spatialEco (version 0.1-5)

breeding.density: Breeding density areas (aka, core habitat areas)

Description

Calculates breeding density areas base on population counts and spatial point density.

Usage

breeding.density(x, pop, p = 0.75, bw = 6400, b = 8500, self = TRUE)

Arguments

x

sp SpatialPointsDataFrame object

pop

Population count/density column in x@data

p

Target percent of population

bw

Bandwidth distance for the kernel estimate (default 8500)

b

Buffer distance (default 8500)

self

(TRUE/FALSE) Should source observations be included in density (default TRUE)

Value

A list object with:pop.pts sp point object with points identified within the specified ppop.area sp polygon object of buffered points specified by parameter bbandwidth Specified distance bandwidth used in identifying neighbour countsbuffer Specified buffer distance used in buffering points for pop.areap Specified population percent

References

Doherty, K.E., J.D. Tack, J.S. Evans, D.E. Naugle (2010) Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number L10PG00911

Examples

require(sp)
n=1500
bb <- rbind(c(-1281299,-761876.5),c(1915337,2566433.5))
  bb.mat <- cbind(c(bb[1,1], bb[1,2], bb[1,2], bb[1,1]),   
                  c(bb[2,1], bb[2,1], bb[2,2], bb[2,2]))  
    bbp <- Polygon(bb.mat)
     s <- spsample(bbp, n, type='random')
        pop <- SpatialPointsDataFrame(s, data.frame(ID=1:length(s), 
                                 counts=runif(length(s), 1,250)))

    bd75 <- breeding.density(pop, pop='counts', p=0.75, b=8500, bw=6400)
     plot(bd75$pop.area, main='75% breeding density areas')
       plot(pop, pch=20, col='black', add=TRUE)
         plot(bd75$pop.pts, pch=20, col='red', add=TRUE)