Andrew C. Parnell

TL;DR: 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.

TL;DR: The ellipses are unbiased with respect to sample size, and their estimation via Bayesian inference allows robust comparison to be made among data sets comprising different sample sizes, which opens up more avenues for direct comparison of isotopic niches across communities.

TL;DR: Stable isotope mixing models are increasingly used to quantify consumer diets, but may be misused and misinter- preted, and major challenges to their effective application are addressed.

TL;DR: In this article, stable isotope mixing models (SIMMs) are used to quantify the proportional contributions of various sources to a mixture, and a compositional component of the model is based on the isometric log ratio (ilr) transform of Egozcue (2003).

TL;DR: Through MixSIAR, an inclusive, rich, and flexible Bayesian tracer mixing model framework implemented as an open-source R package, the disparate array of mixing model tools are consolidated into a single platform, diversified the set of available parameterizations, and provided developers a platform upon which to continue improving mixing model analyses in the future.