breeding.density
Breeding density areas (aka, core habitat areas)
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
Note
The breeding density areas model identifies the Nth-percent population exhibiting the highest spatial density and counts/frequency. It then buffers these points by a specified distance to produce breeding area polygons. If you would like to recreate the results in Doherty et al., (2010), then define bw = 6400m and b[if p < 0.75 b = 6400m, | p >= 0.75 b = 8500m]
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)
# }