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Novel methods improve prediction of species’ distributions from occurrence data

by Jane Elith, Catherine H Graham, RP Robert P Anderson, Miroslav Dudík, Simon Ferrier, ANTOINE Guisan, Robert J Hijmans, Falk Huettmann, John R Leathwick, Anthony Lehmann, Jin Li, Lucia G Lohmann, Bette A Loiselle, Glenn Manion, Craig Moritz, Miguel Nakamura, Yoshinori Nakazawa, Jacob Mcc Overton, A. Townsend Peterson, S.J. Steven J. Phillips, Karen Richardson, Ricardo Scachetti-Pereira, Robert E Schapire, Jorge Soberón, Stephen Williams, Mary S Wisz, Niklaus E Zimmermann, Catherine H. Graham, Robert P. Anderson, Robert J. Hijmans, John R. Leathwick, Lucia G. Lohmann, Bette A. Loiselle, Jacob McC. M. Overton, Steven J. Phillips, Robert E. Schapire, Mary S. Wisz, Niklaus E. Zimmermann, M Dudyk, S Freer, J McOverton, A.Townsend Peterson show all authors
Ecography ()
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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.\n

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