ecospat.occupied.patch {ecospat} | R Documentation |
This function determines the occupied patch of a species using standard IUCN criteria (AOO, EOO) or predictive binary maps from Species Distribution Models.
ecospat.occupied.patch (bin.map, Sp.occ.xy, buffer = 0)
bin.map |
Binary map (single layer or raster stack) from a Species Distribution Model. |
Sp.occ.xy |
xy-coordinates of the species presence. |
buffer |
numeric. Calculate occupied patch models from the binary map using a buffer (predicted area occupied by the species or within a buffer around the species, for details see ?extract). |
Predictive maps derived from SDMs inform about the potential distribution (or habitat suitability) of a species. Often it is useful to get information about the area of the potential distribution which is occupied by a species, e.g. for Red List assessments.
a RasterLayer with value 1.
Frank Breiner frank.breiner@wsl.ch
IUCN Standards and Petitions Subcommittee. 2016. Guidelines for Using the IUCN Red List Categories and Criteria. Version 12. Prepared by the Standards and Petitions Subcommittee. Downloadable from http://www.iucnredlist.org/documents/RedListGuidelines.pdf
ecospat.rangesize
, ecospat.mpa
, ecospat.binary.model
library(raster)
library(dismo)
### make a maxent model
# path to maxent.jar file
path<- paste0(system.file(package="dismo"), "/java/maxent.jar")
if (file.exists(path) & require(rJava) & require(igraph)) {
# get predictor variables
fnames <- list.files(path=paste(system.file(package="dismo"), '/ex', sep=''),
pattern='grd', full.names=TRUE )
predictors <- stack(fnames)
#plot(predictors)
# file with presence points
occurence <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='')
occ <- read.table(occurence, header=TRUE, sep=',')[,-1]
colnames(occ) <- c("x","y")
occ <- ecospat.occ.desaggregation(occ,min.dist=1)
# fit a domain model, biome is a categorical variable
me <- maxent(predictors, occ, factors='biome')
# predict to entire dataset
pred <- predict(me, predictors)
plot(pred)
points(occ)
}
### to convert suitability to binary map
mpa.cutoff <- ecospat.mpa(pred,occ)
pred.bin.mpa <- ecospat.binary.model(pred,mpa.cutoff)
names(pred.bin.mpa) <- "me.mpa"
pred.bin.arbitrary <- ecospat.binary.model(pred,0.5)
names(pred.bin.arbitrary) <- "me.arbitrary"
### calculate occupied patch
mpa.ocp <- ecospat.occupied.patch(pred.bin.mpa,occ)
arbitrary.ocp <- ecospat.occupied.patch(pred.bin.arbitrary,occ)
par(mfrow=c(1,2))
plot(mpa.ocp) ## occupied patches: green area
points(occ,col="red",cex=0.5,pch=19)
plot(arbitrary.ocp)
points(occ,col="red",cex=0.5,pch=19)
## with buffer:
mpa.ocp <- ecospat.occupied.patch(pred.bin.mpa,occ, buffer=500000)
arbitrary.ocp <- ecospat.occupied.patch(pred.bin.arbitrary,occ, buffer=500000)
plot(mpa.ocp) ## occupied patches: green area
points(occ,col="red",cex=0.5,pch=19)
plot(arbitrary.ocp)
points(occ,col="red",cex=0.5,pch=19)