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Copy pathRSCRIPTBATCH_OVERSEE_climate_change_annual_div_ensemble.R
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RSCRIPTBATCH_OVERSEE_climate_change_annual_div_ensemble.R
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# --------------------------------------------------------------------------------------------------------------------------------
library("raster")
library("sp")
library("dplyr")
library("stringr")
library("reshape2")
library("tidyverse")
library("biomod2")
library("viridis")
library("scales")
library("maps")
# --------------------------------------------------------------------------------------------------------------------------------
firstup <- function(x) {
substr(x,1,1) <- toupper(substr(x,1,1))
x
}
### 1°) Set the working directories, vectors etc.
WD <- getwd()
setwd( paste(WD,"/biology/species_data_v9v3.1/total_background/niche.modelling_future_bis_rcp85/", sep = "") )
zoo.wd <- getwd()
setwd( paste(WD,"/biology/phytoplankton_15_01_19/total_background/species_data/niche.modelling_future_bis_rcp85/", sep = "") )
phyto.wd <- getwd()
setwd(WD)
# Vector of SDMs
SDMs <- c('GAM','RF')#,'GLM','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")
rcp <- "rcp85"
### 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$TSS),]
setwd(WD)
### And for phyto
res <- lapply(pools, function(p) {
message(paste("Loading scores for pool ",p, sep = ""))
setwd( paste(phyto.wd,"/eval_scores_",p,"/", sep = "") )
phyto_spp <- str_replace_all(dir(), "eval_scores_", "")
phyto_spp <- str_replace_all(phyto_spp, ".Rdata", "")
scores <- lapply(phyto_spp, function(sp) {
message(paste("Loading scores for ",sp, sep = ""))
s <- get(load(paste("eval_scores_",sp,".Rdata", sep = "")))
s$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),]
setwd(WD)
### 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[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
# scP
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)]
### 03/04/20: Get the modelled species class and rbind phyto and zooplato in one table (for Appendix S8)
# setwd(paste(WD,"/","biology", sep=""))
# #phyto_spp
# #zoo_spp
# phyto <- data.frame(species = phyto_spp); zoo <- data.frame(species = zoo_spp)
# classifP <- get(load("classif_phytoplankton.Rdata"))
# classifZ <- get(load("classif_zooplankton.Rdata"))
# colnames(classifP) ; colnames(classifZ)
# unique(classifP$species) ; unique(phyto) # OK phyto is clear
# unique(classifZ$species) ; unique(zoo) # OK zoo is clear
# # Add classif columns for phyto
# phyto$genus <- NA
# phyto$family <- NA
# phyto$order <- NA
# phyto$class <- NA
# phyto$phylum <- NA
# phyto$group <- NA
# # Add classif columns for zoo
# zoo$genus <- NA
# zoo$family <- NA
# zoo$order <- NA
# zoo$class <- NA
# zoo$phylum <- NA
# zoo$group <- NA
# #sp <- unique(phyto$species)[3]
# for(sp in unique(phyto$species)) {
# message(paste(sp, sep = ""))
# phyto[phyto$species == sp,"genus"] <- as.character(classifP[classifP$species == sp,"genus"])
# phyto[phyto$species == sp,"family"] <- as.character(classifP[classifP$species == sp,"family"])
# phyto[phyto$species == sp,"order"] <- as.character(classifP[classifP$species == sp,"order"])
# phyto[phyto$species == sp,"class"] <- as.character(classifP[classifP$species == sp,"class"])
# phyto[phyto$species == sp,"phylum"] <- as.character(classifP[classifP$species == sp,"phylum"])
# phyto[phyto$species == sp,"group"] <- as.character(classifP[classifP$species == sp,"group"])
# }
#
# for(sp in unique(zoo$species)) {
# message(paste(sp, sep = ""))
# zoo[zoo$species == sp,"genus"] <- as.character(classifZ[classifZ$species == sp,"genus"])
# zoo[zoo$species == sp,"family"] <- as.character(classifZ[classifZ$species == sp,"family"])
# zoo[zoo$species == sp,"order"] <- as.character(classifZ[classifZ$species == sp,"order"])
# zoo[zoo$species == sp,"class"] <- as.character(classifZ[classifZ$species == sp,"class"])
# zoo[zoo$species == sp,"phylum"] <- as.character(classifZ[classifZ$species == sp,"phylum"])
# zoo[zoo$species == sp,"group"] <- as.character(classifZ[classifZ$species == sp,"group"])
# }
#
# ### Add trophic level
# phyto$level <- "Phytoplankton"
# zoo$level <- "Zooplankton"
#
# # Rbind
# table <- rbind(phyto, zoo)
# # Replace "_" in the species names
# table$species <- str_replace_all(as.character(table$species),"_"," ")
# write.table(table, file = "table_appendixS8.txt", sep = ";")
### 3°) For each kingdom and for each time periods, extratc species monthly HSI, average to obtain mean annual composition and save
# zoo_divs_baseline <- lapply(pools, function(p) {
#
# message(paste(" ", sep = ""))
# message(paste("Computing annual Shannon diversity index for pool || ", p, sep = ""))
# message(paste(" ", sep = ""))
# # Extract monthly probabilities
# res <- lapply(months, function(m)
# {
# message(paste(" ", sep = ""))
# message(paste("Retrieving probabilities for ", m, sep = ""))
# message(paste(" ", sep = ""))
# # Load env variables
# setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
# env <- read.table(paste("glob_stack_month_",m,"_21_02_19.txt", sep = ""), h = T, sep = ";")
# env <- env[-which(env$SSS < 20),]
# env <- env[-which(env$Bathy > -175),]
# # Load phyto probas
# message(paste("Loading zoo projections ================================ ", sep = ""))
# setwd(paste(zoo.wd,"/",p,"/", sep = ""))
# require("parallel")
# zoo_spp <- gsub("\\(|\\)", "", zoo_spp)
# probas_zoo <- lapply(X = str_replace_all(zoo_spp, "_", "."), FUN = function(sp) {
#
# # Got to species dir
# setwd(paste(zoo.wd,"/",p,"/",sp,"/", sep = ""))
# message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
#
# # Need to modify sp when there are 2 names and add brackets
# if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# # Then add brackets around the second piece
# sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
# strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
# strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
# } # eo if loop
#
# # If the 4 seasonal projections are done
# if( sum(grepl("proj_projection_", dir())) == 24 )
# {
#
# ### Load projections for each SDM
# setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m, sep = ""),"/", sep = "") )
# d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# # GAM
# resModelGam <- d[,"GAM",,]
# resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
# resModelGam <- (resModelGam/1000)
# # GLM
# resModelGlm <- d[,"GLM",,]
# resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
# resModelGlm <- (resModelGlm/1000)
# # RF
# resModelRF <- d[,"RF",,]
# resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
# resModelRF <- (resModelRF/1000)
# # ANN
# resModelANN <- d[,"ANN",,]
# resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
# resModelANN <- (resModelANN/1000)
#
# # Return
# return( data.frame(cell_id = paste(env$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))]) )
# return( tbl_zoo[,c(1:4,9)] )
# }
#
# ) # eo lapply
# # rbind into one table ? (then average per cell_id for average )
# table.zoo <- dplyr::bind_rows(res)
# rm(res)
# # Compute annual HSI for each species
# comm <- dcast(table.zoo, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T)
# # dim(comm); summary(comm)
# rm(table.zoo)
# # Return zoo.div
# comm$pool <- p
# return(comm)
#
# } # eo fun
#
# ) # eo 1st lapply - pools
# # Rbind
# zoo_div_base <- dplyr::bind_rows(zoo_divs_baseline)
#
# # Melt, average, and dcast?
# m_zoo_div_base <- melt(zoo_div_base, id = c("cell_id","x","y","pool"))
# colnames(m_zoo_div_base)[c(5:6)] <- c("species","HSI")
#
# # Re-dcast
# d_zoo_div_base <- dcast(m_zoo_div_base, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T)
#
# # Save
# setwd(WD)
# write.table(d_zoo_div_base, file = "table_zoo_annual_composition_baseline.txt", sep = "\t")
# rm(m_zoo_div_base,zoo_div_base,zoo_divs_baseline)
# gc()
#
#
# # -------------------------------------------------------
#
# rcp <- "rcp85"
#
# ### Next, same but for future zoopl composition
# zoo_divs_fut <- lapply(pools, function(p) {
#
# message(paste(" ", sep = ""))
# message(paste("Computing annual Shannon diversity index 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 zoo probas
# message(paste("Loading zoo projections ================================ ", sep = ""))
# setwd(paste(zoo.wd,"/",p,"/", sep = ""))
# require("parallel")
# zoo_spp <- gsub("\\(|\\)", "", zoo_spp)
# 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))]) )
# return( tbl_zoo[,c(1:4,9)] )
# }
#
# ) # eo lapply
# # rbind into one table ? (then average per cell_id for average )
# table.zoo <- dplyr::bind_rows(res)
# rm(res)
# # Compute annual HSI for each species
# comm <- dcast(table.zoo, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T)
# # dim(comm); summary(comm)
# rm(table.zoo)
# # Return zoo.div
# comm$pool <- p
# return(comm)
#
# } # eo fun
#
# ) # eo 1st lapply - pools
# # Rbind
# zoo_div_fut <- dplyr::bind_rows(zoo_divs_fut)
#
# # Melt, average, and dcast?
# m_zoo_div_fut <- melt(zoo_div_fut, id = c("cell_id","x","y","pool"))
# colnames(m_zoo_div_fut)[c(5:6)] <- c("species","HSI")
#
# # Re-dcast
# d_zoo_div_fut <- dcast(m_zoo_div_fut, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T)
#
# # Save
# setwd(WD)
# write.table(d_zoo_div_fut, file = "table_zoo_annual_composition_2100-2071_rcp85.txt", sep = "\t")
# rm(m_zoo_div_fut,zoo_div_fut,zoo_divs_fut)
# gc()
#
#
#
# # -------------------------------------------------------
#
# ### And now, phytoplankton baseline composition
# phyto_divs_baseline <- lapply(pools, function(p) {
#
# message(paste(" ", sep = ""))
# message(paste("Computing annual Shannon diversity index for pool || ", p, sep = ""))
# message(paste(" ", sep = ""))
# # Extract monthly probabilities
# res <- lapply(months, function(m)
# {
# message(paste(" ", sep = ""))
# message(paste("Retrieving probabilities for ", m, sep = ""))
# message(paste(" ", sep = ""))
# # Load env variables
# setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
# env <- read.table(paste("glob_stack_month_",m,"_21_02_19.txt", sep = ""), h = T, sep = ";")
# env <- env[-which(env$SSS < 20),]
# env <- env[-which(env$Bathy > -175),]
#
# # Load phyto probas
# message(paste("Loading zoo projections ================================ ", sep = ""))
# setwd(paste(phyto.wd,"/",p,"/", sep = ""))
# require("parallel")
# probas <- lapply(X = str_replace_all(phyto_spp, "_", "."), FUN = function(sp) {
#
# # Got to species dir
# setwd(paste(phyto.wd,"/",p,"/",sp,"/", sep = ""))
# message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
#
# # Need to modify sp when there are 2 names and add brackets
# if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# # Then add brackets around the second piece
# sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
# strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
# strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
# } # eo if loop
#
# # If the 4 seasonal projections are done
# if( sum(grepl("proj_projection_", dir())) == 24 ) {
#
# ### Load projections for each SDM
# setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m, sep = ""),"/", sep = "") )
# d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# # GAM
# resModelGam <- d[,"GAM",,]
# resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
# resModelGam <- (resModelGam/1000)
# # GLM
# resModelGlm <- d[,"GLM",,]
# resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
# resModelGlm <- (resModelGlm/1000)
# # RF
# resModelRF <- d[,"RF",,]
# resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
# resModelRF <- (resModelRF/1000)
# # ANN
# resModelANN <- d[,"ANN",,]
# resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
# resModelANN <- (resModelANN/1000)
#
# # Return
# return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
# GLM = resModelGlm, GAM = resModelGam, RF = resModelRF, ANN = resModelANN ) )
#
# } else {
#
# message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
#
# } # eo if else loop
#
# } ) # eo lapply
# # cbind SDMs' mean HSI
# tbl_phyto <- dplyr::bind_rows(probas)
# rm(probas); gc()
# # Compute average HSI (average across SDMs)
# tbl_phyto$mean_HSI <- rowMeans( as.matrix(tbl_phyto[,c(5:length(tbl_phyto))]) )
# return( tbl_phyto[,c(1:4,9)] )
# }
# ) # eo lapply
# # rbind into one table ? (then average per cell_id for average )
# table.phyto <- dplyr::bind_rows(res)
# rm(res)
# # Compute annual HSI for each species
# comm <- dcast(table.phyto, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T)
# rm(table.phyto)
# # Return zoo.div
# comm$pool <- p
# return(comm)
#
# } # eo fun
#
# ) # eo 1st lapply - pools
# # Rbind
# phyto_div_base <- dplyr::bind_rows(phyto_divs_baseline)
#
# # Melt, average, and dcast?
# m_phyto_div_base <- melt(phyto_div_base, id = c("cell_id","x","y","pool"))
# colnames(m_phyto_div_base)[c(5:6)] <- c("species","HSI")
# # Re-dcast
# d_phyto_div_base <- dcast(m_phyto_div_base, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T)
#
# # Save
# setwd(WD)
# write.table(d_phyto_div_base, file = "table_phyto_annual_composition_baseline.txt", sep = "\t")
# rm(phyto_divs_baseline,m_phyto_div_base,phyto_div_base); gc()
#
#
# # -------------------------------------------------------
#
# phyto_divs_fut <- lapply(pools, function(p) {
#
# message(paste(" ", sep = ""))
# message(paste("Computing annual Shannon diversity index 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")
# probas <- 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 & 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$y, species = gsub("\\.","_",sp),
# GLM = resModelGlm, GAM = resModelGam, RF = resModelRF, ANN = resModelANN ) )
#
# } else {
#
# message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
#
# } # eo if else loop
#
# } ) # eo lapply
# # cbind SDMs' mean HSI
# tbl_phyto <- dplyr::bind_rows(probas)
# rm(probas); gc()
# # Compute average HSI (average across SDMs)
# tbl_phyto$mean_HSI <- rowMeans( as.matrix(tbl_phyto[,c(5:length(tbl_phyto))]) )
# return( tbl_phyto[,c(1:4,9)] )
# }
#
# ) # eo lapply
# # rbind into one table ? (then average per cell_id for average )
# table.phyto <- dplyr::bind_rows(res)
# rm(res)
# # Compute annual HSI for each species
# comm <- dcast(table.phyto, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T)
# rm(table.phyto)
# # Return zoo.div
# comm$pool <- p
# return(comm)
#
# } # eo fun
#
# ) # eo 1st lapply - pools
# # Rbind
# phyto_div_fut <- dplyr::bind_rows(phyto_divs_fut)
#
# # Melt, average, and dcast?
# m_phyto_div_fut <- melt(phyto_div_fut, id = c("cell_id","x","y","pool"))
# colnames(m_phyto_div_fut)[c(5:6)] <- c("species","HSI")
#
# # Re-dcast
# d_phyto_div_fut <- dcast(m_phyto_div_fut, cell_id + x + y ~ species, fun.aggregate = mean, na.rm = T)
#
# # Save
# setwd(WD)
# write.table(d_phyto_div_fut, file = "table_phyto_annual_composition_2100-2071_rcp85.txt", sep = "\t")
# rm(m_phyto_div_fut,phyto_div_fut,phyto_divs_fut)
# gc()
# -------------------------------------------------------
### 05/09/19: Same as above, but extract MONTHLY baseline and future community compositons
# for testing
# m <- "apr"
# p <- "p1"
### 25/09/19: Extract baseline and future species composition (phyto & zoo) for bis projections ! (keep pools information)
months <- c("jan","feb","mar","apr","may","jun","jul","aug","sep","oct","nov","dec")
rcp <- "rcp85"
for(m in months) {
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ", m, sep = ""))
message(paste(" ", sep = ""))
# Load env variables
setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
env <- read.table(paste("glob_stack_month_",m,"_21_02_19.txt", sep = ""), h = T, sep = ";")
env <- env[-which(env$SSS < 20),]
env <- env[-which(env$Bathy > -175),]
env$x2 <- env$x
env[env$x < 0 ,"x2"] <- (env[env$x < 0 ,"x"]) + 360
env$id <- paste(env$x2, env$y, sep = "_")
env <- env[order(env$id),]
zoo_divs_baseline_monthly <- mclapply(pools, function(p) {
message(paste(" ", sep = ""))
message(paste("Retrieving monthly community composition for pool || ", p, sep = ""))
message(paste(" ", sep = ""))
# Load species probas
message(paste("Loading zoo projections ================================ ", sep = ""))
setwd(paste(zoo.wd,"/",p,"/", sep = ""))
require("parallel")
zoo_spp <- gsub("\\(|\\)", "", zoo_spp)
probas_zoo <- lapply(X = str_replace_all(zoo_spp, "_", "."), FUN = function(sp) {
# Got to species dir
setwd(paste(zoo.wd,"/",p,"/",sp,"/", sep = ""))
message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
# Need to modify sp when there are 2 names and add brackets
if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# Then add brackets around the second piece
sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
} # eo if loop
# If the 4 seasonal projections are done
if( sum(grepl("proj_projection_", dir())) == 24 )
{
# Load projections for each SDM
setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m, sep = ""),"/", sep = "") )
d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# GAM
resModelGam <- d[,"GAM",,]
resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
resModelGam <- (resModelGam/1000)
# GLM
#resModelGlm <- d[,"GLM",,]
#resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
#resModelGlm <- (resModelGlm/1000)
# RF
resModelRF <- d[,"RF",,]
resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
resModelRF <- (resModelRF/1000)
#ANN
#resModelANN <- d[,"ANN",,]
#resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
#resModelANN <- (resModelANN/1000)
# Return
return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
GAM = resModelGam, RF = resModelRF) ) #, GLM = resModelGlm, RF = resModelRF, ANN = resModelANN ) )
} else {
message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
} # eo if else loop
} # eo FUN
) # eo lapply
# cbind SDMs' mean HSI
tbl_zoo <- dplyr::bind_rows(probas_zoo)
rm(probas_zoo); gc()
# Compute monthly average HSI (average across SDMs)
tbl_zoo$mean_HSI <- rowMeans( as.matrix(tbl_zoo[,c(5:6)]) )
tbl_zoo$pool <- p
# Return
return( tbl_zoo[,c(1:4,7:8)] )
}, mc.cores = 5
) # eo mclapply
# rbind into one table ? (then average per cell_id for average )
table.zoo <- dplyr::bind_rows(zoo_divs_baseline_monthly)
# Compute species' average HSI across pool and dcast to have species as columns
d_zoo_div_base <- dcast(table.zoo, cell_id + x + y + pool ~ species, fun.aggregate = mean, na.rm = T, value.var = "mean_HSI")
# head(d_zoo_div_base); dim(d_zoo_div_base); summary(d_zoo_div_base)
# And print monthly baseline composition as .txt file
setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition")
write.table(d_zoo_div_base, file = paste("table_zoo_monthly_composition_baseline_",m,"_bis.txt", sep = ""), sep = "\t")
rm(d_zoo_div_base, table.zoo, zoo_divs_baseline_monthly, env)
gc()
setwd(WD)
} # eo for loop - m in months
# Same as above, but for the future conditions
rcp <- "rcp85"
for(m in months) {
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ",m, sep = ""))
message(paste(" ", sep = ""))
# Load env variables
#mm <- firstup(m)
#setwd(paste("/net/kryo/work/fabioben/OVERSEE/data/future/GFDL-ESM2SM/future_monthly_clims/diff/",rcp, sep = ""))
#env <- read.table(paste("clim_2100-2071_rcp85_diff_",mm,"_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
### 25/09/19: for bis projections, you must follow the same coordinates as in the baseline monthly clim
setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
env <- read.table(paste("glob_stack_month_",m,"_21_02_19.txt", sep = ""), h = T, sep = ";")
env <- env[-which(env$SSS < 20),]
env <- env[-which(env$Bathy > -175),]
#
env$x2 <- env$x
env[env$x < 0 ,"x2"] <- (env[env$x < 0 ,"x"]) + 360
env$id <- paste(env$x2, env$y, sep = "_")
#
env <- env[order(env$id),]
# Load probability tables for various pools
zoo_divs_fut_monthly <- mclapply(pools, function(p) {
message(paste(" ", sep = ""))
message(paste("Retrieving monthly future composition for pool || ",p,sep = ""))
message(paste(" ", sep = ""))
setwd(paste(zoo.wd,"/",p,"/", sep = ""))
require("parallel")
zoo_spp <- gsub("\\(|\\)", "", zoo_spp)
probas_zoo <- lapply(X = str_replace_all(zoo_spp, "_", "."), FUN = function(sp) {
# Go to species dir
setwd(paste(zoo.wd,"/",p,"/",sp,"/", sep = ""))
message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
# Need to modify sp when there are 2 names and add brackets
if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# Then add brackets around the second piece
sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
} # eo if loop
# If the 12 monthly projections are done for present & future
if( sum(grepl("proj_projection_", dir())) == 24 ) {
# Load projections for each SDM
setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m,"_2100_GFDLESM2M", sep = ""), sep = "") )
d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_2100_GFDLESM2M_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# GAM
resModelGam <- d[,"GAM",,]
resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
resModelGam <- (resModelGam/1000)
# GLM
#resModelGlm <- d[,"GLM",,]
#resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
#resModelGlm <- (resModelGlm/1000)
# RF
resModelRF <- d[,"RF",,]
resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
resModelRF <- (resModelRF/1000)
# ANN
#resModelANN <- d[,"ANN",,]
#resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
#resModelANN <- (resModelANN/1000)
# Return
return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
GAM = resModelGam, RF = resModelRF) ) #, GLM = resModelGlm, RF = resModelRF, ANN = resModelANN ) )
} else {
message(paste("Skipping for not projection (yet)", sp, " ================================", sep = ""))
} # eo if else loop
} # eo FUN
) # eo lapply
# rbind results from sdms
tbl_zoo <- dplyr::bind_rows(probas_zoo)
rm(probas_zoo); gc()
# Compute monthly average HSI (average across SDMs)
tbl_zoo$mean_HSI <- rowMeans( as.matrix(tbl_zoo[,c(5:6)]) )
tbl_zoo$pool <- p
return( tbl_zoo[,c(1:4,7,8)] )
}, mc.cores = 5
) # eo mclapply
# rbind into one table ? (then average per cell_id for average )
table.zoo <- dplyr::bind_rows(zoo_divs_fut_monthly)
# Compute species' average HSI across pool and dcast to have species as columns
d_zoo_div_fut <- dcast(table.zoo, cell_id + x + y + pool ~ species, fun.aggregate = mean, na.rm = T, value.var = "mean_HSI")
# dim(d_zoo_div_fut); head(d_zoo_div_fut); summary(d_zoo_div_fut)
# And print monthly baseline composition as .txt file
setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition")
write.table(d_zoo_div_fut, file = paste("table_zoo_monthly_composition_2100-2000","_GFDL-ESM2M_",rcp,"_",m,"_bis.txt", sep = ""), sep = "\t")
rm(d_zoo_div_fut, zoo_divs_fut_monthly, table.zoo, env)
gc()
setwd(WD)
} # eo for loop - m in months
# -------------------------------------------------------
# m <- "apr"
# And for phytoplankton
for(m in months) {
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ", m, sep = ""))
message(paste(" ", sep = ""))
# Load env variables
setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
env <- read.table(paste("glob_stack_month_",m,"_21_02_19.txt", sep = ""), h = T, sep = ";")
env <- env[-which(env$SSS < 20),]
env <- env[-which(env$Bathy > -175),]
env$x2 <- env$x
env[env$x < 0 ,"x2"] <- (env[env$x < 0 ,"x"]) + 360
env$id <- paste(env$x2, env$y, sep = "_")
env <- env[order(env$id),]
phyto_divs_baseline_monthly <- mclapply(pools, function(p) {
message(paste(" ", sep = ""))
message(paste("Retrieving monthly community composition for pool || ", p, sep = ""))
message(paste(" ", sep = ""))
# Load species probas
setwd(paste(phyto.wd,"/",p,"/", sep = ""))
require("parallel")
probas_phyto <- lapply(X = str_replace_all(phyto_spp, "_", "."), FUN = function(sp) {
# Got to species dir
setwd(paste(phyto.wd,"/",p,"/",sp,"/", sep = ""))
message(paste("Loading projections for ", sp, " ================================ ", sep = ""))
# Need to modify sp when there are 2 names and add brackets
if( length(strsplit(sp, ".", fixed = TRUE)[[1]]) == 3) {
# Then add brackets around the second piece
sp <- paste(strsplit(sp, ".", fixed = TRUE)[[1]][1],".(",
strsplit(sp, ".", fixed = TRUE)[[1]][2],").",
strsplit(sp, ".", fixed = TRUE)[[1]][3], sep = "" )
} # eo if loop
# If the 4 seasonal projections are done
if( sum(grepl("proj_projection_", dir())) == 24 ) {
# Load projections for each SDM
setwd( paste(paste("proj_projection_",gsub("\\.","_",sp),"_",m, sep = ""),"/", sep = "") )
d <- get(load( paste("proj_projection_", gsub("\\.","_",sp),"_",m,"_", gsub("\\(|)","",sp), ".RData", sep = "") ))
# GAM
resModelGam <- d[,"GAM",,]
resModelGam <- apply(resModelGam, 1, mean, na.rm = F)
resModelGam <- (resModelGam/1000)
# GLM
# resModelGlm <- d[,"GLM",,]
#resModelGlm <- apply(resModelGlm, 1, mean, na.rm = F)
#resModelGlm <- (resModelGlm/1000)
# RF
resModelRF <- d[,"RF",,]
resModelRF <- apply(resModelRF, 1, mean, na.rm = F)
resModelRF <- (resModelRF/1000)
# ANN
#resModelANN <- d[,"ANN",,]
#resModelANN <- apply(resModelANN, 1, mean, na.rm = F)
#resModelANN <- (resModelANN/1000)
# Return
return( data.frame(cell_id = paste(env$x, env$y, sep = "_"), x = env$x, y = env$y, species = gsub("\\.","_",sp),
GAM = resModelGam, RF = resModelRF) ) #, GLM = resModelGlm, RF = resModelRF, ANN = resModelANN ) )
} else {
message(paste("Skipping for not projection (yet)", sp, " ================================", sep = "") )
} # eo if else loop
} # eo FUN
) # eo lapply
# cbind SDMs' mean HSI
tbl_phyto <- dplyr::bind_rows(probas_phyto)
rm(probas_phyto); gc()
# Compute monthly average HSI (average across SDMs)
tbl_phyto$mean_HSI <- rowMeans( as.matrix(tbl_phyto[,c(5,6)]) )
tbl_phyto$pool <- p
# Return
return( tbl_phyto[,c(1:4,7,8)] )
}, mc.cores = 5
) # eo mclapply
# rbind into one table ? (then average per cell_id for average )
table.phyto <- dplyr::bind_rows(phyto_divs_baseline_monthly)
# Compute species' average HSI across pool and dcast to have species as columns
d_phyto_div_base <- dcast(table.phyto, cell_id + x + y + pool ~ species, fun.aggregate = mean, na.rm = T, value.var = "mean_HSI")
# dim(d_phyto_div_base); head(d_phyto_div_base); summary(d_phyto_div_base)
# And print
setwd("/net/kryo/work/fabioben/OVERSEE/data/tables_composition")
write.table(d_phyto_div_base, file = paste("table_phyto_monthly_composition_baseline_",m,"_bis.txt", sep = ""), sep = "\t")
rm(d_phyto_div_base, table.phyto, phyto_divs_baseline_monthly, env)
gc()
setwd(WD)
} # eo for loop - m in months
### And for future phyto monthly composition
for(m in months) {
message(paste(" ", sep = ""))
message(paste("Retrieving probabilities for ",m, sep = ""))
message(paste(" ", sep = ""))
# Load env variables
#mm <- firstup(m)