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Dental topography and diet in marsupials and comparisons with primates

de Vries, D.; Gao, A.; Croft, D. A.; Brown, G.; Burkart, N.; Connell, A.; Gahan, E.; Spinks, E.; Beck, R. M. D.

2025-09-04 evolutionary biology
10.1101/2025.08.29.673041 bioRxiv
Show abstract

Dental topography is a powerful tool for investigating the relationship between diet and tooth shape in mammals. Here, we test whether dental topographic metrics (DTMs) can accurately predict diet in marsupials based on 81 lower molars from 43 extant species in 12 families (representing six of the seven extant orders), and compare them to DTMs of primates with similar diets. Specific DTMs tested included Dirichlet normal energy (DNE) and variants (ariaDNE, convex DNE, and ariaDNE CV), orientation patch count rotated (OPCR), and Relief Index (RFI). We also investigate the use of the ratio of trigonid to talonid height (TriTaHI) as an additional DTM. Highest dietary classification accuracy using a leave-one-out quadratic discriminant function analysis for marsupials was reached when ariaDNE, RFI, molar size, and TriTaHI of lower second and third molars were combined, resulting in 69.2% dietary accuracy. In contrast, the primate sample of lower second molars reached the highest classification accuracy of 80.4% when only ariaDNE, RFI, and molar size were used. Notably, 3D meshes from epoxy casts showed consistently higher OPCR and ariaDNE CV values than meshes from original specimens. When using a cross-validated approach, using the primate sample to predict marsupial diets and vice versa, only ariaDNE, RFI, and molar size were needed to obtain the maximum classification accuracy (69.7% when classifying primates using the marsupial training set; and 61.8% vice versa). The comparative dataset of this study will be very useful for studies aiming to reconstruct the diets of fossil mammals that lack phylogenetically close extant analogues.

Published in Journal of Mammalian Evolution · not in our set (fewer than 10 published preprints to learn from) · training set

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