Novel methods improve prediction of species' distributions from occurrence data

Elith, Jane, Graham, Catherine H., Anderson, Robert P., Dudík, Miroslav, Ferrier, Simon, Guisan, Antoine, Hijmans, Robert J., Huettmann, Falk, Leathwick, John R., Lehmann, Anthony, Li, Jin, Lohmann, Lucia G., Loiselle, Bette A., Manion, Glenn, Moritz, Craig, Nakamura, Miguel, Nakazawa, Yoshinori, Overton, Jacob McC., Peterson, A. Townsend, Phillips, Steven J., Richardson, Karen, Scachetti-Pereira, Ricardo, Schapire, Robert E., Soberón, Jorge, Williams, Stephen, Wisz, Mary S., and Zimmermann, Niklaus E. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29 (2). pp. 129-151.

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Abstract

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Item ID: 1610
Item Type: Article (Refereed Research - C1)
Keywords: ecology; modelling; species distribution
ISSN: 1600-0587
Date Deposited: 21 Aug 2007
FoR Codes: 06 BIOLOGICAL SCIENCES > 0699 Other Biological Sciences > 069999 Biological Sciences not elsewhere classified @ 34%
05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050202 Conservation and Biodiversity @ 33%
06 BIOLOGICAL SCIENCES > 0603 Evolutionary Biology > 060302 Biogeography and Phylogeography @ 33%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970106 Expanding Knowledge in the Biological Sciences @ 100%
Citation Count from Web of Science Web of Science 1894
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