Finding what you don’t know: Testing SDM methods for poorly known species

Abstract

AbstractAimA 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.LocationNorth Carolina, USA.TaxonSouth Mountains Grey‐cheeked Salamander (Plethodon meridianus).MethodsUsing the sparse, existing georeferenced records ofP. 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.ResultsRoughly 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 ofP. meridianus.Main ConclusionsGeographically 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.