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土壤变暖导致碳利用效率降低和微生物周转率增加。

Reduced carbon use efficiency and increased microbial turnover with soil warming.

机构信息

Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, Tennessee.

Environmental Sciences Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee.

出版信息

Glob Chang Biol. 2019 Mar;25(3):900-910. doi: 10.1111/gcb.14517. Epub 2018 Dec 12.

DOI:10.1111/gcb.14517
PMID:30417564
Abstract

Global soil carbon (C) stocks are expected to decline with warming, and changes in microbial processes are key to this projection. However, warming responses of critical microbial parameters such as carbon use efficiency (CUE) and biomass turnover (rB) are not well understood. Here, we determine these parameters using a probabilistic inversion approach that integrates a microbial-enzyme model with 22 years of carbon cycling measurements at Harvard Forest. We find that increasing temperature reduces CUE but increases rB, and that two decades of soil warming increases the temperature sensitivities of CUE and rB. These temperature sensitivities, which are derived from decades-long field observations, contrast with values obtained from short-term laboratory experiments. We also show that long-term soil C flux and pool changes in response to warming are more dependent on the temperature sensitivity of CUE than that of rB. Using the inversion-derived parameters, we project that chronic soil warming at Harvard Forest over six decades will result in soil C gain of <1.0% on average (1st and 3rd quartiles: 3.0% loss and 10.5% gain) in the surface mineral horizon. Our results demonstrate that estimates of temperature sensitivity of microbial CUE and rB can be obtained and evaluated rigorously by integrating multidecadal datasets. This approach can potentially be applied in broader spatiotemporal scales to improve long-term projections of soil C feedbacks to climate warming.

摘要

全球土壤碳(C)储量预计将随着变暖而减少,而微生物过程的变化是这一预测的关键。然而,对于关键微生物参数(如碳利用效率(CUE)和生物量周转率(rB))的变暖响应,我们还不太了解。在这里,我们使用概率反演方法来确定这些参数,该方法将微生物-酶模型与哈佛森林 22 年的碳循环测量数据相结合。我们发现,温度升高会降低 CUE,但会增加 rB,并且二十年的土壤变暖会增加 CUE 和 rB 的温度敏感性。这些温度敏感性是从数十年的野外观测中得出的,与从短期实验室实验中获得的值形成对比。我们还表明,长期土壤 C 通量和对变暖的响应库变化对 CUE 温度敏感性的依赖性大于 rB。使用反演得出的参数,我们预测哈佛森林在六十年的慢性土壤变暖将导致表层矿物层的土壤 C 增益平均<1.0%(第 1 和第 3 四分位数:损失 3.0%和增益 10.5%)。我们的研究结果表明,可以通过整合数十年数据集来获得和严格评估微生物 CUE 和 rB 的温度敏感性估计值。该方法可以在更广泛的时空尺度上应用,以提高对土壤 C 对气候变暖反馈的长期预测。

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