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Ecological Modelling 162 (2003) 211232 Evaluating predictive models of species' distributions
 

Summary: Ecological Modelling 162 (2003) 211­232
Evaluating predictive models of species' distributions:
criteria for selecting optimal models
Robert P. Andersona,b,, Daniel Lewc, A. Townsend Petersonb
a Division of Vertebrate Zoology (Mammalogy), American Museum of Natural History, Central Park West at 79th Street,
New York, NY 10024-5192, USA
b Natural History Museum & Biodiversity Research Center and Department of Ecology & Evolutionary Biology,
University of Kansas, 1345 Jayhawk Boulevard, Lawrence, KS 66045-7561, USA
c Museo de Historia Natural La Salle, Fundación La Salle, Apartado 1930, Caracas 1010-A, Venezuela
Received 21 November 2001; received in revised form 12 August 2002; accepted 4 September 2002
Abstract
The Genetic Algorithm for Rule-Set Prediction (GARP) is one of several current approaches to modeling species' distribu-
tionsusingoccurrencerecordsandenvironmentaldata.Becauseofstochasticelementsinthealgorithmandunderdetermination
of the system (multiple solutions with the same value for the optimization criterion), no unique solution is produced. Fur-
thermore, current implementations of GARP utilize only presence data--rather than both presence and absence, the more
general case. Hence, variability among GARP models, which is typical of genetic algorithms, and complications in interpret-
ing results based on asymmetrical (presence-only) input data make model selection critical. Generally, some locality records
are randomly selected to build a distributional model, with others set aside to evaluate it. Here, we use intrinsic and extrinsic
measures of model performance to determine whether optimal models can be identified based on objective intrinsic criteria,
without resorting to an independent test data set. We modeled potential distributions of two rodents (Heteromys anomalus

  

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

 

Collections: Environmental Sciences and Ecology