diversity: Alpha Diversity

In tesselle/tabula: Analysis and Visualization of Archaeological Count Data

Alpha Diversity

Description

Computes multiple alpha diversity indices.

Usage

diversity(object, ...)

## S4 method for signature 'matrix'
diversity(object, ..., evenness = FALSE, unbiased = FALSE)

## S4 method for signature 'data.frame'
diversity(object, ..., evenness = FALSE, unbiased = FALSE)

Arguments

object	A m \times p numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). A data.frame will be coerced to a numeric matrix via data.matrix().
...	Currently not used.
evenness	A logical scalar: should an evenness measure be computed instead of an heterogeneity/dominance index? Only available for shannon, simpson and brillouin indices.
unbiased	A logical scalar: should the bias-corrected estimator be used? Only available for shannon, simpson and chao1 indices.

Details

Alpha diversity refers to diversity at the local level, assessed within a delimited system. It is the diversity within a uniform habitat of fixed size.

Diversity measurement assumes that all individuals in a specific taxa are equivalent and that all types are equally different from each other (Peet 1974). A measure of diversity can be achieved by using indices built on the relative abundance of taxa. These indices (sometimes referred to as non-parametric indices) benefit from not making assumptions about the underlying distribution of taxa abundance: they only take relative abundances of the species that are present and species richness into account. Peet (1974) refers to them as indices of heterogeneity.

Diversity indices focus on one aspect of the taxa abundance and emphasize either richness (weighting towards uncommon taxa) or dominance (weighting towards abundant taxa; Magurran 1988).

Evenness is a measure of how evenly individuals are distributed across the sample.

Value

A data.frame with the following columns:

size

Sample size.

observed

Number of observed taxa/types.

shannon

Shannon-Wiener diversity index.

brillouin

Brillouin diversity index.

simpson

Simpson dominance index.

berger

Berger-Parker dominance index.

menhinick

Menhinick richness index.

margalef

Margalef richness index.

chao1

Chao1 estimator.

ace

Abundance-based Coverage Estimator.

squares

Squares estimator.

Note

The berger and simpson methods return a dominance index, not the reciprocal or inverse form usually adopted, so that an increase in the value of the index accompanies a decrease in diversity.

Author(s)

N. Frerebeau

References

Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-94-015-7358-0")}.

Peet, R. K. (1974). The Measurement of Species Diversity. Annual Review of Ecology and Systematics, 5(1), 285-307. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1146/annurev.es.05.110174.001441")}.

See Also

Other diversity measures: evenness(), heterogeneity(), occurrence(), plot.DiversityIndex(), plot.RarefactionIndex(), profiles(), rarefaction(), richness(), she(), similarity(), simulate(), turnover()

Examples

## Data from Conkey 1980, Kintigh 1989
data("cantabria")

## Alpha diversity
diversity(cantabria)

## Shannon diversity index
(h <- heterogeneity(cantabria, method = "shannon"))
(e <- evenness(cantabria, method = "shannon"))

as.data.frame(h)

tesselle/tabula documentation built on June 15, 2025, 12:08 a.m.