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RSCRIPTBATCH_OVERSEE_climate_change_correcte.R
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RSCRIPTBATCH_OVERSEE_climate_change_correcte.R
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### ================================================================================================================
library("raster")
library("sp")
library("rJava")
library("stringr")
library("reshape2")
library("tidyverse")
library("biomod2")
### ================================================================================================================
### Set the dataset and the background selection strategy
set <- "v9v3.1"
strat <- "total"
### Set the working directories
# For zooplankton species data :
setwd("/net/kryo/work/fabioben/OVERSEE/data/biology/species_data_v9v3.1/total_background/")
WD <- getwd()
# Niche modelling dir
# 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 spp names
files <- dir()[grep(".txt",dir())]
species <- stringr::str_replace_all(string = files, pattern = paste("data_",strat,"_", sep =""), replacement = "")
species <- stringr::str_replace_all(string = species, pattern = ".txt", replacement = "")
# species
### Load the env stacks (winter and summer, like January & August) for projection
setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
apr <- read.table("glob_stack_month_apr_21_02_19.txt", h = T, sep = ";")
jul <- read.table("glob_stack_month_jul_21_02_19.txt", h = T, sep = ";")
oct <- read.table("glob_stack_month_oct_21_02_19.txt", h = T, sep = ";")
jan <- read.table("glob_stack_month_jan_21_02_19.txt", h = T, sep = ";")
colnames(apr)[7] <- "dSST"
colnames(jul)[7] <- "dSST"
colnames(oct)[7] <- "dSST"
colnames(jan)[7] <- "dSST"
apr <- apr[-which(apr$SSS < 20),]
apr <- apr[-which(apr$Bathy > -175),]
jul <- jul[-which(jul$SSS < 20),]
jul <- jul[-which(jul$Bathy > -175),]
oct <- oct[-which(oct$SSS < 20),]
oct <- oct[-which(oct$Bathy > -175),]
jan <- jan[-which(jan$SSS < 20),]
jan <- jan[-which(jan$Bathy > -175),]
### Load the remaining 8 climatologies for annual proj of diversity
annual <- TRUE
# annual <- FALSE
if(annual == TRUE) {
feb <- read.table("glob_stack_month_feb_21_02_19.txt", h = T, sep = ";")
mar <- read.table("glob_stack_month_mar_21_02_19.txt", h = T, sep = ";")
may <- read.table("glob_stack_month_may_21_02_19.txt", h = T, sep = ";")
jun <- read.table("glob_stack_month_jun_21_02_19.txt", h = T, sep = ";")
aug <- read.table("glob_stack_month_aug_21_02_19.txt", h = T, sep = ";")
sep <- read.table("glob_stack_month_sep_21_02_19.txt", h = T, sep = ";")
nov <- read.table("glob_stack_month_nov_21_02_19.txt", h = T, sep = ";")
dec <- read.table("glob_stack_month_dec_21_02_19.txt", h = T, sep = ";")
colnames(feb)[7] <- "dSST"
colnames(mar)[7] <- "dSST"
colnames(may)[7] <- "dSST"
colnames(jun)[7] <- "dSST"
colnames(aug)[7] <- "dSST"
colnames(sep)[7] <- "dSST"
colnames(nov)[7] <- "dSST"
colnames(dec)[7] <- "dSST"
feb <- feb[-which(feb$SSS < 20),]
feb <- feb[-which(feb$Bathy > -175),]
mar <- mar[-which(mar$SSS < 20),]
mar <- mar[-which(mar$Bathy > -175),]
may <- may[-which(may$SSS < 20),]
may <- may[-which(may$Bathy > -175),]
jun <- jun[-which(jun$SSS < 20),]
jun <- jun[-which(jun$Bathy > -175),]
aug <- aug[-which(aug$SSS < 20),]
aug <- aug[-which(aug$Bathy > -175),]
sep <- sep[-which(sep$SSS < 20),]
sep <- sep[-which(sep$Bathy > -175),]
nov <- nov[-which(nov$SSS < 20),]
nov <- nov[-which(nov$Bathy > -175),]
dec <- dec[-which(dec$SSS < 20),]
dec <- dec[-which(dec$Bathy > -175),]
} # eo if loop : annual switch
future <- TRUE
rcp <- "rcp85"
if(future == TRUE) {
setwd(paste("/net/kryo/work/fabioben/OVERSEE/data/future/GFDL-ESM2SM/future_monthly_clims/diff/",rcp, sep = ""))
future.jan <- read.table(paste("clim_2100-2071_",rcp,"_diff_Jan_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.feb <- read.table(paste("clim_2100-2071_",rcp,"_diff_Feb_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.mar <- read.table(paste("clim_2100-2071_",rcp,"_diff_Mar_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.apr <- read.table(paste("clim_2100-2071_",rcp,"_diff_Apr_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.may <- read.table(paste("clim_2100-2071_",rcp,"_diff_May_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.jun <- read.table(paste("clim_2100-2071_",rcp,"_diff_Jun_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.jul <- read.table(paste("clim_2100-2071_",rcp,"_diff_Jul_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.aug <- read.table(paste("clim_2100-2071_",rcp,"_diff_Aug_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.sep <- read.table(paste("clim_2100-2071_",rcp,"_diff_Sep_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.oct <- read.table(paste("clim_2100-2071_",rcp,"_diff_Oct_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.nov <- read.table(paste("clim_2100-2071_",rcp,"_diff_Nov_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
future.dec <- read.table(paste("clim_2100-2071_",rcp,"_diff_Dec_GFDL-ESM2M_24_07_19_v3.txt", sep = ""), h = T, sep = "\t")
setwd(WD)
} # eo if loop : future vs baseline switch
### Set modelling options
myBiomodOption <- BIOMOD_ModelingOptions(
GLM = list( type = 'quadratic',
interaction.level = 0,
myFormula = NULL,
test = 'AIC',
family = binomial("logit"),
mustart = 0.5,
control = glm.control(epsilon = 1e-08, maxit = 50, trace = FALSE)),
GAM = list(algo = 'GAM_mgcv',
type = 's_smoother',
k = 5,
interaction.level = 0,
myFormula = NULL,
family = binomial("logit"),
method = 'GCV.Cp',
optimizer = c('outer','newton'),
select = FALSE,
knots = NULL,
paraPen = NULL,
control = list(nthreads = 1, irls.reg = 0, epsilon = 1e-07
, maxit = 200, trace = FALSE, mgcv.tol = 1e-07, mgcv.half = 15
, rank.tol = 1.49011611938477e-08
, nlm = list(ndigit=7, gradtol=1e-06, stepmax=2, steptol=1e-04, iterlim=200, check.analyticals=0)
, optim = list(factr=1e+07)
, newton = list(conv.tol=1e-06, maxNstep=5, maxSstep=2, maxHalf=30, use.svd=0)
, outerPIsteps = 0, idLinksBases = TRUE, scalePenalty = TRUE, keepData = FALSE) ),
RF = list(do.classif = T,
ntree = 750,
nodesize = 10 )
) # eo modelling options
### Define niche modelling function and projection for mclapply
SpeciesNicheModelling <- function(sp = species) {
# Get the data
setwd(WD)
message(paste("Modelling ",sp, " =========================================================", sep = ""))
data <- read.table(paste("data_",strat,"_",sp,".txt", sep = ""), h = T, sep = ";")
### 03/05/19: watch out for NAs in env predictors like Chl or PAR that might remove presences & absences
data2 <- na.omit( data[,c(vars,"obs")] )
n <- nrow( data2[data2$obs == 1,] )
rm(data2)
# If n >= 85, continue, otherwise got to next species
if(n >= 75) {
### Initialisation: data formatting
myRespName <- str_replace_all(sp, "_", ".")
myRespName <- gsub("\\(|\\)", "", myRespName)
# the presence/absences data for our species
myResp <- as.numeric(data$obs)
# the XY coordinates of species data
myRespXY <- data[,c("x","y")]
# Environmental variables
# colnames(data)[c(21)] <- c("deltaT")
### Add Lon as a predictor
# data$Lon <- (data$x) + 180
myExpl <- data[,vars]
# weights vector
weights <- na.omit(data[,c(vars,"weights")])[,"weights"]
# formatage des données
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = myRespName
)
### Modelling
setwd(second.wd)
myBiomodModelOut <- BIOMOD_Modeling(myBiomodData,
models = SDMs,
models.options = myBiomodOption,
NbRunEval = length(eval_runs),
DataSplit = 80,
Yweights = weights,
VarImport = 0,
models.eval.meth = c('ROC','TSS','KAPPA'),
SaveObj = FALSE,
do.full.models = FALSE
) # eo modelling
### Get evaluation scores
scores <- data.frame(get_evaluations(myBiomodModelOut))
# Use lapply to summarize all scores (10 scores per 6 SDMs) AND PROBABILITY THRESH
scores.ls <- lapply(SDMs, function(sdm) {
# Retrieve all scores for the sdm 's'
tss <- scores["TSS",grep(paste("Testing.data.",sdm, sep = ""), colnames(scores))]
tss_cutoffs <- scores["TSS",grep(paste("Cutoff.",sdm, sep = ""), colnames(scores))]
colnames(tss) <- paste(sdm, eval_runs, sep = "_")
colnames(tss) <- paste(sdm, eval_runs, sep = "_")
tss <- t(tss)
tss_cutoffs <- t(tss_cutoffs)
colnames(tss_cutoffs) <- "Cutoff_TSS"
# Same with AUC
auc <- scores["ROC",grep(paste("Testing.data.",sdm, sep = ""), colnames(scores))]
auc_cutoffs <- scores["ROC",grep(paste("Cutoff.",sdm, sep = ""), colnames(scores))]
colnames(auc) <- paste(sdm, eval_runs, sep = "_")
colnames(auc_cutoffs) <- paste(sdm, eval_runs, sep = "_")
auc <- t(auc)
auc_cutoffs <- t(auc_cutoffs)
colnames(auc_cutoffs) <- "Cutoff_AUC"
# Same with Cohen's KAPPA
kappa <- scores["KAPPA",grep(paste("Testing.data.",sdm, sep = ""), colnames(scores))]
kappa_cutoffs <- scores["KAPPA",grep(paste("Cutoff.",sdm, sep = ""), colnames(scores))]
colnames(kappa) <- paste(sdm, eval_runs, sep = "_")
colnames(kappa_cutoffs) <- paste(sdm, eval_runs, sep = "_")
kappa <- t(kappa)
kappa_cutoffs <- t(kappa_cutoffs)
colnames(kappa_cutoffs) <- "Cutoff_Kappa"
return(cbind(tss, tss_cutoffs, auc, auc_cutoffs, kappa, kappa_cutoffs))
} ) # eo lapply
scores.tbl <- data.frame(do.call(rbind, scores.ls))
# scores.tbl
rm(scores, scores.ls)
### Make projections (they will be printed in the species folder)
message(paste("Projecting ",sp, " =========================================================", sep = ""))
# Project niches in April conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = apr[,vars],
proj.name = paste("projection",sp,"apr", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Project niches in July conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = jul[,vars],
proj.name = paste("projection",sp,"jul", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Project niches in Octpber conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = oct[,vars],
proj.name = paste("projection",sp,"oct", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Project niches in January conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = jan[,vars],
proj.name = paste("projection",sp,"jan", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Create a switch for annual projections (annual = T/F)
if( annual == TRUE ) {
# Feb
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = feb[,vars],
proj.name = paste("projection",sp,"feb", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Mar
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = mar[,vars],
proj.name = paste("projection",sp,"mar", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# May
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = may[,vars],
proj.name = paste("projection",sp,"may", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Jun
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = jun[,vars],
proj.name = paste("projection",sp,"jun", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Aug
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = aug[,vars],
proj.name = paste("projection",sp,"aug", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Sep
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = sep[,vars],
proj.name = paste("projection",sp,"sep", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Nov
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = nov[,vars],
proj.name = paste("projection",sp,"nov", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Dec
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = dec[,vars],
proj.name = paste("projection",sp,"dec", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
} else {
message(paste("============== NOT PERFORMING ANNUAL PROJECTIONS ==============", sep = ""))
} # eo if else loop
if(future == TRUE) {
# Project niches in future April conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.apr[,vars],
proj.name = paste("projection",sp,"apr_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Project niches in future July conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.jul[,vars],
proj.name = paste("projection",sp,"jul_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Project niches in future October conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.oct[,vars],
proj.name = paste("projection",sp,"oct_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Project niches in future January conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.jan[,vars],
proj.name = paste("projection",sp,"jan_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Feb
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.feb[,vars],
proj.name = paste("projection",sp,"feb_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Mar
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.mar[,vars],
proj.name = paste("projection",sp,"mar_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# May
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.may[,vars],
proj.name = paste("projection",sp,"may_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Jun
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.jun[,vars],
proj.name = paste("projection",sp,"jun_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Aug
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.aug[,vars],
proj.name = paste("projection",sp,"aug_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Sep
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.sep[,vars],
proj.name = paste("projection",sp,"sep_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Nov
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.nov[,vars],
proj.name = paste("projection",sp,"nov_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
# Dec
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = future.dec[,vars],
proj.name = paste("projection",sp,"dec_2100_GFDLESM2M", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
} else {
message(paste("============== NOT PERFORMING FUTURE PROJECTIONS ==============", sep = ""))
}
### Save evaluation scores
setwd(paste(WD,"/","niche.modelling_future/eval_scores_",p,"/", sep = ""))
save(scores.tbl, file = paste("eval_scores_",sp,".Rdata", sep = ""))
setwd(WD)
} else {
message(paste("NOT ENOUGH PRESENCES WITH ENV PREDICTORS VALUES || n = ", n, sep = ""))
}
} # eo NicheModelling
# pools <- paste("p",c(1:4),sep = "")
# For testing:
p <- "p2"
# Set pool of variables
if(p == "p1") {
vars <- c("SST","dSST","dO2","logNO3","logChl")
} else if (p == "p2") {
vars <- c("SST","dSST","dO2","logSiO2","logChl")
} else if (p == "p3") {
vars <- c("SST","dSST","dO2","logSiO2","logChl","Nstar")
} else if (p == "p4") {
vars <- c("SST","dSST","dO2","logNO3","logChl","Sistar")
} # eo if else loop
require("parallel")
setwd(paste(WD,"/niche.modelling_future/","eval_scores_",p,"/", sep = ""))
# dir() # just need to replace the dot by an underscore and there you go
donespp <- str_replace_all(dir(), ".Rdata", "")
donespp <- str_replace_all(donespp, "eval_scores_", "")
donespp <- gsub("\\.", "_", donespp) # But also need to add brackets for some annoying species names
# OK Now remove the 'donespp' from 'species'
species2 <- species[!(species %in% donespp)]
# Plus those that present NAs in the summary tables of eval_scores
species2 <- c("Calanus_helgolandicus","Centropages_typicus",species2)
setwd(paste(WD,"/niche.modelling_future/",p,"/", sep = ""))
second.wd <- getwd()
setwd(WD)
mclapply(X = species2, SpeciesNicheModelling, mc.cores = 25)
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