Accurate identification of medically important Aedes mosquitoes (Diptera: Culicidae) in Thailand through DNA barcoding, wing geometric morphometrics, and machine learning
4
Issued Date
2025-01-01
Resource Type
eISSN
2667114X
Scopus ID
2-s2.0-105020929035
Journal Title
Current Research in Parasitology and Vector Borne Diseases
Volume
8
Rights Holder(s)
SCOPUS
Bibliographic Citation
Current Research in Parasitology and Vector Borne Diseases Vol.8 (2025)
Suggested Citation
Laojun S., Changbunjong T., Kaewthamasorn M., Charnwichai P., Kaewmee S., Wichit S., Hamel R., Chaiphongpachara T. Accurate identification of medically important Aedes mosquitoes (Diptera: Culicidae) in Thailand through DNA barcoding, wing geometric morphometrics, and machine learning. Current Research in Parasitology and Vector Borne Diseases Vol.8 (2025). doi:10.1016/j.crpvbd.2025.100334 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/113052
Title
Accurate identification of medically important Aedes mosquitoes (Diptera: Culicidae) in Thailand through DNA barcoding, wing geometric morphometrics, and machine learning
Corresponding Author(s)
Other Contributor(s)
Abstract
Mosquito-borne diseases remain a significant public health concern, underscoring the need for accurate species-level identification of vector species, including Aedes mosquitoes. Identification based solely on morphology is often limited by interspecific overlap, environmentally induced phenotypic plasticity, and physical damage to field-collected specimens. This study evaluated nine Aedes species (Ae. aegypti, Ae. albopictus, Ae. chrysolineatus, Ae. lineatopennis, Ae. macfarlanei, Ae. poicilius, Ae. vexans, Ae. vigilax, and Ae. vittatus) and a related taxon (Aedeomyia catasticta) in Thailand, using DNA barcoding, wing geometric morphometric (WGM) analysis, and the Random Forests (RF) machine learning algorithm. DNA barcoding of the cytochrome c oxidase subunit 1 (cox1) gene showed strong concordance with morphological classifications, confirming its reliability for species-level identification. Across all 10 species, sequence similarity with GenBank and the Barcode of Life Data System ranged from 96% to 100%, highlighting reliable identification when robust references are available. WGM analysis revealed significant wing shape differences among species (P < 0.05), with 91.05% classification accuracy. The Mahalanobis distance and RF algorithms, applied to newly field-collected specimens assigned as unknown species, demonstrated strong discriminatory power, both achieving 100% accuracy for seven species based on wing shape. Slightly lower accuracy was observed for three species, with Mahalanobis distance achieving 90% (one misclassified individual) and the RF algorithm 80% (two misclassified individuals). These findings present a practical guideline for identifying Aedes mosquitoes and a related taxon in Thailand by integrating approaches. Accurate species identification is essential for selecting targeted vector control strategies and enhancing the effectiveness of Aedes-borne disease surveillance and management.