<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 <italic>Amplimerlinius</italic> and <italic>Pratylenchoides</italic>. 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 <italic>Amplimerlinius</italic> and <italic>Pratylenchoides</italic>.</p>
</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.

Numerical Taxonomy Helps Identification of Merliniidae and Telotylenchidae (Nematoda: Tylenchoidea) from Iran

Journal of Nematology

This work is licensed under the Creative Commons Attribution 4.0 International License.

Numerical taxonomy was used for identification and grouping of the genera, species, and populations in the families Merliniidae and Telotylenchidae. The...

Numerical Taxonomy Helps Identification of Merliniidae and Telotylenchidae (Nematoda: Tylenchoidea) from Iran

Published Online: Jul 24, 2017

Page range: 207 - 222

Received: May 12, 2021

DOI: https://doi.org/10.21307/jofnem-2017-065

Keywords
Amplimerlinius, correlation, hierarchical cluster analysis, principal component analysis

© 2017 Reza Ghaderi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Numerical Taxonomy Helps Identification of Merliniidae and Telotylenchidae (Nematoda: Tylenchoidea) from Iran

Published Online: Jul 24, 2017

Page range: 207 - 222

Received: May 12, 2021

DOI: https://doi.org/10.21307/jofnem-2017-065

KeywordsAmplimerlinius, correlation, hierarchical cluster analysis, principal component analysis

© 2017 Reza Ghaderi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Amplimerlinius, correlation, hierarchical cluster analysis, principal component analysis