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

 `1` ```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 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28``` ```# 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
-------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.