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This article deals with the evolution of community self-organization in public administration. Within the literature of interactive governance, increasing attention is being paid to how communities take initiative in dealing with societal issues. However, we know little about the factors contributing to the durability of self-organization. We analyzed three cases of community self-organization in three different countries: the United Kingdom, the United States, and the Netherlands. We found that community self-organization initiatives are strongly embedded in governmental environments, leading to different modes of interaction that change from phase to phase and in response to reciptiveness (or the lack thereof) among government counterparts. These modes of interaction strongly influence the evolution of community self-organization efforts. Moreover, we conclude that it is important that self-organized citizen initiatives represent and capture the perspectives and interests of large groups of citizens. This condition positively influences the evolution and duration of citizen initiatives. Those who manage to link with other citizens, including via community and volunteer organizations, can succeed. Those who do not can lose their legitimacy and fail.

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Summary1. Collaborative adaptive management (CAM) is regularly touted as the best way to handle natural resource management in the face of uncertainty, change and conflict. Successful applications of CAM have, however, been elusive in practice. 2. This article examines the Glen Canyon Dam Adaptive Management Program (AMP) in the United States, and other CAM efforts, to illustrate why and how procedural shortcomings may lead to natural resource management failures and reflect on how they may be overcome. 3. Synthesis and applications. To increase the chance of success, CAM efforts should set clear overarching goals and concrete and measurable objectives, employ tools and incentives to facilitate participation and foster collaboration, implement well-defined joint fact-finding protocols to promote shared learning and manage scientific uncertainty, and commit to monitoring and adapting their management regimes over time. Even in complex and contentious resource management contexts, future CAM efforts that integrate these design elements are likely to lead to more effective natural resource management.

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