摘要：SummaryMany groups of stingless insects have independently evolved mimicry of bees to fool would-be predators. To investigate this mimicry, we trained artificial intelligence (AI) algorithms—specifically, computer vision—to classify citizen scientist images of bees, bumble bees, and diverse bee mimics. For detecting bees and bumble bees, our models achieved accuracies of91.71%and88.86%, respectively. As a proxy for a natural predator, our models were poorest in detecting bee mimics that exhibit both aggressive and defensive mimicry. Using the explainable AI method of class activation maps, we validated that our models learn from appropriate components within the image, which in turn provided anatomical insights. Our t-SNE plot yielded perfect within-group clustering, as well as between-group clustering that grossly replicated the phylogeny. Ultimately, the transdisciplinary approaches herein can enhance global citizen science efforts as well as investigations of mimicry and morphology of bees and other insects.Graphical abstractDisplay OmittedHighlights•AI models for classifying bees and bumble bees achieved 92% and 89% accuracy•AI models were fooled most by bee mimics exhibiting both aggressive and defensive mimicry•Class activation maps explained the anatomical reasoning of AI model classifications•t-SNE plot exhibited perfect phylogenetic clustering within and between groupsArtificial intelligence; Bioinformatics; Computing methodology; Entomology; Zoology
