PARENT SESSION
Oral Session # 44: Resource Management I: Theory; Aquatic Systems.
Presiding: C Swan
Wednesday, August 6. 8:00 AM to 11:30 AM, SITCC Meeting Room 106.

A probability network of eutrophication models for the Neuse River estuary.

Borsuk, Mark^{1, 3}, Stow, Craig^{*,1, 2}, Reckhow, Kenneth¹, ¹ Duke University, Durham, NC, USA³ SIAM, EAWAG, Dubendorf, Switzerland² University of South Carolina, Columbia, SC, USA

ABSTRACT- We describe a flexible method for ecological prediction based on a Bayesian probability network. The graphical structure explicitly represents cause-and-effect assumptions between ecosystem variables that may be obscured under other approaches. These assumptions allow the complex causal chain linking management actions to ecological consequences to be factored into an articulated sequence of conditional relationships. Each of these relationships can then be quantified independently using an approach suitable for the type and scale of information available. To demonstrate the application of the approach to a specific ecological problem, we present a detailed case study concerning eutrophication management in the Neuse River estuary, North Carolina. Relationships among variables were quantified using a variety of methods, including: process-based models statistically fit to long-term monitoring data, Bayesian hierarchical modeling of cross-system data gathered from the literature, multivariate regression modeling of mesocosm experiments, and probability judgments elicited from scientific experts. The integrated network was then used to generate predictions of the policy-relevant ecosystem variables under alternative nutrient management strategies.

Key words: Neuse, Bayesian, estuary, causal