Calibration chain transformation improves the comparability of organic hydrogen and oxygen stable isotope data
Calibration chain transformation improves the comparability of organic hydrogen and oxygen stable isotope data
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DOI：
10.1111/2041-210x.13556
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发表时间：
2021-02-07
影响因子：
6.6
通讯作者：
Bowen, Gabriel J.
中科院分区：
文献类型：
--
作者：
Magozzi, Sarah;Bataille, Clement P.;Bowen, Gabriel J.
动物组织的稳定氢和氧同位素组成（分别为δ H-2和δ O-18）已被用于推断地理来源或移动性，前提是组织的同位素组成与当地水源的同位素组成系统相关。需要已知来源样品的同位素数据来量化这些组织-环境关系。虽然许多这样的数据已经公布，并可以重复使用的研究人员，用于校准和分析程序的不同datasets.We开发的算法，使用从二级标准的比较分析的结果，这些数据的可比性限制了标准的差异转换数据之间的参考尺度和估计这些转换中固有的不确定性。我们将该算法应用于过去20年来发表的已知来源的角蛋白数据的汇编。我们表明，转换提高了来自不同实验室的数据的可比性，转换后的数据表明，不同动物类群和类群之间的角蛋白-水关系存在有生态生理意义的差异。汇编的数据和算法可在ASSIGNR r-该软件包支持地理来源研究，并更普遍地提供了一种克服地球化学数据集成和再利用中若干挑战的方法。
Stable hydrogen and oxygen isotopic compositions (delta H-2 and delta O-18, respectively) of animal tissues have been used to infer geographical origin or mobility based on the premise that the isotopic composition of tissue is systematically related to that of local water sources. Isotopic data for known-origin samples are required to quantify these tissue-environment relationships. Although many of such data have been published and could be reused by researchers, differences in the standards used for calibration and analytical procedures for different datasets limit the comparability of these data.We develop an algorithm that uses results from comparative analysis of secondary standards to transform data among reference scales and estimate the uncertainty inherent in these transformations. We apply the algorithm to a compilation of known-origin keratin data published over the past similar to 20 years.We show that transformation improves the comparability of data from different laboratories, and that the transformed data suggest ecophysiologically meaningful differences in keratin-water relationships among different animal groups and taxa.The compiled data and algorithms are freely available in the ASSIGNR r-package to support geographical provenance research, and more generally offer a methodology overcoming several challenges in geochemical data integration and reuse.