Improving climate change risk management strategies in grasslands: A prototype application of state and transition simulation modelling

Background/Question/Methods

The Great Plains grasslands of North America provide a multitude of ecosystem services including clean water, forage, habitat, recreation, and pollination of native and agricultural plants. Increasingly variable climate and burgeoning societal demands for goods and services pose unique challenges to managers of these lands. A general lack of quantitative information regarding the effects of varied management strategies on these spatially heterogeneous landscapes complicates our understanding of the processes within them. Given the paucity of studies it is difficult to quantify the interaction of environmental (e.g. drought) influences and managerial strategies, such as grazing intensity and seasonality or fire frequency and behavior. This presents unique challenges to managers seeking to understand, explain, and justify proposed management strategies. We therefore developed a decision support system for helping to understand the risks and impacts of climate and management on production of forage and, ultimately, maintenance of goods and services. This novel decision support system merges two distinct tools which act in concert to produce state-of-the-art ecosystem modelling capabilities. First, the Rangeland Vegetation Simulator (RVS), deterministically estimates growth, succession, and fuels and second, the State-and-Transition Simulation Model (ST-SIM) enables stochastic modelling of ecological processes such as plant community development and response to climate.

Results/Conclusions

The Loamy Plains Ecological Site was chosen to prototype this system and identify uses and limitations of its application on the Great Plains. A gradient of grazing intensities from low, moderate and high were tested across a simulation period of 15 years (2000 to 2014). In each year annual production, vegetation cover and height, composition and fuelbed properties were quantified and calibrated and validated against field referenced data. The calibrated model closely matched observations of annual production and emulated the temporal variation in climate. Heavy grazing increased the area of a sodgrass state by about 30% while simultaneously decreasing annual production by approximately 18%. In addition, the heavy grazing scenario reduced overall fuel loadings by about 40, while the moderate and light grazing scenarios did not increase the abundance of the sodgrass state but enabled increasing cool season (C3) graminoids. The grazing strategies examined provide a range of outcomes influencing annual production, fuelbed properties and species assemblages. Results demonstrate the inherent resiliency of this shortgrass steppe to moderate levels of herbivory. And application of this system to quantify management outcomes will become increasingly important in the future for species conservation as demands for sustainable goods and services continue to increase.