沼泽
环境科学
氮气
环境化学
潮坪
土壤碳
总有机碳
碳纤维
水文学(农业)
海洋学
生态学
土壤科学
化学
湿地
生物
地质学
土壤水分
岩土工程
地貌学
沉积物
数学
有机化学
算法
复合数
作者
T. F. Fan,Jiafang Huang,Guopeng Liang,Shengen Liu,Dehong Hu,Lin Su,Yi Liu,Yixiong Cai,Shihua Li,Pingping Guo,Min Luo,Chuan Tong
摘要
Abstract Tidal marshes serve as critical carbon (C) sinks, yet face increasing threats from global environmental changes. While previous research has documented how nitrogen (N) loading and sea‐level rise affect total C pools individually, their impacts on soil organic carbon (SOC) stabilization remain critically underexplored, particularly when these factors co‐occur in tidal marsh ecosystems. Through a 3‐yr field experiment, we analyzed how these factors, alone and combined, impact SOC stabilization by examining SOC fraction dynamics. Results showed that N loading increased particulate organic carbon (POC) by 18% and decreased mineral‐associated organic carbon (MAOC) by 13%, reducing SOC stabilization. Conversely, increased inundation raised MAOC by 31% and decreased POC by 19%, promoting SOC stabilization. The decreased MAOC under N loading stemmed from reduced fungal necromass C, while the increased POC related to lower phenol oxidase activity. In contrast, with increased inundation, MAOC rose due to iron‐bound organic C (Fe‐OC) accumulation, while POC declined from increased phenol oxidase activity. When both factors were applied together, SOC stabilization remained at control levels. This occurred because the combined effect maintained oxidative enzyme activities and thus retained POC levels. The simultaneous reduction in fungal necromass C and enhancement of Fe‐OC associations established complementary mechanisms that maintained MAOC at levels equivalent to control. Our findings reveal that N loading and increased inundation drive contrasting patterns of SOC stabilization, while their combination produces uniquely stabilized C dynamics. This insight challenges single‐factor predictions and underscores the importance of multi‐factor experiments in understanding ecosystem responses under concurrent global change scenarios.
科研通智能强力驱动
Strongly Powered by AbleSci AI