This SSP project resulted in a final report and two publications (Nest occurrence and survival of King Rails in fire-managed coastal marshes in North Carolina and Virginia, King Rail (Rallus elegans) Occupancy and Abundance in Fire Managed Coastal Marshes in North Carolina and Virginia). The project explored this use of Bayesian network modeling using the King Rail as a case study.
Although Bayesian network (BN) models have been promoted to the conservation community as models well-suited to support adaptive management strategies, there have been few tests of these claims. To test the value of BNs to support U.S. Fish and Wildlife Service and U.S. Geological Service's Strategic Habitat Conservation approach to adaptive management, we modeled habitat occupancy of breeding King Rail, Rallus elegans, in Eastern North Carolina and Southeastern Virginia. The limited egional empirical data for this species, combined with its priority conservation status, made it an ideal candidate to explore strengths and weaknesses of an expert-based Bayesian modeling approach. Specifically, we evaluated whether BN models initiated with expert knowledge and incrementally updated with empirical data could effectively support the definition of population and habitat objectives at regional and local (e.g., refuge) scales. Following two years of field surveys, we compared occupancy predictions from the original expert-only BN model, using a variety of BN models updated with different methods and with different data, and empirically-derived detection-adjusted occupancy estimates calculated in the program PRESENCE. To interpret differences among these models, we considered the relative contribution of spatial data error, expert error, and uncertainty to overall model error. Our results demonstrate how BN models can advance conservation for poorly documented species. We also provide recommendations to maximize the utility of expert knowledge within BNs designed to support the U.S Fish and Wildlife Service's and U.S. Geological Service's adaptive management processes.