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RSCRIPTBATCH_OVERSEE_indivual_diversity_change.R
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RSCRIPTBATCH_OVERSEE_indivual_diversity_change.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("betapart")
world2 <- map_data("world2")
# --------------------------------------------------------------------------------------------------------------------------------
### Set the working directories, vectors etc.
WD <- getwd()
rcp <- "rcp85"
# Vectors
SDMs <- c('GAM','GLM','RF','ANN')
# Vector of months
months <- c("jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec")
# Vector of pools
pools <- c("p1","p2","p3","p4")
# Vector of earth system models
ESMs <- c("CESM-BEC","CNRM-PISCES","GFDL-TOPAZ","IPSL-PISCES","MRI-NEMURO")
### In a mclapply, compute changes in annual alpha+beta diversity for each combination of: SDM x pool x ESM
### (maps for annual 80 combinations)
# esm <- "CESM-BEC"
# sdm <- "GAM"
# p <- "p2"
diversity.changer <- function(esm = ESMs) {
setwd(WD)
message(paste("", sep = ""))
message(paste("Computing changes in diversity for ",esm, sep = ""))
message(paste("", sep = ""))
for(sdm in SDMs) {
message(paste("Based on ",sdm, sep = ""))
for(p in pools) {
message(paste("and pool = ",p, sep = ""))
setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections")
require("dplyr")
# Load monthly baseline projections
basies.zoo <- lapply(months, function(m) {
b <- read.table(paste("table_zoo_mon_composition+div_baseline_",p,"_",sdm,"_",m,".txt", sep=""), h = T, sep = "\t")
#b$month <- m
return(b)
} # eo FUN
) # eo lapply - basies.zoo
# Rbind
base.zoo <- bind_rows(basies.zoo)
basies.phyto <- lapply(months, function(m) {
b <- read.table(paste("table_phyto_mon_composition+div_baseline_",p,"_",sdm,"_",m,".txt", sep=""), h = T, sep = "\t")
#b$month <- m
return(b)
} # eo FUN
) # eo lapply - basies.phyto
# Rbind
base.phyto <- bind_rows(basies.phyto)
# Load monthly future projections
future.zoo <- lapply(months, function(m) {
b <- read.table(paste("table_zoo_mon_composition+div_2100-2000_",esm,"_",p,"_",sdm,"_",m,".txt", sep=""), h = T, sep = "\t")
#b$month <- m
return(b)
} # eo FUN
) # eo lapply - future.zoo
# Rbind
fut.zoo <- bind_rows(future.zoo)
future.phyto <- lapply(months, function(m) {
b <- read.table(paste("table_phyto_mon_composition+div_2100-2000_",esm,"_",p,"_",sdm,"_",m,".txt", sep=""), h = T, sep = "\t")
#b$month <- m
return(b)
} # eo FUN
) # eo lapply - future.phyto
# Rbind
fut.phyto <- bind_rows(future.phyto)
# dim(base.zoo); dim(base.phyto); dim(fut.zoo); dim(fut.phyto)
rm(future.phyto, future.zoo, basies.phyto, basies.zoo) ; gc()
### Compute annual average diversity (H' and rich) and mean % change in SR
message(paste("Calculating changes in richness", sep = ""))
ann.base.phyto <- data.frame(base.phyto %>% group_by(cell_id) %>%
summarize(x = unique(x), y = unique(y), rich = mean(rich, na.rm = T))
)
ann.base.zoo <- data.frame(base.zoo %>% group_by(cell_id) %>%
summarize(x = unique(x), y = unique(y), rich = mean(rich, na.rm = T))
)
ann.fut.phyto <- data.frame(fut.phyto %>% group_by(cell_id) %>%
summarize(x = unique(x), y = unique(y), rich = mean(rich, na.rm = T))
)
ann.fut.zoo <- data.frame(fut.zoo %>% group_by(cell_id) %>%
summarize(x = unique(x), y = unique(y), rich = mean(rich, na.rm = T))
)
# dim(ann.base.phyto); dim(ann.base.zoo); dim(ann.fut.phyto); dim(ann.fut.zoo)
base <- data.frame(id = ann.base.phyto$cell_id, x = ann.base.phyto$x, y = ann.base.phyto$y,
rich_phyto = ann.base.phyto$rich, rich_zoo = ann.base.zoo$rich)
fut <- data.frame(id = ann.fut.zoo$cell_id, x = ann.fut.zoo$x, y = ann.fut.zoo$y,
rich_phyto = ann.fut.phyto$rich, rich_zoo = ann.fut.zoo$rich)
base$rich <- (base$rich_phyto)+(base$rich_zoo)
fut$rich <- (fut$rich_phyto)+(fut$rich_zoo)
# # Maps, first choose appropriate limits for scale
# min <- floor(min(c(base$rich_phyto,base$rich_zoo), na.rm = T))
# max <- floor(max(c(base$rich_phyto,base$rich_zoo), na.rm = T))
#
# map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = rich_phyto), data = base) +
# scale_fill_viridis(name = "Annual richness", limits = c(min,max) ) +
# geom_contour(colour = "grey75", binwidth = 50, size = 0.25, aes(x = x, y = y, z = rich_phyto), data = base) +
# 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 = rich_zoo), data = base) +
# scale_fill_viridis(name = "Annual richness", limits = c(min,max) ) +
# geom_contour(colour = "grey75", binwidth = 50, size = 0.25, aes(x = x, y = y, z = rich_zoo), data = base) +
# 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 = rich), data = base) +
# scale_fill_viridis(name = "Annual richness") + geom_contour(colour = "grey75", binwidth = 50,
# size = 0.25, aes(x = x, y = y, z = rich), data = base) +
# 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") )
#
# setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections/maps")
# ggsave(plot = map1, filename = paste("map_annual_rich_phyto_baseline_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# ggsave(plot = map2, filename = paste("map_annual_rich_zoo_baseline_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# ggsave(plot = map3, filename = paste("map_annual_rich_tot_baseline_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
# Calculate % diff rich for all 3 groups
base <- base[order(base$id),]
fut <- fut[order(fut$id),]
base$fut_rich_tot <- fut[which(fut$id %in% base$id),c("rich")]
base$fut_rich_phyto <- fut[which(fut$id %in% base$id),c("rich_phyto")]
base$fut_rich_zoo <- fut[which(fut$id %in% base$id),c("rich_zoo")]
### Save base (you'll need it to compute Mann-Whitney tests)
setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections/")
save(base, file = paste("table_base+fut_SR_",esm,"_",sdm,"_",p,".Rdata", sep = ""))
# changes <- data.frame(id = base$id, x = base$x, y = base$y,
# diff_rich_tot = (base$fut_rich_tot)-(base$rich),
# diff_rich_phyto = (base$fut_rich_phyto)-(base$rich_phyto),
# diff_rich_zoo = (base$fut_rich_zoo)-(base$rich_zoo)
# ) # eo ddf
# # summary(changes)
# # Add percentage changes
# changes$perc_rich_tot <- ((changes$diff_rich_tot)/base$rich)*100
# changes$perc_rich_phyto <- ((changes$diff_rich_phyto)/base$rich_phyto)*100
# changes$perc_rich_zoo <- ((changes$diff_rich_zoo)/base$rich_zoo)*100
#
# min <- floor(min(changes[,c("perc_rich_tot","perc_rich_phyto","perc_rich_zoo")], na.rm = T) )
#
# map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc_rich_tot), data = changes[changes$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") )
#
# ggsave(plot = map1, filename = paste("map_annual_perc_rich_tot_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# ggsave(plot = map2, filename = paste("map_annual_perc_rich_phyto_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# ggsave(plot = map3, filename = paste("map_annual_perc_rich_zoo_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
# ### Save changes in richness (you'll compute ensemble from those)
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections/")
# save(changes, file = paste("table_ann_changes_",esm,"_",sdm,"_",p,".Rdata", sep = ""))
# # Make room for change sin beta div
# rm(ann.base.phyto,ann.base.zoo,ann.fut.phyto,ann.fut.zoo,base,fut,map1,map2,map3,changes); gc()
#
# ### Computing changes in beta diversity
# message(paste("Computing species mean annual composition", sep = ""))
# # Compute mean annual species' HSI (melt+dcast) Remove NAs?
# # dim(base.zoo); dim(base.phyto); dim(fut.zoo); dim(fut.phyto)
# base.phyto <- na.omit(base.phyto)
# base.zoo <- na.omit(base.zoo)
# fut.phyto <- na.omit(fut.phyto)
# fut.zoo <- na.omit(fut.zoo)
# # Melt
# m.base.phyto <- melt(base.phyto[,c(1:c(length(base.phyto)-2))], id.vars = c("cell_id","x","y") )
# m.base.zoo <- melt(base.zoo[,c(1:c(length(base.zoo)-2))], id.vars = c("cell_id","x","y") )
# m.fut.phyto <- melt(fut.phyto[,c(1:c(length(fut.phyto)-2))], id.vars = c("cell_id","x","y") )
# m.fut.zoo <- melt(fut.zoo[,c(1:c(length(fut.zoo)-2))], id.vars = c("cell_id","x","y") )
# colnames(m.base.zoo)[c(4,5)] <- c("species","HSI")
# colnames(m.base.phyto)[c(4,5)] <- c("species","HSI")
# colnames(m.fut.phyto)[c(4,5)] <- c("species","HSI")
# colnames(m.fut.zoo)[c(4,5)] <- c("species","HSI")
# # Dcast with averaging as a FUN
# d.base.zoo <- dcast(m.base.zoo, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T, value.var = "HSI")
# d.base.phyto <- dcast(m.base.phyto, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T, value.var = "HSI")
# d.fut.zoo <- dcast(m.fut.zoo, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T, value.var = "HSI")
# d.fut.phyto <- dcast(m.fut.phyto, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T, value.var = "HSI")
# # dim(d.base.zoo); dim(d.base.phyto); dim(d.fut.zoo); dim(d.fut.phyto)
#
# # Re-order to make sure they can be combined
# d.base.zoo <- d.base.zoo[order(d.base.zoo$cell_id),]
# d.base.phyto <- d.base.phyto[order(d.base.phyto$cell_id),]
# d.fut.zoo <- d.fut.zoo[order(d.fut.zoo$cell_id),]
# d.fut.phyto <- d.fut.phyto[order(d.fut.phyto$cell_id),]
# # Make room
# rm(m.base.phyto,m.base.zoo,m.fut.phyto,m.fut.zoo); gc()
#
# # Save annual comp tables too (useful for species' range shifts)
# save(d.base.zoo, file = paste("table_ann_compo_zoo_baseline_",sdm,"_",p,".Rdata", sep = ""))
# save(d.base.phyto, file = paste("table_ann_compo_phyto_baseline_",sdm,"_",p,".Rdata", sep = ""))
# save(d.fut.zoo, file = paste("table_ann_compo_zoo_2100-2000_",esm,"_",sdm,"_",p,".Rdata", sep = ""))
# save(d.fut.phyto, file = paste("table_ann_compo_phyto_2100-2000_",esm,"_",sdm,"_",p,".Rdata", sep = ""))
#
# # Bind 'em
# base <- cbind(d.base.phyto[which(d.base.phyto$cell_id %in% unique(d.base.zoo$cell_id)),],
# d.base.zoo[which(d.base.zoo$cell_id %in% unique(d.base.phyto$cell_id)),c(4:length(d.base.zoo))]
# )
# fut <- cbind(d.fut.phyto[which(d.fut.phyto$cell_id %in% unique(d.fut.zoo$cell_id)),],
# d.fut.zoo[which(d.fut.zoo$cell_id %in% unique(d.fut.phyto$cell_id)),c(4:length(d.fut.zoo))]
# )
# # Remove points in the colnames
# colnames(base)[c(4:865)] <- gsub("[.]","",as.character(colnames(base)[c(4:865)]))
# colnames(fut)[c(4:865)] <- gsub("[.]","",as.character(colnames(fut)[c(4:865)]))
# # dim(base); dim(fut)
# base <- na.omit(base)
# fut <- na.omit(fut)
# base <- base[which(base$cell_id %in% unique(fut$cell_id)),]
# fut <- fut[which(fut$cell_id %in% unique(base$cell_id)),]
#
# # Calculating beta div changes for various thresholds
# thresholds <- seq(from = 0.35, to = 0.45, by = 0.01)
# # t <- 0.35
# message(paste("Calculating changes in beta div", sep = ""))
# thresh <- lapply(thresholds, function(t) {
#
# base2 <- base
# fut2 <- fut
# # Get months names based on couples
# message(paste("Using t = ",t, sep = ""))
# # Convert to P/1 using rbinom and initial probability
# for(sp in colnames(base2)[c(4:865)] ) {
# base2[,c(sp)][base2[,c(sp)] > t] <- 1
# base2[,c(sp)][base2[,c(sp)] <= t] <- 0
# # Future 2 now
# fut2[,c(sp)][fut2[,c(sp)] > t] <- 1
# fut2[,c(sp)][fut2[,c(sp)] <= t] <- 0
# } # eo for loop
# # And compute beta.div changes
# M = nrow(base2[,c(4:865)])
# N = ncol(base2[,c(4:865)])
#
# ### With paralelling :
# require("doParallel")
# require("plyr")
# registerDoParallel(cores = 15)
# # Compute beta.div changes ofr all plankton
# div <- data.frame()
# d <- cbind(base2[,c(4:865)], fut2[,c(4:865)])
# d$bit <- cut(1:M, 50, labels = F)
# div <- ddply(d, ~ bit, function(x) {
# beta.temp(x[,1:N], x[,(N+1):(2*N)],"jaccard")
# },.parallel = T
# ) # eo ddply
# # summary(div)
#
# # For phytoplankton
# M = nrow(base2[,c(4:341)])
# N = ncol(base2[,c(4:341)])
# div.phyto <- data.frame()
# d <- cbind(base2[,c(4:341)], fut2[,c(4:341)])
# d$bit <- cut(1:M, 50, labels = F)
# # head(d)
# div.phyto <- ddply(d, ~ bit, function(x) {
# beta.temp(x[,1:N], x[,(N+1):(2*N)],"jaccard")
# },.parallel = T
# ) # eo ddply
#
# # For zooplankton
# M = nrow(base2[,c(342:865)])
# N = ncol(base2[,c(342:865)])
# div.zoo <- data.frame()
# d <- cbind(base2[,c(342:865)], fut2[,c(342:865)])
# d$bit <- cut(1:M, 50, labels = F)
# div.zoo <- ddply(d, ~ bit, function(x) {
# beta.temp(x[,1:N], x[,(N+1):(2*N)],"jaccard")
# },.parallel = T
# ) # eo ddply
#
# if( sum(is.na(div$beta.jtu)) >= 1 ) {
# # summary(div.phyto)
# div[which(is.na(div$beta.jac)),c("beta.jac","beta.jne","beta.jtu")] <- 0
# div[which(is.na(div$beta.jne) & !is.na(div$beta.jac)),c("beta.jne")] <- 1
# div[which(is.na(div$beta.jtu) & !is.na(div$beta.jac)),c("beta.jtu")] <- 1
# }
#
# if( sum(is.na(div.phyto$beta.jtu)) >= 1 ) {
# # summary(div.phyto)
# div.phyto[which(is.na(div.phyto$beta.jac)),c("beta.jac","beta.jne","beta.jtu")] <- 0
# div.phyto[which(is.na(div.phyto$beta.jne) & !is.na(div.phyto$beta.jac)),c("beta.jne")] <- 1
# div.phyto[which(is.na(div.phyto$beta.jtu) & !is.na(div.phyto$beta.jac)),c("beta.jtu")] <- 1
# }
#
# if( sum(is.na(div.zoo$beta.jtu)) >= 1 ) {
# # summary(div.phyto)
# div.zoo[which(is.na(div.zoo$beta.jac)),c("beta.jac","beta.jne","beta.jtu")] <- 0
# div.zoo[which(is.na(div.zoo$beta.jne) & !is.na(div.zoo$beta.jac)),c("beta.jne")] <- 1
# div.zoo[which(is.na(div.zoo$beta.jtu) & !is.na(div.zoo$beta.jac)),c("beta.jtu")] <- 1
# }
#
# # Gather all in a data.frame and return
# div$x <- base2$x
# div$y <- base2$y
# div$id <- factor(paste(div$x, div$y, sep = "_"))
# # And add zoo and phyto components of beta.div
# div$jac.phyto <- div.phyto$beta.jac
# div$jne.phyto <- div.phyto$beta.jne
# div$jtu.phyto <- div.phyto$beta.jtu
# div$jac.zoo <- div.zoo$beta.jac
# div$jne.zoo <- div.zoo$beta.jne
# div$jtu.zoo <- div.zoo$beta.jtu
# # Add the couple
# div$thresh <- factor(t)
# # Return(div)
# rm(d); gc()
# return(div)
#
# } # eo 2nd FUN
#
# ) # eo 2nd lapply - annual
# # Rbind
# detach("package:plyr", unload = T)
# require("dplyr")
# table <- dplyr::bind_rows(thresh)
# rm(thresh); gc()
# # Compute average changes across thresholds
# div <- data.frame(table %>%
# group_by(id) %>%
# summarise(x = unique(x), y = unique(y),
# jac = mean(beta.jac,na.rm=T), jne = mean(beta.jne,na.rm=T), jtu = mean(beta.jtu,na.rm=T),
# jac_phyto = mean(jac.phyto,na.rm=T), jne_phyto = mean(jne.phyto,na.rm=T), jtu_phyto = mean(jtu.phyto,na.rm=T),
# jac_zoo = mean(jac.zoo,na.rm=T), jne_zoo = mean(jne.zoo,na.rm=T), jtu_zoo = mean(jtu.zoo,na.rm=T),
# diff_all = mean(diff_all,na.rm=T), diff_phyto = mean(diff_phyto,na.rm=T), diff_zoo = mean(diff_zoo,na.rm=T)
# ) # eo summarise
# ) # eo ddf
#
# message(paste("Saving changes in beta div", sep = ""))
# rm(table); gc()
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections/")
# save(div, file = paste("table_ann_changes_beta.div_",esm,"_",sdm,"_",p,".Rdata", sep = ""))
#
# setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections/maps")
# # Annual plankton jac
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jac), data = div) +
# scale_fill_viridis(name = "Annual Jaccard", limits = c(0,1)) +
# geom_contour(colour = "grey75", binwidth = 0.25, size = 0.25, aes(x = x, y = y, z = jac), data = div) +
# 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") )
#
# ggsave(plot = map, filename = paste("map_annual_jac_tot_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
# # Annual plankton nestedness
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jne), data = div) +
# scale_fill_viridis(name = "Annual Nestedness", limits = c(0,1)) +
# geom_contour(colour = "grey75", binwidth = 0.25, size = 0.25, aes(x = x, y = y, z = jne), data = div) +
# 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") )
#
# ggsave(plot = map, filename = paste("map_annual_jne_tot_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
# # Annual plankton turn-over
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jtu), data = div) +
# scale_fill_viridis(name = "Annual Turn-over", limits = c(0,1)) +
# geom_contour(colour = "grey75", binwidth = 0.25, size = 0.25, aes(x = x, y = y, z = jtu), data = div) +
# 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") )
#
# ggsave(plot = map, filename = paste("map_annual_jtu_tot_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
# ### Phyto
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jac_phyto), data = div) +
# scale_fill_viridis(name = "Annual Jaccard index", limits = c(0,1)) +
# geom_contour(colour = "grey75", binwidth = 0.25, size = 0.25, aes(x = x, y = y, z = jac_phyto), data = div) +
# 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") )
#
# ggsave(plot = map, filename = paste("map_annual_jac_phyto_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
# # Annual Phyto nestedness
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jne_phyto), data = div) +
# scale_fill_viridis(name = "Annual Nestedness", limits = c(0,1)) +
# geom_contour(colour = "grey75", binwidth = 0.25, size = 0.25, aes(x = x, y = y, z = jne_phyto), data = div) +
# 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") )
#
# ggsave(plot = map, filename = paste("map_annual_jne_phyto_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
# # Annual Phyto turn-over
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jtu_phyto), data = div) +
# scale_fill_viridis(name = "Annual Turn-over", limits = c(0,1)) +
# geom_contour(colour = "grey75", binwidth = 0.25, size = 0.25, aes(x = x, y = y, z = jtu_phyto), data = div) +
# 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") )
#
# ggsave(plot = map, filename = paste("map_annual_jtu_phyto_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
#
# ### Zoo
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jac_zoo), data = div) +
# scale_fill_viridis(name = "Annual Jaccard index", limits = c(0,1)) +
# geom_contour(colour = "grey75", binwidth = 0.25, size = 0.25, aes(x = x, y = y, z = jac_zoo), data = div) +
# 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") )
#
# ggsave(plot = map, filename = paste("map_annual_jac_zoo_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jne_zoo), data = div) +
# scale_fill_viridis(name = "Annual Nestedness", limits = c(0,1)) +
# geom_contour(colour = "grey75", binwidth = 0.25, size = 0.25, aes(x = x, y = y, z = jne_zoo), data = div) +
# 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") )
#
# ggsave(plot = map, filename = paste("map_annual_jne_zoo_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
# # Annual Zoo turn-over
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jtu_zoo), data = div) +
# scale_fill_viridis(name = "Annual Turn-over", limits = c(0,1)) +
# geom_contour(colour = "grey75", binwidth = 0.25, size = 0.25, aes(x = x, y = y, z = jtu_zoo), data = div) +
# 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") )
#
# ggsave(plot = map, filename = paste("map_annual_jne_zoo_",esm,"_",sdm,"_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
#
# ### Clean stuff and exit loop
# rm(base, fut, d.base.phyto, d.base.zoo, d.fut.phyto, d.fut.zoo, map)
# gc()
setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections/")
} # eo for loop - sdm in SDMs
} # eo for loop - sdm in SDMs
} # eo FUN - diversity.changer
require("parallel")
mclapply(X = ESMs, FUN = diversity.changer, mc.cores = 5)
# --------------------------------------------------------------------------------------------------------------------------------