Global drivers and patterns of microbial abundance in soil

Authors

  • Hector M. Serna-Chavez,

    1. Department of Ecological Science, VU University Amsterdam, Amsterdam, The Netherlands
    2. Department of Life Sciences, University of Coimbra, Coimbra, Portugal
    Current affiliation:
    1. Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
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  • Noah Fierer,

    1. Department of Ecology and Evolutionary Biology and the Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
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  • Peter M. van Bodegom

    Corresponding author
    • Department of Ecological Science, VU University Amsterdam, Amsterdam, The Netherlands
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  • Editor: Martin Sykes

Correspondence: Peter M. van Bodegom, Department of Ecological Science, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.

E-mail: p.m.van.bodegom@vu.nl

Abstract

Aim

While soil microorganisms play key roles in Earth's biogeochemical cycles, methodological constraints and sparse data have hampered our ability to describe and understand the global distribution of soil microbial biomass. Here, we present a comprehensive quantification of the environmental drivers of soil microbial biomass.

Location

Global.

Methods

We used a comprehensive global dataset of georeferenced soil microbial biomass estimates and high-resolution climatic and soil data.

Results

We show that microbial biomass carbon (CMic) is primarily driven by moisture availability, with this single variable accounting for 34% of the global variance. For the microbial carbon-to-soil organic carbon ratio (CMic/COrg), soil nitrogen content was an equally important driver as moisture. In contrast, temperature was not a significant predictor of microbial biomass patterns at a global scale, while temperature likely has an indirect effect on microbial biomass by influencing rates of evapotranspiration and decomposition. As our models explain an unprecedented 50% of the global variance of CMic and CMic/COrg, we were able to leverage gridded environmental information to build the first spatially explicit global estimates of microbial biomass and quantified the global soil microbial carbon pool to equal 14.6 Pg C.

Main Conclusions

Our unbiased models allowed us to build the first global spatially explicit predictions of microbial biomass. These patterns show that soil microbial biomass is not primarily driven by temperature, but instead, biomass is more heterogeneous through the effects of moisture availability and soil nutrients. Our global estimates provide important data for integration into large-scale carbon and nutrient models that may imply a major step forward in our ability to predict the global carbon balance, now and in a future climate.

Ancillary