BAT (version 2.11.0)

kernel.arrangement: Functional arrangement of kernel density hypervolumes.

Description

Functional arrangement of a community, measuring the distribution of species within the total functional space at multiple spatial scales. This is a specific function to be used with an Hypervolume object, preferably, from kernel.build function.

Usage

kernel.arrangement(
  comm,
  stat = "PNCP",
  distances = seq(0, 1, 0.01),
  type = "SES",
  alpha = 0.05,
  runs = 99,
  plot = TRUE
)

Value

A list with observed PNCP or NNCP values, a matrix of simulated values at r distances (r x sim), and standard effect size.

Arguments

comm

A 'Hypervolume' object, built using function kernel.build.

stat

statistic to be calculated. One of c("PNCP", "NNCP"), meaning "cumulative proportion of pairwise neighbors" and "cumulative proportion of nearest neighbors", respectively.

distances

vector of distances to be considered in calculations

type

Envelope type for testing significance. One of c("ecdf", "norm", "SES"), meaning "empirical cumulative distribution", "normalized envelope" (between 0-1, 0.5 indicate randomness, more than 0.5 - clustered; less than 0.5 - inhibition), and "standardized effect size" respectively.

alpha

alpha value to consider in significance testing (p-value).

runs

number of simulations for significance testing

plot

whether to plot the results

Details

This function measures the functional arrangement (Carvalho & Cardoso, 2025) of a n-dimensional hypervolume, namely the distribution of species within the total trait space at multiple spatial scales.

References

Carvalho, J.C. & Cardoso, P. (2025) Quantifying species distribution within the functional space.

Examples

if (FALSE) {
comm = c(100,3,0,5,3)
names(comm) = c("SpA", "SpB", "SpC", "SpD", "SpE")

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

hv = kernel.build(comm, trait, method.hv = "svm", svm.nu = 0.01, svm.gamma = 0.25)
kernel.arrangement(hv)
}