iForce: R function to compute a negative exponential dispersal kernel

In TarekHattab/iSDM: Invasive Species Distribution Modelling

R function to compute a negative exponential dispersal kernel

Description

R function to compute a negative exponential dispersal kernel

Usage

iForce(occData,coords,a,envData,binary=TRUE,longlat=NULL)

Arguments

occData	Either a SpatialPointsDataFrame as defined in package sp, a data.frame or a matrix object containing species data
coords	Optional 2 columns matrix containing the X and Y coordinates of occData (only consider if occData is a a data.frame or a matrix object)
a	An integer between 0 and 1 that controls the form of the dispersal kernel
envData	Either a SpatialPixelsDataFrame or a SpatialGridDataFrame as defined in package sp or a Raster or RasterLayer as defined in package raster. This object will be used to determine the area for which the negative exponential dispersal kernel will be calculated
binary	A logic indicating whether the occData object contains presences/absences data or only the geographical coordinates of presences data (default=TRUE)
longlat	A logic indicating whether point coordinates are projected (longitude-latitude decimal degrees) or not (for plane) (default=TRUE)

Value

Returns a RasterLayer object of the negative exponential dispersal kernel

References

Tarek Hattab, Carol Ximena Garzon Lopez, Michael Ewald, Sandra Skowronek, Raf Aerts, Helene Horen, Boris Brasseur, Emilie Gallet-Moron, Fabien Spicher, Guillaume Decocq, Hannes Feilhauer, Olivier Honnay, Pieter Kempeneers, Sebastian Schmidtlein, Ben Somers, Ruben Van De Kerchove, Duccio Rocchini and Jonathan Lenoir (Accpeted). A unified framework to model the potential and realized distributions of invasive species within the invaded range. Diversity and Distributions.

Examples

## Not run: 
library(raster)
library(sp)
envData<-getData('worldclim', var='bio', res=10)
envData<-crop(envData,extent(-8,15,38,55))
envData<-envData[[1]]

#Generate randomly a SpatilaPointsDataFrame containing occurrences
xy<-coordinates(envData)[sample(which(is.na(values(envData))==FALSE),100),]
occ<-ifelse(xy[,2]>50,0,1)
occData<-SpatialPointsDataFrame(coords=xy,data=as.data.frame(occ), 
proj4string = CRS(proj4string(envData)))

par(mfrow=c(3,3),mar=c(1.5,1.5,1.5,1.5))
for (a in c(0.01,0.03,0.05,0.06,0.09,0.1,0.3,0.6,0.9)){
propagule<-iForce(occData,envData=envData,a=a,binary=TRUE,longlat=TRUE)
plot(propagule,main=paste("a = ",a))
plot(occData,col=ifelse(occData@data[,1]==1,1,0),add=TRUE,cex=0.3)}

## End(Not run)

TarekHattab/iSDM documentation built on Feb. 1, 2023, 8:17 p.m.