Mixtures of GAMs for habitat suitability analysis with overdispersed presence/absence data

Authors:
David R. J. Pleydell;Stéphane Chrétien
Affiliations:
UMR 6249 Laboratoire Chrono-Environnement, Université de Franche-Comté, Place Leclerc, 25030 Besançon Cedex, France;Laboratoire de Mathématiques, UMR CNRS 6623 et Université de Franche Comté, 16 Route de Gray, 25030 Besançon Cedex, France
Venue:
Computational Statistics & Data Analysis
Year:
2010

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Abstract

A new approach to species distribution modelling based on unsupervised classification via a finite mixture of GAMs incorporating habitat suitability curves is proposed. A tailored EM algorithm is outlined for computing maximum likelihood estimates. Several submodels incorporating various parameter constraints are explored. Simulation studies confirm that under certain constraints, the habitat suitability curves are recovered with good precision. The method is also applied to a set of real data concerning presence/absence of observable small mammal indices collected on the Tibetan plateau. The resulting classification was found to correspond to species level differences in habitat preference described in the previous ecological work.