Population distribution models: species distributions are better modeled using biologically relevant data partitions
- Equal contributors
1 Florida Museum of Natural History, Division of Mammals, University of Florida, Dickinson Hall, Gainesville, FL 32611, USA
2 Department of Biology, University of Florida, Bartram-Carr Hall, Gainesville, FL 32611, USA
3 Fort Lauderdale Research and Education Center, University of Florida, 3205 College Ave., Davie, FL 33314, USA
BMC Ecology 2011, 11:20 doi:10.1186/1472-6785-11-20Published: 19 September 2011
Predicting the geographic distribution of widespread species through modeling is problematic for several reasons including high rates of omission errors. One potential source of error for modeling widespread species is that subspecies and/or races of species are frequently pooled for analyses, which may mask biologically relevant spatial variation within the distribution of a single widespread species. We contrast a presence-only maximum entropy model for the widely distributed oldfield mouse (Peromyscus polionotus) that includes all available presence locations for this species, with two composite maximum entropy models. The composite models either subdivided the total species distribution into four geographic quadrants or by fifteen subspecies to capture spatially relevant variation in P. polionotus distributions.
Despite high Area Under the ROC Curve (AUC) values for all models, the composite species distribution model of P. polionotus generated from individual subspecies models represented the known distribution of the species much better than did the models produced by partitioning data into geographic quadrants or modeling the whole species as a single unit.
Because the AUC values failed to describe the differences in the predictability of the three modeling strategies, we suggest using omission curves in addition to AUC values to assess model performance. Dividing the data of a widespread species into biologically relevant partitions greatly increased the performance of our distribution model; therefore, this approach may prove to be quite practical and informative for a wide range of modeling applications.