spatialEco (version 1.3-2)

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 p

• pop.area sp polygon object of buffered points specified by parameter b

• bandwidth Specified distance bandwidth used in identifying neighbor counts

• buffer Specified buffer distance used in buffering points for pop.area

• p 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. Number L10PG00911

Examples

# NOT RUN {
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)

# }