REND: Functional Evenness, Richness and Divergence of Communities,...

In TPD: Methods for Measuring Functional Diversity Based on Trait Probability Density

Description

REND computes Functional Richness, Functional Evenness and Functional Divergence, the three primary components of functional diversity (Mason et al. 2005) for single or multiple traits. Although these components were originally intended to be calculated for communities, REND also allows to compute them for populations or species. In the case of communities, all the calculations are based on the TPDc of the considered communities; therefore results are independent of any underlying feature of the species that compose the communities.

Usage

REND(TPDc = NULL, TPDs = NULL)

Arguments

TPDc	An object of class "TPDcomm", generated with the TPDc function, containing the TPDc of the considered communities.
TPDs	An object of class "TPDsp", generated with the TPDs function, containing the TPDs of the considered populations or species.

Value

REND returns a list with an element for each of the provided parameters (ie. communities and/or populations/species). These lists contain in turn one element for the Functional Richness of each unit, one for Functional Evenness, and one for Functional Divergence.

References

Mason, NWH, Mouillot, D, Lee, WG and Wilson, JB (2005), Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos, 111: 112–118.

Examples

# 1.  Compute the TPDs of five different species. SP3 is in the center of
#   the trait space, and the rest of species in the corners
set.seed(1)
species_ex <- c(rep("SP1",20), rep("SP2",20), rep("SP3",20), rep("SP4",20),
             rep("SP5",20))
traits_ex <- data.frame(trait1 = c(rnorm(20, 10, 1),
                                   rnorm(20, 10, 1),
                                   rnorm(20, 15, 1),
                                   rnorm(20, 20, 1),
                                   rnorm(20, 20, 1)),
                        trait2 = c(rnorm(20, 10, 1),
                                   rnorm(20, 20, 1),
                                   rnorm(20, 15, 1),
                                   rnorm(20, 10, 1),
                                   rnorm(20, 20, 1)))
species_TPDs <- TPDs (species = species_ex, traits = traits_ex)
#2. Five different communities with different abundances of each species
abundances_ex <- matrix(c(0.05, 0.05, 0.8,  0.05, 0.05, # 1. Low divergence
                   0.9,  0,    0,    0,    0.1,  # 2. High divergence
                   0,    0,    1,    0,    0,    # 3. Low Richness
                   0.2,  0.2,  0.2,  0.2,  0.2,  # 4. High Evenness
                   0.8,  0.05, 0.05, 0.05, 0.05), # 5. Low Evenness
                   ncol = 5, byrow = TRUE, dimnames = list(paste0("Comm.",1:5),
                   unique(species_ex)))

example_TPDc <- TPDc (TPDs = species_TPDs, sampUnit = abundances_ex)
#3. Estimate functional richness, evenness and divergence
example_RicEveDiv <- REND (TPDc = example_TPDc)

Example output

Loading required package: ggplot2
Loading required package: ks
-------Calculating densities for One population_Multiple species-----------

Calculating FRichness of communities
Calculating FEvenness of communities
Calculating FDivergence of communities

TPD documentation built on July 3, 2019, 1:05 a.m.