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Analyzing mixing systems using a new generation of Bayesian tracer mixing models

The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and flexible Bayesian tracer (e.g., stable isotope) mixing model...

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Autores principales: Stock, Brian C., Jackson, Andrew L., Ward, Eric J., Parnell, Andrew C., Phillips, Donald L., Semmens, Brice X.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6015753/
https://www.ncbi.nlm.nih.gov/pubmed/29942712
http://dx.doi.org/10.7717/peerj.5096
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author Stock, Brian C.
Jackson, Andrew L.
Ward, Eric J.
Parnell, Andrew C.
Phillips, Donald L.
Semmens, Brice X.
author_facet Stock, Brian C.
Jackson, Andrew L.
Ward, Eric J.
Parnell, Andrew C.
Phillips, Donald L.
Semmens, Brice X.
author_sort Stock, Brian C.
collection PubMed
description The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and flexible Bayesian tracer (e.g., stable isotope) mixing model framework implemented as an open-source R package. Using MixSIAR as a foundation, we provide guidance for the implementation of mixing model analyses. We begin by outlining the practical differences between mixture data error structure formulations and relate these error structures to common mixing model study designs in ecology. Because Bayesian mixing models afford the option to specify informative priors on source proportion contributions, we outline methods for establishing prior distributions and discuss the influence of prior specification on model outputs. We also discuss the options available for source data inputs (raw data versus summary statistics) and provide guidance for combining sources. We then describe a key advantage of MixSIAR over previous mixing model software—the ability to include fixed and random effects as covariates explaining variability in mixture proportions and calculate relative support for multiple models via information criteria. We present a case study of Alligator mississippiensis diet partitioning to demonstrate the power of this approach. Finally, we conclude with a discussion of limitations to mixing model applications. Through MixSIAR, we have consolidated the disparate array of mixing model tools into a single platform, diversified the set of available parameterizations, and provided developers a platform upon which to continue improving mixing model analyses in the future.
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spelling pubmed-60157532018-06-25 Analyzing mixing systems using a new generation of Bayesian tracer mixing models Stock, Brian C. Jackson, Andrew L. Ward, Eric J. Parnell, Andrew C. Phillips, Donald L. Semmens, Brice X. PeerJ Conservation Biology The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and flexible Bayesian tracer (e.g., stable isotope) mixing model framework implemented as an open-source R package. Using MixSIAR as a foundation, we provide guidance for the implementation of mixing model analyses. We begin by outlining the practical differences between mixture data error structure formulations and relate these error structures to common mixing model study designs in ecology. Because Bayesian mixing models afford the option to specify informative priors on source proportion contributions, we outline methods for establishing prior distributions and discuss the influence of prior specification on model outputs. We also discuss the options available for source data inputs (raw data versus summary statistics) and provide guidance for combining sources. We then describe a key advantage of MixSIAR over previous mixing model software—the ability to include fixed and random effects as covariates explaining variability in mixture proportions and calculate relative support for multiple models via information criteria. We present a case study of Alligator mississippiensis diet partitioning to demonstrate the power of this approach. Finally, we conclude with a discussion of limitations to mixing model applications. Through MixSIAR, we have consolidated the disparate array of mixing model tools into a single platform, diversified the set of available parameterizations, and provided developers a platform upon which to continue improving mixing model analyses in the future. PeerJ Inc. 2018-06-21 /pmc/articles/PMC6015753/ /pubmed/29942712 http://dx.doi.org/10.7717/peerj.5096 Text en © 2018 Stock et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Conservation Biology
Stock, Brian C.
Jackson, Andrew L.
Ward, Eric J.
Parnell, Andrew C.
Phillips, Donald L.
Semmens, Brice X.
Analyzing mixing systems using a new generation of Bayesian tracer mixing models
title Analyzing mixing systems using a new generation of Bayesian tracer mixing models
title_full Analyzing mixing systems using a new generation of Bayesian tracer mixing models
title_fullStr Analyzing mixing systems using a new generation of Bayesian tracer mixing models
title_full_unstemmed Analyzing mixing systems using a new generation of Bayesian tracer mixing models
title_short Analyzing mixing systems using a new generation of Bayesian tracer mixing models
title_sort analyzing mixing systems using a new generation of bayesian tracer mixing models
topic Conservation Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6015753/
https://www.ncbi.nlm.nih.gov/pubmed/29942712
http://dx.doi.org/10.7717/peerj.5096
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