ABSTRACT: Herbivorous primary consumers are a key intermediate trophic linkage between primary production from microalgae, macrophytes, and vascular plants and higher-level consumers. Fatty acid (FA) biomarkers are useful for evaluating trophic interactions in aquatic ecosystems because of clear phylogenetic separation of algal group FA signatures. We used a FA-based Bayesian mixing model (FASTAR) to generate quantitative diet estimates of 3 algal phyla for an intertidal herbivorous isopod, Idotea wosnesenskii, at 6 sites in Puget Sound, Washington. We generated a ‘resource library’ of FA signatures of isopods fed diverse algal diets in 10-wk feeding trials and used these empirical data to parameterize FASTAR, thus accounting for isopod modification of dietary FA. The FA profiles of fast growing juvenile Idotea were closely related to the signatures of their diets, and several polyunsaturated FA (PUFA) were highly correlated between diet and consumer (e.g. ΣC18 ω6 + C18 ω3, 20:4ω6, and 20:5ω3). We used the model to characterize individual isopod diet variability within sites and to test whether isopods utilize specific algal phyla preferentially or in similar proportions to algae available in the field. The results identified both variation in resource utilization among individual isopods within certain sites, and site level similarities with total available algal cover. Body mass index of wild isopods was highest at sites where the model indicated high utilization (e.g. >30%) of both green and brown algae and low support from red algae. This novel FA-based mixing model approach demonstrated the potential for quantitative diet estimations of fast-growing aquatic herbivorous consumers.
KEY WORDS: Fatty acids · Idotea wosnesenskii · Feeding trial · Biomarker trophic enrichment · Bayesian mixing model · Quantitative diet estimation
Full text in pdf format Supplementary material | Cite this article as: Galloway AWE, Eisenlord ME, Dethier MN, Holtgrieve GW, Brett MT
(2014) Quantitative estimates of isopod resource utilization using a Bayesian fatty acid mixing model. Mar Ecol Prog Ser 507:219-232. https://doi.org/10.3354/meps10860
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