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Abstract ContextThe > 25,000 km²Flint Hills ecoregion in eastern Kansas and northeastern Oklahoma, USA, is an economically and ecologically important area encompassing the largest remaining tallgrass prairie ecosystem in North America. Prescribed fires are used routinely to control invasive woody species and improve forage production for the beef-cattle industry. However, burning releases harmful pollutants that, at times, contribute to air quality problems for communities across a multi-state area. ObjectivesEstablish a modeling framework for synthesizing long-term ecological data in support of Flint Hills tallgrass prairie management goals for identifying how much, where, and when rangeland burning can be conducted to maximize ecological and economic benefits while minimizing regional air quality impacts. MethodsWe used EPA’s VELMA ecohydrology model to synthesize long-term experimental data at the 35 km²Konza Prairie Biological Station (KPBS) describing the effects of climate, fire, grazing, topography, and soil moisture and nutrient dynamics on tallgrass prairie productivity and fuel loads; and to spatially extrapolate that synthesis to estimate grassland productivity and fuel loads across the nearly 1000 times larger Flint Hills ecoregion to support prescribed burning smoke trajectory modeling using the State of Kansas implementation of the U.S. Forest Service BlueSky framework. ResultsVELMA provided a performance-tested synthesis of KPBS data from field observations and experiments, thereby establishing a tool for regionally simulating the combined effects of climate, fire, grazing, topography, soil moisture, and nutrients on tallgrass prairie productivity and fuel loads. VELMA’s extrapolation of that synthesis allowed difficult-to-quantify fuel loads to be mapped across the Flint Hills to support environmental decision making, such as forecasting when, where, and how prescribed burning will have the least impact on downwind population centers. ConclusionsOur regional spatial and temporal extrapolation of VELMA’s KPBS data synthesis posits that the effects of integrated ecohydrological processes operate similarly across tallgrass prairie spatial scales. Based on multi-scale performance tests of the VELMA-BlueSky toolset, our multi-institution team is confident that it can assist stakeholders and decision makers in realistically exploring tallgrass prairie management options for balancing air quality, tallgrass prairie sustainability, and associated economic benefits for the Flint Hills ecoregion and downwind communities. 

Investigations into plant–herbivore interactions are of importance for understanding grassland ecosystem dynamics. Our research quantified the effects of vegetation heterogeneity at a patch scale of 30 m on bison space use in a tallgrass prairie through the analyses of the resource utilization function. In addition, we assessed the vegetation heterogeneity associated with bison locations by comparing the patch-scale vegetation characteristics between areas with high and low bison space use through Mann–Whitney U tests. Furthermore, we simulated the interactions between bison and vegetation patches (2 × 2 m) during the early growing season for the lowland topographic positions using agent-based modeling (ABM) as a preliminary study of linking bison foraging site selection with vegetation responses to bison grazing dynamically. The bison grazing strategy in the ABM of the grassland system was adjusted to ensure consistency in the vegetation pattern variations related to bison space use between the simulation and the empirical on-the-ground observations. The results indicated the following: (1) The effects of the patch-scale vegetation heterogeneity on the bison foraging site selection varied across the seasons, which were most evident in the middle of the growing season. (2) A relatively high level of bison space use generally resulted in diverse grassland canopies with high variability and interspersion. (3) From the ABM of the grassland system, it can be implied that bison select patches with high quality and quantity at the beginning of the growing season; as the vegetation quality and quantity improve overall, the bison graze randomly. This pattern was confirmed by observations of the bison foraging site selection in our study site. The ABM proved to be valuable in exploring and elucidating the underlying mechanisms of the grassland dynamics with a native North American grazer.