Date of Graduation


Document Type


Degree Type



Davis College of Agriculture, Natural Resources and Design


Wildlife and Fisheries Resources

Committee Chair

John W. Edwards

Committee Co-Chair

J. Todd Petty

Committee Member

Harry A. Spiker

Committee Member

Michael P. Strager


After nearly being extirpated from the state, black bears in Maryland have rebounded to a point where recreational harvest has now become an important management tool. Having a better understanding of bear population parameters, movements, and harvest vulnerability allows managers to implement hunting more effectively and responsibly. To estimate demographics of the Maryland bear population, we implemented noninvasive genetic sampling of bear hair during summer 2011. We used a model-based sampling design that allowed us to collect samples more efficiently. We used presence-only maximum entropy (Maxent) modeling to classify the study area based on predicted probability of bear occurrence, and allocated the majority of our hair snares to areas with high or medium probabilities. Using microsatellite analysis and mark-recapture methods, we estimated the bear population at 701 individuals. This represents a nearly doubling of the population since the previous estimate in 2005. Our density estimate (0.25 bears/km2) is comparable to other estimates from southeastern and mid-Atlantic states. Our sampling approach did lead to more efficient sample collection, with more hair samples collected at snares located in areas with predicted high or medium probability of bear occurrence than those in low probability areas. However, in the eastern portion of our study area, where bear occurrence is presumed to be much lower, our sampling effort seemed insufficient to collect enough samples for reliable abundance estimation. As a first step toward quantifying harvest vulnerability, we used Global Positioning System (GPS) units to record movements and spatial behaviors of 108 bear hunters during the 2005--2007 Maryland bear hunting seasons. Median values showed that hunters traveled 2.9 km per hunting event, but only 0.6 km from their starting point. Hunters did not seem to show any preferential use of areas based on the landscape metrics we examined (e.g., elevation, distance from nearest road) except cover type, where 81% of locations were in deciduous forests. We found few differences between spatial behaviors of groups of hunters based on harvest success, residency, and previous bear hunting experience, as classified using post-hunt mail surveys. One notable difference is that successful hunters used steeper slopes than unsuccessful hunters. We also found that hunter perceptions of total distance traveled and distance from nearest roads were often highly inaccurate, showing that hunter surveys are not a useful tool for collecting those data. For Garrett County, Maryland, we used the hunter locations to create a Maxent model of the spatial distribution of harvest pressure. We also created a model using fall telemetry locations of female bears and compared the models to identify areas of high (i.e., high hunter and high bear occurrence) and low (i.e., low hunter and high bear occurrence) harvest vulnerability. Both models showed higher probability of occurrence on public lands. Both high and low vulnerability areas comprised small portions of the county. The low vulnerability areas included 9 larger blocks (>1 km 2), which were 2.3 times steeper, 2.0 times farther from roads, and 1.5 times farther from streams than the medians for the study area. Those characteristics may limit hunter access to and use of the areas. Our predicted high vulnerability areas did not correspond to most previous bear harvest locations, indicating that our definition of harvest vulnerability often does not translate to actual harvest. Finally, we used GPS collars to track female bear locations in Garrett County and examine home range dynamics. Fixed kernel estimates for annual, spring, summer, and fall home ranges were 10.40 km 2, 8.93 km2, 16.08 km2, and 19.35 km 2, respectively. Fall and summer home ranges were larger than spring home ranges, but summer and fall ranges were similar. Solitary females had mean spring home ranges 6.9 times larger than females with cubs-of-the-year, but ranges did not differ during other seasons. Bears exhibited high levels of home range fidelity, with home range centroids shifting little among seasons or years. Intraspecific overlap of home ranges occurred during all 3 seasons, but was most common in summer. The results of this study provide Maryland bear biologists and managers with essential information about the state's bear population. Home range estimates represent important baseline information to determine appropriate spatial scales of management. The abundance estimates will be used to set proper harvest quotas with the goal of slowing the bear population growth. The hunter movement analysis and harvest vulnerability modeling may be used by managers to adjust harvest regulations to increase the efficacy of the hunting seasons.