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Summary: Novel methods improve prediction of species' distributions from
occurrence data
Jane Elith*, Catherine H. Graham*, Robert P. Anderson, Miroslav DudiŽk, 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, Steven J. Phillips, Karen Richardson, Ricardo Scachetti-Pereira, Robert E. Schapire,
Jorge SoberoŽn, Stephen Williams, Mary S. Wisz and Niklaus E. Zimmermann
Elith, J., Graham, C. H., Anderson, R. P., DudiŽk, M., Ferrier, S., Guisan, A., Hijmans, R. J.,
Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G.,
Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. McC., Peterson, A. T., Phillips, S. J.,
Richardson, K. S., Scachetti-Pereira, R., Schapire, R. E., SoberoŽn, J., Williams, S., Wisz, M. S. and
Zimmermann, N. E. 2006. Novel methods improve prediction of species' distributions from
occurrence data. Á/ Ecography 29: 129Á/151.
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
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