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Complexity fosters learning in collaborative adaptive management

María E. Fernández-Giménez, Department of Forest and Rangeland Stewardship and Center for Collaborative Conservation, Colorado State University, Fort Collins, CO, USA
David J. Augustine, USDA-ARS Rangeland Resources and Systems Research Unit, Fort Collins, CO, USA
Lauren M. Porensky, USDA-ARS Rangeland Resources and Systems Research Unit, Fort Collins, CO, USA.
Hailey Wilmer, USDA-ARS Rangeland Resources and Systems Research Unit, Fort Collins, CO, USA
Justin D. Derner, USDA-ARS Rangeland Resources and Systems Research Unit, Cheyenne, WY, USA
David D. Briske, Department of Ecosystem Science and Management Texas A&M University, College Station, TX, USA
Michelle O. Stewart, Department of Forest and Rangeland Stewardship and Center for Collaborative Conservation, Colorado State University, Fort Collins, CO, USA

DOI: http://dx.doi.org/10.5751/ES-10963-240229

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Abstract

Learning is recognized as central to collaborative adaptive management (CAM), yet few longitudinal studies examine how learning occurs in CAM or apply the science of learning to interpret this process. We present an analysis of decision-making processes within the collaborative adaptive rangeland management (CARM) experiment, in which 11 stakeholders use a structured CAM process to make decisions about livestock grazing and vegetation management for beef, vegetation, and wildlife objectives. We analyzed four years of meeting transcripts, stakeholder communications, and biophysical monitoring data to ask what facilitated and challenged stakeholder decision making, how challenges affected stakeholder learning, and whether CARM met theorized criteria for effective CAM. Despite thorough monitoring and natural resource agency commitment to implementing collaborative decisions, CARM participants encountered multiple decision-making challenges born of ecological and social complexity. CARM was effective in achieving several of its management objectives, including reduced ecological uncertainty, knowledge coproduction, and multiple-loop social learning. CARM revealed limitations of the idealized CAM cycle and challenged conceptions of adaptive management that separate reduction of scientific uncertainty from participatory and management dimensions. We present a revised, empirically grounded CAM framework that depicts CAM as a spiral rather than a circle, where feedback loops between monitoring data and management decisions are never fully closed. Instead, complexities including time-lags, trade-offs, path-dependency, and tensions among stakeholders’ differing types of knowledge and social worlds both constrain decision making and foster learning by creating disorienting dilemmas that challenge participants’ pre-existing mental models and relationships. Based on these findings, we share recommendations for accelerating learning in CAM processes.

Key words

adaptive management; collaboration; environmental governance; knowledge coproduction; North American Great Plains; social learning

Copyright © 2019 by the author(s). Published here under license by The Resilience Alliance. This article  is under a Creative Commons Attribution-NonCommercial 4.0 International License.  You may share and adapt the work for noncommercial purposes provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.

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Ecology and Society. ISSN: 1708-3087