Within an organism, lipids are depleted in (13)C relative to proteins and carbohydrates (more negative delta(13)C), and variation in lipid content among organisms or among tissue types has the potential to introduce considerable bias into stable isotope analyses that use delta(13)C. 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 delta(13)C? (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 delta(13)C after analysis by measuring the lipid content, the C:N ratio, and the effect of lipid content on delta(13)C (Deltadelta(13)C) 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 Deltadelta(13)C, and between C:N and Deltadelta(13)C. For plants, C:N was not a good predictor of lipid content or Deltadelta(13)C, but we found a strong relationship between carbon content and lipid content, lipid content and Deltadelta(13)C, and between and carbon content and Deltadelta(13)C. 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 delta(13)C between end members is <10-12 per thousand. The vast majority of studies using natural variation in delta(13)C fall within these criteria. Both direct lipid extraction and mathematical normalization reduce biases in delta(13)C, but mathematical normalization simplifies sample preparation and better preserves the integrity of samples for delta(15)N analysis.
