A random forest isoscape model of bioavailable Sr for South America: a focus on southern Brazil

Abstract

In recent years, advances in machine learning have greatly improved the generation of maps showing the geographic distribution of isotope ratios (isoscapes), which have become essential tools for environmental, mobility and provenance studies in both modern and archaeological contexts. Among the various isotopic systems employed, strontium (Sr) is particularly useful because its 87 Sr /  86 Sr ratio in the environment is largely controlled by the underlying geology through the composition of local soils and rocks. In this work, we present a new dataset of bioavailable 87 Sr /  86 Sr ratios derived from n =233 plant samples collected across southern Brazil, covering the states of Santa Catarina and Rio Grande do Sul (ca. 370 000 km 2 ). The measured ratios span from 0.70521 to 0.76039 and capture the bioavailable Sr isotope signatures over all major geological units in the region. We combined these new data with an extensive compilation of published bioavailable Sr measurements from across South America (including plants, fauna, ancient human remains, shells, snails, lichens, water and soils) to construct three random forest Sr isoscapes using different subsets of the combined dataset at the regional and continental scales. The first model incorporates the entire dataset (“All” dataset, n = 883 sites), the second is based on plant + fauna + lichen + human ( n = 661 sites) and the third is limited to plant + lichen samples ( n = 531 sites). Among the three models, the full dataset model shows lower predictive power, while the plant + fauna + lichen + human and the plant + lichen models yield better results, with similar RMSE (0.0049 and 0.0054) and R 2 values (ca. 0.76). Compared to existing Sr isoscapes of South America, our models significantly enhance both spatial coverage and resolution of bioavailable Sr predictions, particularly in southern Brazil. The new bioavailable Sr isotope dataset from Santa Catarina and Rio Grande do Sul states is available at https://doi.org/10.5281/zenodo.17988601 (Scaggion et al., 2025a) and the compiled literature dataset is reported as supplementary material.