Bayesian stable isotope mixing models effectively characterize the diet of an Arctic raptor

1. Bayesian stable isotope mixing models (BSIMMs) for δ13C and δ15N can be a useful tool to reconstruct diets, characterize trophic relationships, and assess spatiotemporal variation in food webs. However, use of this approach typically requires a priori knowledge on the level of enrichment occurring between the diet and tissue of the consumer being sampled (i.e., a trophic discrimination factor or TDF). 2. TDFs derived from captive feeding studies are highly variable, and it is challenging to select the appropriate TDF for diet estimation in wild populations. We introduce a novel method for estimating TDFs in a wild population: a proportionally balanced equation that uses high-precision diet estimates from nest cameras installed on a subset of nests in lieu of a controlled feeding study (TDFCAM). 3. We tested the ability of BSIMMs to characterize diet in a free-living population of gyrfalcon (Falco rusticolus) nestlings by comparing model output to high-precision nest camera diet estimates. We analyzed the performance of models formulated with a TDFCAM against other relevant TDFs and assessed model sensitivity to an informative prior. We applied the most parsimonious model inputs to a larger sample to analyze broad-scale temporal dietary trends. 4. BSIMMs fitted with a TDFCAM and uninformative prior had the best agreement with nest camera data, outperforming TDFs derived from captive feeding studies. BSIMMs produced with a TDFCAM produced reliable diet estimates at the nest level and accurately identified significant temporal shifts in gyrfalcon diet within and between years. 5. Our method of TDF estimation produced more accurate estimates of TDFs in a wild population than traditional approaches, consequently improving BSIMM diet estimates. We demonstrate how BSIMMs can complement a high-precision diet study by expanding its spatiotemporal scope of inference and recommend this integrative methodology as a powerful tool for future trophic studies.

1. 针对碳同位素比值δ¹³C与氮同位素比值δ¹⁵N的贝叶斯稳定同位素混合模型（Bayesian stable isotope mixing models，BSIMMs），是重建生物食性、刻画营养关系以及评估食物网时空变异的有效工具。但该方法的应用通常需要先验信息，即明确被采样消费者的食性与其组织之间的同位素富集水平（即营养分馏因子（trophic discrimination factor，TDF））。
2. 从圈养饲喂实验中获取的TDF变异程度极高，在野生种群中为食性估算筛选合适的TDF极具挑战。本研究提出一种可用于野生种群TDF估算的新方法：一种比例平衡方程，该方法利用安装在部分鸟巢上的巢相机所得到的高精度食性估算结果，替代受控饲喂实验（该方法所得TDF记为TDFCAM）。
3. 本研究通过将BSIMMs的输出结果与高精度巢相机食性估算结果进行对比，验证了其在野生矛隼（Falco rusticolus）雏鸟种群食性刻画中的性能。我们对比了采用TDFCAM构建的模型与其他相关TDF构建的模型的表现，并评估了模型对信息先验的敏感性。随后我们将最简约的模型输入参数应用于更大规模的样本，以分析大范围的时间食性变化趋势。
4. 采用TDFCAM与无信息先验构建的BSIMMs与巢相机数据的吻合度最高，其表现优于从圈养饲喂实验中获得的TDF所构建的模型。采用TDFCAM构建的BSIMMs可在巢水平上得到可靠的食性估算结果，并能准确识别出矛隼食性在年内及年际间的显著时间变化。
5. 相较于传统方法，本研究提出的TDF估算方法可在野生种群中得到更精准的TDF估算结果，进而优化了BSIMM的食性估算效果。本研究证明，通过拓展食性推断的时空范围，BSIMMs可作为高精度食性研究的补充手段，并推荐该整合方法作为未来营养生态研究的有力工具。