Changes between Version 14 and Version 15 of Eda Description

Timestamp:

Jul 7, 2010 8:43:02 AM (15 years ago)

Author:

celine

Comment:

--

Eda Description

@@ -10 +10 @@
 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 two European hydrographical databases, CCM v2.1 (Catchment Characterisation and Modelling) (Vogt et al. 2007) and Ecrins (European Catchment and RIvers Network System).
+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  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 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's expertise score…) 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 and professional fisheries data set from the ONEMA.
-The data sets used to extract the water quality parameters are obtained from the RNABE database (/ROM database of ONEMA?).
+Values of the explanatory variables are calculated for each segment of the river network. The distance to the sea  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 CRU (Mitchell et al., 2004). Elevation and slope comes from the CCM layer. The obstacle pressure (characteristics, number of dams, Steinbach's expertise score…) 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 and professional fisheries data set from the ONEMA.
+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.
@@ -20 +20 @@
 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 geographical areas. 
+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. 
 
 REFERENCES