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

PeerJ

article

Brian C. Stock¹  Andrew L. Jackson²  Eric J. Ward³  Andrew C. Parnell⁴  Donald L. Phillips⁵  Brice X. Semmens¹ 

[1] Scripps Institution of Oceanography, University of California;Department of Zoology, School of Natural Sciences, University of Dublin, Trinity College;Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration;School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin;EcoIsoMix.com

关键词: Stable isotopes;    Mixing models;    Fatty acids;    Trophic ecology;    Bayesian statistics;    MixSIR;    SIAR;   

DOI  :  10.7717/peerj.5096

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY