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一种新的植物-微生物养分竞争理论解决了观测结果和模型预测之间的不一致性。

A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions.

机构信息

Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA.

出版信息

Ecol Appl. 2017 Apr;27(3):875-886. doi: 10.1002/eap.1490. Epub 2017 Mar 13.

Abstract

Terrestrial plants assimilate anthropogenic CO through photosynthesis and synthesizing new tissues. However, sustaining these processes requires plants to compete with microbes for soil nutrients, which therefore calls for an appropriate understanding and modeling of nutrient competition mechanisms in Earth System Models (ESMs). Here, we survey existing plant-microbe competition theories and their implementations in ESMs. We found no consensus regarding the representation of nutrient competition and that observational and theoretical support for current implementations are weak. To reconcile this situation, we applied the Equilibrium Chemistry Approximation (ECA) theory to plant-microbe nitrogen competition in a detailed grassland N tracer study and found that competition theories in current ESMs fail to capture observed patterns and the ECA prediction simplifies the complex nature of nutrient competition and quantitatively matches the N observations. Since plant carbon dynamics are strongly modulated by soil nutrient acquisition, we conclude that (1) predicted nutrient limitation effects on terrestrial carbon accumulation by existing ESMs may be biased and (2) our ECA-based approach may improve predictions by mechanistically representing plant-microbe nutrient competition.

摘要

陆地植物通过光合作用和合成新组织来同化人为 CO。然而,要维持这些过程,植物就必须与微生物竞争土壤养分,因此需要对地球系统模型 (ESM) 中的养分竞争机制有适当的理解和建模。在这里,我们调查了现有的植物-微生物竞争理论及其在 ESM 中的实施情况。我们发现,关于养分竞争的表示方法没有达成共识,而且对当前实施情况的观测和理论支持都很薄弱。为了解决这一问题,我们将平衡化学近似 (ECA) 理论应用于详细的草地氮示踪研究中的植物-微生物氮竞争,发现当前 ESM 中的竞争理论未能捕捉到观察到的模式,而 ECA 预测简化了养分竞争的复杂性,并对氮观测进行了定量匹配。由于植物的碳动态受到土壤养分获取的强烈调节,我们得出结论:(1) 现有 ESM 对陆地碳积累的预测养分限制效应可能存在偏差;(2) 我们基于 ECA 的方法可以通过从机理上表示植物-微生物养分竞争来提高预测能力。

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