Multi-isotope fingerprints (O, Sr and Pb) in archaeological animal bone bioapatite: Similarity search and the suitability for provenance analysis in a geologically complex Alpine region

Tschetsch, L.; Mussauer, A.; Mauder, M.; Lohrer, J.; Kroeger, P.; Mayr, C.; Soellner, F. and Grupe, G. (2020): Multi-isotope fingerprints (O, Sr and Pb) in archaeological animal bone bioapatite: Similarity search and the suitability for provenance analysis in a geologically complex Alpine region. In: Archaeometry, Vol. 62: pp. 35-52

Abstract

Multi-isotope fingerprints in the bioapatite of archaeological skeletons are mostly superior over single isotope analyses for provenance studies. Gaussian mixture model (GMM) clustering is a novel tool for a similarity search among multidimensional data sets and at the same time permits the evaluation of the structural importance of particular isotopic ratios in the data set. We applied three GMM clustering experiments on multi-isotope fingerprints-stable strontium (Sr), lead (Pb) and oxygen (O) isotopic ratios-established in 217 archaeological animal bones excavated along a specific transect across the European Alps. This reference region had been in use since prehistoric times by humans who crossed the Alps from north to south, and vice versa. The resulting clusters permit a spatial assignment of the specimens with a very high probability, in particular with regard to the geological complexity of the region. A combination of Sr with Pb stable isotopes led to an optimal differentiation between the southern and northern Alpine forelands that cannot be distinguished from each other by Sr-87/Sr-86 ratios alone, while the contribution of delta O-18 is not particularly high. The isotopic mapping and subsequent cluster analysis is suitable for the analysis of archaeological human finds and the reconstruction of the direction of transalpine mobility and trade.

• Item Type: Journal article
• Faculties: Biology > Department Biology II; Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 500 Science > 570 Life sciences; biology; 000 Computer science, information and general works > 004 Data processing computer science
• ISSN: 0003-813X
• Language: English