, Volume 152, Issue 1, pp 179-189

Getting to the fat of the matter: models, methods and assumptions for dealing with lipids in stable isotope analyses

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Within an organism, lipids are depleted in 13C relative to proteins and carbohydrates (more negative δ13C), and variation in lipid content among organisms or among tissue types has the potential to introduce considerable bias into stable isotope analyses that use δ13C. Despite the potential for introduced error, there is no consensus on the need to account for lipids in stable isotope analyses. Here we address two questions: (1) If and when is it important to account for the effects of variation in lipid content on δ13C? (2) If it is important, which method(s) are reliable and robust for dealing with lipid variation? We evaluated the reliability of direct chemical extraction, which physically removes lipids from samples, and mathematical normalization, which uses the carbon-to-nitrogen (C:N) ratio of a sample to normalize δ13C after analysis by measuring the lipid content, the C:N ratio, and the effect of lipid content on δ13C (Δδ13C) of plants and animals with a wide range of lipid contents. For animals, we found strong relationships between C:N and lipid content, between lipid content and Δδ13C, and between C:N and Δδ13C. For plants, C:N was not a good predictor of lipid content or Δδ13C, but we found a strong relationship between carbon content and lipid content, lipid content and Δδ13C, and between and carbon content and Δδ13C. Our results indicate that lipid extraction or normalization is most important when lipid content is variable among consumers of interest or between consumers and end members, and when differences in δ13C between end members is <10–12‰. The vast majority of studies using natural variation in δ13C fall within these criteria. Both direct lipid extraction and mathematical normalization reduce biases in δ13C, but mathematical normalization simplifies sample preparation and better preserves the integrity of samples for δ15N analysis.

Communicated by Jay Rosenheim.