Applications of 3D modeling in cryptic species classification of molluscs

Abstract

Classification of cryptic species is important for assessing biodiversity and conducting ecological studies. However, morphological classification methods face the loss of morphological information due to subjectivity in geometric morphometrics, while an incomplete database and horizontal gene transfer limit the molecular approach. A novel approach combining 3D modeling and artificial intelligence algorithms using morphological and molecular data was developed for species classification. Cryptic species from the Vignadula genus were used to test the feasibility of this new approach. Molecular identification results as data labels were used for training models, and for validating classification results of machine learning and deep learning. Our approach achieved accuracies of over 80% in distinguishing between V. atrata and V. mangle, which were identified by molecular data along China’s coast. The result of the confusion matrix indicated the misidentified individuals were due to the morphological similarity in the intermediate zone. The feature importance analysis highlighted the significant contribution of average curvature—a 3D feature—to the task, indicating the feasibility of the 3D model in cryptic species classification. Utilizing 3D models and artificial intelligence, this study presents a novel approach for classifying cryptic species of molluscs.