Novel Methods Improve Prediction of Species' Distributions from Occurrence Data. Elith, J., Graham, C. H., Anderson, R. P., Dud́ık, 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. M., Townsend Peterson, A., Phillips, S. J., Richardson, K., Scachetti-Pereira, R., Schapire, R. E., Soberón, J., Williams, S., Wisz, M. S., & Zimmermann, N. E. 29(2):129–151.
Novel Methods Improve Prediction of Species' Distributions from Occurrence Data [link]Paper  doi  abstract   bibtex   
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.
@article{elithNovelMethodsImprove2006,
  title = {Novel Methods Improve Prediction of Species' Distributions from Occurrence Data},
  author = {Elith, Jane and Graham, Catherine H. and Anderson, Robert P. and Dud́ık, Miroslav and Ferrier, Simon and Guisan, Antoine and Hijmans, Robert J. and Huettmann, Falk and Leathwick, John R. and Lehmann, Anthony and Li, Jin and Lohmann, Lucia G. and Loiselle, Bette A. and Manion, Glenn and Moritz, Craig and Nakamura, Miguel and Nakazawa, Yoshinori and Overton, Jacob M. and Townsend Peterson, A. and Phillips, Steven J. and Richardson, Karen and Scachetti-Pereira, Ricardo and Schapire, Robert E. and Soberón, Jorge and Williams, Stephen and Wisz, Mary S. and Zimmermann, Niklaus E.},
  date = {2006-04},
  journaltitle = {Ecography},
  volume = {29},
  pages = {129--151},
  issn = {0906-7590},
  doi = {10.1111/j.2006.0906-7590.04596.x},
  url = {https://doi.org/10.1111/j.2006.0906-7590.04596.x},
  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.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-585800,~to-add-doi-URL,artificial-intelligence,comparison,computational-science,conservation,data,ecology,environmental-modelling,environmental-predictors,habitat-suitability,machine-learning,model-comparison,modelling,multiauthor,niche-modelling,presence-absence,presence-only,species-distribution},
  number = {2}
}
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