Machine learning can accurately assign fossil and extant species to crown toxicoferan (Reptilia: Squamata) groups using inner ear shape data

Because the inner ear is involved in gaze stabilization, balance, and hearing, fossil inner ear endocast morphology has been used to infer the palaeoecology of extinct species. These results have been used to inform major evolutionary transitions, including the ecological origin of snakes. However, prior studies found only modest correlations between inner ear shape and ecological traits, and did not apply machine learning approaches, which could potentially reveal greater predictive relationships between inner ear morphology and ecology. Here, we combine three-dimensional geometric morphometrics with machine learning to evaluate the performance of inner ear morphology as a predictor of habitat use and phylogenetic affinities across a broad sample of toxicoferans (snakes, anguimorphs, and iguanians) representing 73 extant species and 4 fossil species. We find a weak correlation between habitat and inner ear morphology, but machine learning models cannot accurately predict habitat preference in extant species (44% accuracy). In contrast, we find a strongly predictive relationship (95% accuracy) between inner ear shape and higher-order classification. Our results demonstrate that the inner ear shape data we measured strongly predicts evolutionary classifications rather than habitat use in crown toxicoferan squamates. We conclude that machine learning provides a versatile analytical approach to reconstruction of palaeobiology.