kernel.arrangement {BAT}R Documentation

Functional arrangement of kernel density hypervolumes.

Description

Functional arrangement of a community, measuring the distribution of stochastic points within the total functional space at different distances.

Usage

kernel.arrangement(
  comm,
  stat = "rneig",
  distance = seq(0, 1, 0.01),
  pool = NULL,
  type = "SES",
  alpha = 0.05,
  runs = 99,
  plotValues = TRUE
)

Arguments

comm

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

stat

statistic to be calculated. One of c("rneig", "nnpair"), meaning "nearest neighbor" and "all neighbors" respectively.

distance

vector of distances to be considered in calculations

pool

Species pool coordinates to use for null model construction. When NULL (default), the function performs a random displacement null model using the environmental space defined in the hypervolume object. When specified (typically coordinates from hyper.build output), the function instead performs a random selection null model, drawing species randomly from the provided pool coordinates. Must be a matrix or data.frame of coordinates matching the hypervolume dimensions.

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.

plotValues

Whether to plot "rneig" or "nnpair" values for all distances.

Details

This function measures the functional arrangement (Carvalho & Cardoso, subm.) of a n-dimensional hypervolume, namely the distribution of stochastic points within the total trait space from small to large functional distances.

Value

A list with observed rneig or nnpair values, the confidence limits and standard effect size.

References

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

Examples

## Not run: 
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)

## End(Not run)

[Package BAT version 2.10.0 Index]