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西南峡谷型喀斯特区坡地土壤矿物质的空间分布特征
引用本文:李莎莎,范夫静,宋同清,黄国勤,曾馥平,彭晚霞,杜虎.西南峡谷型喀斯特区坡地土壤矿物质的空间分布特征[J].生态学报,2014,34(18):5320-5327.
作者姓名:李莎莎  范夫静  宋同清  黄国勤  曾馥平  彭晚霞  杜虎
作者单位:中国科学院亚热带农业生态研究所亚热带农业生态过程重点实验室, 长沙 410125;中国科学院环江喀斯特生态系统观测研究站, 环江 547100;中国科学院亚热带农业生态研究所亚热带农业生态过程重点实验室, 长沙 410125;中国科学院环江喀斯特生态系统观测研究站, 环江 547100;江西农业大学, 南昌 330045;中国科学院亚热带农业生态研究所亚热带农业生态过程重点实验室, 长沙 410125;中国科学院环江喀斯特生态系统观测研究站, 环江 547100;江西农业大学, 南昌 330045;中国科学院亚热带农业生态研究所亚热带农业生态过程重点实验室, 长沙 410125;中国科学院环江喀斯特生态系统观测研究站, 环江 547100;中国科学院亚热带农业生态研究所亚热带农业生态过程重点实验室, 长沙 410125;中国科学院环江喀斯特生态系统观测研究站, 环江 547100;中国科学院亚热带农业生态研究所亚热带农业生态过程重点实验室, 长沙 410125;中国科学院环江喀斯特生态系统观测研究站, 环江 547100
基金项目:中国科学院战略性先导科技专项(XDA05070404);国家自然科学基金项目(31370485,U1033004)
摘    要:探明峡谷型喀斯特土壤矿物质的分布规律可以为喀斯特地区植被恢复和生态重建提供参考。基于动态监测样地(200 m×300 m)的网格取样,采用经典统计分析和地统计学方法分析土壤矿物质(SiO2、Fe2O3、CaO、MgO、Al2O3、MnO)的空间分布特征。结果表明,研究区土壤矿物质含量差异较大,但变异系数不大,SiO2和Al2O3占了土壤矿物质总量的85.99%;SiO2、Al2O3、MgO、MnO均服从正态分布,Fe2O3、CaO分别经过平方和倒数转换后也服从正态分布。土壤各矿物质半变异函数的最佳拟合模型均为指数模型,块金值与基台值比C0/(C0+C)均较小,具有中等或强烈的空间相关性,表明空间变异主要由结构性因素引起;Al2O3和MnO的变程较大,空间连续性较好,其它矿物质的变程较小且相近,空间依赖性较强;Kriging等值线图表明峡谷型喀斯特区土壤SiO2和MnO具有相似的空间分布,受坡位和人为干扰共同影响,基本呈现坡顶高、坡脚低的分布格局;Fe2O3、CaO和MgO的空间分布也相似,斑块较破碎,主要受地形的影响;Al2O3的空间格局呈单峰分布,沿海拔的升高而升高。因此,减少干扰、增加植被覆盖对土壤矿物质具有良好的保持和调控作用。

关 键 词:土壤矿物质  组成特征  空间分布  峡谷型喀斯特
收稿时间:2014/5/9 0:00:00
修稿时间:2014/8/11 0:00:00

Spatial variation of soil minerals in the gorge Karst region, southwest China
LI Shash,FAN Fujing,SONG Tongqing,HUANG Guoqin,ZENG Fuping,PENG Wanxia and DU Hu.Spatial variation of soil minerals in the gorge Karst region, southwest China[J].Acta Ecologica Sinica,2014,34(18):5320-5327.
Authors:LI Shash  FAN Fujing  SONG Tongqing  HUANG Guoqin  ZENG Fuping  PENG Wanxia and DU Hu
Institution:Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China;Karst Station for Ecosystem in Huanjiang, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Huanjiang 547100, China;Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China;Karst Station for Ecosystem in Huanjiang, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Huanjiang 547100, China;Jiangxi Agricultural University, Nanchang, Nanchang 330045, China;Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China;Karst Station for Ecosystem in Huanjiang, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Huanjiang 547100, China;Jiangxi Agricultural University, Nanchang, Nanchang 330045, China;Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China;Karst Station for Ecosystem in Huanjiang, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Huanjiang 547100, China;Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China;Karst Station for Ecosystem in Huanjiang, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Huanjiang 547100, China;Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China;Karst Station for Ecosystem in Huanjiang, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Huanjiang 547100, China
Abstract:Spatial variation analysis of soil minerals is useful for ecological restoration and vegetation reconstruction. In this study, the spatial variation of soil minerals over a typical sloping farmland was investigated in a gorge karst region in southwestern China. The total study area (300 m × 200 m) was divided into grids of 20 m× 20 m and included 212 sample points. Soil minerals (SiO2, Al2O3, Fe2O3, CaO, MgO, and MnO), in surface soil were measured. The spatial patterns of soil minerals were analyzed with classical statistics and geostatistics methods. The differences of the content of the six minerals were large, but the variation coefficients were small. The sum of SiO2 and Al2O3 accounted for 85.99% of the sum of the six mineral components. SiO2, Al2O3, MgO, and MnO, while Fe2O3 and CaO were normally distributed after square and reciprocal transformed, respectively. SiO2, Al2O3, Fe2O3, CaO, MgO, and MnO were best fitted by an Exponential model, with high coefficients (R2) or low residual sum of squares (RSS) indicating that the fitted models could reflect the spatial variation of soil minerals. The nugget (C0) was low (3.0%-43.6%), indicating that the soil minerals were strongly autocorrelated over the study region, and that their spatial patterns were influenced by structural factors. These spatial patterns varied over a small range (25.5-210.3 m). The spatial ranges of SiO2 and MnO were large and similar (210.3 m and 195.9 m, respectively), and other were relatively small with strong spatial dependence. On Kriging contour maps, SiO2 and MnO had similar spatial pattern of high values on the slope top and low values on the bottom slope, and were affected by slope position and anthropogenic disturbances; the patches of the Fe2O3, CaO and MnO were relatively homogeneous, and were mainly influenced by topography. Al2O3 posed a unimodal distribution, increasing along elevation ascending. Therefore, reducing disturbance and increasing vegetation would play good roles in protecting and regulating soil minerals.
Keywords:soil mineral  composition  spatial pattern  gorge karst region
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