Geometric morphometric wing analysis as a tool to discriminate female mosquitoes from different suburban areas of Chiang Mai province, Thailand

Mosquitoes are hematophagous insects that transmit parasites and pathogens with devastating effects on humans, particularly in subtropical regions. Different mosquito species display various behaviors, breeding sites, and geographic distribution; however, they can be difficult to distinguish in the field due to morphological similarities between species and damage caused during trapping and transportation. Vector control methods for controlling mosquito-borne disease epidemics require an understanding of which vector species are present in the area as well as the epidemiological patterns of disease transmission. Although molecular techniques can accurately distinguish between mosquito species, they are costly and laborious, making them unsuitable for extensive use in the field. Thus, alternative techniques are required. Geometric morphometrics (GM) is a rapid and inexpensive technique that can be used to analyze the size, shape, and shape variation of individuals based on a range of traits. Here, we used GM to analyze the wings of 1,040 female mosquitoes from 12 different species in Thailand. The right wing of each specimen was removed, imaged microscopically, and digitized using 17 landmarks. Wing shape variation among genera and species was analyzed using canonical variate analysis (CVA), while discriminant function analysis was used to cross-validate classification reliability based on Mahalanobis distances. Phenetic relationships were constructed to illustrate the discrimination patterns for genera and species. CVA of the morphological variation among Aedes, Anopheles, Armigeres, Culex, and Mansonia mosquito genera revealed five clusters. In particular, we demonstrated a high percentage of correctly-distinguished samples among Aedes (97.48%), Armigeres (96.15%), Culex (90.07%), and Mansonia (91.67%), but not Anopheles (64.54%). Together, these findings suggest that wing landmark-based GM analysis is an efficient method for identifying mosquito species, particularly among the Aedes, Armigeres, Culex, and Mansonia genera.