optimal.params.gst: Estimation of local immigration using GST(k) statistics

Description

Functions optimal.params.gst(), GST.k() and I.k() apply to count data collected over a network of community samples k (species by sample matrix). A theoretical relationship between GST(k) statistics and local immigration numbers I(k), in the context of a spatially-implicit neutral community model (Munoz et al 2008), is used to provide GST(k) and I(k) statistics any sample k. If requested, optimal.params.gst() further provides the user with confidence bounds.

Usage

optimal.params.gst(D, exact = TRUE, ci = FALSE, cint = c(0.025, 0.975), nbres = 100)
GST.k(D, exact = TRUE)
I.k(D, exact = TRUE)

Arguments

A data table including species counts in a network of community samples (columns)

exact

If TRUE, exact similarity statistics are calculated (sampling without replacement) while, if false, approximate statistics (sampling with replacement) are considered (see Munoz et al 2008 for further statistical discussion)

Specifies whether bootstraps confidence intervals of immigration estimates are to be calculated

cint

Bounds of the confidence interval, if ci = TRUE

nbres

Number of rounds of the bootstrap procedure for confidence interval calculation, if ci = T

Value

GSTA vector of 0 to 1 GST(k) numbers (specific output of GST.k)
nkNumber of individuals within samples (length = number of samples)
distribSpecies counts of the merged dataset (output of GST.k and I.k)
IImmigration estimates (output of I.k and optimal.params.gst)
mCorresponding immigration rates (output of I.k and optimal.params.gst). Specific outputs of optimal.params.gst when ci = T (bootstrap procedure)
IciConfidence interval of I(k)
mciConfidence interval of m(k)
IbootTable of bootstrapped values of I(k)
mbootTable of bootstrapped values of im(k)

References

Francois Munoz, Pierre Couteron and B.R. Ramesh (2008). Beta-diversity in spatially implicit neutral models: a new way to assess species migration. The American Naturalist 172(1): 116-127

Examples

Run this code

data(ghats)
optimal.params.gst(ghats)