Comparing Conceptual Model Simulations for Ecosystem-Based Management

Chris R Kelble, Atlantic Oceanographic and Meteorological Laboratory - NOAA, Ocean Chemistry & Ecosystems Division, Miami, United States, Neda Trifonova, University of Aberdeen, Aberdeen, United Kingdom, Jonathan Reum, Joint Institute for the Study of the Atmosphere and Ocean, Seattle, United States, Robert Wildermuth, University of Massachusetts Dartmouth, New Bedford, United States, Christopher Harvey, NOAA Northwest Fisheries Science Center, Seattle, WA, United States and Sean Lucey, NOAA Northeast Fisheries Science Center, Woods Hole, MA, United States
Ecosystem-based management (EBM) of resources, services and human activities is inherently complex, due to the myriad interacting system components and processes, the many sources of uncertainty, and the necessity of tradeoffs in decision-making. Conceptual models can be highly valuable tools in addressing these challenges. They depict components, processes and linkages that make up a social-ecological system, which can range from the environmental processes that influence basic physical, chemical and biological properties to the governance systems and social patterns that regulate and influence human activities. Conceptual models can also serve as the basis for dynamic analyses to simulate scenarios. These simulation approaches ask how the system might respond to changes in one or more of its elements, as a function of the conceptual model structure, the perceived strengths of its internal linkages and cycles, and the degree of uncertainty about its composition. Such efforts represent an important frontier of ecosystem modeling and management because they can combine diverse ecological and social components and processes within a common modeling format. Three simulation approaches have received particular attention in the ecosystem science literature for their ability to simulate social-ecological networks derived from conceptual models: Bayesian belief networks, fuzzy-logic cognitive mapping and qualitative network modeling. We apply these three approaches to test common scenarios across three unique conceptual models to address two questions: (1) Do the three modeling methods produce similar results when common scenarios are applied? and (2) Do these approaches provide clear guidance and science support for EBM of complex social-ecological systems, with respect to likely scenario outcomes, trade-offs, unforeseen results and knowledge gaps? We will present these results focusing on the applicability and differences among the three approaches for providing Ecosystem Based Management advice.