entropart-package {entropart} | R Documentation |

## Entropy Partitioning to Measure Diversity

### Description

Functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.

Estimation-bias corrections are available.

### Details

In the entropart package, individuals of different "species" are counted in several "communities" which may (or not) be agregated to define a "metacommunity". In the metacommunity, the probability to find a species in the weighted average of probabilities in communities. This is a naming convention, which may correspond to plots in a forest inventory or any data organized the same way.

Basic functions allow computing diversity of a community. Data is simply a vector of probabilities (summing up to 1) or of abundances (integer values that are numbers of individuals). Calculate entropy with functions such as `Tsallis`

, `Shannon`

, `Simpson`

, `Hurlbert`

or `GenSimpson`

and explicit diversity (i.e. effective number of species) with `Diversity`

and others. By default, the best available estimator of diversity will be used, according to the data.

Communities can be simulated by `rCommunity`

, explicitely declared as a species distribution (`as.AbdVector`

or `as.ProbaVector`

), and plotted.

Phylogenetic entropy and diversity can be calculated if a phylogenetic (or functional), ultrametric tree is provided. See `PhyloEntropy`

, `Rao`

for examples of entropy and `PhyloDiversity`

to calculate phylodiversity, with the state-of-the-art estimation-bias correction. Similarity-based diversity is calculated with `Dqz`

, based on a similarity matrix.

The simplest way to import data is to organize it into two text files. The first file should contain abundance data: the first column named `Species`

for species names, and a column for each community.

The second file should contain the community weights in two columns. The first one, named `Communities`

should contain their names and the second one, named `Weights`

, their weights.

Files can be read and data imported by code such as:

Abundances <- read.csv(file="Abundances.csv", row.names = 1) Weights <- read.csv(file="Weights.csv") MC <- MetaCommunity(Abundances, Weights)

The last line of the code calls the `MetaCommunity`

function to create an object that will be used by all metacommunity functions, such as `DivPart`

(to partition diversity), `DivEst`

(to partition diversity and calculate confidence interval of its estimation) or `DivProfile`

(to compute diversity profiles).

A full documentation is available in the vignette. Type: `vignette("entropart")`

. A quick introuction is in `vignette("introduction", "entropart")`

.

### Author(s)

Eric Marcon, Bruno Herault

### References

Grabchak, M., Marcon, E., Lang, G., and Zhang, Z. (2017). The Generalized Simpson's Entropy is a Measure of Biodiversity. *Plos One*, 12(3): e0173305.

Marcon, E. (2015) Practical Estimation of Diversity from Abundance Data. *HAL* 01212435: 1-27.

Marcon, E. and Herault, B. (2015). entropart: An R Package to Measure and Partition Diversity. *Journal of Statistical Software*, 67(8): 1-26.

Marcon, E., Herault, B. (2015). Decomposing Phylodiversity. *Methods in Ecology and Evolution* 6(3): 333-339.

Marcon, E., Herault, B., Baraloto, C. and Lang, G. (2012). The Decomposition of Shannon's Entropy and a Confidence Interval for Beta Diversity. *Oikos* 121(4): 516-522.

Marcon, E., Scotti, I., Herault, B., Rossi, V. and Lang G. (2014). Generalization of the partitioning of Shannon diversity. *PLOS One* 9(3): e90289.

Marcon, E., Zhang, Z. and Herault, B. (2014). The decomposition of similarity-based diversity and its bias correction. *HAL* hal-00989454(version 3).

*entropart*version 1.6-13 Index]