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Demography of American black bears in a semiarid environment
Brenden M. Orocu, Janene Auger, Hal L. Black, Randy T. Larsen, Brock R. McMillan, Mark C. Belk
Ursus 2026 (37e5), 1-16, (6 April 2026). https://doi.org/10.2192/URSUS-D-24-00022
KEYWORDS: American black bear, demography, elasticity, population growth rate, semiarid environment, Transition matrix model, Ursus americanus, vital rates
The American black bear (Ursus americanus) has one of the broadest geographic distributions of any mammalian carnivore in North America. Populations occur from high to low elevations and from mesic to arid environments, and their demographic traits have been documented in a wide variety of environments. However, the demography of American black bears in semiarid environments, which comprise a significant portion of the geographic range, is poorly documented. Semiarid environments are characterized by highly variable environmental and climatic conditions across seasons and among years. To determine whether environmental variation is related to demographic traits of black bears, we used data from a long-term mark–recapture study of black bears in the semiarid environment of eastern Utah, USA, from 1991 to 2019. Traits associated with reproduction (i.e., cub production, cub survival, and yearling survival) were more variable compared with adult survival rates. Cub production was strongly associated with measures of drought in the previous year. Wetter years resulted in higher cub production the next spring. Demographic analysis suggests a stable population (λ H 0.96) dominated by adult life stages where the highest reproductive value is found in old adults. Vital rates and demographic patterns fall within the range of patterns found in other populations of black bears in western North America; however, yearling survival and estimates of fecundity were lower in this population in a semiarid environment. Black bear life histories and resulting demography in this semiarid environment appear to be a response to the uncertainty associated with resource availability. We suggest additional long-term studies in underrepresented environments for comparison with our results and conclusions.
Intelligent bear deterrence system based on computer vision: Reducing human–bear conflicts in remote areas
Pengyu Chen, Teng Fei, John A. Kupfer, Yunyan Du, Jiawei Yi, Yi Li
Ursus 2026 (37e6), 1-11, (8 April 2026). https://doi.org/10.2192/URSUS-D-25-00010
KEYWORDS: bear detection and deterrence, brown bear, computer vision, deterrence systems, human–bear conflict, internet of things, Tibetan plateau, Ursus arctos pruinosus
Human–bear conflicts on the Tibetan Plateau threaten both local livelihoods and the conservation of Tibetan brown bears (Ursus arctos pruinosus). To address this challenge, we developed a low-power, network-independent deterrence system that combines computer vision with Internet of Things (IoT) hardware. The system integrates a YOLOv5-MobileNet detection model deployed on a low-power edge artificial intelligence (AI) board with a solar-powered bear spray device. We compiled a data set of 1,243 wildlife images (including 795 bears with 100 infrared captures for nighttime detection, plus other common objects and animals such as mastiffs, yaks, humans, and vehicles), from which 80% were used for training and 20% for validation. Validation showed robust performance (mean average precision = 91.4%, recall = 93.6%). In 100 controlled activation tests involving simulated approaches by bears, humans, and other animals, the spray deployed within 0.2 seconds of detection with 97.2% accuracy, confirming timely and reliable responses. A 30-day field trial in Zadoi County, Qinghai Province, China, recorded 3 successful deterrence events without false activations. By using energy-efficient components and ensuring continuous and stable system operation, this solution provides a practical, sustainable, and scalable approach to mitigating human–bear conflicts, effectively enhancing human safety and bear conservation in remote areas without network or grid coverage.