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变暖条件下提高微生物碳利用效率可预测全球土壤异养呼吸。

Increasing microbial carbon use efficiency with warming predicts soil heterotrophic respiration globally.

机构信息

State Key Laboratory of Grassland Agro-ecosystems, School of Life Sciences, Lanzhou University, Lanzhou, China.

School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA.

出版信息

Glob Chang Biol. 2019 Oct;25(10):3354-3364. doi: 10.1111/gcb.14738. Epub 2019 Jul 24.

Abstract

The degree to which climate warming will stimulate soil organic carbon (SOC) losses via heterotrophic respiration remains uncertain, in part because different or even opposite microbial physiology and temperature relationships have been proposed in SOC models. We incorporated competing microbial carbon use efficiency (CUE)-mean annual temperature (MAT) and enzyme kinetic-MAT relationships into SOC models, and compared the simulated mass-specific soil heterotrophic respiration rates with multiple published datasets of measured respiration. The measured data included 110 dryland soils globally distributed and two continental to global-scale cross-biome datasets. Model-data comparisons suggested that a positive CUE-MAT relationship best predicts the measured mass-specific soil heterotrophic respiration rates in soils distributed globally. These results are robust when considering models of increasing complexity and competing mechanisms driving soil heterotrophic respiration-MAT relationships (e.g., carbon substrate availability). Our findings suggest that a warmer climate selects for microbial communities with higher CUE, as opposed to the often hypothesized reductions in CUE by warming based on soil laboratory assays. Our results help to build the impetus for, and confidence in, including microbial mechanisms in soil biogeochemical models used to forecast changes in global soil carbon stocks in response to warming.

摘要

气候变暖将在何种程度上通过异养呼吸刺激土壤有机碳(SOC)损失仍不确定,部分原因是 SOC 模型中提出了不同甚至相反的微生物生理学和温度关系。我们将有竞争力的微生物碳利用效率(CUE)-年平均温度(MAT)和酶动力学-MAT 关系纳入 SOC 模型,并将模拟的比质量土壤异养呼吸速率与多个已发表的测量呼吸数据集进行比较。测量数据包括全球分布的 110 个旱地土壤和两个大陆到全球范围的跨生物群落数据集。模型数据比较表明,正的 CUE-MAT 关系最能预测全球分布土壤的比质量土壤异养呼吸速率。当考虑到驱动土壤异养呼吸-MAT 关系的更复杂模型和竞争机制(例如,碳底物可用性)时,这些结果是稳健的。我们的研究结果表明,温暖的气候选择了具有更高 CUE 的微生物群落,而不是基于土壤实验室测定的变暖导致 CUE 降低的常见假设。我们的研究结果有助于为包括土壤生物地球化学模型中的微生物机制提供动力,并增强对其的信心,这些模型用于预测变暖对全球土壤碳储量的影响。

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