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RSCRIPTBATCH_OVERSEE_ensemble_beta.div_average.R
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RSCRIPTBATCH_OVERSEE_ensemble_beta.div_average.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()
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")
ESMs <- c("CESM-BEC","CNRM-PISCES","GFDL-TOPAZ","IPSL-PISCES","MRI-NEMURO")
thresholds <- seq(from = 0.35, to = 0.45, by = 0.01)
# t <- 0.41
# esm <- "CESM-BEC"
res <- lapply(ESMs, function(esm) {
message(paste("", sep = ""))
message(paste("Converting HSI to 1/0 for ",esm, sep = ""))
message(paste("", sep = ""))
if(esm %in% c("CNRM-PISCES","GFDL-TOPAZ","IPSL-PISCES","MRI-NEMURO")) {
detach("package:dplyr", unload = T)
} # eo if loop
annual <- lapply(thresholds, function(t) {
# Get months names based on couples
message(paste("Using t = ",t, sep = ""))
# Load the baseline composition for these two months, for both zoo and phyto
base <- read.table(paste("table_annual_mean_compo_plankton_baseline.txt", sep = ""), sep = "\t")
# colnames(base)
base <- base[,c(1:(length(base)-2))]
phyto.fut <- read.table(paste("table_annual_mean_compo_phyto_2100-2000_rcp85_",esm,".txt", sep = ""), sep = "\t")
zoo.fut <- read.table(paste("table_annual_mean_compo_zoo_2100-2000_rcp85_",esm,".txt", sep = ""), sep = "\t")
# dim(base); dim(phyto.fut); dim(zoo.fut)
# Cbind to have all plankton
fut <- cbind(phyto.fut, zoo.fut[,c(4:length(zoo.fut))])
rm(phyto.fut,zoo.fut);gc()
# 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)]))
base <- na.omit(base)
fut <- na.omit(fut)
# dim(base); dim(fut)
# Compute richness based on HSI
base$rich_plankton <- rowSums(as.matrix(base[,c(4:865)]))
base$rich_phyto <- rowSums(as.matrix(base[,c(4:341)]))
base$rich_zoo <- rowSums(as.matrix(base[,c(342:865)]))
fut$rich_plankton <- rowSums(as.matrix(fut[,c(4:865)]))
fut$rich_phyto <- rowSums(as.matrix(fut[,c(4:341)]))
fut$rich_zoo <- rowSums(as.matrix(fut[,c(342:865)]))
# Uneven because monthly climatologies -> make them even
base <- base[order(base$cell_id),]
fut <- fut[order(fut$cell_id),]
base <- base[which(base$cell_id %in% unique(fut$cell_id)),]
fut <- fut[which(fut$cell_id %in% unique(base$cell_id)),]
# summary(base$rich_plankton); summary(fut$rich_plankton)
# Convert to P/1 using rbinom and initial probability
for(sp in colnames(base)[c(4:865)] ) {
message(paste("Converting probabilities for ",sp, sep = ""))
base[,c(sp)][base[,c(sp)] > t] <- 1
base[,c(sp)][base[,c(sp)] <= t] <- 0
# Future 2 now
fut[,c(sp)][fut[,c(sp)] > t] <- 1
fut[,c(sp)][fut[,c(sp)] <= t] <- 0
} # eo for loop
# head(base); head(fut)
# And compute beta.div changes
M = nrow(base[,c(4:865)])
N = ncol(base[,c(4:865)])
### With paralelling :
require("doParallel")
require("plyr")
registerDoParallel(cores = 25)
# Compute beta.div changes ofr all plankton
div <- data.frame()
d <- cbind(base[,c(4:865)], fut[,c(4:865)])
d$bit <- cut(1:M, 50, labels = F)
div <- ddply(d, ~ bit, function(x) {
beta.temp(x[,1:N], x[,(N+1):(2*N)],"jaccard")
},.parallel = T
) # eo ddply
# summary(div)
# For phytoplankton
M = nrow(base[,c(4:341)])
N = ncol(base[,c(4:341)])
div.phyto <- data.frame()
d <- cbind(base[,c(4:341)], fut[,c(4:341)])
d$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(base[,c(342:865)])
N = ncol(base[,c(342:865)])
div.zoo <- data.frame()
d <- cbind(base[,c(342:865)], fut[,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 <- base$x
div$y <- base$y
div$id <- factor(paste(div$x, div$y, sep = "_"))
div$rich_all_base <- base$rich_plankton
div$rich_phyto_base <- base$rich_phyto
div$rich_zoo_base <- base$rich_zoo
div$rich_all_fut <- fut$rich_plankton
div$rich_phyto_fut <- fut$rich_phyto
div$rich_zoo_fut <- fut$rich_zoo
# Compute differences
div$diff_all <- (div$rich_all_fut) - (div$rich_all_base)
div$diff_phyto <- (div$rich_phyto_fut) - (div$rich_phyto_base)
div$diff_zoo <- (div$rich_zoo_fut) - (div$rich_zoo_base)
# And add zoo and phyto components of beta.div
div$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$beta.jtu
# div[is.na(div$jac.phyto),c("x","y","jac.phyto")]
# Add the couple
div$thresh <- factor(t)
# Return(div)
rm(d,base.m1,base.m2); gc()
return(div)
} # eo 2nd FUN
) # eo 2nd lapply - annual
# Rbind
detach("package:plyr", unload = T)
require("dplyr")
table <- bind_rows(annual)
# Compute average changes across thresholds
div <- data.frame(table %>%
group_by(id) %>%
summarise(x = unique(x), y = unique(y),
rich_all_base = mean(rich_all_base,na.rm=T), rich_phyto_base = mean(rich_phyto_base,na.rm=T), rich_zoo_base = mean(rich_zoo_base,na.rm=T),
rich_all_fut = mean(rich_all_fut,na.rm=T), rich_phyto_fut = mean(rich_phyto_fut,na.rm=T), rich_zoo_fut = mean(rich_zoo_fut,na.rm=T),
jac = mean(beta.jac,na.rm=T), jne = mean(beta.jne,na.rm=T), jtu = mean(beta.jtu,na.rm=T),
jac_phyto = mean(jac.phyto,na.rm=T), jne_phyto = mean(jne.phyto,na.rm=T), jtu_phyto = mean(jtu.phyto,na.rm=T),
jac_zoo = mean(jac.zoo,na.rm=T), jne_zoo = mean(jne.zoo,na.rm=T), jtu_zoo = mean(jtu.zoo,na.rm=T),
diff_all = mean(diff_all,na.rm=T), diff_phyto = mean(diff_phyto,na.rm=T), diff_zoo = mean(diff_zoo,na.rm=T)
) # eo summarise
) # eo ddf
# summary(div)
div$ESM <- esm
# Return div
rm(table); gc()
return(div)
} # eo 1st FUN
) # eo 1st lapply - thresholds
# Rbind
require("dplyr")
ddf <- bind_rows(res)
dim(ddf)
head(ddf); unique(ddf$ESM)
summary(ddf)
# Save
write.table(ddf, file = "table_annual_comm_beta.div_ensemble.txt", sep = "\t")
dim(ddf[ddf$ESM == "GFDL-TOPAZ",]); dim(ddf[ddf$ESM == "IPSL-PISCES",]); dim(ddf[ddf$ESM == "MRI-NEMURO",])
### Map changes in beta.div for each ESM
for(esm in ESMs) {
# Mapping time
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/maps")
message(paste("Mapping annual beta div changes for ",esm, sep = ""))
d <- ddf[ddf$ESM == esm,]
require("viridis")
# Annual plankton jac
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jac), data = d) +
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 = d) +
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_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Annual plankton nestedness
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jne), data = d) +
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 = d) +
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_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Annual plankton turn-over
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jtu), data = d) +
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 = d) +
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_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
### Phyto
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jac_phyto), data = d) +
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 = d) +
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,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Annual Phyto nestedness
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jne_phyto), data = d) +
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 = d) +
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,".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 = d) +
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 = d) +
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,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
### Zoo
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jac_zoo), data = d) +
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 = d) +
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,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Annual Zoo nestedness
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jne_zoo), data = d) +
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 = d) +
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,".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 = d) +
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 = d) +
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_zoo_",esm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85")
} #
### And map ensemble projections
ens <- data.frame(ddf %>%
group_by(id) %>%
summarize(x = unique(x), y = unique(y),
jac = mean(jac,na.rm=T), jne = mean(jne,na.rm=T), jtu = mean(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)
)
) # eo ddf - ens
summary(ens)
setwd("/net/hydro/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/maps")
# Annual plankton jac
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jac), data = ens) +
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 = ens) +
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_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Annual plankton nestedness
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jne), data = ens) +
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 = ens) +
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_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Annual plankton turn-over
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jtu), data = ens) +
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 = ens) +
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_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
### Phyto
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jac_phyto), data = ens) +
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 = ens) +
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_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Annual Phyto nestedness
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jne_phyto), data = ens) +
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 = ens) +
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_","ensemble",".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 = ens) +
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 = ens) +
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_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
### Zoo
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jac_zoo), data = ens) +
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 = ens) +
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_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
# Annual Zoo nestedness
map <- ggplot() + geom_raster(aes(x = x, y = y, fill = jne_zoo), data = ens) +
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 = ens) +
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_","ensemble",".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 = ens) +
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 = ens) +
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_zoo_","ensemble",".jpg", sep = ""), dpi = 300, width = 7, height = 5)
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