Modelling potential areas of human-wildlife conflict using community-based data: A case study of black bears (Ursus americanus) in Massachusetts
Human-wildlife conflict has intensified with the continued expansion of human populations into natural habitats. American black bears ( Ursus americanus ) are increasingly coming into contact with people, particularly as bears return to areas where they were previously extirpated. Citizen science offers a promising tool for predicting where such conflicts might occur by providing large-scale data on human-wildlife interactions. We used citizen science observations to model areas of overlap between humans and bears in Massachusetts. Additionally, we created a distribution model by integrating designed research data with citizen science data. Through simulations, we evaluated how to best augment citizen science with designed research data to enhance species distribution modeling. Our model showed high sensitivity in detecting areas where humans and bears overlap, with high housing density and proximity to roads predicting overlap. The inclusion of designed research data improved accuracy of distribution in less-populated areas, although some bias remained. When simulating behavioral differences between urban and forest-dwelling bears, strategic designed research data helped detect elusive forest bears, which are less likely to be observed by people. Overall, incorporating citizen science into conflict models can lead to better predictions of where interactions will occur and therefore will be a helpful tool for developing management strategies as human-wildlife conflict increases.