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

Citation

Johnson, Devin et al. (2020), Bayesian stable isotope mixing models effectively characterize the diet of an Arctic raptor, Dryad, Dataset, https://doi.org/10.5061/dryad.6m905qfxs

Abstract

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. 

Methods

This dataset contains the following files:

"camera_data.csv": Raw prey delivery observations from gyrfalcon nest cameras.

"GYRF_isotopes.csv": Raw stable isotope values (d13C & d15N) from gyrfalcon red blood cells.

"prey_isotopes.csv": Raw stable isotope values (d13C & d15N) from muscle tissue samples from gyrfalcon prey species.

Further methodological details can be found in the article to which this dataset is supplement (JAE-2020-00518).

Usage Notes

Further details on this dataset and how it was used can be found in the attached RMarkdown document (JAE-2020-00518.Rmd). 

*note*: the "MixSIAR" data folder contains subsets of the three main files formatted for use in the "MixSIAR" R package. Due to the computational intensity of these mixing models, we included the code we used to generate said models, then import them into the RMarkdown document via a separate .RData file (JAE-2020-00518_environment.RData).

*note*: Some plots were generated using a package we wrote: "SIAplotR". A tar.gz file for this package is included so figures can be recreated exactly as they appear in the manuscript using the RMarkdown file.