Approaches to Incorporating Climate Change Effects in State and Transition Simulation Models of Vegetation

Understanding landscape vegetation dynamics often involves the use of scientifically-based modeling tools that
are capable of testing alternative management scenarios given complex ecological, management, and social conditions. State-and-transition simulation model (STSM) frameworks and software such as PATH and VDDT are
commonly used tools that simulate how landscapes might look and function in the future. Until recently, however,
STSMs did not explicitly include climate change considerations. Yet the structure of STSMs makes them highly
conducive to the incorporation of any probabilistic phenomenon. The central task in making a STSM climate-sensitive is describing the relevant processes in terms of probabilistic transitions. We discuss four different approaches we have implemented to inform climate-induced changes in vegetation and disturbance probabilities in STSMs using the dynamic global vegetation model MC1. These approaches are based on our work in several landscapes in the western United States using different modeling frameworks. Developing STSMs that consider future climate change will greatly broaden their utility, allowing managers and others to explore the roles of various processes and agents of change in landscape-level vegetation dynamics. However, numerous caveats exist. Regardless of how STSMs are made climate-sensitive, they neither simulate plant physiological responses directly nor predict landscape states by simulating landscape processes mechanistically. They are empirical models that reflect the current understanding of system properties and processes, help organize state-ofthe-art knowledge and information, and serve as tools for quickly assessing the potential ramifications of management strategies. As such, they highlight the need for new
research, while providing projections based on the best available information.