Modeling Microbial Processes: From the Earth Down or the Microbe up?
Thursday, August 14, 2014: 8:00 AM-11:30 AM
202, Sacramento Convention Center
Xiaofeng Xu, Auburn University, AL
Joshua P. Schimel, University of California, Santa Barbara
Wyatt Hartman, Lawrence Berkeley National Laboratory
Parallel advances in developing ecosystem biogeochemical process models and characterizing soil microbial community structure suggest the potential to incorporate microbial population dynamics and physiological processes into large-scale Earth system models. However, this emerging effort faces the challenge: which processes to represent and how? From a top down modeling perspective, identifying the critical processes and then developing approaches to model them, we may lack understanding of the driving mechanisms and the data to develop parameterizations. An alternative approach is more bottom-up, working from the community data to develop relationships and develop models that explain them—but these data sets may be more complex than current ecosystem models can accommodate. For ecosystem modelers to explicitly incorporate microbial mechanisms into Earth system models, and so better simulate and predict biogeochemistry-climate feedbacks, we need to bridge the gaps between these top-down and bottom-up, model- vs. data-driven approaches to microbial dynamics and element cycling. This organized special session will invite experts to discuss current issues in the field of modeling microbial processes to predict global climate change dynamics and feedbacks. The objectives of this session are 1) to enhance communication between microbial data scientists and ecosystem modelers; 2) to review the status of microbial data for improving ecosystem models; 3) to promote designing laboratory and field experiments to test and parameterize models of microbial processes, and 4) to identify opportunities for data-model integration to better simulate and predict biogeochemical feedbacks in the earth-climate system. Speakers will identify critical knowledge gaps and present case studies illustrating both data-guided model development, and model-driven experiments to determine appropriate mechanisms and improve data synthesis. Case study presentations will be contextualized by opening and closing talks emphasizing the overall progress and gaps in the field, and opportunities for future collaboration to improve synthesis of modeling and data-driven process studies. The significance of data-model integration as a mechanism to advance understanding of microbial processes, including carbon and nutrient cycling, and trace gas fluxes will be emphasized. Progress in these areas and the data-model integration process will be of interest to many members of ESA including microbiologists, ecosystem ecologists, ecosystem modelers, and data scientists.