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Envisioning the future of wildlife in a changing climate: Collaborative learning for adaptation planning

doi: 10.1002/wsb.62
AbstractNatural resource managers are tasked with assessing the impacts of climate change on conservation targets and developing adaptation strategies to meet agency goals. The complex, transboundary nature of climate change demands the collaboration of scientists, managers, and stakeholders in this effort. To share, integrate, and apply knowledge from these diverse perspectives, we must engage in social learning. In 2009, we initiated a process to engage university researchers and agency scientists and managers in collaborative learning to assess the impacts of climate change on terrestrial fauna in the state of Wisconsin, USA. We constructed conceptual Bayesian networks to depict the influence of climate change, key biotic and abiotic factors, and existing stressors on the distribution and abundance of 3 species: greater prairie‐chicken (Tympanuchus cupido), wood frog (Lithobates sylvaticus), and Karner blue butterfly (Plebejus melissa samuelis). For each species, we completed a 2‐stage expert review that elicited dialogue on information gaps, management opportunities, and research priorities. From our experience, collaborative network modeling proved to be a powerful tool to develop a common vision of the potential impacts of climate change on conservation targets. © 2011 The Wildlife Society.
- United States Department of the Interior United States
- United States Department of the Interior United States
- Wisconsin Department of Natural Resources United States
- AUS (United States) United States
- Wisconsin Department of Natural Resources United States
vulnerability assessment, collaborative management, General. Including nature conservation, geographical distribution, adaptation, QH1-199.5, social learning, Bayesian network, climate change
vulnerability assessment, collaborative management, General. Including nature conservation, geographical distribution, adaptation, QH1-199.5, social learning, Bayesian network, climate change
