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

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  • Pleydell, David R.J.
  • Chrétien, Stéphane

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.

Suggested Citation

  • Pleydell, David R.J. & Chrétien, Stéphane, 2010. "Mixtures of GAMs for habitat suitability analysis with overdispersed presence/absence data," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1405-1418, May.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:5:p:1405-1418
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    References listed on IDEAS

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    1. Zhu, Hongtu & Zhang, Heping, 2006. "Asymptotics for estimation and testing procedures under loss of identifiability," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 19-45, January.
    2. Vaniscotte, Amélie & Pleydell, David R.J. & Raoul, Francis & Quéré, Jean Pierre & Jiamin, Qiu & Wang, Qian & Tiaoying, Li & Bernard, Nadine & Coeurdassier, Michael & Delattre, Pierre & Takahashi, Keni, 2009. "Modelling and spatial discrimination of small mammal assemblages: An example from western Sichuan (China)," Ecological Modelling, Elsevier, vol. 220(9), pages 1218-1231.
    3. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
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