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Volume 27, Issue 3 p. 875-886
Article

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

Qing Zhu

Corresponding Author

Qing Zhu

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

E-mail: [email protected]Search for more papers by this author
William J. Riley

William J. Riley

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

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Jinyun Tang

Jinyun Tang

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

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First published: 23 December 2016
Citations: 57
Corresponding Editor: David S. Schimel.

Abstract

Terrestrial plants assimilate anthropogenic CO2 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 15N 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 15N 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.