ecospat (version 3.2)

ecospat.occupied.patch: Extract occupied patches of a species in geographic space.)

Description

This function determines the occupied patch of a species using standard IUCN criteria (AOO, EOO) or predictive binary maps from Species Distribution Models.

Usage

ecospat.occupied.patch (bin.map, Sp.occ.xy, buffer = 0)

Arguments

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).

Value

a RasterLayer with value 1.

Details

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.

References

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

See Also

ecospat.rangesize, ecospat.mpa, ecospat.binary.model

Examples

# NOT RUN {
# }
# NOT RUN {
library(raster)
library(dismo)

### make a maxent model

# copy maxent.jar file in the right folder
path.from<-system.file("extdata", "maxent.txt", package="ecospat")
path.to <- paste0(system.file(package="dismo"), "/java/maxent.txt")
path.to.renamed <- paste0(system.file(package="dismo"), "/java/maxent.jar")
file.copy(path.from,path.to,overwrite = TRUE)
file.rename(path.to, path.to.renamed)

if (file.exists(path.to.renamed) & 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)
# }
# NOT RUN {
# }