An ensemble machine learning bioavailable strontium isoscape for Eastern Canada
Maël Le Corre, Felipe Dargent, Vaughan Grimes, Joshua Wright, Steeve D. Côté, Megan S. Reich, Jean-Noël Candau, Marrissa Miller, Brent Holmes, Clément P. Bataille, Kate Britton
Abstract
Bioavailable strontium isotope ratios ( 87 Sr/ 86 Sr) distribution across the landscape mainly follow the underlying lithology, making 87 Sr/ 86 Sr baseline maps (isoscapes) powerful tools for provenance studies. 87 Sr/ 86 Sr has already been used in Eastern Canada (EC) to track food and human remains origins, or to reconstruct animal mobility. While bioavailable 87 Sr/ 86 Sr isoscapes for EC can be extrapolated from global datasets using random forest modelling (RF), no regionally calibrated isoscape exists. Here, we produce a regionally calibrated bioavailable 87 Sr/ 86 Sr isoscape by analysing plants collected at 136 sites across EC, incorporating updated geological variables and applying a novel ensemble machine learning (EML) framework. We generated and compared isoscapes generated by the traditional RF and the EML approaches. Adding local bioavailable 87 Sr/ 86 Sr to a global dataset significantly improved the model prediction with a drastic increase of predicted 87 Sr/ 86 Sr and increased spatial uncertainty in the northern Canadian craton. EML produced similar 87 Sr/ 86 Sr predictions but with tighter spatial uncertainty distribution. Regionally calibrated RF and EML isoscapes significantly outperformed the global bioavailable RF isoscape, confirming the requirement for collecting local data in data-poor regions. This isoscape provides a baseline in EC to monitor and manage the movements and provenance of agricultural products, natural resources, endangered/harmful migratory species, and archaeological human remains and artifacts.