Tree species responses to changing fire and climate in the Greater Yellowstone Ecosystem

Climate-driven changes in fire disturbance could potentially alter tree species regeneration catalyzing abrupt changes in forest composition, structure, and function in the Greater Yellowstone Ecosystem. Our ability to predict species' responses under novel conditions is dependent on the development of ecosystem models that can represent complex interactions between disturbance, climate, species competition, forest demography, and increasing atmospheric carbon dioxide. Dynamic Global Vegetation Models (DGVMs) are process-based models that represent these interactions and are therefore widely used to predict climate change impacts on terrestrial vegetation, frequently at the continental to global scale. DGVMs require input parameters including physiology, biochemistry, structure, and allocation to characterize generalized plant functional types. The potential for modeling vegetation at the landscape scale with DGVMs is promising, but presents a challenge for parameterizing individual species with limited data. Yet when accomplished, the species-level approach is exciting because it enables investigation of interactions between species composition, forest demography and fire dynamics. Here we present results from parameterization of the dominant tree species in the Greater Yellowstone Ecosystem (GYE) for the DGVM LPJ-GUESS to explore the causes of recent changes in plant productivity detected from satellite-derived vegetation indices. Results from fractional factorial model simulations indicate a strong carbon dioxide fertilization effect on post-fire forest regeneration, while climate change is reducing post-fire productivity. These results suggest that the ability of plants to benefit from increasing atmospheric carbon dioxide depends on the bioclimatic context and the physiology of individual species and demonstrates the utility of process-based DGVMs for modeling species responses to future novel climate and disturbance conditions.