A Maximum Entropy Approach to Species Distribution Modeling Steven J. Phillips AT&T Labs Research, 180 Park Avenue, Florham Park, NJ 07932 PHILLIPS @ RESEARCH . ATT. COM Miroslav Dud k MDUDIK @ CS . PRINCETON . EDU Robert E. Schapire SCHAPIRE @ CS . PRINCETON . EDU Princeton University, Department of Computer Science, 35 Olden Street, Princeton, NJ 08544 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, speci cally, 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 over tting when the number of examples is small, and explore the interpretability of models constructed using maxent. 1. Introduction We study the problem of modeling the geographic distribution of a given animal or plant species. This is a critical problem in conservation biology: to save a threatened species, one
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