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Andrew C. Parnell
Researcher at Maynooth University
Publications - 171
Citations - 11077
Andrew C. Parnell is an academic researcher from Maynooth University. The author has contributed to research in topics: Computer science & Bayesian probability. The author has an hindex of 33, co-authored 138 publications receiving 8677 citations. Previous affiliations of Andrew C. Parnell include University College Dublin & Dublin City University.
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Source partitioning using stable isotopes: coping with too much variation.
TL;DR: This work outlines a framework that builds on recently published Bayesian isotopic mixing models and presents a new open source R package, SIAR, to allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.
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Comparing isotopic niche widths among and within communities: SIBER - Stable Isotope Bayesian Ellipses in R.
TL;DR: The ellipses are unbiased with respect to sample size, and their estimation via Bayesian inference allows robust comparison to be made among data sets comprising different sample sizes, which opens up more avenues for direct comparison of isotopic niches across communities.
Journal ArticleDOI
Best practices for use of stable isotope mixing models in food-web studies
Donald L. Phillips,Richard Inger,Stuart Bearhop,Andrew L. Jackson,Jonathan W. Moore,Andrew C. Parnell,Brice X. Semmens,Eric J. Ward +7 more
TL;DR: Stable isotope mixing models are increasingly used to quantify consumer diets, but may be misused and misinter- preted, and major challenges to their effective application are addressed.
Posted Content
Bayesian Stable Isotope Mixing Models
Andrew C. Parnell,Donald L. Phillips,Stuart Bearhop,Brice X. Semmens,Eric J. Ward,Jonathan W. Moore,Andrew L. Jackson,Richard Inger +7 more
TL;DR: In this article, stable isotope mixing models (SIMMs) are used to quantify the proportional contributions of various sources to a mixture, and a compositional component of the model is based on the isometric log ratio (ilr) transform of Egozcue (2003).
Journal ArticleDOI
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 +5 more
TL;DR: Through MixSIAR, an inclusive, rich, and flexible Bayesian tracer mixing model framework implemented as an open-source R package, the disparate array of mixing model tools are consolidated 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.