|FERNANDEZ-GIMENEZ, MARIA - Colorado State University
|BRISKE, DAVID - Texas A&M University
|STEWART, MICHELLE - Colorado State University
Submitted to: Ecology and Society
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/22/2019
Publication Date: 6/18/2019
Citation: Fernandez-Gimenez, M., Augustine, D.J., Wilmer, H.N., Porensky, L.M., Derner, J.D., Briske, D., Stewart, M. 2019. Complexity fosters learning in collaborative adaptive management. Ecology and Society. 24(2):29. https://doi.org/10.5751/ES-10963-240229.
Interpretive Summary: Collaborative adaptive management (CAM) is an approach to natural resource management that attempts to bring together people from many different types of backgrounds and livelihoods to actively work together in making management decision and learning from the outcomes of those decisions. While learning is recognized as being important to CAM, few studies have examined how learning occurs in CAM. In this paper, we describe first four years of the Collaborative Adaptive Rangeland Management (CARM) experiment, in which 11 stakeholders make decisions about livestock grazing and vegetation management in order to achieve multiple outcomes for beef production, vegetation composition and structure, and wildlife conservation. Despite thorough monitoring and agency commitment to implementing collaborative decisions in CARM, participants encountered challenges related to the complexity of both the ecological and social components of the rangeland ecosystem they are managing. Nevertheless, the experiment highlights advances in how monitoring data were collected, visualized, interpreted, and used, and how this led to collaborative learning. We suggest that complexity fosters learning by creating both disorienting dilemmas that challenge people’s existing mental models, and by creating an atmosphere in which people learn from other stakeholder’s perspectives and experiences. The CARM experiment shows limitations in the idealized cycle of adaptive management. We present a revised framework that describes CAM as a complex and non-linear process involving continual feedbacks between short-term and longer-term learning cycles.
Technical Abstract: Collaborative adaptive management (CAM) merges the structured, learning by doing features of adaptive management with a collaborative approach that seeks to achieve shared understanding and novel solutions by bringing diverse stakeholders and their multiple knowledges to decision-making. CAM promises to advance both evidence-based and participatory decision-making and to improve social and environmental outcomes in an increasingly variable and uncertain environment. While learning is recognized as central to CAM, few longitudinal studies examine how learning occurs in CAM or apply the science of learning to interpret this process. We report on the first four years of the Collaborative Adaptive Rangeland Management (CARM) experiment, in which 11 stakeholders make decisions about livestock grazing and vegetation management using a structured CAM process based on jointly developed objectives for beef production, vegetation composition and structure, and wildlife conservation. Despite thorough monitoring and agency commitment to implementing collaborative decisions in CARM, participants encountered multiple decision-making challenges born of ecological and social complexity. Nevertheless, CARM shows significant progress towards key criteria for effective CAM, including advances in how monitoring data were collected, visualized, interpreted, and used; deepening engagement of stakeholders in each other’s social worlds; and multiple-loop learning. Insights from learning science help explain how complexity fosters learning by creating both disorienting dilemmas that challenge participants’ existing mental models, and conditions for learning about and from participants’ different social worlds and knowledge systems. The CARM experiment reveals key limitations in idealized cycles of adaptive management (AM) and CAM, and challenges conceptions of AM that seek to separate reduction of scientific uncertainty from participatory and management dimensions. We present a revised framework that describes CAM as a complex and non-linear process in which relational and normative multiple-loop learning inform and depend upon cognitive single-loop learning about ecological processes.