Supporting Landscape-Level Risk Assessment and Decision-Making in Managed Ecosystems Using Novel Modeling Methods

Abstract

The process of translating fundamental environmental research into evidence-based policies meant to protect and manage our ecosystems has long fallen short of expectations. Policymakers’ desires for straightforward solutions with short-term, certain outcomes are constantly at odds with the nature of uncertainty in environmental research and decision modeling. The current methodologies of bridging the science-policy gap by encouraging researchers to communicate the uncertainties more clearly in their models and relate the results of individual studies to the entirety of complex decision-making frameworks are inadequate. Through this thesis, I explore the role of research within the boundary of science and policy and the ways in which we might increase the translation from research to evidence-based policy. Three different model frameworks employ three methodologies to reach this goal (1) Contextualize model outputs in previously existing policy frameworks; (2) Utilize environmental indicators as model outputs that are known to resonate with policy-makers and the public; and, (3) Collaboratively construct model structures to match to the pre-existing mental models of policy and decisionmakers. By understanding the balance between model complexity, uncertainty, and interpretability researchers can better understand how to integrate across research, social, and political boundaries to effectively inform policy.