Methods for comparative assessment of the results of cluster analysis of hydrobiocenoses structure (by the example of zooplankton communities of the Linda River, Nizhny Novgorod region)

Abstract

In this paper we present modern approaches to the classification of hydrobiological samples based on various metrics of species-structure similarity—Euclidean distance, Renkonen index, and the cosine of the angle between the species abundances vectors. We use the cophenetic correlation coefficient, Gower distance, and Shepard-like plot for the justification of clustering method. For the choice of the optimal number of clusters, we apply approaches based on silhouette widths and binary matrices representing partitions. An analysis of the spatial structure of zooplankton communities in the small Linda River shows that average agglomerative clustering is an optimal algorithm for objects of this type. A comparative analysis of the results of cluster analysis on the basis of different similarity metrics shows that the most adequate classification can be obtained using the cosine of the angle between the species abundances vectors and the Renkonen index, whereas the classification based on the Euclidean distances is less successful from the biological point of view. Approaches outlined in this paper allow researchers to make quantitative decisions about key elements of classification, greatly reducing the subjectivity of the cluster analysis results.