Using Gaussian Mixture Model clustering for multi-isotope analysis of archaeological fish bones for...

Modern methods in mass spectrometry permit fast accumulation of a huge amount of data. The analysis of multi-isotope data sets of archaeological remains is of increasing importance for the study of palaeobiodiversity. However, common bivariate isotopic data analysis fails to detect certain patterns in a multi-dimensional data set. This problem can be solved by cluster analysis.

Gaussian Mixture Model (GMM) clustering was applied to a multi-isotope data set including 184 individual mass spectrometric measurements (δ(13) Ccollagen , δ(15) Ncollagen , δ(13) Ccarbonate , and δ(18) Ocarbonate values) of archaeological fish bones (n = 46) from the Viking Haithabu and medieval Schleswig sites in northern Germany. The number of components was first restricted to the expected number of three (freshwater, brackish, and marine environment). Subsequently, classification was conducted with respect to an optimal Bayesian Information Criterion (BIC).

Restriction of the number of components to three clusters leads to the expected clustering results according to the gross ecological niches (freshwater, brackish, marine). The isotopic data of fish bone were, however, optimally clustered into four clearly separated, reasonable groups, namely a freshwater, a brackish, and two marine groups. The two marine clusters differ in their oxygen isotope ratios, indicating different water temperature and therefore probably imported fish. Restriction of the number of clusters resulted in better training and test results.

The GMM clustering method is applicable to complex multi-dimensional stable isotope data sets established by isotope ratio mass spectrometry (IRMS). This exemplary application resulted in an identification of habitat preferences and non-local individuals. Depending on the scientific question to be solved, restriction of the cluster size could lead to a better reproducibility; however, with loss of dissolution. Copyright © 2016 John Wiley & Sons, Ltd.