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- Title
Linear Morphometry of Male Genitalia Distinguishes the Ant Genera Monomorium and Syllophopsis (Hymenoptera: Formicidae) in Madagascar.
- Authors
Rasoarimalala, Nomena F.; Ramiadantsoa, Tanjona; Rakotonirina, Jean Claude; Fisher, Brian L.
- Abstract
Simple Summary: We evaluated linear morphometry of male genitalia as a diagnostic method to distinguish the genera and species of Monomorium and Syllophopsis (Hymenoptera: Formicidae). We measured 10 morphometric characters on the male genitalia from 10 species of Monomorium and 5 species of Syllophopsis. We used three datasets, raw data, ratio data, and RAV data, and analyzed them using multivariate methods: hierarchical clustering (Ward's method), Principal Component Analysis (PCA), Non-Metric Multidimensional Scaling analyses (NMDS), Linear Discriminant Analysis (LDA), and Conditional Inference Trees (CITs). The ratio data were most effective in separating the two genera, while the raw data were more effective at species-level delimitation. The findings highlighted the potential for a broader application of genitalia-based morphometric analyses in ant systematics. Morphometric analyses of male genitalia are routinely used to distinguish genera and species in beetles, butterflies, and flies, but are rarely used in ants, where most morphometric analyses focus on the external morphology of the worker caste. In this work, we performed linear morphometric analysis of the male genitalia to distinguish Monomorium and Syllophopsis in Madagascar. For 80 specimens, we measured 10 morphometric characters, especially on the paramere, volsella, and penisvalvae. Three datasets were made from linear measurements: mean (raw data), the ratios of characters (ratio data), and the Removal of Allometric Variance (RAV data). The following quantitative methods were applied to these datasets: hierarchical clustering (Ward's method), unconstrained ordination methods including Principal Component Analysis (PCA), Non-Metric Multidimensional Scaling analyses (NMDS), Linear Discriminant Analysis (LDA), and Conditional Inference Trees (CITs). The results from statistical analysis show that the ratios proved to be the most effective approach for genus-level differentiation. However, the RAV method exhibited overlap between the genera. Meanwhile, the raw data facilitated more nuanced distinctions at the species level compared with the ratios and RAV approaches. The CITs revealed that the ratios of denticle length of the valviceps (SeL) to the paramere height (PaH) effectively distinguished between genera and identified key variables for species-level differentiation. Overall, this study shows that linear morphometric analysis of male genitalia is a useful data source for taxonomic delimitation.
- Subjects
FISHER discriminant analysis; PRINCIPAL components analysis; MULTIDIMENSIONAL scaling; HIERARCHICAL clustering (Cluster analysis); LINEAR statistical models
- Publication
Insects (2075-4450), 2024, Vol 15, Issue 8, p605
- ISSN
2075-4450
- Publication type
Article
- DOI
10.3390/insects15080605