Research highlight: Citizen science in adaptive management

This post is the first in a series of research highlights published through the Commons Lab blog. These posts are designed to highlight new research contributions that we believe are particularly valuable for supporting citizen science and crowdsourcing within federal agencies, and among their collaborators.

Aceves-Bueno, E., et al. (2015). Citizen science as an approach for overcoming insufficient monitoring and inadequate stakeholder buy-in in adaptive management: Criteria and evidence. Ecosystems, 18, 3, 493-506.

In adaptive management, natural resources are managed through an iterative, short-term process where data about current conditions inform future decisions. While involving citizen scientists in adaptive management seems promising, potential barriers include inadequate project design and lack of stakeholder buy-in. Through a review and analysis of 83 citizen science projects, a group of 15 students and 2 faculty at UCSB’s Bren School of Environmental Science & Management argue that adaptive monitoring can overcome these barriers, while also shedding light on key questions—such as participant motivation—that face the field at large.

Image credit: http://www.doi.gov/initiatives/AdaptiveManagement/images/amdiagrmlrgbst.gif

Researchers begin by breaking down barriers to “inadequate monitoring” and stakeholder engagement. For monitoring to be successful, the following conditions must be met:

  • Monitoring must take place
  • Data must be relevant to management actions, quantitative and subject to QA/QC controls
  • Monitoring must be cost-effective
  • Monitoring must occur at appropriate temporal and spatial scales

To ensure stakeholder engagement:

  • Community stakeholders must be identified and engaged
  • Managers must provide motivation and incentives for participation
  • Decision-makers must be accountable to stakeholders

Regarding criteria for monitoring, this research largely supports previous work. The authors find, for example, that 81% of citizen science projects use QA/QC mechanisms, and that data quality is largely a “minor” or “critical but fixable or workable” concern. These findings offer new supporting evidence to back up the common claim that volunteers collect data that is as accurate, and as actionable, as the data collected by professionals.

Analysis around stakeholder engagement is novel and through provoking. For example, Aceves-Bueno’s team differentiates between community members, “defined as those with a direct stake in management outcomes,” and volunteers, who “participate in citizen science even though they have no direct economic or health interest in the resource being managed” (p. 498). Based on this classification, 28% of the 83 studies surveyed involved community members, while 72% relied on volunteers. When community members are involved, building trust to support buy-in is a key consideration, achieved through mechanisms like involving participants in project design and in the identification of appropriate incentives. The authors also report that longer-term monitoring, such as the type supported by eBird, is more likely to be successful when volunteers, as opposed to community members, are involved.

Regarding volunteer motivation, five motivational categories were identified: knowledge (75% of projects), sense of place (49%), action (29%), tools and technology (25%), and economic incentives (22%). Analysis revealed an interesting connection between motivation and use of data for management: “The use of sense of place, technology, and action to encourage participants was associated with a higher likelihood of using the data for management…whereas the use of knowledge attainment to motivate participants was negatively correlated with the use of data for management” (p. 503). This is an especially interesting finding given that many projects have explicit goals of educating participants. Are educational and management goals truly at odds, or are projects simply designed to prioritize one impact over the other, instead of simultaneously addressing both?

The authors conclude that citizen science can address key shortcomings of adaptive management, provided that monitoring conditions are carefully designed and stakeholder buy-in is achieved. In addition, this paper suggests important ways for meeting both conditions, laying out a tentative blueprint for how citizen science may support adaptive management. More work such as this research, which examines the impacts of citizen science beyond supporting scientific registration or educational goals, is needed to support a growing and evolving field.

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