Experience, Attitudes, and Demographic Factors Influence the Probability of Reporting Human–Black Bear Interactions

• Published in: Wildlife Society bulletin Vol. 42; no. 1; pp. 22 - 31
• Main Authors: WILBUR, RYAN C., LISCHKA, STACY A., YOUNG, JESSICA R., JOHNSON, HEATHER E.
• Format: Journal Article
• Language: English
• Published: Wildlife Society 01.03.2018
• Subjects: Colorado; human–wildlife conflict; mail survey; Original Article; reporting behavior; Ursus americanus; wildlife management
• ISSN: 1938-5463; 2328-5540; 1938-5463
• DOI: 10.1002/wsb.854

Interactions between people and American black bears (Ursus americanus) have been increasing throughout the United States, with negative interactions becoming a major management challenge for wildlife agencies. To monitor the number, location, and severity of these conflicts, wildlife agencies typically rely on voluntary public reports. Although trends in voluntary reports are commonly assumed to reflect actual trends in human–bear interactions, recent research suggests an individual’s likelihood of reporting interactions may be biased, influenced by attitudes toward the species and its management, previous experiences with wildlife, or demographic factors. During 2012, we used a mail survey of residents in the vicinity of Durango, Colorado, USA, (n = 1,667) to explore the relative importance of tolerance for black bears, satisfaction with bear management, personal experience with bears, and demographic traits as predictors of a resident’s decision to report interactions to the authorities. We found that residents’ experiences with bears were most important in predicting reporting behavior, followed closely by attitudes related to tolerance for bears, and satisfaction with management; demographic factors had relatively little influence. Respondents were more likely to report when they had seen black bears near their homes, had been threatened by bears, were intolerant of bears, dissatisfied with management, and were female. Although several variables in our analyses were influential in explaining reporting behavior, the overall predictive power of our models was low (R 2 = 0.17), suggesting future investigations of reporting behavior should include a broader set of covariates. Our results indicate that public reports represent a biased measure of human–bear interactions, and management agencies should either account for bias, or collect different types of interaction data, when assessing patterns of bear activity.