m,".txt",sep=""), sep = "\t")
# # Melt after removing the div metrics from the columns
# m.table.phyto <- reshape2::melt(data = table.phyto, id.vars = colnames(table.phyto)[c(1:4)] )
# colnames(m.table.phyto)[c(5:6)] <- c("species","HSI")
# m.table.phyto$month <- m
# # head(m.table.phyto); dim(m.table.phyto)
# return(m.table.phyto)
# } # eo FUN
# ) # eo lapply
# # Rbind
# table <- dplyr::bind_rows(res2)
# dim(table) ; head(table)
# rm(res2); gc()
#
# # Compute mean annual species' level HSI
# message(paste("Computing phytoplankton species' mean annual HSI, for ",esm, sep = ""))
# annual <- data.frame(table %>%
# group_by(cell_id,species) %>%
# summarize(x = unique(x), y = unique(y), mean = mean(HSI,na.rm=T))
# ) # eo ddf
# # head(annual); dim(annual)
# # Dcast
# d_annual <- dcast(annual, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T, value.var = "mean")
# # head(d_annual); dim(d_annual)
# # Save
# message(paste("Saving table of phytoplankton species' mean annual HSI, for ",esm, sep = ""))
# write.table(d_annual, file = paste("table_annual_mean_compo_phyto_2100-2000_rcp85_",esm,".txt", sep = ""), sep = "\t")
# #rm(annual, table); gc()
#
# }, mc.cores = 5
#
# ) # eo mclapply
# Check those vibes
# test <- read.table("table_annual_mean_compo_phyto_2100-2000_rcp85_GFDL-TOPAZ.txt", h = T, sep = "\t")
# dim(test)
### And now zooplankton
# esm <- "IPSL-PISCES"
# months <- c("jan","feb","mar")
res <- mclapply(ESMs, function(esm) {
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
res2 <- lapply(months, function(m) {
# Extract monthly files
message(paste("Extracting zooplankton baseline monthly composition for ",esm,", for ",m, sep = ""))
table.zoo <- read.table(paste("table_zoo_mon_composition_2100-2000_",esm,"_rcp85_",m,".txt",sep=""), sep = "\t")
# Melt after removing the div metrics from the columns
m.table.zoo <- reshape2::melt(data = table.zoo, id.vars = colnames(table.zoo)[c(1:4)])
colnames(m.table.zoo)[c(5:6)] <- c("species","HSI")
m.table.zoo$month <- m
# head(m.table.zoo); dim(m.table.zoo)
return(m.table.zoo)
} # eo FUN
) # eo lapply
# Rbind
table <- dplyr::bind_rows(res2)
# dim(table) ; head(table)
rm(res2); gc()
# Compute mean annual species' level HSI
message(paste("Computing zooplankton species' mean annual HSI, for ",esm, sep = ""))
annual <- data.frame(table %>%
group_by(cell_id,species) %>%
summarize(x = unique(x), y = unique(y), mean = mean(HSI,na.rm=T))
) # eo ddf
# head(annual); dim(annual)
# Dcast
d_annual <- dcast(annual, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T, value.var = "mean")
# head(d_annual); dim(d_annual)
# Save
message(paste("Saving table of zooplankton species' mean annual HSI, for ",esm, sep = ""))
write.table(d_annual, file = paste("table_annual_mean_compo_zoo_2100-2000_rcp85_",esm,".txt", sep = ""), sep = "\t")
#rm(annual, table); gc()
}, mc.cores = 5
) # eo mclapply
# ---------------------------------------------------------------
### 3°) Map the monthly and annual differences in richness and H'
ESMs <- c("CESM-BEC","CNRM-PISCES","GFDL-TOPAZ","IPSL-PISCES","MRI-NEMURO")
# esm <- "GFDL-TOPAZ"
for(esm in ESMs) {
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85")
message(paste("Loading annual community projections for ",esm, sep = ""))
base <- read.table("table_annual_mean_compo_plankton_baseline.txt", h = T, sep = "\t")
# remove last 2 cols
base <- base[,c(1:(length(base)-2))]
fut.phyto <- read.table(paste("table_annual_mean_compo_phyto_2100-2000_rcp85_",esm,".txt", sep = ""), h = T, sep = "\t")
fut.zoo <- read.table(paste("table_annual_mean_compo_zoo_2100-2000_rcp85_",esm,".txt", sep = ""), h = T, sep = "\t")
# dim(fut.phyto) ; dim(fut.zoo) ; colnames(fut.phyto) ; colnames(fut.zoo)
base <- base[order(base$cell_id),]
fut.phyto <- fut.phyto[order(fut.phyto$cell_id),]
fut.zoo <- fut.zoo[order(fut.zoo$cell_id),]
# Merge (cbind) fut.phyto and fut.zoo for full plankton diversity estimates
fut <- cbind(fut.phyto, fut.zoo[,c(4:length(fut.zoo))]) # dim(fut)
# 4:341 -> phyto; 342:865 -> zoopl
# Derive multiple diversity metrics for each group (total/phyto/zoo)
base$rich_total <- rowSums(as.matrix(base[,c(4:length(base))]))
base$rich_phyto <- rowSums(as.matrix(base[,c(4:341)]))
base$rich_zoo <- rowSums(as.matrix(base[,c(342:865)]))
require("vegan")
base$Htotal <- diversity(base[,c(4:865)], index = "shannon")
base$Hphyto <- diversity(base[,c(4:341)], index = "shannon")
base$Hzoo <- diversity(base[,c(342:865)], index = "shannon")
# Same but for fut
fut$rich_total <- rowSums(as.matrix(fut[,c(4:length(fut))]))
fut$rich_phyto <- rowSums(as.matrix(fut[,c(4:341)]))
fut$rich_zoo <- rowSums(as.matrix(fut[,c(342:865)]))
fut$Htotal <- diversity(fut[,c(4:865)], index = "shannon")
fut$Hphyto <- diversity(fut[,c(4:341)], index = "shannon")
fut$Hzoo <- diversity(fut[,c(342:865)], index = "shannon")
# And derive % changes ! head(fut[,c(1:4)]); head(base[,c(1:4)])
message(paste("Calculating changes in diversity for ",esm, sep = ""))
base$fut_rich_tot <- fut[which(fut$cell_id %in% base$cell_id),c("rich_total")]
base$fut_rich_phyto <- fut[which(fut$cell_id %in% base$cell_id),c("rich_phyto")]
base$fut_rich_zoo <- fut[which(fut$cell_id %in% base$cell_id),c("rich_zoo")]
base$fut_Htotal <- fut[which(fut$cell_id %in% base$cell_id),c("Htotal")]
base$fut_Hphyto <- fut[which(fut$cell_id %in% base$cell_id),c("Hphyto")]
base$fut_Hzoo <- fut[which(fut$cell_id %in% base$cell_id),c("Hzoo")]
changes <- data.frame(id = base$cell_id, x = base$x, y = base$y,
diff_rich_tot = (base$fut_rich_tot)-(base$rich_total),
diff_rich_phyto = (base$fut_rich_phyto)-(base$rich_phyto),
diff_rich_zoo = (base$fut_rich_zoo)-(base$rich_zoo),
diff_Htot = (base$fut_Htotal)-(base$Htotal),
diff_Hphyto = (base$fut_Hphyto)-(base$Hphyto),
diff_Hzoo = (base$fut_Hzoo)-(base$Hzoo)
) # eo ddf
# summary(changes)
# Add percentage changes
changes$perc_rich_tot <- ((changes$diff_rich_tot)/base$rich_total)*100
changes$perc_rich_phyto <- ((changes$diff_rich_phyto)/base$rich_phyto)*100
changes$perc_rich_zoo <- ((changes$diff_rich_zoo)/base$rich_zoo)*100
changes$perc_Htot <- ((changes$diff_Htot)/base$Htotal)*100
changes$perc_Hphyto <- ((changes$diff_Hphyto)/base$Hphyto)*100
changes$perc_Hzoo <- ((changes$diff_Hzoo)/base$Hzoo)*100
# Mapping time with cobtours and diverging color gradients
message(paste("Mapping annual changes in diversity (richness and H') for ",esm, sep = ""))
# Define the minimum limts for the richness diff limits of the
min <- floor(min(changes[,c("perc_rich_tot","perc_rich_phyto","perc_rich_zoo")], na.rm = T) )
min2 <- floor(min(changes[,c("perc_Htot","perc_Hphyto","perc_Hzoo")], na.rm = T) )
max2 <- abs(min2)
map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_tot), data = changes[changes$perc_rich_tot < 50,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_rich_tot >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_tot),
data = changes[changes$perc_rich_tot < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_phyto), data = changes[changes$perc_rich_phyto < 50,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_rich_phyto >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_phyto),
data = changes[changes$perc_rich_phyto < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map3 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_zoo), data = changes[changes$perc_rich_zoo < 50,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_rich_zoo >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_zoo),
data = changes[changes$perc_rich_zoo < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map4 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Htot), data = changes[changes$perc_Htot < max2,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_Htot >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Htot),
data = changes[changes$perc_Htot < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map5 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Hphyto), data = changes[changes$perc_Hphyto < max2,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_Hphyto >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Hphyto),
data = changes[changes$perc_Hphyto < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map6 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Hzoo), data = changes[changes$perc_Hzoo < max2,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_Hzoo >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Hzoo),
data = changes[changes$perc_Hzoo < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
# Save maps as .jpg for now
setwd(paste(getwd(),"/","maps", sep = ""))
ggsave(plot = map1, filename = paste("map_annual_perc_rich_tot_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map2, filename = paste("map_annual_perc_rich_phyto_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map3, filename = paste("map_annual_perc_rich_zoo_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map4, filename = paste("map_annual_perc_Htot_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map5, filename = paste("map_annual_perc_Hphyto_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map6, filename = paste("map_annual_perc_Hzoo_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Saving estimates
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85")
message(paste("Saving diversity changes estimates for ",esm, sep = ""))
message(paste("", sep = ""))
write.table(changes, file = paste("table_annual_changes_",esm,".txt",sep=""), sep = "\t")
} # eo for loop
### And monthly
# esm <- "GFDL-TOPAZ"
# m <- "apr"
for(esm in ESMs) {
for(m in months) {
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85")
message(paste("Loading monthly community projections for ",m," || ",esm, sep = ""))
base.phyto <- read.table(paste("table_phyto_mean_compo_baseline_",m,".txt", sep = ""),)
base.zoo <- read.table(paste("table_zoo_mean_compo_baseline_",m,".txt", sep = ""),)
fut.phyto <- read.table(paste("table_phyto_mean_compo_2100-2000_",esm,"_rcp85_",m,".txt", sep = ""), h = T, sep = "\t")
fut.zoo <- read.table(paste("table_zoo_mean_compo_2100-2000_",esm,"_rcp85_",m,".txt", sep = ""), h = T, sep = "\t")
# colnames(base.phyto); colnames(base.zoo); colnames(fut.phyto); colnames(fut.zoo)
# dim(base.phyto); dim(base.zoo); dim(fut.phyto); dim(fut.zoo)
base.phyto <- base.phyto[order(base.phyto$cell_id),]; base.zoo <- base.zoo[order(base.zoo$cell_id),]
fut.phyto <- fut.phyto[order(fut.phyto$cell_id),]; fut.zoo <- fut.zoo[order(fut.zoo$cell_id),]
# Merge for total plankton diversity changes
base <- cbind(base.phyto[,c(1:341)], base.zoo[,c(4:527)])
fut <- cbind(fut.phyto[,c(1:341)], fut.zoo[,c(4:527)])
# Derive multiple diversity metrics for each group (total/phyto/zoo)
require("vegan")
base$rich_total <- rowSums(as.matrix(base[,c(4:length(base))]))
base$rich_phyto <- rowSums(as.matrix(base[,c(4:341)]))
base$rich_zoo <- rowSums(as.matrix(base[,c(342:865)]))
base$Htotal <- diversity(base[,c(4:865)], index = "shannon")
base$Hphyto <- diversity(base[,c(4:341)], index = "shannon")
base$Hzoo <- diversity(base[,c(342:865)], index = "shannon")
# Same but for fut
fut$rich_total <- rowSums(as.matrix(fut[,c(4:length(fut))]))
fut$rich_phyto <- rowSums(as.matrix(fut[,c(4:341)]))
fut$rich_zoo <- rowSums(as.matrix(fut[,c(342:865)]))
fut$Htotal <- diversity(fut[,c(4:865)], index = "shannon")
fut$Hphyto <- diversity(fut[,c(4:341)], index = "shannon")
fut$Hzoo <- diversity(fut[,c(342:865)], index = "shannon")
# And derive % changes ! head(fut[,c(1:4)]); head(base[,c(1:4)])
message(paste("Calculating changes in diversity for ",m," || ",esm, sep = ""))
base$fut_rich_tot <- fut[which(fut$cell_id %in% base$cell_id),c("rich_total")]
base$fut_rich_phyto <- fut[which(fut$cell_id %in% base$cell_id),c("rich_phyto")]
base$fut_rich_zoo <- fut[which(fut$cell_id %in% base$cell_id),c("rich_zoo")]
base$fut_Htotal <- fut[which(fut$cell_id %in% base$cell_id),c("Htotal")]
base$fut_Hphyto <- fut[which(fut$cell_id %in% base$cell_id),c("Hphyto")]
base$fut_Hzoo <- fut[which(fut$cell_id %in% base$cell_id),c("Hzoo")]
changes <- data.frame(id = base$cell_id, x = base$x, y = base$y,
diff_rich_tot = (base$fut_rich_tot)-(base$rich_total),
diff_rich_phyto = (base$fut_rich_phyto)-(base$rich_phyto),
diff_rich_zoo = (base$fut_rich_zoo)-(base$rich_zoo),
diff_Htot = (base$fut_Htotal)-(base$Htotal),
diff_Hphyto = (base$fut_Hphyto)-(base$Hphyto),
diff_Hzoo = (base$fut_Hzoo)-(base$Hzoo)
) # eo ddf
# summary(changes)
# Add percentage changes
changes$perc_rich_tot <- ((changes$diff_rich_tot)/base$rich_total)*100
changes$perc_rich_phyto <- ((changes$diff_rich_phyto)/base$rich_phyto)*100
changes$perc_rich_zoo <- ((changes$diff_rich_zoo)/base$rich_zoo)*100
changes$perc_Htot <- ((changes$diff_Htot)/base$Htotal)*100
changes$perc_Hphyto <- ((changes$diff_Hphyto)/base$Hphyto)*100
changes$perc_Hzoo <- ((changes$diff_Hzoo)/base$Hzoo)*100
# Mapping time with cobtours and diverging color gradients
message(paste("Mapping annual changes in diversity (richness and H') for ",m," || ",esm, sep = ""))
# Define the minimum limts for the richness diff limits of the
min <- floor(min(changes[,c("perc_rich_tot","perc_rich_phyto","perc_rich_zoo")], na.rm = T) )
min2 <- floor(min(changes[,c("perc_Htot","perc_Hphyto","perc_Hzoo")], na.rm = T) )
max2 <- abs(min2)
# min; min2; max2
map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_tot), data = changes[changes$perc_rich_tot < 50,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_rich_tot >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_tot),
data = changes[changes$perc_rich_tot < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_phyto), data = changes[changes$perc_rich_phyto < 50,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_rich_phyto >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_phyto),
data = changes[changes$perc_rich_phyto < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map3 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_zoo), data = changes[changes$perc_rich_zoo < 50,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_rich_zoo >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_zoo),
data = changes[changes$perc_rich_zoo < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map4 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Htot), data = changes[changes$perc_Htot < max2,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_Htot >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Htot),
data = changes[changes$perc_Htot < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map5 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Hphyto), data = changes[changes$perc_Hphyto < max2,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_Hphyto >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Hphyto),
data = changes[changes$perc_Hphyto < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map6 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Hzoo), data = changes[changes$perc_Hzoo < max2,]) +
geom_raster(aes(x = x, y = y), data = changes[changes$perc_Hzoo >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Hzoo),
data = changes[changes$perc_Hzoo < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
# Save maps as .jpg for now
setwd(paste(getwd(),"/","maps", sep = ""))
ggsave(plot = map1, filename = paste("map_mon_perc_rich_tot_",esm,"_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map2, filename = paste("map_mon_perc_rich_phyto_",esm,"_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map3, filename = paste("map_mon_perc_rich_zoo_",esm,"_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map4, filename = paste("map_mon_perc_Htot_",esm,"_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map5, filename = paste("map_mon_perc_Hphyto_",esm,"_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map6, filename = paste("map_mon_perc_Hzoo_",esm,"_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Saving estimates
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85")
message(paste("Saving diversity changes estimates for ",m," || ",esm, sep = ""))
message(paste("", sep = ""))
write.table(changes, file = paste("table_mon_changes_",esm,"_",m,".txt",sep=""), sep = "\t")
} # eo for loop - m in months
} # eo for loop - esm in ESMs
# ---------------------------------------------------------------
### 3°) Map average across ESMs (ensemble projections)
### A) Annual
ESMs <- c("CESM-BEC","CNRM-PISCES","GFDL-TOPAZ","IPSL-PISCES","MRI-NEMURO")
res <- mclapply(ESMs, function(esm) {
# Read the changes table
d <- read.table(paste("table_annual_changes_",esm,".txt", sep = ""), sep = "\t", h = T)
d$ESM <- esm
return(d)
}, mc.cores = 5
) # eo mclapply - esm
# Rbind
ens <- dplyr::bind_rows(res)
dim(ens); str(ens); summary(ens); unique(ens$ESM)
rm(res); gc()
# Compute ensemble projections
ensemble <- data.frame(ens %>%
group_by(id) %>%
summarize(x = unique(x), y = unique(y), perc_rich_tot = mean(perc_rich_tot,na.rm=T),
perc_rich_phyto = mean(perc_rich_phyto,na.rm=T), perc_rich_zoo = mean(perc_rich_zoo,na.rm=T),
perc_Htot = mean(perc_Htot,na.rm=T), perc_Hphyto = mean(perc_Hphyto,na.rm=T), perc_Hzoo = mean(perc_Hzoo,na.rm=T)
)
) # eo ddf
head(ensemble); summary(ensemble)
# Define the minimum limts for the richness diff limits of the
min <- floor(min(ensemble[,c("perc_rich_tot","perc_rich_phyto","perc_rich_zoo")], na.rm = T) )
min2 <- floor(min(ensemble[,c("perc_Htot","perc_Hphyto","perc_Hzoo")], na.rm = T) )
max2 <- abs(min2); min; min2; max2
map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_tot), data = ensemble[ensemble$perc_rich_tot < 50,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_rich_tot >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_tot),
data = ensemble[ensemble$perc_rich_tot < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_phyto), data = ensemble[ensemble$perc_rich_phyto < 50,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_rich_phyto >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_phyto),
data = ensemble[ensemble$perc_rich_phyto < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map3 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_zoo), data = ensemble[ensemble$perc_rich_zoo < 50,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_rich_zoo >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_zoo),
data = ensemble[ensemble$perc_rich_zoo < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map4 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Htot), data = ensemble[ensemble$perc_Htot < max2,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_Htot >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Htot),
data = ensemble[ensemble$perc_Htot < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map5 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Hphyto), data = ensemble[ensemble$perc_Hphyto < max2,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_Hphyto >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Hphyto),
data = ensemble[ensemble$perc_Hphyto < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map6 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Hzoo), data = ensemble[ensemble$perc_Hzoo < max2,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_Hzoo >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Hzoo),
data = ensemble[ensemble$perc_Hzoo < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
# Save maps as .jpg for now
ggsave(plot = map1, filename = paste("map_annual_perc_rich_tot_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map2, filename = paste("map_annual_perc_rich_phyto_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map3, filename = paste("map_annual_perc_rich_zoo_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map4, filename = paste("map_annual_perc_Htot_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map5, filename = paste("map_annual_perc_Hphyto_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map6, filename = paste("map_annual_perc_Hzoo_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
### B) Monthly (12*6 maps)
for(m in months) {
message(paste("Mapping ensemble diversity projections for ",m, sep = ""))
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/")
ESMs <- c("CESM-BEC","CNRM-PISCES","GFDL-TOPAZ","IPSL-PISCES","MRI-NEMURO")
res <- mclapply(ESMs, function(esm) {
# Read the changes table
d <- read.table(paste("table_mon_changes_",esm,"_",m,".txt", sep = ""), sep = "\t", h = T)
d$ESM <- esm
return(d)
}, mc.cores = 5
) # eo mclapply - esm
# Rbind
ens <- dplyr::bind_rows(res)
# dim(ens); str(ens); summary(ens); unique(ens$ESM)
rm(res); gc()
# Compute ensemble projections
ensemble <- data.frame(ens %>%
group_by(id) %>%
summarize(x = unique(x), y = unique(y), perc_rich_tot = mean(perc_rich_tot,na.rm=T),
perc_rich_phyto = mean(perc_rich_phyto,na.rm=T), perc_rich_zoo = mean(perc_rich_zoo,na.rm=T),
perc_Htot = mean(perc_Htot,na.rm=T), perc_Hphyto = mean(perc_Hphyto,na.rm=T), perc_Hzoo = mean(perc_Hzoo,na.rm=T)
)
) # eo ddf
# head(ensemble); summary(ensemble)
# Define the minimum limts for the richness diff limits of the
min <- floor(min(ensemble[,c("perc_rich_tot","perc_rich_phyto","perc_rich_zoo")], na.rm = T) )
min2 <- floor(min(ensemble[,c("perc_Htot","perc_Hphyto","perc_Hzoo")], na.rm = T) )
max2 <- abs(min2); min; min2; max2
map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_tot), data = ensemble[ensemble$perc_rich_tot < 50,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_rich_tot >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_tot),
data = ensemble[ensemble$perc_rich_tot < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_phyto), data = ensemble[ensemble$perc_rich_phyto < 50,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_rich_phyto >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_phyto),
data = ensemble[ensemble$perc_rich_phyto < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map3 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_zoo), data = ensemble[ensemble$perc_rich_zoo < 50,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_rich_zoo >= 50,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = perc_rich_zoo),
data = ensemble[ensemble$perc_rich_zoo < 50,] ) +
scale_fill_gradient2(name = "Richness difference\n(%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map4 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Htot), data = ensemble[ensemble$perc_Htot < max2,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_Htot >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Htot),
data = ensemble[ensemble$perc_Htot < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map5 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Hphyto), data = ensemble[ensemble$perc_Hphyto < max2,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_Hphyto >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Hphyto),
data = ensemble[ensemble$perc_Hphyto < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map6 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_Hzoo), data = ensemble[ensemble$perc_Hzoo < max2,]) +
geom_raster(aes(x = x, y = y), data = ensemble[ensemble$perc_Hzoo >= max2,], fill = "#b2182b") +
geom_contour(colour = "grey60", binwidth = 2.5, size = 0.25, aes(x = x, y = y, z = perc_Hzoo),
data = ensemble[ensemble$perc_Hzoo < max2,] ) +
scale_fill_gradient2(name = "H' difference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min2,max2)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
# Save maps as .jpg for now
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/maps/")
ggsave(plot = map1, filename = paste("map_mon_perc_rich_tot_","ensemble_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map2, filename = paste("map_mon_perc_rich_phyto_","ensemble_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map3, filename = paste("map_mon_perc_rich_zoo_","ensemble_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map4, filename = paste("map_mon_perc_Htot_","ensemble_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map5, filename = paste("map_mon_perc_Hphyto_","ensemble_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map6, filename = paste("map_mon_perc_Hzoo_","ensemble_",m,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
} # eo for loop - m in months
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