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Bayesian stable isotope mixing models
Author(s) -
Parnell Andrew C.,
Phillips Donald L.,
Bearhop Stuart,
Semmens Brice X.,
Ward Eric J.,
Moore Jonathan W.,
Jackson Andrew L.,
Grey Jonathan,
Kelly David J.,
Inger Richard
Publication year - 2013
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2221
Subject(s) - compositional data , multivariate statistics , smoothing , mixing (physics) , bayesian probability , mixture model , range (aeronautics) , statistical model , computer science , parametric statistics , econometrics , univariate , model selection , statistics , mathematics , physics , materials science , quantum mechanics , composite material
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 © 2013 John Wiley & Sons, Ltd.