The identification of Aquaculture Opportunity Areas (AOAs) in the Gulf of Alaska represents a significant effort to balance sustainable aquaculture growth with existing ocean uses. To facilitate this, a Multi-Criteria Decision Analysis (MCDA) spatial suitability model was developed, integrating data layers representing potential conflicts across five key categories: National Security, Industry & Navigation, Cultural Resources, Natural Resources, and Aquaculture & Fisheries. These data layers not only come from diverse sectors but also have different formats and standards as well as varying levels of conflict with potential aquaculture farms. Synthesizing them into a cohesive output that represents relative suitability for aquaculture is a complex modeling process that reflects the interconnected nature of ecosystems and industry, especially in such a dynamic environment as coastal Alaska. Understanding how individual data layers and modeling decisions drive the output improves our ability to interpret the results in the Alaska AOA Atlas and provides insights to inform future modeling efforts. We analyzed which data layers exert the highest and lowest quantifiable influence on relative suitability and attempt to explain their behaviors. Furthermore, we evaluated the concepts of correlation and redundancy in the interaction of data layers. This work improves the transparency of the model mechanics to ensure stakeholders and policymakers can trust that suitability modeling accurately reflects the complex trade-offs required for marine spatial planning in the Gulf of Alaska and elsewhere.
Keywords: aquaculture opportunity areas, spatial suitability modeling, multi-criteria decision analysis, sensitivity analysis, Gulf of Alaska