An integrative approach to DNA barcoding, geometric morphometrics, and machine learning for field identification of Culex mosquitoes (Diptera: Culicidae), with implications for vector-borne disease surveillance

Culex mosquitoes are of considerable medical and veterinary importance, acting as vectors of arboviruses such as Japanese encephalitis, Rift Valley fever, and West Nile virus, as well as the filarial parasite Wuchereria bancrofti. Accurate identification of Culex species, however, remains challengin...

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Published in:Acta tropica Vol. 271; p. 107885
Main Authors: Laojun, Sedthapong, Changbunjong, Tanasak, Kamoltham, Thavatchai, Chaiphongpachara, Tanawat
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.11.2025
Subjects:
Animals
Culex - anatomy & histology
Culex - classification
Culex - genetics
DNA Barcoding, Taxonomic - methods
Female
Machine Learning
Molecular methods
Mosquito vectors
Mosquito Vectors - anatomy & histology
Mosquito Vectors - classification
Mosquito Vectors - genetics
Mosquito-borne diseases
Species identification
Vector Borne Diseases - epidemiology
Vector Borne Diseases - transmission
Wing morphometrics
Wings, Animal - anatomy & histology
Mosquito-borne diseases
Mosquito vectors
Molecular methods
Species identification
Wing morphometrics
ISSN:0001-706X, 1873-6254, 1873-6254
Online Access:Get full text
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Summary:Culex mosquitoes are of considerable medical and veterinary importance, acting as vectors of arboviruses such as Japanese encephalitis, Rift Valley fever, and West Nile virus, as well as the filarial parasite Wuchereria bancrofti. Accurate identification of Culex species, however, remains challenging due to their close morphological similarity, frequent damage to field-collected specimens, and the limited availability of trained taxonomists. To address these challenges, this study employed an integrative framework combining DNA barcoding, wing geometric morphometrics (GM), and Random Forest (RF) to improve the identification of 12 Culex species (Cx. bicornutus, Cx. bitaeniorhynchus, Cx. brevipalpis, Cx. fuscocephala, Cx. gelidus, Cx. hutchinsoni, Cx. nigropunctatus, Cx. pseudovishnui, Cx. quinquefasciatus, Cx. sinensis, Cx. sitiens, and Cx. tritaeniorhynchus) in Thailand. DNA barcoding successfully validated the morphological identifications, with nucleotide sequences from representative specimens showing strong concordance with the GenBank and Barcode of Life Data Systems (BOLD) databases (≥96 %), confirming the reliability of morphological diagnoses. Complementarily, wing GM demonstrated stronger discriminatory power: Mahalanobis distance analysis revealed all species to be significantly different (p < 0.05), and a cross-validated reclassification test achieved 82.18 % performance with an adjusted total accuracy of 80 %. For field identification of unknown specimens, both Mahalanobis distance and RF produced comparable results, yielding very high accuracy (80 %–100 %) for eight species. Overall, the integration of DNA barcoding, wing GM, and machine learning offers a robust and practical framework for strengthening mosquito-borne disease surveillance. Nonetheless, as each method has distinct strengths and limitations, their application should be carefully adapted to specific epidemiological and operational contexts.
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ISSN:0001-706X
1873-6254
1873-6254
DOI:10.1016/j.actatropica.2025.107885