Joel C Hoffman1, Michael E Sierszen1, Anne M Cotter1. 1. US EPA Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd, Duluth, MN, 55804, USA.
Abstract
RATIONALE: Normalizing δ(13) C values of animal tissue for lipid content is necessary to accurately interpret food-web relationships from stable isotope analysis. To reduce the effort and expense associated with chemical extraction of lipids, various studies have tested arithmetic mass balance to mathematically normalize δ(13) C values for lipid content; however, the approach assumes that lipid content is related to the tissue C:N ratio. METHODS: We evaluated two commonly used models for estimating tissue lipid content based on C:N ratio (a mass balance model and a stoichiometric model) by comparing model predictions to measure the lipid content of white muscle tissue. We then determined the effect of lipid model choice on δ(13) C values normalized using arithmetic mass balance. To do so, we used a collection of fish from Lake Superior spanning a wide range in lipid content (5% to 73% lipid). RESULTS: We found that the lipid content was positively related to the bulk muscle tissue C:N ratio. The two different lipid models produced similar estimates of lipid content based on tissue C:N, within 6% for tissue C:N values <7. Normalizing δ(13) C values using an arithmetic mass-balance equation based on either model yielded similar results, with a small bias (<1‰) compared with results based on chemical extraction. CONCLUSIONS: Among-species consistency in the relationship between fish muscle tissue C:N ratio and lipid content supports the application of arithmetic mass balance to normalize δ(13) C values for lipid content. The uncertainty associated with both lipid extraction quality and choice of model parameters constrains the achievable precision of normalized δ(13) C values to about ±1.0‰. Published in 2015. This article is a U.S. Government work and is in the public domain in the U.S.A.
RATIONALE: Normalizing δ(13) C values of animal tissue for lipid content is necessary to accurately interpret food-web relationships from stable isotope analysis. To reduce the effort and expense associated with chemical extraction of lipids, various studies have tested arithmetic mass balance to mathematically normalize δ(13) C values for lipid content; however, the approach assumes that lipid content is related to the tissue C:N ratio. METHODS: We evaluated two commonly used models for estimating tissue lipid content based on C:N ratio (a mass balance model and a stoichiometric model) by comparing model predictions to measure the lipid content of white muscle tissue. We then determined the effect of lipid model choice on δ(13) C values normalized using arithmetic mass balance. To do so, we used a collection of fish from Lake Superior spanning a wide range in lipid content (5% to 73% lipid). RESULTS: We found that the lipid content was positively related to the bulk muscle tissue C:N ratio. The two different lipid models produced similar estimates of lipid content based on tissue C:N, within 6% for tissue C:N values <7. Normalizing δ(13) C values using an arithmetic mass-balance equation based on either model yielded similar results, with a small bias (<1‰) compared with results based on chemical extraction. CONCLUSIONS: Among-species consistency in the relationship between fish muscle tissue C:N ratio and lipid content supports the application of arithmetic mass balance to normalize δ(13) C values for lipid content. The uncertainty associated with both lipid extraction quality and choice of model parameters constrains the achievable precision of normalized δ(13) C values to about ±1.0‰. Published in 2015. This article is a U.S. Government work and is in the public domain in the U.S.A.
Authors: Michael E Sierszen; Lee S Schoen; Jessica M Kosiara; Joel C Hoffman; Matthew J Cooper; Donald G Uzarski Journal: J Great Lakes Res Date: 2019 Impact factor: 2.480
Authors: Ryan F Lepak; Jacob M Ogorek; Krista K Bartz; Sarah E Janssen; Michael T Tate; Yin Runsheng; James P Hurley; Daniel B Young; Collin A Eagles-Smith; David P Krabbenhoft Journal: Environ Sci Technol Lett Date: 2022-03-21
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Authors: Sarah E Janssen; Joel C Hoffman; Ryan F Lepak; David P Krabbenhoft; David Walters; Collin A Eagles-Smith; Greg Peterson; Jacob M Ogorek; John F DeWild; Anne Cotter; Mark Pearson; Michael T Tate; Roger B Yeardley; Marc A Mills Journal: Sci Total Environ Date: 2021-03-13 Impact factor: 10.753
Authors: Ryan F Lepak; Joel C Hoffman; Sarah E Janssen; David P Krabbenhoft; Jacob M Ogorek; John F DeWild; Michael T Tate; Christopher L Babiarz; Runsheng Yin; Elizabeth W Murphy; Daniel R Engstrom; James P Hurley Journal: Proc Natl Acad Sci U S A Date: 2019-11-04 Impact factor: 11.205
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