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Bayesian stable isotope mixing models

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Bayesian stable isotope mixing models. / Parnell, Andrew C; Phillips, Donald L; Bearhop, Stuart et al.
In: Environmetrics, Vol. 24, No. 6, 09.2013, p. 387-399.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Parnell, AC, Phillips, DL, Bearhop, S, Semmens, BX, Ward, EJ, Moore, JW, Jackson, AL, Grey, J, Kelly, DJ & Inger, R 2013, 'Bayesian stable isotope mixing models', Environmetrics, vol. 24, no. 6, pp. 387-399. https://doi.org/10.1002/env.2221

APA

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, 24(6), 387-399. https://doi.org/10.1002/env.2221

Vancouver

Parnell AC, Phillips DL, Bearhop S, Semmens BX, Ward EJ, Moore JW et al. Bayesian stable isotope mixing models. Environmetrics. 2013 Sept;24(6):387-399. doi: 10.1002/env.2221

Author

Parnell, Andrew C ; Phillips, Donald L ; Bearhop, Stuart et al. / Bayesian stable isotope mixing models. In: Environmetrics. 2013 ; Vol. 24, No. 6. pp. 387-399.

Bibtex

@article{c9bf9f639aef46c2a557ce09cdc5c037,
title = "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.",
keywords = "stable isotope analysis, mixing models, Bayesian hierarchical model, compositional data, time series",
author = "Parnell, {Andrew C} and Phillips, {Donald L} and Stuart Bearhop and Semmens, {Brice X} and Ward, {Eric J} and Moore, {Jonathan W} and Jackson, {Andrew L} and Jonathan Grey and Kelly, {David J.} and Richard Inger",
year = "2013",
month = sep,
doi = "10.1002/env.2221",
language = "English",
volume = "24",
pages = "387--399",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "6",

}

RIS

TY - JOUR

T1 - Bayesian stable isotope mixing models

AU - Parnell, Andrew C

AU - Phillips, Donald L

AU - Bearhop, Stuart

AU - Semmens, Brice X

AU - Ward, Eric J

AU - Moore, Jonathan W

AU - Jackson, Andrew L

AU - Grey, Jonathan

AU - Kelly, David J.

AU - Inger, Richard

PY - 2013/9

Y1 - 2013/9

N2 - 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.

AB - 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.

KW - stable isotope analysis

KW - mixing models

KW - Bayesian hierarchical model

KW - compositional data

KW - time series

U2 - 10.1002/env.2221

DO - 10.1002/env.2221

M3 - Journal article

VL - 24

SP - 387

EP - 399

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

IS - 6

ER -