Source partitioning using stable isotopes: coping with too much variation.

TLDR: This work outlines a framework that builds on recently published Bayesian isotopic mixing models and presents a new open source R package, SIAR, to allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.

Abstract: Background Stable isotope analysis is increasingly being utilised across broad areas of ecology and biology. Key to much of this work is the use of mixing models to estimate the proportion of sources contributing to a mixture such as in diet estimation. Methodology By accurately reflecting natural variation and uncertainty to generate robust probability estimates of source proportions, the application of Bayesian methods to stable isotope mixing models promises to enable researchers to address an array of new questions, and approach current questions with greater insight and honesty. Conclusions We outline a framework that builds on recently published Bayesian isotopic mixing models and present a new open source R package, SIAR. The formulation in R will allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.