Soil Nitrogen Transformations Respond Diversely to Multiple Levels of Nitrogen Addition in a Tibetan Alpine Steppe

矿化(土壤科学) 硝化作用 生态系统 土壤碳 氮气 化学 氮气循环 环境化学 草原 生物量(生态学) 环境科学 土壤科学 生态学 土壤水分 生物 有机化学
作者
Chao Mao,Dan Kou,Yunfeng Peng,Shuqi Qin,Qiwen Zhang,Yuanhe Yang
出处
期刊:Journal Of Geophysical Research: Biogeosciences [Wiley]
卷期号:126 (5) 被引量:3
标识
DOI:10.1029/2020jg006211
摘要

Abstract Elevated reactive nitrogen (N) input could modify soil N transformations, regulating ecosystem functions such as soil N retention and loss. Although multiple hypotheses advocate nonlinear variations in soil N transformations with continuous N input, there still lacks empirical evidences for the responses of soil N transformations to multiple N additions. Here, based on a manipulative N addition experiment and a 15 N pool dilution approach, we explored changes in soil gross N transformations with eight N addition levels and associated mechanisms in a Tibetan alpine steppe. Our results showed that soil gross N mineralization rate (GNM) increased first and then stabilized with increasing N additions. Meanwhile, soil microbial immobilization rate (MIM) exhibited an initially increased and subsequently declined pattern under various N addition levels. In contrast, soil gross nitrification rate (GN) increased linearly across multiple N addition levels. Our results also revealed that variations in GNM were mainly regulated by aboveground vegetation N pool‐induced changes in dissolved organic N content along the N addition gradient. Meanwhile, changes in GN were dominantly modified by soil pH‐induced variations in ammonia‐oxidizing archaea abundance across multiple N addition levels. Additionally, alterations in MIM under various N input levels were primarily controlled by microbial biomass which was regulated by dissolved organic carbon content under low N input and NH 4 + ‐N content at high N level, respectively. Overall, patterns and drivers of soil N transformations observed in this study provide valuable benchmark for Earth system models to better predict ecosystem N dynamics under global N‐enrichment scenarios.

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