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<p>The temperature sensitivity of soil microbial respiration, commonly quantified using the Q<sub>10</sub> coefficient, is a key parameter in carbon cycle models. Uncovering how environmental factors affect in situ Q<sub>10</sub> values can therefore provide critical insight into potential shifts in global carbon stocks under climate change. We collected data from previously published field experiments that measured soil microbial respiration across a range of temperatures. We hypothesized that the Q<sub>10</sub> coefficient of in situ soil microbial respiration would vary based on environmental factors including mean annual temperature (MAT), mean annual precipitation (MAP), plant cover type, pH, soil C:N, and latitude. Linear regression revealed that Q<sub>10</sub> correlates negatively with MAT and MAP and positively with pH and absolute latitude. Additionally, average Q<sub>10</sub> varied significantly across different plant cover types; it was highest in mountain grasslands and lowest in tropical moist forests. Variation in microbial Q<sub>10</sub> across environmental factors may arise from underlying mechanisms such as enzyme kinetics, substrate availability and complexity, and microbial adaptation. To capture patterns in Q<sub>10</sub> more comprehensively, we developed a multiple linear regression model of Q<sub>10</sub> based on the most individually significant environmental drivers and applied it to public datasets to generate a global map of predicted Q<sub>10</sub>. Q<sub>10</sub> was higher in high-latitude and high-altitude regions, where large permafrost carbon stores are vulnerable to thawing and decomposition. We also compared fits between the Q<sub>10</sub> equation and a model produced from macromolecular rate theory (MMRT). We found that the MMRT model had the superior fit and may be better suited to model temperature sensitivity of complex biological reactions. Overall, our results emphasize that relationships between microbial Q<sub>10</sub> and environmental variables should be accounted for in climate models. Incorporating these variations in the Q<sub>10</sub> parameter, rather than using a fixed value, will help predict whether CO<sub>2</sub> emissions will be buffered or exacerbated by soil microbial respiration under climate change.</p>

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The Q10 of in situ microbial soil respiration varies with mean annual temperature, precipitation, pH, and plant cover: a meta-analysis and spatial prediction of Q10

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月2日

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  • Melanie T. Hacopian,
  • Eduardo Misael Choreño-Parra,
  • Lirio Hadomi Aquino De Araujo,
  • Andrea Zhou,
  • Charlotte J. Alster,
  • James T. Randerson,
  • Kathleen K. Treseder

摘要

The temperature sensitivity of soil microbial respiration, commonly quantified using the Q10 coefficient, is a key parameter in carbon cycle models. Uncovering how environmental factors affect in situ Q10 values can therefore provide critical insight into potential shifts in global carbon stocks under climate change. We collected data from previously published field experiments that measured soil microbial respiration across a range of temperatures. We hypothesized that the Q10 coefficient of in situ soil microbial respiration would vary based on environmental factors including mean annual temperature (MAT), mean annual precipitation (MAP), plant cover type, pH, soil C:N, and latitude. Linear regression revealed that Q10 correlates negatively with MAT and MAP and positively with pH and absolute latitude. Additionally, average Q10 varied significantly across different plant cover types; it was highest in mountain grasslands and lowest in tropical moist forests. Variation in microbial Q10 across environmental factors may arise from underlying mechanisms such as enzyme kinetics, substrate availability and complexity, and microbial adaptation. To capture patterns in Q10 more comprehensively, we developed a multiple linear regression model of Q10 based on the most individually significant environmental drivers and applied it to public datasets to generate a global map of predicted Q10. Q10 was higher in high-latitude and high-altitude regions, where large permafrost carbon stores are vulnerable to thawing and decomposition. We also compared fits between the Q10 equation and a model produced from macromolecular rate theory (MMRT). We found that the MMRT model had the superior fit and may be better suited to model temperature sensitivity of complex biological reactions. Overall, our results emphasize that relationships between microbial Q10 and environmental variables should be accounted for in climate models. Incorporating these variations in the Q10 parameter, rather than using a fixed value, will help predict whether CO2 emissions will be buffered or exacerbated by soil microbial respiration under climate change.