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has been cited by the following article:

• TITLE: The Bio-Geographical Regions Division of Global Terrestrial Animal by Multivariate Similarity Clustering Analysis Method

  AUTHORS: Qi Shen, Jiqi Lu, Shujie Zhang, Zhixing You, Yingdang Ren, Xiaocheng Shen

  KEYWORDS: Global Animal, Multivariate Similarity Clustering Analysis, Biogeography, Regionalization

  JOURNAL NAME: Open Journal of Ecology, Vol.12 No.3, March 30, 2022

  ABSTRACT: A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorithms. The faunal dataset included almost all terrestrial and freshwater fauna, a total of 4631 families, 141,814 genera, and 1,334,834 species. Our findings demonstrated that suitable results were only obtained with the MSCA method, which was associated with distinct hierarchies, reasonable structuring, and furthermore, conformed to biogeographical criteria. A total of seven kingdoms and 20 sub-kingdoms were identified. We discovered that the clustering results for the higher and lower animals did not differ significantly, leading us to consider that the analysis result is convincing as the first zoogeographical division scheme for global all terrestrial animals.