Estimation of local immigration using GST(k) statistics

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

D

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)

ci

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

GST

A vector of 0 to 1 GST(k) numbers (specific output of GST.k)

nk

Number of individuals within samples (length = number of samples)

distrib

Species counts of the merged dataset (output of GST.k and I.k)

I

Immigration estimates (output of I.k and optimal.params.gst)

m

Corresponding immigration rates (output of I.k and optimal.params.gst). Specific outputs of optimal.params.gst when ci = T (bootstrap procedure)

Ici

Confidence interval of I(k)

mci

Confidence interval of m(k)

Iboot

Table of bootstrapped values of I(k)

mboot

Table 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

Author

Francois Munoz

See also

optimal.params,optimal.params.sloss

Examples

data(ghats)
optimal.params.gst(ghats)
#> $I
#>  [1] 16.774694 12.915263 18.654831  8.420867  8.638719  7.334220 23.669878
#>  [8] 10.362868 12.024855 19.147550 12.596480  8.274654 11.324572  4.841787
#> [15] 34.973891 10.915280 28.603709 11.719364 18.204995 16.323013 27.496478
#> [22] 27.646331  3.826457  1.323756  7.927096 14.818397 26.328859 10.753630
#> [29] 29.014992 14.957210  8.205501  8.468848  8.681752 13.086981 17.554385
#> [36]  4.362208 17.625929 16.267331 10.960310  9.976792  9.353981 13.502007
#> [43]  4.643317  2.569371 13.644226 34.402876 16.861746 12.533388 17.380008
#> [50] 25.281964
#> 
#> $m
#>  [1] 0.072688619 0.046808801 0.040061500 0.037690602 0.021890195 0.012036449
#>  [7] 0.070726047 0.028677179 0.034953449 0.034741242 0.035324184 0.027835046
#> [13] 0.077393510 0.011989317 0.084687899 0.023477997 0.113235506 0.049717443
#> [19] 0.080837440 0.072122640 0.114809530 0.103681647 0.009359611 0.003800362
#> [25] 0.030733862 0.125773200 0.040610345 0.024070605 0.092106701 0.043997332
#> [31] 0.009290590 0.017626217 0.030821139 0.070707194 0.099427637 0.007414154
#> [37] 0.027090726 0.031087992 0.016071772 0.013537457 0.014910212 0.023139607
#> [43] 0.008179990 0.003683320 0.047934314 0.096799654 0.044861564 0.025343866
#> [49] 0.044181218 0.054927123
#>