Measures of Microbial Biomass for Soil Carbon Decomposition Models

Melanie A Mayes1, Jennifer Dabbs2, Jessica M Steinweg3, Christopher W Schadt4, Laurel Anne Kluber5, Gangsheng Wang6 and Sindhu Jagadamma5, (1)Oak Ridge National Laboratory, Environmental Science Division, Oak Ridge, TN, United States, (2)University of Arizona, Department of Geosciences, Tucson, AZ, United States, (3)University of Wisconsin - Baraboo, Biological Sciences, Baraboo, WI, United States, (4)Oak Ridge National Laboratory, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge, TN, United States, (5)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (6)University of Oklahoma, Institute for Environmental Genomics, Department of Microbiology and Plant Biology, Norman, OK, United States
Abstract:
Explicit parameterization of the decomposition of plant inputs and soil organic matter by microbes is becoming more widely accepted in models of various complexity, ranging from detailed process models to global-scale earth system models. While there are multiple ways to measure microbial biomass, chloroform fumigation-extraction (CFE) is commonly used to parameterize models.. However CFE is labor- and time-intensive, requires toxic chemicals, and it provides no specific information about the composition or function of the microbial community. We investigated correlations between measures of: CFE; DNA extraction yield; QPCR base-gene copy numbers for Bacteria, Fungi and Archaea; phospholipid fatty acid analysis; and direct cell counts to determine the potential for use as proxies for microbial biomass. As our ultimate goal is to develop a reliable, more informative, and faster methods to predict microbial biomass for use in models, we also examined basic soil physiochemical characteristics including texture, organic matter content, pH, etc. to identify multi-factor predictive correlations with one or more measures of the microbial community. Our work will have application to both microbial ecology studies and the next generation of process and earth system models.