|FERNANDEZ-GIMINEZ, MARIA - Colorado State University|
|STEWART, MICHELLE - Colorado State University|
|BRISKE, DAVID - Texas A&M University|
Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 9/26/2017
Publication Date: 2/9/2018
Citation: Fernandez-Giminez, M.E., Wilmer, H.N., Augustine, D.J., Porensky, L.M., Stewart, M., Derner, J.D., Briske, D. 2018. Keys to success for data-driven decision making: Lessons from participatory monitoring and collaborative adaptive management. Society for Range Management Meeting Abstracts. Abstract Proceedings of the 71st Society for Range Management, Technical Training, and Trade Show. Jan 28 - Feb 2, 2018, Sparks, NV.
Technical Abstract: Recent years have witnessed a call for evidence-based decisions in conservation and natural resource management, including data-driven decision-making. Adaptive management (AM) is one prevalent model for integrating scientific data into decision-making, yet AM has faced numerous challenges and limitations. Collaborative adaptive management (CAM) seeks to overcome some of these limitations, especially “buy-in” by managers and other stakeholders. This presentation draws on the literature on participatory monitoring and a case study of collaborative adaptive rangeland management (CARM) to distill key lessons for data-driven decision making in rangeland management. Studies of participatory monitoring show that data are more likely to lead to management actions when resource users/managers are actively involved in the monitoring process. The CARM case study illustrates that even when resource users and managers are involved in identifying monitoring objectives and indicators, and interpreting data, it may take considerable time to develop 1) trust and mutual respect between those who collect and analyze data (often researchers), and those who use the data to make decisions (such as agency managers, ranchers, conservation organizations), and 2) a shared understanding of what the data mean. Further, it is important to recognize that all data (including scientific data and local knowledge) are interpreted in light of an individual’s existing knowledge and social context, which influence how a person makes sense of and applies the data. These challenges and successes within the CARM case study and broader experiences with participatory monitoring suggest key measures that researchers and managers can take to develop effective data-driven decision making programs for rangelands.