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从骨架中用稳定的同位素重建古老饮食 - - 关于多维数据集的解释

Veronika Zacherl¹, Gisela Grupe¹, Andrea Göhring¹

¹Ludwig Maximilian University Munich, Faculty of Biology, Department of Biology I, Anthropology and Human Genomics, Großhaderner Str. 2, 82152 Planegg-Martinsried, Germany.

Anthropologischer Anzeiger; Bericht uber die biologisch-anthropologische Literatur

|December 7, 2023

概括

高斯混合模型 (GMM) 聚类通过分析多维同位素特征来增强古食重建. 这种方法揭示了饮食模式,包括碳酸盐的重要性,超过了传统分析.

科学领域:

考古学测量是指考古学测量的方法.
同位素分析分析同位素分析
古代饮食重建的重建

背景情况:

骨遗骸的稳定同位素分析在考古学中至关重要.
多维同位素数据集需要先进的解释方法.
与传统的双变量评估相比,集群分析提供了更好的模式检测.

研究的目的:

为了证明高斯混合模型 (GMM) 聚类对古饮食重建的实用性.
重新评估来自上巴伐利亚的早期中世纪骨同位素数据.
突出GMM在解释复杂的同位素特征方面的好处.

主要方法:

将高斯混合模型 (GMM) 集群应用于四维同位素数据集 (δ13Collagen, δ15N, δ13Ccarbonate, δ18Ocarbonate) 的应用.
从中世纪早期的骨发现中对27个个体进行分析,包括特殊埋葬的骨.
集群分析的代过程,以确定必要的同位素系统和噪音数据.

主要成果:

在数据集中,GMM聚类成功地确定了不同的饮食模式.
三种同位素系统 (δ13Collagen, δ15N和 δ13Ccarbonate) 对于准确的古典饮食重建至关重要.
δ13C碳酸盐值提供了关于饮食中的碳水化合物和脂肪的关键信息,有助于分离集群.

结论:

高斯混合模型 (GMM) 聚类是从多维同位素数据中重建古饮食的强大工具.
加入 δ13C碳酸盐显著提高了饮食分析的分辨率.
在这个早期中世纪的人口中,饮食模式和坟墓用品之间没有发现直接的相关性.

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