ArticlePloS one2016
Morphometric Wing Characters as a Tool for Mosquito Identification.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Landmark-based morphometrics with fewer landmarks: Some examples for medical entomology.PLoS neglected tropical diseases · 2026Article
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- Effects of temperature and nutrition stress on Anopheles stephensi, an Indian urban malaria vector.Malaria journal · 2025Article
- A Short-Read Amplicon Sequencing Protocol and Bioinformatic Pipeline for Ecological Surveillance of Dipteran Disease Vectors.Molecular ecology resources · 2025Article
- Carry-over effects in Culex species along a land use gradient with differences in microclimatic conditions.Parasites & vectors · 2025Article
- Taxonomic signal in the wing cells ofJournal of advanced veterinary and animal research · 2025Article
- Variations in the Bacterial Ecosystems of Mosquito Populations - Haikou and Sanya Cities, Hainan Province, China, 2019.China CDC weekly · 2025Article
- Accurate identification of medically importantCurrent research in parasitology & vector-borne diseases · 2025Article
- WingAnalogy: a computer vision-based tool for automated insect wing asymmetry and morphometry analysis.Scientific reports · 2024Article
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- A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images.Scientific reports · 2024Article
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- Wing geometric morphometrics and COI barcoding of Culex pipiens subgroup in the Republic of Korea.Scientific reports · 2024Article
- Unraveling biotypes of the northern house mosquito, Culex pipiens s.l. (Diptera: Culicidae): molecular differentiation and morphometric analysis.Journal of insect science (Online) · 2024Article
- Using geometric wing morphometrics to distinguish Aedes japonicus japonicus and Aedes koreicus.Parasites & vectors · 2023Article
- Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest.Scientific reports · 2023Article
- Enrichment of phenotype among biological forms of Anopheles stephensi Liston through establishment of isofemale lines.Parasites & vectors · 2023Article
- Human biting mosquitoes and implications for West Nile virus transmission.Parasites & vectors · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mosquitoes are responsible for the transmission of important infectious diseases, causing millions of deaths every year and endangering approximately 3 billion people around the world. As such, precise identification of mosquito species is crucial for an understanding of epidemiological patterns of disease transmission. Currently, the most common method of mosquito identification relies on morphological taxonomic keys, which do not always distinguish cryptic species. However, wing geometric morphometrics is a promising tool for the identification of vector mosquitoes, sibling and cryptic species included. This study therefore sought to accurately identify mosquito species from the three most epidemiologically important mosquito genera using wing morphometrics. Twelve mosquito species from three epidemiologically important genera (Aedes, Anopheles and Culex) were collected and identified by taxonomic keys. Next, the right wing of each adult female mosquito was removed and photographed, and the coordinates of eighteen digitized landmarks at the intersections of wing veins were collected. The allometric influence was assessed, and canonical variate analysis and thin-plate splines were used for species identification. Cross-validated reclassification tests were performed for each individual, and a Neighbor Joining tree was constructed to illustrate species segregation patterns. The analyses were carried out and the graphs plotted with TpsUtil 1.29, TpsRelw 1.39, MorphoJ 1.02 and Past 2.17c. Canonical variate analysis for Aedes, Anopheles and Culex genera showed three clear clusters in morphospace, correctly distinguishing the three mosquito genera, and pairwise cross-validated reclassification resulted in at least 99% accuracy; subgenera were also identified correctly with a mean accuracy of 96%, and in 88 of the 132 possible comparisons, species were identified with 100% accuracy after the data was subjected to reclassification. Our results showed that Aedes, Culex and Anopheles were correctly distinguished by wing shape. For the lower hierarchical levels (subgenera and species), wing geometric morphometrics was also efficient, resulting in high reclassification scores.
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