The potential for double-loop learning to enable landscape conservation efforts

As conservation increases its emphasis on implementing change at landscape-level scales, multi-agency, cross-boundary, and multi-stakeholder networks become more important. These elements complicate traditional notions of learning. To investigate this further, we examined structures of learning in the Landscape Conservation Cooperatives (LCCs), which include the entire US and its territories, as well as parts of Canada, Mexico, and Caribbean and Pacific island states. We used semi-structured interviews, transcribed and analyzed using NVivo, as well as a charrette-style workshop to understand the difference between the original stated goals of individual LCCs and the values and purposes expressed as the collaboration matured. We suggest double-loop learning as a theoretical framework appropriate to landscape-scale conservation, recognizing that concerns about accountability are among the valid points of view that must be considered in multi-stakeholder collaborations. Methods from the social sciences and public health sectors provide insights on how such learning might be actualized.