Abstract
Sustainable fisheries management requires an understanding of interactions between fish, marine environments, fishing activities, and fisheries governance. Managing with imperfect information about these interactions can result in undesirable differences between the expected and realized management outcomes. Management Strategy Evaluation (MSE) is a model simulation method that rigorously tests management alternatives before they are implemented to help align expected and realized management outcomes. Tests can be conducted in the context of natural variability, uncertain stock status, and imperfect management implementation to assess potential trade-offs between management alternatives and identify alternatives that are robust to these uncertainties. The goal of this dissertation is to develop tools that support the integration of MSE into existing management processes, using three case studies from the Northeast U.S. as examples. Chapter 1demonstrates the viability of statistical tree analysis to synthesize MSE results for an Ecosystem Based Fisheries Management (EBFM) case study. Statistical tree analysis identified EBFM procedures capable of meeting combinations of catch, biomass, biodiversity, and economic goals, but ceilings that limited total ecosystem catch tended to mask the impact of other EBFM interventions. Chapter 2 expands the realism of this MSE framework to include technical interactions for multi-species groundfish fisheries and assess their impact on EBFM performance. Together, the impacts of fishing behaviors, advice implementation, and technical interactions drove outcomes more strongly than EBFM interventions for most scenarios. Chapter 3 develops communication guidance and a novel visualization tool to support public engagement with MSE. Applied use of this tool advanced communication software development which has lagged behind MSE modeling software and provided decision-support to select a new harvest control rule for Atlantic herring. These studies highlight opportunities to advance MSE applications to support both scientific and regulatory decision making by improving workflow reproducibility, streamlining results communication and leveraging synchronicity between MSE and EBFM to advance modeling.