|Title||Monitoring in adaptive co-management: Toward a learning based approach|
|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Cundill G., Fabricius C.|
|Journal||Journal of Environmental Management|
|Keywords||adaptive co-management, collaborative monitoring, complexity, social learning|
To review and synthesise information in relation to monitoring and learning in the context of adaptive co-management
Integrated assessment and complex system monitoring is a central aspect of managing dynamic social-ecological systems. However, methodological approaches to integrate learning into monitoring processes are poorly understood. This article reviews and synthesises information in relation to monitoring and learning in the context of adaptive co-management. Two key challenges for monitoring adaptive co-management processes and outcomes include complexity and scale. A methodological approach is presented which emphasises a cyclical learning process that includes: problem identification; defining the system of interest; identifying the institutional structure (in which data collection, analysis and action will occur); designing the monitoring system; collective action and implementation of the monitoring system; information sharing and learning; and reviewing the monitoring system and problem identification. The authors argue the need to move beyond the creation of more conceptual models, and to focus greater attention on methodologies and approaches that allow resource practitioners to better cope with uncertainty and change in complex social-ecological systems.
Adaptive learning processes that are attempting to understand and manage complex, uncertain and dynamic social-ecological systems require the integration of learning into monitoring frameworks. These frameworks are cyclical but not linear, with various steps revisited in an iterative manner to allow an adequate building of knowledge around the problem, definition of the system, institutional structures and so forth. An important consideration is the sensitivity of measures used to evaluate the system and any associated learning. These measures should be sensitive to change within the timeframes of the project(s).