Clément P. Bataille

Mapping multiple source effects on the strontium isotopic signatures of ecosystems from the circum-Caribbean region

A method for mapping strontium isotope ratio (87Sr/86Sr) variations in bedrock and water has been recently developed for use in the interpretation of 87Sr/86Sr datasets for provenance studies. The mapping process adopted the simplifying assumption that strontium (Sr) comes exclusively from weathering of the underlying bedrock. The scope of this bedrock-only mapping method is thus limited to systems where the contributions of other sources of Sr are minimal. In this paper, we build on this 87Sr/86Sr mapping method by developing a mixing model of Sr fluxes from multiple sources to the bioavailable Sr pool. The new multiple source model includes: (1) quantitative calculations of Sr fluxes from bedrock weathering using an empirical rock weathering model; and (2) addition of sub-models calculating the contribution of Sr fluxes from atmospheric aerosols based on outputs from global climate model simulations. We compared the performance of the new multiple source model and the bedrock-only mapping method in predicting observed values from two datasets of bioavailable 87Sr/86Sr from the circum-Caribbean region (Antilles and Mesoamerica). Although the bedrock-only method performs relatively well in Mesoamerica (n = 99, MAE = 0.00011, RMSE = 0.00073), its prediction accuracy is lower for the Antillean dataset (n = 287, MAE = 0.0021, RMSE = 0.0027). In comparison, the new multiple source model, which accounts for the deposition of sea salt and mineral dust aerosols, performs comparably well in predicting the observed 87Sr/86Sr values in both datasets (MAE = 0.00040, RMSE = 0.00087 and MAE = 0.00014, RMSE = 0.0010). This study underscores the potential of using process-oriented spatial modeling to improve the predictive power of Sr isoscapes over large spatial scales and to refine sampling strategies and bioavailable Sr dataset interpretations for provenance studies.

Continental igneous rock composition: A major control of past global chemical weathering

Changes in the isotopic composition of the continental crust control the strontium isotope ratio in seawater. The composition of igneous rocks in the continental crust has changed throughout Earth’s history. However, the impact of these compositional variations on chemical weathering, and by extension on seawater and atmosphere evolution, is largely unknown. We use the strontium isotope ratio in seawater [(87Sr/86Sr)seawater] as a proxy for chemical weathering, and we test the sensitivity of (87Sr/86Sr)seawater variations to the strontium isotopic composition (87Sr/86Sr) in igneous rocks generated through time. We demonstrate that the 87Sr/86Sr ratio in igneous rocks is correlated to the epsilon hafnium (εHf) of their hosted zircon grains, and we use the detrital zircon record to reconstruct the evolution of the 87Sr/86Sr ratio in zircon-bearing igneous rocks. The reconstructed 87Sr/86Sr variations in igneous rocks are strongly correlated with the (87Sr/86Sr)seawater variations over the last 1000 million years, suggesting a direct control of the isotopic composition of silicic magmatism on (87Sr/86Sr)seawater variations. The correlation decreases during several time periods, likely reflecting changes in the chemical weathering rate associated with paleogeographic, climatic, or tectonic events. We argue that for most of the last 1000 million years, the (87Sr/86Sr)seawater variations are responding to changes in the isotopic composition of silicic magmatism rather than to changes in the global chemical weathering rate. We conclude that the (87Sr/86Sr)seawater variations are of limited utility to reconstruct changes in the global chemical weathering rate in deep times.

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

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.