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.

我们测试了贝叶斯混合模型 MixSIAR 的性能，以根据脂肪酸 (FA) 定量预测消费者的饮食。六个物种的已知饮食经过控制喂养实验，与根据其 FA 建模的饮食预测进行了比较。测试对象包括鱼类、鸟类和哺乳动物，并代表具有不同 FA 成分的消费者。我们证明，当消费者改变饮食（饮食转换）时，具有 FA 数据的 MixSIAR 可以准确识别消费者的饮食、主要猎物的贡献，并可以检测到不存在的猎物。如果消费者由于生理限制而采用低脂饮食，结果就会受到影响。将潜在猎物物种的先验信息纳入模型可以提高模型性能。即使使用来自不同猎物的营养修改值（校准系数，CC），饮食预测也是合理的。当使用来自不同饮食的消费者的 CC 或使用不同饮食的平均 CC 值时，模型表现良好。我们证明，MixSIAR 与 FA 是正确估计饮食的强大方法，特别是如果用于补充其他方法。