Interrelationships among methods of estimating microbial biomass across multiple soil orders and biomes

Buell, Zachary W.; Dabbs, Jenny; Steinweg, J. M.; Kluber, Laurel A.; Phillips, Jana R.; Yang, Zamin K.; Roth, Spencer W.; Miller, R. Michael; Gutknecht, Jessica L. M.; Schadt, Christopher Warren; Mayes, Melaine A.

doi:10.1016/j.soilbio.2025.109844

Interrelationships among methods of estimating microbial biomass across multiple soil orders and biomes

Journal Article · Thu May 08 00:00:00 EDT 2025 · Soil Biology and Biochemistry

DOI:https://doi.org/10.1016/j.soilbio.2025.109844· OSTI ID:2568518

^[1]; Dabbs, Jenny ^[2]; ^[3]; Kluber, Laurel A. ^[4]; ^[5]; Yang, Zamin K. ^[5]; ^[6]; ^[7]; ^[1]; ^[5]; ^[5]

Univ. of Minnesota, Saint Paul, MN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Tetra Tech, Inc., Superior, CO (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Appalachian State University, Boone, NC (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Novozymes Inc, Salem, VA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pluton Biosciences, St. Louis, MO (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)

Understanding the role of soil microbes is critical to ecosystem processes, and more thorough comparisons of measurement proxies for soil microbial biomass could broaden the inclusion of explicit microbial parameterization in soil carbon cycling and earth system models. We measured physical, chemical, and biological data from eight soil orders representing 11 major biomes and four climate regions. Four prominent methods to measure microbial abundance—chloroform fumigation extraction (CFE), total DNA yield, gene copy number by quantitative polymerase chain reaction (GCN), and phospholipid fatty acids (PLFA)—were compared to assess their relationships with each other and with soil characteristics. Correlations were observed when comparing methods, with CFE correlating strongly with total DNA yield, GCN, and PLFA; CFE with bacterial GCN and bacterial PLFA; and to a lesser extent, total PLFA and total DNA yield. Correlations improved with the removal of organic soils (Histosols, Gelisols). Comparisons involving extracted DNA were improved by correcting for clay content, due to DNA extraction inefficiencies in clay-rich soils. Correlations involving fungi (PLFA or GCN) were always less significant. These methods could serve as reliable, inter-relatable proxies for the estimation of total soil microbial biomass while recognizing that the proxies are less effective at parsing differences between bacteria and fungi. Here, we provide specific equations to relate measures of soil microbial biomass by these four different methods to enable microbial models to utilize a greater diversity of observed data sources in parameterizations and simulations. Caveats for the equations and their values are also discussed.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS); USDA; SeaGrant

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2568518

Alternate ID(s):: OSTI ID: 3002407

Journal Information:: Soil Biology and Biochemistry, Journal Name: Soil Biology and Biochemistry Vol. 208; ISSN 0038-0717

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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