Bayesian stable isotope mixing models

TitleBayesian stable isotope mixing models
Publication TypeJournal Article
Year of Publication2013
AuthorsParnell A.C, Phillips D.L, Bearhop S., Semmens B.X, Ward E.J, Moore J.W, Jackson A.L, Grey J., Kelly D.J, Inger R.
JournalEnvironmetrics
Volume24
Pagination387-399
Date Published2013/09
ISBN Number1180-4009
Accession NumberWOS:000325001200004
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. Copyright (c) 2013 John Wiley & Sons, Ltd.  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. Copyright (c) 2013 John Wiley & Sons, Ltd.

Short TitleEnvironmetrics
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