Location: Rangeland Resources & Systems ResearchTitle: Citizen science in natural resources: Lessons learned from stakeholder engagement in participatory research using collaborative adaptive management Author
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/15/2017
Publication Date: N/A
Citation: N/A Interpretive Summary:
Technical Abstract: Under the traditional “loading-dock” model of research, stakeholders are involved in determining priorities prior to research activities and then recieve one-way communication about findings after research is completed. This approach lacks iterative engagement of stakeholders during the research project where information could be shared, knowledge produced and insights gained, and trust built between stakeholders and researchers. Collaborative adaptive management (CAM) links the structured, learning-by-doing features of adaptive management with a collaborative approach that seeks to achieve shared understanding and solutions by bringing diverse stakeholders and their multiple knowledges to decision-making. Here, we showcase lessons learned from citizen science practitioners in the Collaborative Adaptive Rangeland Management (CARM) experiment, located on the USDA Agricultural Research Service Central Plains Experimental Range, a Long-Term Agro-ecosystem Research (LTAR) network site located on the shortgrass steppe of eastern Colorado. Eleven stakeholders representing ranchers from the Crow Valley Livestock Cooperative, Inc., public resource management agencies and University Extension, and non-governmental organizations are engaged with a multi-disciplinary research team of range scientists, ecologists and social scientists. The Stakeholder Group makes decisions about the spatiotemporal movement of livestock and vegetation management using a structured CAM process based on jointly developed objectives for ranch profitability and drought resilience, vegetation composition and structure, and wildlife conservation. Insights from the science of learning help explain how complexity fosters learning by creating both disorienting dilemmas that challenge participants’ existing mental models, and conditions that foster learning about and from participants’ different social worlds and knowledge systems. In CARM, decision-making was constrained by different parts of the biophysical system responding at different rates to specific management actions, and by social complexities. Social scientists documented multiple-loop learning, for example, when the Stakeholder Group revised collective understanding of their roles and committed to seeking mutually beneficial solutions rather than advocating individual positions.