Description Usage Arguments Author(s) References See Also Examples
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.
1 2 3 |
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.
blend
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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)
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