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The Genetic Algorithm for Rule-Set Prediction (GARP) is one of several current approaches to modeling species’ distributions using occurrence records and environmental data. Because of stochastic elements in the algorithm and underdetermination of the system (multiple solutions with the same value for the optimization criterion), no unique solution is produced. Furthermore, 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 interpreting 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 …