作 者 ：彭晚霞,宋同清,曾馥平,王克林*,刘璐,杜虎,鹿士杨,殷庆仓
期 刊 ：生态学报 2010年 30卷 24期 页码：6787~6797
关键词：土壤水分；空间变化；影响因子；景观类型；喀斯特峰丛洼地；
Keywords:soil water, spatial variation, impact factors, landscape types, Karst cluster-peak-depression region,
摘 要 ：基于动态监测样地(200 m×40 m)的网格(10 m×10 m)取样，用地统计学方法研究了喀斯特峰丛洼地4类典型生态景观类型旱季表层土壤(0—10 cm)水分的空间变化，通过主成分分析和相关分析，探讨了其生态学过程和机制。结果表明，沿严重、重度、中度和轻度的干扰递减梯度，喀斯特峰丛洼地产生了农作物(Ⅰ)→人工林(Ⅱ)→次生林(Ⅲ)→原生林(Ⅳ) 的4类典型生态景观格局变化，土壤水分显著提高，变异系数逐渐增大；4类生态景观类型的土壤水分均具有良好的空间自相关性，正负空间自相关距离反映了性质不同的两大斑块，Ⅰ、Ⅲ和Ⅳ下半部斑块的半径为50 m，拐点在坡地和洼地的分界处，Ⅱ的下半部斑块的半径为75 m，拐点是土地利用方式的转折点；不同景观类型空间变异特征不同，Ⅰ、Ⅱ、Ⅲ和Ⅳ的半变异函数分别符合指数模型、高斯模型、指数模型和球状模型，基台值(C0+C)升高，变程缩小，系统的空间总变异增强，其中Ⅰ和Ⅳ的\[C0/(C0+C)\]值分别为48.3%和39.4%，空间相关中等，Ⅱ和Ⅲ的\[C0/(C0+C)\]值≤25%，空间相关强烈；Kriging等值线图清楚表明Ⅰ和Ⅳ土壤水分呈凸型分布，Ⅱ呈单峰分布，Ⅲ呈凹型分布。主成分分析显示除海拔和坡位始终是影响4类生态景观类型土壤水分的主导因子外，不同景观类型的其他主导因子不同，且同一因子在不同景观类型与土壤水分的正负作用关系和相关程度也不同。因此，应根据4类典型生态景观类型土壤水分的空间变化及主要影响因子制定相应的水资源合理利用和管理策略。
Abstract:In this paper geo-statistical theory and methods were used to study the spatial variation of soil water and the key impact factors in four landscape types in dry season in Karst cluster-peak-depression region, based on the grid sampling (10 m×10 m) in the permanent monitoring plots (200 m×40 m). A probe into the ecological process and mechanism of soil water was made through principal component analysis and correlation analysis. The results indicated that the soil water in dry season remarkably increased and coefficients of variation (CV) increased with the landscape transition from crops (Ⅰ) to manmade forest (Ⅱ), to secondary forest (Ⅲ), to primary forest (Ⅳ) along the descending gradient of disturbance in Karst cluster-peak-depression region. Good spatial autocorrelation existed in soil water in dry season in the four landscape types, among which negative and positive spatial autocorrelation distances reflected two different patches being. The radii of the latter half patches in Ⅰ, Ⅲ, and Ⅳ landscape types were approximately 50 m, where was just located in the boundary of the slope and the depression. The radium of the latter half patch in Ⅱ landscape type was about 75 m, where the location of the turning point reflected the transition of land uses. The spatial variation characteristics differed in the four landscape types. The semi-variance functions of soil water in Ⅰ, Ⅱ, Ⅲ and Ⅳ stages fit exponential, Gaussian, exponential and spherical models best, respectively. The sill (C0+C) and total spatial variance increased, while range decreased, along the descending gradient of disturbance. The values of \[C0/(C0+C)\] in Ⅰ and Ⅳ were 48.3% and 39.4%, respectively, which indicated that medium spatial correlation existed. While those in Ⅱ and Ⅲ were less than 25%, which indicated that strong spatial correlation existed. The Kriging contour maps showed the soil water in Ⅰand Ⅳ landscape types with convex distribution, Ⅱ with unimodal distribution, and Ⅲ with concave distribution. The results of primary component analysis suggested that elevation and the slope position were the key impact factors of soil water in the four landscape types in dry season. Aside from elevation and slope position, other key impact factors were different in the four landscape types. Moreover, even though the same factor in the four landscape types, details about functions and correlations differed through correlation analysis. Therefore, corresponding strategies of rational usage and management of water resources should be made according to the different key impact factors of spatial variation of soil water in the four typical landscape types in dry season in Karst cluster-peak-depression region.
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