Ground validation of presence-only modelling with rare species: a case study on barbastelles Barbastella barbastellus (Chiroptera: Vespertilionidae).
Abstract
We evaluated the accuracy of presence-only modelling for predicting the distribution of rare species, when data are scarce and usually collected with sampling biases. We modelled the potential distribution in Portugal of one of the rarest European bats Barbastella barbastellus and subsequently ground-validated predictions by using acoustic transects. We used ecological niche factor analysis (ENFA) and maximum entropy (Maxent) modelling to build distribution models of B. barbastellus, and determined which ecological factors were more relevant for each model. As ENFA only accepts continuous variables, we built one Maxent model using the same variables as ENFA and another using land cover as a categorical variable. Ecological niche factor analysis and both Maxent models predicted similar areas of occurrence in central and northern regions of Portugal, although ENFA predicted suitable habitat over a wider range. Conversely, there was substantial disagreement on the location of high-suitability areas in the south. This could be a consequence of a different choice of important variables made by each model. Native woodland and average temperature were the most relevant variables for Maxent, while in ENFA B. barbastellus was linked to higher altitudes although avoiding production forests and infrastructures. Threshold-independent and -dependent statistics showed that Maxent models outperformed ENFA, probably as a consequence of divergent predictions in the new areas of occurrence. Overall, 15 new B. barbastellus sites were discovered and known distribution was extended c. 100 km to the south. Synthesis and applications. Our results support the use of presence-only modelling as an indispensible tool for survey design as shown by the discovery of B. barbastellus populations outside of the previously known range. ENFA seems to be more suited to determining a species' potential distribution, although failing to extrapolate it. In contrast, Maxent is better suited to determining a species' realized distribution. It was successful in predicting occurrence in previously unsurveyed areas and can be recommended as a technique for determination of a conservative distribution for a species. Maxent modelling would greatly aid biodiversity conservation, especially when it is necessary to develop survey plans or first assessments of a species' distribution.