Quantifying microbial control of soil organic matter dynamics at macrosystem scales
Quantifying microbial control of soil organic matter dynamics at macrosystem scales
复制标题
大系统尺度下土壤有机质动态的微生物控制
DOI：
10.1007/s10533-021-00789-5
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发表时间：
2021-04-26
期刊：
影响因子：
4
通讯作者：
Wieder, William R.
中科院分区：
文献类型：
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作者：
Bradford, Mark A.;Wood, Stephen A.;Wieder, William R.
土壤有机质（SOM）的库存，分解和持久性在很大程度上是当地控制的产物。然而，控制的形状和互动在多个时空尺度，从SOM的宏系统模式出现。SOM营业额的理论认识到所产生的空间和时间的制约性的控制，发挥了跨宏观系统的效果大小，并通过进化和社区组装过程夫妇。例如，气候历史塑造植物功能性状，这反过来又与当代气候相互作用，影响SOM动态。选择和组装也塑造了土壤分解者群落的功能特征，但目前尚不清楚这些特征如何反过来影响SOM周转的时间宏系统模式。在这里，我们回顾的证据，建立的期望，选择和组装应该产生分解社区的宏观系统，有不同的功能影响SOM动态。这种知识在土壤生态地球化学模型中的表示会影响全球变化下预计的SOM响应的幅度和方向。然而，这些预测的不确定性很高，可信度很低。为了解决这些问题，我们提出的情况下，需要一套协调的经验做法，这需要（1）更多地使用统计方法在地球化学，适合因果推理;（2）长期的，宏观系统规模的，观察和实验网络，以揭示条件的影响大小，嵌入式相关性，在控制SOM营业额;以及（3）使用多个测量颗粒来捕获控制和结果中的局部和宏观尺度变化，以避免通过数据汇总模糊因果关系的理解。当一起使用时，沿着与基于过程的模型，以综合知识和指导进一步的实证工作，我们相信这些做法将迅速推进微生物控制SOM的理解和改善碳循环预测，指导气候适应和缓解政策。
Soil organic matter (SOM) stocks, decomposition and persistence are largely the product of controls that act locally. Yet the controls are shaped and interact at multiple spatiotemporal scales, from which macrosystem patterns in SOM emerge. Theory on SOM turnover recognizes the resulting spatial and temporal conditionality in the effect sizes of controls that play out across macrosystems, and couples them through evolutionary and community assembly processes. For example, climate history shapes plant functional traits, which in turn interact with contemporary climate to influence SOM dynamics. Selection and assembly also shape the functional traits of soil decomposer communities, but it is less clear how in turn these traits influence temporal macrosystem patterns in SOM turnover. Here, we review evidence that establishes the expectation that selection and assembly should generate decomposer communities across macrosystems that have distinct functional effects on SOM dynamics. Representation of this knowledge in soil biogeochemical models affects the magnitude and direction of projected SOM responses under global change. Yet there is high uncertainty and low confidence in these projections. To address these issues, we make the case that a coordinated set of empirical practices are required which necessitate (1) greater use of statistical approaches in biogeochemistry that are suited to causative inference; (2) long-term, macrosystem-scale, observational and experimental networks to reveal conditionality in effect sizes, and embedded correlation, in controls on SOM turnover; and (3) use of multiple measurement grains to capture local- and macroscale variation in controls and outcomes, to avoid obscuring causative understanding through data aggregation. When employed together, along with process-based models to synthesize knowledge and guide further empirical work, we believe these practices will rapidly advance understanding of microbial controls on SOM and improve carbon cycle projections that guide policies on climate adaptation and mitigation.