34
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references42

          • Record: found
          • Abstract: not found
          • Article: not found

          Best practices for use of stable isotope mixing models in food-web studies

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            PRIMARY CONSUMER δ13C AND δ15N AND THE TROPHIC POSITION OF AQUATIC CONSUMERS

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Combining sources in stable isotope mixing models: alternative methods.

              Stable isotope mixing models are often used to quantify source contributions to a mixture. Examples include pollution source identification; trophic web studies; analysis of water sources for soils, plants; or water bodies, and many others. A common problem is having too many sources to allow a unique solution. We discuss two alternative procedures for addressing this problem. One option is a priori to combine sources with similar signatures so the number of sources is small enough to provide a unique solution. Aggregation should be considered only when isotopic signatures of clustered sources are not significantly different, and sources are related so the combined source group has some functional significance. For example, in a food web analysis, lumping several species within a trophic guild allows more interpretable results than lumping disparate food sources, even if they have similar isotopic signatures. One result of combining mixing model sources is increased uncertainty of the combined end-member isotopic signatures and consequently the source contribution estimates; this effect can be quantified using the IsoError model (http://www.epa.gov/wed/pages/models/isotopes/isoerror1_04.htm). As an alternative to lumping sources before a mixing analysis, the IsoSource mixing model (http://www.epa.gov/wed/pages/models/isosource/isosource.htm) can be used to find all feasible solutions of source contributions consistent with isotopic mass balance. While ranges of feasible contributions for each individual source can often be quite broad, contributions from functionally related groups of sources can be summed a posteriori, producing a range of solutions for the aggregate source that may be considerably narrower. A paleo-human dietary analysis example illustrates this method, which involves a terrestrial meat food source, a combination of three terrestrial plant foods, and a combination of three marine foods. In this case, a posteriori aggregation of sources allowed strong conclusions about temporal shifts in marine versus terrestrial diets that would not have otherwise been discerned.
                Bookmark

                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                21 June 2018
                2018
                : 6
                : e5096
                Affiliations
                [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
                Author information
                http://orcid.org/0000-0002-2393-6747
                http://orcid.org/0000-0001-7334-0434
                http://orcid.org/0000-0002-4359-0296
                Article
                5096
                10.7717/peerj.5096
                6015753
                29942712
                5e87e91c-ba91-4e5c-afd9-848e13e17cfa
                © 2018 Stock et al.

                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) and either DOI or URL of the article must be cited.

                History
                : 23 April 2018
                : 5 June 2018
                Funding
                Funded by: Cooperative Institute for Marine Ecosystems and Climate (CIMEC)
                Funded by: Center for the Advancement of Population Assessment Methodology (CAPAM)
                Funded by: National Science Foundation Graduate Research Fellowship
                Award ID: DGE-1144086
                Funding was provided in part by the Cooperative Institute for Marine Ecosystems and Climate (CIMEC) and the Center for the Advancement of Population Assessment Methodology (CAPAM). Brian C. Stock received support from the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1144086. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Conservation Biology
                Ecology
                Ecosystem Science
                Soil Science
                Statistics

                stable isotopes,mixing models,fatty acids,trophic ecology,bayesian statistics,mixsir,siar

                Comments

                Comment on this article