J. Elith et al., "Novel methods improve predictions of species' distributions from occurrence data," Ecography, 29:129-51, 2006. (Cited in 128 papers)
Jane Elith of the University of Melbourne and Catherine Graham of SUNY Stony Brook led the team that compared 16 modeling methods for predicting distributions of 226 plant and animal species from six world regions. They used species-occurrence records from museums, herbaria, and incidental surveys, coupled with environmental data. In general, they found that recently developed modeling methods outperformed more traditional, widely used methods, especially for noisy species data. "You can use pretty pathetic data and make decent predictions," says Graham.
The authors tested the accuracy of each model with independently collected data from designed surveys of the same species in the same regions. This provided a nonbiased evaluation of which predictive models fit the data best, says Elith.
Joshua Plotkin of the University of Pennsylvania describes the paper ...
