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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