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

In ecospat: Spatial Ecology Miscellaneous Methods

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 (a SpatRaster) 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).

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.

Value

a SpatRaster with value 1.

Author(s)

Frank Breiner [e-mail] with the contribution of Flavien Collart

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

library(terra)
library(dismo)

# coordinates of the plots
xy <- ecospat.testData[,2:3]

# environmental data
predictors <- terra::rast(system.file("extdata","ecospat.testEnv.tif",package="ecospat"))
env <- terra::extract(predictors,xy,ID=FALSE)  
spData <- cbind.data.frame(occ=ecospat.testData$Veronica_alpina,env)
mod <- glm(occ~ddeg0+I(ddeg0^2)+srad68+I(srad68^2),data=spData,family = binomial())

# predict to entire dataset
pred <- terra::predict(predictors,mod,type="response") 
plot(pred)
points(xy[spData$occ==1,])
  
### make binary maps
#arbitratry threshold
pred.bin.arbitrary <- ecospat.binary.model(pred,0.3)
names(pred.bin.arbitrary) <- "me.arbitrary"
# use MPA to convert suitability to binary map
mpa.cutoff <- ecospat.mpa(pred,xy[spData$occ==1,])
pred.bin.mpa <- ecospat.binary.model(pred,mpa.cutoff)
names(pred.bin.mpa) <- "me.mpa"

mpa.ocp  <- ecospat.occupied.patch(pred.bin.mpa,xy[spData$occ==1,])
arbitrary.ocp  <- ecospat.occupied.patch(pred.bin.arbitrary,xy[spData$occ==1,])
par(mfrow=c(1,2))
plot(mpa.ocp) ## occupied patches: green area
points(xy[spData$occ==1,],col="red",cex=0.5,pch=19)
plot(arbitrary.ocp)
points(xy[spData$occ==1,],col="red",cex=0.5,pch=19)


## with buffer:
mpa.ocp  <- ecospat.occupied.patch(pred.bin.mpa,xy[spData$occ==1,], buffer=5000)
arbitrary.ocp  <- ecospat.occupied.patch(pred.bin.arbitrary,xy[spData$occ==1,], buffer=5000)

plot(mpa.ocp) ## occupied patches: green area
points(xy[spData$occ==1,],col="red",cex=0.5,pch=19)
plot(arbitrary.ocp)
points(xy[spData$occ==1,],col="red",cex=0.5,pch=19)

ecospat documentation built on July 4, 2024, 5:06 p.m.