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Script#24.1_OVERSEE_climate_change_diversity_change.R
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Script#24.1_OVERSEE_climate_change_diversity_change.R
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##### 21/05/2019 - ETHZ - Fabio Benedetti © UP Group, IBP, ETH Zürich
##### Script for :
# - loading the results of the niche models for both zoo and phyto (similar predictors set)
# - exclude taxa with aveg TSS < 0.3
# - combine probabilities to compute sum of mean HSI
# - from each pool annual div, plot ensemble diversity, for the present and the future !
# - Combine and compute change sin diversity between 2100-2071 and 2000-1971
# - Also examine changes in composition (Bray-Curtis)
### Last update: 22/08/2019
# --------------------------------------------------------------------------------------------------------------------------------
library("raster")
library("sp")
library("dplyr")
library("stringr")
library("reshape2")
library("tidyverse")
library("biomod2")
library("viridis")
library("scales")
library("maps")
world2 <- map_data(map = "world2")
# --------------------------------------------------------------------------------------------------------------------------------
### 1°) Set the working directories, vectors etc.
WD <- getwd()
setwd( paste(WD,"/biology/species_data_v9v3.1/total_background/niche.modelling_future_rcp85/", sep = "") )
zoo.wd <- getwd()
setwd( paste(WD,"/biology/phytoplankton_15_01_19/total_background/species_data/niche.modelling_future_rcp85/", sep = "") )
phyto.wd <- getwd()
setwd(WD)
# Vector of SDMs
SDMs <- c('GLM','GAM','RF','ANN')
# Vector of eval_runs :
eval_runs <- c("RUN1","RUN2","RUN3","RUN4","RUN5","RUN6","RUN7","RUN8","RUN9","RUN10")
# 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")
# p <- "p4"
### 2°) Plot distrbution of models'TSS values for zoo and phyto separately, using boxplots per SDM and facet per pool
res <- lapply(pools, function(p) {
message(paste("Loading scores for pool ",p, sep = ""))
setwd( paste(zoo.wd,"/eval_scores_",p,"/", sep = "") )
zoo_spp <- str_replace_all(dir(), "eval_scores_", "")
zoo_spp <- str_replace_all(zoo_spp, ".Rdata", "")
scores <- lapply(zoo_spp, function(sp) {
message(paste("Loading scores for ",sp, sep = ""))
s <- get(load(paste("eval_scores_",sp,".Rdata", sep = "")))
s$species <- sp
return(s)
}
) # eo lapply
table <- dplyr::bind_rows(scores)
table$pool <- p
rm(scores)
return(table)
}
) # eo lapply
# Rbind
table_scores_zoo <- do.call(rbind,res)
table_scores_zoo$SDM <- factor(t(data.frame(str_split(as.character(rownames(table_scores_zoo)), pattern = "_", n = 2)))[,1])
table_scores_zoo$kingdom <- "Zooplankton"
dim(table_scores_zoo); head(table_scores_zoo)
summary(table_scores_zoo)
rm(res)
table_scores_zoo[is.na(table_scores_zoo$TSS),]
# Plot distribution of TSS
#bplot <- ggplot(na.omit(table_scores_zoo), aes(x = factor(SDM), y = TSS, fill = factor((SDM)))) +
#geom_boxplot(colour = "black") + scale_fill_brewer(name = "", palette = "YlGnBu") + theme_bw() + xlab("") +
#scale_y_continuous(limits = c(0,1)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
#facet_grid(~ factor(pool) )
setwd(WD)
#ggsave(plot = bplot, filename = "boxplot_scores_zoo_21_05_19.jpg", dpi = 300, height = 6, width = 10)
### And for phyto
res <- lapply(pools, function(p) {
message(paste("Loading scores for pool ",p, sep = ""))
setwd( paste(phyto.wd,"/eval_scores_",p,"/", sep = "") )
phyto_spp <- str_replace_all(dir(), "eval_scores_", "")
phyto_spp <- str_replace_all(phyto_spp, ".Rdata", "")
scores <- lapply(phyto_spp, function(sp) {
message(paste("Loading scores for ",sp, sep = ""))
s <- get(load(paste("eval_scores_",sp,".Rdata", sep = "")))
s$species <- sp
return(s)
}
) # eo lapply
table <- do.call(rbind, scores)
table$pool <- p
rm(scores)
return(table)
}
) # eo lapply
# Rbind
table_scores_phyto <- do.call(rbind,res)
table_scores_phyto$SDM <- factor(t(data.frame(str_split(as.character(rownames(table_scores_phyto)), pattern = "_", n = 2)))[,1])
table_scores_phyto$kingdom <- "Phytoplankton"
dim(table_scores_phyto); head(table_scores_phyto)
summary(table_scores_phyto)
rm(res)
table_scores_phyto[is.na(table_scores_phyto$TSS),]
# Plot distrbution of TSS
#bplot <- ggplot(na.omit(table_scores_phyto), aes(x = factor(SDM), y = TSS, fill = factor((SDM)))) +
#geom_boxplot(colour = "black") + scale_fill_brewer(name = "", palette = "YlGnBu") + theme_bw() + xlab("") +
#scale_y_continuous(limits = c(0,1)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
#facet_grid(~ factor(pool) )
setwd(WD)
#ggsave(plot = bplot, filename = "boxplot_scores_phyto_21_05_19.jpg", dpi = 300, height = 6, width = 10)
### Identify the species to use for ensemble diversity
require("dplyr")
scZ <- data.frame(na.omit(table_scores_zoo) %>%
group_by(species) %>%
summarise(avg_TSS = mean(TSS), sd_TSS = sd(TSS))
) # eo ddf
scZ[scZ$avg_TSS <= 0.3,] # none, only probel with ONE RUNE of S.magnus
zoo_spp <- unique(scZ$species)
zoo_spp <- zoo_spp[!(zoo_spp %in% c("Spinocalanus_magnus"))]
# For phytoplankton
scP <- data.frame(table_scores_phyto %>%
group_by(species) %>%
summarise(avg_TSS = mean(TSS), sd_TSS = sd(TSS))
) # eo ddf
phyto_spp <- unique(scP$species)
sp2rm <- scP[scP$avg_TSS <= 0.3,"species"] # generally taxa with presences all over the place
sp2rm <- c(sp2rm,"Actiniscus_pentasterias")
phyto_spp <- phyto_spp[!(phyto_spp %in% sp2rm)]
### 3°) For each kingdom, separately:
# - load each pool's species HSI, compute sum of mean HSI to obtain monthly diversity estimates
# - compute annual diversity estimate for each pool separately
# - exclude grid cells where extrapolation is too frequent
# - compute ensemble projection of diversity (species richness and Shannon H')
### NOTE: will need 2 lapply (one for pool, one for months) and 1 mclapply (for extracting the species' HSI)
zoo_divs <- lapply(pools, function(p) {
message(paste(" ", sep = ""))
message(paste("Computing annual diversity for pool || ", p, sep = ""))
message(paste(" ", sep = ""))
# Extract monthly probabilities
res <- lapply(months, function(m)
{
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ", m, sep = ""))
message(paste(" ", sep = ""))
# Load env variables
setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
env <- read.table(paste("glob_stack_month_",m,"_21_02_19.txt", sep = ""), h = T, sep = ";")
env <- env[-which(env$SSS < 20),]
env <- env[-which(env$Bathy > -175),]
# Load HSI
message(paste("Loading zoo projections ================================ ", sep = ""))
setwd(paste(zoo.wd,"/",p,"/", sep = ""))
require("parallel")
zoo_spp <- gsub("\\(|\\)", "", zoo_spp)
# sp <- "Acartia.Acartia.danae"
probas_zoo <- lapply(X = str_replace_all(zoo_spp, "_", "."), FUN = function(sp) {
# Got to species dir
setwd(paste(zoo.wd,"/",p,"/",sp,"/", sep = ""))
message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
# Need to modify sp when there are 2 names and add brackets
if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# Then add brackets around the second piece
sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
} # eo if loop
# If the 12 monthly projections are done for present & future
if( sum(grepl("proj_projection_", dir())) == 24 ) {
# Load projections for each SDM
setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m, sep = ""),"/", sep = "") )
d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# GAM
resModelGam <- d[,"GAM",,]
resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
resModelGam <- (resModelGam/1000)
# GLM
resModelGlm <- d[,"GLM",,]
resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
resModelGlm <- (resModelGlm/1000)
# RF
resModelRF <- d[,"RF",,]
resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
resModelRF <- (resModelRF/1000)
# ANN
resModelANN <- d[,"ANN",,]
resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
resModelANN <- (resModelANN/1000)
# Return
return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
GLM = resModelGlm, GAM = resModelGam, RF = resModelRF, ANN = resModelANN ) )
} else {
message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
} # eo if else loop
} ) # eo lapply
# cbind SDMs' mean HSI
tbl_zoo <- dplyr::bind_rows(probas_zoo)
rm(probas_zoo); gc()
# Compute average HSI (average across SDMs)
tbl_zoo$mean_HSI <- rowMeans( as.matrix(tbl_zoo[,c(5:length(tbl_zoo))]) )
# Compute richness from sum of HSI
rich_zoo <- data.frame(tbl_zoo %>% group_by(cell_id) %>% summarise(x = unique(x), y = unique(y), rich = sum(mean_HSI)) )
colnames(rich_zoo)[4] <- m
rm(tbl_zoo)
return(rich_zoo)
}
) # eo lapply
for(i in 1:12) { names(res[[i]])[4] <- "div" } # eo for loop
# rbind into one table ? (then average per cell_id for average )
table.zoo <- dplyr::bind_rows(res)
# rm(res)
# Compute annual div
zoo.div <- data.frame(table.zoo %>% group_by(cell_id) %>%
summarise(x = unique(x), y = unique(y), cumul = sum(div, na.rm = T),
mean = mean(div, na.rm = T), NAs = sum(is.na(div)) )
) # eo ddf
rm(table.zoo)
# summary(zoo.div)
# Return zoo.div
zoo.div$pool <- p
return(zoo.div)
} # eo fun
) # eo 1st lapply - pools
# Rbind
zoo_div <- dplyr::bind_rows(zoo_divs)
dim(zoo_div); head(zoo_div)
summary(zoo_div)
unique(zoo_div$pool)
# Considering how long (>1h) it takes to compute every the diversity estimates, you should save the 'zoo_div' object
setwd(zoo.wd)
write.table(zoo_div, file = "table_zoo_annual_div_2000-1971.txt", sep = "\t")
rm(zoo_divs)
## For phyto now
phyto_divs <- lapply(pools, function(p) {
message(paste(" ", sep = ""))
message(paste("Computing annual diversity for pool || ", p, sep = ""))
message(paste(" ", sep = ""))
# Extract monthly probabilities
res <- lapply(months, function(m)
{
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ", m, sep = ""))
message(paste(" ", sep = ""))
# Load env variables
setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
env <- read.table(paste("glob_stack_month_",m,"_21_02_19.txt", sep = ""), h = T, sep = ";")
env <- env[-which(env$SSS < 20),]
env <- env[-which(env$Bathy > -175),]
# Load phyto probas
message(paste("Loading zoo projections ================================ ", sep = ""))
setwd(paste(phyto.wd,"/",p,"/", sep = ""))
require("parallel")
# sp <- "Tripos.vultur"
probas_phyto <- lapply(X = str_replace_all(phyto_spp, "_", "."), FUN = function(sp) {
# Got to species dir
setwd(paste(phyto.wd,"/",p,"/",sp,"/", sep = ""))
message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
# Need to modify sp when there are 2 names and add brackets
if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# Then add brackets around the second piece
sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
} # eo if loop
# If the 12 monthly projections are done, for present and future
if( sum(grepl("proj_projection_", dir())) == 24 ) {
# Load projections for each SDM
setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m, sep = ""),"/", sep = "") )
d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# GAM
resModelGam <- d[,"GAM",,]
resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
resModelGam <- (resModelGam/1000)
# GLM
resModelGlm <- d[,"GLM",,]
resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
resModelGlm <- (resModelGlm/1000)
# RF
resModelRF <- d[,"RF",,]
resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
resModelRF <- (resModelRF/1000)
# ANN
resModelANN <- d[,"ANN",,]
resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
resModelANN <- (resModelANN/1000)
# Return
return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
GLM = resModelGlm, GAM = resModelGam, RF = resModelRF, ANN = resModelANN ) )
} else {
message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
} # eo if else loop
} ) # eo lapply
# cbind SDMs' mean HSI
tbl_phyto <- dplyr::bind_rows(probas_phyto)
rm(probas_phyto); gc()
# Compute average HSI (average across SDMs)
tbl_phyto$mean_HSI <- rowMeans( as.matrix(tbl_phyto[,c(5:length(tbl_phyto))]) )
# Compute richness from sum of HSI
rich_phyto <- data.frame(tbl_phyto %>% group_by(cell_id) %>% summarise(x = unique(x), y = unique(y), rich = sum(mean_HSI)) )
colnames(rich_phyto)[4] <- m
rm(tbl_phyto)
return(rich_phyto)
}
) # eo lapply
for(i in 1:12) { names(res[[i]])[4] <- "div" } # eo for loop
# rbind into one table ? (then average per cell_id for average )
table.phyto <- dplyr::bind_rows(res)
rm(res)
# Compute annual div
phyto.div <- data.frame(table.phyto %>% group_by(cell_id) %>%
summarise(x = unique(x), y = unique(y), cumul = sum(div, na.rm = T),
mean = mean(div, na.rm = T), NAs = sum(is.na(div)) )
)# eo ddf
rm(table.phyto)
# summary(phyto.div)
phyto.div$pool <- p
return(phyto.div)
} # eo fun
) # eo 1st lapply - pools
# Rbind
phyto_div <- dplyr::bind_rows(phyto_divs)
dim(phyto_div); head(phyto_div)
summary(phyto_div)
unique(phyto_div$pool)
setwd(phyto.wd)
write.table(phyto_div, file = "table_phyto_annual_div_2000-1971.txt", sep = "\t")
rm(phyto_divs)
gc()
setwd(WD)
### And now, the same but for future projections based on GFDL-ESM2M
rcp <- "rcp85"
zoo_divs <- lapply(pools, function(p) {
message(paste(" ", sep = ""))
message(paste("Computing annual diversity for pool || ", p, sep = ""))
message(paste(" ", sep = ""))
# Extract monthly probabilities
res <- lapply(months, function(m) {
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ", m, sep = ""))
message(paste(" ", sep = ""))
if(m == "jan") {
mm <- "Jan"
} else if(m == "feb") {
mm <- "Feb"
} else if(m == "mar") {
mm <- "Mar"
} else if(m == "apr") {
mm <- "Apr"
} else if(m == "may") {
mm <- "May"
} else if(m == "jun") {
mm <- "Jun"
} else if(m == "jul") {
mm <- "Jul"
} else if(m == "aug") {
mm <- "Aug"
} else if(m == "sep") {
mm <- "Sep"
} else if(m == "oct") {
mm <- "Oct"
} else if(m == "nov") {
mm <- "Nov"
} else if(m == "dec") {
mm <- "Dec"
} #
# Load env variables
setwd(paste("/net/kryo/work/fabioben/OVERSEE/data/future/GFDL-ESM2SM/future_monthly_clims/diff/",rcp, sep = ""))
env <- read.table(paste("clim_2100-2071_rcp85_diff_",mm,"_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
# Load HSI
message(paste("Loading zoo projections ================================ ", sep = ""))
setwd(paste(zoo.wd,"/",p,"/", sep = ""))
require("parallel")
zoo_spp <- gsub("\\(|\\)", "", zoo_spp)
# sp <- "Acartia.Acartia.danae"
probas_zoo <- lapply(X = str_replace_all(zoo_spp, "_", "."), FUN = function(sp) {
# Got to species dir
setwd(paste(zoo.wd,"/",p,"/",sp,"/", sep = ""))
message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
# Need to modify sp when there are 2 names and add brackets
if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# Then add brackets around the second piece
sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
} # eo if loop
# If the 12 monthly projections are done for present & future
if( sum(grepl("proj_projection_", dir())) == 24 ) {
# Load projections for each SDM
setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m,"_2100_GFDLESM2M", sep = ""), sep = "") )
d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_2100_GFDLESM2M_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# GAM
resModelGam <- d[,"GAM",,]
resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
resModelGam <- (resModelGam/1000)
# GLM
resModelGlm <- d[,"GLM",,]
resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
resModelGlm <- (resModelGlm/1000)
# RF
resModelRF <- d[,"RF",,]
resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
resModelRF <- (resModelRF/1000)
# ANN
resModelANN <- d[,"ANN",,]
resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
resModelANN <- (resModelANN/1000)
# Return
return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
GLM = resModelGlm, GAM = resModelGam, RF = resModelRF, ANN = resModelANN ) )
} else {
message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
} # eo if else loop
} ) # eo lapply
# cbind SDMs' mean HSI
tbl_zoo <- dplyr::bind_rows(probas_zoo)
rm(probas_zoo); gc()
# Compute average HSI (average across SDMs)
tbl_zoo$mean_HSI <- rowMeans( as.matrix(tbl_zoo[,c(5:length(tbl_zoo))]) )
# unique(tbl_zoo$species)
# Compute richness from sum of HSI
rich_zoo <- data.frame(tbl_zoo %>% group_by(cell_id) %>% summarise(x = unique(x), y = unique(y), rich = sum(mean_HSI)) )
colnames(rich_zoo)[4] <- m
rm(tbl_zoo)
return(rich_zoo)
}
) # eo lapply
for(i in 1:12) { names(res[[i]])[4] <- "div" } # eo for loop
# rbind into one table ? (then average per cell_id for average )
table.zoo <- dplyr::bind_rows(res)
# rm(res)
# Compute annual div
zoo.div <- data.frame(table.zoo %>% group_by(cell_id) %>%
summarise(x = unique(x), y = unique(y), cumul = sum(div, na.rm = T),
mean = mean(div, na.rm = T), NAs = sum(is.na(div)) )
) # eo ddf
rm(table.zoo)
# summary(zoo.div)
# Return zoo.div
zoo.div$pool <- p
return(zoo.div)
} # eo fun
) # eo 1st lapply - pools
# Rbind
zoo_div <- dplyr::bind_rows(zoo_divs)
dim(zoo_div); head(zoo_div)
summary(zoo_div)
unique(zoo_div$pool)
# Considering how long (>1h) it takes to compute every the diversity estimates, you should save the 'zoo_div' object
setwd(zoo.wd)
write.table(zoo_div, file = "table_zoo_annual_div_2100-2071.txt", sep = "\t")
rm(zoo_divs)
gc()
## For phyto now
phyto_divs <- lapply(pools, function(p) {
message(paste(" ", sep = ""))
message(paste("Computing annual diversity for pool || ", p, sep = ""))
message(paste(" ", sep = ""))
# Extract monthly probabilities
res <- lapply(months, function(m) {
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ", m, sep = ""))
message(paste(" ", sep = ""))
if(m == "jan") {
mm <- "Jan"
} else if(m == "feb") {
mm <- "Feb"
} else if(m == "mar") {
mm <- "Mar"
} else if(m == "apr") {
mm <- "Apr"
} else if(m == "may") {
mm <- "May"
} else if(m == "jun") {
mm <- "Jun"
} else if(m == "jul") {
mm <- "Jul"
} else if(m == "aug") {
mm <- "Aug"
} else if(m == "sep") {
mm <- "Sep"
} else if(m == "oct") {
mm <- "Oct"
} else if(m == "nov") {
mm <- "Nov"
} else if(m == "dec") {
mm <- "Dec"
} #
# Load env variables
setwd(paste("/net/kryo/work/fabioben/OVERSEE/data/future/GFDL-ESM2SM/future_monthly_clims/diff/",rcp, sep = ""))
env <- read.table(paste("clim_2100-2071_rcp85_diff_",mm,"_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
# Load phyto probas
message(paste("Loading zoo projections ================================ ", sep = ""))
setwd(paste(phyto.wd,"/",p,"/", sep = ""))
require("parallel")
# sp <- "Tripos.vultur"
probas_phyto <- lapply(X = str_replace_all(phyto_spp, "_", "."), FUN = function(sp) {
# Got to species dir
setwd(paste(phyto.wd,"/",p,"/",sp,"/", sep = ""))
message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
# Need to modify sp when there are 2 names and add brackets
if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# Then add brackets around the second piece
sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
} # eo if loop
# If the 12 monthly projections are done, for present and future
if( sum(grepl("proj_projection_", dir())) == 24 ) {
# Load projections for each SDM
setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m,"_2100_GFDLESM2M", sep = ""), sep = "") )
d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_2100_GFDLESM2M_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# GAM
resModelGam <- d[,"GAM",,]
resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
resModelGam <- (resModelGam/1000)
# GLM
resModelGlm <- d[,"GLM",,]
resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
resModelGlm <- (resModelGlm/1000)
# RF
resModelRF <- d[,"RF",,]
resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
resModelRF <- (resModelRF/1000)
# ANN
resModelANN <- d[,"ANN",,]
resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
resModelANN <- (resModelANN/1000)
# Return
return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
GLM = resModelGlm, GAM = resModelGam, RF = resModelRF, ANN = resModelANN ) )
} else {
message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
} # eo if else loop
} ) # eo lapply
# cbind SDMs' mean HSI
tbl_phyto <- dplyr::bind_rows(probas_phyto)
rm(probas_phyto); gc()
# Compute average HSI (average across SDMs)
tbl_phyto$mean_HSI <- rowMeans( as.matrix(tbl_phyto[,c(5:length(tbl_phyto))]) )
# unique(tbl_phyto$species)
# Compute richness from sum of HSI
rich_phyto <- data.frame(tbl_phyto %>% group_by(cell_id) %>% summarise(x = unique(x), y = unique(y), rich = sum(mean_HSI)) )
colnames(rich_phyto)[4] <- m
rm(tbl_phyto)
return(rich_phyto)
}
) # eo lapply
for(i in 1:12) { names(res[[i]])[4] <- "div" } # eo for loop
# rbind into one table ? (then average per cell_id for average )
table.phyto <- dplyr::bind_rows(res)
rm(res)
# Compute annual div
phyto.div <- data.frame(table.phyto %>% group_by(cell_id) %>%
summarise(x = unique(x), y = unique(y), cumul = sum(div, na.rm = T),
mean = mean(div, na.rm = T), NAs = sum(is.na(div)) )
) # eo ddf
rm(table.phyto)
# summary(phyto.div)
phyto.div$pool <- p
return(phyto.div)
} # eo fun
) # eo 1st lapply - pools
# Rbind
phyto_div <- dplyr::bind_rows(phyto_divs)
dim(phyto_div); head(phyto_div)
summary(phyto_div)
unique(phyto_div$pool)
setwd(phyto.wd)
write.table(phyto_div, file = "table_phyto_annual_div_2100-2071.txt", sep = "\t")
rm(phyto_divs)
gc()
# -------------------------------------------------------
### 12/09/2019: Meanwhile, examine preliminary changes in annual plankton diversity
require("maps")
world2 <- map_data(map = "world2")
p <- "p1"
months <- c("jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec")
# Extract zoo annual HSI for the baseline period
res <- lapply(months, function(m) {
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ", m, sep = ""))
message(paste(" ", sep = ""))
# Load env variables
setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
env <- read.table(paste("glob_stack_month_",m,"_21_02_19.txt", sep = ""), h = T, sep = ";")
env <- env[-which(env$SSS < 20),]
env <- env[-which(env$Bathy > -175),]
# Load HSI
message(paste("Loading zoo projections ================================ ", sep = ""))
setwd(paste(zoo.wd,"/",p,"/", sep = ""))
require("parallel")
zoo_spp <- gsub("\\(|\\)", "", zoo_spp)
# sp <- "Acartia.Acartia.danae"
probas_zoo <- mclapply(X = str_replace_all(zoo_spp, "_", "."), FUN = function(sp) {
# Got to species dir
setwd(paste(zoo.wd,"/",p,"/",sp,"/", sep = ""))
message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
# Need to modify sp when there are 2 names and add brackets
if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# Then add brackets around the second piece
sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
} # eo if loop
# If the 12 monthly projections are done for present & future
if( sum(grepl("proj_projection_", dir())) == 24 ) {
# Load projections for each SDM
setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m, sep = ""),"/", sep = "") )
d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# GAM
resModelGam <- d[,"GAM",,]
resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
resModelGam <- (resModelGam/1000)
# GLM
resModelGlm <- d[,"GLM",,]
resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
resModelGlm <- (resModelGlm/1000)
# RF
resModelRF <- d[,"RF",,]
resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
resModelRF <- (resModelRF/1000)
# ANN
resModelANN <- d[,"ANN",,]
resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
resModelANN <- (resModelANN/1000)
# Return
return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
GLM = resModelGlm, GAM = resModelGam, RF = resModelRF, ANN = resModelANN ) )
} else {
message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
} # eo if else loop
}, mc.cores = 25 ) # eo lapply
# cbind SDMs' mean HSI
tbl_zoo <- dplyr::bind_rows(probas_zoo)
rm(probas_zoo); gc()
# Compute average HSI (average across SDMs)
tbl_zoo$mean_HSI <- rowMeans( as.matrix(tbl_zoo[,c(5:length(tbl_zoo))]) )
# Compute richness from sum of HSI
rich_zoo <- data.frame(tbl_zoo %>% group_by(cell_id) %>% summarise(x = unique(x), y = unique(y), rich = sum(mean_HSI)) )
colnames(rich_zoo)[4] <- m
rm(tbl_zoo)
return(rich_zoo)
}
) # eo lapply
for(i in 1:12) { names(res[[i]])[4] <- "div" } # eo for loop
# rbind into one table ? (then average per cell_id for average )
table.zoo <- dplyr::bind_rows(res)
#dim(table.zoo)
#head(table.zoo)
# Compute annual div
zoo.div.baseline <- data.frame(table.zoo %>% group_by(cell_id) %>%
summarise(x = unique(x), y = unique(y), cumul = sum(div, na.rm = T),
mean = mean(div, na.rm = T), NAs = sum(is.na(div)) )
) # eo ddf
summary(zoo.div.baseline)
rm(table.zoo, res)
# Extract zoo annual HSI for the future period period
rcp <- "rcp85"
res <- lapply(months, function(m) {
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ", m, sep = ""))
message(paste(" ", sep = ""))
if(m == "jan") {
mm <- "Jan"
} else if(m == "feb") {
mm <- "Feb"
} else if(m == "mar") {
mm <- "Mar"
} else if(m == "apr") {
mm <- "Apr"
} else if(m == "may") {
mm <- "May"
} else if(m == "jun") {
mm <- "Jun"
} else if(m == "jul") {
mm <- "Jul"
} else if(m == "aug") {
mm <- "Aug"
} else if(m == "sep") {
mm <- "Sep"
} else if(m == "oct") {
mm <- "Oct"
} else if(m == "nov") {
mm <- "Nov"
} else if(m == "dec") {
mm <- "Dec"
} #
# Load env variables
setwd(paste("/net/kryo/work/fabioben/OVERSEE/data/future/GFDL-ESM2SM/future_monthly_clims/diff/",rcp, sep = ""))
env <- read.table(paste("clim_2100-2071_rcp85_diff_",mm,"_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
# Load HSI
message(paste("Loading zoo projections ================================ ", sep = ""))
setwd(paste(zoo.wd,"/",p,"/", sep = ""))
require("parallel")
zoo_spp <- gsub("\\(|\\)", "", zoo_spp)
# sp <- "Acartia.Acartia.danae"
probas_zoo <- mclapply(X = str_replace_all(zoo_spp, "_", "."), FUN = function(sp) {
# Got to species dir
setwd(paste(zoo.wd,"/",p,"/",sp,"/", sep = ""))
message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
# Need to modify sp when there are 2 names and add brackets
if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# Then add brackets around the second piece
sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
} # eo if loop
# If the 12 monthly projections are done for present & future
if( sum(grepl("proj_projection_", dir())) == 24 ) {
# Load projections for each SDM
setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m,"_2100_GFDLESM2M", sep = ""), sep = "") )
d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_2100_GFDLESM2M_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# GAM
resModelGam <- d[,"GAM",,]
resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
resModelGam <- (resModelGam/1000)
# GLM
resModelGlm <- d[,"GLM",,]
resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
resModelGlm <- (resModelGlm/1000)
# RF
resModelRF <- d[,"RF",,]
resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
resModelRF <- (resModelRF/1000)
# ANN
resModelANN <- d[,"ANN",,]
resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
resModelANN <- (resModelANN/1000)
# Return
return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
GLM = resModelGlm, GAM = resModelGam, RF = resModelRF, ANN = resModelANN ) )
} else {
message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
} # eo if else loop
}, mc.cores = 25 ) # eo lapply
# cbind SDMs' mean HSI
tbl_zoo <- dplyr::bind_rows(probas_zoo)
rm(probas_zoo); gc()
# Compute average HSI (average across SDMs)
tbl_zoo$mean_HSI <- rowMeans( as.matrix(tbl_zoo[,c(5:length(tbl_zoo))]) )
# unique(tbl_zoo$species)
# Compute richness from sum of HSI
rich_zoo <- data.frame(tbl_zoo %>% group_by(cell_id) %>% summarise(x = unique(x), y = unique(y), rich = sum(mean_HSI)) )
colnames(rich_zoo)[4] <- m
rm(tbl_zoo)
return(rich_zoo)
} # eo lapply
) # eo lapply
for(i in 1:12) { names(res[[i]])[4] <- "div" } # eo for loop
# rbind into one table ? (then average per cell_id for average )
table.zoo <- dplyr::bind_rows(res)
# dim(table.zoo)
# head(table.zoo)
# Compute annual div
zoo.div.fut <- data.frame(table.zoo %>% group_by(cell_id) %>%
summarise(x = unique(x), y = unique(y), cumul = sum(div, na.rm = T),
mean = mean(div, na.rm = T), NAs = sum(is.na(div)) )
) # eo ddf
summary(zoo.div.fut)
rm(table.zoo,res)
### Make maps and compute difference
dim(zoo.div.baseline); dim(zoo.div.fut)
# And flip x coordinates for mapping
zoo.div.baseline$x2 <- zoo.div.baseline$x
zoo.div.baseline[zoo.div.baseline$x < 0 ,"x2"] <- (zoo.div.baseline[zoo.div.baseline$x < 0 ,"x"]) + 360
#
zoo.div.fut$x2 <- zoo.div.fut$x
zoo.div.fut[zoo.div.fut$x < 0 ,"x2"] <- (zoo.div.fut[zoo.div.fut$x < 0 ,"x"]) + 360
map1 <- ggplot() + geom_raster(aes(x = x2, y = y, fill = mean), data = na.omit(zoo.div.baseline)) +
scale_fill_viridis(name = "Zooplankton richness\n(baseline)", discrete = F) +
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 = x2, y = y, fill = mean), data = na.omit(zoo.div.fut)) +
scale_fill_viridis(name = "Zooplankton richness\n(2071-2100)", discrete = F) +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
# Save
setwd(WD)
ggsave(plot = map1, filename = "map_zoo_rich_baseline.jpg", dpi = 300, width = 7, height = 5)
ggsave(plot = map2, filename = "map_zoo_rich_future_rcp85.jpg", dpi = 300, width = 7, height = 5)
### Gut, now calculate differences, but first make sure both ddf are following the same coordinates
summary(zoo.div.baseline)
# x = -179.5 - 179.5
summary(zoo.div.fut)
# x = 0.50 - 359.50
### OK, need to convert the longitudes of obs.clims to re-center them on the Pacific Ocean (== modelled)
zoo.div.baseline$x2 <- zoo.div.baseline$x
zoo.div.baseline[zoo.div.baseline$x < 0 ,"x2"] <- (zoo.div.baseline[zoo.div.baseline$x < 0 ,"x"]) + 360
# zoo.div.baseline$x2 = zoo.div.fut$x ?
# Change ids
zoo.div.baseline$cell_id <- factor(paste(zoo.div.baseline$x2, zoo.div.baseline$y, sep = "_"))
zoo.div.fut$cell_id <- factor(paste(zoo.div.fut$x, zoo.div.fut$y, sep = "_"))
head(zoo.div.baseline$cell_id); head(zoo.div.fut$cell_id)
# Make them
zoo.div.fut <- zoo.div.fut[order(zoo.div.fut$cell_id),]
zoo.div.baseline <- zoo.div.baseline[order(zoo.div.baseline$cell_id),]
head(zoo.div.baseline); head(zoo.div.fut)
# Restrict to the same cells
# The elements of setdiff(x,y) are those elements in x but not in y.
length(setdiff(zoo.div.fut$cell_id, zoo.div.baseline$cell_id)) # 23247 cells that are not in baseline
length(setdiff(zoo.div.baseline$cell_id, zoo.div.fut$cell_id)) # 0 cells not future
zoo.div.fut2 <- zoo.div.fut
zoo.div.baseline2 <- zoo.div.baseline
#
zoo.div.fut2 <- zoo.div.fut2[zoo.div.fut2$cell_id %in% unique(zoo.div.baseline2$cell_id),]
zoo.div.baseline2 <- zoo.div.baseline2[zoo.div.baseline2$cell_id %in% unique(zoo.div.fut2$cell_id),]
zoo.div.fut2 <- zoo.div.fut2[order(zoo.div.fut2$cell_id),]
zoo.div.baseline2 <- zoo.div.baseline2[order(zoo.div.baseline2$cell_id),]
# dim(zoo.div.baseline2); dim(zoo.div.fut2)
# summary(zoo.div.baseline2); summary(zoo.div.fut2)
# zoo.div.baseline2[1:100,]
# zoo.div.fut2[1:100,]
diff <- data.frame(x = zoo.div.baseline2$x2, y = zoo.div.baseline2$y, diff = (zoo.div.fut2$mean)-(zoo.div.baseline2$mean) ) # eo ddf
diff$perc <- (diff$diff)/(zoo.div.baseline2$mean)
diff$perc <- (diff$perc)*100
summary(diff)
# Flip x coordinates
diff$x2 <- diff$x
diff[diff$x < 0 ,"x2"] <- (diff[diff$x < 0 ,"x"]) + 360
map3 <- ggplot() + geom_raster(aes(x = x, y = y, fill = diff), data = diff) +
scale_fill_gradient2(name = "Richness difference\n(2100-2000)", low = "#3288bd", high = "#d53e4f", mid = "white") +
geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
# Save
map4 <- ggplot() + geom_raster(aes(x = x, y = y, fill = perc), data = diff) +
scale_fill_gradient2(name = "Richness difference\n(2100-2000)", low = "#3288bd", high = "#d53e4f", mid = "white") +
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(WD)
ggsave(plot = map3, filename = "map_zoo_rich_diff_rcp85.jpg", dpi = 300, width = 7, height = 5)
ggsave(plot = map4, filename = "map_zoo_rich_diff_perc_rcp85.jpg", dpi = 300, width = 7, height = 5)
### Ok, gut, now the same for phytoplankton ?
# Extract phyto annual HSI for the baseline period
res <- lapply(months, function(m) {
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ", m, sep = ""))
message(paste(" ", sep = ""))
# Load env variables
setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
env <- read.table(paste("glob_stack_month_",m,"_21_02_19.txt", sep = ""), h = T, sep = ";")
env <- env[-which(env$SSS < 20),]
env <- env[-which(env$Bathy > -175),]
# Load HSI
message(paste("Loading zoo projections ================================ ", sep = ""))
setwd(paste(phyto.wd,"/",p,"/", sep = ""))
require("parallel")
probas_phyto <- mclapply(X = str_replace_all(phyto_spp, "_", "."), FUN = function(sp) {