Bayesian stable isotope mixing models
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 +9 more
TLDR
This paper proposes a compositional mixture of the food sources corrected for various metabolic factors based on the isometric log‐ratio transform, which can apply a range of time series and non‐parametric smoothing relationships.Abstract:
In this paper, we review recent advances in stable isotope mixing models (SIMMs) and place them into an overarching Bayesian statistical framework, which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantifying the diet of organisms based on the food sources they have been observed to consume. At the centre of the multivariate statistical model we propose is a compositional mixture of the food sources corrected for various metabolic factors. The compositional component of our model is based on the isometric log-ratio transform. Through this transform, we can apply a range of time series and non-parametric smoothing relationships. We illustrate our models with three case studies based on real animal dietary behaviour.read more
Citations
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Best practices for use of stable isotope mixing models in food-web studies
Donald L. Phillips,Richard Inger,Stuart Bearhop,Andrew L. Jackson,Jonathan W. Moore,Andrew C. Parnell,Brice X. Semmens,Eric J. Ward +7 more
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.
Journal ArticleDOI
Analyzing mixing systems using a new generation of Bayesian tracer mixing models.
Brian C. Stock,Andrew L. Jackson,Eric J. Ward,Andrew C. Parnell,Donald L. Phillips,Brice X. Semmens +5 more
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.
Journal ArticleDOI
Unifying error structures in commonly used biotracer mixing models
Brian C. Stock,Brice X. Semmens +1 more
TL;DR: A new parameterization is introduced that unifies these mixing model error structures, as well as implicitly estimates the rate at which consumers sample from source populations (i.e., consumption rate) and outperforms existing models and provides an estimate of consumption.
Journal ArticleDOI
Reviews and syntheses: Isotopic approaches to quantify root water uptake: a review and comparison of methods
TL;DR: In this article, different methods used for locating/quantifying relative contributions of water sources to RWU (i.e., graphical inference, statistical (e.g., Bayesian) multi-source linear mixing models) are reviewed with emphasis on their respective advantages and drawbacks.
References
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