mahasuhab: Habitat Suitability Mapping with Mahalanobis Distances.
In adehabitat: Analysis of Habitat Selection by Animals

Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function computes the habitat suitability map of an area for a species, given a set of locations of the species occurences (Clark et al. 1993). This function is to be used in habitat selection studies, when animals are not identified.

1	mahasuhab(kasc, pts, type = c("distance", "probability"))

`kasc`	a raster map of class `kasc`
`pts`	a data frame with two columns, giving the coordinates of the species locations
`type`	a character string. Whether the raw `"distance"` should be returned, or rather the `"probability"` (see details).

Let assume that a set of locations of the species on an area is available (gathered on transects, or during the monitoring of the population, etc.). If we assume that the probability of detecting an individual is independent from the habitat variables, then we can consider that the habitat found at these sites reflects the habitat use by the animals.

The Mahalanobis distances method has become more and more popular during the past few years to derive habitat suitability maps. The niche of a species is defined as the probability density function of presence of a species in the multidimensionnal space defined by the habitat variables. If this function can be assumed to be multivariate normal, then the mean vector of this distribution corresponds to the optimum for the species.

The function mahasuhab first computes this mean vector as well as the variance-covariance matrix of the niche density function, based on the value of habitat variables in the sample of locations. Then, the *squared* Mahalanobis distance from this optimum is computed for each pixel of the map. Thus, the smaller this squared distance is for a given pixel, and the better is the habitat in this pixel.

Assuming multivariate normality, squared Mahalanobis distances are approximately distributed as Chi-square with n-1 degrees of freedom, where n equals the number of habitat characteristics. If the argument type = "probability", maps of these p-values are returned by the function. As such these are the probabilities of a larger squared Mahalanobis distance than that observed when x is sampled from the niche.

Returns a raster map of class asc.

The computation of the squared Mahalanobis distances inverts the variance-covariance matrix of the niche density function (see ?mahalanobis). It is therefore important that the habitat variables considered are not too correlated among each other. When the habitat variables are too correlated, the variance-covariance matrix is singular and cannot be inverted.

Note also that it is recommended to scale the variables before the computation, so that they all have the same variance, and therefore the same weight in the analysis (see examples below).

Clement Calenge clement.calenge@oncfs.gouv.fr

Clark, J.D., Dunn, J.E. and Smith, K.G. (1993) A multivariate model of female black bear habitat use for a geographic information system. Journal of Wildlife Management, 57, 519–526.

asc for further information on objects of class asc, kasc for additional information on objects of class kasc, domain for another method of habitat suitability mapping, and mahalanobis for information on the computation of Mahalanobis distances.

## loads the data
data(lynxjura)
ka <- lynxjura$map
lo <- lynxjura$locs[,1:2]

## We first scale the maps
df <- kasc2df(ka)
pc <- dudi.pca(df$tab, scannf=FALSE)
tab <- pc$tab
ka <- df2kasc(tab, df$index, ka)

## habitat suitability mapping
hsm <- mahasuhab(ka, lo, type = "probability")
plot(hsm, main = "Habitat suitability map for the Lynx",
     plot.axes = { points(lo, pch = 16, cex=0.5)})

Loading required package: ade4
Loading required package: tkrplot
Loading required package: tcltk
Loading required package: shapefiles
Loading required package: foreign

Attaching package: 'shapefiles'

The following objects are masked from 'package:foreign':

    read.dbf, write.dbf

Loading required package: sp

************************************************
************************************************
THE PACKAGE adehabitat IS NOW DEPRECATED!!!!!!!
 It is dangerous to use it, as bugs will no longer be corrected.
It is now recommended to use the packages adehabitatMA, adehabitatLT, adehabitatHR, and adehabitatHS.
These 4 packages are the future of adehabitat.
 They have a vignette explaining in detail how they can be used.
They implement more methods than adehabitat
They are based on the more common and more clever spatial classes implemented in sp.
Bugs are corrected frequently.
Really, avoid to use the classical adehabitat, unless you have a very good reason for it.

*****THIS IS THE VERY LAST WARNING*****
 This is the last version of adehabitat submitted to CRAN (at the time of writing: 2015-03-27).
 THE NEXT VERSION OF adehabitat WILL JUST BE A VIRTUAL PACKAGE LOADING ALL THE PACKAGES DESCRIBED ABOVE.

Warning messages:
1: no DISPLAY variable so Tk is not available 
2: loading Rplot failed