distinctiveness_alt: Truncated Functional Distinctiveness

View source: R/distinctiveness_alt.R

distinctiveness_altR Documentation

Truncated Functional Distinctiveness

Description

Computes functional distinctiveness from a site-species matrix (containing presence-absence or relative abundances) of species with provided functional distance matrix considering only species within a given range in the functional space. Basically species are cutoff when their dissimilarity is above the input threshold. The sites-species matrix should have sites in rows and species in columns, similar to vegan package defaults.

Usage

distinctiveness_alt(pres_matrix, dist_matrix, given_range)

Arguments

pres_matrix

a site-species matrix (presence-absence or relative abundances), with sites in rows and species in columns

dist_matrix

a species functional distance matrix

given_range

a numeric indicating the dissimilarity range at which the the other species are considered maximally dissimilar

Details

The Functional Distinctiveness of a species is the average functional distance from a species to all the other in the given community. It is computed as such:

D_i (T) = Σ_(j = 0, j != i)^N [d_ij/T + θ(d_ij - T) (1 - d_ij/T)] / S - 1

with D_i the functional distinctiveness of species i, N the total number of species in the community and d_ij the functional distance between species i and species j. T is the chosen maximal range considered. The function θ(d_ij - T) is an indicator function that returns 1 when d_ij ≥ T and 0 when d_ij < T. IMPORTANT NOTE: in order to get functional rarity indices between 0 and 1, the distance metric has to be scaled between 0 and 1.

Value

a similar matrix from provided pres_matrix with Distinctiveness values in lieu of presences or relative abundances, species absent from communities will have an NA value (see Note section)

Note

Absent species should be coded by 0 or NA in input matrices.

When a species is alone in its community the functional distinctiveness cannot be computed (denominator = 0 in formula), and its value is assigned as NaN.

For speed and memory efficiency sparse matrices can be used as input of the function using as(pres_matrix, "dgCMatrix") from the Matrix package. (see vignette("sparse_matrices", package = "funrar"))


funrar documentation built on Sept. 23, 2022, 5:10 p.m.