We modified the stable isotope mixing model MixSIR to infer primary producer contributions to consumer diets based on their fatty acid composition. To parameterize the algorithm, we generated a ‘consumer-resource library’ of FA signatures of Daphnia fed different algal diets, using 34 feeding trials representing diverse phytoplankton lineages. This library corresponds to the resource or producer file in classic Bayesian mixing models such as MixSIR or SIAR. Because this library is based on the FA profiles of zooplankton consuming known diets, and not the FA profiles of algae directly, trophic modification of consumer lipids is directly accounted for. To test the model, we simulated hypothetical Daphnia comprised of 80% diatoms, 10% green algae, and 10% cryptophytes and compared the FA signatures of these known pseudo-mixtures to outputs generated by the mixing model. The algorithm inferred these simulated consumers were comprised of 82% (63-92%) [median (2.5th to 97.5th percentile credible interval)] diatoms, 11% (4-22%) green algae, and 6% (0-25%) cryptophytes. We used the same model with published phytoplankton stable isotope (SI) data for δ13C and δ15N to examine how a SI based approach resolved a similar scenario. With SI, the algorithm inferred that the simulated consumer assimilated 52% (4-91%) diatoms, 23% (1-78%) green algae, and 18% (1-73%) cyanobacteria. The accuracy and precision of SI based estimates was extremely sensitive to both resource and consumer uncertainty, as well as the trophic fractionation assumption. These results indicate that when using only two tracers with substantial uncertainty for the putative resources, as is often the case in this class of analyses, the underdetermined constraint in consumer-resource SI analyses may be intractable. The FA based approach alleviated the underdetermined constraint because many more FA biomarkers were utilized (n < 20), different primary producers (e.g., diatoms, green algae, and cryptophytes) have very characteristic FA compositions, and the FA profiles of many aquatic primary consumers are strongly influenced by their diets.
Oregon Institute of Marine Biology, University of Oregon, Charleston, Oregon, 97420, United States of America;Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, 98195, United States of America;School of Aquatic and Fishery Science, University of Washington, Seattle, Washington, 98195, United States of America;Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, 98112, United States of America;Department of Ecosystem and Conservation Science, University of Montana, Missoula, Montana 59812, United States of America;Department of Zoology, University of Otago, PO Box 56, Dunedin, 9054, New Zealand;WasserCluster - Biological Station Lunz, Dr. Carl Kupelwieser Prom. 5, A-3293 Lunz am See, Austria;Aquatic Ecology, University of Hamburg, Ohnhorststraße 18, Hamburg, D-22609, Germany;Integrated Water Resources Management, Norwegian Institute for Water Research (NIVA), Gaustadallen 21, 0349, Oslo, Norway;Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, 98195, United States of America;Department of Biology, University of Eastern Finland, Box 111, 80101, Joensuu, Finland;Department of Biological and Environmental Science, University of Jyväskylä, PL 35 (YA), 40014, Jyväskylä, Finland;Limnology/Department of Ecology and Genetics, Uppsala University, Norbyvägen 18 D, SE-75236, Uppsala, Sweden
Recommended Citation:
Aaron W. E. Galloway,Michael T. Brett,Gordon W. Holtgrieve,et al. A Fatty Acid Based Bayesian Approach for Inferring Diet in Aquatic Consumers[J]. PLOS ONE,2015-01-01,10(6)