Rationale: Stable isotope analysis (SIA) is a powerful tool for examining diet and food-web dynamics. SIA assumes "you are what you eat" relative to carbon (C) and nitrogen (N). However, fractionation of carbon during lipid synthesis violates this assumption; high-lipid tissues do not reflect δ(13) C values of diet and therefore have the potential to skew mixing model results and diet interpretations, making corrections necessary.
Methods: Brook Trout (Salvelinus fontinalis) white muscle and liver samples from several fish species representing the temperate North American cold- and warm-water fish community were corrected for lipids via chemical lipid extraction and mathematical lipid normalization. To assess the accuracy of model-predicted lipid-free δ(13) C values calculated from four normalization models, we compared model-predicted values with those measured after lipid extraction.
Results: We found that chemical lipid extraction is unnecessary for Brook Trout white muscle tissue with low initial lipid content. However, in tissues with C:N ratios greater than 3.5, lipid extraction increased δ(13) C values in fish liver by more than 1.0 ‰, indicating that liver lipid content is sufficient to bias δ(13) C values. We also found that lipids were accurately accounted for with mathematical normalization and recommend that tissues with C:N ratios greater than 3.5 be corrected mathematically.
Conclusions: Our findings indicate that mathematical normalization is sufficient to account for bias in δ(13) C values associated with lipid content in fish tissues when C:N ratios are above 3.5. C:N ratios below 3.5 indicate that tissues have insufficient levels of lipid to bias the δ(13) C values. Generally, these findings support the use of more timely and cost-effective processing and analysis methods in future aquatic food-web studies utilizing SIA. Copyright © 2016 John Wiley & Sons, Ltd.
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http://dx.doi.org/10.1002/rcm.7480 | DOI Listing |
Am J Gastroenterol
March 2002
Division of Gastroenterology, Portland VAMC and Oregon Health Sciences University, 97207, USA.
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