DeStefano, Stephen

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Research Professor, Department of Environmental Conservation
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DeStefano
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Stephen
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Environmental Sciences
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As a member of the Cooperative Research Unit program and Unit Leader for the Massachusetts Cooperative Fish and Wildlife Research Unit, Dr. DeStefano directs a program of research, education, and service focused on fish and wildlife ecology, habitat relationships, and conservation biology and management, in cooperation with several agencies and organizations. His research interests are broadly defined by wildlife biology and terrestrial ecology, with the underlying focus and common themes of population ecology (demography, population dynamics, survival analysis), wildlife-habitat relationships (response to vegetation structure, use-availability analyses, correlation to population demography), the influence of anthropogenic factors (urban-suburban development, disturbance) on wildlife populations, and the science and management of game populations and “overabundant” wildlife. These topics have numerous implications for ecological research and wildlife management, especially as they relate to conservation biology, recovery of endangered species, human-wildlife interactions, and the impact of human activities on wildlife populations.
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    PublicationOpen Access
    Forecasting Seasonal Habitat Connectivity in a Developing Landscape
    (2020) Zeller, Katherine A.; Bauder, Jacan M.; Bauder, Javan M.; Destefano, Stephen
    Connectivity and wildlife corridors are often key components to successful conservation and management plans. Connectivity for wildlife is typically modeled in a static environment that reflects a single snapshot in time. However, it has been shown that, when compared with dynamic connectivity models, static models can underestimate connectivity and mask important population processes. Therefore, including dynamism in connectivity models is important if the goal is to predict functional connectivity. We incorporated four levels of dynamism (individual, daily, seasonal, and interannual) into an individual-based movement model for black bears (Ursus americanus) in Massachusetts, USA. We used future development projections to model movement into the year 2050. We summarized habitat connectivity over the 32-year simulation period as the number of simulated movement paths crossing each pixel in our study area. Our results predict black bears will further colonize the expanding part of their range in the state and move beyond this range towards the greater Boston metropolitan area. This information is useful to managers for predicting and addressing human–wildlife conflict and in targeting public education campaigns on bear awareness. Including dynamism in connectivity models can produce more realistic models and, when future projections are incorporated, can ensure the identification of areas that offer long-term functional connectivity for wildlife.