Water, Risk, Policy

Full Title

Risk perception in provision of aquatic ecosystem services


Nutrient pollution is a major problem for many aquatic systems, because it feeds harmful cyanobacterial blooms (HCBs). It can be moderated through good governance, but to date management has been reactive, with decision makers responding to problems only after they become acute rather than preventing them, even when there is ample advance warning from scientists [1,2]. According to Webster [3,4], reactive governance leads to a process called the policy treadmill. In this, the left-hand cycle starts with an environmental problem, which sends signals to scientists, decision-makers, firms, the public, and other actors. These signals can cause increases in political concern when those who “hear” them have political power. Greater political concern increases the probability that management will switch to a more effective cycle, but the transition depends on other factors, such as available technologies and policy options. Disconnects occur whenever problem signals fail to reach or fail to motivate decision makers. There are many types of disconnects, but one of the most important occurs when there are differences between “objective” risk signals and subjective risk perceptions [5–7]. Of course, no measures of risk are perfectly objective, but we use the term to differentiate between empirical data/science and individual beliefs.

Disconnects between the actual and perceived level of risk can delay or stop the feedback loop depicted on the right in Figure 1, preventing the buildup of concern and related transition to more effective management. This delay in turn allows a build-up of ecological damage that can trigger large and possibly irreversible regime shifts, such as eutrophication [8,9]. Therefore it is important to understand the conditions under which disconnects emerge between objective and subjective risk perception, including social interactions that can affect aggregate perceptions of risk (i.e. political concern) [10,11]. We are most interested in the psychological and socio-economic filters that speed up or slow down policy response by dampening or amplifying objective risk signals.

We propose to convene a group of diverse scholars with expertise on the biophysical, psychological, socio-economic, health, and regulatory aspects of water quality issues to:

  • Develop comparable measures of objective risk signals for HCBs in the Lake Erie and Lake Champlain basins.
  • Compile data sources on subjective risks/beliefs to create a dataset of psychological and socio-economic risk signals to integrate with the objective risk database.
  • Use systems modeling techniques to test the hypothesis that amplification of subjective risk perceptions may trigger a shift in policy cycle from ineffective to effective; and dampening of subjective risk perceptions may trigger policy cycle to shift from effective to ineffective cycle.


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2. Karkkainen BC. Panarchy and Adaptive Change: Around the Loop and Back Again. Minnesota J Law, Sci Technol. 2005;7: 59–77.

3. Webster DG. Beyond the Tragedy in Global Fisheries. Cambrdge: MIT Press; 2015.

4. Webster DG. The action cycle / structural context framework : a fisheries application. 2015;20.

5. Pachur T, Hertwig R, Steinmann F. How do people judge risks: availability heuristic, affect heuristic, or both? J Exp Psychol Appl. 2012;18: 314–30. doi:10.1037/a0028279

6. Chesson H, Viscusi WK. The heterogeneity of time-risk tradeoffs. J Behav Decis Mak. 2000;13: 251–258.

7. Willis HH, Dekay ML. The roles of group membership, beliefs, and norms in ecological risk perception. Risk Anal. 2007;27: 1365–1380. doi:10.1111/j.1539-6924.2007.00958.x

8. Walker B, Salt D. Resilience Thinking: Sustaining Ecosystems and People in a Changing World. Washington DC: Island Press; 2006.

9. Gunderson LH. Adaptive dancing: interactions between social resilience an ecological crises. In: Berkes F, Colding J, Folke C, editors. Navigating Social-Ecological Systems: Building Resilience for Complexity and Change. Cambridge: Cambridge University Press; 2003. pp. 33–52.

10. Kuran T, Sunstein C. Availability Cascades and Risk Regulation. Stanford Law Rev. 1999;51: 683–768.

11. Kasperson JX, Kasperson RE, Pidgeon N, Slovic P. The social amplification of risk: Assessing fifteen years of research and theory. In: Pidgeon N, Kasperson RE, Slovic P, editors. The social amplification of risk. Cambridge: Cambridge Universirty Press; 2003. pp. 13–46.

Project Type
Team Synthesis Project
Principal Investigators
D.G. Webster, Dartmouth College
Semra Aytur, University of New Hampshire
Mark Axelrod, Michigan State University
Joseph Hamm, Michigan State
Robyn Wilson, The Ohio State University
Jade Mitchell, Michigan State University
Louie Rivers, University of North Carolina, Chapel Hill
Victoria Kelly Turner, Kent State University
Christopher Koliba, University of Vermont
Andrew Schroth, University of Vermont
Jeffrey Michael Reutter, Ohio State
Kevin Patrick Carlin, UNH
Elizabeth Doran, University of Vermont
Patrick Bitterman, University of Vermont
Emily Jenkins, Michigan State University
Neil Kamman, Lake Champlain Basin Program
Michelle Selzer, Michigan Department of Environmental Quality
Jiyoung Lee, Ohio State University
Donna Kashian, Wayne State
Craig Stow, NOAA

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