Is There a Minimum Number of Landmarks That Optimizes the Geometric Morphometric Analysis of Mosquito (Diptera, Culicidae) Wings?

Wing geometric morphometrics and COI barcoding of Culex pipiens subgroup in the Republic of Korea

Two members of the Culex pipiens subgroup, Culex pallens and Culex pipiens f. molestus, are known to occur in the Republic of Korea (ROK). These species exhibit morphologically similar features and are challenging to distinguish below the species level. Therefore, this study utilized wing geometric morphometrics (GM) on the right wing of the Culex pipiens subgroup, alongside sequencing of the cytochrome c oxidase subunit I (COI) region. Mosquitoes were collected from 11 locations between June and October (2020-2022) to minimize regional and seasonal variations. Additionally, Culex pipiens f. pipiens, which is not native to the ROK, was included in the analysis. Culex tritaeniorhynchus, Aedes albopictus, and Anopheles sinensis, the primary vectors in the ROK, were used as outgroups for comparison. All three taxa in the Culex pipiens subgroup could be identified with an 82.4%-97.0% accuracy using GM. However, a comparison of the COI regions of the Culex pipiens subgroup revealed no clear differences between the taxa. These data can be used for accurate identification, contributing to effective mosquito control, in addition to providing a foundation for evolutionary and ecological studies on wing shape differences.

Unsupervised machine learning and geometric morphometrics as tools for the identification of inter and intraspecific variations in the Anopheles Maculipennis complex

Geometric morphometric analysis was combined with two different unsupervised machine learning algorithms, UMAP and HDBSCAN, to visualize morphological differences in wing shape among and within four Anopheles sibling species (An. atroparvus, An. melanoon, An. maculipennis s.s. and An. daciae sp. inq.) of the Maculipennis complex in Northern Italy. Specifically, we evaluated: 1) wing shape variation among and within species; 2) the consistencies between groups of An. maculipennis s.s. and An. daciae sp. inq. identified based on COI sequences and wing shape variability; and 3) the spatial and temporal distribution of different morphotypes. UMAP detected at least 13 main patterns of variation in wing shape among the four analyzed species and mapped intraspecific morphological variations. The relationship between the most abundant COI haplotypes of An. daciae sp. inq. and shape ordination/variation was not significant. However, morphological variation within haplotypes was reported. HDBSCAN also recognized different clusters of morphotypes within An. daciae sp. inq. (12) and An. maculipennis s.s. (4). All morphotypes shared a similar pattern of variation in the subcostal vein, in the anal vein and in the radio-medial cross-vein of the wing. On the contrary, the marginal part of the wings remained unchanged in all clusters of both species. Any spatial-temporal significant difference was observed in the frequency of the identified morphotypes.  Our study demonstrated that machine learning algorithms are a useful tool combined with geometric morphometrics and suggest to deepen the analysis of inter and intra specific shape variability to evaluate evolutionary constrains related to wing functionality.

Geometric morphometrics and machine learning as tools for the identification of sibling mosquito species of the Maculipennis complex (Anopheles)

Geometric morphometrics allows researchers to use the specific software to quantify and to visualize morphological differences between taxa from insect wings. Our objective was to assess wing geometry to distinguish four Anopheles sibling species of the Maculipennis complex, An. maculipennis s. s., An. daciae sp. inq., An. atroparvus and An. melanoon, found in Northern Italy. We combined the geometric morphometric approach with different machine learning alghorithms: support vector machine (SVM), random forest (RF), artificial neural network (ANN) and an ensemble model (EN). Centroid size was smaller in An. atroparvus than in An. maculipennis s. s. and An. daciae sp. inq. Principal component analysis (PCA) explained only 33% of the total variance and appeared not very useful to discriminate among species, and in particular between An. maculipennis s. s. and An. daciae sp. inq. The performance of four different machine learning alghorithms using procrustes coordinates of wing shape as predictors was evaluated. All models showed ROC-AUC and PRC-AUC values that were higher than the random classifier but the SVM algorithm maximized the most metrics on the test set. The SVM algorithm with radial basis function allowed the correct classification of 83% of An. maculipennis s. s. and 79% of An. daciae sp. inq. ROC-AUC analysis showed that three landmarks, 11, 16 and 15, were the most important procrustes coordinates in mean wing shape comparison between An. maculipennis s. s. and An. daciae sp. inq. The pattern in the three-dimensional space of the most important procrustes coordinates showed a clearer differentiation between the two species than the PCA. Our study demonstrated that machine learning algorithms could be a useful tool combined with the wing geometric morphometric approach.

Evidence of Wing Shape Sexual Dimorphism in Aedes (Stegomyia) albopictus in Mallorca, Spain

The Asian tiger mosquito Aedes albopictus (Skuse, 1894) is a highly invasive species widely distributed on the Spanish Mediterranean coast and the Balearic archipelago. Most studies involving this species in Spain have been focused on surveillance and control methods. However, micro-evolutionary studies for Ae. albopictus in Spain have been traditionally neglected. Morphological diversity could be the result of long-term evolutionary diversification in responses to selective pressures such as temperature, precipitation, food availability, predation, or competition that may influence flight activity, host-seeking, and blood-feeding behavior. Wing geometric morphometric have been used not only to study micro- and macro-evolution in mosquitoes but also in studies of population structuring and sexual dimorphism. Therefore, the main goal of this study was to investigate the wing shape patterns of Ae. albopictus populations to unveil sexual dimorphism that could provide information about their ecology and behavior. Mosquito eggs were collected using oviposition traps at the main campus of the University of the Balearic Islands (Palma de Mallorca, Spain) and reared under laboratory conditions. In order to study wing shape variation patterns in Ae. albopictus males and females, the left wing of each adult mosquito was removed and analyzed based on 18 landmarks. Our results indicated strong levels of sexual dimorphism between Ae. albopictus males and females. Furthermore, according to the cross-validated reclassification test, males were correctly distinguished from females with an accuracy of 84% and females from males 75%. We observed a significant sexual dimorphism in the wing shape patterns of Ae. albopictus when considering different seasonal patterns (spring vs. autumn). Our results suggested that selective pressures may affect males differently to females. Host-seeking, blood-feeding, and oviposition behavior of females may act as a major driver for wing shape sexual dimorphism. These results should be considered for the development of more effective and targeted mosquito control strategies.