niche_overlap_index: Index of habitat niche overalp
In cyrilmi/myfunctions: Rpackage cyril

Description Usage Arguments Details Value Author(s) Examples

Derive an index (0: complete separation, 1: complete overlap) from a kselect object to estimate a degree of habitat niche overlap between species/indviduals

1	niche_overlap_index(X, kernel = "gaussian", bw = "nrd0")

`X`	a kselect (adehabitatHS) object
`kernel`	a character string giving the smoothing kernel to be used. Default is "gaussian". See `density()`
`bw`	the smoothing bandwidth to be used to compute the kernel density. Default is "nrd0". See `density()`
`permutations`	logical, if permutations should be computed or not
`nb.permutations`	number of permutations to compute
`hypothesis`	the H0 to be tested: c("segregation","overlap")

Uses scores obtained for each individual on each dimension of the ecological space of the Kselect and compute a non-parametric kernel estimation. Area of overlap between distribution of the two indviduals is calculated. Eigen values of the kslect are then used to calculated an overlap weihgted index.

O_{ij} = \int_{b}^{a} ≤ft [ f(x) - g(x) \right ]dx

\bar{O_{ij}} = \frac{∑_{e}^{n} O_{ij_{e}} λ _{e}}{∑_{e}^{n} λ _{e}}

i and j are the two individuals to be compared, f(x) and f(x) the two probability functions of indviduals i and j and e each of the axes.

A list of paired matrices with an overlap index between each individuals calculated on each axis and a Weighted (based on eigen values of the kselect) overlap index calculated on all axes

Cyril Milleret

library(adehabitatHS)
data(puechabonsp)

locs <- puechabonsp$relocs
map <- puechabonsp$map

## compute the home range of animals (e.g. using the minimum convex polygon)
 
pc <- mcp(locs[,"Name"])
hr <- hr.rast(pc, map)

## Compute the number of relocation in each pixel of the map
cp <- count.points(locs[,"Name"], map)

## prepares the data for the kselect analysis
x <- prepksel(map, hr, cp)
tab <- x$t

## We call a new graphic window
## A K-select analysis
acp <- dudi.pca(tab, scannf = FALSE, nf = 4)
kn <- kselect(acp, x$factor, x$weight,
              scannf = FALSE, nf = 4)

# compute habitat niche overlap 
niche_overlap_index(kn, kernel = "gaussian", bw = "nrd0",permutations=TRUE,nb.permutations=100,hypothesis="segregation")