| 1 | = A statistic model Eel Density Analysis (EDA 2.0) to predict the European eel (Anguilla Anguilla) density in the French river network. = |
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| 4 | Since the early 1980s, the European eel ('' Anguilla anguilla '') stock has been declining and continues to decline at an alarming rate and it’s presently considered outside safe biological limits. |
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| 6 | EDA 2.0 (Eel Density Analysis) is a modelling tool using a GIS based approach to predict yellow eel densities. The model is based on a hydrographical database (BD_Carthage® – a spatial referencing system for surface water in France) and (or could be based on two European hydrographical databases: ?) the CCM (Catchment Characterisation and Modelling) River and Catchment Database and the European catchments and Rivers network System (Ecrins). The level of precision of these layers differs; compared to CCM, ECRINS offers a smaller number of elementary catchments. |
| 7 | This EDA 2.0 model could be/is carried out in three (more?) geographical areas: the Brittany region (Leprévost, 2007), the Loire-Brittany basin (Hoffmann, 2008) and France (Beaulaton in French EMP) (and the Rhône and Vilaine basin cf. mail-POSE model and dataset characterization matrices). The Brittany model allowed to test the obstacles grid from Pierre Steinbach. |
| 8 | An analysis with a Generalized Additive Model (GAM) is performed using the electrofishing data set. GAMs, semi-parametric extensions of generalised linear models (GLM) (Hastie and Tibshirani, 1990), are flexible and allow the combination of both linear and complex additive responses within the same model. They are performed using the ‘gam’ library in the R software. The best model is selected by the Akaike’s Information Criterion (AIC). |
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| 10 | The presence/absence and density of yellow eel are obtained from the Aquatic environment and fish database (BDMAP - more than 16,000 fishing samples collected on more than 6,000/8,968 (?) sample stations (cf.coordination juillet 2008)) from the National Office of Water and the Aquatic Environments (ONEMA) and other databases from the IAV in the Vilaine watershed and the BGM database in other Brittany watershed. |
| 11 | Yellow eel densities (YE) are related to different parameters: fishing methods used, environmental conditions (distance to the sea, relative distance, temperature, Strahler stream order, elevation and slope…), anthropogenic conditions (obstacles, fisheries…) and time (year trends). |
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| 13 | The distance to the sea, the relative distance (between the distance to the sea and the total length of the river) are calculated from the BDCarthage water network and could be calculated from the CCM and the Ecrins water network. |
| 14 | The temperatures are extracted from the CRU (Climate Research Unit) and Worlclim (www.worldclim.org/). Elevation and slope are extracted from the National Height Elevation Database (BD ALTI® - spatial resolution of 50m) from the National Geographic Institute. |
| 15 | The obstacle pressure (characteristics, Steinbach rank…) comes from the National list of obstacles to river flows (ROE) from the ONEMA. |
| 16 | Glass eel fisheries data set comes from Castelnaud (1994), non-professional/leisure fisheries data set from the ONEMA and professional fisheries data set from Thomas Changeux and Wenes (2004). |
| 17 | The data sets used to extract the water quality parameters (which ones?) are obtained from the ROM or/and ? the RHP from the ONEMA. |
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| 19 | For each point of the French river network (a 2 kilometres segment), the values of those different variables are calculated and allow to predict the yellow eel densities and also the densities in the pristine conditions without anthropogenic impacts (obstacles impacts and fisheries). |
| 20 | With the water surface, the yellow eel densities are transformed into yellow eel quantities. |
| 21 | 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 (5%). |
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| 25 | REFERENCES |
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| 27 | 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. |
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| 29 | Hastie, T.J. and Tibshirani, R.J., 1990. Generalized Additive Models, New York: Chapman and Hall. |
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| 31 | Hoffmann, 2008. Modélisation de l’impact des ouvrages sur les densités d’anguilles, dans le bassin Loire-Bretagne. Rapport de stage. |
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| 33 | Leprévost, 2007. Développement d’un indicateur pour caractériser l’impact migratoire sur le stock d’anguille européenne à l’échelle des basins. Mémoire technique. |
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