Premolar Ecomorphology in Anthropoid Primates: A Machine Learning Approach.

Reconstructing the diets of extinct taxa is essential for understanding their ecologies and evolutionary histories, yet traditional methods and proxies such as molar morphology have limited resolution. The potential of premolar morphology as a dietary proxy remains underexplored, and advanced computational methods have rarely been applied to improve dietary inference in paleontology. We integrate Random Forest (RF) machine learning and comparative phylogenetic methods to identify and rank dental proxies for diet in a large sample of anthropoid primates. We quantify dietary trends in premolar topography and cusp relief and find that premolar protoconid relief is a strong predictor of dietary category, especially for distinguishing hard-object feeders, which outperformed traditional proxies on molars and incisors. We also identify sexually dimorphic dietary trends in honing premolars. Feature selection improved classification accuracy by 5%-11% compared to unpruned models, with the highest accuracy achieved by a model incorporating premolar, molar, and incisor data. These findings establish robust new dental proxies for dietary inference and demonstrate the potential of machine learning and a multi-tooth approach in ecomorphological research. By expanding the toolkit for reconstructing the diets of extinct primates, we establish a framework that may help clarify the ecological pressures that have shaped the evolution of modern clades including that of the human lineage.