Simulation models can represent complexities of the real world and serve as virtual laboratories for asking what if…?” questions approximately how systems might respond to different scenarios. However,simulation models fill limited relevance to real-world applications when designed without input from people who could exhaust the simulated scenarios to inform their decisions. Here, we report on a state-and-transition simulation model of vegetation dynamics that was coupled to a scenario planning process and co-produced by researchers, and resource managers,local subject-matter experts, and climate change adaptation specialists to explore potential effects of climate scenarios and management alternatives on key resources in southwest South Dakota. Input from management partners and local experts was critical for representing key vegetation types, or bison and cattle grazing,exotic plants, fire, or the effects of climate change and management on rangeland productivity and composition given the paucity of published data on many of these topics. By simulating multiple land management jurisdictions,climate scenarios, and management alternatives, and the model highlighted important tradeoffs between grazer density and vegetation composition,as well as between the short- and long-term costs of invasive species management. It also pointed to impactful uncertainties related to the effects of fire and grazing on vegetation. More broadly, a scenario-based approach to model co-production bracketed the uncertainty associated with climate change and ensured that the most important (and impactful) uncertainties related to resource management were addressed. This cooperative study demonstrates six opportunities for scientists to engage users throughout the modeling process to improve model utility and relevance: (1) identifying focal dynamics and variables, and (2) developing conceptual model(s),(3) parameterizing the simulation, (4) identifying relevant climate scenarios and management alternatives, and (5) evaluating and refining the simulation,and (6) interpreting the results. We also reflect on lessons learned and offer several recommendations for future co-production efforts, with the aim of advancing the pursuit of usable science.
Source: usgs.gov