Identifying morphs of the Asian Hornet (Vespa velutina) and other pests of quarantine importance with geometric morphometrics

Abstract

Abstract In this work assess the use of geometric morphometrics (GMM) as an identification tool for insects of quarantine importance that are difficult to identify based on traditional external morphology alone. We use the Asian hornet (Vespa velutina) as a case study because it is a highly variable species, with a broad natural distribution, and has been accidentally introduced in many regions of the world as a result of commerce. We used landmark data of the forewings of an extensive set of samples (previously considered “subspecies” of the Asian hornets) from across the native range of this species in southeastern Asia to assess the affinity of a dead specimen of the species that was intercepted dead on a mail parcel in Utah, USA in 2020. We find that GMM analysis of the forewing is able to statistically distinguish almost all color morphs from the native range with moderate-sized samples and that automated classification using discriminant function analysis is able to classify individuals with an overall accuracy of about 90%, ranging from 60–100% accurate depending on the morph. Discriminant function classification and continuous-trait maximum-likelihood clustering based on forewing shape both suggest that the Utah specimen belongs to the nigrithorax color morph, which has a native range in southern China, but which has also been introduced to Europe and Korea. The GMM approach bears considerable promise for refining the identification and provenancing of intercepted specimens accurately and inexpensively and can be made even more powerful by increasing the coverage of the wing shape database.