Anthropogenic disturbance decouples coastal soil organic carbon and bulk density

IF 14.3 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Environmental Science and Ecotechnology Pub Date : 2026-07-01 Epub Date: 2026-06-26 DOI:10.1016/j.ese.2026.100728
Feixue Shen, Lin Yang, Xiuqiang Peng, Dianpeng Li, Chenconghai Yang, Yue Pu, Mao Guo, Yuru Yan, Chenghu Zhou
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引用次数: 0

Abstract

Coastal blue carbon ecosystems play a pivotal role in mitigating global climate change through rapid sediment burial and efficient carbon preservation. However, accurate carbon accounting is severely hindered by the systematic neglect of soil bulk density (BD) variations and the uncritical reliance on terrestrial-derived pedotransfer functions, masking hidden uncertainties in regional carbon stock assessments. Here we present a high-resolution, multi-depth assessment of soil organic carbon (SOC) content and BD across a 1-m vertical gradient in the intensively managed coastal zone of Jiangsu Province, China. Machine learning frameworks reveal a striking spatial and vertical decoupling between SOC and BD driven by divergent environmental controls: SOC content responds to soil depth and ocean salinity, whereas BD aligns with hydro-geomorphic distance to the coast. Vegetation mediates a tight vertical negative coupling (p < 0.001) in natural salt marshes, whereas anthropogenic activities decouple this relationship in croplands, restricting standard pedotransfer function predictability in all layers (R2 ≤ 0.22). Across all layers, conventional spatial estimation models yield high baseline prediction uncertainty (RMSE = 0.22 g cm−3). These findings demonstrate that independent, high-resolution BD profiling is indispensable for valid blue carbon verification. Our results establish a transferable baseline to optimize depth-specific sampling and refine global coastal carbon accounting models under intensifying human pressures.

Abstract Image

人为干扰使沿海土壤有机碳和体积密度解耦。
沿海蓝碳生态系统通过快速沉积物掩埋和有效的碳保存,在减缓全球气候变化方面发挥着关键作用。然而,由于系统地忽视土壤体积密度(BD)变化和不加鉴别地依赖陆源土壤转移函数,掩盖了区域碳储量评估中隐藏的不确定性,严重阻碍了准确的碳核算。本文采用高分辨率、多深度的方法对江苏省沿海集约化管理带1 m垂直梯度上的土壤有机碳(SOC)含量和土壤有机碳(BD)进行了评估。机器学习框架揭示了在不同的环境控制下,土壤有机碳和土壤盐度之间存在显著的空间和垂直解耦:土壤有机碳含量与土壤深度和海洋盐度有关,而土壤有机碳含量与到海岸的水文地貌距离有关。在天然盐沼中,植被介导了紧密的垂直负耦合(p < 0.001),而在农田中,人为活动使这种关系解耦,限制了所有层的标准土壤传递函数可预测性(r2≤0.22)。在所有层中,传统的空间估计模型产生很高的基线预测不确定性(RMSE = 0.22 g cm-3)。这些发现表明,独立的、高分辨率的BD分析对于有效的蓝碳验证是必不可少的。我们的研究结果建立了一个可转移的基线,以优化特定深度的采样,并在人类压力加剧的情况下完善全球沿海碳核算模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
20.40
自引率
6.30%
发文量
11
审稿时长
18 days
期刊介绍: Environmental Science & Ecotechnology (ESE) is an international, open-access journal publishing original research in environmental science, engineering, ecotechnology, and related fields. Authors publishing in ESE can immediately, permanently, and freely share their work. They have license options and retain copyright. Published by Elsevier, ESE is co-organized by the Chinese Society for Environmental Sciences, Harbin Institute of Technology, and the Chinese Research Academy of Environmental Sciences, under the supervision of the China Association for Science and Technology.
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