blender.basics: Basic landscape calculations
In blender: Analyze biotic homogenization of landscapes

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.

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

`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`.

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

  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)