Priming effect and microbial diversity in ecosystem functioning and response to global change: a modeling approach using the SYMPHONY model

生态系统 环境科学 矿化(土壤科学) 陆地生态系统 土壤碳 生态学 浸出(土壤学) 土壤科学 土壤水分 生物
作者
Nazia Perveen,Sébastien Barot,Gaël Alvarez,Katja Klumpp,Raphaël Martin,Alain Rapaport,Damien Herfurth,Frédérique Louault,Sébastien Fontaine
出处
期刊:Global Change Biology [Wiley]
卷期号:20 (4): 1174-1190 被引量:117
标识
DOI:10.1111/gcb.12493
摘要

Integration of the priming effect (PE) in ecosystem models is crucial to better predict the consequences of global change on ecosystem carbon (C) dynamics and its feedbacks on climate. Over the last decade, many attempts have been made to model PE in soil. However, PE has not yet been incorporated into any ecosystem models. Here, we build plant/soil models to explore how PE and microbial diversity influence soil/plant interactions and ecosystem C and nitrogen (N) dynamics in response to global change (elevated CO2 and atmospheric N depositions). Our results show that plant persistence, soil organic matter (SOM) accumulation, and low N leaching in undisturbed ecosystems relies on a fine adjustment of microbial N mineralization to plant N uptake. This adjustment can be modeled in the SYMPHONY model by considering the destruction of SOM through PE, and the interactions between two microbial functional groups: SOM decomposers and SOM builders. After estimation of parameters, SYMPHONY provided realistic predictions on forage production, soil C storage and N leaching for a permanent grassland. Consistent with recent observations, SYMPHONY predicted a CO2 -induced modification of soil microbial communities leading to an intensification of SOM mineralization and a decrease in the soil C stock. SYMPHONY also indicated that atmospheric N deposition may promote SOM accumulation via changes in the structure and metabolic activities of microbial communities. Collectively, these results suggest that the PE and functional role of microbial diversity may be incorporated in ecosystem models with a few additional parameters, improving accuracy of predictions.

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