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

Brian C Stock; Andrew L Jackson; Eric J Ward; Andrew C Parnell; Donald L Phillips; Brice X Semmens

doi:10.7287/peerj.preprints.26884v1

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

Brian C Stock ¹, Andrew L Jackson², Eric J Ward³, Andrew C Parnell⁴, Donald L Phillips⁵, Brice X Semmens ¹

1 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA

2 Department of Zoology, School of Natural Sciences, University of Dublin, Trinity College, Dublin, Ireland

3 Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA

4 School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

5 EcoIsoMix.com, Corvallis, OR, USA

DOI: 10.7287/peerj.preprints.26884v1

Published: 2018-04-24
Accepted: 2018-04-24

Subject Areas: Conservation Biology, Ecology, Ecosystem Science, Soil Science, Statistics
Keywords: stable isotopes, mixing models, fatty acids, trophic ecology, Bayesian statistics, MixSIR, SIAR

Copyright: © 2018 Stock et al.
Licence: This is an open access article distributed under the terms of the Creative Commons Attribution License, 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 Preprints) and either DOI or URL of the article must be cited.

Cite this article: Stock BC, Jackson AL, Ward EJ, Parnell AC, Phillips DL, Semmens BX. 2018. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ Preprints 6:e26884v1 https://doi.org/10.7287/peerj.preprints.26884v1

Abstract

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.