Resilience Science and Analytics for Ocean Resource Management

Margaret Kurth, United States and Igor Linkov, US Army Engineer Research and Development Center, Concord, MA, United States
In light of the systemic threat that climate changes poses to ocean health and ecosystem service provision, science and policy are converging on resilience as an overarching objective of management action. Generating actionable information for management to support a resilience objective must be based in the complex physical realities of ocean and coastal systems including their capacity to resist change, thresholds for stress, and shifting regime states and, at the same time, on the capacity of dependent communities to plan and prepare, absorb, recover from and adapt to stressors. In general though, resilience-oriented decision making has not been sufficiently supported by theory and science; specific applications require specific frameworks and analytics.

This presentation will review methodologies of resilience assessment developed by the Risk and Decision Science team of the U.S. Army Corps of Engineering Research and Development Center, which are ripe for customization to a variety of natural resource contexts. The first, a decision analytical approach, can support assessment across a set of system domains including physical, social, cognitive, and informational. Alternatively, a network science technique can be employed to model system functions under normal and disrupted scenarios. Both are designed to accommodate metrics that are tailored to particular resilience inquiries. Case study applications will be included to illustrate their utility.

Ocean resource management decisions stem from best-available information about their implications for ecosystem health and the recipients of subsequent ecosystem goods and services, which is rife with complexity and uncertainty. Resilience analytics, paired with techniques such as adaptive management, can support decisions-making for oceans. Explicating coupled human-ocean systems into their relevant relationships and explicitly framing them as co-resilient can facilitate analytics and in turn, support decision-making.