Resource polygon geometry predicts Bayesian stable isotope mixing model bias

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 514:1-12 (2014) - DOI: https://doi.org/10.3354/meps11017 FEATURE ARTICLE Resource polygon geometry predicts Bayesian stable isotope mixing model bias Michael T. Brett* Civil & Environmental Engineering, University of Washington, Seattle, Washington 98105, USA *Corresponding author: mtbrett@uw.edu ABSTRACT: Bayesian mixing model analyses of resource and consumer stable isotope composition are commonly used to infer elemental, energetic, and trophic pathways in aquatic and terrestrial food webs. However, the outputs of these models may be biased towards prior or null generalist assumptions, but the magnitude of this potential bias is unknown. I conducted a series of experiments to determine how this bias is affected by the geometry and end-member uncertainty of resource polygons. These experiments showed that bias is mostly due to isotopic overlap between resources and is very strongly correlated in a sigmoid manner with the normalized surface area of stable isotope resource polygons. The normalized surface area, a classic signal to noise ratio in bivariate space, is calculated by scaling the x and y ordinates by the mean standard deviations (SD) for δ13C and δ15N, respectively. When equilateral 3-resource polygons have a surface area <3.4 SD2, the outputs of Bayesian mixing models primarily reflect the prior generalist assumption. The back-calculated bias for 85 recently published triangular polygons averaged 50 ± 28% (± SD). Analyses of regular resource polygons with 4 to 6 resources required 3.1 to 8.0 times larger normalized surface areas to constrain bias. Furthermore, polygons with 4 or more resources gave poor outcomes for minor diet components. There was a strong bias for resources similar, and against resources dissimilar, to the dominant resource. Overall, Bayesian methods applied to underdetermined models and poorly resolved data very often give results that are highly biased towards centrist and generalist solutions. KEY WORDS: Bayesian mixing models · Stable isotopes · Trophic pathways · MixSIR · SIAR Full text in pdf format Information about this Feature Article Supplementary material NextCite this article as: Brett MT (2014) Resource polygon geometry predicts Bayesian stable isotope mixing model bias. Mar Ecol Prog Ser 514:1-12. https://doi.org/10.3354/meps11017 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 514. Online publication date: November 06, 2014 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2014 Inter-Research.