| 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 will in its next version be tested 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.[[BR]] |
| 7 | | Within the POSE project, this EDA 2.0 model will be implemented in at least four geographical areas: the Brittany region (Leprévost, 2007), the Loire-Brittany basin (Hoffmann, 2008) , the Rhône and Vaccaress basins, and the Basque coutry river basins. For management purpose, it will also be implemented across all of France (Beaulaton in French EMP). The implementation of the model in Loire Brittany allowed to predict the effect of river obstacles on densities, and 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), and Kappa when presence-absence models were used. |
| | 6 | EDA 2.0 (Eel Density Analysis) is a modelling tool based on a GIS river network database to predict yellow eel densities and silver escapement. The principle of this approach is (1) to relate observed yellow eel densities to different parameters: fishing method, environmental conditions (distance to the sea, relative distance, temperature, Strahler stream order, elevation and slope…), anthropogenic conditions (obstacles, fisheries…) and time (year trends), (2) to calculate the yellow eel density in each reach by applying the statistical model calibrated in step 1, (3) to calculate the yellow eel stock abundance by multiplying these densities by the surfaces of the reaches (4) to calculate a potential escapement from the catchment (mortality during downstream mortality are neglected) from the yellow eel stock estimate in step 3. |
| | 7 | It is also possible to give an estimate of the pristine escapement by running the EDA model with anthropogenic conditions artificially set to zero. |
| 10 | | The presence/absence and densities 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 (?) sampling stations (cf.coordination juillet 2008)) from the National Office of Water and the Aquatic Environments (ONEMA) and other databases from the 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). |
| | 9 | It presently runs with BD_Carthage®, a spatial referencing system for surface water in France and will be tested on two European hydrographical databases, CCM (Catchment Characterisation and Modelling) (Vogt et al. 2007) and Ecrins (European Catchment and RIvers Network System). |
| | 10 | The presence/absence and densities of yellow eel in France are obtained from the Aquatic environment and fish database (BDMAP - more than 16,000 fishing samples collected on more than 6,000/8,968 (?) sampling stations (cf.coordination juillet 2008)) from the National Office of Water and the Aquatic Environments (ONEMA) and other databases from the Brittany watershed. |
| 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 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). |
| | 12 | Values of the explanatory variables are calculated for each segment of the river network. The distance to the sea, 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 CRU (Mitchell et al., 2004), 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. The obstacle pressure (characteristics, Steinbach rank…) comes from the National list of obstacles to river flows (ROE) from the ONEMA. 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). |
