Bayesian stable isotope mixing models

• DOI: 10.1002/env.2221
• Publication Date: 2013-07-19T11:15:26Z
• Authors: 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
• Keywords: Compositional data; Smoothing; Univariate

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 a mixture. The most widely application is quantifying diet organisms based on food they have been observed consume. At centre multivariate model propose compositional mixture corrected metabolic factors. component our isometric log‐ratio transform. Through transform, can apply range time series non‐parametric smoothing relationships. We illustrate with three case studies real animal dietary behaviour. Copyright © 2013 John Wiley & Sons, Ltd.