N Ernande

Jean-Claude Niquil

K Dauvin

Grangeré

Aurore Raoux
email: aurore.raoux@unicaen.fr

Jean-Philippe Pezy

Bruno Ernande

Nathalie Niquil

Jean-Claude Dauvin

Karine Grangeré

Isotopic analyses, a good tool to validate models in the context of Marine Renewable Energy development and cumulative impacts

Keywords: Offshore Wind Farm, Ecopath with Ecosim, Trophic Level, Isotopic Nitrogen analysis, English Channel

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Isotopic analyses, a good tool to validate models in the context of marine renewable energy development and cumulative impacts 1 Introduction

Clean renewable energy from Marine Renewable Energy (MRE) has increased steadily throughout the Northern Europe as a goal of reducing future carbon emissions. Of all MRE, Offshore Wind Farms (OWF) has seen consistent growth in capacity and it is by far the most technically advanced of all MRE [START_REF] Leung | Wind energy development and its environmental impact: A review[END_REF][START_REF] Willsteed | Obligations and aspirations: A critical evaluation of offshore wind farm cumulative impact assessments[END_REF]. The French government are currently planning the construction of three OWFs in the eastern basin of the English Channel (Fécamp, Dieppe-Le Tréport and Courseulles-sur-mer). Environmental impact studies following the BACI protocol are performed before the implementation, during the exploitation, and during the dismantlement, to investigate the impacts of these OWF structures on the surrounding marine ecosystems [START_REF] Wilding | Turning off the DRIP ('Data-rich, information-poor')rationalizing monitoring with a focus on marine renewable energy developments and the benthos[END_REF]. All these studies provide a large amount of data on environmental effects at the species level.

However, as [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF][START_REF] Raoux | Measuring sensitivity of two Ospar indicators for a coastal food web model under Offshore Wind Farm construction[END_REF] pointed out, while these studies endeavour to consider the sensitivity of some ecological compartments (benthos, fish and marine mammals), they fail to do so if not taking into account the trophic links between the compartments. Thus, at the beginning of the OWF development in France, a research gap has been identified in the uncertainties around the potential impact of OWF installation on the trophic web structure and ecosystem functioning [START_REF] Bailey | Assessing environmental impactsof offshore wind farms: lessons learned and recommendations for the future[END_REF]. In this context, and as a complementary approach to the traditional impact assessments, there is a need to adopt an integrated ecosystem approach trough modelling tools that consider the ecosystem as a whole [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF][START_REF] Raoux | Measuring sensitivity of two Ospar indicators for a coastal food web model under Offshore Wind Farm construction[END_REF]Pezy et al., 2018).

Quantitative trophic web models can be used for this purpose since they describe the interactions between species at different trophic levels (from prokaryote to top predators)

and are based on the quantification of flows of energy and matter in ecosystems. Among these different existing approaches, the Ecopath with Ecosim (EwE) model has been intensively used and developed over the last three decades [START_REF] Colléter | Global overview of the applications of the Ecopath with Ecosim modelling approach using the EcoBase models repository[END_REF][START_REF] Colléter | Global overview of the applications of the Ecopath with Ecosim modelling approach using the EcoBase models repository[END_REF] and was recognised by the US National Oceanographic and Atmospheric Administration (NOAA)

as one of the ten biggest scientific breakthroughs in its 200 year history (Heymans et al., 2016). The EwE model has been used to analyse the impact of a wide range of anthropogenic perturbations on marine ecosystems through the world such as fishing activities [START_REF] Heymans | Regime shifts in the Northern Benguela ecosystem: challenges for management[END_REF], invasion of species [START_REF] Langseth | Modelling species invasions in Ecopath with Ecosim: an evaluation using Laurentian Great Lakes models[END_REF], dumping operations (Pezy et al., 2017(Pezy et al., , 2018a)), and infrastructure development [START_REF] Tecchio | The mosaic of habitats of the Seine estuary: Insights from food-web modelling and network analysis[END_REF]. With the planning of the construction of three OWF, the English Channel has become a hotspot for future OWF development. Moreover, this area is one of the most human impacted area, with some of the highest cumulative impacts in the worldwide ocean [START_REF] Halpern | A global map of human impact on marine ecosystems[END_REF]. Recently, [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF][START_REF] Raoux | Measuring sensitivity of two Ospar indicators for a coastal food web model under Offshore Wind Farm construction[END_REF] used the EwE approach in the context of OWF construction and cumulative impacts in the eastern part of the English Channel. In their study, [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF][START_REF] Raoux | Measuring sensitivity of two Ospar indicators for a coastal food web model under Offshore Wind Farm construction[END_REF] built a food web model at the site of Courseulles-sur-mer OWF construction to describe the situation "before" the construction of the wind farm and then performed simulation to analyse the potential impact of the reef and reserve effect (spatial restriction of fisheries for safety measures) on the ecosystem structure and functioning.

This holistic view of the OWF effect on the ecosystem through trophic web modelling could be replicated on other potential installation sites, and used to analyse the long-term reef and reserve effects in the context of global change. Using quantitative modelling to assess cumulative impacts during OWF development would allow bringing new knowledge to policy makers. Results could also be used by the project owner and the "Design Office", which have the responsibility to provide the Environmental Impact Assessment. However, the main drawback that could limit the utilisation of EwE models for management purposes are that model validations are rare, which may lead to a problem of "quality assurance" (Heymans et al., 2016). In addition, this problem of "quality assurance" is compounded by the fact that the data used (biomass and stomach content) to build Ecopath models are often not collected from the study site (same sediment type, depth, and season) but from literature which can induce a bias in the model and so compromise the quality of the results of the model (Plagányi and Butterworth, 2004).

Thus, in the context of OWF development and cumulative impacts there is an urgent need to validate models in order to test the predictions. For that, the validation should be an integral part of the modelling process. However, it is necessary to have an independent data set with information not use in the model inputs.

Concerning EwE models, special attention has been given to the validation of functional compartments trophic levels calculated by Ecopath using Stable Isotope Analyses (SIA) [START_REF] Kline | Cross-validation of trophic level estimates from a mass balance model of Prince William Sound using 15N/14N data[END_REF][START_REF] Pauly | Fishing down marine food webs[END_REF][START_REF] Polunin | Trophic-level dynamics inferred from stable isotopes of carbon and nitrogen[END_REF]Dame and Christian, 2008;[START_REF] Nilsen | Trophic studies in a high latitude fjord ecosystem: a comparison of stable isotope analyses (δ13C and δ15N) and trophic level estimates from a mass-balance model[END_REF][START_REF] Milessi | Trophic mass-balance model of a subtropical coastal lagoon, including a comparison with a stable isotope analysis of the food-web[END_REF][START_REF] Navarro | Comparison of ecosystem modelling and isotopic approach as ecological tools to investigate food webs in the NW Mediterranean Sea[END_REF][START_REF] Lassalle | An assessment of the trophic structure of the Bay of Biscay continental shelf food web: Comparing estimates derived from an ecosystem model and isotopic data[END_REF][START_REF] Deehr | Using stable isotope analysis to validate effective trophic levels from Ecopath models of areas closed and open to shrimp trawling in core sound[END_REF]. Compared to modelling, SIA constitutes a completely different approach to analyse trophic interactions within ecosystems [START_REF] Fry | Review of stable isotopic investigations of food-webs in seagrass meadows[END_REF] and the δ 15 N value can be used to describe the trophic level of consumers [START_REF] Hobson | Determination of trophic relationships within a high Arctic marine food web using δ13C and δ15N analysis[END_REF][START_REF] Loc'h | Benthic community and food web structure on the continental shelf of the Bay of Biscay (North Eastern Atlantic) revealed by stable isotopes analysis[END_REF]. This approach is based on the fact that there is a relationship between the isotopic signatures of consumer and their prey [START_REF] Peterson | Stable isotopes in ecosystem studies[END_REF]. When a predator consumes its prey, there is an energy transfer and the isotopic nitrogen values show a predictable increase in the isotopic ratio between 0.5 and 5‰ across trophic levels [START_REF] Minagawa | Step wise enrichment of 15N along food-chains: further evidence and the relation between d15N and animal age[END_REF][START_REF] Post | Using stable isotopes to estimate trophic position: models, methods and assumptions[END_REF]. Despite the wide use of both EwE and SIA, few attempts have been made to compare these methods [START_REF] Dame | A statistical test of network analysis: can it detect differences in food web properties?[END_REF]Christian, 2008, Lasalle et al., 2014).

In this study, EwE models from two locations were assessed: the site of the future Courseulles-sur-mer Offshore Wind Farm (CSM OWF) [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF], and the future Dieppe-Le-Tréport (DLT OWF) [START_REF] Pezy | What are the most suitable indices to detect the structural and functional changes of benthic community after a local and short-term disturbance?[END_REF]Pezy et al., in press). The trophic levels estimated from these two models were compared using trophic levels estimated from independent nitrogen isotope data.

Materials and methods

Study area

The eastern English Channel (eEC), where the CSM OWF and DLT OWF will be built in the next years, is a shallow epi-continental area located between France and England. It is about 35 000 km² and it is delimited by the Cotentin peninsula in the west and the Dover Strait to the east. The maximum water depth never exceeds 70 m in the trench running through the centre of the English Channel [START_REF] Carpentier | Channel Habitat Atlas for marine Resource Management, final report / Atlas des habitats des resources marines de la Manche orientale, rapport final (CHARM phase II). Interreg 3a Programme[END_REF]. The tidal range is greater than 5 m on the French coast but is closer to 2 m on the English side [START_REF] Dauvin | Mer côtière à forte pression anthropique propice au développement d'une Gestion Intégrée : exemple du bassin oriental de la Manche (Atlantique nord-est)[END_REF].

These currents play an essential role in the sediment [START_REF] Larsonneur | The superficial sediments of the English Channel and its western approaches[END_REF] and benthic communities [START_REF] Cabioch | Distribution des peuplements benthiques dans la partie orientale de la baie de Seine[END_REF][START_REF] Dauvin | History of benthic research in the English Channel: from general patterns of communities to habitat mosaic description[END_REF] distributions. The eEC is also the focus of many human activities such as transportation, fishing, sediment deposit, and sediment extraction [START_REF] Dauvin | Mer côtière à forte pression anthropique propice au développement d'une Gestion Intégrée : exemple du bassin oriental de la Manche (Atlantique nord-est)[END_REF][START_REF] Dauvin | Are western and eastern basin of the English two separate ecosystems[END_REF] and is considered by [START_REF] Halpern | A global map of human impact on marine ecosystems[END_REF] as one of the most anthropized sea of the world.

The Courseulles-sur-mer Offshore Wind Farm (CSM OWF) project

The CSM OWF will be located 10 to 16 km offshore from the Calvados coast between 31 and 34 m of depth. It will be located on the coarse sand and gravels benthic communities of the Bay of Seine (Fig. 1). The OWF will represent an area of 50 km 2 . A total of 75 turbines (each 6 MW) capable of producing 450 MW will be installed by Eoliennes Offshore du Calvados" (EOC) in the next years. The monopile foundation will have a diameter of 7 m and will be driven into the seabed. The wind farm turbines will be connected via an interarray network consisting of 33 kV AC cables which will link up to an offshore transformer substation located within the wind farm. From this station, power will be exported via two 225 kV AC marine cables.

Dieppe-Le Tréport Offshore Wind Farm (DLT OWF) project

The prime contractor of the project is "Eoliennes en mer Dieppe-Le Tréport", a subsidiary of Engie (formerly named GDF Suez). The proposed OWF will be located at distances of 15.5 km and 17 km offshore from the coast off Le Tréport and Dieppe, respectively (Figure 1). The water depth ranges from 12 to 25 m. The OWF will cover a total area of approximately 92 km 2 , and will comprise 62 turbines with a capacity of 8 MW each giving a combined nameplate capacity of 496 MW. The foundations are composed of jacket structures. As for the CSM OWF, wind farm turbines will be connected via an interarray network consisting of 33 kV AC cables which will link up to an offshore transformer substation located within the wind farm. From this station, power will be exported via two 225 kV AC marine cables to the continent.

<Figure 1>

Data collection

Trophic modelling approach

The pre-existing Ecopath models

In order to gain further knowledge on the ecosystem structure and functioning before the construction of the CSM OWF and the DLT OWF, two Ecopath models [START_REF] Polovina | Model of a coral reef ecosystem. The ECOPATH model and its application to French Frigate Shoals[END_REF][START_REF] Christensen | Ecopath with Ecosim: methods, capabilities and limitations[END_REF][START_REF] Christensen | Ecopath with Ecosim version 6 User Guide[END_REF] describing the initial state of the both sites were built [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF]Pezy et al., 2018. Ecopath is a mass-balance (i.e.

neglecting year-to year changes in biomass compared to flow values) single-solution model (i.e. returning only one value per flow) in which the ecosystem is represented by functional groups, which are composed of species or group of species with ecological or biological similarities. Each functional group is parameterized with biomass (B, gC m -2 ), production to biomass ratio (P/B, year -1 ), consumption to biomass ratio (Q/B, year -1 ) and a matrix diet which represents the interactions between predators and prey in the ecosystem. et al., 2017). Benthic invertebrates were grouped into four functional groups: predators, filter feeders, bivalves and deposit feeders [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF]. The benthic cephalopods, benthopelagic cephalopod, suprabenthos, zooplankton, bacteria, primary producer, and detritus were represented by one group respectively. The seabirds were grouped into two groups and marine mammals into four groups. 26 of the organism groups were obtained from sampling studies.

The parameterization of an

The DLT Ecopath model was composed of 28 compartments from primary producers to marine mammals. Fish were grouped into five groups (whiting, Ammodytidae, fish benthos feeders, demersal flatfish, and pelagic planktivorous fish). Benthic invertebrates were divided into 13 groups with a special subdivision for 6 of these groups (consumed (C) and Merosuprabenthos, holosuprabenthos, holozooplankton, bacteria, primary producer and detritus were represented by one group respectively. 21 of the functional groups were obtained from sampling operations (following methods described by [START_REF] Raoux | Measuring sensitivity of two Ospar indicators for a coastal food web model under Offshore Wind Farm construction[END_REF].

The calculated Pedigree indices for these two models were 0.52 and 0.73 for the CSM Ecopath Model and the DLT Ecopath model respectively. These values were calculated from the EwE pedigree routine which quantifies the input parameter uncertainties [START_REF] Christensen | Ecopath with Ecosim: methods, capabilities and limitations[END_REF].

A full description and sources of information of the input and output parameters of the CSM and the DLT Ecopath models can be found in [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF] and Pezy et al. (2018) respectively.

Trophic Level in Ecopath

The Trophic Level (TL) defines the trophic position of an organism within the food web (Lindeman, 1942). Ecopath allows calculating a TL for each functional group in the model.

The formula corresponds to the weighted average of the trophic levels of the prey of a functional group, with primary producers and non-living material set at a level of 1:

(Eq. 1)

where DC ji is the fraction of the prey i in the diet of the predator j.

Stable isotopes analyses

Sampling and samples preparation

Cephalopods were sampled from the GOV ("Grande ouverture Verticale"= high opening) bottom trawl survey in the Bay of Seine during the CGFS (Channel Ground Fish Survey) survey conducted in October 2009 by IFREMER. Other samples of benthic invertebrates were sampled at the site of implantation of the future CSM OWF in October 2015 using a Rallier du Baty dredge.

Fish and benthic invertebrates were collected at the site of the implantation of the future DLT OWF in February 2016 using a 3 m beam trawl and with a 0.1 m 2 Van Veen grab respectively.

130 samples corresponding to nine species were analysed for nitrogen stable isotope to study the trophic level of each species at the CSM future OWF and 102 samples corresponding to 16 species at the DLT future OWF (Table 1 andTable 2). All samples were kept frozen until processed in laboratory. The preparation of the tissue for stable isotopic analyses varied according to taxon. However, we chose to collect the muscle because the nitrogen isotopic signature of the latter is less variable over time than tissues from other organs such as the digestive gland or heart tissue [START_REF] Pinnegar | Differential fractionation of d13C and d15N among fish tissues: implications for the study of trophic interactions[END_REF]. For bivalves, a sample was taken for the abductor muscle, for cephalopods, a mantle tissue was taken, for crabs a mantle tissue was taken from chelipeds, for ophiurid a sample was taken from the arms, for sea urchins, the muscle of the lantern of Aristotle was used and for fish a sample of white dorsal muscle was taken.

<Table 1> <Table 2>

All samples collected were oven dried (60°C for 48 h) and then each dried sample was ground into a homogeneous powder using a mixer mill. Approximately 1 mg of powder was weighed into small tin cups. Isotopic analyses were performed with elemental analyser EA 3000 (EuroVector) coupled with a mass spectrometer [START_REF] Pinnegar | Differential fractionation of d13C and d15N among fish tissues: implications for the study of trophic interactions[END_REF] Results are expressed in standard δ notation based on international standards [N 2 for δ 15 N [START_REF] Peterson | Stable isotopes in ecosystem studies[END_REF]] according to the following the equation:

δX (‰) = [ (R sample / R standard ) -1 ] × 1000 (Eq. 2)
where X= 15 N, R sample = 15 N/ 14 N in sample and R standard = 15 N/ 14 N in international standards.

Trophic level calculation from the isotopic analyses

The trophic level of each species was calculated according to the following equation [START_REF] Minagawa | Step wise enrichment of 15N along food-chains: further evidence and the relation between d15N and animal age[END_REF]:

TL = λ + (δ 15 N organism -δ 15 N base ) / ΔN (Eq. 3)
where δ 15 N organism is the isotopic signatures of the organism, ΔN is the average δ 15 N enrichment from prey to predator assumed to be 3.4‰ [START_REF] Minagawa | Step wise enrichment of 15N along food-chains: further evidence and the relation between d15N and animal age[END_REF] and the δ 15 N base is the mean value of a species close to the trophic web base chosen as trophic baseline and λ its trophic level. In our study, the benthic filter feeders, Aequipecten opercularis (Linnaeus, 1758), was used to establish isotopic baseline as this species integrates the short term spatial and temporal variability displayed by primary producers [START_REF] Jennings | Smaller predator-prey body size ratios in longer food chains[END_REF]. Thus, λ= 2.18.

It is worth to note that published isotopic analyses (Trophic Levels) of 23 taxa (zooplankton, benthic invertebrates and fish) sampled in October 2009 in the Bay of Seine were also used (Kopp et al., 2015) for the comparison of the TLs calculated from the CSM Ecopath with those calculated from SIA. Isotopic results are detailed for all species sampled in the Bay of Seine in Kopp et al. (2015).

Comparison between Ecopath mass balanced model and isotope results

The correlation between TL derived from SIA and those derived from the two Ecopath models were tested using the Spearman-rank correlation coefficient test [START_REF] Zar | Biostatistical analysis[END_REF]. In the case of multi-species functional groups, the TL of the functional group was calculated as the mean TL of the species within the functional group weighted by their biomass proportions.

The same method was applied to calculate the TL of multi-species functional groups from SIA.

Results

Results showed that the TLs estimated by the both Ecopath mass balanced models (CSM and DLT) were highly and positively correlated with the SIA (CSM: r 2 Spearman = 0,79; p < 0.0001 (Figure 2; Table 1); DLT: r 2 Spearman = 0,95; p < 0.0004 (Figure 3 ; Table 2).

<Figure 2> <Table 1>

In fact, the Figure 2 revealed that half of the points are located either above or below the 1:1 line of perfect agreement (or first bisector) in the range of TLs being studied. This result suggested that the EwE approach tended to slightly underestimate the trophic positions of the functional groups located above the first bisector and to slightly over estimate the trophic positions of the functional groups located under the first bisector. For instance, according to the CSM Ecopath model, the zooplankton presented a TL of 2 whereas the zooplankton TL derived from SIA revealed a TL of 2.56. On the same line, the EwE approach also underestimated the TLs of the following groups: Benthopelagic cephalopods, mackerel, European sea bass, Atlantic horse mackerel, gurnard, European pilchard, European sprat, sea bream and Benthic inv, predators. However, the EwE approach overestimated the TL of the following functional groups: Benthic cephalopods, sharks and rays, Atlantic cod (Gadus morhua), whiting (Merlangius merlangus), pounting (Trisopterus luscus), poor cod (Trisopterus minutus), fish benthos feeders, other flat fish, King Scallops (Pecten maximus)

and Benthic inv, deposit feeders. In addition, perfect agreements were found for two compartments, one mono-species functional group corresponding to the plaice (Pleuronectes platessa) (17), and a multi-species functional group corresponding to the bivalves (20) (Figure 2; Table 1). Finally, the cephalopods showed the biggest difference between the TLs estimated by both methods. This difference did not exceed 20% for any of the studied trophic compartment.

<Figure 3> <Table 2>

Concerning the comparison of the TLs derived from the DLT Ecopath model and those from SIA, the results showed that most of the points were located under the first bissector suggested that the EwE approach tended to slightly overestimate the trophic positions of the functional groups located under the first bisector (Figure 3; Table 2). For instance, the EwE approach overestimated the TLs of the following functional groups: fish benthos feeders, flat fish, benthic invertebrates not consumed and the benthic invertebrates filter feeders not consumed. However, the EwE approach underestimated the TL of the benthic invertebrates scavengers not consumed. In fact, according to the DLT Ecopath model, this functional group presented a TL of 3.36 whereas its TL derived from SIA revealed a TL of 3.2 (Figure 3, Table 2). However, it is worth noting that these results were based on a low number of trophic groups and so they should be taken with precaution.

Discussion

Importance of local data

The objective of the present study was to evaluate the CSM OWF model and the DLT OWF model through the comparison of the TLs computed by these two models with TLs estimated from independent nitrogen isotope data. Results showed that TLs estimated by the Ecopath mass balanced models were consistent with the TLs estimated by the SIA as an independent test of validity. These results suggested that diet data use in the CSM Ecopath model and in DLT Ecopath were of good quality and highlighted the importance to build model based on high-quality source data. In addition, these results are consistent by the high value of the pedigree index of these two Ecopath models. In, fact the pedigree index of the CSM Ecopath model (0.5) and of the DLT Ecopath model (0.7) are situated at the maximum of the range (0.16 to 0.7) reported in [START_REF] Morissette | Complexity, Cost and Quality of Ecosystem Models and TheirImpact on Resilience: A Comparative Analysis, with Emphasis on MarineMammals and the Gulf of St[END_REF]. These pedigree high values can be explained by the fact that most of functional group biomass data, in the two models, were obtained from local, highly replicated and detailed sampling [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF]Pezy et al., 2018). In addition, the fish diet compositions of the models were derived from local stomach content studies [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF]Pezy et al., 2018). It is worth to note that in most cases, Ecopath models are built with biomass data not collected from the study site but using literature data that can induce a bias in the model. This is mainly due to the lack of data in many ecosystem studies. In addition, the diet matrix used to build Ecopath models is not always based on local stomach contents which can also induce a bias in the model and so can compromise the Ecopath mass balance results (Plagányi and Butterworth, 2004). In fact, as pointed out by Kopp et al. (2015), marine organism diet can vary significantly between individuals of a given species in different areas. In additions, there can be significant temporal and spatial variability in the diet of marine organisms or during ontogenetic shift [START_REF] Grangeré | Spatial and temporal dynamics of biotic and abiotic features of temperate coastal ecosystems as revealed by a combination of ecological indicators[END_REF][START_REF] Gaudron | The Comparison of δ13C Values of a Deposit-and a Suspension-Feeder Bio-Indicates Benthic vs. Pelagic Couplings and Trophic Status in Contrasted Coastal Ecosystems[END_REF]. Thus, highlight the importance of site-associated data in the construction of the EwE model.

Comparison of the TLs derived from the EwE approach and SIA

The popular utilisation of both the Ecopath mass balanced model and SIA make their approach tended to estimate incorrectly the TLs of the detritivorous species. This can be explained by the fact that in Ecopath, a TL of 1 is automatically associated to detritus leading to an underestimation of TLs compared to SIA [START_REF] Nilsen | Trophic studies in a high latitude fjord ecosystem: a comparison of stable isotope analyses (δ13C and δ15N) and trophic level estimates from a mass-balance model[END_REF][START_REF] Navarro | Comparison of ecosystem modelling and isotopic approach as ecological tools to investigate food webs in the NW Mediterranean Sea[END_REF].

Other potential factors could partly explain the deviation observed between the TLs derived from the two Ecopath models and those calculated from SIA. First, the temporal scale associated with the methods. In Ecopath, TLs are commonly calculated based on data from stomach content analyses, which only records what a predator has eaten recently, whereas the SIA provide information on diet integrated over a period of several weeks or months [START_REF] Stowasser | Experimental study on the effect of diet on fatty acid and stable isotope profiles of the squid Lolliguncula brevis[END_REF]. In this perspective, collecting muscle tissue is important since nitrogen isotopic signature has less temporal variability than tissue from other organs such as the digestive gland [START_REF] Pinnegar | Differential fractionation of d13C and d15N among fish tissues: implications for the study of trophic interactions[END_REF]. This is what we did in this study. A second potential source of discrepancy between the two approaches could be the use of a standard isotopic fractionation values (in the calculation of TLs with the SIA approach). The value of 3.4‰ used in this study is considered as a robust average for δ 15 N isotopic fractionation [START_REF] Minagawa | Step wise enrichment of 15N along food-chains: further evidence and the relation between d15N and animal age[END_REF] but it can range from 0.5 to 5.5‰ [START_REF] Post | Using stable isotopes to estimate trophic position: models, methods and assumptions[END_REF]. The fractionation and isotopic signature can also vary among and within species, and at different stages of development according to their growth rate (DeNiro andEpstein 1978, 1981;[START_REF] Weidel | Rates and components of carbon turnover in fish muscle: insights from bioenergetics models and a whole-lake 13C addition[END_REF], and as a function of the sea temperature [START_REF] Barnes | Effect of temperature and ration size on carbon and nitrogen stable isotope trophic fractionation[END_REF].

Moreover, feeding differences and fish migration between locations can also cause variation in δ 15 N values [START_REF] Deegan | Evidence for spatial variability in estuarine foodwebs[END_REF]. Finally, the aggregation of species within compartments in the Ecopath models, and the resulting composition of the compartments can influence the TL calculation, and could partly explain the deviation between the two approaches. In this study, aggregation and definition of the functional groups were based on the biological and ecological characteristics of the species, such as their food preference, size, and commercial importance, as well as on data availability [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF]Pezy et al., 2018). However, the difference observed in the zooplankton TL could be explained by the fact that the CSM Ecopath model has only one compartment of zooplankton including both prey (mesozooplankton) and predators (copepods), whereas SIA were made only on copepods, which have a higher trophic level than mesozooplankton. Moreover, the SIA were done on copepods that had not undergone fasting, which could introduce a bias in isotopic analysis. Finally, the differences observed in the cephalopods TLs could be explained by the fact that the prey species with a low trophic level are often digested more rapidly than prey species of higher trophic levels, and would therefore be underestimated during stomach analyses. Finally, it is worth noting that the results from the DLT

Conclusion

In the context of the energy transition, the French government is planning the construction of OWFs in the next decade. Several studies have been done to document the environmental conditions and ecosystem functioning in selected sites before the OWF construction.

However, these studies endeavor to consider the sensitivity of some ecological compartments, but they fail in taking into account the trophic links between the compartments. Thus, there is lacking holistic studies on the effects of the OWF constructions and operation. Trophic web models appear to be a good complementary approach to the traditional OWF impact assessments. They allow to consider the ecosystem as a whole, and to simulate the effects of the OWF construction on the ecosystem structure and functioning [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning ?[END_REF][START_REF] Raoux | Measuring sensitivity of two Ospar indicators for a coastal food web model under Offshore Wind Farm construction[END_REF]Pezy et al., 2018b). This holistic view of the OWF effects on the ecosystem could with advantage be replicated on other site in the English Channel, and a potentially useful to analyses the long-term OWF effects in the context of climate change (Pezy et al., 2018b). In order to use these models for management purposes, there is an urgent need to validate these models to better simulate the potential impacts associated with this OWF development. In this study, we used isotope ratios of nitrogen as a validation tool for determining the trophic levels accuracy computed by two Ecopath models that describe the ecosystem before the implantation of two futures OWF (CSM and DLT OWF).

Results showed a good correlation between Ecopath-calculated trophic levels of the two ecosystems and the δ 15 N values. These results indicated that the diet data used for the two Ecopath models were of good quality. However, as mentioned below these results must be taken with precaution as they are based on only two cases and the results from DLT case were based on a low number of trophic groups. Nonetheless, the SIA seems to be a good independent tool to validate Ecopath models. SIA could allow to quantify the uncertainty associated with the diets of the different compartments, and could complement the result of the pedigree index already in use in Ecopath. Ideally, isotopic analyses should be done before the model construction, and they could be used to define the model trophic compartments. In the context of OWF development and cumulative impacts, we strongly believe that this step of validation is essential in order to use these models and simulations by policy makers.

and the LEMAR laboratory (Brest, France) for their help in the isotopic analyses and more particularly François Le Lo'ch and Jean-Marie Munaron. The authors are also grateful to M.

Haraldsson for revising the English style and grammar. zooplankton) and fish by circles (black: flatfish; grey: demersal fish; white: pelagic fish). See

Table 1 for matching between number and functional groups. 2 for matching between number and functional groups.

  Ecopath model is based on two master equations. The first one is the balance equation, which describe the production term: Production = Catch + Predation + Biomass accumulation + Net migration + Other mortality The second equation ensures energy balance for each trophic group: Consumption = Production + Respiration + Unassimilated food The CSM Ecopath model consisted of 37 compartments ranging from primary producers (phytoplankton) to top predators (sea birds and marine mammals). In the model, fish were grouped into 18 groups; 6 functional groups (sharks and rays, gurnards, piscivorous, planktivorous, benthos feeders, and otherflatfish) and 12 single species compartments (mackerel (Scomber scombrus), European seabass (Dicentrarchus labrax), Atlantic cod (Gadus morhua), whiting (Merlangius merlangus), Atlantic horse mackerel (Trachurus trachurus), pouting (Trisopterus luscus), poor cod (Trisopterus minutus), European pilchard (Sardina pilchardus), European sprat (Sprattus sprattus) sea bream (Spondyliosoma cantharus), common sole (Solea solea), and European plaice (Pleuronectes platessa)) (Raoux

  non consumed (NC)): Branchiostoma lanceolatum, predators (C & NC), scavengers (C & NC), filter feeders (C & NC), selective deposit feeders (C & NC), non-selective deposit feeders (C & NC), grazers (C & NC). The consumed organisms were those where the species were found in the fish stomach contents. The cetaceans, seals, cephalopods, meiofauna

  at the Plateau d'Analyse d'Isotopie de Normandie (PLAtin, SFR ICORE) at the University of Caen Normandie (France) and at the LEMAR laboratory (Brest University, France).

  comparison and cross validation highly relevant. However, to date very few attempts have been made to validate the Ecopath model results with results from SIA. Among these few attempts,[START_REF] Kline | Cross-validation of trophic level estimates from a mass balance model of Prince William Sound using 15N/14N data[END_REF], Nilsen et al. (2008), Milessi et al. (2010), Lasalle et al. (2014), Dheer et al. (2014) also found a positive correlation between the TLs estimated by the EwE and the SIA approach. However, Dheer et al. (2014) highlighted that the EwE

Figure captions Figure 1 :Figure 2 :

 12 Figure captions Figure 1: Location of the Courseulles-sur-Mer future offshore wind farm (which corresponds to the CSM Ecopath model) and of the Dieppe-Le Tréport future offshore wind farm (which corresponds to the CSM Ecopath model) (Modified from[START_REF] Larsonneur | The superficial sediments of the English Channel and its western approaches[END_REF]

Figure 3 :

 3 Figure3: TLs estimated from stable isotopes (TL IA ) plotted against their corresponding levels estimated by the Ecopath model (TL Ecopath ) for the DLT OWF trophic web. See Table2 for

  Figure 2

Table 1 :

 1 Comparison between the TLs derived from the CSM ecopath model and the TLs

	608
	609	calculated from SIA
	610
	611
		Number	Functional group	TLs Ecopath	TLs IA	Source for IA
		1	Benthic cephalopods	3.92	3.22 ± 0.16	This study
		2	Benthopelagic cephalopods	4.07	4.22 ± 0.25	Kopp et al.,
		3	Fish, mackerel	3.14	3.30 ± 0.18	Kopp et al.,
		4	Fish, European seabass	3.75	3.98 ± 0.25	Kopp et al.,
		5	Fish, sharks and rays	4.15	3.46 ± 0.2	Kopp et al.,
		6	Fish, Atlantic cod	4.03	3.66 ± 0.2	Kopp et al.,
		7	Fish, whiting	4.12	3.98 ± 0.25	Kopp et al.,
		8	Fish, Atlantic horse mackerel	3.90	4.03 ± 0.26	Kopp et al.,
		9	Fish, gurnard	3.50	3.90 ± 0.25	Kopp et al.,
		10	Fish, pouting	3.76	3.61 ± 0.23	Kopp et al.,
		11	Fish, poor cod	3.72	3.28 ± 0.21	Kopp et al.,
		12	Fish, European pilchard	2.80	2.95 ± 0.23	Kopp et al.,
		13	Fish European sprat	3.00	3.12 ± 0.16	Kopp et al.,
		14	Fish, benthos feeders	3.76	3.48 ± 0.18	Kopp et al.,
		15	Fish, sea bream	3.20	3.71 ± 0.22	Kopp et al.,
		16	Fish, sole	3.44	3.32 ± 0.18	Kopp et al.,
		17	Fish, European plaice	3.37	3.37 ± 0.17	Kopp et al.,
		18	Fish, other flatfish	3.35	3.21 ± 0.16	Kopp et al.,
		19	Benthic inv. predators	3.07	3.22 ± 0.16	This study
		20	Benthic inv. bivalves	2.10	2.11 ± 0.05	This study
		21	King scallop	2.10	2.00 ± 0.16	Kopp et al.,
		22	Benthic inv. deposit feeders	2.21	2.02 ± 0.024	This study
		23	Zooplankton	2.00	2.56 ± 0.17	Kopp et al.,
	612

Table 2 :

 2 Comparison between the TLs derived from the DLT Ecopath model and the TLs calculated from SIA

	Number	Functional group	TLs Ecopath TLs IA	Source
	1	Fish benthos feeders	3.51	3.32 ± 0.16
	2 3 4	Fish flatfish Benthic inv. predator not consumed Benthic inv. filter feeders not consumed	3.38 3.09 2.25	3.35 ± 0.13 2.84 ±0.12 2.10 ± 0.11	study This
	5	Benthic inv. Scavenger not consumed	3.36	3.42 ± 0.18

ACKNOWLEDGEMENTS

This work was co-funded by the Normandie Region and by the company "Eoliennes Offshore du Calvados" (EOC) to support Aurore Raoux PhD. It was also funded by the ANRT (Eoliennes en mer Dieppe-Le Tréport and the French State) to support Jean-Philippe Pezy PhD. We acknowledge, for their help in the data sampling, the captain and the crew of the Oceanographic Vessel "Celtic Warrior". We also acknowledge Lilia Leconte, for her help in the sample preparation of the specimens collected at the site of implantation of the future DLT OWF. The isotopic analyses of the Courseulles-sur-mer were funded by the UMR BOREA and the Trophik project (ANR/FEM EMR-ITE 2015). We also acknowledge, the Plateau d'Analyse d'Isotopie de Normandie (PLAtin, SFR ICORE) at the University of Caen (France)


Maxence Ferrari
email: maxence.ferrari@univ-tln.fr

Hervé Glotin
email: glotin@univ-tln.fr

Ricard Marxer
email: ricard.marxer@lis-lab.fr

END TO END RAW AUDIO DEEP LEARNING OF TRANSIENTS, APPLICATION TO BIOACOUSTICS

INTRODUCTION

In this paper, we propose a raw audio deep learning approach to click classification. The advantage of having a neural network that works directly on the audio samples is that there is no need to find manually the best representation (spectrogram, MEL bands, . . . ), and avoids the work needed to tune all the hyperparameters that come with those representations (window size, stride ...). The way in which the information is extracted is learned from the data. The model proposed, named UpDimV2 was built to answer the challenge DOCC10 [START_REF] Ferrari | Docc10: Open access dataset of marine mammal transient studies and end-to-end cnn classification[END_REF][START_REF] Ferrari | Study of a Biosonar Based on the Modeling of a Complete Chain of Emission-Propagation-Reception with Validation on Sperm Whales[END_REF] that we created based on data from the DCLDE challenge [START_REF] Glotin | Dclde challenge[END_REF] and the Sphyrna expedition [START_REF] Ferrari | Efficient artifacts filter by density-based clustering in long term 3d whale passive acoustic monitoring with five hydrophones fixed under an autonomous surface vehicle[END_REF].

MATERIAL AND METHOD

The network is composed of multiple UpDim blocks. As shown in figure 1, an UpDim block is composed of two ResNet blocks [START_REF] He | Deep residual learning for image recognition[END_REF] followed by the UpDim operation, which adds a dimension (also known as unsqueeze or expand dim). Thus a 1D example with a width and feature dimension with the respective size of w and f will become a 2D example with a height, a width and a feature dimension, with their respective sizes of w, f and 1. The UpDim operator can then be applied once again to turn this 2D example into a 3D example.

The DOCC10 dataset consists of clicks centered in a window of 8192 samples. This was motivated by the possibility of analysing clicks in a window of 4096 samples while being able to offset this shorter window. The combination of DCLDE and Sphyrna Odyssey brought this new dataset to a total count of 134,080, that we split into a training set of 113,120 clicks and a test set of 20,960 clicks for the DOCC10 challenge, which produces an approximately 85-15 split. The test set is balanced with 2096 clicks per class. For the challenge, the test set was split into a private test set (90%) and a public test set (10%). This split was done randomly, so that the classes are no longer perfectly balanced to prevent participant to use this information. The training set is also perfectly balanced with 11,312 clicks per class.

RESULTS

Since the release of the DOCC10 challenge in early 2020, 28 challengers have participated. The full up-to-date leaderboard can be found on the Challenge Data website (https://challengedata.ens. fr/participants/challenges/32/ranking/ public). The top two scores were obtained by the same team, who used a semi-supervised approach on the test set, hence the score gap with the other participants.

DISCUSSION AND CONCLUSION

An alternate version of the DOCC10 dataset, called DOCC7, has been generated. It has the same samples, but restricted to only 7 species, which are Gg, Gma, La, Mb, Me, Pm, and Zc. The reason for the removal of UDA and UDB is more straightforward. When the DCLDE dataset was made, they used clustering methods to detect the various species. These two labels were then given to dolphin species that could not be identified. We decided to leave them in the DOCC10 challenge since they still represent clicks that belong to groups of dolphins, even if they do not represent only one species, unlike the other labels. These clusters are also useful to train a classifier that would be used after a click detector, and prevent it from classifying these dolphin clicks as another species. However, trained networks (with various architectures from various labs) have shown that, unlike the seven other classes in DOCC7, the trained networks had lower accuracy on the UDA and UDB labels. We believe that the networks' prediction might not be wrong, meaning that these classes have a higher label noise. Finally, the Ssp were also removed for two reasons. Firstly, Stenella is a genus and not a species unlike the other remaining classes. Secondly, there seems to be a large covariate shift between the training and test sets for this class. A slew of reasons could explain this difference between the training and test set, such as different species, different groups, different types of clicks, or mislabeling. This model was also tested on the DOCC7 database, achieving an accuracy of 95.09%. Applications of this model will be conducted in the CARI-MAM project.

ACKNOWLEDGEMENTS

This research was partly funded by AID Agency, Region Haut de France, ANR-18-CE40-0014 SMILES, MARIT-TIMO FEDER GIAS projects on advanced studies on whale-ship anti-collision system, and ANR-20-CHIA-

Table 1 .

 1 Topology of UpDimV2 model Dimensions are given in NHWC order. Horizontal lines separate each residual block. 0014-01 national Chair in Artificial Intelligence for Bioacoustics ADSIL (H. Glotin).

			UpDim block
	ResNet block		ResNet block
	Conv	Conv	Conv	Conv
	BN		BN
	ReLU	BN	ReLU	BN
		+	ReLU	+	ReLU	UpDim
	1x1			1x1
		Figure 1. Architecture of an UpDim block
	Layer name Input size	Kernel	Strides	Out features
	Conv-1D	N * 4096 * 1	3	1	32
	Conv-1D	N * 4096 * 32	3	2	32
	Skip	N * 4096 * 1	1	2	32
	Conv-1D	N * 2048 * 32	3	2	64
	Conv-1D	N * 1024 * 64	3	2	128
	Skip	N * 2048 * 32	1	4	128
	Conv-2D	N * 1024 * 128 * 1	3*3	1*1	32
	Conv-2D	N * 1024 * 128 * 32	3*3	2*2	32
	Skip	N * 1024 * 128 * 1	1*1	2*2	32
	Conv-2D	N * 512 * 64 * 32	3*3	2*2	64
	Conv-2D	N * 256 * 32 * 64	3*3	2*2	128
	Skip	N * 512 * 64 * 32	1*1	4*4	128
	Conv-3D	N * 128 * 16 * 128 * 1 3*3*3	1*2*1	32
	Conv-3D	N * 128 * 8 * 128 * 32 3*3*3	2*2*2	64
	Skip	N * 128 * 8 * 128 * 1 1*1*1	2*4*2	64
	Conv-3D	N * 64 * 4 * 64 * 64 3*3*3	2*2*2	128
	Conv-3D	N * 32 * 2 * 32 * 128 3*3*3	2*2*2	256
	Skip	N * 64 * 8 * 64 * 64 1*1*1	4*4*4	256
	Softmax	N * 16 * 1 * 16 * 256 16*1*1
	MaxPool	N * 16 * 1 * 16 * 256 16*1*1
	Flatten	N *	1 * 1 * 16 * 256
	Dense	N * 4096			1024
	Dense	N * 1024			512
	Dense	N * 512			7


Ghassen Halouani
email: ghassen.halouani@ifremer.fr

Ching Villanueva

Aurore Raoux

Jean Claude Dauvin

Frida Ben

Rais Lasram

Eric Foucher

François Le Loc'h H

Georges Safi

Emma Araignous

Jean Paul Robin

Nathalie Niquil

A spatial food web model to investigate potential spillover effects of a fishery closure in an offshore wind farm

Keywords: Ecopath with Ecosim, Ecospace, Marine Renewable Energy, Ecosystembased approach, Fishing, Spillover effects

 An Ecospace model was developed for the extended Bay of Seine  Potential effects of a fishery closure in an offshore wind farm were evaluated  Spillover effect could mitigate the impact of access loss on fishing activities  The spillover effect is highly localized around the offshore wind farm  The offshore wind farm could concentrate highly mobile predators

Introduction

Motivated by the urgent need to reduce the emission of greenhouse gases, Marine Renewable Energy (MRE) development has grown considerably in the last decade [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning?[END_REF][START_REF] Raoux | Measuring sensitivity of two OSPAR indicators for a coastal food web model under offshore wind farm construction[END_REF]. Of these technologies, Offshore Wind Farm (OWF) is a mature technology that has seen consistent growth in capacity and it is by far the most technically advanced of all MRE [START_REF] Wilding | Turning off the DRIP ('Data-rich, information-poor')rationalising monitoring with a focus on marine renewable energy developments and the benthos[END_REF]. This rapid growth of OWF has raised concerns over their potential impacts on the ecosystems [START_REF] Bailey | Assessing environmental impacts of offshore wind farms: lessons learned and recommendations for the future[END_REF][START_REF] Bergström | Effects of offshore wind farms on marine wildlife-a generalized impact assessment[END_REF]. In fact, some studies have highlighted that OWF construction could disturb marine invertebrates, fish, and mammals via the generation of noise and electromagnetic fields [START_REF] Bergström | Effects of offshore wind farms on marine wildlife-a generalized impact assessment[END_REF][START_REF] Zettler | The impact of wind engine constructions on benthic growth patterns in the western Baltic[END_REF]. On the other hand, OWF construction creates new habitats for sessile benthic species through the introduction of hard substrate [START_REF] Coolen | Benthic biodiversity on old platforms, young wind farms, and rocky reefs[END_REF][START_REF] Wilhelmsson | Fouling assemblages on offshore wind power plants and adjacent substrata[END_REF]. This observation is known as the "reef effect" and is considered as one of the most important OWF effects on the marine environment [START_REF] Krone | Mobile demersal megafauna at common offshore wind turbine foundations in the German Bight (North Sea) two years after deployment -increased production rate of Cancer pagurus[END_REF][START_REF] Wilhelmsson | Fouling assemblages on offshore wind power plants and adjacent substrata[END_REF]. Adding to this reef effect, spatial restrictions such as exclusion zones of fisheries activities (trawl and dredge) are likely to be implemented around turbines and cables for navigation safety, which could lead the operational OWF to act as a marine reserve generating increased biodiversity and abundance for many taxa [START_REF] Hammar | Offshore Wind Power for Marine Conservation[END_REF][START_REF] Shields | Marine renewable energy technology and environmental interactions[END_REF][START_REF] Yates | Offshore Energy and Marine Spatial Planning[END_REF].

In this context, the French government has planned the construction of three OWFs in the eastern basin of the English Channel along the Normandy coast (Courseulles-sur-Mer, Fécamp and Dieppe-Le Tréport). As in most other European countries, these future OWFs are subjected to environmental impact assessment and monitoring studies to investigate the impacts of these new structures on ecosystems [START_REF] Wilding | Turning off the DRIP ('Data-rich, information-poor')rationalising monitoring with a focus on marine renewable energy developments and the benthos[END_REF]. However, OWF impact assessment and monitoring protocols are still under development and several studies have pointed out significant shortcomings [START_REF] Lindeboom | Short-term ecological effects of an offshore wind farm in the Dutch coastal zone$\mathsemicolon$ a compilation[END_REF][START_REF] Wilding | Turning off the DRIP ('Data-rich, information-poor')rationalising monitoring with a focus on marine renewable energy developments and the benthos[END_REF][START_REF] Pezy | An ecosystem approach for studying the impact of offshore wind farms: a French case study[END_REF]. For instance, although the call for holistic approaches and Ecosystem-Based Management (EBM) of marine ecosystems is well-established, attention has tended to focus on some iconic species because of their protection status or public acclaim [START_REF] Wilding | Turning off the DRIP ('Data-rich, information-poor')rationalising monitoring with a focus on marine renewable energy developments and the benthos[END_REF]. Thus, the OWF impacts on the whole ecosystem remain insufficiently known and these studies could fail to detect serious impacts on the ecosystem [START_REF] Bailey | Assessing environmental impacts of offshore wind farms: lessons learned and recommendations for the future[END_REF][START_REF] Pezy | An ecosystem approach for studying the impact of offshore wind farms: a French case study[END_REF]. In accordance with EBM and environmental legislation requirements, [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning?[END_REF]Raoux et al. ( , 2019) ) highlighted the need to adopt a holistic approach to the impact of OWF on ecosystem functioning with trophic web modelling tools as a complementary approach to the traditional impact assessments. Such trophic web models have been applied to provide global system indicators reflecting the structure and functioning of ecosystems. In addition, they can provide information on the overall ecosystem status and could be used as a baseline for EBM decisions [START_REF] Raoux | Measuring sensitivity of two OSPAR indicators for a coastal food web model under offshore wind farm construction[END_REF][START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning?[END_REF]Safi et al., 2019).

In 2017, Raoux et al. investigated the applicability of the Ecopath with Ecosim (EwE) approach coupled with Ecological Network Analysis (ENA) indices in the context of OWF construction of the Courseulles-sur-Mer (CSM) area in the Bay of Seine, France.

An Ecopath model composed of 37 compartments, from phytoplankton to seabirds, was built to describe the situation "before" the construction of the CSM wind farm. The model was then run to predict the positive impact of the wind farm on the biomass of targeted benthic and fish compartments subjected to the reef effect produced by the foundations, scour protections and cable routes. ENA indices were calculated under two scenarios ("before" and "after") corresponding to the current state and the operational phase of the OWF to analyze food web properties. One of the main results was that total ecosystem activity, recycling and ecosystem maturity increased after the construction of OWF [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning?[END_REF][START_REF] Raoux | Measuring sensitivity of two OSPAR indicators for a coastal food web model under offshore wind farm construction[END_REF].

The purpose of the present study is to investigate the potential spillover effects of a fishery closure in an offshore wind farm. To achieve this, a spatially explicit model of the extended Bay of Seine was built, based on the use of the Ecospace module of the EwE software. This module simulates the spatial and temporal dynamics of the food web (Christensen and Walters, 2004a;[START_REF] Walters | Ecospace: Prediction of Mesoscale Spatial Patterns in Trophic Relationships of Exploited Ecosystems, with Emphasis on the Impacts of Marine Protected Areas[END_REF]. In order to reach a point of sensitivity where the effects would be observable, the spatial model includes a total fishing exclusion zone in the area intended for wind farm constructions and evaluates potential effects of the wind farm exploitation phase at ecosystem and fishery levels.

The overall goal of this research is to consider both ecosystem complexity and fishing activities to address questions related to the spatial effects of setting an offshore wind farm infrastructure as an exclusion zone, and the potential adjacent benefits due to the spillover effect.

Material and Methods

The study area

The extended Bay of Seine (eBoS) is a shallow coastal ecosystem located on the northwestern French coast and opening onto the Eastern English Channel to the limit of the French Exclusive Economic Zone (Fig. 1). The eBoS covers approximately 13500 km 2 and it is generally composed of soft sediment (i.e. coarse sands, fine sands and muddy fine sands) [START_REF] Dauvin | History of benthic research in the English Channel: From general patterns of communities to habitat mosaic description[END_REF]. The mean depth of the study area is about 35 m with a maximum tidal amplitude up to 7.5 m height near the mouth of the Seine estuary. The intertidal zone and the shallowest subtidal zone (i.e. 0 -5 m depth) was not considered given the specificity of its ecological functioning. In its eastern south part, the eBoS receives the Seine river which is highly loaded with nutrients [START_REF] Guillaud | Biogeochemical modelling in the Bay of Seine (France): an improvement by introducing phosphorus in nutrient cycles[END_REF]. Furthermore, the eBoS constitutes an important nursery, feeding, and breeding ground for several marine species [START_REF] Rochette | Effect of nursery habitat degradation on flatfish population: Application to Solea solea in the Eastern Channel[END_REF].

The Bay of Seine concentrates high fishing effort and is one of the main King scallop (Pecten maximus) producing areas in France. Commercial fisheries operating in the eBoS area are diversified and include several métiers. The main fleets and gears considered in this study are nets targeting demersal fish, pelagic and bottom trawls targeting small pelagic fish, bottom trawls targeting demersal fish and cephalopods, pelagic trawls targeting demersal fish, dredge targeting king scallop and other fishing gears [START_REF] Carpentier | Channel Habitat Atlas for marine Resource Management, final report / Atlas des habitats des ressources marines de la Manche orientale, rapport final (CHARM phase II), INTERREG 3a Programme[END_REF].

Model development

This work was based on the widely used Ecopath with Ecosim (EwE) software (version 6.5; www.ecopath.org) for the modelling of aquatic food webs (Christensen and Walters, 2004a;[START_REF] Polovina | Model of a coral reef ecosystem 11[END_REF]. Basic concepts, capabilities and limitations of this modelling approach are described in detail in [START_REF] Christensen | Ecopath with Ecosim: a user's guide[END_REF] and Christensen and Walters (2004a). The spatial simulations presented in this study required the implementation of Ecopath, Ecosim and Ecospace modules in the eBoS ecosystem. Details on the input data and computational aspect of these modules are described in the Appendix (A). Spatial data maps were constructed using R (R Core Team, 2019).

Ecopath model

The parameterization of a mass-balanced Ecopath model is based on two master equations and a resulting set of linear equations to describe the trophic interactions among functional groups of organisms. The first one describes the production term (Eq.1):

𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 = 𝐶𝑎𝑡𝑐ℎ + 𝑃𝑟𝑒𝑑𝑎𝑡𝑖𝑜𝑛 + 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝑎𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 + 𝑁𝑒𝑡 𝑚𝑖𝑔𝑟𝑎𝑡𝑖𝑜𝑛 + 𝑂𝑡ℎ𝑒𝑟 𝑚𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦
The second equation ensures energy balance for each functional group (Eq. 2):

𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 + 𝑅𝑒𝑠𝑝𝑖𝑟𝑎𝑡𝑖𝑜𝑛 + 𝑈𝑛𝑎𝑠𝑠𝑖𝑚𝑖𝑙𝑎𝑡𝑒𝑑 𝑓𝑜𝑜𝑑

The Ecopath model of eBoS is an update of a previously constructed Ecopath model developed by [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning?[END_REF] for the future site of the offshore wind farm of CSM.

The main differences of the model developed in this work are: 1/ the enlargement of the geographical area covered by the Ecopath model of eBoS. Indeed, a larger area better reflect a closed system when there is lack of accurate information about the dynamic of migratory species, 2/ the addition of six new functional groups, and 3/ the definition of new fishing fleets. The eBoS Ecopath model was balanced by slightly modifying the model inputs (especially diet composition). This step aims to satisfy the constraint of mass balance and an Ecotrophic Efficiency lower than one since the main input parameters (i.e. biomass, production/biomass, and consumption/biomass) were re-estimated in this model. All the details related to the update of the Ecopath model of [START_REF] Raoux | Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning?[END_REF] are presented in the Appendix (A).

The mass-balanced model of eBoS represents the situation of the ecosystem in 2000, (the first year of the dataset (Table 1)), and comprises 43 functional groups composed of more than 72 species including phyto-and zooplankton (4), benthos (7), exploited bivalves (1), fish (20), cephalopods (2), seabirds (3), marine mammals (3), discards

(1), detritus (1).

Ecosim model

An Ecosim model was implemented based on parameters inherited from the eBOS Ecopath model in order to provide temporal dynamic simulation capabilities at the ecosystem level (Christensen and Walters, 2004a) Where 𝑎 𝑖𝑗 is the effective search rate of predator 𝑗 for prey 𝑖, 𝑣 𝑖𝑗 is the transfer rate between vulnerable and an invulnerable component, 𝐵 𝑖 is the biomass of the prey and 𝐵 𝑗 is the biomass of the predator. The consumption rates of the modelled species are computed based on the concept of "Foraging arena". The biomass of each prey 𝑖 is divided into vulnerable 𝑉 𝑖 and invulnerable components(𝐵 𝑖 -𝑉 𝑖 ). The exchange rate between the two components depends on the transfer rate 𝑣 𝑖𝑗 (Christensen and Walters, 2004b;[START_REF] Walters | Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments[END_REF] and only the biomass of the vulnerable component is available to predators. The transfer rate 𝑣 𝑖𝑗 represents the impact of predator's biomass on the predation mortality of a given prey since it determines if the control is top-down, bottom-up or wasp-waist [START_REF] Christensen | Ecopath with Ecosim: a user's guide[END_REF]. During the calibration procedure, the best values of vulnerability were estimated in such way to improve the fit of Ecosim predictions to the observed data by using the same value for all prey to a single predator. In this study, the eBoS Ecosim model was constructed to predict the ecosystem effects of fishing over the period 2000 -2015 in order to reproduce the historical patterns of landings (Appendix B). During the calibration procedure, the Ecosim model of the Bay of Seine was fitted to the available time series of landings (2000 -2015) obtained from the IFREMER database SACROIS (Système d 'Information Halieutique, 2017). For this purpose, several time series were implemented in the model (e.g. time series of catches, fishing effort by métiers, primary production); more details are available in Table 1.

The time-dynamic simulations created by Ecosim were calibrated with an automated stepwise procedure, which searches for vulnerability parameters that minimize differences between predicted outputs and observed time series of catches. The procedure is derived from the one described in Piroddi et al., (2016) in turn based on [START_REF] Mackinson | Which forcing factors fit? Using ecosystem models to investigate the relative influence of fishing and changes in primary productivity on the dynamics of marine ecosystems[END_REF]. The goodness of fit was evaluated by calculating the total sum of squared deviations (SS) and the Akaike's Information Criterion (AIC). The calibration was carried out using the module "Fit to time Series" of Ecosim (EwE. 6.5).

The estimation of the primary production anomaly contributed to the reduction of deviations between model predicted catches and observed catches and thereby to the improvement of model performance. Ecosim fitted time series were used as starting point to initialize the spatial simulations [START_REF] Walters | Ecospace: Prediction of Mesoscale Spatial Patterns in Trophic Relationships of Exploited Ecosystems, with Emphasis on the Impacts of Marine Protected Areas[END_REF]. In Ecospace, the biomass of each functional group is allocated across two-dimensional spatial grid with equally sized homogenous cells. This base map is divided into different habitats to which functional groups and fishing fleets are assigned. The biomass pools linked by trophic flows, can move among fixed spatial reference points according to the "Eulerian" approach which treats movement as flows of organisms without retaining information about their movement history (origin and past features) [START_REF] Walters | Ecospace: Prediction of Mesoscale Spatial Patterns in Trophic Relationships of Exploited Ecosystems, with Emphasis on the Impacts of Marine Protected Areas[END_REF].

The implementation of an Ecospace model starts by defining a grid of spatial cells.

Each cell of the base map is assigned to a land or water value and to a specific habitat type. The distribution of the functional groups across the spatial domain is governed by the habitat assignment, the environmental preference function, dispersal rates and foraging behavior. Despite the fact that the extended Bay of Seine is an open ecosystem, the species migration was not considered due to the lack of data, therefore net inputs or outputs of organisms in the considered zone in terms of trophic flows are neglected. For each cell, biomass and consumption rates of functional groups are driven by the trophic interactions inherited from Ecopath and through Ecosim differential equations described in details by [START_REF] Walters | Representing Density Dependent Consequences of Life History Strategies in Aquatic Ecosystems: EcoSim II[END_REF][START_REF] Walters | Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments[END_REF]. After the assignment of fishing fleets to the existing habitats, the fishing mortality is distributed by fleet over the spatial domain based on a relatively simple "gravity model". Ecospace represents spatial distribution of fishing mortality in such way that the amount of effort allocated to each cell is assumed to be proportional to the relative profitability rate in that same cell [START_REF] Christensen | Ecopath with Ecosim: a user's guide[END_REF][START_REF] Walters | Ecospace: Prediction of Mesoscale Spatial Patterns in Trophic Relationships of Exploited Ecosystems, with Emphasis on the Impacts of Marine Protected Areas[END_REF]. This representation allows the model to predict the fishing effort by fleet in a more realistic way. The base map of the Ecospace eBoS consists of a raster grid map of 70 rows and 101 columns, each cell is 0.015° x 0.015° latitude-longitude resolution (≈ 1.6 km side). Several layers of information have been implemented to define the distribution of functional groups and fishing effort:

-The map of the study area: definition of land and water cells and the position of the main ports.

-The bathymetry of the study area extracted from the GEBCO (General Bathymetric Chart of the Oceans) database at 15 arc-second intervals, downloaded from (https://www.gebco.net/).

-The area of the future CSM offshore wind farm implemented as a Marine

Protected Area (MPA).

-Two sub-areas around the offshore wind farm: a first sub-area adjacent to the wind farm 3.2 km wide and a second sub-area adjacent to first one also 3.2 km wide.

-The map of the primary production extracted from the SeaWifs satellite data (processing level: Level 3, resolution: 0.083 ° (Lat) x 0.083 ° (Long)). This map represents the relative concentration of chlorophyll a in the Bay of Seine for the year 2000 (https://podaac.jpl.nasa.gov/).

-The map of the main benthic habitats of the Bay of Seine based on the seafloor type, namely, gravels, sandy gravel, coarse sands, Ophiotrix fragilis patches, fine sand more or less silted, middle dune sands, scallop shell deposit (derived from several benthos campaigns: LANICE, GIE-GMO, PECTOW and Benthoseine) [START_REF] Baffreau | Mapping benthic communities: An indispensable tool for the preservation and management of the eco-socio-system in the Bay of Seine[END_REF] In Ecospace eBoS, the species distribution is driven by the environmental preference function to bathymetry for fish, cephalopods, dolphins and benthic feeders seabirds (estimated from CGFS occurrence data) (Appendix C.2) and the habitat foraging usage for benthic species (Appendix C.3). A fraction of the biomass of each functional group moves into adjacent cells according to random walk movements. This movement is governed by the dispersal rate parameter, which represents the ability of functional groups to move within the base map. The values of dispersal rates recommended by [START_REF] Christensen | Ecopath with Ecosim: a user's guide[END_REF] were applied for the majority of the functional groups, which are of three magnitudes (i.e. 300 km.year -1 for pelagic species, 30 km.year -1 for demersal species, and 3 km.year -1 for non-dispersing species). These values were adjusted manually for some functional groups during the validation of Ecospace (e.g the dispersal rate of marine mammals and birds is equal to 500 km.year -1 (Appendix C.1). When an organism moves to an "unsuitable" (non-assigned) habitat, the values of the basic dispersal rate were multiplied by a factor ranging from 1 to 3 (Appendix C.1). Concerning the relative vulnerability to predation, for benthic groups (e.g. suprabenthos, benthic invertebrate filter feeders, and benthic invertebrate predators), due to their low mobility, their relative vulnerabilities are three times higher in unsuitable habitats. All the other groups are twice more vulnerable to predation in unsuitable habitat, and they are less likely to consume and find appropriate food [START_REF] Christensen | Ecopath with Ecosim: a user's guide[END_REF]) (Appendix C.1).

The spatial fishing mortality depends on fishing fleet distribution. The gravity model spreads the fishing effort inherited from Ecosim across all habitats open to fishing. In the reference scenario, all fleets could fish everywhere except for "Dredge" which assigned to a specific area limited to the stock of King scallop. The fishing effort is distributed proportionally to the "attractiveness" of each cell 𝐴 𝑛,𝑘 .

𝐴 𝑛,𝑘 = ( ∑ 𝑝 𝑘,𝑖 . 𝑞 𝑘,𝑖 . 𝐵 𝑖,𝑛 𝐼 𝑖=1

𝐶 𝑛,𝑘 )

1 𝜎

Where 𝑛 is the cell, 𝑘 is the fleet, 𝑝 𝑘,𝑖 is the price of functional group 𝑖 for fleet 𝑘, 𝑞 𝑘,𝑖 is the catchability of functional group 𝑖 by fleet 𝑘, 𝐵 𝑖,𝑛 is the biomass of group 𝑖 in cell 𝑛, 𝐶 𝑛,𝑘 is the cost for fleet 𝑘 of fishing in cell 𝑛 and 𝜎 measures variation among fishermen in the perception of profit from fishing in cell 𝑛 [START_REF] Romagnoni | The Ecospace model applied to the North Sea: Evaluating spatial predictions with fish biomass and fishing effort data[END_REF].

In Ecospace eBoS, costs are based on the map of sailing costs calculated from the "distance from port" map and effort related cost (Ecopath default value for all fleets).

The effective power 1 𝜎 controls fleets distribution, high value of 𝜎 correspond to a smoother distribution of the fishing effort throughout the map. The effective power and the total efficiency multiplier (the multiplier factor for effort) were set to the default value 1.

Offshore wind farm simulations

The future wind farm composed of 64 turbines will be located 10 -16 km offshore from Courseulles-sur-Mer (Fig. 1). During the exploitation phase of turbines, around 20% of the installation area is scheduled to be closed to all fishing activities (EDF personal communication). In the present study, two simulations were run for a period of 15 years to assess the Marine Protected Area (MPA) effect through the evaluation of "what if scenarios". A reference scenario, which corresponds to the observed ecosystem for the period 2000 -2015 with no changes and an "exclusion" scenario in which a MPA is assigned to the offshore wind farm area (≈ 70 km 2 ). The scenario corresponding to a closure of 20% of the wind farm area was not presented in this article because the resolution is not fine enough to detect any changes.

Several biomass, catch and trophic level-based indicators were calculated to quantify the impact of the installation of the wind farm (e.g. biomass, catches and discards of exploited groups, Marine Trophic Index (MTI) defined as the mean trophic level (TL) of fisheries landings of species with trophic levels > 3.25, trophic level of catches, trophic level of the community at two different cut-offs (3.25 and 4) corresponding the lowest TL values used in the computation of the indicator (it considers all organisms above cut-off TL). In order to investigate the potential spillover effects from the exclusion area, these indicators were calculated in three sub-areas, i/ sub-area 1: the MPA area (also called "no fishery area" or "exclusion area"), ii/ sub-area 2: a first area 3.2 km wide surrounding sub-area 1 and, iii/ sub-area 3: a second area 3.2 km wide surrounding sub-area 2 (Fig. 2).

Results and discussion

Reference scenario

The reference scenario maps were averaged to capture the mean state of the eBoS ecosystem during the period 2000 -2015. In Ecospace eBoS, CGFS data served to define species preferences in terms of habitat. Since CGFS fisheries surveys are conducted yearly in the eastern English Channel in October the predicted maps of biomass should be considered as an autumnal representation of the ecosystem (Fig. 3). In order to assess the accuracy of Ecospace outputs and avoid a misrepresentation of the geographic distribution of modelled groups, predicted maps were compared to species distribution maps from Channel Habitat Atlas for marine Resource Management [START_REF] Carpentier | Channel Habitat Atlas for marine Resource Management, final report / Atlas des habitats des ressources marines de la Manche orientale, rapport final (CHARM phase II), INTERREG 3a Programme[END_REF]

Analysis of the spillover effects

In order to analyze the spillover effects, the percentage of change by sub-area was calculated by comparing the averaged outputs over 15 years of the reference scenario with the offshore wind farm scenario. Results of the implementation of the offshore wind farm scenario revealed substantial changes in biomass of demersal species which display a percentage of change higher than 9% in sub-area 1 (Fig. 4, Demersal biomass). Nonetheless, the spillover effects for demersal species in sub-areas 2 and 3 is less pronounced than for other functional groups because of their low mobility.

Besides, pelagic species characterized by a high dispersal rate could benefit from the spillover effect by increasing their biomass to almost 2%. For invertebrates, the simulation results exhibit a different pattern. Indeed, the implementation of the offshore wind farm has limited effects in the exclusion zone. Figure 4 reveals that the biomass of invertebrates increases only by 0.5% in sub-area 1. However, their biomass could decrease up to 1% in surrounding sub-areas. This result might be explained by the increase of both predation pressure inside the exclusion zone (sub-area 1) and fishing mortality around the wind farm in sub-areas (2 and 3). The intensification of fishing pressure on benthic invertebrates is clearly illustrated by an important increase of catches of King scallop Pecten maximus in sub-areas 2 and 3. The changes in catches are similar across the main exploited groups of fish, invertebrate, demersal and pelagic species. Potential catches increase up to 8% in sub-area 2 and 4% in sub-area 3, which could be interpreted as a result of re-allocation of the fishing effort in the cells around the offshore wind farm (since the spillover effect increased the profitability of sub-areas 2 and 3).

It is likely that the increase of catches does not balance the decline of total landings following the closure of sub-area 1 to fishing activities. However, the shortfall in catches could be mitigated by the predicted change of catch composition. Indeed, the slight increase of the trophic level of catches and the Marine Trophic Index in the areas surrounding the wind farm indicates an increase in the proportion of high trophic level species in the catch composition, which generally have high economic value (e.g.

European seabass, benthic cephalopods, and Atlantic cod). For the trophic level of the community at thresholds 3.25 and 4, the predicted increase in sub-areas 1 and 2 is attributed to an increase of the proportion of top predators. This result highlights that high trophic level species (TL>3.25) could benefit from the installation of the offshore wind farm and the resulting fishing ban in sub-area 1. Higher trophic level species also benefit in sub-areas 2 and 3 even despite the increase of fishing mortality. The spatial analysis of the spillover effects revealed that, for most of the groups, it is limited to adjacent areas around the offshore wind farm in less than 3 km radius range and diminish rapidly with increasing distance, especially for commercial and demersal species (Fig. 5). These findings are in line with previous findings which showed that spillover is a common phenomenon around no-take marine reserves, but at relatively small scales [START_REF] Harmelin-Vivien | Gradients of abundance and biomass across reserve boundaries in six Mediterranean marine protected areas: Evidence of fish spillover?[END_REF][START_REF] Halpern | Spillover from marine reserves and the replenishment of fished stocks[END_REF]. Furthermore, the intensity of the spillover effects seems to vary among the different groups. For example, the group of invertebrates exhibit an opposite trend due to a higher fishing pressure on King scallop Pecten maximus in the sub-area bordering the wind farm.

Moreover, the simulation of a no fishing area suggested that for highly mobile species, the spillover effect is less intense and more diffused in space, such as for pelagic species and the group of marine mammals and birds. These predictions are consistent with previous results which support that the spillover effect differs by species mobility [START_REF] Kellner | Fishing the Line Near Marine Reserves in Single and Multispecies Fisheries[END_REF].

The model displayed certain spatial patterns in the distribution of the spillover effects over the Bay of Seine (Fig. 5). This is illustrated by the presence of three well defined sub-areas: a first zone adjacent to the offshore wind farm with positive spillover effects (increase of biomass), a second zone bordering the first one with no spillover effects (no change in biomass) and a third zone afterwards with a decrease of biomass). This pattern could be interpreted as being a result of species movement toward more suitable habitats through the net emigration of fish as proposed by [START_REF] Rowley | Marine reserves in fisheries management[END_REF] and the redistribution of the fishing effort over the study area. In fact, the increase of the predicted biomass, in the sub-area 2, could be explained by local movements of functional groups, which tend to spend more time in areas where the conditions are more suitable for two main reasons: 1) the offshore wind farm plays the role of a Marine Protected Area (MPA) after the closure of fishing activities. Hence, the exploited species could benefit from the absence of fishing mortality, and 2) the offshore wind farm and surrounding areas offer more feeding opportunity for all predators given the spillover effects. Therefore, it is very likely that the predicted increase of biomass in sub-areas 1, 2 and 3 is due to the spillover effects and species movements. For invertebrates, the decrease of their biomass only occurs around the no-take area because of a higher fishing pressure on King Scallop, which has a high abundance localized around the future wind farm. Furthermore, the patterns observed in figure 5 show that for less mobile species such as invertebrates the impact of spillover (increase of biomass around the no-take) area is very limited in contrast to more mobile groups (e.g. marine mammals and pelagic fish) for which the effect of the spillover could cover a larger area. Figure 6 shows spatial changes, in terms of catches and discards, after the implementation of the offshore wind farm. The Ecospace model predicted an increase of catches, up to 20%, in some cells in the areas surrounding the wind farm site. The pattern of change of catches was very similar to biomass changes (Fig. 6). Ecospace simulations corroborate the view that the impacts of banning fishing activities within the wind farm are local (less than 3 km radius range around the wind farm) and they are not likely to affect the global trophic dynamic of the eBoS ecosystem. These results are in line with previous findings regarding the potential effects of small sized marine protected areas on the catches of the Gulf of Gabès, Tunisia [START_REF] Abdou | Exploring the potential effects of marine protected areas on the ecosystem structure of the Gulf of Gabes using the Ecospace model[END_REF] and the net contribution of spillover from marine reserves to fishery catches [START_REF] Goñi | Net contribution of spillover from a marine reserve to fishery catches[END_REF]. The Ecospace modelling of eBoS predicted very low (less than 0.01%) spatial changes in the community trophic level and catch composition that resulted from the fishing ban around the wind farm. However, despite the weakness of the signal to detect ecosystem responses, maps of trophic indicators reveal a very clear and consistent spatial pattern (Fig. 7). Indeed, both trophic level of catches and community illustrate a slight increase of the proportion of higher trophic level organisms inside and bordering the offshore wind farm. The decrease of TL indicators, in more remote areas, confirms the hypothesis that top predators would concentrate around the wind farm site, where they would maximize their feeding opportunities [START_REF] Pérez-Jorge | Can Static Habitat Protection Encompass Critical Areas for Highly Mobile Marine Top Predators? Insights from Coastal East Africa[END_REF].

A further explanation may also relate to the absence of fishing inside the exclusion area. Indeed, predators in sub-area 1 take advantage of the fishing ban and benefit from the higher survival rate. This finding suggests that the exclusion zone inside the wind farm would result in the concentration of top predators from surroundings areas [START_REF] Gell | Benefits beyond boundaries: the fishery effects of marine reserves[END_REF]. Moreover, the predicted increase in the proportion of top predators concurred with the observations of [START_REF] Ecoutin | Ecological field experiment of short-term effects of fishing ban on fish assemblages in a tropical estuarine MPA[END_REF] on the effects of a fishing ban on fish assemblages in The Sine Saloum Delta in Senegal.

Uncertainty and limitations of the model

Incorporating ecosystem considerations in the analysis of potential effects of the implementation of offshore wind farms is a relevant approach to understand ecosystem response and prevent conflict between the main users (i.e. fishing industry and marine renewable energy) [START_REF] Alexander | Spatial ecosystem modelling of marine renewable energy installations: Gauging the utility of Ecospace[END_REF]. Such approach requires using complex models like Ecospace in order to represent spatio-temporal dynamics of species interactions. Therefore, it is a major issue to consider uncertainties and limitations associated with this modelling approach in the interpretation of results.

In fact, some limitations related to both data availability and our understanding of the ecosystem occurred. For instance, the model was constructed as a "closed" system because it was not possible to simulate trophic inflows and outflows related to species migration due to lack of data. Moreover, input parameters in Ecopath, Ecosim and Ecospace models do not have the same level of uncertainty. Most of the data were obtained from the Bay of Seine (e.g. biomass, landings, fishing effort) but some parameters were obtained from adjacent ecosystems (i.e. Western English Channel or North Sea) or set by default (e.g. dispersal rates). Moreover, the large number of parameters in ecosystem models makes the sensitivity analysis a complex task to implement and requires high computational resources [START_REF] Romagnoni | The Ecospace model applied to the North Sea: Evaluating spatial predictions with fish biomass and fishing effort data[END_REF][START_REF] Song | Chapter 19 -Sensitivity in Ecological Modeling: From Local to Regional Scales[END_REF]. Therefore, a simple sensitivity analysis limited to the dispersal rate parameter was performed to test the robustness of the results by functional group inside each sub-area (Appendix E). Besides the uncertainties associated with the type and source of data, there are some limitations inherent to mass-balanced models (e.g.

the diet composition of consumers is fixed during the simulation period). The limitations related to model hypothesis and assumptions of Ecopath, Ecosim and Ecospace were discussed in details in [START_REF] Ainsworth | Ten common mistakes made in Ecopath with Ecosim modelling[END_REF] and Christensen & Walters (2004a). More discussions about the uncertainty regarding the definition of functional groups are also detailed in [START_REF] Essington | Evaluating the sensitivity of a trophic mass-balance model (Ecopath) to imprecise data inputs[END_REF] and [START_REF] Plagányi | A critical look at the potential of Ecopath with ecosim to assist in practical fisheries management[END_REF]. Given the different sources of uncertainty in the modelling process and the lack of rigorous sensitivity analysis, the present study was based on emerging patterns to reduce the uncertainty relative to the reliability of inputs and model complexity. Furthermore, spatial observations were not sufficient for the majority of functional groups to perform a quantitative validation. Nevertheless, the abundant samples of the King scallop (Pecten maximus) could be used to perform a regional validation similar to the method ) is too coarse to represent the species habitat at the scale of the installations. Indeed, the size of each cell in Ecospace eBoS grid (0.015° X 0.015°) is not fine enough to analyze both the "reef effect" which may be important at local scale and the "spillover effect" which operates at larger scale. In order to overcome this limitation, coupling a 3D hydrodynamic-biogeochemical model to a Dynamic Energy Budget (DEB) model may help to evaluate the impact of wind turbine structure on benthic species (e.g. mussels) at a small scale [START_REF] Maar | Local effects of blue mussels around turbine foundations in an ecosystem model of Nysted off-shore wind farm, Denmark[END_REF].

The predictions of this high-resolution model could be then integrated into Ecospace as a forcing function.

Given the model structure, this study focuses on the operational phase of an offshore wind farm rather than the whole cycle (prospecting, installation and decommissioning).

Therefore, some long-lasting impacts which may have cascading effects on the food web were not included. Furthermore, it is important to interpret the predictions for marine mammals and seabirds carefully since the model do not consider all the drivers which could affect their distribution (e.g. risk of collisions with rotor blades, migration barriers, low frequency noise from operating turbines).

Conclusions

This study represents a first attempt to provide insight into potential impacts of the deployment of an offshore wind farm in the French waters with a special focus on the spillover effect from a spatial closure of the wind farm to fishing activities. The implementation of the Ecospace model allowed investigating ecosystem consequences of turning the offshore wind farm site into a Marine Protected Area.

Although, the closure area is scheduled to cover around 20% of the site, the objective of this simulation is to evaluate how a more extended total closure could affect fishing activities and the main components of the ecosystem. Such an analysis could then be useful for spatial planning decision makers about new offshore wind farms.

The findings of this study suggest that the spillover effects could mitigate the negative impact on fishing activities because of 1/ an increase of catches (up to 7% close to the wind farm) and 2/ a slight modification in the composition of catches leading to an increase in the proportion of high trophic level species. However, the influence of spillover effects is limited in space and the expected positive effects are highly localized in areas around the offshore wind farm site. The analysis of the spillover effects at the scale of the Bay of Seine suggested a spatial pattern, which shows that the exclusion zone could play the role of a "Fish Aggregating Device" by attracting predators from surrounding areas. Despite the limitations inherited from the underlying Ecopath with Ecosim models and input data used in the parameterization of the Ecospace eBoS, this study serves as a tool of a more holistic approach to address questions regarding the potential effects of the implementation of offshore wind farms. This approach could be used with complementary studies on benthos, marine mammals and seabirds to address potential compatibility and synergies between fishing activities, marine conservation and marine renewable energy and provide a baseline for an assessment tool for Ecosystem Based Management decisions in the Bay of Seine.

Fig. 1 .

 1 Fig. 1. Map of the study area "extended Bay of Seine" (eBoS) and the location of the future Courseulles-sur-Mer offshore wind farm.

Fig. 2 .

 2 Fig. 2. Map of sub-areas of interest inside the study area of eBoS Ecospace model. Sub-area 1 (white) corresponds to the location of the future offshore wind farm of Courseulles-sur-Mer (CSM).

  Fig. 3. Biomass distribution of 40 functional groups predicted by Ecospace eBoS under the reference scenario representing the mean state of the Bay of Seine ecosystem during the period 2000 -2015, (red: high biomass, blue: low biomass).

Fig. 4 .

 4 Fig. 4. The percentage of change represents the comparison of the averaged outputs over 15 years of the offshore wind farm scenario with the reference scenario inside each sub-area (sub-area 1: inside the MPA area, sub-area 2: a first area 3.2 km wide

Fig. 5 .

 5 Fig. 5. The spatial relative impact of the implementation of the offshore wind farm on the biomass of commercial species, invertebrates, demersal species, pelagic species, marine mammals and birds (blue: the value of the indicator is higher than the reference scenario, red: the value of the indicator is lower than the reference scenario). The maps were averaged over the period 2000 -2015.

Fig. 6 .

 6 Fig. 6. The spatial relative Impact of the implementation of the offshore wind farm on total catches, fish catches, invertebrates catches, demersal catches, pelagic catches and total discards (blue: the value of the indicator is higher than the reference scenario, red: the value of the indicator is lower than the reference scenario). The maps were averaged over the period 2000 -2015.

Fig. 7 .

 7 Fig. 7. The spatial relative impacts of the implementation of the offshore wind farm by trophic level-based indicators: Trophic Level of catches, Marine Trophic Index, Trophic Level of the community, Trophic Level of the community set at 2, Trophic Level of the community set at 3.25 and Trophic Level of the community set at 4. (blue: the value of the indicator is higher than the reference scenario, red: the value of the indicator is lower than the reference scenario).

  used by De Mutsert et al. (2017) to test the performance of their Ecospace model in the lower Mississippi River Delta. Other limitations are related to the trade-off between the spatial scale and the resolution during the implementation of the Ecospace model. Indeed, although the spatial resolution of Ecospace eBoS was suitable to analyze spillover effects in the extended Bay of Seine, it was not relevant to simulate scenarios at a finer scale such as the 20% closure scheduled by the operator of the wind farm. The spatial resolution of the Ecospace eBoS (each cell ≈ 2.5 km 2

𝑖 is the net growth efficiency, 𝑄 𝑖𝑗 is the consumption rate of group 𝑖 by group 𝑗, 𝐼 𝑖 is the immigration rate, 𝑒 𝑖 is the emigration rate, 𝑀 𝑖 corresponds to the other natural mortality rate and 𝐹 𝑖 is the fishing mortality rate. The second equation defines the consumption of a predator 𝑖 on its prey 𝑗 for each time step.

				. The time-dynamic simulations of
	the food web result from two main equations, one of which to express the biomass
	dynamic:	𝑑𝐵 𝑖 𝑑𝑡	𝑛 = 𝑔 𝑖 ∑ 𝑄 𝑗𝑖 -∑ 𝑄 𝑖𝑗 + 𝐼 𝑖 -(𝑀 𝑖 + 𝐹 𝑖 + 𝑒 𝑖 )𝐵 𝑖 𝑛 𝑗=1 𝑗=1
	Where	𝑑𝐵 𝑖 𝑑𝑡	represents the growth rate of group 𝑖 during the time interval 𝑑𝑡 in terms of
	biomass, 𝑔 𝑄 𝑖𝑗 =	𝑎 𝑖𝑗 𝑣 𝑖𝑗 𝐵 𝑖 𝐵 𝑗 2𝑣 𝑖𝑗 + 𝑎 𝑖𝑗 𝐵 𝑗

Table 1 .

 1 Time series data used to fit the Ecosim model of the Bay of Seine Ecospace is the spatial and time dynamic module of the EwE software. It inherits all the key elements of Ecopath and Ecosim models. Ecopath baseline biomasses and

	Time series	Period	Target group	Sources	Type (in Ecosim)
				 King scallop
				 Fish limande
				 Fish flounder
				 Fish european plaice
				 Fish sole
				 Fish sea bream
				 Fish benthos feeders
				 Fish planctivorous
	Catches	(t.km -2 )	2000 -2015	 Fish piscivorous  Fish atlantic horse mackerel  Fish european pilchard  Fish pouting  Fish gurnard	SACROIS data (Système d'Information Halieutique, 2017) http://sih.ifremer.fr/	Catch (reference)
				 Fish whiting
				 Fish atlantic cod
				 Fish sharks
				 Fish rays
				 Fish european seabass
				 Fish mackerel
				 Benthic cephalopods
				 Benthopelagic cephalopods

Acknowledgments

The work was funded by the TROPHIK project (ANR/FEM EMR-ITE 2015) and supported by France Energies Marines and the French State, managed by the National Research Agency under the "Investissements d'avenir" program (ANR-10-IEED-0006-12) and by a postdoc grant from IFREMER (coordinated by MC Villanueva). The authors acknowledge the contribution of several researcher from IFREMER and University of Caen Normandy for providing expertise for the construction and validation of the Ecospace model. We thank two anonymous reviewers for their insightful and constructive comments and suggestions.