An ensemble machine learning bioavailable strontium isoscape for Eastern Canada

Bioavailable strontium isotope ratios (87Sr/86Sr) distribution across the
landscape mainly follow the underlying lithology, making 87Sr/86Sr
baseline maps (isoscapes) powerful tools for provenance studies. 87Sr/86Sr
has already been used in Eastern Canada (EC) to track food and human
remains origins, or to reconstruct animal mobility. While bioavailable
87Sr/86Sr 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 87Sr/86Sr 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 87Sr/86Sr to a global dataset significantly improved the
model prediction with a drastic increase of predicted 87Sr/86Sr and
increased spatial uncertainty in the northern Canadian craton. EML
produced similar 87Sr/86Sr 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.