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Heterogeneity ratio: a measure of beta‐diversity and its use in community classification
Author(s) -
Kobayashi Shiro
Publication year - 1987
Publication title -
ecological research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.628
H-Index - 68
eISSN - 1440-1703
pISSN - 0912-3814
DOI - 10.1007/bf02346919
Subject(s) - similarity (geometry) , statistics , mathematics , cluster analysis , beta diversity , sample size determination , sample (material) , diversity index , index (typography) , measure (data warehouse) , cluster (spacecraft) , artificial intelligence , computer science , biology , ecology , data mining , chromatography , biodiversity , species richness , chemistry , world wide web , image (mathematics) , programming language
Fifteen previously proposed similarity indices are examined for the effects of sample size and/or group size (the number of samples included in a cluster). The three indices of C λ, NESS , and C ′λ are free from effects, but the former two are unsuitable for arithmetic averaging unless all of the sample sizes are equal. Thus clustering using C ′λ is found to be superior to the combination of any other similarity index and the group‐average strategy. Unfortunately none of these measures have the desirable property of measuring the difference in component species among samples independent of the alpha‐diversity. A new index of similarity ( HR ) is developed based on the assumption that community from which samples are taken is described by a logseries distribution. This new index measures the beta‐diversity among samples without the influence of sample size and group size, and has the advantage that the significance of fusing samples can statistically be tested. An example clustering with HR is shown and compared with those obtained by other clustering strategies.