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FRAME—Monte Carlo model for evaluation of the stable isotope mixing and fractionation

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  • Maciej P Lewicki
  • Dominika Lewicka-Szczebak
  • Grzegorz Skrzypek

Abstract

Bayesian stable isotope mixing models are widely used in geochemical and ecological studies for partitioning sources that contribute to various mixtures. However, none of the existing tools allows accounting for the influence of processes other than mixing, especially stable isotope fractionation. Bridging this gap, new software for the stable isotope Fractionation And Mixing Evaluation (FRAME) has been developed with a user-friendly graphical interface (malewick.github.io/frame). This calculation tool allows simultaneous sources partitioning and fractionation progress determination based on the stable isotope composition of sources/substrates and mixture/products. The mathematical algorithm applies the Markov-Chain Monte Carlo model to estimate the contribution of individual sources and processes, as well as the probability distributions of the calculated results. The performance of FRAME was comprehensively tested and practical applications of this modelling tool are presented with simple theoretical examples and stable isotope case studies for nitrates, nitrites, water and nitrous oxide. The open mathematical design, featuring custom distributions of source isotope signatures, allows for the implementation of additional processes that alternate the characteristics of the final mixture and its application for various range of studies.

Suggested Citation

  • Maciej P Lewicki & Dominika Lewicka-Szczebak & Grzegorz Skrzypek, 2022. "FRAME—Monte Carlo model for evaluation of the stable isotope mixing and fractionation," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-29, November.
    • Handle: RePEc:plo:pone00:0277204
    DOI: 10.1371/journal.pone.0277204
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    1. Andrew C. Parnell & Donald L. Phillips & Stuart Bearhop & Brice X. Semmens & Eric J. Ward & Jonathan W. Moore & Andrew L. Jackson & Jonathan Grey & David J. Kelly & Richard Inger, 2013. "Bayesian stable isotope mixing models," Environmetrics, John Wiley & Sons, Ltd., vol. 24(6), pages 387-399, September.
    2. Andrew C Parnell & Richard Inger & Stuart Bearhop & Andrew L Jackson, 2010. "Source Partitioning Using Stable Isotopes: Coping with Too Much Variation," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-5, March.
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