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RSCRIPTBATCH_OVERSEE_indivual_diversity_ensemble.R
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RSCRIPTBATCH_OVERSEE_indivual_diversity_ensemble.R
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# --------------------------------------------------------------------------------------------------------------------------------
library("tidyverse")
library("RColorBrewer")
library("reshape2")
library("viridis")
library("scales")
library("maps")
library("geosphere")
library("rgdal")
library("raster")
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")
# --------------------------------------------------------------------------------------------------------------------------------
### 1°) Examine ensembles of baseline annual species richness
setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections")
# format: "table_ann_changes_",esm,"_",sdm,"_",p,".Rdata"
files <- dir()[grep("div_baseline_",dir())]; files
require("parallel")
# f <- files[1]
res <- mclapply(files, function(f) {
# Useless message
message(paste("Loading results from ",f, sep = ""))
t <- read.table(f, sep = "\t", h = T) # dim(t); colnames(t)
# Get attributes from filename
terms <- do.call(cbind, strsplit(as.character(f),"_"))
group <- terms[2,1]
sdm <- terms[7,1]
p <- terms[6,1]
m <- terms[8,1]
mm <- str_replace(as.character(m),".txt","")
ddf <- data.frame(id = t$cell_id, x = t$x, y = t$y,
month = mm, group = group,
sdm = sdm, pool = p, rich = t$rich
) # eo ddf
# Return
return(ddf)
}, mc.cores = 35
) # eo mclapply
# Rbind
table.base <- dplyr::bind_rows(res)
dim(table.base)
head(table.base)
rm(res); gc()
table.base <- table.base[order(table.base$id),]
### Computing ensembles (all, SDM/ESM/pool)
ens <- data.frame(table.base %>% group_by(id,group) %>%
summarize(x = unique(x), y = unique(y), mean.rich = mean(rich, na.rm = T), sd.rich = sd(rich, na.rm = T) )
) # ddf
summary(ens)
head(ens)
# Dcast to separate between
dens <- dcast(ens, id + x + y ~ group, value.var = c("mean.rich") )
dim(dens)
summary(dens)
dens$tot <- (dens$phyto)+(dens$zoo)
# Define limts of the color scale
#min <- floor(min(c(dens$phyto,dens$zoo), na.rm = T))
#max <- floor(max(c(dens$phyto,dens$zoo), na.rm = T))
### 23/06/2020: As Niki suggested, rather plot % of species modelled
dens$phyto2 <- (dens$phyto)/336
dens$zoo2 <- (dens$zoo)/524
dens$tot2 <- (dens$tot)/860
summary(dens)
min <- 0
max <- 0.7
# min;max
map1 <- ggplot() + geom_tile(aes(x = x, y = y, fill = tot2), data = na.omit(dens)) +
#scale_fill_viridis(name = "Mean annual\nrichness", begin = 0, end = 1, limits = c(min,max)) +
scale_fill_viridis(name = "", rescaler = function(x, to = c(0,1), from = NULL) {
ifelse(x < 0.4, scales::rescale(x, to = to, from = c(min(x, na.rm = T), 0.4)), 1) } ) +
geom_contour(colour = "grey75", binwidth = 0.1, size = 0.25, aes(x = x, y = y, z = tot2), data = na.omit(dens)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("GM","60°E","120°E","180°","120°W","60°W","GM"), expand = c(0,0)) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","Eq","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"), legend.position = "none")
map2 <- ggplot() + geom_tile(aes(x = x, y = y, fill = phyto2), data = na.omit(dens)) +
#scale_fill_viridis(name = "Mean annual\nrichness", limits = c(min,max) ) +
scale_fill_viridis(name = "", rescaler = function(x, to = c(0,1), from = NULL) {
ifelse(x < 0.4, scales::rescale(x, to = to, from = c(min(x, na.rm = T), 0.4)), 1) } ) +
geom_contour(colour = "grey75", binwidth = 0.1, size = 0.25, aes(x = x, y = y, z = phyto2), data = na.omit(dens)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("GM","60°E","120°E","180°","120°W","60°W","GM"), expand = c(0,0)) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","Eq","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"), legend.position = "none")
map3 <- ggplot() + geom_tile(aes(x = x, y = y, fill = zoo2), data = na.omit(dens)) +
#scale_fill_viridis(name = "Mean annual\nrichness", limits = c(min,max) ) +
scale_fill_viridis(name = "", rescaler = function(x, to = c(0,1), from = NULL) {
ifelse(x < 0.4, scales::rescale(x, to = to, from = c(min(x, na.rm = T), 0.4)), 1) } ) +
geom_contour(colour = "grey75", binwidth = 0.1, size = 0.25, aes(x = x, y = y, z = zoo2), data = na.omit(dens)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("GM","60°E","120°E","180°","120°W","60°W","GM"), expand = c(0,0)) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","Eq","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"), legend.position = "none")
setwd(WD)
ggsave(plot = map2, filename = paste("map_ensemble_annual_rich_phyto_baseline.jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map3, filename = paste("map_ensemble_annual_rich_zoo_baseline.jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map1, filename = paste("map_ensemble_annual_rich_tot_baseline.jpg", sep = ""), dpi = 300, width = 7, height = 5)
### 09/08/2020: and the true postscript versions for Niki
grDevices::cairo_ps(filename = "map_ensemble_annual_rich_phyto_baseline.ps", width = 7, height = 5, fallback_resolution = 300)
print(map2)
dev.off()
grDevices::cairo_ps(filename = "map_ensemble_annual_rich_zoo_baseline.ps", width = 7, height = 5, fallback_resolution = 300)
print(map3)
dev.off()
grDevices::cairo_ps(filename = "map_ensemble_annual_rich_tot_baseline.ps", width = 7, height = 5, fallback_resolution = 300)
print(map1)
dev.off()
### 23/06/2020: And plot zonal patterns, one per SDM for baseline richness
head(table.base)
zonal <- data.frame(table.base %>%
group_by(group,sdm,y) %>%
summarise(rich = mean(rich,na.rm=T) )
) # eo ddf
summary(zonal)
plot2 <- ggplot() +
geom_path(aes(y = y, x = rich/336), data = na.omit(zonal[zonal$group == "phyto" & zonal$sdm == "GLM",]), colour = "black", linetype = "solid") +
geom_path(aes(y = y, x = rich/336), data = na.omit(zonal[zonal$group == "phyto" & zonal$sdm == "GAM",]), colour = "black", linetype = "longdash") +
geom_path(aes(y = y, x = rich/336), data = na.omit(zonal[zonal$group == "phyto" & zonal$sdm == "ANN",]), colour = "black", linetype = "dashed") +
geom_path(aes(y = y, x = (rich/336)*3.2), data = na.omit(zonal[zonal$group == "phyto" & zonal$sdm == "RF",]), colour = "grey50", linetype = "solid") +
scale_x_continuous(name = "", limits = c(0,0.55), breaks = c(0,0.1,0.2,0.3,0.4,0.5,0.6)) +
scale_y_continuous(position = "left", name = "Latitude", limits = c(-90,90),
breaks = c(-90,-60,-30,0,30,60,90), labels = c("90°S","60°S","30°S","Eq","30°N","60°N","90°N")) +
theme_classic()
plot3 <- ggplot() +
geom_path(aes(y = y, x = rich/524), data = na.omit(zonal[zonal$group == "zoo" & zonal$sdm == "GLM",]), colour = "black", linetype = "solid") +
geom_path(aes(y = y, x = rich/524), data = na.omit(zonal[zonal$group == "zoo" & zonal$sdm == "GAM",]), colour = "black", linetype = "longdash") +
geom_path(aes(y = y, x = rich/524), data = na.omit(zonal[zonal$group == "zoo" & zonal$sdm == "ANN",]), colour = "black", linetype = "dashed") +
geom_path(aes(y = y, x = (rich/524)*3.2), data = na.omit(zonal[zonal$group == "zoo" & zonal$sdm == "RF",]), colour = "grey50", linetype = "solid") +
scale_y_continuous(position = "left", name = "Latitude", limits = c(-90,90),
breaks = c(-90,-60,-30,0,30,60,90), labels = c("90°S","60°S","30°S","Eq","30°N","60°N","90°N")) +
scale_x_continuous(name = "", limits = c(0,0.55), breaks = c(0,0.1,0.2,0.3,0.4,0.5,0.6)) +
theme_classic()
# Dcast to separate between
dzon <- dcast(zonal, sdm + y ~ group, value.var = c("rich") )
dim(dzon) ; summary(dzon)
dzon$tot <- (dzon$phyto)+(dzon$zoo)
plot1 <- ggplot() +
geom_path(aes(y = y, x = tot/860), data = na.omit(dzon[dzon$sdm == "GLM",]), colour = "black", linetype = "solid") +
geom_path(aes(y = y, x = tot/860), data = na.omit(dzon[dzon$sdm == "GAM",]), colour = "black", linetype = "longdash") +
geom_path(aes(y = y, x = tot/860), data = na.omit(dzon[dzon$sdm == "ANN",]), colour = "black", linetype = "dashed") +
geom_path(aes(y = y, x = (tot/860)*3.2), data = na.omit(dzon[dzon$sdm == "RF",]), colour = "grey50", linetype = "solid") +
scale_y_continuous(position = "left", name = "Latitude", limits = c(-90,90),
breaks = c(-90,-60,-30,0,30,60,90), labels = c("90°S","60°S","30°S","Eq","30°N","60°N","90°N")) +
scale_x_continuous(name = "", limits = c(0,0.55), breaks = c(0,0.1,0.2,0.3,0.4,0.5,0.6)) +
theme_classic()
# zonal <- data.frame(dens %>%
# group_by(y) %>%
# summarise(mean_plankton = mean(tot, na.rm=T), sd_plankton = sd(tot, na.rm=T),
# mean_phyto = mean(phyto, na.rm=T), sd_phyto = sd(phyto, na.rm=T),
# mean_zoo = mean(zoo, na.rm=T), sd_zoo = sd(zoo, na.rm=T) )
# ) # eo ddf
# plot1 <- ggplot() + geom_ribbon(aes(y = y, xmin = mean_plankton - sd_plankton, xmax = mean_plankton + sd_plankton),
# fill = "grey70", data = na.omit(zonal)) +
# geom_path(aes(y = y, x = mean_plankton), data = na.omit(zonal), colour = "black" ) + scale_x_continuous(limits = c(0,290)) +
# xlab("Mean annual richness") + scale_y_continuous(position = "left", name = "Latitude (°)") +
# theme_classic()
#
# plot2 <- ggplot() + geom_ribbon(aes(y = y, xmin = mean_phyto - sd_phyto, xmax = mean_phyto + sd_phyto),
# fill = "grey70", data = na.omit(zonal)) +
# geom_path(aes(y = y, x = mean_phyto), data = na.omit(zonal), colour = "black" ) + scale_x_continuous(limits = c(0,200)) +
# xlab("Mean annual richness") + scale_y_continuous(position = "left", name = "Latitude (°)") +
# theme_classic()
# #
# plot3 <- ggplot() + geom_ribbon(aes(y = y, xmin = mean_zoo - sd_zoo, xmax = mean_zoo + sd_zoo),
# fill = "grey70", data = na.omit(zonal)) +
# geom_path(aes(y = y, x = mean_zoo), data = na.omit(zonal), colour = "black" ) + scale_x_continuous(limits = c(0,200)) +
# xlab("Mean annual richness") + scale_y_continuous(position = "left", name = "Latitude (°)") +
# theme_classic()
#
ggsave(plot = plot1, filename = "plot_zonal_ann_plankton_rich_baseline_ensemble.jpg", dpi = 300, width = 3, height = 4)
ggsave(plot = plot2, filename = "plot_zonal_ann_phyto_rich_baseline_ensemble.jpg", dpi = 300, width = 3, height = 4)
ggsave(plot = plot3, filename = "plot_zonal_ann_zoo_rich_baseline_ensemble.jpg", dpi = 300, width = 3, height = 4)
grDevices::cairo_ps(filename = "plot_zonal_ann_plankton_rich_baseline_ensemble.ps", width = 3, height = 4, fallback_resolution = 300)
print(plot1)
dev.off()
grDevices::cairo_ps(filename = "plot_zonal_ann_phyto_rich_baseline_ensemble.ps", width = 3, height = 4, fallback_resolution = 300)
print(plot2)
dev.off()
grDevices::cairo_ps(filename = "plot_zonal_ann_zoo_rich_baseline_ensemble.ps", width = 3, height = 4, fallback_resolution = 300)
print(plot3)
dev.off()
### 20/04/2020: Use grid arrange or ggExtra to arrange the plots in panels that will make them
library("ggpubr")
# ?ggarrange
# ggarrange(plot1,map1, labels = c("A","B"), ncol = 2, nrow = 1, widths = c(1,2.7), align = "v")
# ggarrange(plot2,map2, labels = c("C","D"), ncol = 2, nrow = 1, widths = c(1,2.7), align = "v")
# ggarrange(plot3,map3, labels = c("E","F"), ncol = 2, nrow = 1, widths = c(1,2.7), align = "v")
#ggarrange(plot1,map1,plot2,map2,plot3,map3, labels = c("A","B","C","D","E","F"), ncol = 2, nrow = 3, widths = c(1,2.7) )
setwd("/net/kryo/work/fabioben/OVERSEE/data")
panel <- ggarrange(plot1,map1,plot2,map2,plot3,map3, labels = c("A","B","C","D","E","F"), ncol = 2, nrow = 3, widths = c(1,2.7) )
ggsave(plot = panel, filename = "panel_ensemble_baseline_rich_Fig1A-F.pdf", dpi = 300, width = 8, height = 8)
grDevices::cairo_ps(filename = "panel_ensemble_baseline_rich_Fig1A-F.ps", width = 8, height = 8, fallback_resolution = 300)
print(panel)
dev.off()
### And save one with as .pdf with the color bar
map <- ggplot() + geom_tile(aes(x = x, y = y, fill = zoo2), data = na.omit(dens)) +
scale_fill_viridis(name = "", rescaler = function(x, to = c(0,1), from = NULL) {
ifelse(x < 0.4, scales::rescale(x, to = to, from = c(min(x, na.rm = T), 0.4)), 1) }, limits = c(0,0.7) ) +
geom_contour(colour = "grey75", binwidth = 0.1, size = 0.25, aes(x = x, y = y, z = zoo2), data = na.omit(dens)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed"), legend.position = "bottom")
ggsave(plot = map, filename = paste("map4colorbar1.pdf", sep = ""), dpi = 300, width = 8, height = 5)
### And check ensemble per SDMs
ens.SDM <- data.frame(table %>% group_by(id,group,sdm) %>%
summarize(x = unique(x), y = unique(y), mean.rich = mean(rich, na.rm = T), sd.rich = sd(rich, na.rm = T) )
) # ddf
# dim(ens.SDM[which(ens.SDM$sdm == "GAM" & dens$group == "phyto"),])
min1 <- min(ens.SDM[which(ens.SDM$group == "phyto"),"mean.rich"], na.rm = T)
max1 <- max(ens.SDM[which(ens.SDM$group == "phyto"),"mean.rich"], na.rm = T)
min2 <- min(ens.SDM[which(ens.SDM$group == "zoo"),"mean.rich"], na.rm = T)
max2 <- max(ens.SDM[which(ens.SDM$group == "zoo"),"mean.rich"], na.rm = T)
for(sdm in unique(ens.SDM$sdm) ) {
map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = mean.rich),
data = ens.SDM[which(ens.SDM$group == "phyto" & ens.SDM$sdm == sdm),] ) +
scale_fill_viridis(name = "Annual richness", limits = c(min1,max1)) +
geom_contour(colour = "grey75", binwidth = 50, size = 0.25, aes(x = x, y = y, z = mean.rich),
data = ens.SDM[which(ens.SDM$group == "phyto" & ens.SDM$group == sdm),] ) +
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 = mean.rich),
data = ens.SDM[which(ens.SDM$group == "zoo" & ens.SDM$sdm == sdm),] ) +
scale_fill_viridis(name = "Annual richness", limits = c(min2,max2)) +
geom_contour(colour = "grey75", binwidth = 50, size = 0.25, aes(x = x, y = y, z = mean.rich),
data = ens.SDM[which(ens.SDM$group == "zoo" & ens.SDM$group == sdm),] ) +
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_ensemble_annual_rich_phyto_baseline_",sdm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map2, filename = paste("map_ensemble_annual_rich_zoo_baseline_",sdm,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
} # eo sdm in SDMs
### Put all 4 SDM-scale zonal on the same plot? (with facet)
head(ens.SDM)
zonal <- data.frame(ens.SDM %>%
group_by(y,sdm,group) %>%
summarise(mean = mean(mean.rich, na.rm=T), sd = sd(mean.rich, na.rm=T)
)
) # eo ddf
summary(zonal)
plot1 <- ggplot(data = zonal[zonal$group == "phyto",]) +
geom_ribbon(aes(x = y, ymin = mean - sd, ymax = mean + sd), fill = "grey70" ) +
geom_line(aes(x = y, y = mean), colour = "black" ) +
ylab("Mean annual species richness\n(Phytoplankton)") + xlab("Latitude (°)") +
theme_classic() + coord_flip() + facet_wrap(~factor(sdm), ncol = 2, scales = "fixed")
plot2 <- ggplot(data = zonal[zonal$group == "zoo",]) +
geom_ribbon(aes(x = y, ymin = mean - sd, ymax = mean + sd), fill = "grey70" ) +
geom_line(aes(x = y, y = mean), colour = "black" ) +
ylab("Mean annual species richness\n(Zooplankton)") + xlab("Latitude (°)") +
theme_classic() + coord_flip() + facet_wrap(~factor(sdm), ncol = 2, scales = "fixed")
#
ggsave(plot = plot1, filename = "plot_zonal_ann_phyto_rich_baseline_ensemble_SDMs.jpg", dpi = 300, width = 6, height = 6)
ggsave(plot = plot2, filename = "plot_zonal_ann_zoo_rich_baseline_ensemble_SDMs.jpg", dpi = 300, width = 6, height = 6)
### And check ensemble per pools
ens.pool <- data.frame(table %>% group_by(id,group,pool) %>%
summarize(x = unique(x), y = unique(y), mean.rich = mean(rich, na.rm = T), sd.rich = sd(rich, na.rm = T) )
) # ddf
# summary(ens.pool)
min1 <- min(ens.pool[which(ens.pool$group == "phyto"),"mean.rich"], na.rm = T)
max1 <- max(ens.pool[which(ens.pool$group == "phyto"),"mean.rich"], na.rm = T)
min2 <- min(ens.pool[which(ens.pool$group == "zoo"),"mean.rich"], na.rm = T)
max2 <- max(ens.pool[which(ens.pool$group == "zoo"),"mean.rich"], na.rm = T)
for(p in unique(ens.pool$pool) ) {
map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = mean.rich),
data = ens.pool[which(ens.pool$group == "phyto" & ens.pool$pool == p),] ) +
scale_fill_viridis(name = "Annual richness", limits = c(min1,max1)) +
geom_contour(colour = "grey75", binwidth = 50, size = 0.25, aes(x = x, y = y, z = mean.rich),
data = ens.pool[which(ens.pool$group == "phyto" & ens.pool$pool == p),] ) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = mean.rich),
data = ens.pool[which(ens.pool$group == "zoo" & ens.pool$pool == p),] ) +
scale_fill_viridis(name = "Annual richness", limits = c(min2,max2)) +
geom_contour(colour = "grey75", binwidth = 50, size = 0.25, aes(x = x, y = y, z = mean.rich),
data = ens.pool[which(ens.pool$group == "zoo" & ens.pool$pool == p),] ) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
ggsave(plot = map1, filename = paste("map_ensemble_annual_rich_phyto_baseline_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map2, filename = paste("map_ensemble_annual_rich_zoo_baseline_",p,".jpg", sep = ""), dpi = 300, width = 7, height = 5)
} # eo p in pools
# ---------------------------------------------------------
### 2°) Examine ensembles of changes in annual species richness
setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections")
# format: "table_ann_changes_",esm,"_",sdm,"_",p,".Rdata"
files <- dir()[grep("table_ann_changes_",dir())]; files
# Separate the ones for richness (no "beta.div") from those with beta.div
files2 <- files[grep("beta.div", files)]; files2
files1 <- files[!(files %in% files2)]; files1
require("parallel")
# f <- files1[1]
res <- mclapply(files1, function(f) {
# Useless message
message(paste("Loading results from ",f, sep = ""))
t <- get(load(f))
# Get attributes from filename
terms <- do.call(cbind, strsplit(as.character(f),"_"))
t$ESM <- terms[4,1]
t$SDM <- terms[5,1]
p <- terms[6,1]
t$pool <- str_replace(as.character(p),".Rdata","")
# Return
return(t)
}, mc.cores = 30
) # eo mclapply
# Rbind
table.diff <- dplyr::bind_rows(res)
dim(table.diff)
head(table.diff)
rm(res); gc()
### 26/06/2020: To describe the results based on ALL 80 ensemble members rather than from the spatial variability of the ensemble projection:
# - compute spatial averages for ALL 80 combinations in table, and THEN compute median (±IQR). Focus on those regions:
# - Southern Ocean (< -60°)
# - tropical band (-30°/+30°)
# - temperate latitudes (abs(ens$y) >= 40 & abs(ens$y))
# - arctic ocean: y > 70
### First, add an id for the ensemble member:
table.diff$member <- factor(paste(table.diff$ESM, table.diff$SDM, table.diff$pool, sep = "_"))
unique(table.diff$member) # 80 values as should be
# And a vector of region
table.diff$region <- NA
table.diff[which(table.diff$y <= -60),"region"] <- "SO"
table.diff[which(table.diff$y >= 70),"region"] <- "AO"
table.diff[which(abs(table.diff$y) <= 30),"region"] <- "Tropics"
table.diff[which(abs(table.diff$y) >= 40 & abs(table.diff$y) <= 55),"region"] <- "Temperate"
unique(table.diff$region)
### 07/08/2020: For Niki, compute and map regions of model members agreement according to varying thresholds
ddf <- data.frame(table.diff %>% group_by(id) %>% summarize(
x = unique(x), y = unique(y),
n_posi_tot = sum(perc_rich_tot > 0)/80,
n_nega_tot = sum(perc_rich_tot < 0)/80,
n_posi_phyto = sum(perc_rich_phyto > 0)/80,
n_nega_phyto = sum(perc_rich_phyto < 0)/80,
n_posi_zoo = sum(perc_rich_zoo > 0)/80,
n_nega_zoo = sum(perc_rich_zoo < 0)/80
) # eo summarize
) # eo ddf
head(ddf) ; summary(ddf)
map_agreement_90_tot <- ggplot() + geom_tile(aes(x = x, y = y), data = ddf[ddf$n_posi_tot >= 0.9,], fill = "red") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) + theme_void()
#
map_agreement_85_tot <- ggplot() + geom_tile(aes(x = x, y = y), data = ddf[ddf$n_posi_tot >= 0.85,], fill = "red") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) + theme_void()
#
map_agreement_75_tot <- ggplot() + geom_tile(aes(x = x, y = y), data = ddf[ddf$n_posi_tot >= 0.75,], fill = "red") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) + theme_void()
#
map_agreement_90_phyto <- ggplot() + geom_tile(aes(x = x, y = y), data = ddf[ddf$n_posi_phyto >= 0.9,], fill = "red") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) + theme_void()
#
map_agreement_85_phyto <- ggplot() + geom_tile(aes(x = x, y = y), data = ddf[ddf$n_posi_phyto >= 0.85,], fill = "red") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) + theme_void()
#
map_agreement_75_phyto <- ggplot() + geom_tile(aes(x = x, y = y), data = ddf[ddf$n_posi_phyto >= 0.75,], fill = "red") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) + theme_void()
#
map_agreement_90_zoo <- ggplot() + geom_tile(aes(x = x, y = y), data = ddf[ddf$n_posi_zoo >= 0.9,], fill = "red") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) + theme_void()
#
map_agreement_85_zoo <- ggplot() + geom_tile(aes(x = x, y = y), data = ddf[ddf$n_posi_zoo >= 0.85,], fill = "red") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) + theme_void()
#
map_agreement_75_zoo <- ggplot() + geom_tile(aes(x = x, y = y), data = ddf[ddf$n_posi_zoo >= 0.75,], fill = "red") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("180°W","120°W","60°W","0°E","60°E","120°E","180°E") ) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N") ) + theme_void()
setwd(WD)
grDevices::cairo_ps(filename = "map_model_member_agreement_diff_plankton_90.ps", width = 7, height = 5, fallback_resolution = 300)
print(map_agreement_90_tot)
dev.off()
grDevices::cairo_ps(filename = "map_model_member_agreement_diff_plankton_85.ps", width = 7, height = 5, fallback_resolution = 300)
print(map_agreement_85_tot)
dev.off()
grDevices::cairo_ps(filename = "map_model_member_agreement_diff_plankton_75.ps", width = 7, height = 5, fallback_resolution = 300)
print(map_agreement_75_tot)
dev.off()
grDevices::cairo_ps(filename = "map_model_member_agreement_diff_phytoplankton_90.ps", width = 7, height = 5, fallback_resolution = 300)
print(map_agreement_90_phyto)
dev.off()
grDevices::cairo_ps(filename = "map_model_member_agreement_diff_phytoplankton_85.ps", width = 7, height = 5, fallback_resolution = 300)
print(map_agreement_85_phyto)
dev.off()
grDevices::cairo_ps(filename = "map_model_member_agreement_diff_phytoplankton_75.eps", width = 7, height = 5, fallback_resolution = 300)
print(map_agreement_75_phyto)
dev.off()
grDevices::cairo_ps(filename = "map_model_member_agreement_diff_zooplankton_90.ps", width = 7, height = 5, fallback_resolution = 300)
print(map_agreement_90_zoo)
dev.off()
grDevices::cairo_ps(filename = "map_model_member_agreement_diff_zooplankton_85.ps", width = 7, height = 5, fallback_resolution = 300)
print(map_agreement_85_zoo)
dev.off()
grDevices::cairo_ps(filename = "map_model_member_agreement_diff_zooplankton_75.ps", width = 7, height = 5, fallback_resolution = 300)
print(map_agreement_75_zoo)
dev.off()
### Use dplyr to compute median response per region and member and sign agreement
ddf <- data.frame(na.omit(table.diff) %>% group_by(member,region) %>% summarize(
perc_tot = median(perc_rich_tot,na.rm=T),
perc_phyto = median(perc_rich_phyto,na.rm=T),
perc_zoo = median(perc_rich_zoo,na.rm=T)
) # eo summarize
) # eo ddf
head(ddf)
### Derive sign agreement:
ddf$sign_tot <- NA
ddf$sign_phyto <- NA
ddf$sign_zoo <- NA
for(i in c(1:nrow(ddf))) {
message(paste(i, sep = ""))
### Use 3 if else loops to attribute sign of change
if( ddf[i,"perc_tot"] > 0) {
ddf[i,"sign_tot"] <- "increase"
} else {
ddf[i,"sign_tot"] <- "decrease"
}
#
if( ddf[i,"perc_phyto"] > 0) {
ddf[i,"sign_phyto"] <- "increase"
} else {
ddf[i,"sign_phyto"] <- "decrease"
}
#
if( ddf[i,"perc_zoo"] > 0) {
ddf[i,"sign_zoo"] <- "increase"
} else {
ddf[i,"sign_zoo"] <- "decrease"
}
} # eo for loop
### Display % agreement
for(reg in unique(ddf$region)) {
tot_posi <- ( (nrow(ddf[ddf$region == reg & ddf$sign_tot == "increase",]))/80 )*100
tot_nega <- ( (nrow(ddf[ddf$region == reg & ddf$sign_tot == "decrease",]))/80 )*100
message(paste("% agreement on total plankton increase in SR in the ",reg," is ",tot_posi, "%", sep = ""))
message(paste("% agreement on total plankton decrease in SR in the ",reg," is ",tot_nega, "%", sep = ""))
message(paste("", sep = ""))
# Same with phyto and zoo
phyto_posi <- ( (nrow(ddf[ddf$region == reg & ddf$sign_phyto == "increase",]))/80 )*100
phyto_nega <- ( (nrow(ddf[ddf$region == reg & ddf$sign_phyto == "decrease",]))/80 )*100
message(paste("% agreement in total phytoplankton increase in SR in the ",reg," is ",phyto_posi, "%", sep = ""))
message(paste("% agreement on total phytoplankton decrease in SR in the ",reg," is ",phyto_nega, "%", sep = ""))
message(paste("", sep = ""))
zoo_posi <- ( (nrow(ddf[ddf$region == reg & ddf$sign_zoo == "increase",]))/80 )*100
zoo_nega <- ( (nrow(ddf[ddf$region == reg & ddf$sign_zoo == "decrease",]))/80 )*100
message(paste("% agreement in total zooplankton increase in SR in the ",reg," is ",zoo_posi, "%", sep = ""))
message(paste("% agreement on total zooplankton decrease in SR in the ",reg," is ",zoo_nega, "%", sep = ""))
message(paste("", sep = ""))
}
### And compute median ±IQR of regional averages for each resp var + agreement %
ddf2 <- data.frame(na.omit(ddf) %>% group_by(region) %>% summarize(
med_tot = median(perc_tot, na.rm=T), q25_t = quantile(perc_tot, na.rm=T)[2], q75_t = quantile(perc_tot, na.rm=T)[5],
med_phyto = median(perc_phyto, na.rm=T), q25_p = quantile(perc_phyto, na.rm=T)[2], q75_p = quantile(perc_phyto, na.rm=T)[5],
med_zoo = median(perc_zoo, na.rm=T), q25_z = quantile(perc_zoo, na.rm=T)[2], q75_z = quantile(perc_zoo, na.rm=T)[5]
) # eo summarize
) # eo ddf
ddf2
### And the global numbers
ddf_glob <- data.frame(na.omit(table.diff) %>% group_by(member) %>% summarize(
perc_tot = median(perc_rich_tot,na.rm=T),
perc_phyto = median(perc_rich_phyto,na.rm=T),
perc_zoo = median(perc_rich_zoo,na.rm=T)
) # eo summarize
) # eo ddf
head(ddf_glob)
# Display global reponses
median(ddf_glob$perc_tot) ; IQR(ddf_glob$perc_tot) ; quantile(ddf_glob$perc_tot)
median(ddf_glob$perc_phyto) ; IQR(ddf_glob$perc_phyto) ; quantile(ddf_glob$perc_phyto)
median(ddf_glob$perc_zoo) ; IQR(ddf_glob$perc_zoo) ; quantile(ddf_glob$perc_zoo)
(nrow(ddf_glob[which(ddf_glob$perc_tot > 0),])/80)*100
(nrow(ddf_glob[which(ddf_glob$perc_phyto > 0),])/80)*100
(nrow(ddf_glob[which(ddf_glob$perc_zoo > 0),])/80)*100
### Compute global zonal averages across all 80 ensemble members for zoo and phyto responses and plot panel
glob_zonal <- data.frame(table.diff %>% group_by(member,y) %>% summarize(
perc_tot = mean(perc_rich_tot,na.rm=T),
perc_phyto = mean(perc_rich_phyto,na.rm=T),
perc_zoo = mean(perc_rich_zoo,na.rm=T)
) # eo summarize
) # eo ddf
head(glob_zonal)
# Change labels
glob_zonal$member2 <- rep(c(1:80), each = 179, len = 14320)
summary(glob_zonal)
panel <- ggplot() + geom_path(aes(y = y, x = perc_phyto), data = na.omit(glob_zonal), colour = "black", linetype = "solid") +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N"), position = "right") +
scale_x_continuous(name = "", limits = c(-40,200), breaks = c(-40,-20,0,50,100,200)) +
geom_vline(xintercept = 0, linetype = "dotted") +
theme_classic() + facet_wrap(~factor(member2), nrow = 40, ncol = 20, scales = "fixed")
#
setwd(WD)
ggsave(plot = panel, filename = "ensemble_members_∆SRphyto_zonal.jpg", dpi = 300, width = 30, height = 10)
panel <- ggplot() + geom_path(aes(y = y, x = perc_zoo), data = na.omit(glob_zonal), colour = "black", linetype = "solid") +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","0°N","30°N","60°N","90°N"), position = "right") +
scale_x_continuous(name = "", limits = c(-45,170), breaks = c(-45,-20,0,25,50,100,170)) +
geom_vline(xintercept = 0, linetype = "dotted") +
theme_classic() + facet_wrap(~factor(member2), nrow = 40, ncol = 20, scales = "fixed")
#
ggsave(plot = panel, filename = "ensemble_members_∆SRzoo_zonal.jpg", dpi = 300, width = 30, height = 10)
### Examine the members that highly diverge for phyto (13:16, 29:32)
glob_zonal[which(glob_zonal$member2 %in% c(13:16)),"member"]
glob_zonal[which(glob_zonal$member2 %in% c(29:32)),"member"]
glob_zonal[which(glob_zonal$member2 %in% c(77:80)),"member"]
### 26/06/20: Perform non parametric tests of variance to test how signif the changes in SR are, on global and regional scales
### First, need to combine table.base with table.diff --> already exists in "/net/kryo/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections"
setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition_ensemble_rcp85/Individual_projections")
# Example of data.frame to use
files <- dir()[grep("fut_SR_",dir())]
f <- files[1]
res <- mclapply(files, function(f) {
# Useless message
message(paste("Loading results from ",f, sep = ""))
t <- get(load(f))
# Get attributes from filename
terms <- do.call(cbind, strsplit(as.character(f),"_"))
sdm <- terms[5,1]
esm <- terms[4,1]
p <- terms[6,1]
pool <- str_replace(p,".Rdata","")
t$SDM <- sdm
t$ESM <- esm
t$pool <- pool
t <- t[order(t$id),]
# Return
return(t)
}, mc.cores = 30
) # eo mclapply
# Rbind
table <- dplyr::bind_rows(res)
rm(res); gc()
colnames(table)[6] <- "rich_tot"
dim(table) ; head(table)
### First, add an ensemble member IR
table$member <- paste(table$SDM, table$ESM, table$pool, sep = "_")
### Add region like above
table$region <- NA
table[which(table$y <= -60),"region"] <- "SO"
table[which(table$y >= 70),"region"] <- "AO"
table[which(abs(table$y) <= 30),"region"] <- "Tropics"
table[which(abs(table$y) >= 40 & abs(table$y) <= 55),"region"] <- "Temperate"
### Compute medians of base and future SR per region and member
ens.med <- data.frame(table %>% group_by(region,member) %>% summarize(
base_tot = median(rich_tot,na.rm=T),
base_phyto = median(rich_phyto,na.rm=T),
base_zoo = median(rich_zoo,na.rm=T),
fut_tot = median(fut_rich_tot,na.rm=T),
fut_phyto = median(fut_rich_phyto,na.rm=T),
fut_zoo = median(fut_rich_zoo,na.rm=T)
) # eo summarize
) # eo ddf
summary(ens.med)
head(ens.med)
### Perform wilcox test
wilcox.test(na.omit(ens.med[ens.med$region == "AO","base_zoo"]), na.omit(ens.med[ens.med$region == "AO","fut_zoo"]), paired = T)
wilcox.test(na.omit(ens.med[ens.med$region == "SO","base_zoo"]), na.omit(ens.med[ens.med$region == "SO","fut_zoo"]), paired = T)
wilcox.test(na.omit(ens.med[ens.med$region == "Temperate","base_zoo"]), na.omit(ens.med[ens.med$region == "Temperate","fut_zoo"]), paired = T)
wilcox.test(na.omit(ens.med[ens.med$region == "Tropics","base_zoo"]), na.omit(ens.med[ens.med$region == "Tropics","fut_zoo"]), paired = T)
### Same as above but global
glob.med <- data.frame(table %>% group_by(member) %>% summarize(
base_tot = median(rich_tot,na.rm=T),
base_phyto = median(rich_phyto,na.rm=T),
base_zoo = median(rich_zoo,na.rm=T),
fut_tot = median(fut_rich_tot,na.rm=T),
fut_phyto = median(fut_rich_phyto,na.rm=T),
fut_zoo = median(fut_rich_zoo,na.rm=T)
) # eo summarize
) # eo ddf
head(glob.med)
### Perform wilcox test
wilcox.test(na.omit(glob.med[,"base_tot"]), na.omit(glob.med[,"fut_tot"]), paired = T)
wilcox.test(na.omit(glob.med[,"base_phyto"]), na.omit(glob.med[,"fut_phyto"]), paired = T)
wilcox.test(na.omit(glob.med[,"base_zoo"]), na.omit(glob.med[,"fut_zoo"]), paired = T)
### 23/04/2020: For describing results in the manuscript
# summary(ens)
# IQR(ens$rich_phyto, na.rm = T)
# # colnames(ens)
# summary(ens[abs(ens$y) < 30,])
# IQR(ens[abs(ens$y) < 30,"rich_zoo"], na.rm = T)
#
# summary(ens[abs(ens$y) >= 40 & abs(ens$y) <= 55,])
# IQR(ens[abs(ens$y) >= 40 & abs(ens$y) <= 55,"rich_tot"], na.rm = T)
# IQR(ens[abs(ens$y) >= 40 & abs(ens$y) <= 55,"rich_phyto"], na.rm = T)
# IQR(ens[abs(ens$y) >= 40 & abs(ens$y) <= 55,"rich_zoo"], na.rm = T)
#
# # For the Southern Ocean
# summary(ens[ens$y < -60,])
# IQR(ens[ens$y < -60,"rich_zoo"], na.rm = T)
#
# # For the phyto in the arctic
# summary(ens[ens$y > 75,])
# IQR(ens[ens$y > 75,"rich_phyto"], na.rm = T)
### 01/04/2020: Compute ratio of average to sd for signifiance test?
ens$robust_tot <- (ens$rich_tot)/(ens$sd_rich_tot)
ens$robust_phyto <- (ens$rich_phyto)/(ens$sd_rich_phyto)
ens$robust_zoo <- (ens$rich_zoo)/(ens$sd_rich_zoo)
ggplot() + geom_point(aes(x = rich_tot, y = sd_rich_tot), data = ens, colour = "grey50", alpha = 0.5) +
geom_abline(intercept = 1, linetype = "longdash") + geom_vline(xintercept = 0, linetype = "dashed") +
xlab("Mean plankton richness difference (%)") + ylab("Standard deviation") + theme_classic()
#
ggplot() + geom_point(aes(x = rich_tot, y = robust_tot), data = ens, colour = "grey50", alpha = 0.5) +
geom_hline(yintercept = 1, linetype = "dashed") + xlab("Mean phytoplankton richness difference (%)") +
ylab("Ensemble projection robustness\n(ensemble mean:inter-model std)") + theme_classic()
ggplot() + geom_point(aes(x = rich_phyto, y = robust_phyto), data = ens, colour = "grey50", alpha = 0.5) +
geom_hline(yintercept = 1, linetype = "dashed") + xlab("Mean phytoplankton richness difference (%)") +
ylab("Ensemble projection robustness\n(ensemble mean:inter-model std)") + theme_classic()
#
ggplot() + geom_point(aes(x = rich_zoo, y = robust_zoo), data = ens, colour = "grey50", alpha = 0.5) +
geom_hline(yintercept = 1, linetype = "dashed") + xlab("Mean zooplankton richness difference (%)") +
ylab("Ensemble projection robustness\n(ensemble mean:inter-model std)") + theme_classic()
### Make maps !
### Compute global zonal averages across all 80 ensemble members for zoo and phyto responses and plot panel
ens <- data.frame(table.diff %>% group_by(id) %>% summarize(
x = unique(x), y = unique(y),
rich_tot = mean(perc_rich_tot,na.rm=T),
rich_phyto = mean(perc_rich_phyto,na.rm=T),
rich_zoo = mean(perc_rich_zoo,na.rm=T)
) # eo summarize
) # eo ddf
summary(ens)
min <- floor(min(ens[,c("rich_tot","rich_phyto","rich_zoo")], na.rm = T) ) ; min
map1 <- ggplot() + geom_tile(aes(x = x, y = y, fill = rich_tot), data = ens[ens$rich_tot < 50,]) +
geom_tile(aes(x = x, y = y), data = ens[ens$rich_tot >= 50,], fill = "#b2182b") +
#geom_point(aes(x = x, y = y), data = ens[ens$robust_tot > 1,], colour = "grey30", alpha = 0.1, size = 0.05, shape = 4) +
#geom_contour(colour = "grey25", breaks = 1, size = 0.25, aes(x = x, y = y, z = robust_tot), data = ens) +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = rich_tot), data = ens[ens$rich_tot < 50,] ) +
scale_fill_gradient2(name = "Richness\ndifference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("GM","60°E","120°E","180°","120°W","60°W","GM"), expand = c(0,0)) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","Eq","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed"), legend.position = "none")
map2 <- ggplot() + geom_tile(aes(x = x, y = y, fill = rich_phyto), data = ens[ens$rich_phyto < 50,]) +
geom_tile(aes(x = x, y = y), data = ens[ens$rich_phyto >= 50,], fill = "#b2182b") +
#geom_point(aes(x = x, y = y), data = ens[ens$robust_phyto > 1,], colour = "grey30", alpha = 0.1, size = 0.05, shape = 4) +
#geom_contour(colour = "grey25", breaks = 1, size = 0.25, aes(x = x, y = y, z = robust_phyto), data = ens ) +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = rich_phyto), data = ens[ens$rich_phyto < 50,] ) +
scale_fill_gradient2(name = "Richness\ndifference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("GM","60°E","120°E","180°","120°W","60°W","GM"), expand = c(0,0)) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","Eq","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed"), legend.position = "none")
map3 <- ggplot() + geom_tile(aes(x = x, y = y, fill = rich_zoo), data = ens[ens$rich_zoo < 50,]) +
geom_tile(aes(x = x, y = y), data = ens[ens$rich_zoo >= 50,], fill = "#b2182b") +
#geom_point(aes(x = x, y = y), data = ens[ens$robust_zoo > 1,], colour = "grey30", alpha = 0.1, size = 0.05, shape = 4) +
#geom_contour(colour = "grey25", breaks = 1, size = 0.25, aes(x = x, y = y, z = robust_zoo), data = ens) +
geom_contour(colour = "grey60", binwidth = 25, size = 0.25, aes(x = x, y = y, z = rich_zoo), data = ens[ens$rich_zoo < 50,] ) +
scale_fill_gradient2(name = "Richness\ndifference (%)", low = "#3288bd", high = "#d53e4f", mid = "white", limits = c(min,50)) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("GM","60°E","120°E","180°","120°W","60°W","GM"), expand = c(0,0)) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","Eq","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed"), legend.position = "none")
setwd(WD)
ggsave(plot = map1, filename = "map_ensemble_annual_perc_rich_tot_robust.ps", dpi = 300, width = 7, height = 5)
ggsave(plot = map2, filename = "map_ensemble_annual_perc_rich_phyto_robust.ps", dpi = 300, width = 7, height = 5)
ggsave(plot = map3, filename = "map_ensemble_annual_perc_rich_zoo_robust.ps", dpi = 300, width = 7, height = 5)
grDevices::cairo_ps(filename = "map_ensemble_annual_perc_rich_tot_robust.ps", width = 7, height = 5, fallback_resolution = 300)
print(map1)
dev.off()
grDevices::cairo_ps(filename = "map_ensemble_annual_perc_rich_phyto_robust.ps", width = 7, height = 5, fallback_resolution = 300)
print(map2)
dev.off()
grDevices::cairo_ps(filename = "map_ensemble_annual_perc_rich_zoo_robust.ps", width = 7, height = 5, fallback_resolution = 300)
print(map3)
dev.off()
### Also standard deviation to highlight uncertainties
map1 <- ggplot() + geom_raster(aes(x = x, y = y, fill = sd_rich_tot), data = ens[!is.na(ens$sd_rich_tot),]) +
geom_contour(colour = "#fff7bc", binwidth = 25, size = 0.25, aes(x = x, y = y, z = sd_rich_tot), data = ens[!is.na(ens$sd_rich_tot),]) +
scale_fill_viridis(name = "Uncertainty\n(stdev)", option = "cividis") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map2 <- ggplot() + geom_raster(aes(x = x, y = y, fill = sd_rich_phyto), data = ens[!is.na(ens$sd_rich_phyto),]) +
geom_contour(colour = "#fff7bc", binwidth = 25, size = 0.25, aes(x = x, y = y, z = sd_rich_phyto), data = ens[!is.na(ens$sd_rich_phyto),]) +
scale_fill_viridis(name = "Uncertainty\n(stdev)", option = "cividis") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
map3 <- ggplot() + geom_raster(aes(x = x, y = y, fill = sd_rich_zoo), data = ens[!is.na(ens$sd_rich_zoo),]) +
geom_contour(colour = "#fff7bc", binwidth = 25, size = 0.25, aes(x = x, y = y, z = sd_rich_zoo), data = ens[!is.na(ens$sd_rich_zoo),]) +
scale_fill_viridis(name = "Uncertainty\n(stdev)", option = "cividis") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
setwd(WD)
ggsave(plot = map1, filename = paste("map_ensemble_annual_stdev_rich_tot.jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map2, filename = paste("map_ensemble_annual_stdev_rich_phyto.jpg", sep = ""), dpi = 300, width = 7, height = 5)
ggsave(plot = map3, filename = paste("map_ensemble_annual_stdev_rich_zoo.jpg", sep = ""), dpi = 300, width = 7, height = 5)
### 23/06/2020: And plot zonal patterns by separating ESMs
head(table)
zonal <- data.frame(table.diff %>% group_by(y,ESM) %>%
summarise(perc_tot = mean(perc_rich_tot, na.rm = T),
perc_phyto = mean(perc_rich_phyto, na.rm = T),
perc_zoo = mean(perc_rich_zoo, na.rm = T) )
) # eo ddf
summary(zonal)
unique(zonal$ESM)
plot2 <- ggplot() +
geom_path(aes(y = y, x = perc_phyto), data = na.omit(zonal[zonal$ESM == "MRI-NEMURO",]), colour = "black", linetype = "solid") +
geom_path(aes(y = y, x = perc_phyto), data = na.omit(zonal[zonal$ESM == "IPSL-PISCES",]), colour = "black", linetype = "longdash") +
geom_path(aes(y = y, x = perc_phyto), data = na.omit(zonal[zonal$ESM == "GFDL-TOPAZ",]), colour = "black", linetype = "dashed") +
geom_path(aes(y = y, x = perc_phyto), data = na.omit(zonal[zonal$ESM == "CNRM-PISCES",]), colour = "grey50", linetype = "solid") +
geom_path(aes(y = y, x = perc_phyto), data = na.omit(zonal[zonal$ESM == "CESM-BEC",]), colour = "grey50", linetype = "dashed") +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","Eq","30°N","60°N","90°N"), position = "right") +
scale_x_continuous(name = "", limits = c(-25,95), breaks = c(-25,0,25,50,75,95)) +
geom_vline(xintercept = 0, linetype = "dotted") +
theme_classic()
plot3 <- ggplot() +
geom_path(aes(y = y, x = perc_zoo), data = na.omit(zonal[zonal$ESM == "MRI-NEMURO",]), colour = "black", linetype = "solid") +
geom_path(aes(y = y, x = perc_zoo), data = na.omit(zonal[zonal$ESM == "IPSL-PISCES",]), colour = "black", linetype = "longdash") +
geom_path(aes(y = y, x = perc_zoo), data = na.omit(zonal[zonal$ESM == "GFDL-TOPAZ",]), colour = "black", linetype = "dashed") +
geom_path(aes(y = y, x = perc_zoo), data = na.omit(zonal[zonal$ESM == "CNRM-PISCES",]), colour = "grey50", linetype = "solid") +
geom_path(aes(y = y, x = perc_zoo), data = na.omit(zonal[zonal$ESM == "CESM-BEC",]), colour = "grey50", linetype = "dashed") +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","Eq","30°N","60°N","90°N"), position = "right") +
scale_x_continuous(name = "", limits = c(-25,95), breaks = c(-25,0,25,50,75,95)) +
geom_vline(xintercept = 0, linetype = "dotted") +
theme_classic()
plot1 <- ggplot() +
geom_path(aes(y = y, x = perc_tot), data = na.omit(zonal[zonal$ESM == "MRI-NEMURO",]), colour = "black", linetype = "solid") +
geom_path(aes(y = y, x = perc_tot), data = na.omit(zonal[zonal$ESM == "IPSL-PISCES",]), colour = "black", linetype = "longdash") +
geom_path(aes(y = y, x = perc_tot), data = na.omit(zonal[zonal$ESM == "GFDL-TOPAZ",]), colour = "black", linetype = "dashed") +
geom_path(aes(y = y, x = perc_tot), data = na.omit(zonal[zonal$ESM == "CNRM-PISCES",]), colour = "grey50", linetype = "solid") +
geom_path(aes(y = y, x = perc_tot), data = na.omit(zonal[zonal$ESM == "CESM-BEC",]), colour = "grey50", linetype = "dashed") +
scale_y_continuous(name = "", breaks = c(-90,-60,-30,0,30,60,90), limits = c(-90,90),
labels = c("90°S","60°S","30°S","Eq","30°N","60°N","90°N"), position = "right") +
scale_x_continuous(name = "", limits = c(-25,95), breaks = c(-25,0,25,50,75,95)) +
geom_vline(xintercept = 0, linetype = "dotted") +
theme_classic()
grDevices::cairo_ps(filename = "plot_zonal_ann_plankton_perc_ensemble.ps", width = 3, height = 4, fallback_resolution = 300)
print(plot1)
dev.off()
grDevices::cairo_ps(filename = "plot_zonal_ann_phyto_perc_ensemble.ps", width = 3, height = 4, fallback_resolution = 300)
print(plot2)
dev.off()
grDevices::cairo_ps(filename = "plot_zonal_ann_zoo_perc_ensemble.ps", width = 3, height = 4, fallback_resolution = 300)
print(plot3)
dev.off()
# summary(ens)
# zonal <- data.frame(ens %>%
# group_by(y) %>%
# summarise(mean_plankton = mean(rich_tot, na.rm=T), sd_plankton = sd(rich_tot, na.rm=T),
# mean_phyto = mean(rich_phyto, na.rm=T), sd_phyto = sd(rich_phyto, na.rm=T),
# mean_zoo = mean(rich_zoo, na.rm=T), sd_zoo = sd(rich_zoo, na.rm=T) )
# ) # eo ddf
# summary(zonal)
#
# # Define maxi and mins
# min <- -20
# max <- 70
#
# plot1 <- ggplot() + geom_ribbon(aes(y = y, xmin = mean_plankton - sd_plankton, xmax = mean_plankton + sd_plankton), fill = "grey70", data = na.omit(zonal)) +
# geom_path(aes(y = y, x = mean_plankton), data = na.omit(zonal), colour = "black" ) + scale_x_continuous(limits = c(min,max)) +
# xlab("Mean richness difference (%)") + scale_y_continuous(position = "right", name = "Latitude (°)") +
# geom_vline(xintercept = 0, linetype = "dashed") + theme_classic()
#
# plot2 <- ggplot() + geom_ribbon(aes(y = y, xmin = mean_phyto - sd_phyto, xmax = mean_phyto + sd_phyto), fill = "grey70", data = na.omit(zonal)) +
# geom_path(aes(y = y, x = mean_phyto), data = na.omit(zonal), colour = "black" ) + scale_x_continuous(limits = c(min,max)) +
# xlab("Mean richness difference (%)") + scale_y_continuous(position = "right", name = "Latitude (°)") +
# geom_vline(xintercept = 0, linetype = "dashed") + theme_classic()
# #
# plot3 <- ggplot() + geom_ribbon(aes(y = y, xmin = mean_zoo - sd_zoo, xmax = mean_zoo + sd_zoo), fill = "grey70", data = na.omit(zonal)) +
# geom_path(aes(y = y, x = mean_zoo), data = na.omit(zonal), colour = "black" ) + scale_x_continuous(limits = c(min,max)) +
# xlab("Mean richness difference (%)") + scale_y_continuous(position = "right", name = "Latitude (°)") +
# geom_vline(xintercept = 0, linetype = "dashed") + theme_classic()
#
# #
# ggsave(plot = plot1, filename = "plot_zonal_ann_plankton_perc_ensemble.jpg", dpi = 300, width = 3, height = 4)
# ggsave(plot = plot2, filename = "plot_zonal_ann_phyto_perc_ensemble.jpg", dpi = 300, width = 3, height = 4)
# ggsave(plot = plot3, filename = "plot_zonal_ann_zoo_perc_ensemble.jpg", dpi = 300, width = 3, height = 4)
### 23/06/20: Use ggarrange and save panels
library("ggpubr")
setwd("/net/kryo/work/fabioben/OVERSEE/data")
ggarrange(map1, plot1, map2, plot2, map3, plot3, labels = c("G","H","I","J","K","L"), ncol = 2, nrow = 3, widths = c(2.7,1) )
panel <- ggarrange(map1, plot1, map2, plot2, map3, plot3, labels = c("G","H","I","J","K","L"), ncol = 2, nrow = 3, widths = c(2.7,1) )
ggsave(plot = panel, filename = "panel_ensemble_future_perc_Fig1G-L.eps", dpi = 300, width = 8, height = 8)
grDevices::cairo_ps(filename = "panel_ensemble_future_perc_Fig1G-L.ps", width = 8, height = 8, fallback_resolution = 300)
print(panel)
dev.off()