A bioavailable strontium isoscape for Western Europe

A machine learning approach

Bibliographic Data

ID23328147
AuthorsClément P Bataille (0000-0001-8625-4658, University of North Carolina at Chapel Hill, corresponding author), Isabella C C von Holstein (0000-0002-2538-8457, Vrije Universiteit Amsterdam), J E Laffoon (0000-0002-8821-805X, Leiden University), M Willmes (0000-0001-8734-5324, University of California, Davis), Xiaoming Liu (0000-0001-7904-1056, University of North Carolina at Chapel Hill), Xiao-Ming Liu, G R Davies (0000-0002-6136-9202, Vrije Universiteit Amsterdam)
EditorsJennifer Cotton
Year2018
Volume13
Issue5
Pagese0197386
Publication date2018-05-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePLoS ONE (JOURNAL)
Journal identifiersISSN: 1932-6203 • E-ISSN: 1932-6203
PublisherPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pone.0197386
PMID29847595
OpenAlexW2805524315
LanguageEN
Citations received57
References cited56

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.

Biology · Cartography · Geography · Geolocation · Geospatial analysis · Geostatistics · Kriging · Machine learning · Physical geography · Random forest · Regression · Regression analysis · Remote sensing · Scale (ratio) · Spatial analysis · Spatial ecology · Spatial variability · Statistics · Archaeology and ancient environmental studies · Computer Science · Ecology · Environmental Science · Geology and Paleoclimatology Research · Isotope Analysis in Ecology · Mathematics

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