Maximov V.N.1, Kuznetsova N.A.2 2012. A similarity standard and its use in comparing species compositions with species structures of communities // Russian Entomol. J. Vol.21. No.2: 157–164 [in English].
1 Lomonosov Moscow State University, Faculty of Biology, Leninskie Gory 1-12, Moscow, 119991, Russia. E-mail: V_Maximovv@rambler.ru
2 Moscow State Pedagogical University, Kibalchicha Str.6, Build.5, Moscow 129164, Russia. E-mail: firstname.lastname@example.org
KEY WORDS: new method for community comparisons, similarity standard, Jaccard’s similarity index, Shorygin’s index, Collembola, sea phytoplankton, sea macrobenthos.
Abstract. When comparing the composition and structure of communities using traditional indices, the problem arises of an adequate evaluation of the results related to a lack of statistical criteria for this evaluation. To resolve this problem, a new method for community comparisons is advanced, based on the use of an empirically obtained similarity standard. Soil springtail communities, sea phytoplankton and sea macrobenthos communities serve as model objects. The widely used Jaccard’s similarity index and Shorygin’s coefficient (the sum of the minimum relative abundances of species in the samples to be compared) are chosen as examples. Empirical distributions of these indices for samples taken both in ecologically remote and similar communities are studied. Significance levels for arriving at a decision concerning the degree of similarity in their species compositions and species structures are determined. An express method for creating a similarity standard of species structure is developed, based on regular observations in particular ecological conditions. Using springtail populations, we show how to select a standard dataset to apply this index when comparing communities from various ecosystems and when analyzing seasonal and between-year changes in communities within a single habitat. The use of a similarity standard renders cluster analyses or dendrogram constructions redundant, thus avoiding a diversity of data interpretations.