Adaptive management is broadly recognized as critical for managing natural resources, yet in practice it often fails to achieve intended results for two main reasons: insufficient monitoring and inadequate stakeholder buy-in. Citizen science is gaining momentum as an approach that can inform natural resource management and has some promise for solving the problems faced by adaptive management. Based on adaptive management literature, we developed a set of criteria for successfully addressing monitoring and stakeholder related failures in adaptive management and then used these criteria to evaluate 83 citizen science case studies from peer-reviewed literature. The results suggest that citizen science can be a cost-effective method to collect essential monitoring information and can also produce the high levels of citizen engagement that are vital to the adaptive management learning process. The analysis also provides a set of recommendations for citizen science program design that addresses spatial and temporal scale, data quality, costs, and effective incentives to facilitate participation and integration of findings into adaptive management.
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Of course, there may be other reasons for the failure of citizen science to contribute to management. For example, managers might be reluctant to use citizen science-collected data. We focus here on the two most commonly-cited reasons for failure, while acknowledging that the use of citizen science for adaptive management may face other barriers.
Other scholars (Bonney and others 2009a) have distinguished between contributory, collaborative, and co-created models for PPSR in order to assess whether citizen science can contribute to informal science education. While such a categorization could also be useful for determining when citizen science effectively engages stakeholders, we opt to distinguish instead between types of participants, in part because extracting the information about informal learning was usually not possible from the studies reviewed.
We caution that the search terms we used likely do not cover all of the studies that involve citizen scientists, since terminology is highly variable. Although some programs date back to the late 1800s, we choose to limit the study to more recent papers, following Bonney and others (2009a, b) who argue that “The current concept of citizen science, however, with its integration of explicit and tested protocols for collecting data, vetting of data by professional biologists, and inclusion of specific and measurable goals for public education, has evolved primarily over the past two decades (Bonney 2007; Cohn 2008).” Including earlier papers that are more likely to lack a rigorous protocol would bias our results toward finding that citizen science cannot be useful for adaptive management. Although we inevitably miss some studies from our time period, there is no reason to think that the bias introduced by these omissions makes us more or less likely to conclude that citizen science can be used to resolve the two main problems of adaptive management.
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We gratefully acknowledge the support of the Bren School of Environmental Science & Management at the University of California, Santa Barbara. This paper is the product of an interdisciplinary PhD seminar. The first 15 authors were participants. Anderson and Tague were the faculty leads.
Conceived of and designed study (EAB, ASA, DB, WB, PC, MF, KG, RG, YH, IM, IP, SS, WW, YY, TZ, SA, CT), performed research (EAB, ASA, DB, WB, PC, MF, KG, RG, YH, IM, IP, SS, WW, YY, TZ), analyzed data (EAB, ASA, DB, WB, PC, MF, KG, RG, YH, IM, IP, SS, WW, YY, TZ, SA, CT), wrote paper (EAB, ASA, DB, WB, PC, MF, KG, RG, YH, IM, IP, SS, WW, YY, TZ, SA, CT).
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Aceves-Bueno, E., Adeleye, A.S., Bradley, D. et al. Citizen Science as an Approach for Overcoming Insufficient Monitoring and Inadequate Stakeholder Buy-in in Adaptive Management: Criteria and Evidence. Ecosystems 18, 493–506 (2015) doi:10.1007/s10021-015-9842-4
- citizen science
- community-based monitoring
- Public Participation in Scientific Research
- adaptive management
- natural resource management
- environmental science and management