Dealing with biases introduced by lipids in stable carbon and nitrogen isotope analyses: a solution based on 28 marine invertebrate, fish, and mammal species

Author:

Ouellet JF1,Cabrol J1,Rioux È1,Bordeleau X1,Lesage V1

Affiliation:

1. Marine Mammals Biology and Conservation, Maurice-Lamontagne Institute, Fisheries and Oceans Canada, Mont-Joli, Québec G5H3Z4, Canada

Abstract

Stable carbon (δ13C) and nitrogen (δ15N) isotope ratios are widely used in marine food web and habitat use studies. However, lipids are naturally depleted in 13C relative to proteins and are variable in content, biasing δ13C of bulk samples, with consequences for the accuracy of conclusions. This issue can be resolved either by extracting lipids from samples prior to analysis, a resource-intensive process that can also alter δ15N, or by estimating lipid-free δ13C using one of several equations that differ in degree of sophistication and generalization across taxa. Here, δ13C and δ15N were measured in bulk and lipid-extracted muscle samples from over 2000 specimens of 28 species of marine invertebrates, fishes, and mammals. Our objectives were to compare the effect of lipid extraction on δ13C and δ15N across taxa and evaluate the performance of 5 normalization models, overall and using subsets of species, to propose a model to revert lipid-extracted δ15N back to their bulk values and to identify the best approach for dealing with lipid-related biases. Lipid extraction caused an uneven enrichment in δ13C and δ15N across species. Model taxonomic specificity increased estimation accuracy for both isotopes. While models from Logan et al. (2008; J Anim Ecol 77:838-846) and McConnaughey & McRoy (1979; Mar Biol 53:257-262) were the best at predicting lipid-free δ13C, a linear model reliably estimated δ15N values of lipid-free samples using δ15N values of bulk samples. This study presents a method for reliably estimating δ13C and δ15N values of muscle tissue without resorting to duplicate analyses. This represents a major step toward the harmonization of data sets generated using bulk and lipid-extracted samples.

Publisher

Inter-Research Science Center

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