More complex distribution models or more representative data?

Distribution models for species are increasingly used to summarize species' geography in conservation analyses. These models use increasingly sophisticated modeling techniques, but often lack detailed examination of the quality of the biological occurrence data on which they are based. I analyze the results of the best comparative study of the performance of different modeling techniques, which used pseudo-absence data selected at random. I provide an example of variation in model accuracy depending on the type of absence information used, showing that good model predictions depend most critically on better biological data. Recently, many efforts have focused on creation of models able to predict species' distributions from partial data. These distributional models use known distribution records of a species, as well as environmental and spatial explanatory variables, to build statistical functions for interpolating species' distributions across the environmental spectrum (Guisan & Zimmermann 2000). Models may also extrapolate species' distributions to sets of environmental conditions outside those used to build the models (Peterson 2003). The reliability of