Abstract
To address the holistic aims of marine ecosystem-based management (EBM), ecosystem modelers seek to reflect the diverse processes, priorities, and perspectives of social-ecological systems to describe the equally complex systems we want to manage. Because of the multiple components, relationships, and parameters at play in models of marine ecosystems, structural (or model) uncertainty, which is uncertainty about 1) the number of modeled system components, 2) interactions between these components, and 3) the form and function of these relationships and values for parameters, can affect advice provided for EBM. Employing ecosystem or whole-of-system models in marine resource management presents a conflict between testing the performance of these models and structural uncertainty. Any model used as the basis for decision-making should first be shown to be robust to observation, process, and structural errors through tests of model performance with respect to relevant data. At the same time, test data become more and more scarce as additional processes and variables are included in these assessment models. Building from a conceptual model of the social-ecological system of Georges Bank, this dissertation explored these issues by 1) applying qualitative and Bayesian network modeling tools as simpler alternatives to complex whole-of-system models, 2) addressing model uncertainty in the Bayesian network model through a structural uncertainty framework, and 3) testing the performance of the qualitative and Bayesian network models for decision making through a management strategy evaluation applied at the ecosystem level. Chapter 1 applied qualitative network modeling to evaluate effects of structural uncertainty on management outcomes for twelve social, economic, and conservation objectives. This study compared the sensitivity of outcomes from two management strategies in four model structures of the Georges Bank system that investigate uncertainty in trophic structure and fisheries configuration. This analysis revealed that the resolution used to represent fisheries affects perceived tradeoffs among objectives as well as the reliability of predictions. Chapter 2ivdeveloped and fit a Bayesian network of the Georges Bank system to a 58-year time series of observations and expert information. With a focus on predictive ability of the model, this analysis tested the accuracy of the Bayesian network using within-sample and simulated datasets and measured the sensitivity of model predictions to additional information. The model predicted system states with 73% and 68% accuracy one and two years in advance, respectively, but some indicators were predicted more accurately than others. Chapter 3 demonstrated a framework for assessing effects of structural uncertainty on perceived management objective outcomes by comparing predictions from nine alternate Bayesian network model structures. After applying four management scenarios, differences among model alternatives were summarized from two performance perspectives: 1) optimization of estimated utility and 2) satisficing, or meeting a minimum threshold, for a maximum number of objectives. This structural sensitivity analysis showed that tradeoffs among objectives may be affected by structural uncertainty, contingent on the performance metric(s) used to make decisions. In Chapter 4, multi-model comparison and simulation testing emulating management strategy evaluation were used to evaluate the performance of the qualitative and Bayesian network models developed in Chapters 1 and 2 in an EBM decision context. Performance testing of these models found that 1) resolution of the qualitative network model may be too coarse to adequately represent the Georges Bank system for evaluating multiple cumulative pressures and 2) the Bayesian network model formulated in Chapter 2 may mischaracterize predictions of system states that are important for guiding strategic EBM decisions. Nonetheless, this case study reveals modeling approaches that address model structure that may improve performance of the Georges Bank Bayesian network model. This dissertation demonstrates that addressing structural uncertainty in whole-of-system models is essential for providing EBM advice and outlines a targeted approach to evaluate its effects using a range of qualitative and quantitative modeling tools.