A new method, multivariate similarity clustering analysis (MSCA) method, was established for biogeographical distribution analyzing. General similarity formula (GSF), the core of MSCA method, can be used to calculate the similarity coefficients between 2 and among any ≥ 3 geographical units. Taking the global insects as example, we introduced the steps to use of GSF and consequent clustering processes of this method in details. Firstly, geographical distributions of certain taxa (e.g. Insecta) were categorized into basic geographical units (BGUs); Secondly, similarity coefficients between 2 and among n BGUs were calculated using GSF. Thirdly, hierarchical clustering was conducted according to values of similarity coefficients (from high to low); then a clustering diagram was generated. Finally, a framework of biogeographical division map was established for the target taxa (e.g. Insecta). We concluded that the MSCA method was efficiently applied in analyzing the biogeographical distribution of given biological taxa; the geographical regions regarding global insects were categorized into 7 Realms with 20 sub‐Realms based on the results of MSCA method.

中文翻译：

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多元相似性聚类分析：一种有关生物地理学的新方法及其在全球昆虫中的应用

建立了一种用于生物地理分布分析的新方法，即多元相似聚类分析（MSCA）方法。通用相似度公式（GSF）是MSCA方法的核心，可用于计算2个地理位之间和任何≥3个地理单位之间的相似系数。以全球昆虫为例，我们详细介绍了使用GSF的步骤以及该方法的后续聚类过程。首先，将某些分类群（例如昆虫纲）的地理分布归类为基本地理单位（BGU）。其次，2与n之间的相似系数使用GUF计算BGU。第三，根据相似系数的值（从高到低）进行层次聚类。然后生成聚类图。最后，为目标分类群（如昆虫纲）建立了生物地理分区图的框架。我们得出的结论是，MSCA方法可有效地用于分析给定生物分类群的生物地理分布。根据MSCA方法的结果，有关全球昆虫的地理区域分为7个领域和20个子领域。