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ScrapCode.R
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require(curl)
require(rgbif)
## Old code for later reference
gbif_user <- "ss665"
gbif_pwd <- "9eKUSuZfMY3r"
gbif_email <- "[email protected]"
key <- as.character(paste("taxonKey =",name_suggest(q=species, rank='species')$key[[1]]))
occ.dl <- occ_download(key, "hasCoordinate = TRUE", "hasGeospatialIssue = FALSE",
"year >= 1970", "year <= 2019",
user = gbif_user, pwd = gbif_pwd, email = gbif_email,
curlopts = list())
occ.dl <- occ_download(body = query1, user = gbif_user, pwd = gbif_pwd, email = gbif_email)
get.output <- occ_download_get(key = occ.dl[[1]], "./Species_Pts/", overwrite = TRUE)
df.orig <- occ_download_import(get.output, path = "./Species_Pts")
data.pts <- read.csv("./Species_Pts/Calypte.anna/occurrence.csv")
unzip("./Species_Pts/0041203-181108115102211.zip", exdir = paste0("./Species_Pts/",
gsub(" ", ".", species)))
file.remove(paste0("./Species_Pts/", "0041203-181108115102211", ".zip"))
key <- "0041203-181108115102211"
data.orig <- read.table(paste0("./Species_Pts/",
gsub(" ", ".", species), "/", key, ".csv"), row.names = NULL, header = TRUE,
fill = TRUE)
data.orig <- read.csv(paste0("./Species_Pts/",
gsub(" ", ".", species), "/", key, ".csv"), row.names = NULL, header = TRUE)
query1 <- '{"creator":"ss665",
"notification_address":["[email protected]"],
"format": "SIMPLE_CSV",
"predicate": {
"type":"and","predicates":[
{
"type":"equals",
"key":"TAXON_KEY",
"value":"5229467"
},
{
"type":"equals",
"key":"HAS_COORDINATE",
"value":"TRUE"
},
{
"type":"equals",
"key":"HAS_GEOSPATIAL_ISSUE",
"value":"FALSE"
},
{
"type":"greaterThanOrEquals",
"key":"YEAR",
"value":"1970"
},
]
}
}'
unzip("./Species_Pts/0038393-181108115102211.zip", exdir = "./Species_Pts/Calypte.anna")
read.delim("./Species_Pts/Calypte.anna/occurrence.txt")
dforig <- gbif(genus = "Calypte", species = "anna", geo = TRUE, removeZeros = TRUE, start = 1,
end = 30000)
dforig <- occ_download(scientificName = species, hasCoordinate = TRUE,
eventDate = '1970, 2017', return = "data",
fields = c('decimalLatitude','decimalLongitude','eventDate'
,'locality'), limit = 200000, curlopts=list(noprogress=FALSE),
download = FALSE)
dforig <- occ_search(scientificName = species, hasCoordinate = TRUE,
eventDate = '1970, 2017', return = "data",
fields = c('decimalLatitude','decimalLongitude','eventDate'
,'locality'), limit = 200000, curlopts=list(noprogress=FALSE))
write.csv(dforig, "./Species_Pts/Lophornis.ornatus/gbif.file.csv")
key <- as.character(paste("taxonKey =",name_suggest(q='Lophornis ornatus', rank='species')$key[[1]]))
rdforig <- occ_download(key, "hasCoordinate = TRUE", "hasGeospatialIssue = FALSE",
"year >= 1970", "year <= 2017",
user = gbif_user, pwd = gbif_pwd, email = gbif_email)
get.output <- occ_download_get(key = dforig[[1]], "./Species_Pts/", overwrite = TRUE)
df.orig <- occ_download_import(as.download("./Species_Pts/0038395-181108115102211.zip"), path = "./Species_Pts")
data.pts <- read.csv("./Species_Pts/Calypte.anna/occurrence.csv")
overlap <- function(raster1, raster2){
raster1[raster1>0] <- 1
raster2[raster2>0] <- 1
add <- raster1+raster2
add[add<2] <- 0
return(add)
}
raster_area <- function(area_raster){
area_raster[area_raster==0] <- NA
cell_size <- area(area_raster, na.rm = TRUE, weights = FALSE)
cell_size <- cell_size[!is.na(cell_size)]
raster_area <- length(cell_size) * median(cell_size)
if (length(raster_area) == 0) {return(0)}
return(raster_area)
}
clim <- brick("clim10m")
overlap.df <- data.frame("species" =character(), "Area" = double(),
"Area.Overlap.Upper" = double(), "Area.Overlap.Lower" = double(),
"Area.Upper" = double(), "Area.Lower" = double())
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
for (i in 1:nrow(sample.df)){
print(paste(i, "/", nrow(sample.df)))
species <- sample.df[i, 1]
print(species)
upper.crit <- species.df.full$UCT...C.[species.df.full$Species==species]
lower.crit <-species.df.full$LCT...C.[species.df.full$Species==species]
upper.crit.raster <- clim[[5]] <= upper.crit
lower.crit.raster <- clim[[6]] >= lower.crit
data.thresh <- raster(paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".thresh.tif"))
overlap.upper <- overlap(data.thresh, upper.crit.raster)
overlap.lower <- overlap(data.thresh, lower.crit.raster)
this.row <- data.frame("species" =species, "Area" = raster_area(data.thresh)/1000,
"Area.Overlap.Upper" = raster_area(overlap.upper)/1000,
"Area.Overlap.Lower" = raster_area(overlap.lower)/1000,
"Area.Upper" = raster_area(upper.crit.raster)/1000,
"Area.Lower" = raster_area(lower.crit.raster)/1000)
print(this.row)
overlap.df <- rbind(this.row, overlap.df)
write.csv(overlap.df, "./SummaryTables/areaoverlap.csv")
}
raster_area <- function(area_raster){
area_raster[area_raster==0] <- NA
cell_size <- area(area_raster, na.rm = TRUE, weights = FALSE)
cell_size <- cell_size[!is.na(cell_size)]
raster_area <- length(cell_size) * median(cell_size)
if (length(raster_area) == 0) {return(0)}
return(raster_area)
}
info.df <- data.frame("species" = character(), "threshold" = numeric(), "Suit" = numeric(),
"Area" = numeric())
for (i in 1:nrow(sample.df)){
species <- sample.df[i, 1]
print(species)
print(i)
# Run maxent
for.maxent.full <- read.csv(paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.region.csv", sep = ""), row.names = 1)
predsnums <- c(1, 2, 5, 6, 7)
preds <- for.maxent.full[,predsnums+3]
resp <- for.maxent.full[,"resp"]
thisRegMult <- 1
params <- c(paste("betamultiplier=", thisRegMult, sep=""), "jackknife=false")
maxent.model <- maxent(x = preds, p = resp, path = "./MaxentModels/ScratchDir/",
args = params)
curPredsRaster <- clim[[predsnums]]
names(curPredsRaster) <- colnames(preds)
dir.create(paste0("./MaxentModels/",gsub(" ", ".", species)))
sp.raster <- predict(model = maxent.model, object = curPredsRaster, progress = "text",
filename = paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".full.tif"), overwrite = TRUE)
thresh <- maxent.model@results[38]
sp.raster.thresh <- sp.raster
sp.raster.thresh[sp.raster.thresh<thresh] = 0
avg.suit <- mean(sp.raster[sp.raster>0])
thresh.area <- raster_area(sp.raster.thresh)
plot(sp.raster)
plot(sp.raster.thresh)
writeRaster(sp.raster.thresh, filename = paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".thresh.tif"), overwrite = TRUE)
this.row <- data.frame("species" = species, "threshold" = thresh, "Suit" = avg.suit,
"Area" = thresh.area)
info.df <- rbind(this.row, info.df)
}
write.csv(info.df, "./SummaryTables/maxent.info.csv")
###############################################################################################
require(dplyr)
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
summary.df <- data.frame("Species" = character(), "BelowMaxCrit" = numeric(), "AboveMaxCrit" = numeric(),
"BelowMinCrit" = numeric(), "AboveMinCrit" = numeric(),
"BtwnCrit" = numeric(), "MeanMaxent" = numeric())
for (i in 1:nrow(sample.df)){
species <- sample.df[i, 1]
print(species)
upper.crit <- species.df.full$UCT...C.[species.df.full$Species==species]
lower.crit <-species.df.full$LCT...C.[species.df.full$Species==species]
full.model <- raster(paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".full.tif"))
maxent.vals <- getValues(full.model)[!is.na(getValues(full.model))]
max.temp.vals <- getValues(clim[[5]])[!is.na(getValues(clim[[5]]))]
min.temp.vals <- getValues(clim[[6]])[!is.na(getValues(clim[[6]]))]
vals.merge <- data.frame(maxent.vals, max.temp.vals, min.temp.vals)
vals.df <- dplyr::filter(vals.merge, max.temp.vals != 0 & min.temp.vals != 0 &
!is.na(max.temp.vals) & !is.na(min.temp.vals) &
!is.na(maxent.vals))
# Take mean of maxent values for values that are inside range and outside range.
maxent.below.maxcrit <- mean(dplyr::filter(vals.df, max.temp.vals <= upper.crit)$maxent.vals)
maxent.above.maxcrit <- mean(dplyr::filter(vals.df, max.temp.vals > upper.crit)$maxent.vals)
maxent.below.mincrit <- mean(dplyr::filter(vals.df, min.temp.vals < lower.crit)$maxent.vals)
maxent.above.mincrit <- mean(dplyr::filter(vals.df, min.temp.vals >= lower.crit)$maxent.vals)
maxent.btwn.crit <- mean(dplyr::filter(vals.df, min.temp.vals >= lower.crit & max.temp.vals <= upper.crit)$maxent.vals)
mean.maxent <- mean(maxent.vals)
this.row <- data.frame("Species" = species, "BelowMaxCrit" = maxent.below.maxcrit, "AboveMaxCrit" = maxent.above.maxcrit,
"BelowMinCrit" = maxent.below.mincrit, "AboveMinCrit" = maxent.above.mincrit,
"BtwnCrit" = maxent.btwn.crit, "MeanMaxent" = mean.maxent)
print(this.row)
summary.df <- rbind(this.row, summary.df)
write.csv(summary.df, "./SummaryTables/valueextraction.csv")
}
raster_area <- function(area_raster){
area_raster[area_raster==0] <- NA
cell_size <- area(area_raster, na.rm = TRUE, weights = FALSE)
cell_size <- cell_size[!is.na(cell_size)]
raster_area <- length(cell_size) * median(cell_size)
if (length(raster_area) == 0) {return(0)}
return(raster_area)
}
info.df <- data.frame("species" = character(), "threshold" = numeric(), "Suit" = numeric(),
"Area" = numeric())
for (i in 1:nrow(sample.df)){
species <- sample.df[i, 1]
print(species)
print(i)
# Run maxent
for.maxent.full <- read.csv(paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.region.csv", sep = ""), row.names = 1)
predsnums <- c(1, 2, 5, 6, 7)
preds <- for.maxent.full[,predsnums+3]
resp <- for.maxent.full[,"resp"]
thisRegMult <- 1
params <- c(paste("betamultiplier=", thisRegMult, sep=""), "jackknife=false")
maxent.model <- maxent(x = preds, p = resp, path = "./MaxentModels/ScratchDir/",
args = params)
curPredsRaster <- clim[[predsnums]]
names(curPredsRaster) <- colnames(preds)
dir.create(paste0("./MaxentModels/",gsub(" ", ".", species)))
sp.raster <- predict(model = maxent.model, object = curPredsRaster, progress = "text",
filename = paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".full.tif"), overwrite = TRUE)
thresh <- maxent.model@results[38]
sp.raster.thresh <- sp.raster
sp.raster.thresh[sp.raster.thresh<thresh] = 0
avg.suit <- mean(sp.raster[sp.raster>0])
thresh.area <- raster_area(sp.raster.thresh)
plot(sp.raster)
plot(sp.raster.thresh)
writeRaster(sp.raster.thresh, filename = paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".thresh.tif"), overwrite = TRUE)
this.row <- data.frame("species" = species, "threshold" = thresh, "Suit" = avg.suit,
"Area" = thresh.area)
info.df <- rbind(this.row, info.df)
}
write.csv(info.df, "./SummaryTables/maxent.info.csv")
# Compare
overlap <- function(raster1, raster2){
raster1[raster1>0] <- 1
raster2[raster2>0] <- 1
add <- raster1+raster2
add[add<2] <- 0
return(add)
}
raster_area <- function(area_raster){
area_raster[area_raster==0] <- NA
cell_size <- area(area_raster, na.rm = TRUE, weights = FALSE)
cell_size <- cell_size[!is.na(cell_size)]
raster_area <- length(cell_size) * median(cell_size)
if (length(raster_area) == 0) {return(0)}
return(raster_area)
}
clim <- brick("clim10m")
overlap.df <- data.frame("species" =character(), "Area" = double(),
"Area.Overlap.Upper" = double(), "Area.Overlap.Lower" = double(),
"Area.Upper" = double(), "Area.Lower" = double())
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
for (i in 1:nrow(sample.df)){
print(paste(i, "/", nrow(sample.df)))
species <- sample.df[i, 1]
print(species)
upper.crit <- species.df.full$UCT...C.[species.df.full$Species==species]
lower.crit <-species.df.full$LCT...C.[species.df.full$Species==species]
upper.crit.raster <- clim[[5]] <= upper.crit
lower.crit.raster <- clim[[6]] >= lower.crit
data.thresh <- raster(paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".thresh.tif"))
overlap.upper <- overlap(data.thresh, upper.crit.raster)
overlap.lower <- overlap(data.thresh, lower.crit.raster)
this.row <- data.frame("species" =species, "Area" = raster_area(data.thresh)/1000,
"Area.Overlap.Upper" = raster_area(overlap.upper)/1000,
"Area.Overlap.Lower" = raster_area(overlap.lower)/1000,
"Area.Upper" = raster_area(upper.crit.raster)/1000,
"Area.Lower" = raster_area(lower.crit.raster)/1000)
print(this.row)
overlap.df <- rbind(this.row, overlap.df)
write.csv(overlap.df, "./SummaryTables/areaoverlap.csv")
}
#######################################################
species = species.df[i, 1]
species.df <- read.csv("simple.species.csv", row.names = 1, stringsAsFactors = FALSE)
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
for.maxent.full <- read.csv(paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.region.csv", sep = ""), row.names = 1)
upper.crit <- species.df.full$UCT...C.[species.df.full$Species==species]
lower.crit <-species.df.full$LCT...C.[species.df.full$Species==species]
predsnums <- c(1, 2, 5, 6, 7)
preds <- for.maxent.full[,predsnums+3]
resp <- for.maxent.full[,"resp"]
thisRegMult <- 1
params <- c(paste("betamultiplier=", thisRegMult, sep=""), "jackknife=false", "writeplotdata=true")
maxent.model <- maxent(x = preds, p = resp, path = "./MaxentModels/ScratchDir/",
args = params)
areas.row <- AreaUnderResponseCurves(x = maxent.model, var = c("annualmeantemp", "maxtempwarmestmonth", "mintempcoldestmonth"), at = median,
expand = 10, data = NULL, fun = predict, upper.crit = upper.crit, lower.crit = lower.crit)
require(dplyr)
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
summary.df <- data.frame("Species" = character(), "BelowMaxCrit" = numeric(), "AboveMaxCrit" = numeric(),
"BelowMinCrit" = numeric(), "AboveMinCrit" = numeric(),
"BtwnCrit" = numeric(), "MeanMaxent" = numeric())
for (i in 1:nrow(sample.df)){
species <- sample.df[i, 1]
print(species)
upper.crit <- species.df.full$UCT...C.[species.df.full$Species==species]
lower.crit <-species.df.full$LCT...C.[species.df.full$Species==species]
full.model <- raster(paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".full.tif"))
maxent.vals <- getValues(full.model)[!is.na(getValues(full.model))]
max.temp.vals <- getValues(clim[[5]])[!is.na(getValues(clim[[5]]))]
min.temp.vals <- getValues(clim[[6]])[!is.na(getValues(clim[[6]]))]
vals.merge <- data.frame(maxent.vals, max.temp.vals, min.temp.vals)
vals.df <- dplyr::filter(vals.merge, max.temp.vals != 0 & min.temp.vals != 0 &
!is.na(max.temp.vals) & !is.na(min.temp.vals) &
!is.na(maxent.vals))
# Working on the code for the raster read-in
require(rgdal)
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
species.df <- read.csv("simple.species.csv", row.names = 1, stringsAsFactors = FALSE)
gbif.prj <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
clim <- brick("clim10m")
# Sample DF
sample.df <- species.df[c(154, 376, 393, 19, 409),]
# Read get species points, buffer them based around continents and ecoregions.
# This loop will also create the maxent data frame, so we need climate as well.
ecoregion <- readOGR(dsn = "./SpatialFiles/", "Sm_Ecoregions")
ecoregion.raster <- raster("ecoregionraster10m.gri")
ecoregions.df <- levels(ecoregion.raster)[[1]]
clim <- brick("clim10m")
require(raster)
require(dplyr)
require(enmSdm)
for (i in 1:nrow(sample.df)){
species <- sample.df[i, 1] #ith species
print(species)
print(i)
# spatial points file that I made earlier
species.pts.orig <- readOGR(dsn = paste0("./Species_Pts/", gsub(" ", ".", species), "/"),
layer = paste0(gsub(" ", ".", species),"sp.pts"))
species.pts.thinned <- elimCellDups(SpatialPoints(coordinates(species.pts.orig),
proj4string = crs(clim)), clim[[5]])
#species.pts <- over(species.pts.thinned, species.pts.orig)
species.pts.spdf <- SpatialPointsDataFrame(coords = data.frame(species.pts$dcmlLng, species.pts$dcmlLtt),
proj4string=gbif.prj, data=species.pts)
# get information about ecoregions that each point lies within, creating a buffer of
# 10000 to avoid getting NAs because of coarse borders and islands
#sp.buffer <- buffer(species.pts.spdf, width = 10000, dissolve = FALSE, progress= TRUE)
#print("Buffered, overlapping")
sp.over.df <-as.data.frame(extract(y = SpatialPoints(coordinates(species.pts.spdf), proj4string=gbif.prj),
buffer = 20000, x = ecoregion.raster, small = TRUE, df = TRUE,
fun = function(x){max(x)}))
sp.over <- merge(x = sp.over.df, y = ecoregions.df[,1:3], by.x = "layer", by.y = "ID", all.x = TRUE)
print("Extract 1 Finished")
# need to do this stuff to keep the data frame nice
species.pts.spdf$ecoregion <- sp.over$region
species.pts.spdf$biome <- sp.over$biome
# which regions/biomes do the species reside in?
regions <- unique(species.pts.spdf$ecoregion)
biomes <- unique(species.pts.spdf$biome)
# clipping the ecoregions layer so we only have the regions/biomes
# where the species has been found
these.regions.spdf <- ecoregion[as.vector(!is.na(match(ecoregion$region,regions))),]
these.biomes.spdf <- ecoregion[as.vector(!is.na(match(ecoregion$biome,biomes))),]
# generate random points, same number as observations
print("Generating random points")
random.pts.region <- spsample(these.regions.spdf, n=nrow(species.pts.spdf), type="random")
random.pts.biome <- spsample(these.biomes.spdf, n=nrow(species.pts.spdf), type = "random")
# I want to save this info now
species.pts.df <- as.data.frame(species.pts.spdf@data)
write.csv(species.pts.df, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".editeddata.csv", sep = ""))
print("Extracting...")
# making a two simple data frames for maxent; region and biome
simpledata.df <- cbind(rep(1, nrow(species.pts.df)),species.pts.df[,1:2])
colnames(simpledata.df) <- c("resp", "x", "y")
random.df.region <- data.frame("x"=random.pts.region@coords[,1], "y"=random.pts.region@coords[,2],"resp"=0)
for.maxent.pts.region <- rbind(simpledata.df, random.df.region)
random.df.biome <- data.frame("x"=random.pts.biome@coords[,1], "y"=random.pts.biome@coords[,2],"resp"=0)
for.maxent.pts.biome <- rbind(simpledata.df, random.df.biome)
# Extract climate data
df.clim.region <- as.data.frame(extract(clim, cbind(for.maxent.pts.region$x, for.maxent.pts.region$y)))
df.clim.biome <- as.data.frame(extract(clim, cbind(for.maxent.pts.biome$x, for.maxent.pts.biome$y)))
print(paste(as.character(species), "climate extraction completed"))
# gotta rename the climate variable names for the columns.
bionames <- c("annualmeantemp", "meandiurnalrange", "isothermality", "tempseasonality",
"maxtempwarmestmonth", "mintempcoldestmonth", "tempannualrange",
"meantempwettestquart", "meantempdriestquart",
"meantempwarmestquart", "meantempcoldestquart",
"annualprecip", "precipwettestmo", "precipdriestmo", "precipseasonality",
"precipwettestquart", "precipdriestquart", "precipwarmestquart",
"precipcoldestquart")
colnames(df.clim.region) <- bionames
colnames(df.clim.biome) <- bionames
# Binds climate data and long lat and pres together, in a way that the
# maxent function likes
for.maxent.full.region <- cbind(for.maxent.pts.region, df.clim.region)
for.maxent.full.biome <- cbind(for.maxent.pts.biome, df.clim.biome)
# Save 'em!
write.csv(for.maxent.full.region, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.region.csv", sep = ""))
write.csv(for.maxent.full.biome, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.biome.csv", sep = ""))
write.csv(for.maxent.full.biome, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.csv", sep = ""))
}
for (i in 1:nrow(sample.df)){
species <- sample.df[i, 1] #ith species
print(species)
print(i)
# spatial points file that I made earlier
species.pts.orig <- readOGR(dsn = paste0("./Species_Pts/", gsub(" ", ".", species), "/"),
layer = paste0(gsub(" ", ".", species),"sp.pts"))
species.pts.thinned <- elimCellDups(SpatialPoints(coordinates(species.pts.orig),
proj4string = crs(clim)), clim[[5]])
species.pts <- over(species.pts.thinned, species.pts.orig)
species.pts.spdf <- SpatialPointsDataFrame(coords = data.frame(species.pts$dcmlLng, species.pts$dcmlLtt),
proj4string=gbif.prj, data=species.pts)
# get information about ecoregions that each point lies within, creating a buffer of
# 10000 to avoid getting NAs because of coarse borders and islands
sp.buffer <- buffer(species.pts.spdf, width = 10000, dissolve = FALSE, progress= TRUE)
print("Buffered, overlapping")
# sp.over.df <- as.data.frame(extract(y = SpatialPoints(coordinates(species.pts.spdf), proj4string=gbif.prj),
# buffer = 200000, x = ecoregion.raster, small = TRUE, df = TRUE,
# progress = TRUE))
#
# sp.over.sort <- sp.over.df[order(sp.over.df$layer),]
# sp.over <- sp.over.sort[!duplicated(sp.over.sort$ID),]
# sp.over <- merge(sp.over, ecoregions.df[,2:3], all.x = TRUE)
sp.over <- over(sp.buffer, ecoregion[,c("region", "biome")])
print("Extract 1 Finished")
# need to do this stuff to keep the data frame nice
species.pts.spdf$ecoregion <- sp.over$region
species.pts.spdf$biome <- sp.over$biome
# which regions/biomes do the species reside in?
regions <- unique(species.pts.spdf$ecoregion)
biomes <- unique(species.pts.spdf$biome)
# clipping the ecoregions layer so we only have the regions/biomes
# where the species has been found
these.regions.spdf <- ecoregion[as.vector(!is.na(match(ecoregion$region,regions))),]
these.biomes.spdf <- ecoregion[as.vector(!is.na(match(ecoregion$biome,biomes))),]
# generate random points, same number as observations
print("Generating random points")
random.pts.region <- spsample(these.regions.spdf, n=nrow(species.pts.spdf), type="random")
random.pts.biome <- spsample(these.biomes.spdf, n=nrow(species.pts.spdf), type = "random")
# I want to save this info now
species.pts.df <- as.data.frame(species.pts.spdf@data)
write.csv(species.pts.df, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".editeddata.csv", sep = ""))
print("Extracting...")
# making a two simple data frames for maxent; region and biome
simpledata.df <- cbind(rep(1, nrow(species.pts.df)),species.pts.df[,1:2])
colnames(simpledata.df) <- c("resp", "x", "y")
random.df.region <- data.frame("x"=random.pts.region@coords[,1], "y"=random.pts.region@coords[,2],"resp"=0)
for.maxent.pts.region <- rbind(simpledata.df, random.df.region)
random.df.biome <- data.frame("x"=random.pts.biome@coords[,1], "y"=random.pts.biome@coords[,2],"resp"=0)
for.maxent.pts.biome <- rbind(simpledata.df, random.df.biome)
# Extract climate data
df.clim.region <- as.data.frame(extract(clim, cbind(for.maxent.pts.region$x, for.maxent.pts.region$y)))
df.clim.biome <- as.data.frame(extract(clim, cbind(for.maxent.pts.biome$x, for.maxent.pts.biome$y)))
print(paste(as.character(species), "climate extraction completed"))
# gotta rename the climate variable names for the columns.
bionames <- c("annualmeantemp", "meandiurnalrange", "isothermality", "tempseasonality",
"maxtempwarmestmonth", "mintempcoldestmonth", "tempannualrange",
"meantempwettestquart", "meantempdriestquart",
"meantempwarmestquart", "meantempcoldestquart",
"annualprecip", "precipwettestmo", "precipdriestmo", "precipseasonality",
"precipwettestquart", "precipdriestquart", "precipwarmestquart",
"precipcoldestquart")
colnames(df.clim.region) <- bionames
colnames(df.clim.biome) <- bionames
# Binds climate data and long lat and pres together, in a way that the
# maxent function likes
for.maxent.full.region <- cbind(for.maxent.pts.region, df.clim.region)
for.maxent.full.biome <- cbind(for.maxent.pts.biome, df.clim.biome)
# Save 'em!
write.csv(for.maxent.full.region, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.region.csv", sep = ""))
write.csv(for.maxent.full.biome, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.biome.csv", sep = ""))
write.csv(for.maxent.full.biome, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.csv", sep = ""))
}
species.df <- read.csv("simple.species.csv", row.names = 1, stringsAsFactors = FALSE)
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
df <- data.frame("species" = character(), "full.area" = numeric(), "above.lower.crit" = numeric(), "below.lower.crit" = numeric(),
"above.upper.crit" = numeric(), "below.upper.crit" = numeric(),
"area.between.crits" = numeric())
sample.df <- species.df[c(154, 376, 393, 19, 409, 361, 131, 26, 281, 133),]
for (i in 1:nrow(sample.df)){
species = sample.df[i, 1]
print(species)
for.maxent.full <- read.csv(paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.region.csv", sep = ""), row.names = 1)
upper.crit <- species.df.full$UCT...C.[species.df.full$Species==species]
lower.crit <-species.df.full$LCT...C.[species.df.full$Species==species]
predsnums <- c(1, 2, 5, 6, 7)
preds <- for.maxent.full[,predsnums+3]
resp <- for.maxent.full[,"resp"]
thisRegMult <- c(0.5, 1, 2, 5, 10)
params <- c(paste("betamultiplier=", thisRegMult, sep=""), "jackknife=false", "writeplotdata=true")
maxent.model <- maxent(x = preds, p = resp, path = "./MaxentModels/ScratchDir/",
args = params)
areas.row <- AreaUnderResponseCurves(x = maxent.model, var = c("annualmeantemp", "maxtempwarmestmonth", "mintempcoldestmonth"), at = median,
expand = 10, data = NULL, fun = predict, upper.crit = upper.crit, lower.crit = lower.crit)
this.row <- cbind("species" = species, areas.row)
df <- rbind(this.row, df)
write.csv(df, "./SummaryTables/response.suitability.csv")
}
species.df <- read.csv("simple.species.csv", row.names = 1, stringsAsFactors = FALSE)
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
df <- data.frame("species" = character(), "full.area" = numeric(), "above.lower.crit" = numeric(), "below.lower.crit" = numeric(),
"above.upper.crit" = numeric(), "below.upper.crit" = numeric(),
"area.between.crits" = numeric())
sample.df <- species.df[c(154, 376, 393, 19, 409, 361, 131, 26, 281, 133),]
for (i in 1:nrow(sample.df)){
species = sample.df[i, 1]
print(species)
for.maxent.full <- read.csv(paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.region.csv", sep = ""), row.names = 1)
upper.crit <- species.df.full$UCT...C.[species.df.full$Species==species]
lower.crit <-species.df.full$LCT...C.[species.df.full$Species==species]
predsnums <- c(1, 2, 5, 6, 7)
preds <- for.maxent.full[,predsnums+3]
resp <- for.maxent.full[,"resp"]
thisRegMult <- (1)
#params <- c(paste("betamultiplier=", thisRegMult, sep=""), "jackknife=false", "writeplotdata=true")
maxent.model <- trainMaxEnt(data = for.maxent.full, scratchDir = "./MaxentModels/ScratchDir/",
regMult = thisRegMult, jacknife = FALSE, verbose = TRUE, out = c("model", "tuning"),
testClasses = FALSE, silent = FALSE, classes = "lpqht")
areas.row <- AreaUnderResponseCurves(x = maxent.model[[1]], var = c("annualmeantemp", "maxtempwarmestmonth", "mintempcoldestmonth"), at = median,
expand = 10, data = NULL, fun = predict, upper.crit = upper.crit, lower.crit = lower.crit)
this.row <- cbind("species" = species, areas.row)
df <- rbind(this.row, df)
write.csv(df, "./SummaryTables/response.suitability.region.csv")
}
### Comparison method
overlap <- function(raster1, raster2){
raster1[raster1>0] <- 1
raster2[raster2>0] <- 1
add <- raster1+raster2
add[add<2] <- 0
return(add)
}
raster_area <- function(area_raster){
area_raster[area_raster==0] <- NA
cell_size <- area(area_raster, na.rm = TRUE, weights = FALSE)
cell_size <- cell_size[!is.na(cell_size)]
raster_area <- length(cell_size) * median(cell_size)
if (length(raster_area) == 0) {return(0)}
return(raster_area)
}
clim <- brick("clim10m")
overlap.df <- data.frame("species" =character(), "Area.B" = double(),
"Area.Overlap.Upper.B" = double(), "Area.Overlap.Lower.B" = double(),
"Area.R" = double(), "Area.Overlap.Upper.R" = double(),
"Area.Overlap.Lower.R" = double(), "Area.Upper" = double(),
"Area.Lower" = double())
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
starttime <-Sys.time()
for (i in 1:nrow(sample.df)){
print(paste(i, "/", nrow(sample.df)))
species <- sample.df[i, 1]
print(species)
upper.crit <- species.df.full$UCT...C.[species.df.full$Species==species]
lower.crit <-species.df.full$LCT...C.[species.df.full$Species==species]
upper.crit.raster <- clim[[5]] <= upper.crit
lower.crit.raster <- clim[[6]] >= lower.crit
data.thresh.region <- raster(paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".region.thresh.tif"))
overlap.upper.region <- overlap(data.thresh.region, upper.crit.raster)
overlap.lower.region <- overlap(data.thresh.region, lower.crit.raster)
data.thresh.biome <- raster(paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".biome.thresh.tif"))
overlap.upper.biome <- overlap(data.thresh.biome, upper.crit.raster)
overlap.lower.biome <- overlap(data.thresh.biome, lower.crit.raster)
this.row <- data.frame("species" =species, "Area.b" = raster_area(data.thresh.biome)/1000,
"Area.Overlap.Upper.B" = raster_area(overlap.upper.biome)/1000,
"Area.Overlap.Lower.B" = raster_area(overlap.lower.biome)/1000,
"Area.R" = raster_area(data.thresh.region)/1000,
"Area.Overlap.Upper.R" = raster_area(overlap.upper.region)/1000,
"Area.Overlap.Lower.R" = raster_area(overlap.lower.region)/1000,
"Area.Upper" = raster_area(upper.crit.raster)/1000,
"Area.Lower" = raster_area(lower.crit.raster)/1000)
print(this.row)
overlap.df <- rbind(this.row, overlap.df)
write.csv(overlap.df, "./SummaryTables/areaoverlap.csv")
#cur.runtime <- Sys.time()-starttime
}
species <- species.df[i, 1]
raster_area <- function(area_raster){
area_raster[area_raster==0] <- NA
cell_size <- area(area_raster, na.rm = TRUE, weights = FALSE)
cell_size <- cell_size[!is.na(cell_size)]
raster_area <- length(cell_size) * median(cell_size)
if (length(raster_area) == 0) {return(0)}
return(raster_area)
}
for (i in 1:sample.df){
species <- sample.df[i, 1]
# Run maxent
# Region first
for.maxent.full.region <- read.csv(paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.region.csv", sep = ""), row.names = 1)
predsnums <- c(1, 2, 5, 6, 7)
preds.region <- for.maxent.full.region[,predsnums+3]
resp.region <- for.maxent.full.region[,"resp"]
thisRegMult <- 1
params <- c(paste("betamultiplier=", thisRegMult, sep=""), "jackknife=false")
require(rJava)
maxent.model.region <- maxent(x = preds.region, p = resp.region, path = "./MaxentModels/ScratchDir/",
args = params)
curPredsRaster <- clim[[predsnums]]
names(curPredsRaster) <- colnames(preds.region)
dir.create(paste0("./MaxentModels/",gsub(" ", ".", species)))
sp.raster.region <- predict(model = maxent.model.region, object = curPredsRaster, progress = "text",
filename = paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".full.region.tif"), overwrite = TRUE)
thresh.region <- maxent.model.region@results[38]
sp.raster.thresh.region <- sp.raster.region
sp.raster.thresh.region[sp.raster.thresh.region<thresh.region] = 0
avg.suit.region <- mean(sp.raster.region[sp.raster.region>0])
thresh.area.region <- raster_area(sp.raster.thresh.region)
plot(sp.raster.region)
plot(sp.raster.thresh.region)
writeRaster(sp.raster.thresh.region, filename = paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".region.thresh.tif"), overwrite = TRUE)
# Biome
for.maxent.full.biome <- read.csv(paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.biome.csv", sep = ""), row.names = 1)
preds.biome <- for.maxent.full.biome[,predsnums+3]
resp.biome <- for.maxent.full.biome[,"resp"]
maxent.model.biome <- maxent(x = preds.biome, p = resp.biome, path = "./MaxentModels/ScratchDir/",
args = params)
sp.raster.biome <- predict(model = maxent.model.biome, object = curPredsRaster, progress = "text",
filename = paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".full.biome.tif"), overwrite = TRUE)
thresh.biome <- maxent.model.biome@results[38]
sp.raster.thresh.biome <- sp.raster.biome
sp.raster.thresh.biome[sp.raster.thresh.biome<thresh.biome] = 0
avg.suit.biome <- mean(sp.raster.biome[sp.raster.biome>0])
thresh.area.biome <- raster_area(sp.raster.thresh.biome)
writeRaster(sp.raster.thresh.biome, filename = paste0("./MaxentModels/",gsub(" ", ".", species),"/", gsub(" ", ".", species),
".biome.thresh.tif"), overwrite = TRUE)
this.row <- data.frame("species" = species, "thresholdRegion" = thresh.region, "SuitRegion" = avg.suit.region,
"AreaRegion" = thresh.area.region, "thresholdBiome" = thresh.biome,
"SuitBiome" = avg.suit.biome, "AreaBiome" = thresh.area.biome)
compare <- rbind(this.row, compare)
}
#### old code before I implemented select_max
#cur.runtime <- Sys.time()-starttime
species.df <- read.csv("simple.species.csv", row.names = 1, stringsAsFactors = FALSE)
species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
df <- data.frame("species" = character(), "full.area" = numeric(), "above.lower.crit" = numeric(), "below.lower.crit" = numeric(),
"above.upper.crit" = numeric(), "below.upper.crit" = numeric(),
"area.between.crits" = numeric())
pts.climate.df <- data.frame("species" = character(), "below.maxtemp" = numeric(),
"above.maxtemp" = numeric(), "below.mintemp" = numeric(),
"above.mintemp" = numeric(), "maxtemp.loc" = numeric(),
"mintemp.loc" = numeric(), "maxcrit" = numeric(), "mincrit" = numeric(),
"nobs" = integer())
sample.df <- species.df[c(154, 376, 393, 19, 409, 361, 131, 26, 281, 133),]
# type in biome or region here to get appropriate points
bor = "biome"
par(mfrow = c(1, 3))
for (i in 1:nrow(sample.df)){
species = sample.df[i, 1]
print(species)
for.maxent.full <- read.csv(paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.",bor,".csv", sep = ""), row.names = 1)
upper.crit <- species.df.full$UCT...C.[species.df.full$Species==species]
lower.crit <-species.df.full$LCT...C.[species.df.full$Species==species]
predsnums <- c(1, 2, 5, 6, 7)
preds <- for.maxent.full[,predsnums+3]
resp <- for.maxent.full[,"resp"]
thisRegMult <- 1
params <- c(paste("betamultiplier=", thisRegMult, sep=""), "jackknife=false")
dir.create(paste0("./MaxentModels/",gsub(" ", ".", species)))
maxent.model <- maxent(x = preds, p = resp, path = paste0("./MaxentModels/",gsub(" ", ".", species)),
args = params, silent = FALSE)
areas.row <- AreaUnderResponseCurves(x = maxent.model, var = c("annualmeantemp", "maxtempwarmestmonth", "mintempcoldestmonth"), at = median,
expand = 10, data = NULL, fun = predict, upper.crit = upper.crit, lower.crit = lower.crit)
df <- rbind(cbind("species" = species, areas.row), df)
write.csv(df, paste0("./SummaryTables/response.suitability.",bor,".csv"))
#Evaluating climate at occurence points
species.pts.climate <- dplyr::filter(for.maxent.full, for.maxent.full$resp == 1)
species.pts.climate <- species.pts.climate[!is.na(species.pts.climate$maxtempwarmestmonth),]
below.maxtemp <- sum(species.pts.climate$maxtempwarmestmonth <= upper.crit)
above.mintemp <- sum(species.pts.climate$mintempcoldestmonth >= lower.crit)
above.maxtemp <- sum(species.pts.climate$maxtempwarmestmonth > upper.crit)
below.mintemp <- sum(species.pts.climate$mintempcoldestmonth < lower.crit)
maxtemp.loc <- max(species.pts.climate$maxtempwarmestmonth)
mintemp.loc <- min(species.pts.climate$mintempcoldestmonth)
meantemp.loc <- mean(species.pts.climate$annualmeantemp)
nobs <- nrow(species.pts.climate)
pts.climate.df <- rbind(data.frame("species" = species, "below.maxtemp" = below.maxtemp,
"above.maxtemp" = above.maxtemp, "below.mintemp" = below.mintemp,
"above.mintemp" = above.mintemp, "maxtemp.loc" = maxtemp.loc,
"mintemp.loc" = mintemp.loc, "meantemp.loc" = meantemp.loc,
"nobs" = nobs, "maxcrit" = upper.crit, "mincrit" = lower.crit), pts.climate.df)
write.csv(pts.climate.df, paste0("./SummaryTables/ptssuitability.",bor,".csv"))
}
######################################################################
# species.df <- read.csv("simple.species.csv", row.names = 1, stringsAsFactors = FALSE)
# species.df.full <- read.csv("Species_Table_021619.csv", row.names = 1, stringsAsFactors = FALSE)
#
# sample.df <- species.df[c(154, 376, 393, 19, 409),]
# pts.climate.df <- data.frame("species" = character(), "below.maxtemp" = numeric(),
# "above.maxtemp" = numeric(), "below.mintemp" = numeric(),
# "above.mintemp" = numeric(), "maxtemp.loc" = numeric(),
# "mintemp.loc" = numeric(), "nobs" = integer())
# for (i in 1:nrow(sample.df)){
# species = sample.df[i, 1]
#
# # Just getting maxtemp and mintemp where the species is found and comparing it
#
# for.maxent.full <- read.csv(paste("./Species_Pts/", gsub(" ", ".", species), "/",
# gsub(" ", ".", species), ".maxentdatafile.csv", sep = ""), row.names = 1)
# species.pts.climate <- dplyr::filter(for.maxent.full, for.maxent.full$resp == 1)
# species.pts.climate <- species.pts.climate[!is.na(species.pts.climate$maxtempwarmestmonth),]
# below.maxtemp <- sum(species.pts.climate$maxtempwarmestmonth <= upper.crit)
# above.mintemp <- sum(species.pts.climate$mintempcoldestmonth >= lower.crit)
# above.maxtemp <- sum(species.pts.climate$maxtempwarmestmonth > upper.crit)
# below.mintemp <- sum(species.pts.climate$mintempcoldestmonth < lower.crit)
#
#
# maxtemp.loc <- max(species.pts.climate$maxtempwarmestmonth)
# mintemp.loc <- min(species.pts.climate$mintempcoldestmonth)
# meantemp.loc <- mean(species.pts.climate$annualmeantemp)
# nobs <- nrow(species.pts.climate)
#
# pts.climate.df <- rbind(data.frame("species" = species, "below.maxtemp" = below.maxtemp,
# "above.maxtemp" = above.maxtemp, "below.mintemp" = below.mintemp,
# "above.mintemp" = above.mintemp, "maxtemp.loc" = maxtemp.loc,
# "mintemp.loc" = mintemp.loc, "meantemp.loc" = meantemp.loc,
# "nobs" = nobs), pts.climate.df)
# write.csv(pts.climate.df, "./SummaryTables/ptssuitability")
# }
for (i in 1:nrow(species.df)){
species <- sample.df[i, 1] #ith species
print(species)
print(i)
# spatial points file that I made earlier
species.pts.orig <- readOGR(dsn = paste0("./Species_Pts/", gsub(" ", ".", species), "/"),
layer = paste0(gsub(" ", ".", species),"sp.pts"))
# reference raster for thinning
print("Thinning...")
ref.raster <- raster(crs = crs(species.pts.orig), ext = extent(clim),
resolution = c(0.00833333, 0.00833333))
species.pts.thinned <- elimCellDups(coordinates(species.pts.orig), ref.raster)
species.pts <- over(SpatialPoints(species.pts.thinned, proj4string=gbif.prj), species.pts.orig)
species.pts.spdf <- SpatialPointsDataFrame(coords = data.frame(species.pts$dcmlLng, species.pts$dcmlLtt),
proj4string=gbif.prj, data=species.pts)
# get information about ecoregions that each point lies within, creating a buffer of
# 10000 to avoid getting NAs because of coarse borders and islands
print("Extracting...")
sp.over.df <-as.data.frame(extract(y = SpatialPoints(coordinates(species.pts.spdf), proj4string=gbif.prj),
buffer = 20000, x = ecoregion.raster, small = TRUE, df = TRUE,
fun = function(x){max(x)}))
sp.over <- merge(x = sp.over.df, y = ecoregions.df[,1:3], by.x = "layer", by.y = "ID", all.x = TRUE)
print("Extract 1 Finished")
# need to do this stuff to keep the data frame nice
species.pts.spdf$ecoregion <- sp.over$region
species.pts.spdf$biome <- sp.over$biome
# which regions/biomes do the species reside in?
regions <- unique(species.pts.spdf$ecoregion)
biomes <- unique(species.pts.spdf$biome)
for (i in 1:regions){
}
# clipping the ecoregions layer so we only have the regions/biomes
# where the species has been found
these.regions.spdf <- ecoregion[as.vector(!is.na(match(ecoregion$region,regions))),]
these.biomes.spdf <- ecoregion[as.vector(!is.na(match(ecoregion$biome,biomes))),]
# generate random points, same number as observations
print("Generating random points...")
random.pts.region <- spsample(these.regions.spdf, n=10000, type="random")
random.pts.biome <- spsample(these.biomes.spdf, n=10000, type = "random")
# I want to save this info now
species.pts.df <- as.data.frame(species.pts.spdf@data)
write.csv(species.pts.df, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".editeddata.csv", sep = ""))
print("Extracting climate data...")
# making a two simple data frames for maxent; region and biome
simpledata.df <- cbind(rep(1, nrow(species.pts.df)),species.pts.df[,1:2])
colnames(simpledata.df) <- c("resp", "x", "y")
random.df.region <- data.frame("x"=random.pts.region@coords[,1], "y"=random.pts.region@coords[,2],"resp"=0)
for.maxent.pts.region <- rbind(simpledata.df, random.df.region)
random.df.biome <- data.frame("x"=random.pts.biome@coords[,1], "y"=random.pts.biome@coords[,2],"resp"=0)
for.maxent.pts.biome <- rbind(simpledata.df, random.df.biome)
# Extract climate data
df.clim.region <- as.data.frame(extract(clim, cbind(for.maxent.pts.region$x, for.maxent.pts.region$y)))
df.clim.biome <- as.data.frame(extract(clim, cbind(for.maxent.pts.biome$x, for.maxent.pts.biome$y)))
print(paste(as.character(species), "climate extraction completed"))
# gotta rename the climate variable names for the columns.
bionames <- c("annualmeantemp", "meandiurnalrange", "isothermality", "tempseasonality",
"maxtempwarmestmonth", "mintempcoldestmonth", "tempannualrange",
"meantempwettestquart", "meantempdriestquart",
"meantempwarmestquart", "meantempcoldestquart",
"annualprecip", "precipwettestmo", "precipdriestmo", "precipseasonality",
"precipwettestquart", "precipdriestquart", "precipwarmestquart",
"precipcoldestquart")
colnames(df.clim.region) <- bionames
colnames(df.clim.biome) <- bionames
# Binds climate data and long lat and pres together, in a way that the
# maxent function likes
for.maxent.full.region <- cbind(for.maxent.pts.region, df.clim.region)
for.maxent.full.biome <- cbind(for.maxent.pts.biome, df.clim.biome)
# Save 'em!
write.csv(for.maxent.full.region, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.region.csv", sep = ""))
write.csv(for.maxent.full.biome, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.biome.csv", sep = ""))
write.csv(for.maxent.full.biome, paste("./Species_Pts/", gsub(" ", ".", species), "/",
gsub(" ", ".", species), ".maxentdatafile.csv", sep = ""))
}