土壤呼吸
土壤碳
环境科学
呼吸
中国
灵敏度(控制系统)
碳循环
情感(语言学)
生态系统
大气科学
水文学(农业)
林业
农林复合经营
土壤科学
生态学
土壤水分
地质学
生物
地理
植物
语言学
哲学
岩土工程
考古
电子工程
工程类
作者
Yin Li,Jilin Zhang,Enze Li,Yuan Miao,Shijie Han,Yanchun Liu,Yinzhan Liu,Cancan Zhao,Yaojun Zhang
出处
期刊:Catena
[Elsevier BV]
日期:2022-03-09
卷期号:213: 106197-106197
被引量:23
标识
DOI:10.1016/j.catena.2022.106197
摘要
• Carbon inputs were altered in a broadleaved forest. • Litter removal decreased soil respiration. • Root exclusion enhanced temperature sensitivity of soil respiration ( Q 10 ). • Soil C/N played an important role in regulating Q 10 . Soil respiration, as an important process in global carbon cycle, is drastically stimulated by warming. Changing plant carbon inputs caused by intensified human disturbances and climate change can complexly influence soil respiration and its temperature sensitivity ( Q 10 ). Although a number of experiments have been established in the world to explore the effects of changing carbon inputs, it remains a challenge to predict the direction and magnitude of changes in soil respiration and Q 10 due to high spatiotemporal variability of climatic zone and ecosystem. Therefore, we conducted a field manipulative experiment to examine the impact of litter removal and root exclusion on soil respiration and Q 10 in a deciduous broad-leaved forest in transition zone between subtropical and warm temperate zone. The results showed that litter removal significantly reduced soil respiration, microbial biomass carbon, soil organic carbon, total nitrogen and soil moisture. In addition, litter removal altered soil microbial community composition, expressed in increase of Gram-positive / Gram-negative bacteria ratio and percentage of actinomycetes, and enhanced microbial physiological stress index. Root exclusion significantly increased Q 10 by elevating soil C/N ratio, which supporting “C quality-temperature” hypothesis. The study indicates that root exclusion may accelerate soil respiration under warming. Our study provides insights to improve carbon cycling models and accurately predict the consequences of climate change.
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