P Cantelaube

P A Jayet

Florence Carre

C Bamps

P Zakharov

Keywords: Downscaling, land use, spatial statistics, farm-groups, Farm Accountancy Data Network

Geographical downscaling of outputs provided by an economic farm model calibrated at the regional level

There is a strong need for accurate and spatially referenced information regarding policy making and model linkage. This need has been expressed by land users, and policy and decision makers in order to estimate both spatially and locally the impacts of European policy (like the Common Agricultural Policy) and/or global changes on farm-groups. These entities are defined according to variables such as altitude, economic size and type of farming (referring to land uses). European farm-groups are provided through the Farm Accountancy Data Network (FADN) as statistical information delivered at regional level. The aim of the study is to map locally farm-group probabilities within each region. The mapping of the farmgroups is done in two steps: 1/ by mapping locally the co-variables associated to the farmgroups, i.e. altitude and land uses; 2/ by using regional FADN data as a priori knowledge for transforming land uses and altitude information into farm-groups location probabilities within each region. The downscaling process focuses on the land use mapping since land use data are originally point information located every 18 km. Interpolation of land use data is done at 100 m by using co-variables like land cover, altitude, climate and soil data which are continuous layers usually provided at fine resolution. Once the farm-groups are mapped, European Policy and global changes scenarios are run through an agro-economic model for assessing environmental impacts locally.

Introduction

There is a strong need for accurate and spatially referenced information regarding policy making and model linkage. This need has been expressed by land users, and policy and decision makers in order to estimate spatially and locally the impacts of European policy (like the Common Agricultural Policy) and/or global changes on economic agents and consequently on natural resources.

Economic models are able to provide information on changes of economic agents' behaviour according to different Policy scenarios whereas agronomic or environmental models can provide information on changes in natural resources according to global environmental changes. Within the Common Agricultural Policy (CAP), the farm-types are considered as 2 European policy and /or global changes on economic agents and natural resources means:

1/ to map locally farm-types dealt with economic models, 2/ to assess the impacts of the Policy on the spatial distribution of farm-types by linking economic models with agroenvironmental models.

Locally mapping the farm-types from a regional level (small scale or coarse resolution) to a local level (large scale or fine resolution) is a downscaling issue or a disaggregation process (Bierkens et al., 2000) tackled as the core issue of the paper. The downscaling issue is tackled by using environmental information which can explain farm-types and which is usually provided at fine resolution and at European level. These are: land use taken as farmtype indicator, land cover, soil, climate, and topography used as co-variables. Two examples of application are presented: 1/ the assessment of CAP option scenarios on land use; 2/ the assessment of CAP option scenarios on agricultural N-pollutants.

Conversely to most of approaches used in spatial econometrics where spatial autocorrelation and spatial heterogeneity are basic characteristics of the models themselves, the downscaling occurs independently from the run of the economic model. In other words, we propose to downscale model outputs without changing a line of code into the model programming. [START_REF] Vidal | Geographical use of statistical data, methodological overview. in "Towards agri-environmental indicators[END_REF] noticed that changing the spatial unit used in statistical data redistribution is faster and more effective than setting up a new tool requesting new data. In addition we consider that changing the spatial distribution of data provided by a model from one geographical scale to another is faster than building a new model working at this new spatial unit. This is faster because this approach does not need to calibrate and to validate new models (we mention "models" at plural, taking into account the different fields and the large range of environmental problems for which the economic model could be used). This is more effective, regarding the amount and the quality of data called for a new model based on new spatial units and scale.

Summarily, downscaling of large sets of outputs provided by any run of the economic model through the one-step preliminary disaggregation procedure offers fast user-friendly means devoted to policy analysis and to model linkage. When the models and geo-referenced data are not hard linked, any update of one of them can improve the quality of the global modeling chain.

A first section is dedicated to the material used for the downscaling process where the general concept of the AROPAj economic model used is roughly explained and the economic and environmental data are described. A second section is devoted to the methodology, a third one to the results, followed by a fourth section presenting the impact assessment applications.

Materials

General concepts of the AROPAj economic model

The AROPAj model is an economic model dedicated to the simulation of European Union The main input of the AROPAj model is the Farm Accountancy Data Network (FADN1 ) in which samples supply economic information related to thousands of farmers across the European Union. In practice, estimates can be published only if derived from a sample of at least 15 farms, and the geographical identification consistent with representative information does not refer to a level finer than the "region" level.

The construction steps of AROPAj are (i) the farming design describing the farmer's objective (i.e. maximizing the gross margin), the activities and the bounded "production set" defined by constraints related to different blocks (i.e. crop systems, bovine demography, animal feeding, greenhouse gas emissions, nitrogen balance, …); (ii) the classification of FADN samples into farm-groups (hereafter labeled as farm-groups) which are then the entities to be mapped at fine resolution (this step is described more in detail in section 2.2); (iii) pre-estimating of parameters through linear econometric methods calling for FADN data combined with expert information; (iv) calibration (i.e. re-estimating) of subsets of parameters through Monte Carlo and gradient methods. Different versions of the model are operational. In the next application section, we used the version covering the EU-15 and related to FADN-2002 (101 FADN regions are partitioned into a total of 1074 farm-groups). Each farm-group is assumed to be an individual economic agent, related to a maximization program. Solving of these programs calls for mathematical programming software combining linear programming and mixed integer variables (LMIP). This version represents a large part of "real" farms (a little less than 2 millions farms represented by the model) and of the utilized agricultural area (UAA) devoted to crops and pasture (UAA close to 88 million hectares represented by the model).

Regarding the land use side, it has to be kept in mind that each individual LMIP delivers, among other variables, the optimal annual allocation of the individual UAA. This individual UAA is a fixed resource parameter. Any change in prices, in CAP or in the global environment (when the climate change is assumed to impact the crop growth function provided by the crop model) may lead to change in the land allocation at the farm-group level. Through the linkage to an agronomic crop model (i.e. STICS, see [START_REF] Brisson | STICS: a Generic Model for the simulation of crops and their water and nitrogen balances, Model Validation for Wheat and Maize[END_REF] AROPAj allows to simulate a wide range of alternative economic policy scenarios combined with various management and agro-environmental conditions.

Definition of the AROPAj farm-groups

The second step of the AROPAj economic model leads to transform FADN regional data into EU representative farm-groups through hierarchical clustering applied to observed farms within each FADN region according to three criteria, (1) the average altitude (by 3 elevation classes, i.e. <300 m, 300-600 m and > 600 m), (2) the type of farming (defined by the FADN and disaggregated into 14 types of farming activities from which we exclude grape production and arboriculture), (3) the economic size unit (based on the total standard gross margin, which is defined by the value of output from one hectare or from one animal less the cost of variable inputs required to produce that output). The legal use of such data notably leads to restrain the number of clusters. Moreover agreement with the European Commission is required for an access to the FADN database. Area. The altitude is categorized into 3 classes (less then 300m, between 300 and 600m, over 600m). Slopes were also computed and used together with altitudes in the land cover model (also categorized into 3 classes).

Data used for mapping the farm-groups

Altitude information

Available Land Use information at European level: LUCAS

LUCAS is the Land Use/Cover Area frame Statistical Survey (Delincé, 2001, Bettio et al, 2002, Gallego, ed., 2002 3 ). This survey is coordinated by Eurostat and is based on direct observations described by a common typology all over Europe. The report of accurate and detailed geo-referenced information follows a detailed nomenclature, notably for agricultural categories: 57 land classes including 34 agricultural classes. The LUCAS is a non stratified systematic survey; it is delivered as point information sampled across the EU territory. The point sample consists of 10-points clusters which are set every 18 km (approximately 98 000 points are related to the EU15).

The land cover database: Corine Land Cover

As a part of the European CORINE Database, the CORINE Land Cover 2000 (CLC 4 ) geographic database provides geo-referenced information on land cover over most European countries (including the EU 27 members states) [START_REF] Cec Eea | CORINE Land Cover; guide technique[END_REF][START_REF] Ec-Eea | CORINE Land Cover[END_REF][START_REF] Jrc-Eea | CORINE Land Cover updating for the year 2000[END_REF]. Accurate required data are accessible through the European Environmental Agency (EEA). It has been produced by photo-interpretation of satellite pictures (Landsat ETM images) and refined with ancillary data (aerial photographs, topographic maps, local knowledge …). The CLC describes the land cover according to a 3 level nomenclature ( 5classes for the first level -Artificial surfaces, Agriculture, Forest, Westland and Water Bodies and up to 44 classes for the more detailed level). CLC map has a scale of 1:100,000 (meaning that the location precision of CLC is 100m) and has a minimum mapping unit (the CLC polygons) of at least 25ha. The original vector land cover database converted into a raster format is made available at a 100mx100m raster cell size in Lambert-Azimuth coordinates (see EUROSTAT/GISCO Database Manual, Eurostat 2005). The CLC nomenclature is not detailed enough to distinguish between the different kinds of agricultural land cover. Arable crops are indeed aggregated (even for the 44 class nomenclature) and the CLC is a pluri-annual land cover map, consequently most crops are not reported [START_REF] Gallego | Using CORINE land cover and the point survey LUCAS for area estimation[END_REF].

The European Soil Database

The European Soil Database v2.0 (ESDB 5 ) contains a large number of soil related parameters in raster data files with cell sizes of 1km x 1km. Those rasters are in the public domain but data access should be asked to the Joint Research Center and allow expert users to use the data, for instance to run soil, water and air related models/pedo-transfer rules. Parameters used in this study (all qualitative variables) are :

-Main and secondary limitation to agricultural use, dominant and secondary surface textural class, depth class of an obstacle to roots (from the sub-database Soil Geographic Database of Eurasia, SGDBE) -Topsoil and subsoil water availability, topsoil and subsoil cation exchange capacity (from the sub-database Pedo-Transfer Rules Database, which holds results of application of pedotransfer rules to SGDBE parameters).

These variables are quantitative.

The meteorological data

Method for downscaling the farm-groups

The starting point is assumed to be the availability of results provided by an economic model based on a set of representative farming systems. Due to lack of information or to limited access to information, geo-referenced modeled systems are usually not available at fine resolution. Nevertheless downscaling and mapping of such results can be achieved by using probabilistic linkage between economic activities and land cover.

Let us consider on the one side economic entitiesnamely farm-groupsdefined according to variables such as altitude, economic size and type of farming. A realistic case occurs when statistical information is delivered at regional level. Let us consider on the other side that land cover information meets agricultural activitiesor clusters of agricultural activitiesaccurately in terms of geographical location. These two information sources can be brought together, leading to assess the probabilistic location of economic entities at the finest level allowed by the land cover database.

As described in the material section, the very preliminary step simply consists in considering the agricultural supply-side modelnamely the AROPAj modelwhich refers to a set of representative farm-groups defined at the regional level and acting as autonomous economic agents.

The next two steps related to mapping and downscaling issues, presented in the following, consist in assessing (i) probabilistic location of activities related to observable explanatory variables on a fine resolution grid, (ii) weights related to farm-groups at grid cell level.

Concretely the method is implemented (i) by mapping locally the co-variables associated to the farm-groups, i.e. altitude and land use; (ii) by using regional data related to the Farm Accountancy Data Network (FADN) as a priori knowledge for transforming land use, land cover, climate, soil and altitude information into farm-group location probabilities within each region.

Combining the data into a common database

The overlay relies on two GIS operations: (i) the overlay of CLC polygons with the other georeferenced explanatory variables and also with the FADN regional boundaries; (ii) the combination of the LUCAS observations with layers of explanatory variables. The CLC is available in both a vector and a raster based format. The original vector land cover database converted into a raster format is made available at a 100mx100m raster cell size in Lambert-Azimuth coordinates (see EUROSTAT/GISCO Database Manual, Eurostat 2005). The overlay is realized at the regional level.

Co-location inaccuracies which are possibly accumulated during the different steps of the overlay are not discussed in this paper: We assumed that related errors refer to geographical deviations lower than 100m (see [START_REF] Gallego | Building Agro Environmental Indicators. Focusing on the European area frame survey LUCAS[END_REF], for discussion and remarks about the CLC and LUCAS overlay).

The spatial unit resulting from this geographical overlay corresponds either to a CLC polygon (thus mapping unit of at least 25ha), either to an intersection of a CLC polygon with one (or more) of the other layer's mapping unit (ESDB mapping unit, cell from the 50x50m meteorological grid, DEM polygon or FADN region).

These new polygons are the mapping units of the geographic layer resulting from the downscaling process.

The downscaling process

General concept

The downscaling consists in mapping the AROPAj farm-groups from the administrative regional FADN level (source unit) into a finer resolution level. One way to carry out such a task consists in using co-variables and distributing data at the target level following the distribution of this co-variable at this target level, which is obviously assumed to be known [START_REF] Vidal | Geographical use of statistical data, methodological overview. in "Towards agri-environmental indicators[END_REF].

Co-variable information refers to soil, climate and elevation characteristics. Geo-referenced farm activities come through databases devoted to land use and land cover. It is assumed that these geo-referenced databases can be overlaid with FADN regions maps. [START_REF] Hazeu | A biophysical typology in agri-environmental modelling[END_REF] built a methodology for spatially allocating farm-types. The procedure combines a logit model with a Bayesian highest posterior density estimator in order to distribute spatially an intermediate entity, the Homogenous Spatial Mapping Unit, defined by homogeneous production conditions (given by a combination of the co-variables) rather than administrative boundaries. Afterwards the farm-types are related to Homogenous Spatial Mapping Units (co-variable combinations). However, focusing the disaggregation on the combined intermediate entity rather than on each co-variable can lead to more errors difficult to identify (Bierkens et al., 2000).

The co-variable relates to the data through a mathematical function or a statistical model. We assume that the target unit is a partition of the source unit. Let us consider the quantitative variable Y located on the geographic unit A related to the source level. Let us consider a partition B i of A. Let us define the variables Y i which refer to the unknown distribution of Y over B i . We set:

A B i i Y Y (Equation 1)
Let us use the set of co-variables X i assumed to be known for subunits B i with X "linked"/"related" to Y : 2) Y refers to an output related to a farm-group, A refers to a FADN region, and B i refers to any sub-regional geographical level. Our problem can be so expressed: which would be the probability of locating a farm-group on any B i . In other words, what is the "weight" of the considered farm-group in the agricultural activity on any B i . A preliminary step consists in using the Altitude. This is obviously not sufficient for precise locating of the farm-group but taking into account this criterion will improve the likely location within the region.

) ( i i X f Y (Equation
Hence, as a first step of the GIS operation, the geographic unit A (to which the considered farm-group belongs) is restricted to the sub-units A alt where alt refers to the elevation (according to the regions).

1,2,3 alt alt

A A (Equation 3)

Regarding the co-variables required by the spatial disaggregation, the linkage between farmgroups and geo-referenced indicators of activities comes through the area devoted to agricultural land uses by farm-groups. Estimated areas are provided by the pre-estimating step of the AROPAj build-up (see the section 2.1). The generic variable Y is now replaced by a vector of areas S j,k where j refers to the crop and k refers to the farm-group.

Finally the spatial disaggregation enters in a two-steps process (regarding the FADN region scale). The first step is devoted to the likely location of crops. The second step consists in estimating the likely location of regional farm-groups through the crop location. The first step is made of two sub-steps: (i) spatial econometrics is called for estimating the f-function when X refers to geo-referenced physical observations leading us to prior estimates of crop location; (ii) these prior estimates are used to assess the probability of crop location based on a cross entropy method.

Mapping the crop cover probability

Chakir ( 2009) proposed a 2 step-approach aiming at the estimate of the probability to find a land cover category (agricultural activity) on any geo-referenced cell. The first step is based on the application of a Multinomial Logit model (MNL) which relates the land cover/type of crops through the LUCAS points to a set of explanatory variables, within each FADN-region.

As explained above, explanatory variables are CLC classes7 , Altitude, Slope, Climatic parameters (average annual temperature and annual sum of rain), and Soil characteristic parameters. The MNL is a usual tool of prediction of the probability of occurrence related to a random event which here is the land cover category. The probability of finding the land cover category j on the cell i follows the expression: 4) in which x ij denotes the explanatory variables, ij denotes the error term and β j denotes the vector of parameters. In our problem the observed ij P are provided by the LUCAS database.

'' '1 exp( ) P exp( ) ij j ij ij J ij j j x x (Equation
The estimate of β j leads to the estimate of the probability ij P (denoted by P ij ). The estimating process is conducted separately for each FADN region. Consequently the econometric model (i.e. the parameter β j ) depends on the region.

The second step aims at optimizing the estimates of the land cover probability. The previous econometric model does not take account of information provided by the FADN database such as the regional proportion of land cover related to the various crops. The land cover estimates are now refined in order to minimize the difference between the estimated land use share (derived from probabilities , P ij estimated by the MLN model) and the observed land use share (derived from the FADN data). [START_REF] Chakir | Spatial Downscaling of Agricultural Land-Use Data: An Econometric Approach Using Cross Entropy[END_REF] used the Generalized Cross Entropy method (GCE, see [START_REF] Rubinstein | The Cross-Entropy method for combinatorial and continuous optimization[END_REF][START_REF] Golan | Maximum entropy econometrics: Robust estimationwith limited data[END_REF] applied to the re-estimate of probabilities P I,j of finding the crop j on the cell i (previously estimated by , P ij and hereafter denoted by Q I,j ). This leads to solve the following optimisation programme (separately for each FADN region) :

, , , , min ln ln ij ij EE i j j j i j j Q ij Q Q Q Q P subject to : , , : 0 ij i j Q , , :1 ij j iQ, 1 E j j Q , , : E i j i j j j i j Q u R Q
in which R j, denotes the regional FADN area devoted to the land cover category j (including an additional category devoted to non-FADN land cover), u i denotes the area of the i th raster cell (i.e. the area of the polygons previously defined), denotes a user selected error weight, and j denotes the components of a selected error vector (constrained by The whole procedure is summarized on the Figure 1.

Mapping the farm-groups

Regarding now the co-variables required for the spatial disaggregation, the linkage between farm-groups and geo-referenced indicators of activities will come through the area devoted to Thus, for a given FADN region, let us note jk S the surface occupied by crop-category j into farm-group k. The sum jk j S provides the surface of each farm-group of the region while jk k S provides the area occupied by each crop category into the region. The sum jk jk S

represents the total area (used for agricultural activities) of this FADN region.

Starting from a land cover probability map (at a fine resolution) distinguishing crop categories (as the ones used in AROPAj), a farm-group presence probability distribution can be derived from the crop category shares observed on the FADN region. Let us note ij Q the probability of presence for crop category j on geographic unit i . Let us define ik T related to the farmgroup k and to the geographic unit i : 5)

1 1 J ij jk ik K j jh h QS T S (Equation
Considering that all activities other than crops used in the first step are grouped in one extra land cover class ("Non Agricultural Use") and that the total number of land cover classes is J, it is easy to check that ik T is eligible for a probability. For any FADN region we have :

, : 0 1 ik i k T , 1 :1 K ik k iT
. The probability ik T refers to the relative contribution of the k farm-group (within a region) to the share of agricultural activities present into the i cell belonging to the regional territory (with convenient altitude restriction). For a given k (in 1,..K), ik i T is equal to the area of the farm-group k.

Summarily (Equation 5) provides a probability distribution of the AROPAj farm-groups between the target units following the distribution of land cover categories on those target units. Let us recall that the f function stated by Equation (2) leads to the crop categories probability distribution ij Q through the co-variable x. Availability of ij Q allows us to implement a spatial distribution tool in order to scatter any output related to the farm-groups (that is to say any AROPAj output) over the FADN region.

Results of the downscaling

The spatial disaggregation of farm-groups is the result of the two-steps process described above. Following the first step devoted to the probabilistic location of crops (section 3.2.2), the second step consists then in estimating the probabilistic location of regional farm-groups through the crops location (section 3.2.3). The two steps have been implemented for numerous regions in the EU.

The results of probabilities of farm-group distributions are illustrated by the case of the Bourgogne French region. Figure 2

Spatial disaggregation of agricultural N-pollutants

Knowledge of accurate location of pollution is of high interest regarding at least two aspects that are (i) display of information useful for policy makers and (ii) disposal of quantitative elements as a part of a complete chain of modelling. The latter occurs when an agroeconomic model is linked to a hydro-geological model dedicated to the estimate of nitrate concentration in aquifers. More precisely, the AROPAj model will be linked to the hydrological model MODCOU [START_REF] Ledoux | Agriculture and groundwater nitrate contamination in the Seine basin -The STICS-MODCOU modelling chain[END_REF], which describes surface and groundwater flow at a daily time step. Time and spatial disaggregation will be requested. We focus here on the spatial dimension, when AROPAj outputs need to match the MODCOU grid cells (squared cells from 1 to 16 km 2 ). Our downscaling tool is applied to the AROPAj estimates of yearly presence of N-pollutants sourced from agriculture at the convenient geographical scale.

The AROPAj model has been strongly improved by introducing N-yields functions based on the use of the STICS crop model. The adequate methodology is developed by [START_REF] Godard | Use of the available information at a European level to construct crop nitrogen response curves for the regions of the EU[END_REF]. Additional work devoted to greenhouse gas emission by [START_REF] Durandeau | Coupling biophysical and micro-economic models to assess nitrous oxide emissions from cropland and the effects of mitigation measures on greenhouse emissions[END_REF] has been extended to provide N-pollution functions regarding nitrous oxide (N 2 O, a greenhouse gas), ammonia (NH 3 ) and nitrate (NO 3 ) mainly sourced from the use of nitrate fertilizers. We In this subsection the study area is limited to the Seine river basin (Northern France) which intersects 8 FADN regions and 54 farm-groups, covering more than 9 millions hectares. We use additional information regarding the distribution of agricultural activities (areas of each land cover category) available at a level finer than the FADN region. This finer level refers to small districts named Small Agricultural Regions (SAR)8 . In order to take full advantage of this information, the land cover map is re-calibrated from the GCE step at the SAR district level instead of at the FADN region level as previously (see the 3.2.2 subsection). The farmgroup location is now based on these new estimates of the probability LC ij Q .and re-estimated through (equation 5). The process is illustrated by the Figure 5. The two land cover maps (numbered 1 and 3 on Figure 5) refer to the same total (regional) surface regarding each land cover category (permanent grassland on the figure). But change in the calibration may significantly impact the SAR surface related to a given land category (see the map 2 on And we can suspect other bias source when all databases used in the modeling chain differ in years. Nevertheless accuracy of the (likely) location of the economic model outputs will meet a large range of applications devoted to policy analysis and to model linkage.

Concluding remarks

Our downscaling method helps to achieve the spatial distribution of the AROPAj economic model outputs at the finest geographical resolution level inferred by the most geographically detailed database. This is strongly useful for land use policy making when impact heterogeneity enters the decision process as well as for coupling the economic model with physical models when the analyze of environmental phenomena requests information on high spatial resolution grids. In this sense, the mapping of impacts related to different agricultural policies is a matter of great concern.

The spatial distribution of outputs is provided through the localization of the economic working units of the model, i.e. the farm-groups. The mapping of AROPAj farm-groups is based on the land allocation among agricultural activities related to all farm-groups inside each FADN region. This statistical information in term of location probability is linked to land use information which is interpolated mainly by using Corine land cover as a support (this means that intensive animal producers would be worse located). CORINE land cover is the main support used to establish the land cover map and thus the spatial unit for the disaggregation is the 100x100m CLC raster cells.

The farm-groups location map allows us to highlight the diversity of CAP impacts on land use, as it is shown by the application of the method to assess the impacts of the Luxembourg reform implementation. It is clear that other outputs provided by the AROPAj model through the farm-groups mapping would modify the analysis of land use change from set-aside to abandonment, compared to what a more usual modeling approach based on homogenous regional or national scale would show. In the same line of thinking, the analysis of environmental indicators such as greenhouse gas emissions or such as nitrous pollutants would be strongly affected by the high resolution mapping of results, when policy makers have to take account of individual consequences of policies for making their implementation easier.

With respect to the existing right of access to databases and information, we design a route for the mapping of economic model outputs which is something else than fictive homogeneous regional or national maps. The increasing availability of richer information and the improvement of statistical methods related to spatial disaggregation joined with geographical information systems and more performing computation tools, lead to supply geographically disaggregated results. Two use fields appear. Firstly, a better assessment of environmental impacts related to agricultural activities requires high resolution mapping. This is useful for the improvement of the economic regulation of external effects as well as for the improvement of environmental impact assessment related to any change in policy and in the environment (e.g. climate change). Secondly, back to economic theory and to policy design, interesting perspectives appear regarding for instance the subsidiarity principle in policy making.

Acknowledgment

The GENEDEC project was funded by the European Union under the contract 502184 (2004, 6 th Framework Programme) and aimed at analysing the impacts of CAP change on agriculture and the environment.

The perennial French interdisciplinary PIREN-Seine programme aims at understanding the ecological and economical functioning of the Seine River basin.

(

  EU) farming systems' behavior facing change in economics (Common Agricultural Policy, European Directives regarding environment, the climate change and bio-energy). The AROPAj model aims at providing assessment of the impacts of European agricultural and environmental policies at different scales from the farm level to the EU one. Light presentations of the model are available in papers by De Cara and Jayet (2000), De Cara et al (2005). It is a short-term supply model using mathematical programming tools.

  Meteorological data come from the European MARS meteorological database (Monitoring Agriculture with Remote Sensing 6 , MARS FOOD unit of the EU-JRC). Derived from global atmospheric model, this database holds daily climatic data for Europe interpolated on a 50 x 50 km grid. Data are daily, 10-daily and monthly outputs from the ECMWF (European Centre for Medium-Range Weather Forecast) atmospheric model. The two parameters extracted from this database to be used in this study are the annual average temperature and annual sum of rain (both parameters averaged on years 1990-2000). They are used as quantitative variables.

  bias occurs regarding the fact that the FADN is based on yearly data (not synchronous with other databases) related to a sample of the agricultural holdings in the EU. Nevertheless we have refined estimates of land cover probability linked to FADN and useful for spatial disaggregation of the outputs provided by an agricultural economic model based on FADN. We denote the solution of the CGE programme by LC ij Q .

Figure 1 -

 1 Figure 1 -Procedure to build a crop cover probability map from LUCAS, CLC2000 and other explanatory variables DEM, soil map and climatic data.

  shows the probable location of belonging either to the Altitude <300 class (right) or to the Alt>300 class (left). In the version of the AROPAJ model implemented on EU15 and related to FADN2002, the Bourgogne region leads to cluster FADN sample farms into 10 farm-groups ranged from "40" to "49" (8 on the Alt<300 side and 2 on the Alt>300 side). In addition the figure delivers the numbers of farms of the FADN sample split into FADN farm types which characterize a farm-group.

Figure 2 -Figure 3 -

 23 Figure 2 -Probability of farm-group location in the Bourgogne region: 2 farm-groups (out of 8, #45 and #49) in altitude lower than 300 m, and the 2 farm-groups #40 and #42 in altitude higher than 300 m

Figure 4 -

 4 Figure 4 -Change in probability of presence of cereals when the CAP turns the Luxembourg agreement scenario into the Agenda2000 scenario: mapping at the regional level (EU15, left) and at the 100x100m raster cells level for the region Auvergne.

  provided a set of functions applied to the French agriculture regarding the major crops. The N-yield functions are expressed by ( ) ( ) tN Y N b b a e in which b denotes the asymptotic potential, a denotes the yield related to the 0 N-level, and relates to the curvature, and Npollutant functions are adjusted to expression of the type () P N c d N . All parameters a, b, , c, d depend on the crop and on the farm-group.

Figure 5 -

 5 Figure 5 -Procedure to refine the land cover map with data provided on the Small Agricultural Regions.Example for FADN region 132 ("Picardie", northern France)

Figure 7 -

 7 Figure 7 -N-loss in NO 3 emissions (tN NO3 /ha) sourced from the agricultural activities represented by the AROPAj model on the Seine river Basin (Agenda 2000 scenario).

Figure

  Figure5).

The Digital Elevation Model (DEM) used is the 100mx100m STRM 2003 DEM, a resample (and clipped on Europe) version of the digital topographic data from the SRTM project (NASA Shuttle Radar Topography Mission 2 ) available from the Joint Research Center. The

	original 90m SRTM was resampled in accordance with the recommended INSPIRE
	guidelines (Commission of the European Communities, 2007): resampled at 100m, aligned
	with the INSPIRE reference grid and projected in the ETRS89 Lambert Azimuthal Equal

  For instance, category Cereals in some regions could be split into more classes : Wheat, Maize and Other Cereals (here other than Wheat and Maize), or Wheat, Barley and Other Cereals (now including Maize and not Barley), etc. On the contrary, in some Southern European regions, Cereals may be included into a general class Other Crops while other categories such as Olive Trees or Vineyards are taken into account.

	the different agricultural activities (crop categories) for each farm-group. It is assumed that
	land use diversity within a region reflects the diversity among this region's types of farming.
	Estimated areas for each type of cultivation are provided by the pre-estimating step of the

AROPAj build-up. Crop categorization into AROPAj distinguishes usual crop categories/land uses (Cereals, Permanent Crops, Permanent Grassland, Fresh Vegetables, Oleaginous, Fruit Trees, etc) but varies from one region to another due to the diversity in European agriculture.

http://ec.europa.eu/agriculture/rica/index_en.cfm. FADN is still the only representative and reproducible source of micro-economic data covering EU farming systems, even if costs and goals are widely questionable and discussed(Vrolijk et al,

2004).

http://www2.jpl.nasa.gov/srtm

Area frame surveys: aim, principals and operational survey, http://agrienv.jrc.it/publications/ECpubs/agri-ind/ LUCAS has been deeply restructured in 2006[START_REF] Jacques | The LUCAS project -The new methodology in the 2005/2006 surveys[END_REF], but the data of 2001 were used in this study to "fit" with CLC 2000.

CORINE Land Cover stands for CO-oRdination on INformation of the Environment Land CoverSee http://www.ec-gis.org/docs/F10418/CLCTECHNICAL_GUIDE.PDF and http://ec.europa.eu/agriculture/publi/landscape/about.htm

http://eusoils.jrc.it/esbn/Esbn_overview.html

http://agrifish.jrc.it/marsstat/datadistribution/ http://agrifish.jrc.it/marsfood/ecmwf.htm

It is assumed here that CLC classes "Artificial surfaces" (mostly urban and industrial areas, numbered 1 in the level 1 nomenclature), "Water Bodies" and "Wetland" (nb.4 and 5 in the level 1 nomenclature) are not used for agricultural purpose; hence raster cells belonging to those classes are excluded from the modeling phase.

Small Agricultural Regions (in French 'Petites Régions Agricoles'), units defined by a regrouping of communes within a' Département' (subregional partitioning) having 'similar' agricultural orientations and activities. It is managed by the French institute for statistics and economy (INSEE). http://agreste.agriculture.gouv.fr/definitions_6/zonages_81/index.html Those data were provided by PIREN-Seine project partner INRA SAD-MIRECOURT.

We thank participants and the discussant during the contributed paper session of the XII th EAAE congress held in Ghent on August 2008 for helpful comments.

Technical data overlay at the beginning of the procedure was realized by the JRC team involved in the GENEDEC programme.

We strongly thank Delphine Leconte-Demarsy for her careful reading of the paper.

Applications of the downscaling

Assessment of CAP option impacts on land use

Back to the AROPAj model and to the AROPAj farm-groups, we use the downscaling tool to estimate the impacts of policy change on land use. Land allocation of farm-groups is seen as a typical output of the AROPAj model. The Common Agricultural Policy (CAP) offers a large range of applications which could impact the land allocation. Different CAP options are easily implemented into the AROPAj model thanks to the normative mathematical programming approach, and the model runs provide land allocation results for each farm-group and for any policy option.

We consider two CAP scenarios, namely the Agenda 2000 and the Luxembourg agreement.

The Agenda 2000 refers to the CAP in effect before the reform adopted on June 2003 (i.e.

the Luxembourg agreement). The de-coupling scheme regarding subsidy supply at the core of the Luxembourg agreement progressively enters in force in 2005 (see [START_REF] Jayet | Impact d'une réforme de la Politique Agricole Commune par le découplage[END_REF], for theoretical and applied explanation regarding "de-coupling" in the CAP). Usual geographical visualization of the AROPAj outputs leads to monochrome mapping at the FADN region scale, as the aggregation of the farm-groups output. It is now possible to downscale the results thanks to the farm-groups' contribution related to the agricultural activity spread over all 100mx100m cells within the FADN region. The downscaling process is applied to the mapping of change in the probability of presence of (i) pastures and (ii) cereals when the CAP turns the Agenda 2000 scenario into the Luxembourg agreement scenario. Figure 3 But change impacts are possibly quite complex. With CAP change, some plots (and farms) may be devoted to local higher yield crops while less numerous plots (or farms) are covered by these crops. That leads to possible relocation of production, and so reorganization of agricultural markets and logistics up-stream and down-stream. Some of other dramatic consequences of these changes arise regarding the environment, mainly through change in pollution sources (i.e. Nitrogen losses).


Sabrina Jolly

Anne Bado-Nilles

Florent Lamand

Cyril Turies

Edith Chadili

Jean- Marc Porcher

Stéphane Betoulle

Wilfried Sanchez
email: wilfried.sanchez@ineris.fr

Multi-biomarker approach in wild European bullhead, Cottus sp., exposed to agricultural and urban environmental pressures: practical recommendations for experimental design

Keywords: Fish, Immunotoxicity, European bullhead, Seasonal variability, Multi-biomarker approach

In freshwater ecosystems, a large number of chemical substances are able to disturb homeostasis of fish by modulating one or more physiological functions including the immune system. The aim of this study was to assess multi-biomarker responses including immunotoxicity induced by urban and agricultural pressure in European bullheads living in a small French river basin. For this purpose, a set of biochemical, immunological, physiological and histological parameters was measured in wild bullheads from five locations characterized by various environmental pressures. Moreover, to address effects of physiological status and contamination level variation on biomarker responses, fish were sampled during three periods (April, July and October). Results revealed a clear impact of environmental pressure on fish health and particularly on immunological status. An increase of EROD activity was recorded between upstream and downstream sites. Upstream sites were also characterized by neurotoxicological effects. Fish exhibited upstream/downstream variations of immunological status but strong differences were observed according to sampling season. Conversely, regarding biochemical and immunological effects, no significant response of physiological indexes was recorded related to environmental pressures. According to these results, the European bullhead appears as a valuable model fish species to assess adverse effects in wildlife due to urban and agricultural pressures.

Introduction

Adverse effects induced by several environmental pollutants, including widely used pesticides such as organophosphate, organochlorine or metals in aquatic ecosystems were assessed by many authors [START_REF] Krzystyniak | Approaches to the evaluation of chemical-induced immunotoxicity[END_REF][START_REF] Brousseau | Marqueurs immunologiques[END_REF]. Since these chemicals are known to induce disturbance of immune function in wild organisms, and especially in fish [START_REF] Thomas | Pesticide-induced Immunotoxicity: Are great lakes residents at risk?[END_REF][START_REF] Dautremepuits | Modulation in hepatic and head kidney parameters of carp (Cyprinus carpio L.) induced by copper and chitosan[END_REF], some immune parameters are proposed as ecotoxicological biomarkers. Currently, innate immune response was used as attractive biomarker for immunotoxicological risk assessment studies on mammals [START_REF] Luster | Development of a testing battery to assess chemical-induced immunotoxicity: National Toxicology Program's guidelines for immunotoxicity evaluation in mice[END_REF]. This innate immune response is part of the first-line defense in the immune system of organisms acting against pathogens without prior exposure to any particular microorganism.

Among them, respiratory burst, a crucial reaction of phagocytes to degrade internalized particles and bacteria [START_REF] Huber | Respiratory burst as a biomarker for stress responses[END_REF], and lysozyme activity, a major parameter in the nonspecific immune defense [START_REF] Magnadottir | Innate immunity of fish (overview)[END_REF], are described as relevant indicators of toxicant exposure in aquatic organisms [START_REF] Fournier | Phagocytosis as a biomarker of immunotoxicity in wildlife species exposed to environmental xenobiotics[END_REF][START_REF] Bols | Ecotoxicology and innate immunity in fish[END_REF][START_REF] Reynaud | The effects of polycyclic aromatic hydrocarbons on the immune system of fish: A review[END_REF].

Moreover, immune biomarkers appear as attractive effect-based monitoring tools due to their capacities to predict population disturbances by modification of disease susceptibility [START_REF] Zelikoff | Biomarkers of immunotoxicity in fish and other non-mammalian sentinel species: predictive value for mammals?[END_REF][START_REF] Bols | Ecotoxicology and innate immunity in fish[END_REF]. However, ecotoxicological status of water bodies cannot be reduced to measurement of immunomarkers while, i) immunotoxic pollutants are able to disturb other physiological functions such as reproduction [START_REF] Kime | The effects of pollution on reproduction in fish[END_REF] or xenobiotic metabolism [START_REF] Whyte | Ethoxyresorufin-O-deethylase (EROD) activity in fish as a biomarker of chemical exposure[END_REF], and/or ii) many other pollutants are involved in environmental pollution. Also, immune parameters must be integrated in a multi-biomarker approach based on measurement of a set of complementary parameters. These parameters must be linked to several physiological processes able to reflect the effects of global contamination in investigated organisms [START_REF] Flammarion | Multibiomarker responses in fish from the Moselle River (France)[END_REF]Sanchez et al., 2008a), such as biotransformation, oxidative stress and/or neurotoxic disturbances.

For this purpose, a set of complementary biomarkers involved in fish immunity (i.e. respiratory burst and lysozyme activity) but also in organic compounds biotransformation (i.e. 7-ethoxyresorufin-O-deethylase, EROD; cytochrome P4503A activity, CYP3A, and glutathione-S-transferase, GST activities), protection against oxidative stress (i.e. glutathione peroxydase, GPx,) and neurotoxic disturbances (i.e. acetylcholinesterase activity, AChE) was measured to assess effects of field multi-contamination in wild European bullheads, Cottus sp., exposed to agricultural and urban environmental pressures. This fish was selected due to its wide repartition in French and European freshwater ecosystems. Moreover, this small benthic fish is characterized by a strong sedentary behavior [START_REF] Reyjol | Effects of temperature on biological and biochemical indicators of the life-history strategy of bullheads Cottus gobio[END_REF] that allows assessing local environmental disturbances conversely to migratory or mobile fish species. Some studies have documented the physiological and biochemical responses of this species after field or laboratory chemical exposure and highlighted the bullhead as a relevant model fish species in ecotoxicology [START_REF] Bucher | Prevalence and pathology of Zschokkella nova (Myxosporea) in the liver of bullhead Cottus gobio from a polluted river[END_REF][START_REF] Bucher | Disturbances in the prooxidant -antioxidant balance in the liver of bullhead (Cottus gobio L.) exposed to treated paper mill effluents[END_REF]Dorts et al., 2011aDorts et al., , 2011b)). This work was completed by histological analysis of gonads. Indeed, histology offers a powerful tool for sex determination, identifying the stage of gonad development, documenting presence of intersex and other abnormalities. Histological analysis of reproductive tissues can provide predictive information related to the fitness of organisms and their reproduction potential [START_REF] Blazer | Histopathological assessment of gonadal tissue in wild fishes[END_REF]. Gross indices such as Liver-and Gonado-Somatic Index (LSI and GSI, respectively) and Condition Factor (CF) were also determined. These integrative indicators serve to provide information on energy reserve, possible disease and general condition of fish [START_REF] Mayer | Physiological and nonspecific biomarkers[END_REF]. The effect-based monitoring approach deployed in this study allows integrating effects of environmental pollutants in various biological levels (i.e. biochemical, histological, physiological and morphological). Three different seasons were also chosen: spring (April), summer (July) and autumn (October). In fact, environmental conditions, which are directly (temperature, photoperiod, precipitation, pathogens) or indirectly (anthropogenic pressures) controlled by the season, can affect fish physiology and health [START_REF] Bowden | Seasonal variation and the immune response: A fish perspective[END_REF].

Materials and methods

Site descriptions

This study was performed in the Vesle River basin located in Champagne-Ardenne region (Northeastern France). Five sites were investigated: Bouy, Prunay and Muizon located on the Vesle River; Courtagnon and Serzy in the tributary Ardre River (Fig. 1). The Bouy and Prunay sites, located upstream from the city of Reims, were highly influenced by intensive cereal farming and by agricultural (sugar beets, alfalfa, peas and potatoes; 70% of land use) and viticultural practices (5.5% of land use), respectively. Muizon station was chosen due to its urbanized area location (downstream from the city of Reims), where water quality has been classified as "bad" by the French Water Quality Monitoring Program (RNB) due to high concentration of Diuron, associated with high levels of nitrogen and phosphorous (Seine-Normandie Water Agency, 2007). In the Ardre River, the upstream Courtagnon site was located in a forested sector without direct environmental inputs of chemicals, whereas Serzy station, situated downstream, was located in an intensive wine-growing area with "bad" water quality due to high concentrations of isoproturon, aminotriazole, glyphosate, atrazine and MCPA (Seine-Normandie Water Agency, 2007). Due distance between investigated sites and sedentary character of bullhead, investigated populations were isolated in each of the five stations.

Fish collection and tissue sampling

For each location, 12-20 adult European bullheads of both sexes (mean length 82 ± 6 mm) were caught by electrofishing, which seems to have no effect on immune parameters tested [START_REF] Vanderkooi | The effect of elestroshock on immune function and disease progression in juvenile spring chinook salmon[END_REF] , in April, July and October 2010. Fish were immediately killed by spinal dislocation, weighed, measured and sexed. The liver, the gonads and approximately 10 mg of muscle were collected and weighed. The liver and the muscle were stored at -80 C for biochemical analysis, whereas the gonads were maintained in Bouin's fluid for histological analysis. Trunk kidneys were also removed under aseptic conditions and cut in two parts to measure immunological parameters. A piece of trunk kidney was stored in liquid nitrogen and another piece was pressed through sterilized nylon mesh (40 µm, Dutscher) with Leibovitz 15 Medium (Sigma) containing heparin lithium (10 U.mL-1, Sigma), penicillin (500 U.mL-1, Biochrom AG) and streptomycin (500 µg.mL-1, Biochrom AG) to obtain leucocyte suspension [START_REF] Secombes | Isolation of salmonid macrophages and analysis of their killing activity[END_REF]. Leucocytes were stored for 12 hours at 5 ± 1°C before measuring immune cellular responses.

Morphometric indices

As described by [START_REF] Janssen | The annual ovarian cycle and the influence of pollution on vitellogenesis in the flounder, Pleuronectes flesus[END_REF], CF were calculated as [100 x body weight (g)] / length 3 (cm)]. The LSI was calculated as the [100 x liver wet weight (g)] / total body weight (g)] [START_REF] Sloof | Relative liver weights and xenobioticmetabolizing enzymes of fish from polluted surface in the Netherlands[END_REF]. GSI was calculated as [100 x gonad weight (g)] / fish weight (g)] [START_REF] Lofts | Effects of methyl testosterone on the testes of a hypophysectomized cyprinodont fish, Fundulus heteroclitus[END_REF].

Biochemical analysis

Both liver and muscle were homogenized in an ice-cold potassium phosphate buffer (0.1M, pH 7.5) supplemented with 20% glycerol and 0.2 mM phenylmethylsulfonyl fluoride (PMSF) in order to inhibit proteolysis. Homogenates were centrifuged at 10,000 g, + 4°C for 15 min and the resulting post-mitochondrial fraction was used for biomarker measurements. Protein concentration was determined using the method of [START_REF] Bradford | A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding[END_REF] with bovine serum albumin (Sigma Aldrich chemicals, France) as the standard. Hepatic activities of EROD, CYP3A, GST, GPx were assessed. Briefly, EROD activity was measured according the method developed by Flammarion et al. (1998a). The diluted samples at 25 to 100 µg of proteins were added to phosphate buffer containing 8 µM of 7-ethoxyresorufin and 0.5 mM of NADPH. Formed resorufin was quantified by fluorimetric measurement with 530 nm wavelength excitation and 590 nm wavelength emission. Resorufin was used as standard, and results were expressed in pmol of resorufin/min/mg of proteins. CYP3A activity was measured according to [START_REF] Miller | Fluorimetric high-throughput screening for inhibitors of cytochrome P450[END_REF]. The post-mitochondrial fraction at 60 µg of proteins was added to 200 mM of 7-benzyloxyfluoromethylcoumarin and 2 mM of NADPH.

The formation of 7-hydroxy-4-trifluoromethylcoumarin (HFC) is monitored by fluorescence using an excitation wavelength of 410 nm and an emission wavelength of 510 nm. Data were expressed as pmol of HFC formed/min/mg of proteins using HFC as standard. GST activity assay was conducted according to [START_REF] Habig | Glutathione S-Transferases. The first enzymatic step in mercapturic acid formation[END_REF]. The diluted samples at 12.5 µg of proteins were mixed with 1 mM of chloro dinitro benzene and 1 mM of reduced glutathione.

Enzymatic reaction was monitored spectrophotometrically at 340 nm and results were expressed in U of GST/g of proteins. The diluted samples at 56 µg of proteins were used to determine GPx activity according to the method of [START_REF] Paglia | Studies on the quantitative and qualitative characterization of erythrocyte glutathione peroxidase[END_REF]. Cumene hydroperoxide was used as substrate and enzymatic activity was assessed at 340 nm. Results were expressed in U of GPx/g of proteins.

AChE activity was measured in muscle according to the method developed by [START_REF] Ellman | A new and rapid colorimetric determination of acetylcholinesterase activity[END_REF]. The formation of 5-thio-2-nitro-benzoic acid generated by the sample diluted at 5 µg of proteins or by the standard of AChE from electric eel (Sigma-Aldrich Chemicals), was monitored at 405 nm. The results were expressed as U of AChE/g of proteins.

Immunological parameters

To determine lysozyme concentration, the piece of kidney stored at -80 °C, was homogenized in an ice-cold potassium phosphate buffer (0.05 M, pH 6.2). Homogenates were centrifuged at 3,450 g, + 4 °C for 30 min and the resulting supernatant was used for immune analysis. The protein concentration was determined and the lysozyme activity was measured by a slight modification of the turbidimetric assay described by [START_REF] Studnicka | Lysozyme level in carp (Cyprinus carpio L.)[END_REF]. Sample or lysozyme standard (Sigma-Aldrich) was added to Micrococcus lysodeikticus suspension (0.2 g/L) and the decrease in absorbance was recorded at 450 nm by spectrophotometry. The results were expressed as U of lysozyme/g of proteins.

Concerning determination of respiratory burst activity (Reactive oxygen species (ROS) production), leucocyte suspension samples were centrifuged for 10 min at 400 g at 4°C. The supernatant was eliminated and the leucocytes were re-suspended in 1 mL of L15 medium supplemented with heparin, penicillin and streptomycin. Viable cells were counted by trypan blue exclusion and the cell suspension was diluted with L15 medium to obtain 60,000 viable cells per well. Determination of respiratory burst activity (ROS production) was performed using a modification of the chemiluminescence method of [START_REF] Stave | Chemiluminescence of phagocytic cells isolated from the pronephros of striped bass[END_REF]. Each assay well was filled with a volume of 160 µl containing cell suspension in L15 medium (60,000 cells), luminol (20 µL at 2 x 10 -5 M in L15 medium) and PMA (20 µL at 125 ng.mL -1 ). Controls were carried out with L15 medium. Cellular baseline was determined at this zero time, PMA was immediately added and the level of intracellular luminescence was measured in both unstimulated and stimulated cells. Chemiluminescence was determined at 20°C with a microplate luminometer (VICTOR 2 , 1420 Multilabel counter). Data were collected every 1.30 min for 45 min. The results were expressed in Relative Luminescence Units.

Gonad histology

Dissected tissues were fixed in Bouin's fluid for 24 hours, dehydrated through a series of ethanol, cleared with toluene and finally embedded in paraffin. Sections of 5 µm in thickness were cut using a model RM2245 microtome (Leica Microsystems, Wetzlar, Germany) and stained with hematoxylin and eosin (HE). Gonad sections were then examined under a Zeiss Axioimager z 1 light microscope (Zeiss GmbH, Göttingen, Germany) and gonadal development stages were determined and adapted from the classification described by [START_REF] Jafri | Gametogenesis in roach, Rutilus rutilus (L.) (Cyprinidae: Teleostei)[END_REF]. Oocyte maturation was divided into four different stages according to the cell size, the morphology and the extent of vitellus accumulation: primary oocytes (stage 1), previtellogenic oocytes (stage 2), secondary oocytes (stage 3) and degenerated oocytes (stage 4). Four spermatogenetic stages were differentiated according to the morphology and the size of the cells: spermatogonia (stage 1), spermatocytes (stage 2), spermatids (stage 3) and spermatozoids (stage 4). As several stages coexist in a given gonad, the most advanced stage occurring inside the gonad was chosen to characterize the sexual maturity of fish. The occurrence of abnormalities such as intersex, fibrosis and necrosis were noted.

Statistical analysis

All data are reported as mean ± standard deviation and the SPSS 17.0 software was used for statistical analysis. Normal distribution of data was verified using Kolmogorov-Smirnov test.

Since data sets did not have a normal distribution and homogeneity of variance, the data was log-transformed using F(x) = log (1+x), prior to parametric analysis. A two-way analysis of variance (ANOVA) was performed for each parameter using sites and gender as factors.

When sites by gender interactions were significant (α = 0.05), male and female data were treated separately. However, male and female LSI and GSI data were also treated separately due to physiological differences between genders. Biomarker responses measured at each sampling period were compared using one-way ANOVA followed by the Sidak test (α = 0.05).

Results

Morphometric indices

In April, length (81.3 ± 7.1 mm), weight (7.1 ± 3.2 g) and CF (1.3 ± 0.2 g/cm 3 ) were measured in adult European bullheads at each station and no significant differences were recorded among investigated sites. However, statistical analysis of seasonal variations (data not presented) showed that in July and October, Courtagnon's fish possessed a weak length and weight compared to other sites. Moreover, in July, the Vesle downstream site of Muizon had bigger bullheads than other stations, having the highest CF (1.6 ± 0.9 g/cm 3 ).

Among the investigated gross indices, LSI and GSI showed significant gender differences with high values in female fish compared to male. Assessment of seasonal variations showed that LSI values were lower in October compared to April and July whereas GSI data were lower in July than in April and October. Inter-site comparisons showed a lack of variation for LSI and GSI during the year. Nevertheless, statistical analysis of seasonal variations (data not presented) showed an increase of LSI in fish from Serzy collected either in April and October or in July, in fish located at Serzy, Bouy and Muizon. Concerning GSI, in April, only at the Vesle upstream sites of Serzy and Prunay female values were significantly higher than other stations tested; and in October, the male data were lower in Vesle than Ardre River (Table 1).

Biochemical and immune biomarkers

Only the three biotransformation enzymes tested (EROD, CYP3A and GST) and the lysozyme concentrations were gender dependent. In fact, high EROD, CYP3A and GST and low lysozyme activity were detected in male bullheads compared to female (Table 2 and3).

Moreover, in this study, all selected biomarkers were influenced by seasonal variation. In July, a decrease of biotransformation enzyme activities was recorded in both genders for EROD, CYP3A activities and in male for GST activity. This decrease of GST activity was also observed in October in both male and female bullheads. Conversely, high AChE and respiratory burst values were exhibited for bullheads caught in the same period. An increase of lysozyme and GPx activities was also noticed in July (Table 2 and3).

Independent of seasonal variations, some modulations in biomarker responses were detected at the sites. For biochemical biomarkers (Table 2), in April and July, a gradient of EROD activity was observed in the Vesle and Ardre Rivers with significantly low values measured in fish from the agriculturally-contaminated site of Prunay. When CYP3A values were almost equal at each station, higher GST values were detected in April for Bouy's fish. In the same way, GPx values were higher in Bouy station from April to July. AChE activity was also modified with inhibition recorded in July for Vesle and Ardre upstream sites of Serzy, Prunay and Bouy stations and in October only for Serzy area. Finally, concerning immunomarkers (Table 3), an immunostimulation (respiratory burst and lysozyme concentration) was detected in July at Prunay station. Respiratory burst activity was also characterized by a decreasing trend along the Vesle River in October, with a minimal value of 4530 RLU max in Muizon.

Conversely, bullheads sampled in April shown a trend of lysozyme increase along the Vesle River with a maximal concentration of 14.7 U/g at the Vesle downstream site of Muizon.

Histological analysis of gonads

For each gender and season, the forest site of Courtagnon site possessed specific stages of gonad development compared to others stations sampled where a similar repartition of testicular/ovary development was observed at each sampling date (Fig. 2) and without intersex (data not shown). Moreover, except for Courtagnon, several histological anomalies, such as fibrosis and necrosis, were also observed in bullhead gonads at all sites with a significant gender difference, i.e. female gonads exhibited more anomalies than male gonads (Table 4).

More specifically for male gonads, in April, approximately 30 % of spermatids and 70 % of spermatozoids were detected in the Serzy, Bouy, Prunay and Muizon areas, whereas 100 % of spermatozoids were shown at the Courtagnon station. In July, sites were characterized by the presence of spermatogonia (approximately 35 %) and spermatocytes (approximately 52 %).

At this time, only two sites, Serzy and Prunay, still possessed fish with few spermatozoids (approximately 31 %) in their testes. Moreover, the Courtagnon station was clearly dissimilar, with absence of spermatocytes and with a higher percentage of spermatozoids (57 %) than spermatogonia (43 %). Finally, in October, fish testicles were all in the spermatocyte stage and only Bouy and Muizon possessed some organisms in the spermatid stage (approximately 57 %) (Fig. 2). Moreover, during the entire year, a higher percentage of fish with parasperm (> 50 %) was detected at each sampling area with a maximum values (> 80 %) in April and October (data not shown).

Concerning female gonads, in April, ovary atresia was recorded. However, Prunay possessed some fish (63 %) with secondary oocyte stages; and Bouy was clearly different with the presence of bullhead with only the first three gonad development stages. In July, the Courtagnon area again showed 100 % of fish with ovary atresia, whereas at the four other sites each stage of gonad development was detected. Pre-vitollogenic (approximately 52 %) and secondary oocytes (approximately 48 %) were specifically detected in female fish sampled in October. However, some variations were observed in fish from Courtagnon that exhibited gonads with only secondary (approximately 60 %) and degenerated oocytes (approximately 40 %) (Fig. 2).

Discussion

Basal values and reference selection for multi-biomarker data analysis

Two strategies based on absolute reference definition or on upstream/downstream approach are commonly used to analyze biomarker responses. Both interpretation methods are considered complementary due to the level of information supported by these strategies [START_REF] Sanchez | Comparison of two reference systems for biomarker data analysis in a freshwater biomonitoring context[END_REF]. In this work, the site of Courtagnon could be considered a valuable study area for establishing an absolute reference in the Vesle River basin. Indeed, this site is located in the forested upper area of a Vesle tributary and is not exposed to point source chemical pollutants. However, examination of the reproductive cycle of Courtagnon's bullhead, which resulted from a relationship between variations of bullhead reproductive status and histological examination, showed a clear disturbance of this parameter, with presence of fish exhibiting gonads with secondary or degenerated oocytes in all sampling seasons (Fig. 2). This result could be explained by the specific hydrological status of the Courtagnon site. The site is located near the source of the Ardre River and could be characterized by a lack of temperature variation as observed in other similar sampling locations such as the Vallon du Vivier (Sanchez et al., 2008b) where signs of bullhead reproduction were recorded throughout the year (ONEMA, personal communication). In other stations investigated in this study, the reproductive cycle of bullheads appeared to be in accordance with data reported in the scientific literature describing a spawning period between April and July [START_REF] Zbinden | Biologie, menaces et protection du chabot (Cottus gobio) en Suisse[END_REF]. Before spawning period (April), male gonads possess spermatids, spermatozoids and parasperm and, after spawning period (July), the gametogenesis began again at all sites with the presence of spermatogonia and/or spermatocytes and a reduction of parasperm presence (data not shown). In the same way, secondary oocytes or atresia were recorded in female ovaries in April and July and a new reproductive cycle was begun with the presence of primary and pre-vitellogenic oocytes. Due to the interaction between reproduction and other physiological functions investigated in this study, such as immunity [START_REF] Harris | Modulation of the fish immune system by hormones[END_REF] and biotransformation [START_REF] Kirby | Ethoxyresorufin-O-deethylase (EROD) and vitellogenin (VTG) in flounder (Platichthys flesus): System interaction, crosstalk and implications for monitoring[END_REF], fish from Courtagnon cannot be used to establish reference values for bullhead biomarkers in the Vesle River basin. To bridge this gap, a relative reference system based on an upstream/downstream approach was proposed in this study to analyze multi-biomarker responses. In this context, classification of real induction or inhibition seems to be difficult but this strategy allows for integrating the effects induced by pollution and other environmental factors in the upstream area.

Relationship between environmental pressure and fish responses

A noteworthy fact was that the biomarker response patterns noticed along the Vesle River showed a clear difference between upstream sites characterized by agricultural pressure (Prunay and Bouy) and the downstream site of Muizon affected by urban pressure (Table 2), reflecting adverse effects of environmental pollution on wild fish health. This statement is particularly true in April for biotransformation enzymes and in July and October for AChE activity.

A gradient effect was recorded for the two immunobiomarkers tested, including respiratory burst and lysozyme activities (Table 3), with a decrease of responses between upstream and downstream. As described by several authors [START_REF] Fournier | Phagocytosis as a biomarker of immunotoxicity in wildlife species exposed to environmental xenobiotics[END_REF][START_REF] Reynaud | The effects of polycyclic aromatic hydrocarbons on the immune system of fish: A review[END_REF], this immunosuppressive effect was correlated with exposure to various chemicals discharged in the river, such as heavy metals, PAHs, PCBs and pesticides. The diminution of lysozyme activity could induce an inhibition of their hydrolysis catalyzation of 1,4--linkages between N-acetylmuramic acid and N-acetyl-D-glucosamine residues [START_REF] Yano | The nonspecific immune system: Humoral defense[END_REF]. This action highlighted a direct decrease of pathogen membrane destruction. Moreover, a reduction of phagocytosis capacities was indirectly proved by a decrease of respiratory burst, a crucial reaction to degraded internalized pathogens [START_REF] Huber | Respiratory burst as a biomarker for stress responses[END_REF], and lysozyme activity, which is an activator of phagocytes and acts as an opsonine [START_REF] Yano | The nonspecific immune system: Humoral defense[END_REF]. Therefore, this disturbance of fish immunity denoted a reduction of pathogen destruction and an increase of fish susceptibility to pathogen aggressions.

Concerning biochemical biomarkers, a similar response profile was observed for each parameter tested (EROD, CYP3A, GST, GPx and AChE). In the present work, in wild bullheads an inhibition of EROD activity was suggested at upstream sites. As described previously, this recognized biomarker of exposure to dioxin-like compounds can be inhibited by several pollutants widely used in agricultural activities, such as copper [START_REF] Flammarion | Join effect of copper sulphate and methidathion on rainbow trout (Oncorhynchus mykiss) EROD and AChE activities[END_REF][START_REF] Viarengo | Heavy metal inhibition of EROD activity in liver microsomes from the Bass Dicentrarchus labrax exposed to organic xenobiotics: Role of GSH in the reduction of heavy metal effect[END_REF] or pesticides (Flammarion et al., 1998b), and is strongly detected in the Vesle River due to viticultural and agricultural pressures (ONEMA, personal communication). In addition to the destabilization of catalytic activity, phases I and II of pollutant detoxification and hepatic steroid catabolism were also modified in downstream stations compared to upstream sites, due to a trend of modulation of CYP3A and GST activities, respectively. Moreover, GST is known to be involved in the cellular defense against oxidative stress by conjugating electrophilic compounds to glutathione (GSH) [START_REF] Habig | Glutathione S-Transferases. The first enzymatic step in mercapturic acid formation[END_REF]. Since, oxidative stress could be induced by some pollutants, such as copper and pesticides [START_REF] Dautremepuits | Modulation in hepatic and head kidney parameters of carp (Cyprinus carpio L.) induced by copper and chitosan[END_REF][START_REF] Oliveira | Anguilla anguilla L. oxidative stress biomarkers responses to copper exposure with or without b-naphthoflavone preexposure[END_REF], an increase of antioxidant enzymes, such as GPx, could be detected at downstream stations. These enzymes permit the reduction of intermediate products to maintain cellular viability [START_REF] Reischl | Distribution, adaptation and physiological meaning of thiols from vertebrate hemoglobins[END_REF] and to decrease oxidative species produced during pollutant metabolization in order to restrain cytochrom P450 decline (Della Torre et al., In Press). Finally, a decrease of AChE activity in bullheads from upstream sites was also observed. The AChE activity is frequently described as a relevant and sensitive parameter to assess exposure to neurotic pollutants and particularly pesticides in wild fish [START_REF] Payne | Acetylcholinesterase, an Old Biomarker with a New Future? Field Trials in Association with Two Urban Rivers and a Paper Mill in Newfoundland[END_REF]. Moreover, as obtained in bullhead from sites receiving agricultural run-off in the Vesle and Ardre Rivers, an association between induction of oxidative stress and inhibition of AChE activity in fish exposed to various pesticides such as chlorpyrifos, diazinon and glyphosate [START_REF] Modesto | Roundup® causes oxidative stress in liver and inhibits acetylcholinesterase in muscle and brain of the fish Prochilodus lineatus[END_REF][START_REF] Oruç | Oxidative stress, steroid hormone concentrations and acetylcholinesterase activity in Oreochromis niloticus exposed to chlorpyrifos[END_REF] was demonstrated.

To complete biochemical and immunological analysis, physiological indicators including LSI, GSI and CF were measured in wild bullheads. These parameters are classically used as integrative indicators that provide valuable information on fish heath and reproductive status.

However, due to influence of biotic and environmental factors on these parameters, they are considered as bad discriminators (Sanchez et al., 2008a). In the present work, few significant inter-site variations were recorded for these physiological indexes and observed differences were between fish from both sites of Ardre river and between both Ardre and Vesle rivers. Also, these results could be explained by hydromorphological variations previously described. To our knowledge, no data is available on basal level of somatic indexes and CF in the European bullhead. Consequently, it is not easy to address accurately these physiological changes.

Seasonal variability: a complex effect

Seasonal effects on biomarker responses are well described in fish (Sanchez et al., 2008b).

Indeed, this knowledge is indispensable for a better application and analysis of biomarkers in environmental risk assessment. The present work provided relevant data related to interaction between fish biochemical responses and biotic or environmental factors. A noteworthy fact was that seasonal and also gender effects recorded in the bullhead are in accordance with data available in the scientific literature for other model fish species. A clear gender effect was observed for biotransformation enzyme responses with high values in male fish. This extensively described result is explained by the negative effect of endogenous estradiol on cytochrome P-450 catalytic activity [START_REF] Arukwe | Changes in three hepatic cytochrome P450 subfamilies during a reproductive cycle in turbot (Scophthalmus maximus L.)[END_REF]. This effect is most pronounced during breeding periods. As phase I biotransformation enzymes, GST activity seems to be correlated to spawning period with lower values recorded in fish collected in October. The same pattern was reported in Anguilla anguilla and Mugil cephalus [START_REF] Gorbi | Seasonal variability of metallothioneins, cytochrome P450, bile metabolites and oxyradical metabolism in the European eel Anguilla anguilla L. (Anguillidae) and striped mullet Mugil cephalus L. (Mugilidae)[END_REF] but the involved mechanisms are not understood. A seasonal variation was also measured for GPx activity. If less information is available on seasonal changes of the antioxidant enzymes in fish, any studies reported a similar seasonal pattern in fish such as the three-spined stickleback and explained this effect by the involvement of GPx in steroid metabolism [START_REF] Sanchez | Preliminary investigation of multi-biomarker responses in three-spined stickleback (Gasterosteus aculeatus L.) sampled in contaminated streams[END_REF]. In light of this data, several authors recommend measurement of biomarkers outside the reproductive period to decrease the number of sampled fish and to increase statistical power due to a low variability of investigated responses [START_REF] Flammarion | Cyprinids EROD activities in low contaminated rivers: a relevant statistical approach to estimate reference levels for EROD biomarker ?[END_REF]Sanchez et al., 2008a). However, in the present study, more inter-site differences were recorded in April than in July and October especially for EROD activity, oxidative stress parameters and neurotoxicity biomarkers. A decrease of respiratory burst was observed in fish collected in October compared to July. A number of reports have shown that abiotic factors (e.g. water temperature, photoperiod, pollution, diet) as well as biotic factors (e.g. pathogens) directly or indirectly controlled by the season cycle, affect the immune function of fish [START_REF] Saha | Seasonal changes in the immune activities of common carp (Cyprinus carpio)[END_REF]. These response profiles could be explained by the seasonality of pollution due to agricultural practices in the investigated area since viticulture prophylactic treatments are applied in spring. Also, this study argues for a design of freshwater risk assessment studies based on variability of environmental pressures.

Several sources of pollutants such as agriculture, but also urban activities [START_REF] Hemming | Temporal and spatial variability in the estrogenicity of a municipal wastewater effluent[END_REF][START_REF] Miège | Occurrence of betablockers in effluents of wastewater treatment plants from the Lyon area (France) and risk assessment for the downstream rivers[END_REF], are known to exhibit strong temporal variability. In this context, a monitoring design based only on fish physiological cycles appears as not relevant and could lead to erroneous diagnosis of contamination effects on wildlife. However, a better knowledge of model fish species physiology must be considered as a major requirement for biomarker deployment.

Conclusions

To summarize, the present study was designed to assess multi-biomarker responses including immunotoxicity induced by urban and agricultural pressure in European bullheads living in a small French river basin, the Vesle watershed. Results revealed various kinds of biological effects due to environmental pressure on fish health and particularly on immunological status.

This study also highlighted the high variability of biomarker responses between sites (downstream/upstream effect) and seasons (environmental condition impact). Some cautions about selection of reference sites and seasons to determine freshwater risk assessment were also given: i) Reference site must be established according to the function of chemical and biological qualities of area, but in a river basin monitoring context, an upstream/downstream approach appears as a powerful methodology to analyze multi-biomarker responses and integrate the complexity of contamination;

ii) Historically, measurement of multi-biomarker responses was performed according to fish physiological cycles. However, seasonal variability of environmental pressure cannot be ignored in the design of monitoring programs.

Fig. 1 .

 1 Fig. 1. Sampling sites on Vesle river system, with A = localization of sites in France and B =

Fig. 2 .

 2 Fig. 2. Distribution of gonadal development stages in male and female bullheads sampled in

Fig

  Fig. 1.

Acknowledgements

This work was supported by the French Ministry of Ecology and Sustainable Development (Programme 181-DRC43 and Programme 190 Ecotoxicology). We thank the technical staff from the "Office National de l'Eau et des Milieux Aquatiques" for their assistance in fish sampling. The authors thank also xxx for reviewing English language of this manuscript.


Perceval

W Sanchez

T Burgeot

O Perceval

Perspectives from the French workshop on the development and validation of biomarkers and bioassays for the monitoring of aquatic environments

Perspectives from the French workshop on the development and validation of biomarkers and bioassays

for the monitoring of aquatic environments

Workshop objectives and main outcomes

During the past three decades, there has been an increasing interest in the field of ecotoxicology for development of effect-based monitoring tools (EBMTs) including in vitro tests, biomarkers and whole organism bioassays. This interest is due to the practical application of EBMTs in the surveillance of the quality of water and aquatic ecosystems (ICES, 2008). Within this framework, these ecotoxicological tools seem to offer numerous potential advantages, as they allow one to take into account the cumulative impact of the whole contaminants present in the field, and the bio-available and active fractions of toxicants as well as their degradation bioaccumulation, biotransformation (Lam and Gray, 2003;[START_REF] Hecker | Effect-directed analysis (EDA) in aquatic ecotoxicology: State of the art and future challenges[END_REF]. Hence, they are often seen as complementary tools to conventional environmental monitoring approaches, mostly based on the measurement of a predefined list of chemical substances in various aquatic compartments (water, sediments and biota) and the analyses of indices of biological integrity (OSPAR 2004). Due to these research activities, several core EBMTs (SGIMC, 2011) are now well characterised and may be directly used to assess the quality of the aquatic environment under a European consensus (Table 1;Sanchez and Porcher, 2009;Lyons et al., 2010). However, if EBMTs are widely used in research studies and provide, at geographically and temporally limited scales, data on ecotoxicological status of water bodies, few applications of EBMTs are currently implemented in regulatory environmental monitoring programs.

The workshop on "the development and validation of effect-based monitoring tools for aquatic environment monitoring" was held the 17 th November 2010 in Verneuil en Halatte, France. It was organized by the French Institute for Industrial Environment and Risks (INERIS), the French Research Institute for Exploration of the Sea (IFREMER) and the French Agency for Water and Aquatic Environments (ONEMA). The major aims of this workshop were i) to compile and review state of art on biomarker and bioassay currently used in environmental risk assessment; ii) to identify of scientific and technical limitations hampering the implementation of these biomarkers/bioassays in environmental monitoring programs; and iii) to highlight research needs and organise future applications of EBMTs.

Workshop attendees (i.e. representatives of French research institutions, environmental managers and decision makers) agreed on the potential interest of EBMTs for water body and/or effluent monitoring (Table 2). Moreover, the need of EBMT validation and standardisation recognised in national and international quality assurance programmes was clearly identify as a key challenge to be met for these indicators to be deployed in future marine and freshwater environmental monitoring programmes. The French National Reference Laboratory for Water (AQUAREF), the International Council for the Exploration of the Sea (ICES) and the network of reference laboratories for monitoring of emerging environmental pollutants (NORMAN) were considered as suitable European platforms for the validation and standardization of candidate EBMTs. To complete these conclusions, several research needs and knowledge gaps were identified such as the identification of relevant sentinel species, the definition of reference values and assessment criteria and also development of specific tools for data analysis.

Workshop perspectives and current activities

Since October 2010, several activities were planned and /or organized to facilitate EBMT As recommended by workshop attendees, an action aiming at the standardisation of EBMTs was initiated by the French AQUAREF laboratory. During the first year, the aim is to define a clear validation process for biomarkers and bioassays based on the European experience acquired by the ICES Working Group on Biological Effects of the Chemical Contaminants (WGBEC, 2010) and in the NORMAN Working Group on "Validation of biomarkers and bioassays for aquatic environment monitoring" (WG2). In the following years, this methodology will be applied to biological indicators selected in the EBMT inventory by a working group operating under the mandate of AQUAREF. This exercise will entail validation or rejection of EBMTs, or identification of missing data required to define validation status. To complete this work, a comparison study will be organised during Autumn 2012 in collaboration with the NORMAN network (WG2 and WG on "Fieldrelevance based approaches for hazardous pollutant identification" (WG3)). This campaign is designed i) to compare assay performances for a same EBMT; ii) to assess the potential of various EBMTs for a same effect or mechanism of action; and iii) to evaluate various data analysis tools. For this purpose, two river basins exhibiting reference sites and various environmental pressures will be investigated and chemical contamination of investigated sites will be characterized. This comparison study will be opened to all interested institutions and laboratories working on EBMTs and the conclusion will be discussed in relation with the ICON marine monitoring programme deployed in the European North-East Atlantic coasts [START_REF] Hylland | ICON (Integrated assessment of contaminant impacts on the North Sea): An international workshop[END_REF].

  validation and the methodological transfer to environmental managers in accordance with workshop conclusions. In a first step, an inventory of biomarkers and in vitro bioassays available in French research laboratories was conducted in cooperation with the marine ICES Study Group on Integrated Monitoring of Contaminants and Biological Effects (SGIMC 2011). This work benefited from the contributions by other European countries, including Sweden, Spain, Belgium, Germany, Ireland, Norway, the United Kingdom and The Netherlands. This exhaustive inventory distinguishes well-characterised EBMTs and tools for which development is still needed due to a lack of data related to response specificity, basal level or interactions with physiological and environmental factors. This European-wide survey will be integrated in the technical report on effect-based monitoring tools, which is in preparation by the Sub-Group on Chemical monitoring and emerging pollutants (SG-CMEP) of the Working Group E on chemical aspects (WG-E) of the DG Environment of European Commission.

  According to the workshop conclusions, the work on EBMT validation performed by NORMAN and ICON network and AQUAREF laboratory can significantly contribute to the future implementation of these assessment tools in major environmental legislations related to water and aquatic ecosystems and especially in the European Water Framework Directive and the Marine Strategy Framework Directive. Concurrently, this experience seems to be relevant to develop a strong academic research to develop novel biomarkers and bioassays able to address new challenges in ecotoxicology such as emerging pollutants (manufactured nanoparticles, GMOs), or the combined effects of chemical stress and climate change on aquatic ecosystems. ICES, 2008. Report of the fourth ICES/OPAR Workshop on Integrated Monitoring of Contaminants and their Effects in Coastal and Open sea Areas (WKIMON IV). ICES document CM 2008/ACOM:49 ref.MHC.OSPAR. Lam P.K.S., J.S. Gray, 2003. The use of biomarkers in environmental monitoring programmes. Marine Pollution Bulletin. 46, 182-186. Lyons B.P., J.E.Thain, G.D. Stentiford, K. Hylland, I.M. Davies, A.D. Vethaak, 2010. Using biological effects tools to define good environmental status under the european union marine strategy framework directive. Marine Pollution Bulletin. 60, 1647-1651. Network of reference laboratories for monitoring of emerging environmental pollutants (NORMAN). http://www.norman-network.net/ OSPAR, 2004. OSPAR/ICES Workshop on the evaluation and uptake of background refernce concentrations (B/RCs) and ecotoxicological assessment criteria contaminants in water, sediment and biota. Hazardous substances series, No, 214.ISBN 1-904426-522, p.169. Sanchez W., J.-M. Porcher, 2009. Fish biomarkers for environmental monitoring within the Water Framework Directive. Trends in Analytical Chemistry. 28. 150-158. SGIMC, 2011: Report of the Study Group on Integrated Monitoring of Contaminants and biological effects (SGIMC), 14-18 March 2011, Copenhagen, Denmark. ICES CM 2011/ACOM:30 (www.ices.dk). 265 p.

Table 1 .

 1 List of core Effect-Based Monitoring Tools (EBMTs) that could be used in environmental monitoring programs (adapted fromSanchez and Porcher, 2009; Lyons et al., 2010).

	Type of EBMTs	EBMT name	Data provided
	In vitro bioassay	DR-CALUX	Contamination by dioxin-like
			compounds
	Biomarker	EROD activity	Exposure to dioxin-like
			compounds
	Biomarker	Vitellogenin	Exposure to estrogenic
			compounds
	Biomarker	Lysosomal stability	General health status
	Biomarker	Acetylcholinesterase	Exposure to neurotoxic
			compounds
	Whole-organism bioassay	Growth of sea urchin embryo Toxic potential of
			environmental matrices for
			organisms
	Whole-organism bioassay	Abnormality of bivalve	Toxic potential of
		embryo	environmental matrices for
			organisms

Table 2 .

 2 Summary of workshop conclusions on Effect-Based Monitoring Tools (EBMTs): potential applications according to needs of environmental managers

	Needs of environmental	EBMTs and application	Environmental legislation
	managers	strategies
	Environmental impact of	In vitro and whole organism	Financial regulation
	effluents	bioassays
	Water body monitoring	Biomarker in wild or caged	WFD, MSFD
		organisms
	Monitoring of specific site	Weight of evidence	WFD investigative, MSFD
	impact	approach, Effect-Directed	monitoring
		Analysis
	WFD: Water Framework Directive (2000/60/EC); MSFD: Marine Strategy Framework
	Directive (2008/56/EC)

Acknowledgements

The works presented in the paper were supported by the French Ministry for Ecology and Sustainable Development (MEDDTL, programs 181 and 190) and by the French Agency for Water and Aquatic Environments (ONEMA).