functional_beta function

Functional beta diversity for mixed-type functional traits

Computes turnover of functional diversity using k-prototypes clustering algorithm tailored for mixed-type functional traits (numeric and categorical) to generate an integer vector of cluster assignments. The ranges of each species in a cluster are collapsed to generate a new community matrix based on the presence or absence of cluster membership in a grid cell. A grade of membership model or beta diversity is then fitted to the new reduced community matrix for further analysis.

functional_beta(
  x,
  trait = NULL,
  bin = 10,
  na.rm = "no",
  quick_elbow = FALSE,
  abundance = FALSE,
  ...
)

Arguments

• x: A dataframe or sparse community matrix of species occurrences.

• trait: A data frame with the first column labeled species

  containing the taxonomic groups to be evaluated whereas the remaining columns contain the various functional traits. The variables should be mixed-type combining numeric and categorical variables.

• bin: The desired number of clusters or bins. If elbow=TRUE, the optimal number of clusters is determined by running the analysis multiple times varying from 2 to bin.

• na.rm: Logical, whether NA values should be removed prior to computation

• quick_elbow: Quickly estimate the 'elbow' of a scree plot to determine the optimal number of clusters.

• abundance: Logical, whether the reduced matrix should be returned as presence or absence of cluster representation or as abundances of cluster memberships

• ...: Further arguments passed to or from other methods.

Returns

A list with three dissimilarity matrices capturing: (i) turnover (replacement), (ii) nestedness-resultant component, and (iii) total dissimilarity (i.e. the sum of both components).

For index.family="sorensen" the three matrices are:

• beta.sim A distance object, dissimilarity matrix accounting for spatial turnover (replacement), measured as Simpson pair-wise dissimilarity.
• beta.sne dist object, dissimilarity matrix accounting for nestedness-resultant dissimilarity, measured as the nestedness-fraction of Sorensen pair-wise dissimilarity
• beta.sor dist object, dissimilarity matrix accounting for total dissimilarity, measured as Sorensen pair-wise dissimilarity (a monotonic transformation of beta diversity)

For index.family="jaccard" the three matrices are:

• beta.jtu A distance object, dissimilarity matrix accounting for spatial turnover, measured as the turnover-fraction of Jaccard pair-wise dissimilarity
• beta.jne dist object, dissimilarity matrix accounting for nestedness-resultant dissimilarity, measured as the nestedness-fraction of Jaccard pair-wise dissimilarity
• beta.jac dist object, dissimilarity matrix accounting for beta diversity, measured as Jaccard pair-wise dissimilarity (a monotonic transformation of beta diversity)

Examples

library(terra)
data(africa)
p <- vect(system.file("ex/sa.json", package = "phyloregion"))
fb <- functional_beta(x=africa$comm, trait = africa$trait)
p <- phyloregion(fb[[1]], pol = p)
plot(p)

References

Szepannek, G. (2018) clustMixType: User-friendly clustering of mixed-type data in R. The R Journal, 10 : 200-208.