Evaluation Framework for Large Scale Aquatic System Adaptive Management Programs

This research presents a new evaluation framework for large-scale aquatic adaptive management programs. The evaluation framework was developed by Chad Smith in response to: 1) personal experience with adaptive management failure in large-scale restoration programs, governance structure failure or absence in these programs, and observation of critical overlap between good governance and adaptive management success; and 2) recent literature and scholarship calling for more empirical case studies and analysis, particularly in U.S. river basins, of governance and adaptive management. These information sources suggest a fair exploration of the relationship between adaptive management and governance structure is warranted as it pertains to the implications for successful adaptive management at a large scale. While adaptive management is ubiquitous in most large restoration programs as the management framework of choice, few, if any, examples of successful adaptive management at a large scale exist. Given the amount of federal money spent annually on large restoration programs and the promise of adaptive management, it is curious that examples of success are in short supply. There has been a good deal of recent scholarship on governance and its components and separately on adaptive management but no examples of assessment frameworks that capture the linkages between governance structure/function and adaptive management. The evaluation framework is presented as a practical tool to assess the governance structure and operation of a large-scale program, as well as the structure and operation of adaptive management within the program. It is intended that the methodology, results, and conclusions will serve as an impetus for applying the evaluation framework to further explore the relationship between governance structure and the successful application of adaptive management in large-scale restoration programs.

The five-step Adaptive Management Program Evaluation Framework (AMPEF) was created to serve as a repeatable tool for large-scale restoration programs utilizing adaptive management to assess components/sub-components of governance and adaptive management and point to recommendations for refinement to help those programs move forward in achieving their goals and objectives. The central research hypothesis is governance of a large-scale aquatic system adaptive management program is determinative in successful implementation of adaptive management, thus predicating program success. The new evaluation framework tests this hypothesis by:

1. Specifying key governance and adaptive management components, based on a literature review, other related evaluation methods, and personal experience.
2. Conducting an assessment of these components/sub-components in two programs through program scoping, expert appraisal, interviews, and standardized electronic surveys.
3. Rating both the likelihood of success and the consequences of failure of the key governance and adaptive management components/subcomponents.
4. Assessing programs against a proposed typology for adaptive management.
5. Using the results of this rating to predict the possibility of program success or failure.
6. Prioritizing initial recommendations for reform.

The evaluation framework was developed based on a combination of scholarship and methodological application from the disciplines of risk analysis, governance analysis, and adaptive management analysis. The research also presents a proposed ideal typology for adaptive management in large-scale aquatic recovery programs that is adapted from similar work on governance structures. The typology serves as an attempt to merge governance and adaptive management components to provide qualitative insight into the hypothesis that good governance through a strong process of shared decision making and communication is likely to promote successful adaptive management at a large scale. High levels of communication and data synthesis but unilateral decision making is expected to predict adaptive management being “stuck” in the six-step cycle well before the Adjust step. A similar condition is expected for low levels of communication and data synthesis even in shared decision-making contexts. Little communication and data synthesis (resulting in a “science pile” where data is collected but not analyzed, synthesized, or otherwise communicated to decision-makers) and unilateral decision-making is expected to promote conditions that do not enable adaptive management and instead revert management back to trial and error.