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首页> 外文期刊>International Journal of Ecology & Enviromental Sciences >Spatial Structure of Soil Microbial Communities and the Evaluation of the Spatial Structure Analysis based on Relative Dissimilarity
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Spatial Structure of Soil Microbial Communities and the Evaluation of the Spatial Structure Analysis based on Relative Dissimilarity

机译：土壤微生物群落的空间结构及基于相对差异的空间结构分析评价

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摘要

The spatial structure of soil microbial communities was analyzed across the border of two distinctive grass vegetation patches. Genetic fingerprinting technique was used to assess microbial community structure in binary format data, which is not suitable for conventional semivariogram analysis. An alternative method of geostatistical analysis based on relative dissimilarity provided well fitted models from the experimental "semivariograms". Overall patterns of the models were fairly similar between bacterial and fungal communities, especially fittings (Indicative Goodness of Fit, IGF), sill (C_1) and ranges (a) at comparable scales. A small difference was also noticeable in response to distinctive vegetation settings. The evaluation of the alternative spatial structure analysis was done by comparing the semivariogram and the dissimogram (semivariogram based on dissimilarity) using continuous variables (total soil nitrogen content and C/N ratio from same sampling design). The extremely high fit of the models and prevalent high nugget value from all the dissimograms might be the results of artificiality from using relative dissimilarity instead of semivariance. Thus, one should be careful when using this approach, especially in estimation purposes such as kriging, since prediction is dependent upon the parameters determined in dissimogram models.
机译：在两个独特的草木植被斑块的边界上分析了土壤微生物群落的空间结构。遗传指纹技术用于评估二进制格式数据中的微生物群落结构，不适用于常规的半变异函数分析。基于相对不相似性的地统计分析的另一种方法提供了来自实验“半变异函数”的拟合模型。在细菌和真菌群落之间，模型的总体模​​式相当相似，尤其是在可比较的规模上的拟合（指示拟合度，IGF），窗台（C_1）和范围（a）。对于不同的植被环境，也存在明显的差异。通过使用连续变量（相同采样设计中的土壤总氮含量和碳氮比）比较半变异函数图和简化图（基于不相似性的半变异图），对替代空间结构分析进行了评估。模型的极高拟合度和来自所有残差图的普遍较高的金块值可能是由于使用相对差异而非半方差而人为地造成的。因此，使用这种方法时应特别小心，尤其是在诸如克里金法之类的估计目的中，因为预测取决于在Dissimogram模型中确定的参数。

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