ecospat (version 3.3)

ecospat.Cscore: Pairwise co-occurrence Analysis with calculation of the C-score index.

Description

The function tests for nonrandom patterns of species co-occurrence in a presence-absence matrix. It calculates the C-score index for the whole community and for each species pair. Null communities have column sum fixed.

Usage

ecospat.Cscore (data, nperm, outpath, verbose = FALSE)

Value

The function returns the C-score index for the observed community (ObsCscoreTot), p.value (PValTot) and standardized effect size (SES.Tot). It saves also a table in the working directory where the same metrics are calculated for each species pair (only the table with species pairs with significant p-values is saved in this version)

Arguments

data

A presence-absence dataframe for each species (columns) in each location or grid cell (rows). Column names (species names) and row names (sampling plots).

nperm

The number of permutation in the null model.

outpath

Path to specify where to save the results.

verbose

Boolean indicating whether to print progress output during calculation. Default is FALSE.

Author

Christophe Randin and Manuela D'Amen <[e-mail]>

Details

This function allows to apply a pairwise null model analysis (Gotelli and Ulrich 2010) to a presence-absence community matrix to determine which species associations are significant across the study area. The strength of associations is quantified by the C-score index (Stone and Roberts 1990) and a 'fixed-equiprobable' null model algorithm is applied. The format required for input databases: a plots (rows) x species (columns) matrix. Input matrices should have column names (species names) and row names (sampling plots). NOTE: a SES that is greater than 2 or less than -2 is statistically significant with a tail probability of less than 0.05 (Gotelli & McCabe 2002).

References

Gotelli, N.J. and D.J. McCabe. 2002. Species co-occurrence: a meta-analysis of JM Diamond's assembly rules model. Ecology, 83, 2091-2096.

Gotelli, N.J. and W. Ulrich. 2010. The empirical Bayes approach as a tool to identify non-random species associations. Oecologia, 162, 463-477

Stone, L. and A. Roberts, A. 1990. The checkerboard score and species distributions. Oecologia, 85, 74-79

See Also

ecospat.co_occurrences and ecospat.cons_Cscore

Examples

if (FALSE) {
data<- ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
nperm <- 10000
outpath <- getwd()
Cscore<-ecospat.Cscore(data, nperm, outpath)

}