A Bayesian stable isotope mixing model for coping with multiple isotopes, multiple trophic steps and small sample sizes

We introduce a Bayesian stable isotope mixing model for estimating the relative contributions of different dietary components to tissues consumers within food webs. The is implemented with probabilistic programming language Stan. incorporates isotopes multiple elements (e.g. C, N, H) two trophic levels, when structure web known. In addition, allows inclusion latent levels (i.e. which no empirical data are available) intermediate between sources and measured consumers. Running in simulations driven by real dataset from Finnish lakes, we tested sensitivity posterior distributions altering critical prior parameters assumptions data-generating process. Importantly, found that estimations were particularly sensitive assigned value ω (the fraction H aquatic consumer tissue derived environmental water rather than diet) so reliable this parameter required. When information not available ω, suggest an uninformative should be used. proposed inferences suitable studies where resources collecting new limited, but useful each specific level earlier studies.