Abstract
Numerical taxonomy was used for identification and grouping of the genera, species, and populations in the families Merliniidae and Telotylenchidae. The variability of each of 44 morphometric characters was evaluated by calculation of the coefficient of variability (CV) and the ratio of extremes (max/min) in the range of 1,020 measured females. Also correlation and regression analyses were made between characters to find potential collinearities. Hierarchical cluster analysis (HCA) was used for (i) grouping 21 genera in the superfamily Dolichodoroidea based on literature data coded for states of 18 diagnostic characters, and (ii) for grouping Iranian populations belonging to selected genera. Furthermore, STEPDISC analysis was used for (i) grouping 11 genera of Merliniidae and Telotylenchidae based on the measurements of 35 characters from 1,007 Iranian female specimens, and (ii) grouping measured females of eight species of Amplimerlinius and Pratylenchoides. The multivariate data analysis approach showed robust enough to summarize relationship between morphometric characters and group genera, species, and populations of the nematodes and in particular help to identify the genera and species of Amplimerlinius and Pratylenchoides.
Highlights
Numerical taxonomy was used for identification and grouping of the genera, species, and populations in the families Merliniidae and Telotylenchidae
Different methods of numerical taxonomy including Hierarchical cluster analysis (HCA), factor analysis (FA), principal component analysis (PCA), and multiple regression analysis have been used for identification of plant-parasitic nematodes at different taxonomic ranks, e.g., in Tylenchus (Blackith and Blackith, 1976), Helicotylenchus (Fortuner et al, 1984), Rotylenchus (Zancada and Lima, 1985; Cantalapiedra-Navarrete et al, 2013), Caloosia (Fortuner, 1993), Xiphinema (Lamberti and Ciancio, 1993; Lamberti et al, 2002; Gozel et al, 2006), Longidorus (Ye and Robbins, 2004, 2005), Criconematina (Subbotin et al, 2005), Heterodera (Abdollahi, 2009), Meloidogyne (Mokaram Hesar et al, 2011), Criconemoides (Chenari Bouket, 2013), and Paratylenchus (Akyazi et al, 2015)
The present study aims to provide a concise description of patterns of the morphological and morphometric similarities and differences in data obtained from the Iranian populations of Merliniidae and Telotylenchidae
Summary
Numerical taxonomy was used for identification and grouping of the genera, species, and populations in the families Merliniidae and Telotylenchidae. Numerical taxonomy (or phenetics) was largely developed and popularized by Sneath and Sokal (1973), as a response to the call for a more objective taxonomy This approach consists of applying various mathematical procedures to numerically encoded character state data for the organisms under study. In a classification method, a training set (a portion of data or a different dataset) is used to discover the unknown grouping pattern Methods such as HCA, principal component analysis (PCA), factor analysis (FA), and canonical discriminant analysis (CDA) comprise unsupervised methods that are far more useful than supervised ones (Goodacre et al, 2004)
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