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  • Title: Bayesian stable isotope mixing models effectively characterize the diet of an Arctic raptor.
    Author: Johnson DL, Henderson MT, Anderson DL, Booms TL, Williams CT.
    Journal: J Anim Ecol; 2020 Dec; 89(12):2972-2985. PubMed ID: 33020919.
    Abstract:
    Bayesian stable isotope mixing models (BSIMMs) for δ13 C and δ15 N 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). Trophic discrimination factors 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 ). 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 analysed 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 analyse broad-scale temporal dietary trends. Bayesian stable isotope mixing models 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. 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.
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