Original title: Analyzing mixing systems using a new generation of Bayesian tracer mixing models
A tracerA measurable signal, such as a stable isotope, used to help track the sources of material. is a measurable signal, such as a stable isotope, that can help identify where material came from. A mixing model uses those signals to estimate the shares of different sources in a mixture, such as the sources in an animal’s diet. MixSIAR is an open-source R package that brings different ways to set up these analyses into one framework. It can also test whether fixed or random factors help explain changes in source shares and compare competing models.
Imagine tasting a stew made from several ingredients. You can recognize clues from the ingredients, but the finished stew does not tell you exactly how much of each went in. In this paper, the stew is the mixture, the ingredient clues are tracers, and MixSIAR is a set of tools for estimating the ingredients’ shares and checking whether those shares vary with other factors.
Ecologists use tracers such as stable isotopes to estimate how much different sources contribute to a mixture, including an animal’s diet. MixSIAR brings several analysis choices into one open-source tool and lets researchers test whether factors such as group membership explain differences in source proportions.
The abstract does not report quantitative validation or show that MixSIAR is best for every mixing-model problem. It notes that mixing-model applications have limitations, but does not specify them in detail. The case study demonstrates the approach for alligator diet partitioning; the abstract does not establish how well it applies to every other setting.
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Analyzing mixing systems using a new generation of Bayesian tracer mixing models — Brian C. Stock, Andrew L. Jackson, Eric J. Ward et al. — PeerJ — doi:10.7717/peerj.5096
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper.