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Numerical Taxonomy Helps Identification of Merliniidae

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Numerical Taxonomy Helps Identification of Merliniidae Journal of Nematology 49(2):207–222. 2017. Ó The Society of Nematologists 2017. Numerical Taxonomy Helps Identification of Merliniidae and Telotylenchidae (Nematoda: Tylenchoidea) from Iran REZA GHADERI,HABIBALLAH HAMZEHZARGHANI, AND AKBAR KAREGAR 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 co- efficient 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. Key words: Amplimerlinius, correlation, hierarchical cluster analysis, Pratylenchoides, principal component analysis. The families Merliniidae Siddiqi, 1971 and Teloty- 2008; Holterman et al., 2009; van Megen et al., 2009; lenchidae Siddiqi, 1960 are among the most problem- Panahandeh et al., 2014; Ghaderi et al., 2014) in- atical taxa in Tylenchida Thorne, 1949\Tylenchomorpha formation strongly support placement of Pratylen- De Ley and Blaxter, 2002. There is no overall agree- choides within Merliniidae. Many other taxonomic ment on the taxonomic positions of the included taxa problems of Merliniidae and Telotylenchidae have at the species and\or genus levels or even higher ranks been discussed in Furtuner and Luc (1987), Sturhan among nematologists (Fortuner and Luc, 1987; Maggenti, (2012), Ghaderi et al. (2014), and Azizi et al. (2016). 1991; Brzeski, 1998; Siddiqi, 2000; Decraemer and Hunt, More studies on morphological characters and the 2006, 2013; Andrassy, 2007; Geraert, 2011; Hunt et al., range of intraspecific variation (accompanied by mo- 2013). Some attempts have been made to clarify tax- lecular analyses) are required for achieving a better onomical problems within these families using mor- and more comprehensive view on the taxonomy of phological (Sturhan, 2011, 2012; Ghaderi and Karegar, these nematodes. 2014a, 2014b) or molecular (Ghaderi et al., 2014) ap- Numerical taxonomy (or phenetics) was largely de- proaches, but taxonomic identification of the several veloped and popularized by Sneath and Sokal (1973), included members has remained difficult and uncer- as a response to the call for a more objective taxonomy. tain. Erection of the superfamily Dolichodoroidea sensu This approach consists of applying various mathemati- Siddiqi (2000) or the family Dolichodoridae sensu cal procedures to numerically encoded character state Decraemer and Hunt (2006) for covering all ‘‘awl’’ data for the organisms under study. The products of (dolichodorids), ‘‘sting’’ (belonolaimids), and ‘‘stunt’’ these operations were often taken to be ‘‘unbiased’’ (tylenchorhynchids) nematodes appears no longer jus- indicators of the similarity or difference between the tified according to the recent studies (Subbotin et al., taxa, which were in turn used to arrange taxa in a hier- 2006; Sturhan, 2012; Ghaderi et al., 2014). The concept archy (Quicke, 1993). The word ‘‘character’’ has many of the ‘‘large genus’’ for Tylenchorhynchus Cobb, 1913 is different meanings in taxonomy, but the general idea one of the most controversial problems in Telotylenchi- requires a character to be characteristic sufficiently to dae. Fortuner and Luc (1987) considered Bitylenchus be used to differentiate, classify, or identify taxa. The Filipjev, 1934, Neodolichorhynchus Jairajpuri and Hunt, domain of possible qualitative states or the range of 1984, Telotylenchus Siddiqi, 1960, and several other gen- possible quantitative values is called ‘‘character states’’ era as synonyms of Tylenchorhynchus; although this or ‘‘character values,’’ respectively (Diederich et al., idea was followed by Brzeski (1998), others (Siddiqi, 1997). 2000; Decraemer and Hunt, 2006, 2013; Andrassy, Multivariate data analysis techniques are classified 2007; Geraert, 2011; Hunt et al., 2013) rejected that, into two main supervised and unsupervised groups and partially or completely. For many years, the genus may at the same time be predictive and/or descriptive. Pratylenchoides Winslow, 1958 was thought to be closely Unsupervised methods perform the job of clustering, related to pratylenchids, but recent morphological whereas supervised ones classify the data sets. The basic (Sturhan, 2011, 2012) and molecular (Bert et al., difference between clustering and classification is that in the clustering the data, points are unlabeled, as- Received for publication March 28, 2017. suming no prior knowledge of the previous grouping of Department of Plant Protection, School of Agriculture, Shiraz University, samples. In classification, the data points have ‘‘labels’’ Shiraz, 71441-65186, Iran. E-mail: [email protected]. i.e., there are predefined groups. For instance, hierar- This paper was edited by Eyualem Abebe. chical clustering separates the more similar unlabeled 207 208 Journal of Nematology, Volume 49, No. 2, June 2017 data points (samples) from an experiment and group similarities and differences in data obtained from the them tightly close together. In contrast, in a classifica- Iranian populations of Merliniidae and Telotylenchi- tion method, a training set (a portion of data or a dif- dae. Finally, considering current morphological and ferent dataset) is used to discover the unknown molecular information as a basis, our study attempts grouping pattern. Methods such as HCA, principal to address some taxonomic complications in these component analysis (PCA), factor analysis (FA), and families. canonical discriminant analysis (CDA) comprise un- supervised methods that are far more useful than su- MATERIALS AND METHODS pervised ones (Goodacre et al., 2004). Different methods of numerical taxonomy including Nematode samples: Nematode isolates were collected HCA, FA, PCA, and multiple regression analysis have from the rhizosphere of different plants in fields, or- been used for identification of plant-parasitic nema- chards, plantations, and meadows from different lo- todes at different taxonomic ranks, e.g., in Tylenchus calities in Iran (Fig. 1) during 2010 to 2013. Nematodes (Blackith and Blackith, 1976), Helicotylenchus (Fortuner were extracted from soil samples using the tray method et al., 1984), Rotylenchus (Zancada and Lima, 1985; (Whitehead and Hemming, 1965). Specimens were Cantalapiedra-Navarrete et al., 2013), Caloosia (For- killed and fixed by hot FPG (4:1:1, formaldehyde: tuner, 1993), Xiphinema (Lamberti and Ciancio, 1993; propionic acid: glycerol), processed to anhydrous Lamberti et al., 2002; Gozel et al., 2006), Longidorus (Ye glycerol (De Grisse, 1969), and mounted in glycerol on and Robbins, 2004, 2005), Criconematina (Subbotin permanent slides. All measurements were taken by et al., 2005), Heterodera (Abdollahi, 2009), Meloidogyne a light microscope Zeiss III, equipped with Dino-eye (Mokaram Hesar et al., 2011), Criconemoides (Chenari microscope eyepiece camera and its software Dino Bouket, 2013), and Paratylenchus (Akyazi et al., 2015). Capture version 2.0. Nematode species were identified The present study aims to provide a concise description based on the morphological and morphometric char- of patterns of the morphological and morphometric acters, using identification keys (Geraert, 2011, 2013). FIG. 1. The number of collected samples (from the rhizosphere of different plants) from Iranian provinces during 2010 to 2013. Numerical Taxonomy of Nematodes: Ghaderi et al. 209 Evaluation of morphometric characters: In total, 1,020 species from 11 genera) in order to determine the females belonging to 197 populations of 31 species morphometric discrimination among genera, and from 11 genera of the families Merliniidae and Teloty- (ii) on the sampled populations of Amplimerlinius, lenchidae, collected from Iran, were used for the Geocenamus, Merlinius, and Pratylenchoides in order to present study. For each female, 44 morphometric in- delimit included species in each genus. The analyses dices and ratios (Tables 1,2) were used in the calcula- were based on the 35 characters (Table 6). PCA was tions. The variability of each morphometric character performed with the PRIN-COMP procedure of SAS was estimated by calculation of the CV and the ratio of (Statistical Analysis System, version 9.2) to produce a set extremes in the range of measured females (Max/ of variables (PC) that were linear combinations of the Min). Furthermore, the Max/Min ratios of eight char- original variables. The new variables were ranked ac- acters (Table 3) were estimated for reported pop- cording to the amount of variation accounted for. ulations in the literature plus those recovered in the As grouping in the 3 dimensional (3D)
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