A bioavailable strontium isoscape for Western Europe: A machine learning approach

Clement P. Bataille*, Isabella C.C. von Holstein, Jason E. Laffoon, Malte Willmes, Xiao Ming Liu, Gareth R. Davies

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Strontium isotope ratios (87Sr/86Sr) are gaining considerable interest as a geolocation tool and are now widely applied in archaeology, ecology, and forensic research. However, their application for provenance requires the development of baseline models predicting surficial 87Sr/86Sr variations (“isoscapes”). A variety of empirically-based and process-based models have been proposed to build terrestrial 87Sr/86Sr isoscapes but, in their current forms, those models are not mature enough to be integrated with continuous-probability surface models used in geographic assignment. In this study, we aim to overcome those limitations and to predict 87Sr/86Sr variations across Western Europe by combining process-based models and a series of remote-sensing geospatial products into a regression framework. We find that random forest regression significantly outperforms other commonly used regression and interpolation methods, and efficiently predicts the multi-scale patterning of 87Sr/86Sr variations by accounting for geological, geomorphological and atmospheric controls. Random forest regression also provides an easily interpretable and flexible framework to integrate different types of environmental auxiliary variables required to model the multi-scale patterning of 87Sr/86Sr variability. The method is transferable to different scales and resolutions and can be applied to the large collection of geospatial data available at local and global levels. The isoscape generated in this study provides the most accurate 87Sr/86Sr predictions in bioavailable strontium for Western Europe (R2 = 0.58 and RMSE = 0.0023) to date, as well as a conservative estimate of spatial uncertainty by applying quantile regression forest. We anticipate that the method presented in this study combined with the growing numbers of bioavailable 87Sr/86Sr data and satellite geospatial products will extend the applicability of the 87Sr/86Sr geo-profiling tool in provenance applications.

Original languageEnglish
Article numbere0197386
JournalPLoS ONE
Volume13
Issue number5
DOIs
Publication statusPublished - 30 May 2018

Funding

CPB and XML were supported by the University of North Carolina start-up fund awarded to Xiao-Ming Liu (http://www.unc.edu/). GRD, ICCvH and JEL were supported by the ERC-Synergy project NEXUS1492 under the European Union’s Seventh Framework Programme (FP7/ 2007-2013)/ERC grant agreement no. 319209 (https://ec.europa.eu/research/fp7/index_en.cfm). Partial support for this work was provided by U.S. National Science Foundation grant EF-01241286 (https://www.nsf.gov/). No individual employed or contracted by the funders (other than the named authors) played any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CPB and XML were supported by the University of North Carolina start-up fund awarded to Xiao-Ming Liu (http://www.unc.edu/). GRD, ICCvH and JEL were supported by the ERC-Synergy project NEXUS1492 under the European Union?s Seventh Framework Programme (FP7/ 2007-2013)/ERC grant agreement no. 319209 (https://ec.europa.eu/research/fp7/index_en.cfm). Partial support for this work was provided by U.S. National Science Foundation grant EF-01241286 (https://www.nsf.gov/). No individual employed or contracted by the funders (other than the named authors) played any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

FundersFunder number
ERC-Synergy
U.S. National Science Foundation
University of North Carolina
H2020 European Research CouncilNEXUS1492
Seventh Framework Programme319209
European Research Councilhttps://ec.europa.eu/research/fp7/index_en.cfm
Seventh Framework ProgrammeFP7/ 2007-2013
Crown Property Bureau
National Science FoundationEF-01241286

    Fingerprint

    Dive into the research topics of 'A bioavailable strontium isoscape for Western Europe: A machine learning approach'. Together they form a unique fingerprint.

    Cite this