blender.basics {blender} | R Documentation |
jbar
calculates average Jaccard similarity among sites (columns)
in your landscape as the expected ratio of the intersection between two
sites to to their union:
J.Bar = mean(intersection/union)
jstar
gives an approximation of this value from species
occupancy rates (row sums) as the ratio of the expected intersection
between two randomly chosen sites to the expected union:
J.Star = mean(intersection)/mean(union)
pstar
gives the "effective occupancy" of a landscape, defined in
Harris et al. (2011). A landscape composed entirely of species with this
occupancy rate would have the same J.Star value as the input landscape.
jbar(x)
jstar(x, n = NULL)
pstar(x, n = NULL)
x |
For |
n |
The number of sites in your landscape. Only needed for |
David Jay Harris <DavHarris@UCDavis.edu>
Harris, D. J., K. G. Smith, and P. J. Hanly. 2011. "Occupancy is nine-tenths of the law: Occupancy rates determine the homogenizing and differentiating effects of exotic species." The American Naturalist.
data(PLANTS)
# Calculate key values for Wyoming from raw data
landscape = PLANTS[["WY native table"]]
jbar(landscape)
jstar(landscape)
pstar(landscape)
# jstar and pstar also work if given row means and landscape sizes.
# jbar requires spatial information that is lost during this averaging.
occupancy = rowMeans(landscape)
nsites = ncol(landscape)
jstar(occupancy, nsites)
pstar(occupancy, nsites)