Using hierarchical spatial models to assess the occurrence of an island endemism: the case of Salamandra corsica

Abstract

Background: Island species are vulnerable to rapid extinction, so it is important to develop accurate methods to determine their occurrence and habitat preferences. In this study, we assessed two methods for modeling the occurrence of the Corsican endemic Salamandra corsica, based on macro-ecological and fine habitat descriptors. We expected that models based on habitat descriptors would better estimate S. corsica occurrence, because its distribution could be influenced by micro-environmental gradients. The occurrence of S. corsica was modeled according to two ensembles of variables using random forests. Results: Salamandra corsica was mainly found in forested habitats, with a complex vertical structure. These habitats are associated with more stable environmental conditions. The model based on fine habitat descriptors was better able to predict occurrence, and gave no false negatives. The model based on macro-ecological variables underestimated the occurrence of the species on its ecological boundary, which is important as such locations may facilitate interpopulation connectivity. Conclusions: Implementing fine spatial resolution models requires greater investment of resources, but this is advisable for study of microendemic species, where it is important to reduce type II error (false negatives)