Suppr超能文献

不同森林类型土壤分形特征及其与土壤性质的相关性研究:来自中国北方半湿润地区的见解

Research on the soil fractal characteristics and their correlation with soil properties in various forest types: insights from sub-humid area in Northern China.

作者信息

Wang Yige, Sun Xiangyang, Li Suyan, Wei Bin

机构信息

College of Forestry, Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, 100083, China.

Liaoning Provincial Forestry Development Service Center, Liaoning Forestry and Grassland Administration, Shenyang, 110001, China.

出版信息

Sci Rep. 2025 Apr 1;15(1):11036. doi: 10.1038/s41598-025-92215-1.

Abstract

Soil particle-size distribution (PSD) is one of the most important physical attributes due to its great influence on soil properties related to soil management and degradation. Thus, characterizing variations in the PSDs of soil are a major issue in environmental research. To date, the fractal model could well characterize PSD. Furthermore, scientific understanding and evaluation of forest soil quality is the basis for guiding ecological restoration and improvement of forest soil of degraded stands and select suitable tree species for afforestation purposes. Therefore, in this research the typical forest types: Pinus koraiensis, Pinus sylvestris var. mongholica, Quercus mongolica, Juglans mandshurica and mixed conifer-broadleaf (Pinus koraiensis × Quercus mongolica) forests in the mountains of eastern Liaoning were taken as the study objects. The topsoil (0-20 cm) and sub-topsoil (20-40 cm) samples, and litter were collected, and the relationship between the soil physiochemical properties and particle size characteristics under natural cultivation measures were evaluated and compared. The results indicated that the soil layer composition of forests were mainly sand (> 40%), followed by silt (> 25%) and clay (> 15%). The particles size characteristics showed well sorted (< 0.35), positive skewness (> 0.80) and narrow kurtosis state (1.11-1.61), and the singular fractal dimension (D) of soil was between 1.82 and 2.75. The mean particle size, D, litter and soil properties in forests were higher than those in non-forest cover control plots, and the Ds showed an increasing trend from conifer to broadleaf forests and from pure forest of single species to mixed conifer-broadleaf forests, and the recovery effect of topsoil soil was better. Pearson correlation analysis indicated that there is a positive correlation between physical and chemical indicators, and the singular fractal dimension and capacity dimension are significantly positively correlated with various indicators. Meanwhile, the multifractal dimensions are displayed as capacity dimension > correlation dimension > information dimension, indicating that the PSD is not completely ideal and uniform, thus it is still necessary to use the D to evaluate soil quality in combination with multifractal analysis. In conclusion, D is a sensitive and useful index because it quantifies changes in soil properties and it is highly recommended that broadleaf and mixed conifer-broadleaf forests are suitable for local afforestation for soil restoration purpose. Our results could provide a reliable scientific treatment method for forestry management and reconstruction in sub-humid area in Northern China and the same climate regions around the world.

摘要

土壤颗粒大小分布(PSD)是最重要的物理属性之一,因为它对与土壤管理和退化相关的土壤性质有很大影响。因此,表征土壤PSD的变化是环境研究中的一个主要问题。迄今为止,分形模型能够很好地表征PSD。此外,科学理解和评估森林土壤质量是指导退化林分森林土壤生态恢复和改良以及选择合适造林树种的基础。因此,本研究以辽东山区典型森林类型:红松、樟子松、蒙古栎、胡桃楸和针阔混交林(红松×蒙古栎)为研究对象。采集了表层土壤(0 - 20厘米)、亚表层土壤(20 - 40厘米)样本和凋落物,并评估和比较了自然栽培措施下土壤理化性质与颗粒大小特征之间的关系。结果表明,森林土壤层组成主要为砂粒(> 40%),其次是粉粒(> 25%)和黏粒(> 15%)。颗粒大小特征表现为分选良好(< 0.35)、正偏态(> 0.80)和窄峰态(1.11 - 1.61),土壤的奇异分形维数(D)在1.82至2.75之间。森林中的平均粒径、D、凋落物和土壤性质均高于非森林覆盖对照样地,且D值从针叶林到阔叶林、从单一树种纯林到针阔混交林呈增加趋势,表层土壤的恢复效果更好。Pearson相关分析表明,理化指标之间存在正相关,奇异分形维数和容量维数与各项指标显著正相关。同时,多重分形维数表现为容量维数>关联维数>信息维数,表明PSD并非完全理想和均匀,因此仍有必要结合多重分形分析使用D来评估土壤质量。总之,D是一个敏感且有用的指标,因为它量化了土壤性质的变化,强烈建议阔叶林和针阔混交林适合当地用于土壤恢复目的的造林。我们的结果可为中国北方半湿润地区及世界同气候区域的林业管理和重建提供可靠的科学处理方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f9e/11962097/3cd48df484a7/41598_2025_92215_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验