Carbon saturation level regulates the stability of mineral-associated organic carbon in forest soils

文献类型： 外文期刊

第一作者： Liu, Yuedong

作者： Liu, Yuedong;Hou, Ruixing;Liu, Yuedong;Ndzelu, Batande Sinovuyo;Huang, Yanan

作者机构：

关键词： Mineral-associated organic carbon; Stability; Carbon saturation level; Mineral composition

期刊名称：CATENA （ 影响因子：5.7； 五年影响因子：6.3 ）

ISSN： 0341-8162

年卷期： 2025 年 259 卷

页码：

收录情况： SCI

摘要： The stability of mineral-associated organic carbon (MAOC) serves as a critical determinant of long-term soil organic carbon (SOC) preservation, predominantly governed by mineral-organic binding interactions. However, the regulatory mechanisms of mineral composition and initial carbon saturation level (CSL) on MAOC stability remain poorly understood. In this study, we selected six forest soils from three climatic zones in China, and simulated microbial oxidative degradation using hydrogen peroxide (H2O2) to investigate MAOC chemical stability. The results showed that MAOC contributed 40.84-86.93% of SOC, with spatial variation influenced by the illite content and specific surface area. The remaining MAOC (r-MAOC) after treatment accounted for 25.32-86.66% of MAOC and the oxidation-resistant efficiency was significantly correlated with CSL and clay content. During oxidation MAOC preferentially lost a high proportion of plant-derived organic carbon with relatively weak binding to the mineral surfaces like hydroxyl carbon (1.43-22.10%), while microbial-derived polysaccharide carbon significantly increased by 0.48-19.64%. Under unsaturated conditions, higher CSL levels corresponds with increased MAOC stability, implying that organic matter preferentially binds to and stabilizes on vacant mineral sites. The partial least squares path model (PLS-PM) and random forest model (RFM) analysis indicated that CSL and mineral composition were key determinants of MAOC stability (0.79 and 0.41). This study provides theoretical insights into predicting forest soil carbon stability and contributes to improving global carbon cycle modeling by refining MAOC dynamics.

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