Interactive effects of urea and lipid content confound stable isotope analysis in elasmobranch fishes

Author:

Carlisle Aaron B.1,Litvin Steven Y.1,Madigan Daniel J.2,Lyons Kady3,Bigman Jennifer S.4,Ibarra Melissa5,Bizzarro Joseph J.6

Affiliation:

1. Hopkins Marine Station of Stanford University, 120 Oceanview Blvd., Pacific Grove, CA 93950, USA.

2. Harvard University Center for the Environment, 24 Oxford Street, Cambridge, MA 02138, USA.

3. University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 4N1, Canada.

4. Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada.

5. University of California Davis, One Shields Avenue, Davis, CA 95616, USA.

6. Moss Landing Marine Laboratories, 8272 Moss Landing Road, Moss Landing, CA 95039, USA.

Abstract

Stable isotope analysis (SIA) is becoming a commonly used tool to study the ecology of elasmobranchs. However, the retention of urea by elasmobranchs for osmoregulatory purposes may bias the analysis and interpretation of SIA data. We examined the effects of removing urea and lipid on the stable isotope composition of 14 species of sharks, skates, and rays from the eastern North Pacific Ocean. While effects were variable across taxa, removal of urea generally increased δ15N and C:N. Urea removal had less influence on δ13C, whereas extracting urea and lipid generally increased δ15N, C:N, and δ13C. Because C:N values of nonextracted tissues are often used to infer lipid content and adjust δ13C, shifts in C:N following urea extraction will change the inferred lipid content and bias any mathematical adjustment of δ13C. These results highlight the importance of urea and lipid extraction and demonstrate the confounding effects of these compounds, making it impossible to use C:N of non-urea-extracted samples as a diagnostic tool to estimate and correct for lipid content in elasmobranch tissues.

Publisher

Canadian Science Publishing

Subject

Aquatic Science,Ecology, Evolution, Behavior and Systematics

Reference40 articles.

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5. Burnham, K.P., and Anderson, D.R. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media.

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