Metabolites of Polycyclic Aromatic Hydrocarbons (PAHs) in Bile as Biomarkers of Pollution in European Eel (Anguilla anguilla) from German Rivers

  • F. Nagel
  • U. Kammann
  • C. Wagner
  • R. Hanel


In the light of the alarming decline of the European eel (Anguilla anguilla L.) population, there is an urgent need to define ecological indicators for eel habitat quality. Due to an increasing shortage of glass eels available for local stock enhancement, the decision of whether restocking is a valuable management tool to increase high-quality silver eel escapement to the sea needs to be evaluated. Organic contaminants, such as polycyclic aromatic hydrocarbons (PAHs), are among the major threats to fish in their habitat. Therefore, the aim of the investigation presented here was to examine metabolites of PAHs in eel bile as one possible marker for habitat quality. In total, 170 yellow eels were collected in the rivers Rhine, Ems, Weser, Elbe, Havel, Schlei, Eider, Trave, Warnow, Peene, Uecker, and Oder in 2009. PAH metabolites in eel bile were analyzed using high-performance liquid chromatography with fluorescence detection. Metabolites of pyrene and phenanthrene were investigated. Concentrations of PAH metabolites in eel bile varied significantly between several rivers, with the highest mean concentrations of 1-hydroxypyrene and 1-hydroxyphenanthrene in eel bile from the river Trave (2421 and 632 ng/ml). Moreover, huge differences in the ratio of 1-hydroxypyrene to 1-hydroxyphenanthrene, with the highest mean value in eel bile from the river Ems (7.43) and the lowest mean value in eel bile from the river Uecker (0.70), indicate different sources of PAH contamination. A comparative analysis of PAH-metabolite contamination of eels in different river systems is seen as a first step toward a classification of freshwater habitats for restocking purposes.


PAHs Biliverdin Bile Pigment Bile Fluid River Eider 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors are grateful to Alexander Schulz for skillful technical assistance and to Florian Sambraus for support with data evaluation. Furthermore, we thank our colleagues Klaus Wysujack, Thomas Lang, and Udo Koops, for many useful discussions. This study was partly financed by the EU-Data Collection Regulation (2008/949/EC).


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Johann Heinrich von Thünen-Institut, Federal Research Institute for Rural Areas, Forestry and Fisheries, Institute of Fisheries EcologyHamburgGermany
  2. 2.Leibniz Institute for Baltic Sea ResearchRostockGermany

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