Monitoring bison impacts in prairies: How transferable are predictions from remnants to restorations?

Holly P. Jones, Department of Biological Sciences and Institute for the Study of the Environment, Sustainability, and Energy, Northern Illinois University, DeKalb, IL, Nicholas Barber, San Diego State University, San Diego, CA and Christy N. Wails, Department of Biological Sciences, Northern Illinois University, DeKalb, IL

Background/Question/Methods
Bison are influential grazers in North American grasslands, but were driven to near-extinction in the late 1800’s. Their reintroduction across prairie ecosystems has resulted in significant impacts to prairie plant communities. However, much of the research on impacts of bison have been limited to prairie remnants west of the Mississippi river. These ecosystems generally have lower precipitation and slightly different plant community composition than the eastern tallgrass prairie region. In this study, we ask whether predictions made about plant community response to bison reintroduction from remnant prairies can be used to forecast impacts of bison reintroduction in restored prairies. We used plant percent cover data from Konza prairie, a remnant tallgrass prairie in bison grazed and ungrazed sites to build models of how Shannon diversity and grass:forb changed with bison reintroduction. We then used that model as a test model to predict responses to bison reintroduction at Nachusa Grasslands, a tallgrass prairie restoration in the eastern tallgrass prairie region.
Results/Conclusions
We find that models built with remnant prairie data do not predict well the impacts of bison reintroduction to plant communities in restored eastern tallgrass prairie. Sustaining long-term funding for monitoring restoration outcomes is notoriously difficult, but extremely critical. Much of the science that occurs in restoration is short term, and driven by what concepts or methods are on trend, rather with an eye to comparability to other sites, and enabling predictive forecasting. Our results show the perils of predicting management impacts from one site in one context (remnant prairies with lower precipitation) to another (restored prairies with high precipitation). We call for increased harmonization of methods used to monitor restoration impacts, and an increase in monitoring across different habitats and contexts, to enable more accurate predictive forecasting. This will help to identify site-specific factors that may drive whether and when predictive forecasting will be effective.