Toward Arctic transitions and sustainability: modeling risks and resilience across scales of governance

Abstract

The Arctic region has been the subject of international attention in recent years. The magnitude of impacts from global climate change, land-use change, and speculations about economic development and accessible polar shipping lanes have intensified this focus. As a result, the potential to manage complex ecological, social and political relationships in the context of changes, risks and opportunities is the focus of a large and growing body of research. This dissertation contributes to the expanding scholarship on managing Arctic social-ecological systems for resilience by answering the question: What conditions improve cross-scale learning and resilience in nested social-ecological systems experiencing rapid changes? Using the framework of social-ecological systems and the drivers of change that can transform fundamental relationships within, three studies profile the spatial and temporal dimensions of learning and risk perceptions that impact nested social systems. The first study presents a spatial and temporal analysis of scale- and level-specific processes that impact learning from risks. It draws on four cases to underscore the need for a plurality of risk assumptions in learning for resilience, and sums up essential resources needed to support key decision points for increasing resilience. Two additional studies present research conducted with northern Alaska communities and resource managers. In these studies, I analyzed the extent to which perceptions of risks scale horizontally (between same-level jurisdictions), and vertically (between levels in a dominant jurisdictional structure). These examples illustrate the need for innovative institutions to enhance cross-scale learning, and to balance global drivers of change with local socioeconomic, cultural, and ecological interests. Based on findings of the dissertation research I propose recommendations to optimize the tools and processes of complex decision making under uncertainty.