beta.sample.abund {betapart}

Resampling abundance-based multiple-site dissimilarity for n sites

Description

Resamples the 3 abundance-based multiple-site dissimilarities (balanced variation fraction,abundance-gradient fraction, and overall dissimilarity) for a subset of sites of the original data frame.

Usage

beta.sample.abund(x, index.family="bray", sites = nrow(x), samples = 1)

Arguments

x

data frame, where rows are sites and columns are species

index.family

family of dissimilarity indices, partial match of "bray" or "ruzicka".

sites

number of sites for which multiple-site dissimilarities will be computed. If not specified, default is all sites.

samples

number of repetitions. If not specified, default is 1.

Value

The function returns a list with a dataframe with the resampled 3 multiple-site dissimilarities (balanced variation fraction, abundance-gradient fraction and overall dissimilarity; see beta.multi.abund), a vector with the respective means and a vector with the respective standard deviation.

For index.family="bray":

sampled.values

dataframe containing beta.BRAY.BAL, beta.BRAY.GRA and beta.BRAY for all samples

mean.values

vector containing the mean values of beta.BRAY.BAL, beta.BRAY.GRA and beta.BRAY among samples

sd.values

vector containing the sd values of beta.BRAY.BAL, beta.BRAY.GRA and beta.BRAY among samples

For index.family="ruzicka":

sampled.values

dataframe containing beta.RUZ.BAL, beta.RUZ.GRA and beta.RUZ for all samples

mean.values

vector containing the mean values of beta.RUZ.BAL, beta.RUZ.GRA and beta.RUZ among samples

sd.values

vector containing the sd values of beta.RUZ.BAL, beta.RUZ.GRA and beta.RUZ among samples

Author(s)

Andrés Baselga

References

Baselga, A. 2017. Partitioning abundance-based multiple-site dissimilarity into components: balanced variation in abundance and abundance gradients. Methods in Ecology and Evolution 8: 799-808

See Also

beta.multi.abund, beta.sample

Examples

require(vegan)
data(BCI)
beta.sample.abund(BCI, index.family="bray", sites=10, samples=100)