Integrating Microbial Evolution into Biogeochemical Models to Predict Soil Response to Drought

ended

Soil is both the largest sink and source of organic carbon (C) exchanged with the atmosphere. These exchanges result from biological processes, the primary source being the decomposition of soil organic matter (SOM), which is controlled by physical factors such as climate. As such, soil C emissions are very vulnerable to climate change but can also be reduced with new land management practices if we can predict the outcomes of soil carbon-climate feedbacks. However, predictions from the existing large-scale soil C models strongly diverge, and reveal large uncertainties in the processes and controls at play. One of these uncertainties is the effect of change in precipitation regimes on SOM decomposition mediated by soil microorganisms. Functions describing the decomposition response of soil carbon to soil moisture are static in current large-scale models, yet recent empirical studies show that decay responses under new soil moisture conditions can change due to shifts in microbial communities. Recent evidence suggests that evolution is a key processes driving these shifts in microbial communities. This project proposes to integrate variable decomposition-moisture functions into a large-scale soil C model to reflect precipitation history and carbon substrate influence on microbial responses to changing soil moisture. These functions will be calculated from a mechanistic microbial model that accounts for both ecological and evolutionary processes. The mechanistic model will be an updated version of the trait-based model DEMENT developed by the fellow’s supervisor at the partner institution (UC Irvine). The moisture response functions will be integrated into a commonly used soil carbon model, RothC, that has been incorporated into the global land surface model (ORCHIDEE) of the host institution (LSCE).