R: I Similarity Statistic for Quantifying Niche Overlap

Istat {SDMTools} | R Documentation

## I Similarity Statistic for Quantifying Niche Overlap

### Description

`Istat` computes the I similarity statistic of Warren et al. 2008. It is a method for defining niche overlap from predictions of species' distributions.
NOTE: it is assumed the input data are of the same extent and cellsize, and all values are positive.

### Usage

    Istat(x, y, old = FALSE)

### Arguments

`x` | a vector or matrix of data; the matrix can be a raster of class 'asc' (adehabitat package), 'RasterLayer' (raster package) or 'SpatialGridDataFrame' (sp package)
`y` | a vector or matrix of data with the same dimensions and class of 'x'
`old` | a boolean identifying if "old" equation is to be used (see description). This was kept for legacy issues.

### Details

The I similarity statistic sums the pair-wise differences between two predictions to create a single value representing the similarity of the two distributions. The I similarity statistic ranges from a value of 0, where two distributions have no overlap, to 1 where two distributions are identical (Warren et al., 2008).

NOTE: updated to correct equation but not to worry about old... see explanation at http://enmtools.blogspot.com.au/2010_09_01_archive.html.

### Value

A single value that is the I similarity statistic

### Author(s)

Jeremy VanDerWal jjvanderwal@gmail.com

### References

Warren, D. L., R. E. Glor, M. Turelli, and D. Funk. 2008. Environmental Niche Equivalency versus Conservatism: Quantitative Approaches to Niche Evolution. Evolution 62:2868-2883.

### Examples

    #create some simple objects of class 'asc'
    tasc = as.asc(matrix(1:50,nr=50,nc=50)); print(tasc)
    #modify the asc objects so that they are slightly different
    tasc1 = tasc + runif(n = 2500, min = -1, max = 1)
    tasc2 = tasc + rnorm(n = 2500, mean = 1, sd = 1)

    #ensure all data is positive
    tasc1 = abs(tasc1)
    tasc2 = abs(tasc2)

    #calculate the I similarity statistic
    I = Istat(tasc1,tasc2)
    print(I) #high niche overlap

    #using a more variable map
    tasc2 = tasc + rnorm(n = 2500, mean = 25, sd = 15);tasc2 = abs(tasc2)
    I = Istat(tasc1,tasc2)
    print(I) #lower niche overlap

[Package SDMTools version 1.1-221.2 Index]