表土
马尾松
土壤碳
氨基糖
底土
土壤有机质
农学
化学
环境科学
残留物(化学)
环境化学
总有机碳
土壤水分
土壤科学
植物
生物
有机化学
生物化学
作者
Yafei Shen,Lei Lei,Wenfa Xiao,Ruimei Cheng,Changfu Liu,Xiaoyu Liu,Lin Hu,Lixiong Zeng
标识
DOI:10.1016/j.envres.2023.116081
摘要
A large amount of stable soil organic matter (SOM) is derived from microbial necromass, which can be assessed by quantifying amino sugar biomarkers. Pinus massoniana Lamb. Plantations are widely distributed in China and play a vital role in forest carbon sequestration. However, the patterns of soil microbial residue remain poorly understood. In this study, amino sugars were used to characterize patterns of soil microbial residues at three soil depths (0-10, 10-20, and 20-30 cm) in P. massoniana plantations of different ages (young, middle-aged, near-mature, mature, and over-mature; denoted as YG, MD, NM, MT, and OM, respectively). In the topsoil (0-10 cm), the total nitrogen (TN) content of the OM forest was the highest, whereas the soil organic carbon (SOC) content of the MT forest was the highest. Consistent with changes in SOC and TN, total microbial residue content decreased with increasing soil depth. However, the total microbial residues C to SOC contribution increased considerably with increasing depth, suggesting that more SOC was derived from microbial residues in the subsoil than that from the topsoil. The fungal residue C to SOC contribution was higher than that of bacterial residue C. Total amino sugar content in the topsoil increased with increasing age, and MT and OM had a significantly higher content than that of other forests. At all soil depths, SOC and TN content predominantly determined microbial necromass, whereas soil microbial biomass content predominantly determined microbial necromass in the topsoil; soil pH predominantly determined microbial necromass in the 10-20 cm soil layer; and soil pH and Ca2+ content were the primary factors in the soil layer below 20 cm. The study provides valuable insights into controls of microbial-derived organic C could be applied in Earth system studies for predicting SOC dynamics in forests.
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