Application of Machine Learning on Isotopic Data from Tooth Microsections for Reconstructing Weaning Patterns and Physiological Stress

26 Pages Posted: 18 Aug 2022

See all articles by Elissavet Ganiatsou

Elissavet Ganiatsou

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace

Angeliki Georgiadou

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace

Angelos Souleles

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace

Asterios Aidonis

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace

Tania Protopsalti

Ephorate of Antiquities of Thessaloniki City, Ministry of Culture and Sports

Stavroula Tzevreni

Ephorate of Antiquities of Thessaloniki City, Ministry of Culture and Sports

krino Konstantinidou

Ephorate of Antiquities of Thessaloniki City, Ministry of Culture and Sports

Stella Vasileiadou

Ephorate of Antiquities of Thessaloniki City, Ministry of Culture and Sports

Frank Siegmund

Abt. Ur- und Frühgeschichtliche Archäologie, Department of History, University of Münster

Christina Papageorgopoulou

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace

Abstract

The recent development of measuring stable isotope ratios (δ15N and δ13C) in collagen from tooth microsections provides temporal resolution of dietary changes and has been widely used for the reconstruction of breastfeeding, weaning and physiological stress in archaeological datasets. We applied incremental dentine analysis, measuring the δ15N and δ13C in collagen from first permanent molars of 45 adult individuals from the city of Thessaloniki (4th c. BC-16th c. AD). We were able to reconstruct the diet of 31 individuals from birth up to the age of seven. To this dataset, we added 20 previously published individuals from the same site and we re-examined the weaning ages with WEAN, an automated application for estimating the weaning age based on the measurements of δ15N. Furthermore, we used the k-means machine learning method to discern clusters of different isotopic patterns in the individual profiles and trace possible signals of physiological stress.Our results show that 45 out of 51 individuals were breastfed but weaned at different ages ranging from one to three years old. Five individuals, however, were breastfed for an even shorter period or were never breastfed at all. The weaning diet was comprised mostly of animal protein and C3 plants, while the consumption of small fish and/or C4 plants intensified from the Roman period onwards. Moreover, there were no statistically significant differences between males and females and among chronological periods and the machine learning approach indicated that 9 individuals show evidence of physiological stress, which may be linked to maladaptive breastfeeding patterns. Our study reports new data on breastfeeding and weaning utilizing the implementation of computational methods and illustrates the complexity of the infant feeding practices in ancient societies.

Note:

Funding Information: This research has been co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project title: ECHOES560 Development of a methodology for the digital reconstruction of ancient human biographies through the study of archaeo-anthropological material, project code:T2EDK-00152). This research is funded in the context of the project ‘Breastfeeding and weaning in antiquity: the case of Thessaloniki' (MIS 5049509) under the call for proposals ‘Researchers' support with an emphasis on young researchers564 2nd Cycle' (EDULLL 103). The project is co-financed by Greece and the European Union (European Social Fund ESF) through the Operational Programme ‘Human Resources Development, Education and Lifelong Learning 2014 – 2020'.

Declaration of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Keywords: incremental dentine analysis, carbon and nitrogen isotopes, breastfeeding, unsupervised clustering, Roman, Byzantine, Greece

Suggested Citation

Ganiatsou, Elissavet and Georgiadou, Angeliki and Souleles, Angelos and Aidonis, Asterios and Protopsalti, Tania and Tzevreni, Stavroula and Konstantinidou, krino and Vasileiadou, Stella and Siegmund, Frank and Papageorgopoulou, Christina, Application of Machine Learning on Isotopic Data from Tooth Microsections for Reconstructing Weaning Patterns and Physiological Stress. Available at SSRN: https://ssrn.com/abstract=4186545 or http://dx.doi.org/10.2139/ssrn.4186545

Elissavet Ganiatsou

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace ( email )

Angeliki Georgiadou

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace ( email )

Angelos Souleles

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace ( email )

Asterios Aidonis

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace ( email )

Tania Protopsalti

Ephorate of Antiquities of Thessaloniki City, Ministry of Culture and Sports ( email )

Stavroula Tzevreni

Ephorate of Antiquities of Thessaloniki City, Ministry of Culture and Sports ( email )

Krino Konstantinidou

Ephorate of Antiquities of Thessaloniki City, Ministry of Culture and Sports ( email )

Stella Vasileiadou

Ephorate of Antiquities of Thessaloniki City, Ministry of Culture and Sports ( email )

Frank Siegmund

Abt. Ur- und Frühgeschichtliche Archäologie, Department of History, University of Münster ( email )

Christina Papageorgopoulou (Contact Author)

Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace ( email )

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