OOS 41-8
Feedback responses of soil microbial communities to climate warming

Wednesday, August 12, 2015: 10:30 AM
327, Baltimore Convention Center
Jizhong Zhou, Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK
Mengting Yuan, Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK
Cong Wang, Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK
Xue Guo, Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK
Katherine Todd-Brown, Pacific Northwest National Laboratory, Richland, WA
Liyou Wu, Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK
Zhili He, Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK
Kostas Konstantinidis, Center for Bioinformatics and Computational Genomics, and School of Biology, Georgia Institute of Technology, Atlanta, GA
Yiqi Luo, Microbiology and Plant Biology, University of Oklahoma, Norman, OK
Edward A. G. Schuur, Center for Ecosystem Sciences and Society, and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ
James R. Cole, Center for Microbial Ecology and Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI
James M. Tiedje, Center for Microbial Ecology and Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI
Background/Question/Methods

Understanding the responses of biological communities to climate warming is a central issue in ecology and global change biology, but it is poorly understood, particularly for microbial communities. To advance system-level predictive understanding of the feedbacks of belowground microbial communities to multiple climate change factors and their impacts on soil carbon (C) and nitrogen (N) cycling processes, we have used integrated metagenomic technologies (e.g., target gene and shotgun metagenome sequencing, GeoChip, and isotope) to analyze soil microbial communities from experimental warming sites in Alaska (AK) and Oklahoma (OK), and long-term laboratory incubation. The community structure was determined with high throughput amplicon sequencing, shotgun sequencing and functional gene arrays. The experimental data was then analyzed with a variety of statistical approaches. The functional gene data was subsequently incorporated into global change models to improve their predictability.

Results/Conclusions

Our experimental results showed that microbial functional community structure in permafrost soils was dramatically shifted only after 1.5 year warming, demonstrating rapid microbial responses and high sensitivity of the tundra ecosystem to warming. The short-term warming markedly increased microbial genes/populations involved in not only aerobic but also anaerobic decomposition. Such increases resulted in up to 56% increase in ecosystem respiration (Reco), indicating the vulnerability of the permafrost soil C. Warming significantly enhanced genes for nutrient cycle processes, which likely contributed to the increased (30%) gross primary productivity (GPP). Ultimately, whether the feedback of the permafrost ecosystem to climate warming is negative or positive depends on the balance of the simultaneous increases in Reco and GPP. Similar results were also observed after 5 year warming at both AK and OK long-term experimental sites. In addition, long-term laboratory incubation (>700 days) of soils (>1000 samples) from both AK and OK showed that C quality had dramatic impacts on the temperature sensitivity of soil organic matter decomposition (SOM) and associated microbial community structure.  However, field warming did not affect the dynamics and temperature sensitivity of SOM decomposition. Finally, mathematical analysis indicated that incorporating microbial functional gene information did help to constrain uncertainty of modeling prediction.