BAT (version 2.9.6)

kernel.gamma: Gamma diversity using kernel density hypervolumes.

Description

Estimation of functional richness of multiple sites, based on n-dimensional hypervolumes.

Usage

kernel.gamma(comm)

Value

A single value of gamma.

Arguments

comm

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

Details

Estimates the functional richness (gamma FD) of multiple communities using kernel density hypervolumes, as implemented in Blonder et al. (2014, 2018). Functional richness is expressed as the total volume of the n-dimensional hypervolume (Mammola & Cardoso, 2020). Note that the hypervolume is dimensionless, and that only hypervolumes with the same number of dimensions can be compared in terms of functional richness. Given that the density and positions of stochastic points in the hypervolume are probabilistic, the functional richness of the trait space will intimately depend on the quality of input hypervolumes (details in Mammola & Cardoso, 2020).

References

Blonder, B., Lamanna, C., Violle, C. & Enquist, B.J. (2014) The n-dimensional hypervolume. Global Ecology and Biogeography, 23: 595-609.

Blonder, B., Morrow, C.B., Maitner, B., Harris, D.J., Lamanna, C., Violle, C., ... & Kerkhoff, A.J. (2018) New approaches for delineating n-dimensional hypervolumes. Methods in Ecology and Evolution, 9: 305-319.

Mammola, S. & Cardoso, P. (2020) Functional diversity metrics using kernel density n-dimensional hypervolumes. Methods in Ecology and Evolution, 11: 986-995.

Examples

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

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

hv = kernel.build(comm[1,], trait)
kernel.alpha(hv)
kernel.gamma(hv)
hvlist = kernel.build(comm, trait)
kernel.alpha(hvlist)
kernel.gamma(hvlist)
}