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SHEFS_Species_distribution_model_FS.r
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##########################################################################
# SHEFS Species distribution model
##########################################################################
library(dismo)
library(rgdal)
library(raster)
library(sp)
library(randomForest)
library(kernlab)
library(rJava)
library(doParallel)
library(here)
memory.limit(size=50000000)
#-----------------------------------------------------------------------------
# user section to set directories and chose input data
#-----------------------------------------------------------------------------
# data source
path<-here("Part1/")
func_group<- c('bushmeat','medical','pollinator')
# Input/output directories
predictors_dir <- paste(path,"/Part1_predictors/",func_group[1],"_predictors",sep='')
predictors_LU_dir <- paste(path,"/Part1_predictors/MODIS/",sep='')
occurrence_dir <- paste(path,"/Part1_occurrence/",func_group[1],"/",func_group[1],"_occurrence_in/",sep='')
background_file <- paste(occurrence_dir,func_group[1],"_background/new_",func_group[1],"_background.csv",sep='')
if(!dir.exists(paste(path,"/Part1_occurrence/",func_group[1],"/",func_group[1],"_output/",func_group[1],"_output_SDM/",sep=''))){
dir.create(paste(path,"/Part1_occurrence/",func_group[1],"/",func_group[1],"_output/",func_group[1],"_output_SDM/",sep=''), recursive = T)
}
outdir_SDM <- paste(path,"/Part1_occurrence/",func_group[1],"/",func_group[1],"_output/",func_group[1],"_output_SDM/",sep='')
if(!dir.exists(paste(path,"/Part1_occurrence/",func_group[1],"/",func_group[1],"_output/",func_group[1],"_output_LU/",sep=''))){
dir.create(paste(path,"/Part1_occurrence/",func_group[1],"/",func_group[1],"_output/",func_group[1],"_output_LU/",sep=''), recursive = T)
}
outdir_LU <- paste(path,"/Part1_occurrence/",func_group[1],"/",func_group[1],"_output/",func_group[1],"_output_LU/",sep='')
#-----------------------------------------------------------------------------
# data preparation
#-----------------------------------------------------------------------------
# load predictors and occurrence records
predictors<- list.files(predictors_dir, pattern = 'tif')
predictors_in<-paste(predictors_dir, predictors, sep="/")
predictors_r <- lapply(predictors_in, raster)
predictors <- stack(predictors_r)
print(paste('predictors loaded'))
species <- as.character(list.files(occurrence_dir, pattern = 'csv'))
#for (s in 1:length(species)){}
s=15
occurrence_in <- read.csv(paste(occurrence_dir, species[s], sep=''))
species_name <- substr(species[s],14,nchar(species[s])-4) #this could be more reproducible although need to think of a way
coordinates(occurrence_in) <- ~lon+lat
crs(occurrence_in) <- "+proj=longlat +datum=WGS84 +no_defs"
ext <- extent(occurrence_in)*1.2
print(paste(species_name,' occurrence records loaded'))
# create data frame with presence/background data and predictor values
# occurence training and test set
occurrence_lonlat=cbind.data.frame(occurrence_in$lon,occurrence_in$lat)
colnames(occurrence_lonlat) = c('lon', 'lat')
#removing points which have missing environmental data
envpres <- data.frame(extract(predictors, occurrence_lonlat))
occurrence_lonlat<-occurrence_lonlat[-which(!complete.cases(envpres)),]
envpres<-envpres[complete.cases(envpres),]
set.seed(0)
k<-5
group_pres <- kfold(occurrence_lonlat, k=k)
# pres_train <- occurrence_lonlat[group_pres != 1, ]
# pres_test <- occurrence_lonlat[group_pres == 1, ]
print(paste(species_name,'occurrence training and test set done'))
# Background training and test set (bias correction)
background_in <- read.csv(paste(background_file))
coordinates(background_in) <- ~lon+lat
crs(background_in) <- "+proj=longlat +datum=WGS84 +no_defs"
background_raster <- rasterize(background_in, predictors[[1]], 'X', fun = min)
background_raster_sp<-crop(background_raster, ext) ##added this so all background points are within the same extent as the models
set.seed(0)
backgr <- randomPoints(background_raster_sp, 1000) #removed !is.na as the function automatically excludes NA points and '!is.na' was cancelling it out somehow!
colnames(backgr) = c('lon', 'lat')
set.seed(0)
group_bg <- kfold(backgr, k=k)
# backg_train <- backgr[group_bg != 1, ]
# backg_test <- backgr[group_bg == 1, ]
print(paste(species_name,' background training and test set done'))
# extract predictors training
#train <- rbind(pres_train, backg_train)
#set_frame_train <- c(rep(1, nrow(pres_train)), rep(0, nrow(backg_train)))
envbackg <- data.frame(extract(predictors, backgr))
#envtrainbackg <- data.frame(cbind(pa=set_frame_train, envtrain))
print(paste(species_name,' predictors for presence and background extracted'))
# # extract predictors test
# #test <- rbind(pres_test, backg_test)
# envtestpres <- data.frame( extract(predictors, pres_test) )
# envtestbackg <- data.frame( extract(predictors, backg_test) )
# print(paste(species_name,' test predictors extracted'))
#
envpres_pa<-cbind(1, envpres)
envbackg_pa<-cbind(0, envbackg)
colnames(envpres_pa)[1]<-"pa"
colnames(envbackg_pa)[1]<-"pa"
env_all<-rbind(envpres_pa, envbackg_pa)
#env_all<-rbind(envtrain, envtest)
#-----------------------------------------------------------------------------
# SDM Model ensemble
#-----------------------------------------------------------------------------
# BIOCLIM
print(paste(' '))
print(paste('+++++++++++++++'))
print(paste('+++ Bioclim +++'))
print(paste('+++++++++++++++'))
evl_bc<- list()
cl <- makeCluster(detectCores(), type='PSOCK')
registerDoParallel(cl)
clusterExport(cl,c('bioclim', 'evaluate', 'evl_bc', 'threshold', 'predict'))
eval_bc<-foreach(i = 1:k, .combine = 'c') %dopar% {
pres_train<-occurrence_lonlat[group_pres!=i,]
pres_test<-occurrence_lonlat[group_pres==i,]
backg_test<-backgr[group_bg==i,]
bc <- bioclim(predictors,pres_train)
evl_bc[[i]] <- dismo:::evaluate(pres_test, backg_test, bc,predictors,type="response")
saveRDS(evl_bc, file = paste(outdir_SDM,species_name,"_eval_bc_",i,".ascii",sep=''),ascii=TRUE)
tr_bc <- threshold(evl_bc[[i]], 'spec_sens')
saveRDS(tr_bc, file = paste(outdir_SDM,species_name,"_tr_bc_",i, ".ascii",sep=''),ascii=TRUE)
predict_bioclim <- predict(predictors, bc, ext=ext, progress='')
saveRDS( predict_bioclim, file = paste(outdir_SDM,species_name,"_predict_bioclim_raw_",i,".ascii",sep=''),ascii=TRUE)
predict_bioclim_pa <- predict_bioclim > tr_bc
saveRDS( predict_bioclim_pa, file = paste(outdir_SDM,species_name,"_predict_bioclim_pa_",i,".ascii",sep=''),ascii=TRUE)
print(evl_bc[[i]])
}
stopCluster(cl)
auc_bc <- sapply(eval_bc, function(x){slot(x, "auc")} )
print(auc_bc)
bc_auc<-mean(auc_bc)
# GENERALIZED LINEAR MODEL
print(paste(' '))
print(paste('+++++++++++'))
print(paste('+++ GLM +++'))
print(paste('+++++++++++'))
model_glm1=NULL
vars = names(predictors)
for(i in 1:length(vars)){
xx = combn(vars,i)
if(is.null(dim(xx))){
fla = paste("pa ~", paste(xx, collapse="+"))
model_glm1[[length(model_glm1)+1]]=glm(as.formula(fla),family=binomial(link = "logit"), data=env_all)
} else {
for(j in 1:dim(xx)[2]){
fla = paste("pa ~", paste(xx[1:dim(xx)[1],j], collapse="+"))
model_glm1[[length(model_glm1)+1]]=glm(as.formula(fla),family=binomial(link = "logit"), data=env_all)
}
}
}
AICs = NULL
for(i in 1:length(model_glm1)){
AICs[i] = AIC(model_glm1[[i]])
}
model_glm <- which(AICs==min(AICs))
evl_glm<- list()
cl <- makeCluster((detectCores()-2), type='PSOCK')
registerDoParallel(cl)
clusterExport(cl,c('bioclim', 'evaluate', 'threshold', 'predict'))
eval_glm<-foreach(i = 1:k, .combine = 'c') %dopar% {
pres_train<-envpres_pa[group_pres!=i ,]
pres_test<-envpres_pa[(group_pres==i) ,]
back_test<-envbackg_pa[(group_bg==i),]
back_train<-envbackg_pa[(group_bg!=i),]
envtrain<-rbind(pres_train, back_train)
yy<-names(coef(model_glm1[[model_glm]]))[-1]
fla<-paste("pa ~", paste(yy, collapse="+"))
model_glm_out<-glm(as.formula(fla),family=binomial(link = "logit"), data=envtrain)
evl_glm[[i]] <- evaluate(pres_test, back_test,model= model_glm_out,type="response")
saveRDS(evl_glm[[i]], file = paste(outdir_SDM,species_name,"_eval_glm",i,".ascii",sep=''),ascii=TRUE)
tr_glm <- threshold(evl_glm[[i]], 'spec_sens')
saveRDS(tr_glm, file = paste(outdir_SDM,species_name,"_tr_glm",i,".ascii",sep=''),ascii=TRUE)
predict_glm <- predict(predictors, model_glm1[[model_glm]], ext=ext) #adding in conversion back from logit space
predict_glm <-raster:::calc(predict_glm, fun=function(x){ exp(x)/(1+exp(x))})
saveRDS( predict_glm, file = paste(outdir_SDM,species_name,"_predict_glm_raw",i,".ascii",sep=''),ascii=TRUE)
predict_glm_pa <- predict_glm > tr_glm
saveRDS(predict_glm_pa, file = paste(outdir_SDM,species_name,"_predict_glm_pa",i,".ascii",sep=''),ascii=TRUE)
print(evl_glm[[i]])
}
stopCluster(cl)
auc_gam <- sapply(eval_glm, function(x){slot(x, "auc")} )
print(auc_gam)
gam_auc<-mean(auc_gam)
print(paste('GLM done'))
# MAXENT
print(paste(' '))
print(paste('++++++++++++++'))
print(paste('+++ MAXENT +++'))
print(paste('++++++++++++++'))
jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='')
eval_ma<- list()
for (i in 1:k){
pres_train<-occurrence_lonlat[group_pres!=i ,]
pres_test<-occurrence_lonlat[(group_pres==i) ,]
back_test<-backgr[(group_bg==i),]
back_train<-backgr[(group_bg!=i),]
model_ma <- maxent(predictors, pres_train)#,l1_regularizer=0.7)
saveRDS(model_ma, file = paste(outdir_SDM,species_name,"_model_ma_",i,".ascii",sep=''),ascii=TRUE)
eval_ma[[i]] <- evaluate(pres_test, back_test,model= model_ma,x = predictors)
saveRDS(eval_ma[[i]], file = paste(outdir_SDM,species_name,"_eval_ma",i,".ascii",sep=''),ascii=TRUE)
tr_ma <- threshold(eval_ma[[i]], 'spec_sens')
saveRDS(tr_ma, file = paste(outdir_SDM,species_name,"_tr_ma",i,".ascii",sep=''),ascii=TRUE)
predict_maxent <- predict(model_ma, predictors,ext=ext)
saveRDS(predict_maxent, file = paste(outdir_SDM,species_name,"_predict_maxent_raw_",i,".ascii",sep=''),ascii=TRUE)
plot(predict_maxent)
predict_maxent_pa <- predict_maxent >tr_ma
plot(predict_maxent_pa)
saveRDS(predict_maxent_pa, file = paste(outdir_SDM,species_name,"_predict_maxent_pa",i,".ascii",sep=''),ascii=TRUE)
print(i)
}
auc_ma <- sapply( eval_ma, function(x){slot(x, "auc")} )
print(auc_ma)
ma_auc<-mean(auc_ma)
#
# RANDOM FOREST
print(paste(' '))
print(paste('+++++++++++++++++++++'))
print(paste('+++ Random forest +++'))
print(paste('+++++++++++++++++++++'))
model_rf1 <- paste("pa ~", paste(names(predictors), collapse=" + "))
eval_rf<- list()
for (i in 1:k){
pres_train<-envpres_pa[group_pres!=i ,]
pres_test<-envpres_pa[(group_pres==i) ,]
back_test<-envbackg_pa[(group_bg==i),]
back_train<-envbackg_pa[(group_bg!=i),]
envtrain<-rbind(pres_train, back_train)
model_rf <- randomForest(as.formula(model_rf1), data=envtrain, na.action=na.roughfix)
saveRDS(model_rf, file = paste(outdir_SDM,species_name,"_model_rf.ascii",sep=''),ascii=TRUE)
eval_rf[[i]] <- evaluate(pres_test, back_test,model= model_rf,type="response")
saveRDS(eval_rf[[i]], file = paste(outdir_SDM,species_name,"_eval_rf",i,".ascii",sep=''),ascii=TRUE)
tr_rf <- threshold(eval_rf[[i]], 'spec_sens')
saveRDS(tr_rf, file = paste(outdir_SDM,species_name,"_tr_rf",i,".ascii",sep=''),ascii=TRUE)
predict_rf <- predict(model_rf, as.data.frame(predictors),ext=ext)
saveRDS(predict_rf, file = paste(outdir_SDM,species_name,"_predict_rf_raw_",i,".ascii",sep=''),ascii=TRUE)
plot(predict_rf)
predict_rf_pa <- predict_rf >tr_rf
plot(predict_rf_pa)
saveRDS(predict_rf_pa, file = paste(outdir_SDM,species_name,"_predict_rf_pa",i,".ascii",sep=''),ascii=TRUE)
print(i)
}
auc_rf <- sapply(eval_rf, function(x){slot(x, "auc")} )
print(auc_rf)
rf_auc<-mean(auc_rf)
model_rf <- randomForest(as.formula(model_rf1), data=envtrain, na.action=na.roughfix)
saveRDS(model_rf, file = paste(outdir_SDM,species_name,"_model_rf.ascii",sep=''),ascii=TRUE)
eval_rf <- evaluate(envtestpres, envtestbackg, model_rf)
saveRDS(eval_rf, file = paste(outdir_SDM,species_name,"_eval_rf.ascii",sep=''),ascii=TRUE)
print(eval_rf)
tr_rf <- threshold(eval_rf, 'spec_sens')
saveRDS(tr_rf, file = paste(outdir_SDM,species_name,"_tr_rf.ascii",sep=''),ascii=TRUE)
print(tr_rf)
predict_rf <- predict(predictors, model_rf, ext=ext)
saveRDS(predict_rf, file = paste(outdir_SDM,species_name,"_predict_rf_raw.ascii",sep=''),ascii=TRUE)
plot(predict_rf)
print(predict_rf)
predict_rf_pa <- predict_rf > tr_rf
plot(predict_rf_pa)
saveRDS(predict_rf_pa, file = paste(outdir_SDM,species_name,"_predict_rf_pa.ascii",sep=''),ascii=TRUE)
plot(predict_rf)
points(occurrence_in$lon, occurrence_in$lat)
print(paste('Random forest done'))
# MODEL ENSEMBLE
print(paste(' '))
print(paste('++++++++++++++++++++++'))
print(paste('+++ Model ensemble +++'))
print(paste('++++++++++++++++++++++'))
##need to run the models again on the whole data set - rather than just the training
model_bc_all<-bioclim(predictors, occurrence_lonlat)
yy<-names(coef(model_glm1[[model_glm]]))[-1]
fla<-paste("pa ~", paste(yy, collapse="+"))
model_glm_all<-glm(as.formula(fla),family=binomial(link = "logit"), data=env_all)
model_ma_all <- maxent(predictors, occurrence_lonlat)
model_rf_all <- randomForest(as.formula(model_rf1), data=env_all, na.action=na.roughfix)
predict_bioclim_all <- predict(predictors, model_bc_all,ext=ext, progress='')
predict_glm_all<- predict(predictors, model_glm_all, ext = ext)
predict_glm_all <-raster:::calc(predict_glm_all, fun=function(x){ exp(x)/(1+exp(x))})
predict_maxent_all<-predict(model_ma_all, predictors,ext=ext)
predict_rf_all<-predict(model_rf_all, as.data.frame(predictors),ext=ext)
all_models <- stack(predict_bioclim_all, predict_glm_all, predict_maxent_all, predict_rf_all)
names(all_models) <- c("BIOCLIM", "GLM", "MAXENT","RANDOM FOREST")
auc <- sapply(list(eval_bc, eval_glm, eval_ma, eval_rf), function(x) x@auc)
saveRDS(auc, file = paste(outdir_SDM,species_name,"_all_models_auc.ascii",sep=''),ascii=TRUE)
w <- (auc-0.5)^2
ensemble_raster <- weighted.mean( all_models, w)
plot(ensemble_raster)
saveRDS(ensemble_raster, file = paste(outdir_SDM,species_name,"_ensemble_raster_raw.ascii",sep=''),ascii=TRUE)
print(paste('Ensemble mean done'))
saveRDS(ensemble_raster, file = paste(outdir_SDM,species_name,"_ensemble_eval.ascii",sep=''),ascii=TRUE)
print(paste('Ensemble evaluation done'))
#evaluate ensemble
source("ensemble_evaluate.R")
ens<-ensemble_evaluate(occurrence_lonlat,backgr , ensemble_raster)
tr_ensemble<-threshold(ens, stat="spec_sens")
print(tr_ensemble)
ensemble_raster_pa <- ensemble_raster > tr_ensemble
plot(ensemble_raster_pa)
points(occurrence_in$lon, occurrence_in$lat)
saveRDS(ensemble_raster_pa, file = paste(outdir_SDM,species_name,"_ensemble_raster_pa.ascii",sep=''),ascii=TRUE)
library(maptools)
data(wrld_simpl)
tiff(paste(outdir_SDM,species_name,'_ensemble_pa.tiff',sep=''))
plot(ensemble_raster,legend = FALSE, col = rev(terrain.colors(2)), main=paste('Weighted ensemble mean - ', species_name),xlab="longitue", ylab="latitude")
plot(wrld_simpl, add=TRUE)
legend("bottomleft", legend = c("Absence", "Presence"),box.col = "white", fill = rev(terrain.colors(2)))
box()
points(occurrence_in$lon,occurrence_in$lat)
dev.off()
print(paste(species_name, 'DONE', sep=' '))
#}
####################################################################################
####################################################################################
#
# Land use model
#
####################################################################################
####################################################################################
# load predictor MODIS file
setwd(predictors_LU_dir)
f <- list.files(pattern='tif')
cl <- makeCluster(detectCores(), type='PSOCK')
registerDoParallel(cl)
predictors_LU_in <- lapply(f, raster)
predictors_LU_s <- stack(predictors_LU_in)
predictors_LU <- crop(predictors_LU_s,extent(ensemble_raster))
stopCluster(cl)
print(paste('predictors LU loaded'))
# mask land use with occurrence based on climate
cl <- makeCluster(detectCores(), type='PSOCK')
registerDoParallel(cl)
ensemble_raster[ensemble_raster ==0 ] <- NA
predictor_LU_masked <- mask(predictors_LU, ensemble_raster)
stopCluster(cl)
set.seed(0)
backgr_LU <- randomPoints(!is.na(predictor_LU_masked), 500)
points(backgr_LU)
group <- kfold(backgr_LU, 5)
backg_train <- backgr_LU[group != 1, ]
backg_test <- backgr_LU[group == 1, ]
colnames(backg_train) = c('lon', 'lat')
colnames(backg_test) = c('lon', 'lat')
print(paste('background training and test set LU done'))
# extract predictors training LU
train <- rbind(pres_train, backg_train)
set_frame_train <- c(rep(1, nrow(pres_train)), rep(0, nrow(backg_train)))
envtrain <- extract(predictor_LU_masked, train)
envtrain <- data.frame(cbind(pa=set_frame_train, envtrain))
print(paste('training LU predictors extracted'))
# extract predictors test LU
test <- rbind(pres_test, backg_test)
envtestpres <- data.frame( extract(predictor_LU_masked, pres_test) )
envtestbackg <- data.frame( extract(predictor_LU_masked, backg_test) )
print(paste('test predictors extracted'))
#-----------------------------------------------------------------------------
# LU Model ensemble
#-----------------------------------------------------------------------------
# BIOCLIM
print(paste(' '))
print(paste('+++++++++++++++'))
print(paste('+++ Bioclim +++'))
print(paste('+++++++++++++++'))
model_bc <- bioclim(predictor_LU_masked, pres_train)
saveRDS(model_bc, file = paste(outdir_LU,species_name,"_model_bc_LU.ascii",sep=''),ascii=TRUE)
eval_bc <- evaluate(pres_test, backg_test, model_bc, predictor_LU_masked)
saveRDS(eval_bc, file = paste(outdir_LU,species_name,"_eval_bc_LU.ascii",sep=''),ascii=TRUE)
print(eval_bc)
tr_bc <- threshold(eval_bc, 'spec_sens')
saveRDS(tr_bc, file = paste(outdir_LU,species_name,"_tr_bc_LU.ascii",sep=''),ascii=TRUE)
print(tr_bc)
predict_bioclim <- predict(predictor_LU_masked, model_bc, ext=ext, progress='')
saveRDS( predict_bioclim, file = paste(outdir_LU,species_name,"_predict_bioclim_raw_LU.ascii",sep=''),ascii=TRUE)
print(predict_bioclim)
predict_bioclim <- predict_bioclim > tr_bc
saveRDS( predict_bioclim, file = paste(outdir_LU,species_name,"_predict_bioclim_pa_LU.ascii",sep=''),ascii=TRUE)
plot(predict_bioclim)
points(occurrence_in$lon, occurrence_in$lat)
print(paste('Bioclim done'))
# GENERALIZED LINEAR MODEL
print(paste(' '))
print(paste('+++++++++++'))
print(paste('+++ GLM +++'))
print(paste('+++++++++++'))
cl <- makeCluster(detectCores(), type='PSOCK')
registerDoParallel(cl)
model_glm <- glm(pa ~ MODIS_LCType,
family = binomial(link = "logit"), data=envtrain)
saveRDS( model_glm, file = paste(outdir_LU,species_name,"_model_glm_LU.ascii",sep=''),ascii=TRUE)
eval_glm <- evaluate(envtestpres, envtestbackg, model_glm)
saveRDS( eval_glm, file = paste(outdir_LU,species_name,"_eval_glm_LU.ascii",sep=''),ascii=TRUE)
print(eval_glm)
tr_glm <- threshold(eval_glm, 'spec_sens')
saveRDS( tr_glm, file = paste(outdir_LU,species_name,"_tr_glm_LU.ascii",sep=''),ascii=TRUE)
print(tr_glm)
predict_glm <- predict(predictor_LU_masked, model_glm, ext=ext)
saveRDS( predict_glm, file = paste(outdir_LU,species_name,"_predict_glm_raw_LU.ascii",sep=''),ascii=TRUE)
print(predict_glm)
predict_glm <- predict_glm >tr_glm
stopCluster(cl)
plot(predict_glm)
points(occurrence_in$lon, occurrence_in$lat)
saveRDS( predict_glm, file = paste(outdir_LU,species_name,"_predict_glm_pa_LU.ascii",sep=''),ascii=TRUE)
print(paste('GLM done'))
# MAXENT
print(paste(' '))
print(paste('++++++++++++++'))
print(paste('+++ MAXENT +++'))
print(paste('++++++++++++++'))
jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='')
model_ma <- maxent(predictor_LU_masked, pres_train)#,l1_regularizer=0.7)
saveRDS( model_ma, file = paste(outdir_LU,species_name,"_model_ma_LU.ascii",sep=''),ascii=TRUE)
eval_ma <- evaluate(model_ma, p=pres_test, a=backgr_LU, x=predictor_LU_masked)
saveRDS(eval_ma, file = paste(outdir_LU,species_name,"_eval_ma_LU.ascii",sep=''),ascii=TRUE)
print(eval_ma)
tr_ma <- threshold(eval_ma, 'spec_sens')
saveRDS(tr_ma, file = paste(outdir_LU,species_name,"_tr_ma_LU.ascii",sep=''),ascii=TRUE)
print(tr_ma)
predict_maxent <- predict(model_ma, predictor_LU_masked,ext=ext)
saveRDS(predict_maxent, file = paste(outdir_LU,species_name,"_predict_maxent_raw_LU.ascii",sep=''),ascii=TRUE)
print(predict_maxent)
predict_maxent <- predict_maxent > tr_ma
saveRDS(predict_maxent, file = paste(outdir_LU,species_name,"_predict_maxent_pa_LU.ascii",sep=''),ascii=TRUE)
plot(predict_maxent)
points(occurrence_in$lon, occurrence_in$lat)
print(paste('MAXENT done'))
# RANDOM FOREST
print(paste(' '))
print(paste('+++++++++++++++++++++'))
print(paste('+++ Random forest +++'))
print(paste('+++++++++++++++++++++'))
model_rf1 <- paste("pa ~", paste(names(predictor_LU_masked), collapse=" + "))
model_rf <- randomForest(as.formula(model_rf1), data=envtrain, na.action=na.roughfix)
saveRDS(model_rf, file = paste(outdir_LU,species_name,"_model_rf_LU.ascii",sep=''),ascii=TRUE)
eval_rf <- evaluate(envtestpres, envtestbackg, model_rf)
saveRDS(eval_rf, file = paste(outdir_LU,species_name,"_eval_rf_LU.ascii",sep=''),ascii=TRUE)
print(eval_rf)
tr_rf <- threshold(eval_rf, 'spec_sens')
saveRDS(tr_rf, file = paste(outdir_LU,species_name,"_tr_rf_LU.ascii",sep=''),ascii=TRUE)
print(tr_rf)
predict_rf <- predict(predictor_LU_masked, model_rf, ext=ext)
saveRDS(predict_rf, file = paste(outdir_LU,species_name,"_predict_rf_raw_LU.ascii",sep=''),ascii=TRUE)
print(predict_rf)
predict_rf <- predict_rf > tr_rf
saveRDS(predict_rf, file = paste(outdir_LU,species_name,"_predict_rf_pa_LU.ascii",sep=''),ascii=TRUE)
plot(predict_rf)
points(occurrence_in$lon, occurrence_in$lat)
print(paste('Random forest done'))
# MODEL ENSEMBLE
print(paste(' '))
print(paste('++++++++++++++++++++++'))
print(paste('+++ Model ensemble +++'))
print(paste('++++++++++++++++++++++'))
all_models <- stack(predict_bioclim, predict_glm, predict_maxent, predict_rf)
names(all_models) <- c("BIOCLIM", "GLM", "MAXENT","RANDOM FOREST")
auc <- sapply(list(eval_bc, eval_glm, eval_ma, eval_rf), function(x) x@auc)
saveRDS(auc, file = paste(outdir_LU,species_name,"_all_models_auc_LU.ascii",sep=''),ascii=TRUE)
w <- (auc-0.5)^2
ensemble_raster_LU <- weighted.mean( all_models, w)
saveRDS(ensemble_raster_LU, file = paste(outdir_LU,species_name,"_ensemble_raster_raw_LU.ascii",sep=''),ascii=TRUE)
print(paste('Ensemble mean done'))
#evaluate ensemble
p <- extract(ensemble_raster, occurrence_lonlat)
a <- extract(ensemble_raster, backgr)
p <- stats::na.omit(p)
a <- stats::na.omit(a)
np <- length(p)
na <- length(a)
tr <- p
tr <- sort(unique(round(tr, 8)))
tr <- c(tr - 1e-04, tr[length(tr)] + c(0, 1e-04))
N <- na + np
xc <- new("ModelEvaluation")
xc@presence <- p
xc@absence <- a
R <- sum(rank(c(p, a))[1:np]) - (np * (np + 1) / 2)
xc@auc <- R / (as.numeric(na) * as.numeric(np))
cr <- try(cor.test(c(p, a), c(rep(1, length(p)), rep(0, length(a)))),
silent = TRUE)
if (class(cr) != "try-error") {
xc@cor <- cr$estimate
xc@pcor <- cr$p.value
}
res <- matrix(ncol = 4, nrow = length(tr))
colnames(res) <- c("tp", "fp", "fn", "tn")
xc@t <- tr
for (i in 1:length(tr)) {
res[i, 1] <- length(p[p >= tr[i]])
res[i, 2] <- length(a[a >= tr[i]])
res[i, 3] <- length(p[p < tr[i]])
res[i, 4] <- length(a[a < tr[i]])
}
xc@confusion <- res
a <- res[, 1]
b <- res[, 2]
c <- res[, 3]
d <- res[, 4]
xc@np <- as.integer(np)
xc@na <- as.integer(na)
xc@prevalence <- (a + c) / N
xc@ODP <- (b + d) / N
xc@CCR <- (a + d) / N
xc@TPR <- a / (a + c)
xc@TNR <- d / (b + d)
xc@FPR <- b / (b + d)
xc@FNR <- c / (a + c)
xc@PPP <- a / (a + b)
xc@NPP <- d / (c + d)
xc@MCR <- (b + c) / N
xc@OR <- (a * d) / (c * b)
prA <- (a + d) / N
prY <- (a + b) / N * (a + c) / N
prN <- (c + d) / N * (b + d) / N
prE <- prY + prN
xc@kappa <- (prA - prE) / (1 - prE)
xc
tr_ensemble <- threshold(xc,'spec_sens')
saveRDS(xc, file = paste(outdir_LU,species_name,"_ensemble_eval_LU.ascii",sep=''),ascii=TRUE)
print(paste('Ensemble evaluation LU done'))
ensemble_raster_LU <- ensemble_raster_LU > tr_ensemble
saveRDS(ensemble_raster_LU, file = paste(outdir_LU,species_name,"_ensemble_raster_pa_LU.ascii",sep=''),ascii=TRUE)
tiff(paste(outdir_LU,species_name,'_ensemble_pa_LU.tiff',sep=''))
plot(ensemble_raster_LU,legend = FALSE, col = rev(terrain.colors(2)), main=paste('Weighted ensemble mean SDM-LU - ', species_name),xlab="longitue", ylab="latitude")
plot(wrld_simpl, add=TRUE)
legend("bottomleft", legend = c("Absence", "Presence"),box.col = "white", fill = rev(terrain.colors(2)))
box()
points(occurrence_in$lon,occurrence_in$lat)
dev.off()
print(paste(species_name, 'DONE', sep=' '))
# }