entropart-package: Entropy Partitioning to Measure Diversity

In entropart: Entropy Partitioning to Measure Diversity

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 documentation built on Sept. 11, 2024, 6:20 p.m.