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Cited by 1,091

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Bayesian stable isotope mixing models

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·2013·Environmetrics

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 © 2013 John Wiley & Sons, Ltd.

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Crossref · 10.1002/env.2221

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Metadata

Authors
Parnell, 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.
Venue
Environmetrics
Publisher
Wiley
Published
2013-07-19
Volume
24
Issue
6
Pages
387-399
ISSN
  • 1099-095X (ISSN-L)
  • 1180-4009
OpenAlex
W2592128209
Cited by
1,091
References
4
FWCI
26.19
Type
journal article
Language
en
Open access
closed

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