Old Tools, New Ways of Using Them: Harnessing Expert Opinions to Plan for Surprise in Marine Socio-Ecological Systems

Last modified: 
December 13, 2019 - 11:49am
Type: Journal Article
Year of publication: 2019
Date published: 11/2019
Authors: Rebecca Gladstone-Gallagher, Julie Hope, Richard Bulmer, Dana Clark, Fabrice Stephenson, Stephanie Mangan, Vera Rullens, Ewa Siwicka, Samuel Thomas, Conrad Pilditch, Candida Savage, Simon Thrush
Journal title: Frontiers in Marine Science
Volume: 6

With globally accelerating rates of environmental disturbance, coastal marine ecosystems are increasingly prone to non-linear regime shifts that result in a loss of ecosystem function and services. A lack of early-detection methods, and an over reliance on limits-based approaches means that these tipping points manifest as surprises. Consequently, marine ecosystems are notoriously difficult to manage, and scientists, managers, and policy makers are paralyzed in a spiral of ecosystem degradation. This paralysis is caused by the inherent need to quantify the risk and uncertainty that surrounds every decision. While progress toward forecasting tipping points is ongoing and important, an interim approach is desperately needed to enable scientists to make recommendations that are credible and defensible in the face of deep uncertainty. We discuss how current tools for developing risk assessments and scenario planning, coupled with expert opinions, can be adapted to bridge gaps in quantitative data, enabling scientists and managers to prepare for many plausible futures. We argue that these tools are currently underutilized in a marine cumulative effects context but offer a way to inform decisions in the interim while predictive models and early warning signals remain imperfect. This approach will require redefining the way we think about managing for ecological surprise to include actions that not only limit drivers of tipping points but increase socio-ecological resilience to yield satisfactory outcomes under multiple possible futures that are inherently uncertain.

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