BAT (version 2.9.6)

kernel.dispersion: Functional dispersion of kernel density hypervolumes.

Description

Average distance to centroid or dissimilarity between random points within the boundaries of the kernel density hypervolume.

Usage

kernel.dispersion(comm, func = "dissimilarity", frac = 0.1)

Value

A value or vector of dispersion values for each site.

Arguments

comm

A 'Hypervolume' or 'HypervolumeList' object, preferably built using function kernel.build.

func

Function for calculating dispersion. One of 'divergence', 'dissimilarity' or 'regression'.

frac

A value between 0.01 and 1, indicating the fraction of random points to be used. Default is 0.1.

Details

This function calculates dispersion either: i) as the average distance between stochastic points within the kernel density hypervolume and the centroid of these points (divergence; Laliberte & Legendre, 2010; see also Carmona et al., 2019); ii) as the average distance between all points (dissimilarity, see also function BAT::dispersion); or iii) as the average distance between stochastic points within the kernel density hypervolume and a regression line fitted through the points. The number of stochastic points is controlled by the 'frac' parameter (increase this number for less deviation in the estimation).

References

Carmona, C.P., de Bello, F., Mason, N.W.H. & Leps, J. (2019) Trait probability density (TPD): measuring functional diversity across scales based on TPD with R. Ecology, 100: e02876.

Laliberte, E. & Legendre, P. (2010) A distance-based framework for measuring functional diversity from multiple traits. Ecology 91: 299-305.

Examples

if (FALSE) {
comm = rbind(c(1,3,0,5,3), c(3,2,5,1,0))
colnames(comm) = c("SpA", "SpB", "SpC", "SpD", "SpE")
rownames(comm) = c("Site 1", "Site 2")

trait = data.frame(body = c(1,2,3,4,4), beak = c(1,5,4,1,2))
rownames(trait) = colnames(comm)

hv = kernel.build(comm[1,], trait)
kernel.dispersion(hv)
hvlist = kernel.build(comm, trait, axes = 2)
kernel.dispersion(hvlist)
kernel.dispersion(hvlist, func = "divergence")
}