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盐城滩涂湿地典型植物群落土壤活性有机碳组分分布特征

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国家林业和草原科学数据中心2022-11-29 更新2024-03-06 收录
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本研究以江苏盐城滩涂湿地4 种典型植物群落为对象,分析了0 ~ 10 cm( 表层) 、10 ~ 30cm( 中层) 、30~60 cm( 深层) 3 个层次土壤总有机碳( TOC) 、易氧化碳( EOC) 、水溶性有机碳( DOC) 、颗粒有机碳( POC) 等与土壤因子、地上生物量等之间的相关性。结果表明: 同植物群落间,土壤TOC 含量差异较大,但土壤EOC 和DOC 含量差异相对较小。不同土层间,互花米草群落深层TOC 含量略高,其他群落随深度增加呈下降趋势,POC 含量随土壤深度增加逐渐下降。 冗余分析表明,地上生物量( AGB) 和土壤pH 对3 个土层土壤有机碳组分含量的单独解释率均为最高,土壤还原性物质总量( RS) 的解释率随土壤深度的增加呈上升趋势。土壤含水量( SWC) 在表、中层土壤的单独解释率均为最低,其对土壤有机碳组分含量影响较小。

This study targeted four typical plant communities in the coastal tidal flat wetland of Yancheng, Jiangsu Province, and analyzed the correlations between soil organic carbon components including total organic carbon (TOC), easily oxidized carbon (EOC), dissolved organic carbon (DOC), and particulate organic carbon (POC) at three soil layers (0–10 cm surface layer, 10–30 cm middle layer, and 30–60 cm deep layer) and soil factors as well as aboveground biomass. The results showed that within the same plant community, soil TOC content varied considerably, while the differences in soil EOC and DOC contents were relatively small. Among different soil layers, the deep-layer TOC content of *Spartina alterniflora* community was slightly higher, while those of other communities showed a decreasing trend with increasing soil depth; additionally, POC content gradually declined with increasing soil depth. Redundancy analysis (RDA) revealed that aboveground biomass (AGB) and soil pH had the highest individual explanatory rates for the contents of soil organic carbon components in all three soil layers; the explanatory rate of total soil reducing substances (RS) showed an upward trend with increasing soil depth. Soil water content (SWC) had the lowest individual explanatory rates in surface and middle soil layers, indicating its relatively weak impact on the contents of soil organic carbon components.
提供机构:
国家林业和草原科学数据中心
创建时间:
2022-11-29
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数据集介绍
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背景与挑战
背景概述
该数据集聚焦盐城滩涂湿地典型植物群落的土壤有机碳组分分布,通过分析不同土层的TOC、EOC、DOC和POC含量及其影响因素,揭示了地上生物量和土壤pH是影响有机碳分布的主要因子。数据为PDF格式,来源于国家重点研发计划项目,具有明确的科研应用价值。
以上内容由遇见数据集搜集并总结生成
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