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2000_species_distribution_modelling.R
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options(echo=TRUE)
fit_maxent2 <- function(occ, bg, name, predictors, outdir, template, max_bg_size = 100000,
bg_buffer_width = 1000000, shapefiles = TRUE, features,
replicates, responsecurves = TRUE, rep_args, full_args, random_args,
evaluation, sensitivity = 0.9) {
require(ff)
require(rgeos)
require(sp)
require(raster)
require(rJava)
require(dismo)
require(things)
if (!file.exists(outdir)) stop('outdir does not exist :(', call. = FALSE)
outdir_sp <- file.path(outdir, gsub(' ', '_', name))
if (!file.exists(outdir_sp)) dir.create(outdir_sp)
features <- unlist(strsplit(features, ''))
if (length(setdiff(features, c('l', 'p', 'q', 'h', 't'))) > 1)
stop("features must be a vector of one or more of ',
'l', 'p', 'q', 'h', and 't'.")
b <- aggregate(gBuffer(occ, width = bg_buffer_width, byid = TRUE))
# proj4string(b) <- proj4string(bg)
if (length(occ) < 30) {
warning('Fewer occurrence records than the number of cross-validation ',
'replicates for species ', name,
'. Model not fit for this species.')
return(NULL)
}
# subset bg to the buffer poly (bg = 200 km, bg_poly = IBRA poly)
koppen_occ <- gIntersects(koppen,occ,byid = TRUE)
n <- colSums(koppen_occ)
koppen_subset <- koppen[n > 5,]
koppen_subset_clean <- clgeo_Clean(koppen_subset)
kop_buf <- gIntersection(koppen_subset_clean,b)
bg <- spTransform(bg, crs(kop_buf))
bg_kop_buf <- over(bg, kop_buf) # SLOW
bg_kop_buf_nona <- data.frame(bg[!is.na(bg_kop_buf),])
crp <- raster::crop(template, b)
crp1 <- raster::mask(crp, b)
random <- dismo::randomPoints(crp1, n=nrow(bg_kop_buf_nona))
random_swd <- raster::extract(s, random)
speciesName <- rep(gsub(' ', '_', name), nrow(random_swd))
random_swd1 <- cbind.data.frame(speciesName, random, random_swd)
random_swd_spdf <- SpatialPointsDataFrame(random_swd1[,2:3], random_swd1, proj4string=crs(s.crs))
# Reduce background sample if it's larger than max_bg_size
if (nrow(bg_kop_buf_nona) > max_bg_size) {
message(nrow(bg_kop_buf_nona), ' target species background records, reduced to random ',
max_bg_size, '.')
bg_2 <- bg_kop_buf_nona[sample(nrow(bg_kop_buf_nona), max_bg_size), ]} else {
message(nrow(bg_kop_buf_nona), ' target species background records.')
bg_2 <-bg_kop_buf_nona
}
# Save objects for future reference
if (shapefiles) {
suppressWarnings({
writeOGR(SpatialPolygonsDataFrame(b, data.frame(ID = seq_len(length(b)))),
outdir_sp, 'bg_buffer', 'ESRI Shapefile', overwrite_layer = TRUE)
writeOGR(occ, outdir_sp, 'occ', 'ESRI Shapefile', overwrite_layer = TRUE)
writeOGR(koppen_subset_clean, outdir_sp, 'koppen', 'ESRI Shapefile', overwrite_layer = TRUE)
writeOGR(SpatialPointsDataFrame(random_swd1[,2:3], random_swd1, proj4string=s.crs),
outdir_sp, 'random_bkgr', 'ESRI Shapefile', overwrite_layer = TRUE)
})
}
saveRDS(bg_2, file.path(outdir_sp, 'bg.rds'))
saveRDS(occ, file.path(outdir_sp, 'occ.rds'))
# Sample predictors at occurrence and background points
all.predictors <- names(occ@data[,-c(1:4,20)])
swd_occ <- as.data.frame(occ[,all.predictors])
swd_occ <- swd_occ[,-c(16:17)]
swd_occ <-swd_occ[,predictors]
swd_bg <- as.data.frame(bg_2[,all.predictors])
swd_bg <- swd_bg[,predictors]
saveRDS(swd_occ, file.path(outdir_sp, 'occ_swd.rds'))
saveRDS(swd_bg, file.path(outdir_sp, 'bg_swd.rds'))
swd_bg_spdf <- SpatialPointsDataFrame(bg_2[,2:3], bg_2, proj4string = crs(s.crs))
if (shapefiles) {
writeOGR(occ, outdir_sp, 'occ_swd', 'ESRI Shapefile', overwrite_layer = TRUE)
writeOGR(swd_bg_spdf, outdir_sp, 'bg_swd', 'ESRI Shapefile', overwrite_layer = TRUE)
}
# Combine occ and bg SWD data
swd <- as.data.frame(rbind(swd_occ, swd_bg))
saveRDS(swd, file.path(outdir_sp, 'swd.rds'))
pa <- rep(1:0, c(nrow(swd_occ), nrow(swd_bg)))
random_swd2 <- random_swd[,predictors]
swd1 <- as.data.frame(rbind(swd_occ, random_swd2))
saveRDS(swd1, file.path(outdir_sp, 'swd_random.rds'))
pa1 <- rep(1:0, c(nrow(swd_occ), nrow(random_swd2)))
# Fit model
off <- setdiff(c('l', 'p', 'q', 't', 'h'), features)
if (length(off) > 0) {
off <- c(l = 'linear=FALSE', p = 'product=FALSE', q = 'quadratic=FALSE',
t = 'threshold=FALSE', h = 'hinge=FALSE')[off]
}
off <- unname(off)
if (replicates > 1) {
if (missing(rep_args)) rep_args <- NULL
me_xval <- maxent(swd, pa, path = file.path(outdir_sp, 'xval'),
args = c(paste0('replicates=', replicates),
'responsecurves=TRUE',
off, paste(names(rep_args), rep_args, sep = '=')))
}
if (missing(full_args)) full_args <- NULL
me_full <- maxent(swd, pa, path = file.path(outdir_sp, 'full'),
args = c(off, paste(names(full_args), full_args, sep = '='),
'responsecurves=TRUE'))
if (missing(random_args)) random_args <- NULL
me_random <- maxent(swd1, pa1, path = file.path(outdir_sp, 'rndm_bkgr'),
args = c(off, paste(names(random_args), random_args, sep = '='),
'responsecurves=TRUE'))
if (missing(evaluation)) evaluation <- NULL
eval_mod <- dismo::evaluate(occ, swd_bg_spdf, me_full, predictors = predictors, type = "logistic")
eval_df <- data.frame(threshold = eval_mod@t,
prevalence = eval_mod@prevalence,
TPR = eval_mod@TPR, TNR = eval_mod@TNR, FPR = eval_mod@FPR, FNR = eval_mod@FNR,
PPP = eval_mod@PPP, NPP = eval_mod@NPP, MCR = eval_mod@MCR,
kappa = eval_mod@kappa, TSS = (eval_mod@TPR + eval_mod@TNR) - 1)
th_table <- dismo::threshold(eval_mod, sensitivity = sensitivity)
th_table$species_name <- name
th_table$n.presencen <- eval_mod@np
th_table$n.absencen <- eval_mod@na
th_table$AUC <- eval_mod@auc
th_table$TSS10th <- min(eval_df$TSS[which(abs(eval_df$TPR - 0.9)==min(abs(eval_df$TPR - 0.9)))])
th_table$TSSmax <- max(eval_df$TSS)
th_table$Kappa10th <- min(eval_mod@kappa[which(abs(eval_mod@TPR - 0.9)==min(abs(eval_mod@TPR - 0.9)))])
th_table$KAPPAmax <- max(eval_df$kappa)
colnames(th_table) <- c("th_max.KAPPA", "th_max.spec+sens", "th_no.omission","prevalence",
"th_spec=sens", "th_sensitivity90", "species_name","n.presencen",
"n.absencen","AUC", "TSS10th", "max.TSS", "Kappa10th", "max.KAPPA")
th_table <- th_table[,c(7,8,9,4,10,11,12,13,14,1,2,3,5,6)]
rownames(th_table) <- ""
message("writing evaluation table")
write.csv(th_table, file = paste0(outdir_sp,
"/evaluate_", name, ".csv"), row.names=F)
eval_mod_random <- dismo::evaluate(occ, random_swd_spdf, me_random, predictors = predictors, type = "logistic")
eval_df_random <- data.frame(threshold = eval_mod_random@t,
prevalence = eval_mod_random@prevalence,
TPR = eval_mod_random@TPR, TNR = eval_mod_random@TNR, FPR = eval_mod_random@FPR, FNR = eval_mod_random@FNR,
PPP = eval_mod_random@PPP, NPP = eval_mod_random@NPP, MCR = eval_mod_random@MCR,
kappa = eval_mod_random@kappa, TSS = (eval_mod_random@TPR + eval_mod_random@TNR) - 1)
th_table_random <- dismo::threshold(eval_mod_random, sensitivity = sensitivity)
th_table_random$species_name <- name
th_table_random$n.presencen <- eval_mod_random@np
th_table_random$n.absencen <- eval_mod_random@na
th_table_random$AUC <- eval_mod_random@auc
th_table_random$TSS10th <- min(eval_df_random$TSS[which(abs(eval_df_random$TPR - 0.9)==min(abs(eval_df_random$TPR - 0.9)))])
th_table_random$TSSmax <- max(eval_df_random$TSS)
th_table_random$Kappa10th <- min(eval_mod_random@kappa[which(abs(eval_mod_random@TPR - 0.9)==min(abs(eval_mod_random@TPR - 0.9)))])
th_table_random$KAPPAmax <- max(eval_df_random$kappa)
colnames(th_table_random) <- c("th_max.KAPPA", "th_max.spec+sens", "th_no.omission","prevalence",
"th_spec=sens", "th_sensitivity90", "species_name","n.presencen",
"n.absencen","AUC", "TSS10th", "max.TSS", "Kappa10th", "max.KAPPA")
th_table_random <- th_table_random[,c(7,8,9,4,10,11,12,13,14,1,2,3,5,6)]
rownames(th_table_random) <- ""
message("writing evaluation table")
write.csv(th_table_random, file = paste0(outdir_sp,
"/evaluate_random_", name, ".csv"), row.names=F)
# Save fitted model object, and the model-fitting data.
saveRDS(list(me_xval = me_xval, me_full = me_full, me_random = me_random, swd = swd, pa = pa),
file.path(outdir_sp, 'maxent_fitted.rds'))
return(invisible(NULL))
}
options(bitmapType='cairo')
## Load libraries individually for debugging purposes
library('dismo')
library('sp')
library('maptools')
library('raster')
library('ff')
library('rgdal')
library('rgeos')
library('gdalUtils')
library('rmaxent')
library('readr')
library('dplyr')
library('tidyr')
library('cleangeo')
library('readr')
library('rnaturalearth')
library('rasterVis')
library('RColorBrewer')
library('latticeExtra')
library('data.table')
library('rJava')
## Load libraries
p <- c('ff', 'things', 'raster', 'dismo', 'sp', 'latticeExtra',
'rgdal', 'rgeos', 'gdalUtils', 'rmaxent', 'readr', 'dplyr', 'tidyr','cleangeo',
'readr', 'rnaturalearth', 'rasterVis', 'RColorBrewer', 'latticeExtra', 'data.table')
sapply(p, require, character.only=TRUE)
## Load environmental data and Koppen zones
template <- raster('CHELSA_RASTERS_10km/Current/biol_01_mol.tif')
koppen <- readOGR("Given/Koppen_single_mol.shp")
files_current <- list.files("CHELSA_RASTERS_10km/Current/",full.names = TRUE, pattern = "tif$")
s <- stack(files_current)
s <- dropLayer(s, c(7,8,18,19))
s.crs <- crs(s[[1]])
#only if we want to do the projection for AUS
files_aus <- list.files("CHELSA_RASTERS_1km/Current_cropped_AUS",full.names = TRUE, pattern = "tif$")
s_aus <- stack(files_aus)
s_aus <- dropLayer(s_aus, c(7,8,18,19))
## Load occs, bg, and names of target species
occ <- readRDS("Given/occ_swd_SPDF_single_species_1km_mol_chelsa.rds")
bg1 <- readRDS("Given/bg_swd_df_1rec_per_spp_Chelsa.rds")
spdf.bg <- SpatialPoints(bg1[,c(2:3)], s.crs)
bg <- SpatialPointsDataFrame(spdf.bg, data.frame(bg1))
colnames(bg@data)[1] = "speciesName"
bg <- spTransform(bg, crs(occ[[2]]))
occ_red <- occ[__RANGE__]
## Set parameters
bg_buffer_width <- 1000000
max_bg_size <- 50000
group <- 'plants'
# Load background
names(s) <- c("biol_01",
"biol_02",
"biol_03",
"biol_04",
"biol_05",
"biol_06",
"biol_09",
"biol_10",
"biol_11",
"biol_12",
"biol_13",
"biol_14",
"biol_15",
"biol_16",
"biol_17")
#only if we want to do for AUS
names(s_aus) <- c("biol_01",
"biol_02",
"biol_03",
"biol_04",
"biol_05",
"biol_06",
"biol_09",
"biol_10",
"biol_11",
"biol_12",
"biol_13",
"biol_14",
"biol_15",
"biol_16",
"biol_17")
## Read in predictors
gc()
## Load the function to fit Maxent models
## Fit models
lapply(c('clim1'), function(x) {
outdir <- file.path('__OUTDIR__/',
paste(group,
x, sep = '_'))
if (!dir.exists(outdir)) dir.create(outdir, recursive=TRUE)
clim1 <- c("biol_01","biol_04","biol_05","biol_12","biol_14","biol_15" )
clim2 <- c("biol_01","biol_04","biol_06","biol_12","biol_13","biol_14","biol_15")
preds <- clim1
# switch(x,
# clim1 = clim1,
# clim2 = clim2)
mapply(function(occ, name) {
message('Doing ', name)
name <- gsub('\\*', '', name) # remove asterisks from names
if (!dir.exists(file.path(outdir, gsub(' ', '_', name)))) {
fit_maxent2(occ = occ, bg = bg, predictors = preds, name = name,
outdir = outdir,
template = template, shapefiles = TRUE, features = 'lpq',
replicates = 2) ## Change replicates if necessary
}
}, occ_red, names(occ_red))
})
## project model onto current scenario
### with ff_table
ff_models_FN <- grep(
'full',
list.files('__OUTDIR__/', '\\.lambdas', recursive=TRUE, full=TRUE),
value=TRUE)
## prepare ff table for current
#locs <- which(!is.na(sum(s_aus)[]))
locs <- which(!is.na(sum(s)[]))
locs <- cbind(cell=locs, xyFromCell(s, locs))
cells <- locs[, 'cell']
#If we want AUS
locs_aus <- which(!is.na(sum(s_aus)[]))
locs_aus <- cbind(cell_aus=locs_aus, xyFromCell(s_aus, locs_aus))
cells_aus <- locs_aus[, 'cell_aus']
library(ff)
s_ff <- ff::ff(vmode="double", dim=c(length(cells), nlayers(s)),
filename=ff_swd <- tempfile(fileext='.ff'))
# fill ff_matrix with data
for(i in 1:nlayers(s)) {
s_ff[, i] <- s[[i]][][cells]
}
colnames(s_ff) <- names(s)
s_ff_aus <- ff::ff(vmode="double", dim=c(length(cells_aus), nlayers(s_aus)),
filename=ff_swd_aus <- tempfile(fileext='.ff'))
# fill ff_matrix with data
for(i in 1:nlayers(s_aus)) {
s_ff_aus[, i] <- s_aus[[i]][][cells_aus]
}
colnames(s_ff_aus) <- names(s_aus)
gc()
### apply funciton to write the prediciton map and binary map according to selected threshold
lapply(ff_models_FN, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_current.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- s[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, s_ff[, seq_len(ncol(s_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('full/species\\.lambdas', 'full/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
### apply funciton to write the prediciton map and binary map for Australia
lapply(ff_models_FN, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_current_aus.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- s_aus[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells_aus), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, s_ff_aus[, seq_len(ncol(s_ff_aus))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells_aus] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('full/species\\.lambdas', 'full/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
# projection based on randomly-selected background points
ff_models_random <- grep(
'rndm_bkgr',
list.files('__OUTDIR__//', '\\.lambdas', recursive=TRUE, full=TRUE),
value=TRUE)
lapply(ff_models_random, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_current_random.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- s[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, s_ff[, seq_len(ncol(s_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('rndm_bkgr/species\\.lambdas', 'rndm_bkgr/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
# projection to Australia for model fitted based on randomly-selected background points
lapply(ff_models_random, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_current_aus_random.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- s_aus[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells_aus), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, s_ff_aus[, seq_len(ncol(s_ff_aus))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells_aus] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('rndm_bkgr/species\\.lambdas', 'rndm_bkgr/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
#project onto future scenario
## 2030 RCP 8.5
## prepare ff table for RCP 8.5 ACCESS10 2030 (GCM1)
access_8.5 <- list.files("CHELSA_RASTERS_1km/2030_correct/RCP85/ACCESS10/",full.names = TRUE)
f1 <- stack(access_8.5)
f1 <- dropLayer(f1, c(7,8,18,19))
names(f1) = names(s)
locs1 <- which(!is.na(sum(f1)[]))
locs1 <- cbind(cell=locs1, xyFromCell(f1, locs1))
cells1 <- locs1[, 'cell']
library(ff)
f1_ff <- ff::ff(vmode="double", dim=c(length(cells1), nlayers(f1)),
filename=ff_swd <- tempfile(fileext='.ff'))
# fill ff_matrix with data
for(i in 1:nlayers(f1)) {
f1_ff[, i] <- f1[[i]][][cells1]
}
colnames(f1_ff) <- names(f1)
gc()
### apply funciton to write the prediciton map and binary map according to selected threshold
lapply(ff_models_FN, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_ACCESS_30_8.5.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('full/species\\.lambdas', 'full/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
lapply(ff_models_random, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_ACCESS_30_8.5_random.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('rndm_bkgr/species\\.lambdas', 'rndm_bkgr/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
## 2050 RCP 8.5
## prepare ff table for RCP 8.5 ACCESS10 2050 (GCM1)
access_8.5 <- list.files("CHELSA_RASTERS_1km/2050/RCP85/ACCESS10/",full.names = TRUE)
f1 <- stack(access_8.5)
f1 <- dropLayer(f1, c(7,8,18,19))
names(f1) = names(s)
locs1 <- which(!is.na(sum(f1)[]))
locs1 <- cbind(cell=locs1, xyFromCell(f1, locs1))
cells1 <- locs1[, 'cell']
library(ff)
f1_ff <- ff::ff(vmode="double", dim=c(length(cells1), nlayers(f1)),
filename=ff_swd <- tempfile(fileext='.ff'))
# fill ff_matrix with data
for(i in 1:nlayers(f1)) {
f1_ff[, i] <- f1[[i]][][cells1]
}
colnames(f1_ff) <- names(f1)
gc()
### apply funciton to write the prediciton map and binary map according to selected threshold
lapply(ff_models_FN, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_ACCESS_50_8.5.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('full/species\\.lambdas', 'full/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
lapply(ff_models_random, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_ACCESS_50_8.5_random.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('rndm_bkgr/species\\.lambdas', 'rndm_bkgr/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
########
## prepare ff table for RCP 8.5 ACCESS10 2070 (GCM1)
access_70.8.5 <- list.files("CHELSA_RASTERS_1km/2070/RCP85/ACCESS10/",full.names = TRUE)
f1 <- stack(access_70.8.5)
f1 <- dropLayer(f1, c(7,8,18,19))
names(f1) = names(s)
locs1 <- which(!is.na(sum(f1)[]))
locs1 <- cbind(cell=locs1, xyFromCell(f1, locs1))
cells1 <- locs1[, 'cell']
library(ff)
f1_ff <- ff::ff(vmode="double", dim=c(length(cells1), nlayers(f1)),
filename=ff_swd <- tempfile(fileext='.ff'))
#fill ff_matrix with data
for(i in 1:nlayers(f1)) {
f1_ff[, i] <- f1[[i]][][cells1]
}
colnames(f1_ff) <- names(f1)
gc()
### apply funciton to write the prediciton map and binary map according to selected threshold
lapply(ff_models_FN, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_ACCESS_70_8.5.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('full/species\\.lambdas', 'full/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
lapply(ff_models_random, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_ACCESS_70_8.5_random.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('rndm_bkgr/species\\.lambdas', 'rndm_bkgr/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
#project onto future scenario
## 2030 RCP 8.5
## prepare ff table for RCP 8.5 CESM1BGC 2030 (GCM2)
CESM1BGC_30.8.5 <- list.files("CHELSA_RASTERS_1km/2030_correct/RCP85/CESM1BGC/",full.names = TRUE)
f1 <- stack(CESM1BGC_30.8.5)
f1 <- dropLayer(f1, c(7,8,18,19))
names(f1) = names(s)
locs1 <- which(!is.na(sum(f1)[]))
locs1 <- cbind(cell=locs1, xyFromCell(f1, locs1))
cells1 <- locs1[, 'cell']
library(ff)
f1_ff <- ff::ff(vmode="double", dim=c(length(cells1), nlayers(f1)),
filename=ff_swd <- tempfile(fileext='.ff'))
# fill ff_matrix with data
for(i in 1:nlayers(f1)) {
f1_ff[, i] <- f1[[i]][][cells1]
}
colnames(f1_ff) <- names(f1)
gc()
### apply funciton to write the prediciton map and binary map according to selected threshold
lapply(ff_models_FN, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_CESM1BGC_30_8.5.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('full/species\\.lambdas', 'full/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
lapply(ff_models_random, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_CESM1BGC_30_8.5_random.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('rndm_bkgr/species\\.lambdas', 'rndm_bkgr/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
## prepare ff table for RCP 8.5 CESM1BGC 2050 (GCM2)
access_8.5 <- list.files("CHELSA_RASTERS_1km/2050/RCP85/CESM1BGC/",full.names = TRUE)
f1 <- stack(access_8.5)
f1 <- dropLayer(f1, c(7,8,18,19))
names(f1) = names(s)
locs1 <- which(!is.na(sum(f1)[]))
locs1 <- cbind(cell=locs1, xyFromCell(f1, locs1))
cells1 <- locs1[, 'cell']
library(ff)
f1_ff <- ff::ff(vmode="double", dim=c(length(cells1), nlayers(f1)),
filename=ff_swd <- tempfile(fileext='.ff'))
# fill ff_matrix with data
for(i in 1:nlayers(f1)) {
f1_ff[, i] <- f1[[i]][][cells1]
}
colnames(f1_ff) <- names(f1)
gc()
### apply funciton to write the prediciton map and binary map according to selected threshold
lapply(ff_models_FN, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_CESM1BGC_50_8.5.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('full/species\\.lambdas', 'full/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})
lapply(ff_models_random, function(m) {
species <- basename(dirname(dirname(m)))
outfile <- sprintf('%s/%s_CESM1BGC_50_8.5_random.ff',
(dirname(dirname(m))),
gsub(' ', '_', species))
if(!file.exists(extension(outfile, '.tif'))){
r_pred <- f1[[1]]
message('Doing species: ', gsub(' ', '_', species))
preds_ff <- ff(vmode="double", dim=c(length(cells1), 1),
filename=outfile)
finalizer(preds_ff) <- 'close'
preds_ff[, 1] <-
round(rmaxent::project(m, f1_ff[, seq_len(ncol(f1_ff))],
quiet=TRUE)$prediction_logistic * 1000)
r_pred[cells1] <- preds_ff[, 1]
writeRaster(r_pred, extension(outfile, '.tif'), datatype='INT2S', NAflag=-9999, overwrite=TRUE)
results <- suppressMessages(
read_csv(sub('rndm_bkgr/species\\.lambdas', 'rndm_bkgr/maxentResults.csv', m))
)
thr <- results$`10 percentile training presence logistic threshold`[
nrow(results)]
r_pred_binary <- r_pred >= thr*1000
outfile_binary <- sub('\\.ff$', '_binary', outfile)
writeRaster(r_pred_binary, extension(outfile_binary, '.tif'), datatype='INT1U', overwrite=TRUE)
close(preds_ff)
delete(preds_ff)
rm(preds_ff, r_pred, r_pred_binary)
}
})