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A maximum entropy approach to species distribution modeling
Abstract
We study the problem of modeling species geographic distributions, a critical problem in conservation biology. We propose the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features. We describe experiments comparing maxent with a standard distribution-modeling tool, called GARP, on a dataset containing observation data for North American breeding birds. We also study how well maxent performs as a function of the number of training examples and training time, analyze the use of regularization to avoid overfitting when the number of examples is small, and explore the interpretability of models constructed using maxent.
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Cited by
Maximum entropy modeling of species geographic distributions
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.03.026 · Cited by 15,396
Self-citationNovel methods improve prediction of species’ distributions from occurrence data
Elith*, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., et al. (2006) · Ecography · doi:10.1111/j.2006.0906-7590.04596.x · Cited by 9,088
Self-citationModeling of species distributions with Maxent: new extensions and a comprehensive evaluation
Phillips, S. J., & Dudík, M. (2008) · Ecography · doi:10.1111/j.0906-7590.2008.5203.x · Cited by 5,890
Self-citationOpening the black box: an open‐source release of Maxent
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E. (2017) · Ecography · doi:10.1111/ecog.03049 · Cited by 2,729
Self-citationRethinking receiver operating characteristic analysis applications in ecological niche modeling
Peterson, A. T., Papeş, M., & Soberón, J. (2008) · Ecological Modelling · doi:10.1016/j.ecolmodel.2007.11.008 · Cited by 1,526
Metadata
- Authors
- Phillips, S. J., Dudík, M., & Schapire, R. E.
- Venue
- Twenty-first international conference on Machine learning - ICML '04
- Publisher
- ACM Press
- Published
- 2004
- Pages
- 83
- OpenAlex
- W1979759959
- Cited by
- 2,252
- References
- 0
- Type
- conference abstract
- Language
- en
- Open access
- closed
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