Incorporating uncertainty and prior information into stable isotope mixing models

TLDR

Stable isotope mixing models estimate source contributions to a mixture, but uncertainty is often substantial and has not yet been fully incorporated. The study develops a Bayesian mixing model that estimates probability distributions of source contributions while explicitly accounting for uncertainty in sources, fractionation, and isotope signatures. The model allows optional incorporation of informative prior information and is demonstrated on a predator–prey case study. Accounting for uncertainty alters the variability, magnitude, and rank order of prey contribution estimates, underscoring the need to fully incorporate uncertainty for accurate source estimation.

Abstract

Abstract Stable isotopes are a powerful tool for ecologists, often used to assess contributions of different sources to a mixture (e.g. prey to a consumer). Mixing models use stable isotope data to estimate the contribution of sources to a mixture. Uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. We developed a Bayesian‐mixing model that estimates probability distributions of source contributions to a mixture while explicitly accounting for uncertainty associated with multiple sources, fractionation and isotope signatures. This model also allows for optional incorporation of informative prior information in analyses. We demonstrate our model using a predator–prey case study. Accounting for uncertainty in mixing model inputs can change the variability, magnitude and rank order of estimates of prey (source) contributions to the predator (mixture). Isotope mixing models need to fully account for uncertainty in order to accurately estimate source contributions.