A WILD APPROACH TO UNDERSTANDING KARST: USING A WILDLIFE SPECIES DISTRIBUTION MODEL TO PREDICT CAVE LOCATIONS

Cave entrances directly connect the surface and subsurface geomorphology in karst terrain; therefore, predicting the spatial distribution of these features can bring greater insight into areas on the landscape that are most critical to water flow into and out of the karst groundwater system. Sinkholes and springs are typically assumed to be major locations of inflow and outflow from the groundwater system, respectively, however not all sinkholes and springs are equally connected to the main conduit system. Predicting where on the landscape these zones of higher connectivity exist is a challenge. Wildlife research has a similar issue of understanding the complexities of where a given species is likely to exist on a landscape. For wildlife, species distribution models have been developed to create accurate predictions of species across the landscape using available landscape measurements as predictors. Here we apply a species distribution model, MaxEnt, to predict cave entrance locations in three geomorphic regions of Kentucky. The models were trained with cave locations from the Kentucky Speleological Survey database and landscape predictor variables, including distance from sinkholes, distance from springs, distance from faults, elevation, lithology, slope, and aspect. All three regional models predict locations well, with AUC values exceeding 0.8 (out of a maximum of 1.0). The most important variables for predicting cave entrance locations were consistent between models. Throughout all three models, sinkholes and springs having the largest influence on the likelihood of cave entrance presence. This unique use of species distribution modeling techniques shows that they are potentially valuable tools to understand spatial patterns of other landscape features that are either ephemeral or difficult to identify using standard techniques.