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Estimating δ(15)N fractionation and adjusting the lipid correction equation using Southern African freshwater fishes

Stable isotope analysis is an important tool for characterising food web structure; however, interpretation of isotope data can often be flawed. For instance, lipid normalisation and trophic fractionation values are often assumed to be constant, but can vary considerably between ecosystems, species...

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Detalles Bibliográficos
Autores principales: Taylor, Geraldine C., Hill, Jaclyn M., Jackson, Michelle C., Peel, Richard A., Weyl, Olaf L. F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5443568/
https://www.ncbi.nlm.nih.gov/pubmed/28542647
http://dx.doi.org/10.1371/journal.pone.0178047
Descripción
Sumario:Stable isotope analysis is an important tool for characterising food web structure; however, interpretation of isotope data can often be flawed. For instance, lipid normalisation and trophic fractionation values are often assumed to be constant, but can vary considerably between ecosystems, species and tissues. Here, previously determined lipid normalisation equations and trophic fractionation values were re-evaluated using freshwater fish species from three rivers in the Upper Zambezian floodplain ecoregion in southern Africa. The parameters commonly used in lipid normalisation equations were not correct for the 18 model species (new D and I parameters were estimated as D = 4.46‰ [95% CI: 2.62, 4.85] and constant I = 0 [95% CI: 0, 0.17]). We suggest that future isotopic analyses on freshwater fishes use our new values if the species under consideration do not have a high lipid content in their white muscle tissue. Nitrogen fractionation values varied between species and river basin; however, the average value closely matched that calculated in previous studies on other species (δ(15)N fractionation factor of 3.37 ± 1.30 ‰). Here we have highlighted the need to treat stable isotope data correctly in food web studies to avoid misinterpretation of the data.