U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Microbial Community-Level Regulation Explains Soil Carbon Responses to Long-Term Litter Manipulations
Published: October 31, 2017
Posted: January 17, 2018

A generalizable model modification that improves long-term soil carbon predictions.

The Science
Currently, soil carbon models that explicitly represent microbial activity have large biases in predicted carbon stocks and temporal dynamics. Scientists at Lawrence Berkeley National Laboratory have shown that accounting for density-dependent microbial mortality greatly improves predictions against long-term observations, and improves microbial models for inclusion in Earth System Models.  

The Impact
The proposed model modification addresses a long-standing problem in mechanistic models of soil biogeochemistry and improves predictions of soil carbon storage in response to long-term changes in plant productivity. 

Summary
Changes in climate, atmospheric composition, and land use all have the potential to alter plant inputs to soil in ways that impact soil microbial activity. Many microbial models of soil organic carbon (SOC) decomposition have been proposed recently to advance prediction of SOC dynamics. Most of these models, however, exhibit unrealistic oscillatory behavior and their SOC stocks are insensitive to long-term changes in carbon (C) inputs. DOE National Laboratory Scientists diagnosed the source of these problems in four archetypal microbial models and proposed a density-dependent formulation of microbial turnover, motivated by community-level interactions, that limits population sizes and reduces oscillations. They compared model predictions to 24 long-term carbon-input field manipulations and identified key benchmarks. The proposed formulation reproduces soil carbon responses to long-term carbon-input changes and implies greater SOC storage associated with CO2-fertilization-driven increases in carbon inputs over the coming century compared to recent microbial models. This study provides a simple, yet effective, modification to improve microbial models for inclusion in Earth System Models.

Contacts (BER PM)
Daniel Stover
SC-23.1
Daniel.Stover@science.doe.gov (301-903-0289)

Renu Joseph
SC-23.1
Renu.joseph@science.doe.gov (301-903-9237)

(PI Contact)
Margaret S. Torn
Lawrence Berkeley National Laboratory
mstorn@lbl.gov; 510-495-2223

Funding
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science Program, under contract number DE-AC02-05CH11231.

Publications
K. Georgiou, R. Z. Abramoff, J. Harte, W. J. Riley and M. S. Torn, “Microbial community-level regulation explains soil carbon responses to long-term litter manipulations.” Nature Communications, 8:1223 (2017). [DOI: 10.1038/s41467-017-01116-z]

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Terrestrial Ecosystem Science
  • Research Area: Carbon Cycle, Nutrient Cycling

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

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