Centro de Investigación y Gestión de Recursos Naturales (CIGREN), Instituto de Biología, Facultad de Ciencias, Universidad de Valparaíso, Gran Bretaña, 1111, Playa Ancha, Valparaíso, Chile.
Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, 2052, Australia.
Sci Rep. 2020 Nov 27;10(1):20780. doi: 10.1038/s41598-020-77396-1.
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，该模型可基于脂肪酸 (FA) 定量预测消费者的饮食。将 6 种经过受控喂养实验的已知饮食与从其 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，代表了具有不同 FA 组成的消费者。我们表明，MixSIAR 与 FA 数据相结合，可以准确识别消费者的饮食、主要猎物的贡献，以及当他们改变饮食（饮食切换）时，可以检测到不存在的猎物。如果由于生理限制消费者的饮食脂肪含量低，则结果会受到影响。将潜在猎物物种的先验信息纳入模型可以提高模型性能。即使使用源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。当使用从喂食多样化饮食的消费者中得出的 CC 或使用跨饮食平均的 CC 值时，模型表现良好。我们证明，MixSIAR 与 FA 相结合是一种正确估计饮食的强大方法，特别是如果将其与其他方法结合使用。
