Model prediction vs. reality--testing the predictions of a European eel (Anguilla anguilla) stock dynamics model against the in situ observation of silver eel escapement in compliance with the European eel regulation

• Published in: ICES Journal of Marine Science
• Main Authors: Prigge, E., Marohn, L., Oeberst, R., Hanel, R.
• Format: Text
• Language: English
• Published: Oxford University Press 2013
• Subjects: Original Articles; Anguilla anguilla
• Online Access: http://icesjms.oxfordjournals.org/cgi/content/short/70/2/309; https://doi.org/10.1093/icesjms/fss188

<qd> Prigge, E., Marohn, L., Oeberst R., and Hanel, R. 2013. Model prediction vs. reality—testing the predictions of a European eel ( Anguilla anguilla ) stock dynamics model against the in situ observation of silver eel escapement in compliance with the European eel regulation – ICES Journal of Marine Science, 70: 309–318. </qd>A direct monitoring of European silver eel ( Anguilla anguilla , L) escapement from rivers and estuaries has been proven to be challenging, and a Europe-wide documentation of escaping silver eel numbers therefore hardly seems realistic. To reinforce management decisions, policy-makers are thus widely reliant on the accuracy of escapement models. A 3-year programme of silver eel escapement monitoring was undertaken to compile model input data and revise an eel population model (German Eel Model II; GEM II) already used in the decision-making process of management authorities. By compiling necessary input data and analysing vital system-specific population characteristics, it was possible to compare the documented silver eel escapement with the modelled potential silver eel escapement. Resulting model predictions were close to actually monitored escapement numbers, which were distinctly lower than reference escapement values for the same freshwater system given in the implementation report of the German Eel Management Plans. Applying different commercial and recreational catch scenarios revealed the sensitivity of the model. The results show the potential of the GEM II and highlight the importance of high-quality input data to use model predictions as the basis for management measures.