The use of wing shape for characterising macroevolution in mosquitoes (Diptera: Culicidae)

Influence of insular conditions on wing phenotypic variation in two dominant mosquito vectors, Aedes albopictus and Armigeres subalbatus (Diptera: Culicidae), in the border archipelagos of Thailand

Insects geographically separated into island and mainland populations often exhibit phenotypic variations, a phenomenon known as insular conditions. These conditions can lead to rapid evolutionary changes that affect the morphological characteristics of mosquito vectors. Nevertheless, studies that specifically examine phenotype differences between island and mainland mosquito populations have been limited. In this study, wing variation in size and shape was investigated using the geometric morphometric (GM) technique in two dominant mosquito vectors, Aedes albopictus and Armigeres subalbatus, in the Ranong and Trat archipelagos of Thailand. Significant differences in average wing centroid size (CS) were found in 6 out of 15 population pairs for Ae. albopictus (p < 0.05) and in 5 population pairs for Ar. subalbatus (p < 0.05). After removing the allometric effect, canonical variate analyses (CVA) based on wing shape analysis revealed overlap across all populations for both Ae. albopictus and Ar. subalbatus. However, the statistical analysis indicated that Ar. subalbatus exhibited wing shape differences across all populations (p < 0.05), and most Ae. albopictus populations also displayed distinct wing shapes (p < 0.05), except for the populations from Chang Island and the mainland of Ranong, which showed no significant differences (p > 0.05). These findings enhance our understanding of mosquito adaptability in island regions and provide valuable data for the surveillance and monitoring of vector evolution.

Integrating wing morphometrics and mitochondrial DNA analysis to assess the filaria vector Mansonia uniformis (Diptera: Culicidae) populations in Thailand

Mansonia uniformis (Diptera: Culicidae) is recognized as a vector of Brugia malayi and has been reported to transmit Wuchereria bancrofti, both causing lymphatic filariasis in humans. This study employed geometric morphometrics (GM) to investigate wing shape variation and analyzed genetic diversity through cytochrome c oxidase subunit 1 (COI) gene analyses in Ma. uniformis populations across Thailand. Wing GM analyses indicated significant differences in wing shape based on Mahalanobis distances among nearly all population pairs (p < 0.05), with no significant correlation between wing shape and geographic distance (r = 0.210, p > 0.05). Genetic analyses identified 63 haplotypes and 49 polymorphic sites, with the overall population exhibiting a nucleotide diversity of 0.006 (± 0.001) and a haplotype diversity of 0.912 (± 0.017). Deviations from neutrality, as indicated by Tajima's D and Fu's FS tests for the overall Ma. uniformis populations in Thailand, were statistically significant and negative, suggesting population expansion (both p < 0.05). Analysis of molecular variance revealed no significant genetic structure when all populations were categorized based on collection sites and geographic regions. However, significant differences in FST values were observed between some populations. These findings enhance our understanding of the geographical and genetic factors influencing Ma. uniformis populations, which are crucial for developing effective control strategies in Thailand.

Population genetic structure and wing geometric morphometrics of the filarial vector Armigeres subalbatus (Diptera: Culicidae) in Thailand

Armigeres subalbatus (Diptera: Culicidae) is a mosquito species of significant medical and veterinary importance. It is widely distributed across Southeast and East Asia and is commonly found throughout Thailand. This study assessed the genetic diversity and population structure of Ar. subalbatus in Thailand using the cytochrome c oxidase subunit I (COI) gene sequences. Additionally, wing shape variations among these populations were examined using geometric morphometrics (GM). Our results demonstrated that the overall haplotype diversity (Hd) was 0.634, and the nucleotide diversity (π) was 0.0019. Significant negative values in neutrality tests (p < 0.05) indicate that the Ar. subalbatus populations in Thailand are undergoing a phase of expansion following a bottleneck event. The mismatch distribution test suggests that the populations may have started expanding approximately 16,678 years ago. Pairwise genetic differentiation among the 12 populations based on Fst revealed significant differences in 32 pairs (p < 0.05), with the degree of differentiation ranging from 0.000 to 0.419. The GM analysis of wing shape also indicated significant differences in nearly all pairs (p < 0.05), except for between populations from Nakhon Pathom and Samut Songkhram, and between those from Chiang Mai and Mae Hong Son, suggesting no significant difference due to their similar environmental settings. These findings enhance our understanding of the population structure and phenotypic adaptations of mosquito vectors, providing vital insights for the formulation of more efficacious vector control strategies.

Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification

Mosquitoes (Diptera: Culicidae) comprise over 3500 global species, primarily in tropical regions, where the females act as disease vectors. Thus, identifying medically significant species is vital. In this context, Wing Geometric Morphometry (WGM) emerges as a precise and accessible method, excelling in species differentiation through mathematical approaches. Computational technologies and Artificial Intelligence (AI) promise to overcome WGM challenges, supporting mosquito identification. AI explores computers' thinking capacity, originating in the 1950s. Machine Learning (ML) arose in the 1980s as a subfield of AI, and deep Learning (DL) characterizes ML's subcategory, featuring hierarchical data processing layers. DL relies on data volume and layer adjustments. Over the past decade, AI demonstrated potential in mosquito identification. Various studies employed optical sensors, and Convolutional Neural Networks (CNNs) for mosquito identification, achieving average accuracy rates between 84 % and 93 %. Furthermore, larval Aedes identification reached accuracy rates of 92 % to 94 % using CNNs. DL models such as ResNet50 and VGG16 achieved up to 95 % accuracy in mosquito identification. Applying CNNs to georeference mosquito photos showed promising results. AI algorithms automated landmark detection in various insects' wings with repeatability rates exceeding 90 %. Companies have developed wing landmark detection algorithms, marking significant advancements in the field. In this review, we discuss how AI and WGM are being combined to identify mosquito species, offering benefits in monitoring and controlling mosquito populations.

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.

Wing geometric morphometrics for identification of mosquito species (Diptera: Culicidae) of neglected epidemiological importance

Culicidae comprises more than 3500 species, some of which are responsible for the spread of various human diseases, causing millions of deaths worldwide. Correct identification of these species is essential for the development of surveillance and control strategies. The most common method of mosquito identification is based on specific traits of the external morphology of species. However, identification of mosquitoes by morphological characters can be inaccurate or even unfeasible if the specimen is damaged or there is a lack of distinguishing features, as in the case of cryptic species complexes. Wing geometric morphometrics is a reliable, affordable tool for the identification of mosquito species, including sibling species. More importantly, it can be used in addition to both traditional morphologic identification methods as well as genetic approaches. Here, wing geometric morphometrics was used to identify sixteen mosquito species from eight genera: Aedes, Coquillettidia, Culex, Limatus, Mansonia, Psorophora, Runchomyia, and Wyeomyia. The 390 specimens used here were collected in São Paulo, Brazil using CDC traps, aspiration, and Shannon traps. Allometry was assessed by multivariate regression of the Procrustes coordinates on centroid size followed by canonical variate analysis and a pairwise cross-validated reclassification test. A Neighbor-Joining tree based on Mahalanobis distances was constructed with 1,000 bootstrap replicates using MorphoJ 1.02 and Past 2.17c. The canonical variate analysis of genera resulted in distinct clusters for Culex, Limatus, and Psorophora and partial overlapping between Aedes, Coquilettidia, and Mansonia, and between Runchomyia and Wyeomyia. Pairwise cross-validated reclassification tests indicated that genera were identified with an accuracy of at least 99% and subgenera with a mean accuracy of 96% and that in 160 of the 240 possible comparisons species were identified with an accuracy of 100%. Our results show that the eight genera in the study were correctly distinguished by wing shape, as were subgenera and most species, demonstrating that wing geometric morphometrics can be used for the identification of the mosquito species studied here.