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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. Location: North Carolina, USA. Taxon: South Mountains Grey‐cheeked Salamander (Plethodon meridianus). Methods: Using the sparse, existing georeferenced records of P. meridianus, we built SDMs using a range of methods that previous researchers have argued should work for low sample sizes. We then tested each SDM's ability to accurately predict independent survey data that were not georeferenced prior to our study. We compared SDMs using omission error and AUC. Results: Roughly half of the models successfully predicted survey records in the range centre, and all models had high omission error rates in the range exterior. In the range of interior or exterior, the 'ensemble of small models' technique produced SDMs with high omission error rates. Spatial filtering had a negligible impact on model performance. Most, but not all, models outperformed predictions using distance from known populations. Using one of the best‐performing methods, we developed an improved range map of P. meridianus. Main Conclusions: Geographically peripheral populations were difficult to predict for all SDMs, though some methods were clearly inferior for our data set. We recommend that when sample sizes are low, researchers use Maxent with species‐specific model settings. [ABSTRACT FROM AUTHOR]

One of the primary goals of any systematic, taxonomic or biodiversity study is the characterization of species distributions. While museum collection data are important for ascertaining distributional ranges, they are often biased or incomplete. The Genetic Algorithm for Rule-set Prediction (GARP) is an ecological niche modelling method based on a genetic algorithm that has been argued to provide an accurate assessment of the spatial distribution of organisms that have dispersal capabilities. The primary objective of this study is to evaluate the accuracy of a GARP model to predict the spatial distribution of a non-invasive, non-vagile invertebrate whose full distributional range was unknown. A GARP predictive model based on seven environmental parameters and 42 locations known from historical museum records for species of the trapdoor spider genus Promyrmekiaphila was produced and subsequently used as a guide for ground truthing the model. The GARP model was neither a significant nor an accurate predictor of spider localities and was outperformed by more simplistic BIOCLIM and GLM models. The isolated nature of Promyrmekiaphila populations mandates that environmental layers and their respective resolutions are carefully chosen for model production. Our results strongly indicate that, for modelling the spatial distribution of low vagility organisms, one should employ a modelling method whose results are more conducive to interpretation than models produced by a ‘black box’ algorithm such as GARP. [ABSTRACT FROM AUTHOR]