Accurate identification of medically important Aedes mosquitoes (Diptera: Culicidae) in Thailand through DNA barcoding, wing geometric morphometrics, and machine learning

Mosquito-borne diseases remain a significant public health concern, underscoring the need for accurate species-level identification of vector species, including <i>Aedes</i> 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 <i>Aedes</i> species (<i>Ae. aegypti</i>, <i>Ae. albopictus</i>, <i>Ae. chrysolineatus</i>, <i>Ae. lineatopennis</i>, <i>Ae. macfarlanei</i>, <i>Ae. poicilius</i>, <i>Ae. vexans</i>, <i>Ae. vigilax</i>, and <i>Ae. vittatus</i>) and a related taxon (<i>Aedeomyia catasticta</i>) in Thailand, using DNA barcoding, wing geometric morphometric (WGM) analysis, and the Random Forests (RF) machine learning algorithm. DNA barcoding of the cytochrome <i>c</i> oxidase subunit 1 (<i>cox</i>1) 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 (<i>P</i> < 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 <i>Aedes</i> 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 <i>Aedes</i>-borne disease surveillance and management.