# dissim: Overlap-Based Functional Dissimilarity and its Decomposition In TPD: Methods for Measuring Functional Diversity Based on Trait Probability Density

## Description

`dissim` calculates the functional dissimilarity between pairs of communities or populations, as well as its decomposition into shared and non-shared trait volume.

## Usage

 `1` ```dissim(x = NULL) ```

## Arguments

 `x` Either an object of class "TPDcomm", generated with the `TPDc` function, containing the TPDc of the considered communities, or an object of class "TPDsp", generated with the `TPDs` or `TPDsMean` functions, containing the TPDs of the considered populations or species.

## Value

`dissim` returns the overlap-based functional dissimilarity between all pairs of populations/species/communities, along with the decomposition of dissimilarity between shared and non-shared trait volume.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```# 1. Compute the TPDs of three different species: traits_iris <- iris[, c("Sepal.Length", "Sepal.Width")] sp_iris <- iris\$Species TPDs_iris <- TPDs(species = sp_iris, traits_iris) #2. Compute the TPDc of three different communities: abundances_comm_iris <- matrix(c(c(0.9, 0.1, 0), #I. setosa dominates c(0.0, 0.9, 0.1 ), #I. Versic. dominates; setosa absent c(0.0, 0.1, 0.9 )), #I. virg. dominates; setosa absent ncol = 3, byrow = TRUE, dimnames = list(paste0("Comm.",1:3), unique(iris\$Species))) TPDc_iris <- TPDc(TPDs = TPDs_iris, sampUnit = abundances_comm_iris) #3. Estimate functional dissimilarity example_dissimilarity_comm <- dissim (TPDc_iris) example_dissimilarity_sps <- dissim (TPDs_iris) ```

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