Reducing the uncertainty in estimating soil microbial- derived carbon storage

• 成果类型: Article
• 署名作者: Hua, Han; Qian, Chao; Xue, Ke; Jorgensen, Raine Georg; Keiluweit, Marco; Liang, Chao; Zhu, Xuefeng; Chen, Ji; Sun, Yishen; Ni, Haowei; Ding, Jixian; Huang, Weigen; Mao, Jingdong; Tan, Rong- Xi; Zhou, Jizhong; Crowther, Thomas W.; Zhou, Zhi-Hua; Zhang, Jiabao; Liang, Yuting
• 署名单位: Chinese Academy of Sciences; Nanjing Institute of Soil Science, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Nanjing University; Nanjing University; Universitat Kassel; University of Lausanne; Chinese Academy of Sciences; Shenyang Institute of Applied Ecology, CAS; Aarhus University; Aarhus University; Aarhus University; Old Dominion University; University of Oklahoma System; University of Oklahoma - Norman; Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9294
• DOI: 10.1073/pnas.2401916121
• 发表日期: 2024-08-27
• 关键词: organic-matter; bacterial residues; fungal

摘要

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a crucial role in mitigating climate change and enhancing soil productivity. Microbial- derived carbon (MDC) is the main component of the persistent SOC pool. However, current formulas used to estimate the proportional contribution of MDC are plagued by uncertainties due to limited sample sizes and the neglect of bacterial group composition effects. Here, we compiled the comprehensive global dataset and employed machine learning approaches to refine our quantitative understanding of MDC contributions to total carbon storage. Our efforts resulted in a reduction in the relative standard errors in prevailing estimations by an average of 71% and minimized the effect of global variations in bacterial group compositions on estimating MDC. Our estimation indicates that MDC contributes approximately 758 Pg, representing approximately 40% of the global soil carbon stock. Our study updated the formulas of MDC estimation with improving the accuracy and preserving simplicity and practicality. Given the unique biochemistry and functioning of the MDC pool, our study has direct implications for modeling efforts and predicting the land-atmosphere carbon balance under current and future climate scenarios. Significance Soil organic carbon (SOC) plays a crucial role in mitigating climate change and enhancing soil productivity, with microbial- derived carbon (MDC) being the main component of the persistent SOC pool. However, the current formulas for estimating MDC storage have several limitations, which reduce the reliability of our estimates of global MDC storage. By using a comprehensive dataset and machine learning approaches, we addressed the limitations of the current formulas and proposed unique formulas. Based on these unique formulas, we estimated that the global MDC contributed approximately 758 Pg. This study has direct significance for modeling efforts to predict total terrestrial carbon storage and has great implications for accurately parameterizing next-generation soil-atmospheric C models.