Cooperatively improving tallgrass prairie with adaptive management

Abstract

Adaptive management (AM) is widely used as an approach for learning to improve resource management, but successful AM projects remain relatively uncommon, with few documented examples applied by natural resource management agencies. We used AM to provide insights into actions that would be most beneficial for the management of native tallgrass prairie plant communities in western Minnesota and eastern North and South Dakota, USA. After 9 yr of data collection and learning, we report on whether the condition of the prairie improved with management and which actions and frequency of action allowed improvement. Our approach to AM employed Bayesian inference to generate annual management recommendations at site- and state-dependent scales. We also used a logistic regression approach to complement the output from the AM model and evaluate the more general conditions that led to attaining management goals. Overall, the cover of native plants increased for low-quality sites, and among the management practices considered, we found that burning most effectively enhanced the native prairie plant community and increased the dominance of native indicator species. Contrary to expectations, the results also indicate that grazing on sites that started in a poor condition was less likely to show improvements in the native plant community. Complementing AM with more traditional statistical analyses can help inform the iterative double-loop learning phase of the AM framework. Adaptive management has many challenges, but we demonstrate that multi-agency AM can be successful. Keys to success include starting the project with an in-person, in-depth workshop; standardized protocols and a centralized database; a core project team with multi-disciplinary backgrounds; stability in project leadership; and regular communication to meet annual deadlines.