blender.basics {blender} R Documentation

## Basic landscape calculations

### Description

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.

### Usage

  jbar(x)
jstar(x, n = NULL)
pstar(x, n = NULL)


### Arguments

 x For jbar, a binary data.frame with species as rows and sites as columns. For jstar and pstar, either a data.frame or a numeric vector containing the proportion of sites occupied by each species. n The number of sites in your landscape. Only needed for jstar and pstar if x is numeric.

### Author(s)

David Jay Harris <DavHarris@UCDavis.edu>

### References

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.

### Examples

  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)


[Package blender version 0.1.2 Index]