Cooperatively improving tallgrass prairie with adaptive management

Abstract

Adaptive management (AM) is widely recommended 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 make recommendations for the management of native tallgrass prairie plant communities in western Minnesota and eastern North and South Dakota, USA. After nine years 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 updating to generate annual management recommendations at a site and state-dependent scale. We also used a logistic regression approach to complement the output from the AM model and evaluate the more general conditions which 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 suggest that grazing on sites that started in a poor condition were less likely to show improvements in the native plant community. Complementing AM with more traditional statistical analyses can help inform the iterative doubleloop learning phase of the AM framework. AM 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.