Megan M Skinner1, Amy A Martin2, Barry C Moore1. 1. Washington State University, School of the Environment, P.O. Box 646420, Pullman, WA, 99164-6420, USA. 2. Okanogan Conservation District, 1251 South 2nd Ave, Room, 102, Okanogan, WA, 98840, USA.
Abstract
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.
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 n class="Chemical">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.
Authors: Tiffany N Penland; W Gregory Cope; Thomas J Kwak; Mark J Strynar; Casey A Grieshaber; Ryan J Heise; Forrest W Sessions Journal: Environ Sci Technol Date: 2020-05-12 Impact factor: 9.028
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