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New paradigms for modelling species distributions?

Lookup NU author(s): Professor Stephen Rushton


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1. The management of both desirable and undesirable species requires an understanding of the factors determining their distribution. Quantitative distribution models offer simple methods for formulating the species-habitat link and the means not only for predicting where species should occur, but also for understanding the factors involved. Generalized linear modelling, in particular, links the incidence of species to habitat variables, and has increasingly formed the backbone of the modelling approaches used. New 'data technologies', such as remote sensing and geographical information systems, have further broadened these modelling applications to almost any ecological system and any species for which there are distribution data. 2. Many previous approaches have aimed to identify the most parsimonious model with the best suite of predictors, selected on the basis of null hypothesis testing. However, information-theoretic approaches based on Akaike's information criterion allow the selection of a best approximating model or a subset of models from a set of candidates. Information-theoretic approaches require a deeper understanding of the biology of the system modelled and may well become an improved paradigm for species distribution modelling. 3. Synthesis and applications. This special profile of six papers demonstrates the development in methodology used in species distribution modelling. The papers show how information-theoretic approaches can be coupled with emerging data technologies to address issues of conservation significance. With conservation biology and applied ecology at the forefront of many of the basic science developments so far, we expect these methods to pervade other areas of ecological research more fully in future.

Publication metadata

Author(s): Rushton SP, Ormerod SJ, Kerby G

Publication type: Article

Publication status: Published

Journal: Journal of Applied Ecology

Year: 2004

Volume: 41

Issue: 2

Pages: 193-200

ISSN (print): 0021-8901

ISSN (electronic): 1365-2664

Publisher: Wiley-Blackwell


DOI: 10.1111/j.0021-8901.2004.00903.x


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