Invited review article
Advances in global bioavailable strontium isoscapes

https://doi.org/10.1016/j.palaeo.2020.109849Get rights and content

Highlights

  • We review existing methods to generate 87Sr/86Sr isoscapes.

  • We compile ~17,000 published and newly generated global bioavailable 87Sr/86Sr.

  • We generate and test a global bioavailable 87Sr/86Sr isoscape using random forest.

  • The global 87Sr/86Sr isoscape explains more than 60% of the variance.

  • We highlight potential research directions and remaining knowledge gaps.

Abstract

Strontium isotope ratios (87Sr/86Sr) are a popular tool in provenance applications in archeology, forensics, paleoecology, and environmental sciences. Using bioavailable 87Sr/86Sr in provenance studies requires comparing the 87Sr/86Sr of a sample of interest to that of 87Sr/86Sr baselines. Historically, these baselines required building empirical datasets from plants or local animals to characterize the 87Sr/86Sr available to local ecosystems (bioavailable 87Sr/86Sr). However, researchers are increasingly relying on modeled bioavailable 87Sr/86Sr maps (called isoscapes). We review the advantages and limitations of existing approaches to mapping bioavailable 87Sr/86Sr for provenance studies and propose a globally applicable, scalable, and editable framework for creating bioavailable 87Sr/86Sr isoscapes. This framework relies on: 1) Compiling global bioavailable 87Sr/86Sr data; 2) Mapping 87Sr/86Sr variability in rocks; 3) Leveraging global environmental covariates; and 4) Applying a random forest regression method that integrates these data to predict bioavailable 87Sr/86Sr. When the random-forest model is applied at the global scale it performs well (explaining 60% of the variance of the global bioavailable 87Sr/86Sr dataset), and accounts for geological, geomorphological and atmospheric controls. In data-rich regions (e.g., Europe), the global bioavailable 87Sr/86Sr isoscape can be successfully extrapolated to broad regions without bioavailable 87Sr/86Sr data. However, we also show that this extrapolation may not be valid in exceptionally geologically complex and data-poor regions (e.g., Madagascar). We suggest research directions to improve the accuracy of global bioavailable 87Sr/86Sr isoscapes, which include: 1) Increasing the collection of bioavailable datasets in data-poor regions; 2) Harmonizing data management practices and metadata collection for bioavailable 87Sr/86Sr data; and 3) Relying on advances in remote sensing and geological mapping techniques to improve geological covariates. While significant potential to refine 87Sr/86Sr isoscapes remains, the data products provided in this review form a basis for using 87Sr/86Sr data in large-scale provenance studies, opening new research avenues in a range of fields.

Introduction

Strontium (Sr) isotope ratios (87Sr/86Sr) display a unique and predictable patterns of variability on the Earth's surface that follow the geological age and lithology of bedrocks (Bataille and Bowen, 2012). As rocks interact with the hydrosphere, atmosphere and biosphere, bedrock Sr is transferred to other reservoirs on the Earth's surface, such as soils and plants. Geologists have long recognized and capitalized on this natural 87Sr/86Sr variability to trace the provenance of geological materials (Reviewed in Banner, 2004; Capo et al., 1998; Peucker-Ehrenbrink and Fiske, 2019). In the last few decades, researchers have also recognized the potential for 87Sr/86Sr data to solve new questions in ecology, paleoecology, and archeology (Reviewed in Åberg, 1995; Bentley, 2006; Crowley et al., 2017a; Hobson et al., 2010; Makarewicz and Sealy, 2015). This uptick of interest in 87Sr/86Sr geochemistry has coincided with analytical advances and the development of multi-collector inductively coupled plasma mass spectrometers (MC-ICPMS). This instrumentation and its greater global availability has made 87Sr/86Sr analysis more mainstream by accelerating throughput and enhancing cost-effectiveness while also facilitating the development of new applications in the life sciences such as laser ablation of incrementally growing tissues (e.g., fin rays and otoliths; Brennan et al., 2015b; Willmes et al., 2016). With these advances, 87Sr/86Sr geochemistry has become a critical tool for tracing the mobility and/or geographic origin of biological material in ecology (Reviewed in Hobson et al., 2010), paleoecology (Reviewed in Crowley et al., 2017a), archeology (Reviewed in Bentley, 2006), forensic sciences (Reviewed in Makarewicz and Sealy, 2015), and food sciences (Reviewed in Coelho et al., 2017). All of these applications rely on comparing the 87Sr/86Sr of a given substrate with the isotopic signatures of its potential sources. To facilitate the interpretation of 87Sr/86Sr data in these applications, it is critical to constrain the spatial variability of 87Sr/86Sr in the geosphere, hydrosphere and biosphere.

Isoscapes are spatially explicit predictions of isotopic variations. These predictions can be produced either through geostatistical interpolation of observed isotopic data, or through mechanistic model based on first principles of isotope geochemistry (Bowen and West, 2008). Over the last few decades, isoscapes of hydrogen, carbon, oxygen, and nitrogen have been developed, building upon the growing number of isotopic observations (Bowen and Wilkinson, 2002; Still and Powell, 2010; West et al., 2010a). These isoscapes have become a routine tool to understand movement patterns of animals and humans and environmental and biological processes (West et al., 2010b). Isoscape science has recently contributed to research on many high-profile science questions, from partitioning the global hydrological cycle (Good et al., 2015) to assessing the population dynamics of critical species (Brennan et al., 2019). As such, the field of isotope provenancing is rapidly expanding and entering the realm of data science, for example through large initiatives to integrate relevant data in centralized repositories (Pauli et al., 2017), and community efforts to make modeling products widely accessible (Bowen et al., 2014). Historically, interest in, and development of, Sr isoscapes has lagged hydrogen, oxygen or carbon isotopic systems. The primary reasons are that 87Sr/86Sr analysis is challenging, relatively expensive, and relies on instrumentation that is not as widely available as that needed for conducting light stable isotope analyses. However, 87Sr/86Sr analyses have progressively emerged as a powerful complementary tool in provenance studies due to their unique spatial patterns of isotopic variability, with pioneering work having been conducted in archeology (Ezzo et al., 1997; Price et al., 1994; Sillen et al., 1998), paleoecology (Hoppe et al., 1999), ecology (Chamberlain et al., 1997; Kennedy et al., 2000, Kennedy et al., 2002; Koch et al., 1995a, Koch et al., 1995b; Thorrold and Shuttleworth, 2000), and ecosystem dynamics (Blum et al., 2000; Gosz et al., 1983). In the last decade, the development and application of 87Sr/86Sr isoscapes has grown exponentially, driven by high-profile applications in archeology (e.g., Copeland et al., 2011), paleoecology (e.g., Price et al., 2017), ecology (e.g., Brennan et al., 2019; Glassburn et al., 2018), and forensic science (e.g., Bartelink and Chesson, 2019; Kramer et al., 2020).

This review synthesizes the current state of the rapidly evolving and interdisciplinary research associated with 87Sr/86Sr isoscapes. We begin by reviewing spatial 87Sr/86Sr trends on the Earth surface with a focus on large-scale patterns derived from the interactions of the geosphere, hydrosphere, atmosphere and biosphere. We then compare different approaches for making 87Sr/86Sr isoscapes in terrestrial and freshwater environments. In an effort to better integrate interdisciplinary 87Sr/86Sr data, we present the first global compilation of 87Sr/86Sr data from different environmental substrates. We use this compilation to produce a global model for predicting bioavailable 87Sr/86Sr and demonstrate the potential of using this approach to generate 87Sr/86Sr isoscapes at the regional scale in two regions: Europe and Madagascar. We conclude by discussing key knowledge gaps and new research avenues opened by this global data science approach.

Section snippets

Strontium isotopes geochemistry

Strontium is a divalent alkaline earth trace element with four naturally occurring isotopes: 84Sr (~0.56%), 86Sr (~9.87%), 87Sr (~7.04%) and 88Sr (~82.53%). 84Sr, 86Sr, 87Sr and 88Sr are all stable isotopes (i.e., do not radioactively decay). Unlike the other Sr isotopes, 87Sr is the radiogenic daughter product of rubidium 87 (87Rb; decay constant λ = 1.42 × 10–11 year−1; Steiger and Jäger, 1977). The ratio of 87Sr to the other isotopes is therefore a function of the variable abundance of 87Sr.

Empirical isoscapes

The most commonly used strategy to create biologically available (bioavailable) 87Sr/86Sr baselines is to analyze 87Sr/86Sr in a series of biological samples representing bioavailable Sr pools in a study area (Lengfelder et al., 2019). However, as outlined above, different substrates (e.g., soils, plants, animals, or waters) can integrate 87Sr/86Sr at different spatiotemporal scales depending on the local geo-environmental conditions. Thus, identifying appropriate substrates can be challenging.

Compilation description

To date, no researchers have attempted global bioavailable 87Sr/86Sr modeling. Here we leverage a new global data compilation, mechanistic models, and auxiliary variables integrated in a multivariate random forest regression framework to predict bioavailable 87Sr/86Sr at the global scale. The dataset used in our study is a compilation of 17,240 published and unpublished 87Sr/86Sr analyses from 278 individual studies spanning 8476 individual locations across the globe (Fig. 2). Unpublished data

Regional dataset

We tested the performance of the model in two regions with different geological settings and sampling density to provide guidance on how to use this global bioavailable 87Sr/86Sr isoscape. First, we used data collected through the GEMAS project (Hoogewerff et al., 2019) to test the performance of the global model in a data-rich region. To date, GEMAS is the most systematic and comprehensive continental-scale dataset of bioavailable 87Sr/86Sr. The dataset includes close to 1200 soil samples from

Guidelines for using the global isoscape

Provenance studies have underlying assumptions specific to the sample type and the question being addressed. The predicted bioavailable 87Sr/86Sr isoscape presented here (Fig. 9) is best suited as a broad scale approach for 1) excluding provenance areas and 2) informing where targeted sampling for a specific research question should occur. When samples in question exhibit a limited range in bioavailable 87Sr/86Sr, as is the case for plants, soils, and animals with small feeding ranges (e.g.,

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This paper is dedicated to the memory of Dr. Sean Brennan who sadly passed away in January 2020. Dr. Brennan was a stellar young scientist, and had considerably advanced the Sr isoscape field. His work and his passion will be dearly missed. This work was supported in part by NSERC Discovery Grant RGPIN-2019-05709 (to C.P·B). B.E.C was supported by the UC faculty startup. G.J.B was supported by NSF DBI-1759730. We thank Tom Johnson and Gideon Bartov for analyzing specimens at the University of

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