DOI: 10.5061/dryad.9zw3r22qf
title: An ensemble machine learning bioavailable strontium isoscape for Eastern Canada

description: 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.
description: - Data collection and analyses We completed
 the original bioavailable
 <sup>87</sup>Sr/<sup>86</sup>Sr 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 (<em>Abies balsamea</em> (Mill.)) and
 spruce needles (<em>Picea sp</em>.) were collected at 107
 sites across the boreal forest. Samples were analysed for
 <sup>87</sup>Sr/<sup>86</sup>Sr by Multi-Collector
 Inductively Coupled Plasma Mass Spectrometer (MC-ICP-MS).
 - Mechanistic
 <sup>87</sup>Sr/<sup>86</sup>Sr bedrock
 model The mechanistic
 <sup>87</sup>Sr/<sup>86</sup>Sr bedrock model
 (Bataille et al. 2014) predicts the modern
 <sup>87</sup>Sr/<sup>86</sup>Sr 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 <sup>87</sup>Sr/<sup>86</sup>Sr
 value changing over time following a three-stage history: (1) the initial
 <sup>87</sup>Sr/<sup>86</sup>Sr in
 undifferentiated mantle, (2) the enrichment in <sup>87</sup>Sr
 through <sup>87</sup>Rb decay of the parent crustal rock
 reservoir from its differentiation to the formation of the modern rock,
 (3) the enrichment in <sup>87</sup>Sr of the modern rock since
 its formation. We updated the global mechanistic
 bedrock model (median, 1<sup>st</sup> quartile,
 3<sup>rd</sup> quartile) and the associated global geological
 unit age rasters (min, max, mean) used in Bataille et al. (2020), for the
 province of Québec, using SIGEOM (Système d’Information Géominière,
 Ministère des Ressources naturelles et des Forêts [MRNF], Québec) fine
 scale geological data (Thériault and Beauséjour 2012). Update of the
 geological data (age, nature) was done in ArcGIS 10.3. The mechanistic
 bedrock model was run in R 4.3 (script provided). We provided the updated
 geological age rasters (min, max, mean) and the predicted
 <sup>87</sup>Sr/<sup>86</sup>Sr from bedrock
 (median, 1<sup>st</sup> quartile, 3<sup>rd</sup>
 quartile), used in the random forest and ensemble machine learning
 analyses to produce the bioavailable
 <sup>87</sup>Sr/<sup>86</sup>Sr isoscape for
 Eastern Canada.      - Bioavailable
 <sup>87</sup>Sr/<sup>86</sup>Sr isoscape
 We generated the
 bioavailable <sup>87</sup>Sr/<sup>86</sup>Sr
 isoscape for Eastern Canada using the global
 bioavailable <sup>87</sup>Sr/<sup>86</sup>Sr
 sample dataset from Bataille et al. (2020) updated with samples from
 Eastern Canada and geological, topographic, climate, soil, and aerosol
 deposition variables extracted at each sampling sites.
 We used two machine learning approaches: a random forest analysis
 and an ensemble learning machine analysis. The random forest grows
 multiple regression trees by bagging : for each tree the dataset is
 divided randomly by bootstrap into a training set and a validation set.
 Predictions are then aggregated from all trees. For the ensemble machine
 learning, we used the stacking approach: several different algorithms are
 trained on the dataset and the predictions of these models are used to
 train a meta-learner, generating the ensemble machine learning
 estimates. Two isoscapes were generated with the random
 forest approach: the first one uses the global
 <sup>87</sup>Sr/<sup>86</sup>Sr dataset without
 data from Eastearn Canada and the other one integrates Eastern Canada
 samples. The ensemble machine learning analysis use global
 <sup>87</sup>Sr/<sup>86</sup>Sr dataset with the
 Eastearn Canada data. Analyses were conduct in R 4.3
 (script provided) and we provide the 87Sr/86Sr prediction rasters as well
 as the associated spatial error (standard deviation) rasters for both
 approaches. - References: Bataille,
 C.P., Brennan, S.R., Hartmann, J., Moosdorf, N., Wooller, M.J., and Bowen,
 G.J., 2014. A geostatistical framework for predicting variations in
 strontium concentrations and isotope ratios in Alaskan rivers. Chemical
 Geology, 389, 1–15.
 https://doi.org/10.1016/j.chemgeo.2014.08.030 Bataille,
 C.P., Crowley, B.E., Wooller, M.J., and Bowen, G.J., 2020. Advances in
 global bioavailable strontium isoscapes. Palaeogeography Palaeoclimatology
 Palaeoecology, 555, 109849. https://doi.org/10.1016/j.palaeo.2020.109849 Thériault, R., and Beauséjour, S., 2012. Geological map of Québec, DV2012-07. Ministère des Ressources naturelles et des Forêts, Québec.
description: # 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—was updated for EC using more accurate geological data, following the methodology described in Bataille et al. (2014, *A geostatistical framework for predicting variations in strontium concentrations and isotope ratios in Alaskan rivers*, *Chemical Geology*, 389, [https://doi.org/10.1016/j.chemgeo.2014.08.030](https://doi.org/10.1016/j.chemgeo.2014.08.030)), prior to running the RF and EML models. The RF-based isoscape was generated using both the original dataset from Bataille et al. (2020) and an updated dataset incorporating plant samples from Eastern Canada. The EML-based isoscape was generated using the updated dataset only. This folder contains: \- The updated bioavailable 87Sr/86Sr database (Excel format), based on Bataille et al. (2020), including new plant data from Eastern Canada. \- Rasters of geological variables (predicted bedrock 87Sr/86Sr, geological unit age) updated specifically for this study (additional variables are available from Bataille et al. 2020). \- R scripts to 1) run the bedrock model to predict bedrock 87Sr/86Sr, 2) run the RF and EML analyses. \- The isoscapes generated using RF (with and without EC plant data) and the EML-generated isoscape, along with their corresponding spatial error rasters. ### Files and variables **File: SM1_table_S1.xlsx** Bioavailable 87Sr/86Sr dataset used to generate the isoscape. This is an updated version of the dataset compiled by Bataille et al. (2020), incorporating new samples from Eastern Canada. Due to variability in data formats and completeness across the original source studies, not all fields are available for every record. Missing information is indicated with "NA". **Fields :** * Reference sheet: list of the source studies of the samples with reference id used in the "samples" sheet. * samples sheet: -ID: sample ID for this dataset -Original_ID: source sample ID -Country: geographic origin of the sample -Type1: main type of sample (soil, plant, animal taxa) -Type2, Type3: more detailed type (e.g. species for plant and animals) -Material: description of the material analysed -87Sr/86Sr: strontium (Sr) isotopic ratio. According to the source studies, this ratio was measured using either Thermal Ionization Mass Spectrometry (TIMS) or Inductively Coupled Plasma Mass Spectrometry (ICP-MS). For detailed information on analytical procedures, refer to the original studies. -Conc._Sr: concentration in strontium (ppm) -Sr.1SD, Sr.2SD: measurement error measured at 1 or 2 standard deviation -Latitude/Longitude: sampling site coordinates in decimal degrees -Source.of.data.reference.ID: ID used to identify source studies in "Reference" sheet. #### File: SM3\_script\_bedrock.R **Description:** R script to run the mechanistic bedrock model from Bataille et al. (2014) and predict modern 87Sr/86Sr values in bedrock (median, 1st quartile, 3rd quartile), based on the age and lithology of geological units. The script uses three different equations to estimate 87Sr/86Sr according to bedrock type (igneous/metamorphic rocks, siliciclastic sediments, and carbonate sediments). #### File: SM4\_script\_isoscape.R **Description:** R script to run the random forest (RF) and ensemble machine learning (EML) analyses and generate bioavailable 87Sr/86Sr isoscapes. The script includes: extraction of geological, environmental, and climatic predictors at sampling sites, variable selection for the RF analysis, execution of RF and EML models, and prediction of bioavailable 87Sr/86Sr values across the landscape. #### File: isoscapes.zip **Description:** Output rasters (predicted values and standard deviations) from the RF and EML analyses, including the RF-based isoscapes with and without Eastern Canada (EC) samples, and the EML-based isoscape. All rasters are provided in .tif format (GeoTIFF) and can be opened in R using the *raster* or *terra* packages, or in any GIS software such as QGIS. #### File: projected\_rasters.zip **Description:** Rasters of predicted bedrock 87Sr/86Sr (median, 1st quartile, 3rd quartile) and geological unit age (minimum, maximum, mean), updated from Bataille et al. (2020), and used as predictors for generating the Eastern Canada isoscapes. Additional predictor rasters used in the analyses are available from Bataille et al. (2020). All rasters are provided in .tif format (GeoTIFF) and can be opened in R using the *raster* or *terra* packages, or in any GIS software such as QGIS. ## Sharing/Access information The original bioavailable 87Sr/86Sr dataset and the predictor rasters used in the analysis are available from Bataille et al. (2020): [https://doi.org/10.1016/j.palaeo.2020.109849](https://doi.org/10.1016/j.palaeo.2020.109849) ## Code/software Analyses were conducted using R 4.3. The scripts are compatible with more recent versions of R, except for the EML section. The EML analysis relies on the landmap R package, which depends on the deprecated rgdal package and will not run in recent R versions where rgdal is no longer supported. The landmap package is available at: [https://github.com/Envirometrix/landmap](https://github.com/Envirometrix/landmap)