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Bayesian stable isotope mixing models
Citation:
Parnell, A., D. Phillips, S. Bearhop, B. Semmens, E. Ward, J. Moore, A. Jackson, J. Gray, D. J. Kelly, AND R. Inger. Bayesian stable isotope mixing models. ENVIRONMETRICS. John Wiley & Sons, Ltd., Indianapolis, IN, 24(6):387-399, (2013).
Impact/Purpose:
A manuscript co-authored by an EPA scientist and government and academic colleagues in the U.S., Canada, United Kingdom, and Ireland provides a statistical description of new models that use stable isotope data to inform studies on “who eats who” in food webs. These models incorporate earlier stable isotope mixing models developed at EPA/NHEERL/WED that have been widely used for food web studies worldwide over the last decade. The new comprehensive Bayesian statistical framework provides both increased flexibility in model formulation and increased statistical rigor.
Description:
In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching 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 (ilr) transform of Egozcue et al. (2003). Through this transform we can apply a range of time series and non-parametric smoothing relationships. We illustrate our models with 3 case studies based on real animal dietary behaviour.