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Eel Density Analysis (EDA 2.0): A statistic model to assess European eel (Anguilla Anguilla) escapement in a river network.

Since the early 1980s, the European eel (Anguilla anguilla) stock has been declining and continues to decline at an alarming rate. It is presently considered to be outside safe biological limits (ICES 1999).

EDA 2.0 (Eel Density Analysis) is a modelling tool based on a geolocalized river network database to predict yellow eel densities and silver eel escapement. The principle of this approach is (1) to relate observed yellow eel densities to different parameters: sampling methods, environmental conditions (distance to the sea, relative distance, temperature, elevation and slope…), anthropogenic conditions (obstacles, soil occupation,...) and time (year trends), (2) to calculate the yellow eel density in each reach of river network by applying the statistical model calibrated in step 1, (3) to calculate the overall yellow eel stock abundance by multiplying these densities by the surfaces of the reaches and by summing them (4) to calculate a potential silver eel escapement by converting the yellow eel stock estimated in step 3 into silver eel stock (5) when silver eel mortalities (turbines, fisheries) are known (are estimated) they can be used to assess the silver eel escapement (not yet implemented). It is also possible to give an estimate of the pristine escapement by running the EDA model with anthropogenic conditions artificially set to zero and time variable sets before 1980.

It presently runs with BD_Carthage® v3, a spatial referencing system for surface water in France and will be tested on a European hydrographical databases, CCM v2.1 (Catchment Characterisation and Modelling) (Vogt et al. 2007). The presence/absence and densities of yellow eel in France are obtained from the Aquatic environment and fish database (BDMAP - more than 11 787 fishing samples used, collected on 6 007 sampling stations) from the French National Office of Water and the Aquatic Environments (ONEMA) and other databases from the Brittany watershed.

Values of the explanatory variables are calculated for each segment of the river network. The distance to the sea, to the source and the relative distance (between sea limit and the more upstream source) are directly calculated from the river network topology. The temperatures are extracted from the CCM (and come from the CRU (Mitchell et al., 2004)). Elevation and slope come from the CCM layer. The obstacle pressure (characteristics, number of dams,…) comes from the National list of obstacles to river flows (ROE) from the ONEMA. Glass eel fisheries data set (not yet implemented) comes from Castelnaud (1994), non-professional/leisure fisheries and professional fisheries data set from the ONEMA. The soil occupation is extracted from the European database Corine Land Cover. The data sets used to extract the water quality parameters are obtained from the RNABE database.

The statistical model is calibrated with a Generalized Additive Model (Hastie and Tibshirani, 1990), using the ‘gam’ library in the R software. This semi-parametric extension of generalised linear models is flexible and allows combination of both linear and complex additive responses within the same model. The best model is selected by the Akaike’s Information Criterion (AIC) and Kappa coefficient when presence-absence models were used.

For technical reasons, silver eel densities are unachievable and exhaustive samplings are rare, so an indirect method is used to estimate the silver eel stock from the knowledge of the yellow eel stock. The silver eel stock is obtained with a conversion rate which will be calibrated according to known silver eel productions.

EDA 1.0 has been implemented in the Brittany region (Leprévost, 2007) and the Loire-Brittany basin (Hoffmann, 2008). This implementation of the model allowed predicting the impact of river obstacles on densities and testing the obstacles scoring method of Pierre Steinbach. For management purpose, it has also been implemented at the France scale (Beaulaton, in French EMP). Within the POSE project, this EDA 2.0 model should be implemented in six/eight geographical areas.

Key words

European eel, yellow eel density, silver eel escapement, stock, france, management, model

REFERENCES

Castelnaud G., Guérault D., Désaunay Y. and Elie P., 1994. Production et abondance de la civelle en France au début des années 90. Bulletin Français de la Pêche et de la Pisciculture, 335, 263-288.

Crouzet, C. and W. Simonazzi, 2008. Building the EEA European Catchment and Rivers Network System (ECRINS) from CCM v2.1, European ENvironment Agency: 15.

Hastie, T.J. and Tibshirani, R.J., 1990. Generalized Additive Models, New York: Chapman and Hall.

Hoffmann, 2008. Modélisation de l’impact des ouvrages sur les densités d’anguilles, dans le bassin Loire-Bretagne. Rapport de stage.

ICES, 1999. Report of the ICES Advisory Committee on Fisheries Management. In ICES (Ed.), cooperative research report, 229, Part 2, 393-405.

Leprévost, 2007. Développement d’un indicateur pour caractériser l’impact migratoire sur le stock d’anguille européenne à l’échelle des basins. Mémoire technique.

Mitchell, T. D., Carter, T. R., Jones, P.D., Hulme, M., 2004. A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901–2000) and 16 scenarios (2001–2100), Tyndall Centre Working Paper.

Vogt, J., Soille, P., and al., 2007. A pan-European river and catchment database. Luxembourg, Joint Research Centre-Institute for Environment and Sustainability: 120.