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Forecasting seasonal habitat connectivity in a developing landscapeAuthor(s): Katherine A. Zeller; David W. Wattles; Javan M. Bauder; Stephen DeStefano
Source: Land. 9: 233.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionConnectivity 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 o er long-term functional connectivity for wildlife.
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CitationZeller, Katherine A.; Wattles, David W.; Bauder, Javan M.; DeStefano, Stephen. 2020. Forecasting seasonal habitat connectivity in a developing landscape. Land. 9: 233.
Keywordsdynamic connectivity, black bear, Ursus americanus, individual-based movement model, IBMM, GPS-telemetry, wildlife, corridor, agent-based model
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