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Ecological Modelling 190 (2006) 231259 Maximum entropy modeling of species geographic distributions
 

Summary: Ecological Modelling 190 (2006) 231259
Maximum entropy modeling of species geographic distributions
Steven J. Phillipsa,, Robert P. Andersonb,c, Robert E. Schapired
a AT&T Labs-Research, 180 Park Avenue, Florham Park, NJ 07932, USA
b Department of Biology, City College of the City University of New York, J-526 Marshak Science Building,
Convent Avenue at 138th Street, New York, NY 10031, USA
c Division of Vertebrate Zoology (Mammalogy), American Museum of Natural History, Central Park West at 79th Street,
New York, NY 10024, USA
d Computer Science Department, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA
Received 23 February 2004; received in revised form 11 March 2005; accepted 28 March 2005
Available online 14 July 2005
Abstract
The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase
in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed pres-
ence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data
are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling
species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple
and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In
order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a
lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent

  

Source: Anderson, Robert P. - Department of Biology, City College, City University of New York
Schapire, Robert - Department of Computer Science, Princeton University

 

Collections: Computer Technologies and Information Sciences; Environmental Sciences and Ecology