BAT (version 2.5.1)

kernel.evenness.contribution: Contribution of each observation to the evenness of a n-dimensional hypervolume representing a given species or community.

Description

Contribution of each species or individual to the evenness of one or more kernel hypervolumes.

Usage

kernel.evenness.contribution(
  comm,
  trait,
  method = "gaussian",
  abund = TRUE,
  ...
)

Arguments

comm

A 'Hypervolume' object constructed with the hypervolume R package. Alternatively, a sites x species matrix, data.frame or vector, with incidence or abundance data about the species in the community. Note that the use of 'HypervolumeList' object is not implemented for this function yet.

trait

A matrix of traits for each species in comm (a species for each row and traits as columns). Must be provided only if 'comm' is a sites x species matrix.

method

Method for constructing the 'Hypervolume' object. One of "box" (box kernel density estimation), "gaussian" (Gaussian kernel density estimation), or "svm" (one-class support vector machine). See respective functions of the hypervolume R package for details. Must be provided only if 'comm' is a sites x species matrix. Default is 'gaussian'.

abund

A boolean (T/F) indicating whether abundance data should be used as weights in hypervolume construction. Only works if method = "gaussian". Ignored if a 'Hypervolume' or 'HypervolumeList' is provided as input data.

...

further arguments to be passed for other methods in hypervolume package.

Value

A matrix with the contribution values of each species or individual for each community or species respectively.

Details

The contribution of each observation (species or individual) to the total evenness of a kernel hypervolume. Contribution to evenness is calculated as the difference in evenness between the total hypervolume and a second hypervolume lacking this specific observation (i.e., leave-one-out approach; Mammola & Cardoso, 2020). Note that the contribution of a species or individual can be negative, if the removal of an observation increases the total evenness.

References

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

Examples

# NOT RUN {
comm <- rbind(c(0,3,2,1), c(1,5,6,2), c(0,0,2,1))
rownames(comm) <- c("Community_1", "Community_2", "Community_3")
colnames(comm) <- c("Sp_1", "Sp_2", "Sp_3", "Sp_4")

trait <- cbind(c(2.2,4.4,6.1,8.3), c(0.5,1,0.5,0.4), c(0.7,1.2,0.5,0.4))
rownames(trait) <- c("Sp_1", "Sp_2", "Sp_3", "Sp_4")
colnames(trait) <- c("Trait_1", "Trait_2", "Trait_3")

#Example with community and trait matrices as input data
#kernel.evenness.contribution(comm = comm, trait = trait, method = "gaussian")

#Example with hypervolume as input data
#kernel.evenness.contribution(comm = hypervolume_gaussian(trait))
# }