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A Web-based software for randomization tests of cluster analysis of invertebrate biodiversity in a rice ecosystem

机译:基于Web的软件,用于水稻生态系统中无脊椎动物生物多样性聚类分析的随机测试

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摘要

As an important analytical tool, cluster analysis is widely used in ecological research— e.g., community classification, biological evolution analyses, and biogeographic comparisons (Krebs 1989). Dozens of algorithms for cluster analysis have been developed for common use or special purposes (Zhang and Fang 1982). Most of them, however, do not use appropriate statistical tests in the computation procedures. Thus, we are not able to evaluate statistically the confidence of the classifications in thecluster analysis. Classical statistics can be used to answer the above question when statistical assumptions on data have been met, For example, are the individuals randomly sampled from the population of interest? Do clustered individuals come from different populations or groups that share equal population standard deviations or means? Do the values coincide with a normal distribution or with other known distributions (Manly 1997)? Unfortunately, these assumptions are usually not met in most ecological studies.
机译:作为一种重要的分析工具,聚类分析被广泛用于生态研究中,例如,社区分类,生物演化分析和生物地理比较(Krebs 1989)。已经开发了数十种用于聚类分析的算法,用于通用或特殊用途(Zhang和Fang 1982)。但是,其中大多数都没有在计算过程中使用适当的统计检验。因此,我们无法在聚类分析中统计评估分类的可信度。当满足数据的统计假设时,古典统计可以用来回答上述问题,例如,是否从感兴趣的人群中随机抽取了个体?聚集的个体是否来自具有相同的人口标准偏差或均值的不同人口或群体?这些值是与正态分布还是与其他已知分布一致(Manly 1997)?不幸的是,大多数生态学研究通常无法满足这些假设。

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