Despite methodological advances in functional microbial ecology, integrating this wealth of data into microbial biogeochemical models remains challenging. Data such as omics, stable isotope probing (SIP), and activity measurements can improve our fundamental understanding of biogeochemical cycles, and consequently increase model realism, and reduce uncertainty in model predictions, especially under scenarios of global change. Yet, a substantial knowledge gap remains in how this integration could be achieved. How can models invoke mechanisms that can be interrogated experimentally? What information can realistically be integrated and how much improvement can we expect? At the same time, how can we leverage model-generated hypotheses to design novel experiments to advance empirical ecology and theory?
This session aims to bring together empirical microbial studies and process-based modeling to close this gap. We welcome contributions using omics, SIP, activity-based approaches, and other methods targeting microbial functional traits, as well as modeling studies applying trait-based parameterization, genome-scale metabolic models, machine-learning-based trait inference, or data assimilation. We are particularly interested in how quantitative microbial traits that determine rates of biogeochemical cycling can be extracted from measurements to define functional groups that inform trait-based models. We envision presentations covering these topics in any biogeochemical context-from soils to sediments, from wetlands to oceans.