南宁市桉树人工林土壤有机碳密度与地形因子的关系

Relationships between soil organic carbon density of Eucalyptus plantations and terrain factors in Nanning

桉树人工林土壤有机碳密度与地形因子的关系还不明确,且当前的研究较少地关注复合地形因子,对两者的线性和非线性关系强度的对比研究也较少。以南宁市高峰林场中的桉树人工林地为研究区,采用条件拉丁超立方抽样法布设采样点,通过建立数字高程模型提取和计算得到地形因子的指标;采用线性回归和回归树分别建立桉树人工林土壤有机碳密度-地形因子的线性和非线性回归模型。结果表明:研究区土壤表层、0—50 cm和全剖面的土壤有机碳密度均值分别为2.8、7.7、10.9 kg/m2,属于中等水平;仅坡度在0.05显著性水平上与表层和全剖面土壤有机碳密度显著相关,总体而言,简单地形因子与土壤有机碳密度的相关性普遍好于复合地形因子;随着土壤层次厚度的增加,线性和非线性回归模型对土壤有机碳密度空间变异解释能力都提升;回归模型对土壤有机碳密度空间变异的解释能力大小顺序为:回归树>全变量多元线性回归>逐步线性回归,因此,桉树人工林土壤有机碳密度与地形因子之间的非线性关系比线性关系强。

Eucalyptus plantations have widely expanded in the past two decades, which has triggered much debate with respect to their ecological and environmental impacts. One argument has focused on the organic carbon stocks of soil under Eucalyptus plantations. Although many studies have been devoted to this topic, the relationships between soil organic carbon density under a Eucalyptus plantation and terrain attributes are still unclear. In addition, little attention has been paid to secondary terrain attributes, as well as comparison of linear and non-linear relationships. This study was conducted in the Gaofeng Forest, a hilly area under Eucalyptus plantations with altitudes ranging from 125 m above sea level to 300 m in Nanning, China, and the： linear and non-linear relationships between soil organic carbon density and terrain attributes （including both primary and secondary attributes） were investigated. A 10 m digital elevation model was constructed based on 1：10000 digital topographic data of this area, and several commonly used terrain attributes, i.e., elevation, slope, aspect, plan curvature, profile curvature, and topographic wetness index, were extracted from the model. Then, the conditioned Latin hypcrcube sampling method was used to select representative samples based on these extracted terrain attributes. This method is a form of stratified sampling with the advantage of generating a set of samples that can precisely reflect the shape of a sampled distribution. Fifty sampling sites were selected using this method, of which only 41 were ultimately visited since the remaining 9 were not accessible. At each visited site, a soil profile was dug to the rocks or to a depth of 1.4 m. Soil samples were collected for each diagnostic layer of the profiles, which were then measured for soil organic carbon content using the potassium dichromate-oxidation method. Finally, correlation analysis, multiple linear regression, stepwise multiple linear regression, and regression tree modeling were explored to analyze relationships between the extracted terrain attributes and soil organic carbon density. In order to compare linear and non-linear relationships, pseudo-R^2 values, reflecting the spatial variability of soil organic carbon density explained by the models, were computed. The results showed that the 41 sampling sites represented the topographic conditions of this study area very well, as distributions of their terrain attributes were very similar to those of the whole study area. The soil organic carbon density within soil layer A, the 0--50 cm soil layer, and complete soil profile were 2.8 kg/hm^2, 7.7 kg/hm^2, and 10.9 kg/hm^2, respectively. However, only slope was correlated with the soil organic carbon density of soil layer A and the complete profile at a 0.05 level of significance. In general, primary terrain factors （ i.e., elevation, slope, and aspect） were much more strongly correlated with soil organic carbon density than secondary factors （i.e., plan curvature, profile curvature, and topographic wetness index）. This might be because the primary terrain attributes directly reflect redistribution of material and energy in the soil, whereas the secondary attributes can only reflect the overall environmental characteristics of soil. As soil depth increased, much more of the variability in soil organic carbon density was explained by the linear regression and non-linear tree models be^ilt from the terrain attributes. Comparatively, the non-linear tree models explained most of the variability of soil organic carbon density, followed by multiple linear regression models and stepwise regression models. Thus, it was concluded that non-linear relationships between soil organic carbon density under Eucalyptus plantations and terrain attributes are stronger than their corresponding linear relationships.