<p>Microbial carbon use efficiency (CUE) is a central parameter for understanding soil carbon cycling and is widely used to predict soil organic carbon (SOC) stabilization and accumulation, microbial metabolic efficiency, and ecosystem carbon balance under global change. The isotope-based approaches currently employed to quantify CUE are grounded on a key assumption that assimilated carbon is predominantly allocated to growth and respiration. This assumption implicitly treats growth-related carbon investment as the dominant pathway linking microbial metabolism to SOC formation. However, in arid environments characterized by chronic water scarcity and nutrient impoverishment, microorganisms often allocate assimilated carbon to “non-growth carbon investments”, defined as carbon expenditures that do not directly generate new biomass but support survival, stress tolerance, and environmental persistence. These investments include maintenance respiration, dormancy, structural carbon allocation and extracellular polymeric substance production. This raises substantial uncertainty regarding whether the foundational assumptions of stable isotope probing-based CUE measurements can be directly applied to drylands. From both microbial ecological and methodological perspectives, this paper argues that isotope-based CUE measurements may systematically underestimate the true metabolic efficiency of microorganisms in drylands. We further examine the ecological mechanisms underlying this bias and outline future research directions. We propose that a “Adjusted CUE framework”—one that explicitly incorporates maintenance metabolism and survival-related carbon investments—should be developed for drylands. We also call for the broader adoption of integrative, multi-method approaches in global dryland carbon-cycle research to avoid misinterpreting microbial carbon allocation strategies and to improve the parameterization of SOC models under increasing aridity.</p>

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Measurement of Microbial Carbon Use Efficiency in Drylands: Challenges to the Applicability of 18O Stable Isotope Probing and Future Directions

  • Yunjie Huang,
  • Tong Li,
  • Benfeng Yin,
  • Yuanming Zhang

摘要

Microbial carbon use efficiency (CUE) is a central parameter for understanding soil carbon cycling and is widely used to predict soil organic carbon (SOC) stabilization and accumulation, microbial metabolic efficiency, and ecosystem carbon balance under global change. The isotope-based approaches currently employed to quantify CUE are grounded on a key assumption that assimilated carbon is predominantly allocated to growth and respiration. This assumption implicitly treats growth-related carbon investment as the dominant pathway linking microbial metabolism to SOC formation. However, in arid environments characterized by chronic water scarcity and nutrient impoverishment, microorganisms often allocate assimilated carbon to “non-growth carbon investments”, defined as carbon expenditures that do not directly generate new biomass but support survival, stress tolerance, and environmental persistence. These investments include maintenance respiration, dormancy, structural carbon allocation and extracellular polymeric substance production. This raises substantial uncertainty regarding whether the foundational assumptions of stable isotope probing-based CUE measurements can be directly applied to drylands. From both microbial ecological and methodological perspectives, this paper argues that isotope-based CUE measurements may systematically underestimate the true metabolic efficiency of microorganisms in drylands. We further examine the ecological mechanisms underlying this bias and outline future research directions. We propose that a “Adjusted CUE framework”—one that explicitly incorporates maintenance metabolism and survival-related carbon investments—should be developed for drylands. We also call for the broader adoption of integrative, multi-method approaches in global dryland carbon-cycle research to avoid misinterpreting microbial carbon allocation strategies and to improve the parameterization of SOC models under increasing aridity.