Date of this Version
Smith, C.B. 2020. A Conceptual Model Evaluation Framework for Adaptive Governance and Adaptive Management in Large-Scale Restoration Programs. Doctoral Dissertation. University of Nebraska-Lincoln.
Adaptive management (AM) has become a kind of plastic phrase applied as a formulaic panacea for most major species recovery and ecosystem restoration efforts now underway across the United States. AM emerged as an application of the scientific method to resource management, closely tying management to science learning through experimental actions. The phrase “learning by doing” best captures the premise behind developing an experimental management approach that could be applied on the larger scale of a river system or ecosystem. In nearly five decades of application, however, examples of successful AM implementation at large scales are few and conflict remains over how to achieve the most essential elements of true adaptive management. Emerging theory on governance structures and the ability of those structures to adapt to a changing environment led to development of adaptive governance (AG). With a focus on polycentric structures, self-organization, and decision-making made more inclusive and less top-down, AG appears linked to the notions of AM grounded in constant learning, implementing management actions as experiments, and embracing uncertainty. AG has thus emerged as an integral approach to tackling the challenges of moving large-scale AM programs forward. But few analytical frameworks exist to evaluate governance performance and point to necessary reforms. Similarly, assessment frameworks for AM focus on improving the steps of the AM process but do not capture related linkages to the governance structure under which those AM processes are operated. The central proposition of my dissertation is that governance of a large-scale aquatic system adaptive management program is determinative in successful implementation of adaptive management thus predicating program success. To explore this proposition, I developed and field-trialed a new conceptual model restoration program evaluation framework that incorporates a performance assessment of multiple components and subcomponents of AG and AM; a risk assessment of these AG and AM components; and a typology to place restoration programs in quadrants of possible success, all resulting in recommendations for restoration program reform.
Advisor: Craig Allen