The effect of sample size and species characteristics on performance of different species distribution modeling methods

• Published in: Ecography (Copenhagen) 2006-10, Vol.29 (5), p.773-785
• Main Authors: Hernandez, Pilar A., Graham, Catherine H., Master, Lawrence L., Albert, Deborah L.
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; Biological taxonomies; Datasets; Ecological modeling; Environmental conservation; Fundamental and applied biological sciences. Psychology; General aspects; General aspects. Techniques; Global atmospheric research program; Methods and techniques (sampling, tagging, trapping, modelling...); Modeling; Predictive modeling; Sample size; Spatial models; Species
• ISSN: 0906-7590; 1600-0587
• DOI: 10.1111/j.0906-7590.2006.04700.x

Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox.