Understanding the combined effects of spruce beetle outbreaks and climate change on Rocky Mountains vegetation through ecological modeling

Foster, Adrianna, Department of Environmental Sciences, University of Virginia
Shugart, Herman, Department of Environmental Sciences, University of Virginia

Mean annual temperatures in the western United States have increased in the last few decades, and during the 21st century, it is predicted that this warming trend will continue. In the subalpine zone of the Rocky Mountains, this warming is also predicted to increase the frequency and severity of spruce beetle outbreaks. Climate change itself may also affect vegetation within the Rocky Mountains, potentially leading to shifts in species compositions. These forests are a crucial part of the US’s carbon budget, thus it is important to analyze how climate change and bark beetles in conjunction will affect the biomass and species composition of vegetation in the subalpine zone. UVAFME is an individual-based gap model that simulates the biomass and species composition of a forested landscape through time. UVAFME is first calibrated and parameterized to the southern Rocky Mountain landscape using data on species composition, climate, and site conditions. Species-specific parameter inputs for the 11 major Rocky Mountain species are derived from the scientific literature. The model is then quantitatively and qualitatively validated at two Rocky Mountain sites in Wyoming and Colorado. Results show that UVAFME accurately simulates the vegetation dynamics along an elevation gradient. UVAFME output on size structure (stems ha-1 size class-1) and species-specific biomass (tonnes C ha-1) is comparable to forest inventory data at those locations. A climate sensitivity test is performed in which temperature is first increased linearly by 2°C over 100 years, stabilized for 200 years, cooled back to present climate values over 100 years, and again stabilized for 200 years. This test was conducted to determine what effect elevated temperatures may have on vegetation zonation, and how lasting the changes may be. Results show that elevated temperatures within the southern Rocky Mountains may lead to persistent decreases in biomass and changes in forest composition as species migrate upslope. Without the effect of disturbances, long-term output from the subalpine zone at the southern WY site shows periodic behavior between Engelmann spruce and subalpine fir, indicating that periodicities in forested ecosystems may be more common than previously thought. UVAFME is then updated with a spruce beetle subroutine created for this study that calculates the probability for beetle infestation of each tree on a plot. This probability is based on site characteristics, such as mean spruce size and plot-level basal area; climate factors, such as temperature; and individual tree characteristics, such as tree size, stress level, and proximity to other infested trees. To determine the net effect of both climate and beetle infestation on subalpine vegetation, UVAFME is then run with multiple scenarios that combine beetle infestation with current or altered climate at sites across the Wyoming and Colorado Rocky Mountains. Climate change projections are from the NCAR Community Earth System Model output for the A1B and A2 IPCC scenarios. These results are compared among the different scenarios. Output from these tests show that the combination of spruce beetle infestations and increasing temperatures will cause a greater loss of Engelmann spruce biomass than either climate change or beetle infestation alone. The combination of spruce beetles and climate change additionally results in a further increase in the dominance of lower elevation species, such as lodgepole pine, ponderosa pine, and Douglas-fir. These results are an important step in understanding the possible futures for the vegetation of the subalpine zone in the Rocky Mountains.

PHD (Doctor of Philosophy)
ecological modeling, UVAFME, forest dynamics, Rocky Mountains, bark beetles, disturbances, individual-based gap modeling
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