Evaluating Bayesian stable isotope mixing models of wild animal diet and the effects of trophic discrimination factors and informative priors

George J.F. Swan, Stuart Bearhop, Steve M. Redpath, Matthew J. Silk, Cecily E.D. Goodwin, Richard Inger, Robbie A. McDonald*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)
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Ecologists quantify animal diets using direct and indirect methods, including analysis of faeces, pellets, prey items and gut contents. For stable isotope analyses of diet, Bayesian stable isotope mixing models (BSIMMs) are increasingly used to infer the relative importance of food sources to consumers. Although a powerful approach, it has been hard to test BSIMM performance for wild animals because precise, direct dietary data are difficult to collect. We evaluated the performance of BSIMMs in quantifying animal diets when using δ13C and δ15N stable isotope ratios from the feathers and red blood cells of common buzzard Buteo buteo chicks. We analysed mixing model outcomes with various trophic discrimination factors (TDFs), with and without informative priors, and compared these to direct observations of prey provisioned to chicks by adults at nests, using remote cameras. Although BSIMMs with different TDFs varied markedly in their performance, the statistical package SIDER generated TDFs for both feathers and blood that resulted in model outputs that accorded well with direct observations of prey provisioning. Using feather TDFs derived from captive peregrines Falco peregrinus resulted in estimates of diet composition that were also similar to provisioned prey, although blood TDFs from the same study performed poorly. The inclusion of informative priors, based on conventional analysis of pellet and prey remains, markedly reduced model performance. BSIMMs can provide accurate assessments of diet in wild animals. TDF estimates from the SIDER package performed well. The inclusion of informative priors from conventional methods in Bayesian mixing models can transfer biases into model outcomes, leading to erroneous results.

Original languageEnglish
Pages (from-to)139-149
Number of pages11
JournalMethods in Ecology and Evolution
Issue number1
Early online date22 Oct 2019
Publication statusPublished - 1 Jan 2020

Bibliographical note

Funding information University of Exeter; CONICYT, Grant/ Award Number: 3190800; ERC, Grant/ Award Number: 310820


  • animal diet
  • Bayesian mixing models
  • Bayesian stable isotope mixing models
  • informative priors
  • stable isotopes
  • trophic discrimination factors


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