Developments in data science solutions for carnivore tooth pit classification

Abstract Competition for resources is a key question in the study of our early human evolution. From the first hominin groups, carnivores have played a fundamental role in the ecosystem. From this perspective, understanding the trophic pressure between hominins and carnivores can provide valuable in...

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Autores principales:	Lloyd A. Courtenay, Darío Herranz-Rodrigo, Diego González-Aguilera, José Yravedra
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b720805492744b1187704000715ee269
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spelling	oai:doaj.org-article:b720805492744b1187704000715ee2692021-12-02T17:15:36ZDevelopments in data science solutions for carnivore tooth pit classification10.1038/s41598-021-89518-42045-2322https://doaj.org/article/b720805492744b1187704000715ee2692021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89518-4https://doaj.org/toc/2045-2322Abstract Competition for resources is a key question in the study of our early human evolution. From the first hominin groups, carnivores have played a fundamental role in the ecosystem. From this perspective, understanding the trophic pressure between hominins and carnivores can provide valuable insights into the context in which humans survived, interacted with their surroundings, and consequently evolved. While numerous techniques already exist for the detection of carnivore activity in archaeological and palaeontological sites, many of these techniques present important limitations. The present study builds on a number of advanced data science techniques to confront these issues, defining methods for the identification of the precise agents involved in carcass consumption and manipulation. For the purpose of this study, a large sample of 620 carnivore tooth pits is presented, including samples from bears, hyenas, jaguars, leopards, lions, wolves, foxes and African wild dogs. Using 3D modelling, geometric morphometrics, robust data modelling, and artificial intelligence algorithms, the present study obtains between 88 and 98% accuracy, with balanced overall evaluation metrics across all datasets. From this perspective, and when combined with other sources of taphonomic evidence, these results show that advanced data science techniques can be considered a valuable addition to the taphonomist’s toolkit for the identification of precise carnivore agents via tooth pit morphology.Lloyd A. CourtenayDarío Herranz-RodrigoDiego González-AguileraJosé YravedraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lloyd A. Courtenay Darío Herranz-Rodrigo Diego González-Aguilera José Yravedra Developments in data science solutions for carnivore tooth pit classification
description	Abstract Competition for resources is a key question in the study of our early human evolution. From the first hominin groups, carnivores have played a fundamental role in the ecosystem. From this perspective, understanding the trophic pressure between hominins and carnivores can provide valuable insights into the context in which humans survived, interacted with their surroundings, and consequently evolved. While numerous techniques already exist for the detection of carnivore activity in archaeological and palaeontological sites, many of these techniques present important limitations. The present study builds on a number of advanced data science techniques to confront these issues, defining methods for the identification of the precise agents involved in carcass consumption and manipulation. For the purpose of this study, a large sample of 620 carnivore tooth pits is presented, including samples from bears, hyenas, jaguars, leopards, lions, wolves, foxes and African wild dogs. Using 3D modelling, geometric morphometrics, robust data modelling, and artificial intelligence algorithms, the present study obtains between 88 and 98% accuracy, with balanced overall evaluation metrics across all datasets. From this perspective, and when combined with other sources of taphonomic evidence, these results show that advanced data science techniques can be considered a valuable addition to the taphonomist’s toolkit for the identification of precise carnivore agents via tooth pit morphology.
format	article
author	Lloyd A. Courtenay Darío Herranz-Rodrigo Diego González-Aguilera José Yravedra
author_facet	Lloyd A. Courtenay Darío Herranz-Rodrigo Diego González-Aguilera José Yravedra
author_sort	Lloyd A. Courtenay
title	Developments in data science solutions for carnivore tooth pit classification
title_short	Developments in data science solutions for carnivore tooth pit classification
title_full	Developments in data science solutions for carnivore tooth pit classification
title_fullStr	Developments in data science solutions for carnivore tooth pit classification
title_full_unstemmed	Developments in data science solutions for carnivore tooth pit classification
title_sort	developments in data science solutions for carnivore tooth pit classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b720805492744b1187704000715ee269
work_keys_str_mv	AT lloydacourtenay developmentsindatasciencesolutionsforcarnivoretoothpitclassification AT darioherranzrodrigo developmentsindatasciencesolutionsforcarnivoretoothpitclassification AT diegogonzalezaguilera developmentsindatasciencesolutionsforcarnivoretoothpitclassification AT joseyravedra developmentsindatasciencesolutionsforcarnivoretoothpitclassification
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Developments in data science solutions for carnivore tooth pit classification

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