I’ll be giving a shortened version of my Bayesian stable isotope mixing model talk (title and abstract below) at the Albuquerque Chapter of the American Statistical Association (ACASA) annual meeting on Friday, April 29, 2011. I gave two distinct longer versions of this talk recently as part of job interview talks at St. Louis University and the University of New Mexico. I’m looking forward to the meeting to visit with people who I’ve worked with over the last several years, organizing judging events at science fairs, and other events.
A Bayesian Framework for Stable Isotope Mixing Models
Erik B. Erhardt, The Mind Research Network; Edward J. Bedrick, Division of Epidemiology and Biostatistics, University of New Mexico Health Sciences Center
Stable isotope sourcing is used to estimate proportional contributions of sources to a mixture, such as in the analysis of animal diets and plant nutrient use. Statistical methods for inference on the diet proportions using stable isotopes have focused on the linear mixing model. Existing frequentist methods provide inferences when the diet proportion vector can be uniquely solved for in terms of the isotope ratios. Bayesian methods apply for arbitrary numbers of isotopes and diet sources but existing models are somewhat limited as they assume that trophic fractionation or discrimination are estimated without error or that isotope ratios are uncorrelated. We present a Bayesian model for the estimation of mean diet that accounts for uncertainty in source means and discrimination and allows correlated isotope ratios. This model is easily extended to allow the diet proportion vector to depend on covariates, such as time. Two examples are used to illustrate the methodology.