A 11 ecosystems and human societies depend on a healthy and tlroductive natural environment that contains diverse plant and animal species. The earth's biota is comtlosed of an estimated 1 0 million species of plants, animals, and microbes (Pimm et al. 1995). In the United States, there are an estimated 750.000 stlecies. of which small organisms, such as arthropods and microbes, make up 95%.' Although approximately 60% of the world's food supply comes from rice, wheat, and corn (Wilson 1988), as many as 20,000 other plant species have been used by humans as food. Some vlants and akimals provide human; with essential medicines and other diverse, useful ~r o d u c t s. For instance. some plants i n d microbes help to d&rade chemical pollutants and organic wastes and recycle nutrients throughout the ecosystem. The rapidiy growing world population and increased human activity threaten many of these species. The current extinction rate of species ranges from approximately 1000 to 10,000 times higher than natural extinction rates (Kellert and Wilson

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Aim: A limitation of species distribution models (SDMs) is that species with low sample sizes are difficult to model. Yet, it is often important to know the habitat associations of poorly known species to guide conservation efforts. Techniques have been proposed for modelling species' distributions from a few records, but their performance relative to one another has not been compared. Because these models are built and evaluated with small data sets, sampling error could cause severely biased sampling in environmental space. As a result, SDMs are likely to underpredict geographic distributions given small sample sizes. We perform the first comparison of methods explicitly promoted or developed for predicting the geographic ranges of species with very low sample sizes.

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