New distributional modelling approaches for gap analysis

Synthetic products based on biodiversity information such as gap analysis depend critically on accurate models of species' geographic distributions that simultaneously minimize error in both overprediction and omission. We compared current gap methodologies, as exemplified by the distributional models used in the Maine Gap Analysis project, with an alternative approach, the geographic projections of ecological niche models developed using the Genetic Algorithm for Rule-Set Prediction (GARP). Point-occurrence data were used to develop GARP models based on the same environmental data layers as were used in the gap project, and independent occurrence data used to test both methods. Gap models performed better in avoiding omission error, but GARP better avoided errors of overprediction. Advantages of the point-based approach, and strategies for its incorporation into current gap efforts are discussed.