Bridging morphometrics and Archaeogenetics: a non-destructive approach to species identification of sheep, goats and pigs based on bony labyrinth

The petrous part of the temporal bone is a target for ancient DNA research due to its biomolecular preservation, yet destructive sampling risks losing valuable morphological information. Bony labyrinth morphology, captured by high-resolution 3D imaging, carries strong taxonomic and evolutionary signals, but its bioarchaeological application remains limited. We assessed the potential of bony labyrinth geometric morphometrics (GMM) for species identification of domestic animals by analysing 14 micro-CT-scanned archaeological petrosals from Spain alongside a dataset of 41 modern French and Spanish breeds of sheep and goats, as well as two pigs and two wild boars. Predictive discriminant analysis shows bony labyrinth shape reliably distinguishes these taxa, matching ancient genomic identification. Our findings demonstrate that bony labyrinth morphometrics is a robust, non-destructive approach for taxonomic identification of archaeological specimens, complementing other bioarchaeological proxies (e.g. ancient DNA, palaeoprotein). Integrated with palaeogenomic evidence, this framework enables comprehensive reconstructions of domestication processes and human-animal relationships.

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Publication Details

Journal
World Archaeology
Published
2026-09-07
DOI
https://doi.org/10.1080/00438243.2026.2720450
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
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article

Bridging morphometrics and Archaeogenetics: a non-destructive approach to species identification of sheep, goats and pigs based on bony labyrinth

Vanessa Navarrete, Dani López-Reyes, Joan Bernabéu Aubán, Alfredo Cortell‐Nicolau et al.
World Archaeology
Identification and Quantification in Food
article

Bridging morphometrics and Archaeogenetics: a non-destructive approach to species identification of sheep, goats and pigs based on bony labyrinth

Vanessa Navarrete, Dani López-Reyes, Joan Bernabéu Aubán, Alfredo Cortell‐Nicolau, Ana Elisabete Pires, Valeria Mattiangeli, Renaud Lebrun, Pau Menéndez Molist, Mireia Sabaté Balada, Allowen Evin, Daniel G. Bradley, Cleia Detry, Áine Halpin, Gabriel de Prado, Adeline Hemelsdaël, Daniel Gaspar, Agathe Guignet, Catarina Ginja, Jaume Noguera, Oreto García Puchol, Silvia Valenzuela-Lamas, Lluís Molina Balaguer
article en

Abstract

The petrous part of the temporal bone is a target for ancient DNA research due to its biomolecular preservation, yet destructive sampling risks losing valuable morphological information. Bony labyrinth morphology, captured by high-resolution 3D imaging, carries strong taxonomic and evolutionary signals, but its bioarchaeological application remains limited. We assessed the potential of bony labyrinth geometric morphometrics (GMM) for species identification of domestic animals by analysing 14 micro-CT-scanned archaeological petrosals from Spain alongside a dataset of 41 modern French and Spanish breeds of sheep and goats, as well as two pigs and two wild boars. Predictive discriminant analysis shows bony labyrinth shape reliably distinguishes these taxa, matching ancient genomic identification. Our findings demonstrate that bony labyrinth morphometrics is a robust, non-destructive approach for taxonomic identification of archaeological specimens, complementing other bioarchaeological proxies (e.g. ancient DNA, palaeoprotein). Integrated with palaeogenomic evidence, this framework enables comprehensive reconstructions of domestication processes and human-animal relationships.

World Archaeology
Centre National de la Recherche Scientifique (FR), Consejo Superior de Investigaciones Científicas (ES), University of Lisbon (PT), Universitat de València (ES), Universidade do Porto (PT), Université de Montpellier (FR), Trinity College Dublin (IE), Museu d'Arqueologia de Catalunya (ES), Tenet Healthcare (US), Max Planck Institute for Evolutionary Anthropology (DE), Institut de Recherche pour le Développement (FR), Universitat de Barcelona (ES), Universidade Lusófona (PT)
European Research Council
Openalex Percentile: Top 18%
Identification and Quantification in Food
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