Machine Learning Approach to Support Taxonomic Discrimination of Mayflies Species Based on Morphologic Data

Artificial intelligence (AI) and machine learning (ML) offer objective solutions in the elaboration of taxonomic keys, such as the processing of large numbers of samples, aiding in the species identification, and optimizing the time required for this process. We utilized ML to study the morphological data of eight species of Americabaetis Kluge 1992, a diverse genus in South American freshwater environments. Decision trees were employed, examining specimens from the Museu de Entomologia da Universidade Federal de Vi & ccedil;osa (UFVB/Brazil) and literature data. Eleven morphological traits of taxonomic importance from the literature, including frontal keel, shape of the mouthparts, and abdominal color pattern, were analyzed. The decision tree obtained with the Gini algorithm effectively differentiates eight species (40% of the known species), using only eight morphological characters. Our analysis revealed distinct groups within Americabaetis alphus Lugo-Ortiz and McCafferty 1996a, based on variations in abdominal tracheae pigmentation. This study introduces a novel approach, integrating AI techniques, biological collections, and literature data for aid in the Americabaetis species identification. It provides a valuable tool for taxonomic research on contemporary and extinct mayflies.