The use of genetic algorithms and Bayesian classification to model species distributions

Termansen, M.; McClean, C. J.; Preston, C. D.. 2006 The use of genetic algorithms and Bayesian classification to model species distributions. Ecological Modelling, 192. 410-424. 10.1016/j.ecolmodel.2005.07.009

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Abstract/Summary

This paper develops a method to model species’ spatial distributions from environmental variables. The method is based on a search for an optimal identification of environmental niches to match observed species presence/absence data. The identification is based on Bayesian classification and the optimisation is based on a Genetic Algorithm (GA). The algorithm is tested on an artificial “species” and is shown to perform well. We apply the approach to a random sample of 100 plant species native to the British Isles. This enables an identification of the environmental variables that are most important for capturing the species’ spatial distribution. We show that both climate and land use variables are important for modelling the spatial distribution patterns of the sampled species

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.ecolmodel.2005.07.009
Programmes:	CEH Programmes pre-2009 publications > Biodiversity
UKCEH and CEH Sections/Science Areas:	_ Biological Records Centre
ISSN:	0304-3800
Format Availability:	Electronic, Print
Additional Keywords:	Ecological thresholds, Species distributions modelling, Land use effects, Niche models, Bioclimatic modelling
NORA Subject Terms:	Biology and Microbiology
Date made live:	28 Jun 2007 13:09 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/485

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