Geographic selection bias of occurrence data influences transferability of invasive Hydrilla verticillata distribution models.

ABSTRACT: Due to socioeconomic differences, the accuracy and extent of reporting on the occurrence of native species differs among countries, which can impact the performance of species distribution models. We assessed the importance of geographical biases in occurrence data on model performance using Hydrilla verticillata as a case study. We used Maxent to predict potential North American distribution of the aquatic invasive macrophyte based upon training data from its native range. We produced a model using all available native range occurrence data, then explored the change in model performance produced by omitting subsets of training data based on political boundaries. We also compared those results with models trained on data from which a random sample of occurrence data was omitted from across

SUBMITTER: Barnes MA 

PROVIDER: S-EPMC4203300 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature