r.niche.similarity - GRASS GIS manual

DESCRIPTION
Notes
EXAMPLE
REFERENCES
AUTHOR

NAME

r.niche.similarity - Computes niche overlap or similarity

KEYWORDS

raster, niche modelling

SYNOPSIS

r.niche.similarity

r.niche.similarity --help

r.niche.similarity [-idcm] maps=name[,name,...] [output=name] [nprocs=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-i: I niche similarity
-d: D niche similarity
-c: Correlation
-m: remove NA cells
--overwrite: Allow output files to overwrite existing files
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

maps=name[,name,...] [required]: Input maps
output=name: Name of output text file
nprocs=integer: Number of threads for parallel computing; Default: 1

DESCRIPTION
Notes
EXAMPLE
REFERENCES
AUTHOR

DESCRIPTION

Module r.niche.similarity computes two metrics to quantify niche similarity or overlap between all pairs of input raster layers.

One is the niche equivalency or similarity for two species following Warren et al. (2008) based on Schoeners D (Schoener, 1968). This metric ranges from 0 to 1, representing respectively no overlap and an identical distribution.

The other is the niche overlap metric which indicates the niche overlap from predictions of species distributions with the I similarity statistic of Warren et al. (2009), which is based on Hellinger Distances (van der Vaart, 1998). The statistic ranges from 0 (no overlap) to 1 (the distributions are identical).

By default the results are written to screen, but they can also be written to a text file with two columns for the names of each pair of rasters, a third column for the type of statistic (D or I) and a fourth column for the D or I statistic.

Notes

This implementation is especially suitable if you are working with very large data sets. Results were checked against the nicheOverlap function in the dismo package for R.

If any of the input maps include NODATA cells, these should normally not be included. To ensure this, the -m flag can be set to remove them. This mimics the default behaviour of the nicheOverlap function in the R dismo package. Depending on what the NODATA represents, an alternative approcah is to replace the NODATA with 0 values before running r.niche.overlap.

Figure 1. with the -m flag set, areas with NODATA in any of the input maps are ignored.

EXAMPLE

Create two random rasters

# Set region
g.region rows=18 cols=36 w=10 s=10 res=0.1

# Create rasters r1 and r2
r.mapcalc 'r1 = rand(0.0,1.0)' seed=0
r.mapcalc 'r1 = rand(0.0,1.0)' seed=1

Compute the I and D

# Create rasters r1 and r2
r.niche.similarity -i -d maps=r1,r2

REFERENCES

Warren, D. L., Glor, R. E., & Turelli, M. 2008. Environmental Niche Equivalency Versus Conservatism: Quantitative Approaches to Niche Evolution. Evolution 62(11): 2868-2883
Warren, D. L., R. E. Glor, and M. Turelli. 2010. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33:607-611.
Robert J. Hijmans, Steven Phillips, John Leathwick and Jane Elith (2013). dismo: Species distribution modeling. R package version 0.8-5. http://CRAN.R-project.org/package=dismo
Christoph Heibl and Clement Calenge (2012). phyloclim: Integrating Phylogenetics and Climatic Niche Modeling. R package version 0.9-0. http://CRAN.R-project.org/package=phyloclim

AUTHOR

Paulo van Breugel, Ecodiv.earth, HAS green academy, Innovative Biomonitoring research group, Climate-robust Landscapes research group

SOURCE CODE

Available at: r.niche.similarity source code (history)

Latest change: Saturday Apr 12 06:10:01 2025 in commit: 453cfa2fcea17580814cac32bd92ec2fdab8bf3e