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Bayesian stable isotope mixing models
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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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
General methods for monitoring convergence of iterative simulations
Brooks, S. P., & Gelman, A. (1998) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1998.10474787 · Cited by 6,045
The Statistical Analysis of Compositional Data
Aitchison, J. (1986) · doi:10.1007/978-94-009-4109-0 · Cited by 4,383
Compositional Data Analysis: Theory and Applications
(2011) · Wiley eBooks · doi:10.1002/9781119976462 · Cited by 704 · cited 2×
“It is209 also not sub-compositionally coherent (see Pawlowsky-Glahn and Buccianti, 2011, for further discussion of210 these terms).”
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Cited by
Dietary adjustability of grizzly bears and American black bears in Yellowstone National Park
Fortin, J. K., Schwartz, C. C., Gunther, K. A., Teisberg, J. E., Haroldson, M. A., Evans, M. A., & Robbins, C. T. (2013) · The Journal of Wildlife Management · doi:10.1002/jwmg.483 · Cited by 140
Wißing, C., Rougier, H., Baumann, C., Comeyne, A., Crevecoeur, I., Drucker, D. G., Gaudzinski-Windheuser, S., Germonpré, M., Gómez-Olivencia, A., Krause, J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-41033-3 · Cited by 89
Terrestrial–aquatic trophic linkages support fish production in a tropical oligotrophic river
Correa, S. B., & Winemiller, K. (2018) · Oecologia · doi:10.1007/s00442-018-4093-7 · Cited by 75
Reintroduced bison diet changes throughout the season in restored prairie
Blackburn, R. C., Barber, N. A., & Jones, H. P. (2021) · Restoration Ecology · doi:10.1111/rec.13161 · Cited by 16
Waiki, S. M. P., Colbert, S. L., Wiegner, T. N., Puniwai, N., Nakoa, III, Storie, N. M., Nelson, C. E., Overly, A. N., McDermid, K. J., & Aguiar, D. K. (2025) · Journal of Hydrology: Regional Studies · doi:10.1016/j.ejrh.2024.102122 · Cited by 3
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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- 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. (2013). Bayesian stable isotope mixing models. Environmetrics. https://doi.org/10.1002/env.2221
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