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.
1 2 3 |
D |
A data table including species counts in a network of community samples (columns) |
exact |
If |
ci |
Specifies whether bootstraps confidence intervals of immigration estimates are to be calculated |
cint |
Bounds of the confidence interval, if |
nbres |
Number of rounds of the bootstrap procedure for confidence interval calculation, if ci = T |
GST |
A vector of 0 to 1 |
nk |
Number of individuals within samples (length = number of samples) |
distrib |
Species counts of the merged dataset (output of |
I |
Immigration estimates (output of |
m |
Corresponding immigration rates (output of |
Ici |
Confidence interval of |
mci |
Confidence interval of |
Iboot |
Table of bootstrapped values of |
mboot |
Table of bootstrapped values of i |
Francois Munoz
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
optimal.params
,optimal.params.sloss
1 2 |
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