Evaluating the performance of the Bayesian mixing tool MixSIAR with fatty acid data for quantitative estimation of diet

Alicia I Guerrero,Tracey L Rogers

doi:10.1038/s41598-020-77396-1

Alicia I Guerrero, Tracey L Rogers

Open Access

https://doi.org/10.1038/s41598-020-77396-1

Copy DOI

Journal: Scientific Reports	Publication Date: Nov 27, 2020
Citations: 20	License type: open-access

Affiliation: University of Valparaíso, UNSW Sydney

Abstract

We test the performance of the Bayesian mixing model, MixSIAR, to quantitatively predict diets of consumers based on their fatty acids (FAs). The known diets of six species, undergoing controlled-feeding experiments, were compared with dietary predictions modelled from their FAs. Test subjects included fish, birds and mammals, and represent consumers with disparate FA compositions. We show that MixSIAR with FA data accurately identifies a consumer’s diet, the contribution of major prey items, when they change their diet (diet switching) and can detect an absent prey. Results were impacted if the consumer had a low-fat diet due to physiological constraints. Incorporating prior information on the potential prey species into the model improves model performance. Dietary predictions were reasonable even when using trophic modification values (calibration coefficients, CCs) derived from different prey. Models performed well when using CCs derived from consumers fed a varied diet or when using CC values averaged across diets. We demonstrate that MixSIAR with FAs is a powerful approach to correctly estimate diet, in particular if used to complement other methods.

Highlights

Quantitative dietary studies are important for ecosystem-based management and are needed to predict potential impacts on predator–prey d ynamics[1,2]
This case is based on captive feeding trials conducted on 8 adult spectacled eiders, Somateria fischeri, which were maintained on an initial diet containing 1% Atlantic surf clam, 3% Antarctic krill, 88% Mazuri sea duck formula, 4% blue mussel and 4% Atlantic silverside, for 69 days prior to the start of the feeding experiment
Simulations based on real fatty acids (FAs) data derived from feeding studies allowed us to evaluate the performance of MixSIAR under a variety of scenarios

Summary

Introduction

Quantitative dietary studies are important for ecosystem-based management and are needed to predict potential impacts on predator–prey d ynamics[1,2]. Stock et al.[11] developed another Bayesian framework, MixSIAR, which integrates a set of parameterizations that improve on the error structure of its predecessors SIAR and MixSIR, in terms of their assumptions about the predation p rocess[12] This new generation of Bayesian tracer mixing model has already been widely applied to stable isotope data for ecological studies[13,14,15], it can be used with other biochemical tracers, little is known about its performance with FA data. Neubauer and Jensen[25] developed the mixing model framework “fastinR” that integrates stable isotope and FA data into a joint model to estimate diet This model accounts for compositional constraints on FA data using an additive log-ratio transformation which makes the data approximately normal.

Objectives

Methods

Results

Conclusion