Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change
Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change
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DOI：
10.1038/s41396-019-0510-0
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发表时间：
2020-01-01
期刊：
影响因子：
11
通讯作者：
Allison, Steven D.
中科院分区：
文献类型：
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作者：
Malik, Ashish A.;Martiny, Jennifer B. H.;Allison, Steven D.
微生物在陆地碳循环中至关重要，因为它们的生长、活性和与环境的相互作用在很大程度上控制着最近植物碳输入以及受保护土壤有机碳的命运[1，2]。土壤碳储量反映了微生物分解有机碳和微生物同化碳之间的平衡。这种平衡可能会在改变的环境条件下发生变化[3]，新的研究表明，微生物生理学知识对于预测土壤碳的变化和改善气候变化反馈的预测可能至关重要[4-7]。尽管如此，预测微生物过程对生态系统的影响仍然是一个挑战。在这里，我们认为，这一挑战可以通过确定微生物的生活史策略的基础上，一个有机体的表型特征，或性状，并代表这些战略的生态系统模型。环境变化下土壤碳循环的关键微生物特征是什么？土壤中微生物的生长和存活受到多种性状的影响，这些性状决定了对不同资源可用性和波动的非生物条件的反应[8]。细胞维持活动（不产生生长的活动）包括产生胞外酶以降解和获取资源、生物分子修复机制、维持细胞完整性、渗透平衡、防御、拮抗、细胞信号传导和运动性[9-11]。可以想象，土壤中微生物对维护活动的投资通常很高，因为土壤的资源分布高度不均匀且随时间变化，并且具有极端的水分、温度、pH值和盐度等非生物条件[12，13]。在次优环境条件下的选择压力可能会导致更大的细胞水平的生理分配，以维持相对于生长性状（图1），从而影响土壤碳循环过程。生活史策略代表了一组性状，由于生理或进化的权衡，倾向于相关，不同的策略在不同的环境条件下的青睐。例如，用于资源获取的降解酶生产的代谢投资可以降低细胞生长的效率[14，15]。此外，胁迫耐受性性状可以权衡资源获取和生长产量的投资[12，16-18]。虽然一些应激耐受机制可能具有附带的益处，但如果资源有限，则通常必须以牺牲其他生理过程为代价来支付成本。最终，微生物代谢投资和由此产生的权衡性状与生长产量，资源获取和胁迫耐受性决定了微生物过程的生态系统水平的碳通量的贡献。因此，关于这些微生物特征的信息应该有助于将微生物过程与生态系统碳通量联系起来[19]。
Microorganisms are critical in terrestrial carbon cycling because their growth, activity and interactions with the environment largely control the fate of recent plant carbon inputs as well as protected soil organic carbon [1, 2]. Soil carbon stocks reflect a balance between microbial decomposition of organic carbon and stabilisation of microbial assimilated carbon. The balance can shift under altered environmental conditions [3], and new research suggests that knowledge of microbial physiology may be critical for projecting changes in soil carbon and improving the prognosis of climate change feedbacks [4–7]. Still, predicting the ecosystem implications of microbial processes remains a challenge. Here we argue that this challenge can be met by identifying microbial life history strategies based on an organism’s phenotypic characteristics, or traits, and representing these strategies in ecosystem models. What are the key microbial traits for soil carbon cycling under environmental change? Microbial growth and survival in soil are impacted by multiple traits that determine responses to varying resource availability and fluctuating abiotic conditions [8]. Cellular maintenance activities (those that do not produce growth) include production of extracellular enzymes to degrade and acquire resources, biomolecular repair mechanisms, maintenance of cellular integrity, osmotic balance, defence, antagonism, cell signalling and motility [9–11]. It is conceivable that microbial investment into maintenance activities would be generally high in soils, with their highly heterogeneous and temporally variable resource distribution and stressful abiotic conditions like extremes of moisture, temperature, pH and salinity [12, 13]. Selective pressures in suboptimal environmental conditions could lead to greater cellular-level physiological allocation to maintenance relative to growth traits (Fig. 1) thereby impacting soil carbon cycling processes.Life history strategies represent sets of traits that tend to correlate due to physiological or evolutionary tradeoffs, with different strategies favoured under different environmental conditions. For example, metabolic investments in degradative enzyme production for resource acquisition can reduce the efficiency of cellular growth [14, 15]. Furthermore, stress tolerance traits can tradeoff against investment in resource acquisition and growth yield [12, 16–18]. Although some stress tolerance mechanisms may have collateral benefits, the costs must generally be paid at the expense of other physiological processes if resources are limited. Ultimately, microbial metabolic investments and the resulting tradeoffs among traits linked to growth yield, resource acquisition and stress tolerance determine the contribution of microbial processes to ecosystem level carbon fluxes. Thus, information on these microbial traits should be useful in linking microbial processes with ecosystem carbon fluxes [19].