复旦大学GCB发文揭示干旱驱动的微生物碳利用效率变化及其与土壤碳储存的关系

题目：

Aridity-Driven Change in Microbial Carbon Use Efficiency and Its Linkage to Soil Carbon Storage

期刊： 

Global Change Biology

第一作者：

Junmin Pei

第一发表单位：  

复旦大学

全球变暖通常被预测将增加干旱地区的干旱程度，但干旱变化对微生物碳利用效率（CUE）及其与土壤有机碳（SOC）储存的联系的影响仍未解决，这限制了在气候变化条件下预测土壤碳动态的准确性。通过对中国北方沿着约6000公里的干旱梯度的50个地点的大规模土壤采样，本研究报告了随干旱增加，微生物CUE显著下降（在干旱梯度上大约从0.07至0.59不等）的趋势。干旱对微生物CUE的负面影响进一步通过独立的水分操纵实验得到验证，结果表明，在较低水分条件下CUE较低。干旱诱导的物理化学保护增加或微生物多样性降低主要导致了随干旱增加而CUE的减少。此外，本研究发现微生物CUE与SOC之间存在高度正相关关系，纳入CUE可以提高解释SOC在干旱梯度上变化的解释力。本研究提供了广泛地理尺度上干旱导致微生物CUE减少的实证证据，强调了干旱增加可能通过抑制土壤微生物的碳封存能力而成为SOC损失的关键机制。

土壤有机碳（SOC）储存对于生态系统稳定性和气候调节至关重要。传统理论认为，SOC储存由植物碳输入和SOC分解之间的动态平衡决定。越来越多的文献揭示了土壤微生物通过调节有机质的积累和损失在SOC储存中所扮演的重要角色。微生物碳利用效率（CUE）表示用于生长的碳与用于生长和呼吸的总碳的比例，是捕捉微生物介导的SOC积累和损失的各种途径的一个综合指标。最近的研究强调了微生物CUE在区域到全球尺度上决定SOC储存的关键作用，报告了强烈的正向CUE-SOC关系。微生物CUE是群落层面对环境变化的响应基础，并随着环境条件（如温度和水分水平）而变化。已证实，在预测模型中加入相对于影响环境因素的CUE显著提高了SOC储存的预测准确性。因此，迫切需要阐明气候变化对微生物CUE的影响及其背后的机制，以改进气候-碳循环反馈的预测。

气候变化影响土壤水分，这直接影响微生物的新陈代谢，进而影响CUE。通常，低水分会导致可用于生长的基质减少，而在低水分条件下，由于扩散速度减慢，更多的基质用于维持代谢。与产生胞内溶质和胞外多糖相比，生活在较干燥土壤中的土壤微生物可能会面临更高的代谢成本。在任何一种情况下，土壤水分的减少预计都会抑制CUE，因为较高的代谢成本被引发。此外，土壤水分还可能通过改变微生物群落组成和结构来影响CUE。例如，土壤水分减少可能会通过降低微生物多样性或限制快速生长的类群而抑制CUE，这些类群通常与高CUE相关。因此，尽管有关CUE对水分变化响应的研究较少，且以往的工作主要限于场地尺度，但其结果具有不确定性，不同研究对CUE对水分变化的响应方向报告不一。在全球变化背景下，揭示CUE对水分变化的普遍响应具有迫切性，因为降水模式预计将发生显著变化，从而影响SOC储存。

干旱化加剧被普遍预测会在干旱地区发生，它还可能通过改变土壤理化性质来调节微生物CUE。越来越多的证据表明，通过团聚体的物理保护和钙等矿物的化学保护，理化保护在决定土壤碳周转中具有重要作用。例如，团聚体和矿物的理化保护被证明主要在区域尺度上调节SOC的激发效应。这些机制还可能通过改变团聚体保护和矿物结合有机质的积累，进而影响SOC源的可获取性，从而介导CUE对干旱化变化的响应。此外，由干旱引起的土壤盐渍化可以通过介导内球或外球碳与阳离子的相互作用来影响微生物的基质可用性。然而，干旱变化通过介导理化保护影响CUE的程度仍然未知。

干旱引起的其他因素变化，如土壤条件（如土壤质地和pH值）和基质，也可能调节微生物碳代谢，从而影响CUE。干旱变化可能会单独或协同地引起这些因素的变化（如土壤条件、基质质量和可用性、理化保护和微生物群落）；然而，这些潜在驱动因素在调节干旱引起的CUE变化中的相对重要性仍然未知。考虑到微生物CUE在预测气候变化导致的SOC反馈和增强SOC固定中的关键作用，这一知识空白也亟需填补。

在此，我们旨在揭示干旱变化对微生物CUE的一般影响及其与SOC的联系。我们的具体目标是：（1）揭示干旱变化对微生物CUE的影响，（2）确定调节干旱引起的微生物CUE变化的因素，（3）探索干旱引起的微生物CUE变化与SOC变化的联系。为此，我们从中国北方沿东西向大约6000公里的干旱梯度上收集了50个采样点的土壤，并在田间水分条件下及不同实验水分含量下确定了CUE。此外，为了确定与干旱梯度上CUE变化相关的决定因素，我们分析了土壤条件、理化保护、基质可用性、基质质量和微生物特性。最后，为了探索微生物CUE在解释干旱梯度上SOC变化中的作用，我们进行了随机森林模型和一般线性模型分析。我们假设：（1）干旱加剧将削弱微生物CUE，因为随着干旱加剧，微生物多样性和基质可用性及质量通常会降低；（2）由于理化保护和微生物多样性在介导微生物过程和基质条件中的重要性，理化保护变化和微生物多样性可能在干旱梯度上调节微生物CUE中发挥主要作用；（3）沿干旱梯度会出现正向的SOC-CUE关系，因为高CUE通常与有利于SOC形成的高微生物副产品积累相关。

（注：以上翻译来着ChatGPT，具体文章内容请以原文内容为准。若解读有误欢迎探讨指正。）

Fig. 1: Response of microbial carbon use efficiency (CUE) to moisture changes. (a) Relationships between CUE and the aridity index across 50 sites, where CUE was estimated under field moisture conditions. The dots correspond to the CUE values for the 50 sites, the solid line corresponds to the linear regression between CUE and the aridity index, and the dashed lines correspond to the 95% confidence interval of the linear relationship. (b) Differences in CUE at 20%, 40% and 60% of the water-holding capacity (WHC). The gray dots indicate the values for each site (25 of the 50 sites were selected for the experimental moisture treatment experiment); boxes represent the interquartile range; vertical lines indicate the whiskers; horizontal lines represent the median; and “x” represents the mean value. *p < 0.05; ***p < 0.001.

Fig. 2: Variations in soil organic carbon (SOC) with varying aridity and microbial carbon use efficiency (CUE) and the relative importance of predictors to the variation in SOC along an aridity gradient. (a) Linear relationship of SOC with the aridity index across 50 sites. (b) Linear relationship of the SOC with CUE, which was estimated under field moisture conditions. The dots correspond to values for the 50 sites, the solid lines correspond to the linear regression, and the dashed lines correspond to the 95% confidence interval of the linear relationship. (c) The relative importance of potential predictors to the variation in SOC. Importance is determined by the percentage of increase in the mean square error (MSE) in the random forest analysis. AGBC, aboveground biomass carbon; Bac. diversity, bacterial diversity; BGBC, belowground biomass carbon; Fun. diversity, fungal diversity; Hyd. activity, hydrolase enzyme activity; MAP, mean annual precipitation; MAT, mean annual temperature. *p < 0.05, **p < 0.01.

Fig. 3: Direct and indirect effects of the aridity index, edaphic conditions, physicochemical protection, substrate availability, substrate quality, and microbial properties on microbial carbon use efficiency (CUE). (a) Structural equation modeling (SEM) showing the direct and indirect effects of different groups of factors on CUE. The pentagons represent the first component from the PCAs conducted for edaphic conditions, physicochemical protection, substrate availability, substrate quality, and microbial properties. The black dotted and solid arrows indicate negative and positive relationships, respectively, and the gray arrows indicate nonsignificant relationships; the arrow width is proportional to the strength of the relationship. (b) Standardized total effects (direct plus indirect effects) obtained from the SEM. *p < 0.05; **p < 0.01; ***p < 0.001.

Fig. 4: Response ratios of microbial carbon use efficiency (CUE) to aggregate disruption, glucose addition and microbial community inoculation from external sources. (a) Response of CUE to aggregate disruption. (b) Response of CUE to glucose addition. (c) Response of CUE following inoculation with microorganisms from soils with relatively higher CUE. (d) Response of CUE following inoculation with microorganisms from soils with relatively lower CUE. The labels on the left in panels a and b represent the soil sources, and those in panels c and d represent the different combinations of sterilized soils (letters outside the parentheses) and inoculated microbial communities (letters within the parentheses) from different sources. In all panels: RR, ratio of the CUE of the depicted combination relative to that of the control (the corresponding bulk soil in (a), the deionized water addition treatment in (b), after inoculation with microbial communities from the corresponding soils (own inoculum) in (c, d). The treatment enhances CUE when ln RR > 0 but has a reduction effect when ln RR < 0. D: Soil from a dry area (aridity index of 0.04); S: Soil from a semiarid area (aridity index of 0.28); and M: Soil from a moist area (aridity index of 0.69). The error bars indicate the standard deviation (n = 3).

Fig. 5: Conceptual diagram showing aridity-induced changes in physicochemical protection and the microbial community as major determinants of microbial carbon use efficiency (CUE) and thus SOC storage in response to aridity changes. Increasing aridity enhances physicochemical protection and/or decreases microbial diversity to suppress microbial CUE, leading to a reduction in SOC storage, and vice versa. The relative importance of edaphic conditions, physicochemical protection, substrate availability, substrate quality, and microorganisms in regulating microbial CUE along the aridity gradient is shown on the left, with a longer rectangular shape indicating greater importance for the corresponding factor.