A maximum entropy approach to species distribution modeling

A maximum entropy approach to species distribution modeling 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A maximum entropy approach to species distribution modeling

Association for Computing Machinery — Jul 4, 2004

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Datasource
Association for Computing Machinery
Copyright
Copyright © 2004 by ACM Inc.
ISBN
1-58113-838-5
D.O.I.
10.1145/1015330.1015412
Publisher site
See Article on Publisher Site

Abstract

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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