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 unce..., - Data collection and analyses
We completed the original bioavailable 87Sr/86Sr global database from Bataille et al. (2020) with plant samples from Eastern Canada collected during two independent campaigns between 2018 and 2022: moss, lichen and grass were collected at 28 sites across taiga and tundra habitats; Balsam fir (Abies balsamea (Mill.)) and spruce needles (Picea sp.) were collected at 107 sites across the boreal forest. Samples were analysed for 87Sr/86Sr by Multi-Collector Inductively Coupled Plasma Mass Spectrometer (MC-ICP-MS).
- Mechanistic 87Sr/86Sr bedrock model
The mechanistic 87Sr/86Sr bedrock model (Bataille et al. 2014) predicts the modern 87Sr/86Sr values of the bedrock based on the age and the nature of the geological units. The model assumes that all rocks of a given lithology come from a common parent material with 87Sr/86Sr value changing over time following a three-stage history: (1) the initial 87Sr/86Sr in undifferentiated mantle, (2) the enrichment..., , # An ensemble machine learning bioavailable strontium isoscape for Eastern Canada

[https://doi.org/10.5061/dryad.9zw3r22qf](https://doi.org/10.5061/dryad.9zw3r22qf)

## Description of the data and file structure

The bioavailable strontium (87Sr/86Sr) isoscape for Eastern Canada (EC) was generated using two machine learning approaches: random forest (RF), following the workflow described in Bataille et al. (2020, *Advances in global bioavailable strontium isoscapes*, *Palaeogeography, Palaeoclimatology, Palaeoecology*, 555, 109849, [https://doi.org/10.1016/j.palaeo.2020.109849](https://doi.org/10.1016/j.palaeo.2020.109849)), and ensemble machine learning (EML).

Both approaches rely on a training dataset consisting of bioavailable 87Sr/86Sr measurements sampled globally, along with geological, environmental, and climatic variables in raster format used to predict 87Sr/86Sr across the landscape. Among these variables, predicted bedrock 87Sr/86Sr—a key driver of bioavailable 87Sr/86Sr—wa...,