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Distance-based parametric bootstrap tests for clustering of species ranges

Hennig, C; Hausdorf, B; (2004) Distance-based parametric bootstrap tests for clustering of species ranges. Computational Statistics and Data Analysis , 45 (4) 875 - 895. 10.1016/S0167-9473(03)00091-4.

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Methods to analyze species range data are presented: n species (taxa) are characterized by their presence or absence on c units into which a map is subdivided. Such data occur often in biogeography. Some tests for the existence of clusters of species according to their ranges are proposed. Some distance-based test statistics for the presence of clustering are defined. A null model for the generation of a species and an alternative model for clustering is proposed. The models include a parameter governing the spatial autocorrelation of its occurrence in the cells and they account for the species richness of the individual cells. The distribution of the test statistics can be estimated by a parametric bootstrap simulation (Monte Carlo with estimated parameters) from the null model. The validity of the p-values and the power of the tests are considered by exemplary simulations. The determination of the clusters is also briefly discussed.

Title:Distance-based parametric bootstrap tests for clustering of species ranges
Additional information:Imported via OAI, 7:29:01 24th Apr 2008
Keywords:spatial autocorrelation, presence-absence data, biogeography, clustering under noise, Monte Carlo, double bootstrap
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

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