OOS 73-10
Modeling microbial responses to drying and rewetting

Thursday, August 13, 2015: 4:40 PM
316, Baltimore Convention Center
Steven D. Allison, Ecology and Evolutionary Biology/Earth System Science, University of California, Irvine, CA
Emma L. Aronson, Plant Pathology and Microbiology, UC Riverside, CA

A key goal of biogeochemical models is to predict ecosystem responses to environmental change. Conventional models based on first-order decay of organic matter have difficulty replicating the Birch effect, or pulse of respiration that follows addition of water to a dry soil. In arid and semi-arid ecosystems, these pulses can represent a substantial fraction of soil respiration fluxes. Recently, new models based on microbial physiology and enzyme kinetics have been more successful at replicating pulse responses to drying and rewetting. These models represent the buildup of soluble carbon pools in soil microenvironments during dry periods followed by rapid consumption of soluble carbon by microbial biomass upon wet-up. Still, these new models do not account for different moisture tolerance strategies in the microbial community or the potential for adaptation to the drying-wetting disturbance regime. Trait-based models offer the opportunity to account for these adaptive processes and their effects on process rates, such as carbon mineralization. I modified the trait-based model DEMENT to include moisture dependence in microbial enzyme kinetics and turnover; I then analyzed the model’s ability to match data on litter decomposition and soil respiration in a southern California grassland ecosystem.


Carbon fluxes in southern California are highly sensitive to precipitation pulses, with nearly all litter decomposition occurring in the 6-month rainy season, and soil respiration pulses tracking rain events. Overall, DEMENT qualitatively matched these patterns, consistent with previous microbial models. DEMENT also allowed for tradeoffs among moisture tolerance traits and functional traits related to carbon and nutrient fluxes. These tradeoffs define alternative microbial strategies, with different strategies favored under different precipitation regimes. Thus it becomes possible to model the microbial diversity observed in genetic datasets from precipitation manipulations. Likewise, we can predict adaptive shifts in microbial community composition over time or with precipitation treatment, along with the consequences for biogeochemical function. Thus trait-based models are a powerful tool for predicting ecosystem responses to drying and rewetting, including the Birch effect.