# The environmental impact from an offshore windfarm: Challenge and evaluation methodology based on an ecosystem approach

Jean-Philippe Pezy, Aurore Raoux, Jean-Claude Dauvin

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Jean-Philippe Pezy, Aurore Raoux, Jean-Claude Dauvin. The environmental impact from an offshore windfarm: Challenge and evaluation methodology based on an ecosystem approach. Ecological Indicators, 2020, 114, 10.1016/j.ecolind.2020.106302. hal-03420720

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## 1 Ecological indicators

2

3 The environmental impact from an offshore windfarm: challenge and evaluation methodology based

4 on an ecosystem approach

5

6 Jean-Philippe Pezy¹*, Aurore Raoux¹, Jean-Claude Dauvin¹

7

8 ¹Normandie Univ, UNICAEN, UNIROUEN, Laboratoire Morphodynamique Continentale et Côtière,

9 CNRS UMR 6143 M2C, 24 rue des Tilleuls, 14000 Caen, France

10

11 * Corresponding author: jean-philippe.pezy@unicaen.fr

12

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## 14 ABSTRACT

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16 Responding to pressure to combat climate change by cutting greenhouse gasses emission, Offshore
17 Wind Farm (OWF) is currently the most developed of all the renewable energy options that are based
18 in the marine environment. However, despite its perceived benefits, OWF construction will also lead
19 to direct and indirect impacts on the ecosystem. Nonetheless, surprisingly few studies have sought to
20 combine trophic web modelling tools with the framework of OWF impact assessment. The objective
21 of this study was to develop a holistic and integrated ecosystem approach as a complementary
22 methodology to the traditional OWF environmental impact assessment. To that end, trophic web
23 models coupled with Ecological Network Analysis indices were used to study the structure and
24 functioning of three sedimentary types (sandy gravel, gravelly sand, and medium sand), identified on
25 the proposed site for a new offshore wind farm at Dieppe-Le Tréport (Normandy coast). The results
26 from this study demonstrated that the trophic structures are strongly linked to the sedimentary
27 types. The results also highlighted the existence of a maturity gradient moving between the three
28 types of sediment identified. This study underlined the challenge and the need to have detailed
29 biomass data on a maximum of biological compartments to enable an effective OWF impact
30 assessment. We strongly consider that our approach will provide useful and practical information for
31 environmental decision-makers and stakeholders. The same approach can be applied to other new
32 OWF constructions in European or international waters.
33

33
**KEYWORDS:** Ecological Network Analysis, Ecopath model, Offshore Wind Farm, environmental impact assessment, trophic web

# 1 Introduction

The growing concern over the global climate change threat has led to pressure to move energy production based on fossil fuels to that of renewable sources. In this context, Marine Renewable Energy (MRE) have increased steadily throughout the coastal areas of Northern Europe as they offer the potential to reduce our dependency on fossil fuels (Raoux et al., 2017; Raoux et al., 2019; Pezy et al., 2019). Of these technologies, Offshore Wind Farms (OWF) has seen a consistent growth in generating capacity and it is by far the most technically advanced of all the MRE methods (Wilding et al., 2017). Ecosystem modifications following OWF construction are expected to change local biodiversity through the colonization of OWF foundations by numerous marine organisms resulting in an additional food source for colonizing organisms higher in the food chain and for surrounding soft-bottom communities (Raoux et al., 2017). On the other hand, several studies have shown that OWF also disrupt marine invertebrates, fish, and mammals, especially from the generation of noise and electromagnetic fields (Lovich and Ennen, 2013; Tabassum-Abbasi et al., 2014; Zettler and Pollehne, 2006).

The French government is considering the construction of four OWFs in the English Channel (St Brieuc, Courseulles sur mer, Fécamp and Dieppe-Le Tréport) (Pezy et al., 2019). As in all European countries, such proposals must be subjected to an environmental impact assessment including monitoring studies to investigate the impact from these new structures on the coastal ecosystems (Wilding et al., 2017). However, OWF impact assessment and monitoring protocols are still under development (Scott et al., 2016; Wiesebron et al., 2016) and several studies have pointed out some significant shortcoming (Pezy et al., 2019; Raoux et al., 2019; Wilding et al., 2017). For instance, although there is a well-established requirement for both holistic approaches and Ecosystem-Based Management (EBM) of marine ecosystems, attention tends to be focused on some iconic high profile species (such as marine mammals, birds) because of their protected status and public interest (Wilding et al., 2017). Thus, the impacts from OWFs on the whole ecosystem remain insufficiently known and current studies could fail to detect serious disruption to the local ecosystem (Wilding et al., 2017). Responding to EBM and environmental legislation requirements, both Raoux et al. (2017; 2019) and Pezy et al. (2019) have highlighted the need to adopt a holistic approach to assess the impact of OWF installations on the local ecosystem functioning using trophic web modelling tools as a complementary approach to the traditional impact assessments methodology. Trophic web models have been shown to provide global system indicators reflecting the structure and functioning of
entire ecosystems. In addition, they can provide information on the overall ecosystem status and
70 could be used as a baseline for EBM decisions (Safi et al., 2019).

Amongst the different trophic web modelling approaches currently available, one known as EwE
71 (Ecopath with Ecosim) has been widely used and progressively developed over the last three
72 decades (Colléter et al., 2013). This model has been used to analyse the impact of a wide range of
73 anthropogenic perturbations on marine ecosystems as fishing (Heymans and Tomczack, 2014),
74 introduction of new species (Langseth et al., 2012), sea dumping operations (Pezy et al., 2018a,b)
75 infrastructure development (Tecchio et al., 2015), and most recently, OWF impacts (Raoux et al.,
76 2017; Raoux et al., 2019; Wang et al., 2019). However, surprisingly, few studies have sought to
77 combine the strength of trophic web modelling with the framework of OWF impact assessment
78 (Raoux et al., 2017).

Raoux et al. (2017; 2019) investigated the applicability of the EwE approach coupled with Ecological
80 Network Analysis (ENA) indices in the context of OWF construction in France. They developed an
81 ecopath model composed of 37 compartments, from phytoplankton to seabirds to describe the
82 situation "before" the construction of the wind farm. The model was then used to project the impact
83 of the OWF by increasing the biomass of targeted benthic and fish compartments subjected to reef
84 effect caused by the foundations. ENA indices were calculated for the two periods, "before" and
85 "after", to compare changes in the functioning and structure of the trophic web. One of the main
86 results showed that total ecosystem activity, recycling and ecosystem maturity, all increased after
87 the OWF construction (Raoux et al., 2017, 2019). Results also suggest an increase in detritivory flow
88 and a potential reef effect with respect to apex predators owing to an increased population of
89 benthic fish after OWF construction. However, these studies were based solely on simulated future
90 scenarios instead of using actual post-construction field investigations.
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Wang et al. (2019) also built an Ecopath model applying it before and after the construction of the
92 Rudong OWF (Jiangsu coastal ecosystem, China) based on biological field data collected before and
93 after the construction. These results also indicated that the ecosystem tended to develop towards
94 higher maturity with higher ecosystem activity and recycling following the OWF construction.
95

Finally, Pezy et al. (2019) promoted a sampling strategy to collect data on the different ecosystem
96 compartments of the future Dieppe-Le Tréport (DLT) OWF site (France), adopting a BACI (Before
97 After Control Impact) approach coupled with an ecosystem approach. For that, an Ecopath model
98 was built to characterize the structure and functioning of this ecosystem based on biological field
99 data (zooplankton, suprabenthos, meiofauna, benthos and demersal fish). Data were collected
100 during four successive campaigns (summers 2014/2015 to winter 2015/2016) which enabled
101 seasonality to be taken into account (noting minimum biomass values in winter and maximum
102 in the summer). However, this study modelled the DLT site as a homogenous ecosystem without
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taking into account the three different sediment types (defined as medium sand; gravel sand; gravelly sand) identified on this site (Pezy, 2017) which could respond differently to the OWF construction. In fact, in the English Channel, the distribution of benthic communities is strongly correlated to sediment type and to a longitudinal climatic gradient (Dauvin, 2015).

The same modelling procedure described by Pezy et al. (2019) is used in the present study but developed further with the objective to deepen the understanding of the structure and functioning of DLT OWF site. In particular, the objective was to consider the additional influence of the three different sediment types that makes up the chosen site. This is represented by four approaches:

* Building nine Ecopath models that take into account the three distinct sedimentary types. For each sedimentary habitat, three models were built: one for the winter, one for the summer and one for the whole year.

* Using these results to compare the structure and the functioning of the three sedimentary types throughout the seasons in respect of the holistic properties derived from the Ecological Network Analysis (ENA) indices.

* Evaluating the results in the light of the ecological maturity theory (Odum, 1969, 1971).

* Comparing the structure and the functioning of these three sedimentary types with ecosystems studied with the same methodology (EwE approach) and located in the same sedimentary types.

# 2 Materials and methods

## 2.1 Study area

The DLT OWF will be built in the next years in the eastern English Channel (eEC) which is a shallow epi-continental sea located between France and United Kingdom (UK). It is about 35,000 km² and it is delimited by the Cotentin peninsula in the west and the Dover Strait to the east (Dauvin, 2019). The maximum water depth never exceeds 70 m in the trench running through the centre of the English Channel (Carpentier et al, 2009). The tidal range is greater than 5 m on the French coast but is closer to 2 m on the UK side (Dauvin, 2019). The resulting sea currents play an essential role in the characterisation of the sediment (Larsonneur et al., 1982) and in the makeup and distribution of the benthic community (Dauvin, 2015). The eEC is already subject to many human activities such as shipping, fishing, sediment deposit, and sediment extraction (Dauvin, 2019) and is indeed considered by Halpern et al. (2008) as one of the most disturbed seas of the world.
## 2.2 Dieppe-Le Tréport (offshore wind farm proposal)

The proposer for the project is "Eoliennes en mer Dieppe-Le Tréport", a subsidiary of Engie (formerly GDF Suez). The OWF will be located between 15 and 17 km from the coast running from Le Tréport to Dieppe, (Fig. 1). It will cover a total area of 92 km², and will comprise 62 x 8 MW turbines giving a combined nameplate capacity of 496 MW. The water depth ranges from 12 to 25 m. The wind farm turbines will be connected via an interarray network of 33 kV AC cables which will link up to an offshore transformer sub-station located within the wind farm area. From this station, power will be exported via two 225 kV AC marine cables. The foundations will be composed of jacket structures. The DLT OWF project site includes three different sedimentary habitats: sandy gravel, gravely sand and medium sand (more detailed are given in Pezy, 2017 and Pezy et al., 2019).

<Figure 1>

## 2.3 Modelling approach

In this study, the methodology as in Pezy et al. (2019) was used to build the nine trophic web model (three models at each sedimentary type: one in winter, one in summer and one corresponding to a mean year). The Ecopath with Ecosim (EwE) approach (Christensen and Walters, 2004) was retained to estimate the value of all carbon flows in the nine trophic webs. A detailed description of the EwE approach with the main equations can be found in Pezy et al. (2019).

The biomass data used to build these nine models were collected during four surveys from March 2014 to September 2016. The benthic invertebrates, suprabenthos, demersal fishes, meiofauna, and zooplankton were sampled at two seasons: in March during the winter period and in September during the summer (Pezy et al., 2018). The benthic macrofauna and meiofauna were sampled over 25 stations (20 located inside and five located outside the future OWF), 12 of which were located in sandy gravel, seven gravelly sand and six in medium sand using a 0.1m² Van Veen grab and three replicates per station (Pezy et al., 2018). Demersal fish species and cephalopods biomass were collected by sampling with a 3-m beam trawl. 10 hauls were operated (five inside and five outside the wind farm area) while sailing against the current for a sampling period of 15 min at a speed of approximately 3–3.5 knots (according to the current speed). Among the 10 hauls, three were located in sandy gravel, three were located in gravelly sand and four were located in the medium sand (Pezy et al., 2018). Concerning the zooplankton and the suprabenthos the sampling plan was carried out on two stations: one inside the wind farm area, located in sandy gravel and one outside the wind farm area, located in medium sand, using a WP2 net (200 mm) and a Macer-GIROQ sledge (Dauvin and Lorgeré, 1989) respectively. A full description of the sampling and analytical procedures of these
biomass data can be found in Pezy et al. (2019) and thus need not to be repeated here. The biomass of cetaceans (porpoises and dolphins), seals and planctivorous fish were obtained from another study focused also on the EC (Carpentier et al., 2009). Thus, these three compartments were not reliable to the sedimentary types as they moved at the scale of the whole basin of the English Channel (EC)

On the same line, the data on primary production, bacteria, and detritus were obtained from other studies focused also on the EC (Garcia, 2010). All source data used for obtaining model parameters are listed in the Table 1.

<Table 1>

The functional groups used in this study were chosen according to the biological and ecological characteristics of the species (food preference, size, commercial importance) and data availability. On this basis, 28 groups were selected, 2 marine mammals, 5 fish, 1 cephalopod, 1 lancelet, 14 benthic invertebrates, 1 zooplankton, 1 merosuprabenthos, 1 primary producer, 1 bacteria, and 1 detritus group. 21 of the organism groups were obtained from sampling operations (following methods described by Pezy et al., 2019). Each benthic functional group was further divided in two categories: "consumed (C)" and "not consumed (NC)". The consumed organisms were those where the species were found in the fish stomach contents (Pezy et al., 2019).

The EwE pedigree routine was used to quantify the input parameter uncertainties (Christensen and Walters, 2004). This routine allows categorizing the data origin of each single input (biomass, P/B, Q/B, diet, catch) using pre-defined tables; the key criterion being that inputs from local data have the best confidence and the highest level in the scale (Christensen et al., 2005).

## 2.4 Analysis of the ecosystem organization and emergent properties

For each of the nine Ecopath models, the trophic level of each functional group was calculated from the makeup of its diet. This is calculated as the weighted average of the trophic levels of each of its prey, with primary producers and non-living material being set at a trophic level of 1:

$$TL_j = 1 + \sum_{i=1}^{N} DC_{ji}TL_j$$

where DC<sub>ji</sub> is the fraction of the prey <i>i</i> in the diet of the predator <i>j</i>.
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Ecological Network Analysis (ENA (Ulanowicz, 1986)), indices were calculated for the three types of sediment using the network analysis plug-in included in the EwE protocol (Christensen and Walters, 2004). The following ENA indices were retained:

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* Total System Throughput (T..) is calculated as the sum of all the flows in the food web. It thus characterizes the overall activity and indicates the size of the ecosystem (Latham, 2006).

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* Ascendency (A) is a measure of the system activity linked to its degree of organization (Average Mutual Information) (Ortiz and Wolff, 2002).

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* Finn's Cycling Index (FCI) quantifies the importance of cycling in the system and is presented as the percentage of the total flow generated by cycling (Finn, 1980).

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* The System Omnivory Index (SOI) is calculated as the average of the OI values of the individual group, weighted by the logarithm of each consumer intake (Christensen and Walters, 2004). The SOI represents an overall measure of the complexity of a given ecological network.

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Finally, three more ecosystem attributes were characterized by the following ratios: the total primary production/total respiration (PP/R), the total primary production/total biomass (PP/B) and the total biomass/total system throughput (B/T..).

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# 3 Results

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The calculated Pedigree index for the models is 0.73.

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## 3.1 Biomass profiles and Trophic levels

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Results showed that during the winter, in the sandy gravel and gravely sand, the functional groups dominating the biomass, were the benthic invertebrates, filter feeders NC (Not Consumed) (mostly composed of the dog cockle *Glycymeris glycymeris* and the banded carpet clam *Polititapes rhomboides*) which represented 70 % of the total living biomass of the ecosystem (Table 2). By contrast, in the medium sand, the biomass was dominated by the benthic invertebrates, sub-surface deposit feeders NC (mostly composed of the heart urchin *Echinocardium cordatum*), making up 30 % of the living biomass (Table 2).

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In the summer, the functional group dominating the biomass for all three sedimentary types were the filter feeders NC subgroup of benthic invertebrates, (again mostly composed of the dog cockle *Glycymeris glycymeris*) (Table 2). This functional group represented 75 %, 77 % and 25 % of the total living biomass in the sandy gravel, gravely sand and medium sand respectively (Table 2). The other major groups dominating the biomass in the sandy gravel and gravely sand sediment were the

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benthic invertebrates subgroup surface deposit feeders NC, whereas in the medium sand sediment it
was the sub-surface deposit feeders NC benthic invertebrates that are more common.

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Looking at the whole year, the total living biomass (Bt) values were higher in the sandy gravel (Bt: 86.6 gC.m⁻²) than in the gravelly sand (Bt: 60.7 gC.m⁻²), the value is much lower for the medium sand (Bt: 17.9 gC.m⁻²) (Table 4).

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<Table 2>

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In the nine models, the Trophic Level (TL) of functional groups ranged from 1.0 for primary producers and detritus to a maximum of approximately 4.5 represented by the cetaceans (Table 3). Irrespective of the season or the sediment, the cetaceans had the highest TL followed by the cephalopods and the whiting (*Merlangius merlangus*) in the winter and by the cephalopods and the fish, benthos feeders in the summer.

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<Table 3>

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**3.2 Ecosystem functioning**

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Results relating to the ENA indices showed that the Total total ecosystem activity (measured as the sum of all flows, T..) was higher in the summer than in the winter regardless of the type of sediment (Table 3). When moving from winter to summer, T.. increased by approximately 8.4 %, 35,1 %, 7.2 % in the sandy gravel, the gravelly sand and medium sand respectively (Table 4). A similar pattern was observed with the Ascendency (A), which is a measure of the system activity multiplied, by its degree of organization. The highest values of A were observed in summer for all types of sediment. For instance, the sandy gravel presented an Ascendency value of 1,021 gC.m⁻².Year⁻¹ in the winter and 1,079 gC.m⁻².Year⁻¹ in the summer. In addition, the highest values of the Finn's cycling index (FCI, which gives the percentage of all flows generated by recycling) were also observed during the summer This parameter ranged from 9.5 (medium sand) to 10.5 (sandy gravel) in the winter and from 9.9 (medium sand) to 12.2 (in the sandy gravel and the gravelly sand) in the summer (Table 4).

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Looking at the whole year, it is noteworthy, that the T.., A, FCI values were higher in the sandy gravel and the gravelly sand than in the medium sand (Table 4).

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Finally, these results also highlighted that the System Omnivory Index remained mostly stable across the nine models with a value of 0.2 in the summer regardless the sedimentary types and a value of 0.2 in winter for the sandy gravel and the gravelly sand whereas a value of 0.3 in the medium sand (Table 4).

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273 <Table 4>

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275 Concerning the other ecosystem attributes, results showed that the ratios PPt/R, PPt/B and the Bt/T.. varied with the seasons and the sediment types (Figure 2). PPt/R was higher in the winter 276 irrespective of the type of sediment. The highest values for the ratio PPt/R were observed in the 277 sediment labelled medium sand with a PPt/R= 2.81 in winter and a PPt/R= 1.75 in summer whereas 278 these values for the sandy gravel and the gravelly sand were 1.49 and 1.85 in the winter and 1.04 and 279 0.86 in the summer. A similar pattern is observed for the PPt/B ratio. In fact, the PPt/B was the 280 highest in the winter than in the summer regardless of sediment type with highest values observed in 281 the medium sand (15.3 in winter and 11.72 in summer) (Figure 2). On the opposite, highest values of 282 Bt/T.. were observed in summer regardless of sedimentary types. The highest values were noted for 283 the sandy gravel (0.05 in winter and 0.08 in summer). Looking at the whole year, results showed a 284 decrease of the PPt/R and PPt/B ratio and an increase of the Bt/T.. ratio from medium sand to sandy 285 gravel to gravelly sand (Figure 2). 286

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288 <Figure 2>

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290 **4 Discussion**

291 **4.1 The challenge and need to adopt an ecosystem approach for OWF projects**

292 An important challenge for the scientific community is to assess the possible ecological 293 consequences of new OWF projects to optimize the targeted environmental legislation objectives. 294 However, although, the current understanding of the potential effects of OWFs on marine 295 biodiversity continues to improve as evidence is gathered from operational installations (Dannheim 296 et al., 2019), their potential consequences in terms of changes to the structure and functioning of the 297 local ecosystem remain unclear (Raoux et al., 2017). In fact, environmental impact studies and the 298 related monitoring focus on the detection of change in variables such as diversity, or the abundance 299 of monitored species. Furthermore, these studies are conducted compartment by compartment, 300 which does not allow taking into account the ecosystems complexity (Raoux et al., 2017, 2019; Pezy 301 et al., 2019). Thus, there remains a need for a more integrated approach when assessing the impact 302 of OWF on the ecosystem (Raoux et al., 2017, 2018, 2019) which is in accordance with environmental 303 legislation requirements. In fact, the Water Framework Directive (article 2, paragraph 21) as well as 304 the Marine Strategy Framework Directive (Descriptor 4) both stress the need for assessing the quality 305 of ecosystem structure and its functioning to achieve a Good Environmental Status (Raoux et al., 306 2019; Safi et al., 2019). In addition, as pointed out by Curtin and Prellezo (2010), the EBM framework 307 highlighted the necessity for regulators and stakeholders to understand and consider the whole
ecosystem. In this context, and as a complementary approach to the traditional impact assessments, the objective of the present study was to develop an integrated approach using trophic web modelling tools that consider the ecosystem as a whole instead of a group of food web subsets or independent species. This point is taken up by Safi et al. (2019) who warns that fragmented solutions from "reductionist approaches" do not take into account the whole ecosystem and tend to lead to numerous unpleasant and unintended consequences. Our study also underlined the challenge and the need to have detailed biomass data on a maximum number of biological compartments as well as on fish stomach content which is essential to trophic web models. Because our approach is holistic, the main challenge was thus to acquire detailed biomass data on a wide range of biological compartments (ranging from bacteria to the apex predators) as well as data on fish stomach content which may or may not have already been part of the standard monitoring. The strength of this study is the fact that it is based on local, highly replicated and detailed sampling, which is rarely the case for the majority of Ecopath models (Pezy et al., 2019). Often, they are built with biomass data not collected from the study site and using a diet matrix not based on the species stomach contents of organisms taken from the study site (Pezy et al., 2019) which can induce a bias in the model and compromise the model results (Plaganyi and Butterworth, 2004). Such models cannot be used for management purposes. On the other hand, the study included the analysis of the stomach content of demersal fish, which enabled the identification of the benthos species that are either consumed or not consumed. The results showed that the group with the highest biomass is the "benthic invertebrates, Non-Consumed filter feeders", suggesting that this group could act as a sort of trophic dead end for the fish but still participate in the recycling of energy flow (Pezy et al., 2019).

By processing all of the available data using the ecosystem model, from which various system properties are derived, this study allows a deeper understanding of the biological compartments at the proposed site before the OWF construction and the setting up a sampling framework following construction. As demonstrated by the high value of the pedigree index (0.73), the presented Ecopath models were based on local, highly replicated and detailed samples. In fact, the pedigree index for the current study falls into the upper range of values (0.16 to 0.7 (Morissette, 2007)) obtained for other published models, confirming a high level of confidence with the data. We strongly consider that our approach will provide useful and practical information for environmental decision-makers and stakeholders.

## 4.2 Ecosystems maturity

The PPt/R ratio is a functional index of the ecosystem maturity (Odum, 1969). In immature ecosystems, this ratio is will be higher than 1, approaching 1 as a system matures. The estimated PPt/R values varied between the three sedimentary types and the time of year, suggesting that these
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ecosystems are in different development stages: the sandy gravel and gravelly sand are more mature than the ecosystem present in the sediment described as medium sand (Figure 2, Table 3). The B/T.. ratio will also increase as an ecosystem matures (Odum, 1971) and results showed that these values were higher in the sandy gravel and gravelly sand than in medium sand suggesting also that the first two media sustain a more mature ecosystem regardless the season (Figure 2, Table 3). Both of these observations are reflected by the PPt/B ratios which were higher in the medium sand than either the sandy gravel or the gravelly sand regardless the season. This ratio will in fact decrease as an ecosystem mature. Thus, the energetic attributes reveal that the ecosystems sustained by the sandy gravel and gravelly sand are more mature than the medium sand ecosystem regardless the season (Figure 2, Table 3). Furthermore, these results also highlighted that the three ecosystems are more mature during the summer than during the winter and that there is the existence of a maturity gradient progressing from medium sand to gravel to sandy gravel. Thus, results show two progressive maturity gradients: one during the season and one as a function of the type of sediment.

## 4.3 Ecosystem structure and functioning

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Ecological Network Analysis indices are powerful tools as they can link the ecosystem structure to its functional state revealing its emergent properties (Ulanowicz, 1986). In the current study, the ENA indices were calculated to enable the analysis of the structure and the functioning of an ecosystem developed on three sediment types (sandy gravel, gravelly sand and medium sand) on the proposed site for an OWF during the winter and the summer.

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This analysis revealed that the highest values of the Total system activity (T..) and Ascendency (A) occurred in the two sediments referred to as sandy gravel and gravelly sand regardless of the season (Table 3). These results can be explained by the fact that the sandy gravel and gravelly sand presented the highest total living biomass in comparison to the medium sand regardless of the season (Baeta et al., 2011). Similarly, the highest values of T.. and A (which were observed in summer) and those for the three sedimentary types, could also be linked to the total living biomass. The ascendency parameter also provides an indication of the ecosystem maturity (Ulanowicz, 1986). During maturation, ecosystems develop in order to increase their activity and energy storage, and they tend towards a greater Ascendency value (Ulanowicz, 1986). In other words, high values of A represent a mature system whereas low values indicate a stressed or immature system (Patrício et al., 2006). The current results showed that the sandy gravel and the gravelly sand both seem to support an ecosystem that is more mature than that developing on the medium sand. This observation is reflected by the ratios listed: PPt/R, PPt/B and B/T...

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Concerning the FCI (that is, the flow percentage generated by cycling), the spread of values amongst the six models could be explained by the numbers of bivalve organisms which varied between the
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three sedimentary types and with season with the highest values of FCI correlating with the highest bivalve biomass value. In their simulation, Raoux et al. (2017) highlighted a strong recycling activity linked to a high biomass of bivalves (*Mytilus edulis*) in the vicinity of the foundations of the wind turbine in the Bay of Seine.

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The SOI results (which is a measurement of the complexity of the trophic web) can be considered as an intermediate value when compared with the distribution of the same indices for other existing models of Northern Europe (ranging from 0.14 to 0.36) (Mackinson and Daskalov, 2007).

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These results suggest that the trophic structure was strongly linked to the sedimentary types and their communities.

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## 4.4 Comparison with other similar sedimentary types

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The ENA results from the wind farm proposal at Dieppe-Le Tréport can be compared with other ecosystems studied using the same methodology (Ecopath) and located in similar sediments (Table 5). Raoux et al. (2017) did a modelling of the gravelly sand of the proposed Courseulles-sur-mer OWF site (CSM OWF model) in the Bay of Seine and Pezy et al. (2018) did a modelling of the ecosystem developing in a medium sand sediment of a dumping site called Machu from the eastern part of the Bay of Seine (Table 5).

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The ENA comparison between the gravelly sand of the wind farms planned at Dieppe-Le Tréport and Courseulles-sur-mer revealed that the Total system activity (T..) and the Ascendancy values were higher in the Courseulles study. This might be explained by the fact that the CSM OWF model presents a greater number of compartments (37 against 28 in the current study). These differences could also be explained by the nutrient intake from the Seine (Billen and al., 2013). With respect to the Finn's cycling index, a higher values of recycling was observed in the gravelly sand sediment of the DLT OWF study than in a similar sediment encountered in Courseulles study. This difference might be explained by the higher bivalve biomass found at the Dieppe-Le Tréport site (for more details see Raoux et al., 2017).

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Furthermore, the ENA comparison between the medium sand sediment in the current study and the medium sand of the site at Machu revealed that T.. and A were both higher in the latter case. As previously noted, these results could be explained by the nutrient intake from the Seine. The higher value of the FCI in the Machu model could be explained by a higher biomass of benthic, invertebrate filter feeders (4.7 gC.m⁻², against a value of 3.5 gC.m⁻² measured in the present study).

409

410

411

Finally, the SOI of these four models were close to 0.20 suggesting that these systems have a similar web like structure (each with several pathways and including omnivory relationships between compartments).

412
416 Trophic modelling was used as a tool to study the structure and functioning of ecosystems existing
417 on three sedimentary types (sandy gravel, gravelly sand, medium sand), found at a proposed site for
418 a new offshore wind farm: the effect of season was taken into account. The findings of the study
419 demonstrated that the trophic structure is strongly linked to the sedimentary conditions. These
420 results also highlighted the existence of a maturity gradient from medium sand to gravelly sand to
421 sandy gravel. It is one of the features of the proposed site at Dieppe-Le Tréport that three
422 sedimentary types are present in proportions similar to those estimated across the eastern part of
423 the English Channel (study site: 52.1 % sG; 26.0% gS; 21.9 % S and eastern English Channel: 42.2 %
424 sG; 35.8% gS; 22.0 %). Thus, it might be expected that the present study could be representative of
425 the wider area.

426 By introducing an ecosystem approach through the use of trophic web modelling tools based on
427 local, highly replicated and detailed sampling, as a complementary approach to the traditional impact
428 assessments, this study provides an insight into a more sustainable method for an impact assessment
429 of potential OWF sites. However, it remains important that both regulators and stakeholders
430 properly understand the challenges involved in long-term monitoring and the need to ensure it
431 covers a maximum number of biological compartments and estimation of their biomasses, ranging
432 from the microbial to top predators. This approach will provide useful and practical information for
433 environmental decision-makers and stakeholders. Such a holistic approach could be replicated at
434 other proposed sites for OWF construction.

437 This work was supported by ANRT (Eoliennes en Mer Dieppe-Le Tréport and the French
438 Government). The authors also acknowledge the practical help received in the sampling procedures
439 from the captain and the crew of the Oceanographic Vessel "Celtic Warrior". Finally, we thank C.
440 Burton for post-editing the English style and grammar.
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549

550

Figure captions

551

552

553

**Figure 1:** Map showing the localisation of the proposed OWF at Dieppe-Le Tréport illustrating the three sedimentary types of seabed and sample locations.

554

555

556

**Figure 2:** Variation of ecosystem attributes with season (annual, winter and summer values). (PP/R: total primary production/total system respiration; PP/B: total primary production/total biomass; B/T..: Total Biomass/ Total System Throughputs; sG: sandy gravel, gS: gravelly sand, S: medium sand).

557
<table><caption>**Table 1:** Source data and references for the functional groups included in the models</caption><thead><tr><th></th><th>Biomass</th><th>P/B and Q/B</th><th>Diet</th></tr></thead><tbody><tr><td>Cetaceans and seals</td><td rowspan="2">Carpentier et al., 2009</td><td rowspan="2">Carpentier et al., 2009</td><td>Carpentier et al., 2009</td></tr><tr><td>Cephalopods</td><td>Expertise knowledge</td></tr><tr><td>Fish, planctivorous</td><td></td><td></td><td></td></tr><tr><td>Demersal fih</td><td rowspan="5">Pezy et al., 2018</td><td rowspan="5">Mackinson and Daskalov, 2007<br>Garcia, 2010</td><td>Pezy et al., 2018</td></tr><tr><td>Benthic invertebrates</td><td>Garcia, 2010</td></tr><tr><td>Meiofauna</td><td>Garcia, 2010</td></tr><tr><td>Suprabenthos</td><td>Expertise knowledge</td></tr><tr><td>Zooplankton</td><td>Expertise knowledge</td></tr><tr><td>Bacteria</td><td rowspan="3">Garcia, 2010</td><td rowspan="3">Garcia, 2010</td><td>Chardy and Dauvin, 1992</td></tr><tr><td>Phytoplankton</td><td>-</td></tr><tr><td>Detritus</td><td>-</td></tr></tbody></table>
<table><caption>Table 2: Biomass values for the nine Ecopath models.</caption><thead><tr><th rowspan="2">Compartments</th><th colspan="3">Winter</th><th colspan="3">Summer</th><th colspan="3">Annual</th></tr><tr><th>S</th><th>gS</th><th>sG</th><th>S</th><th>gS</th><th>sG</th><th>S</th><th>gS</th><th>sG</th></tr></thead><tbody><tr><td>Cetaceans</td><td>0.0016</td><td>0.0016</td><td>0.0016</td><td>0.0016</td><td>0.0016</td><td>0.0016</td><td>0.0016</td><td>0.0016</td><td>0.0016</td></tr><tr><td>Seals</td><td>0.0004</td><td>0.0004</td><td>0.0004</td><td>0.0004</td><td>0.0004</td><td>0.0004</td><td>0.0004</td><td>0.0004</td><td>0.0004</td></tr><tr><td>Cephalopods (Sepia officinalis)</td><td>0.0161</td><td>0.0161</td><td>0.0161</td><td>0.0161</td><td>0.0161</td><td>0.0161</td><td>0.0161</td><td>0.0161</td><td>0.0161</td></tr><tr><td>Piscivorous (Merlangius merlangius)</td><td>0.033</td><td>0.046</td><td>0.015</td><td>-</td><td>-</td><td>-</td><td>0.0165</td><td>0.023</td><td>0.0075</td></tr><tr><td>Hyperoplus lanceolatus</td><td>0.075</td><td>0.123</td><td>0.078</td><td>0.005</td><td>0.004</td><td>0.020</td><td>0.04</td><td>0.0635</td><td>0.049</td></tr><tr><td>Fish, benthos feeders</td><td>0.014</td><td>0.011</td><td>0.010</td><td>0.014</td><td>0.003</td><td>0.004</td><td>0.014</td><td>0.007</td><td>0.007</td></tr><tr><td>Fish, flat fish</td><td>0.026</td><td>0.039</td><td>0.031</td><td>0.009</td><td>0.001</td><td>-</td><td>0.017</td><td>0.020</td><td>0.015</td></tr><tr><td>Fish, planctivorous</td><td>0.557</td><td>0.557</td><td>0.557</td><td>0.557</td><td>0.557</td><td>0.557</td><td>0.557</td><td>0.557</td><td>0.557</td></tr><tr><td>Branchiostoma lanceolatum</td><td>0.044</td><td>0.425</td><td>1.660</td><td>0.032</td><td>0.807</td><td>2.036</td><td>0.038</td><td>0.616</td><td>1.848</td></tr><tr><td>Benthic inv, Predator C.</td><td>0.184</td><td>0.080</td><td>0.038</td><td>0.014</td><td>0.078</td><td>-</td><td>0.099</td><td>0.079</td><td>0.019</td></tr><tr><td>N.C.</td><td>0.398</td><td>1.071</td><td>1.117</td><td>0.631</td><td>0.940</td><td>1.445</td><td>0.514</td><td>1.005</td><td>1.281</td></tr><tr><td>Benthic inv, C.</td><td>0.012</td><td>0.001</td><td>0.301</td><td>0.491</td><td>0.203</td><td>0.414</td><td>0.251</td><td>0.102</td><td>0.357</td></tr><tr><td>Scavenger N.C.</td><td>0.942</td><td>0.290</td><td>0.083</td><td>0.007</td><td>0.256</td><td>0.162</td><td>0.474</td><td>0.273</td><td>0.122</td></tr><tr><td>Benthic inv, Filter C.</td><td>0.046</td><td>0.220</td><td>0.247</td><td>0.055</td><td>0.465</td><td>0.642</td><td>0.050</td><td>0.342</td><td>0.444</td></tr><tr><td>feeders N.C.</td><td>1.790</td><td>26.90</td><td>35.44</td><td>5.070</td><td>65.40</td><td>56.98</td><td>3.43</td><td>46.15</td><td>46.21</td></tr><tr><td>Benthic inv, C.</td><td>0.003</td><td>0.016</td><td>0.008</td><td>2.450</td><td>0.015</td><td>0.055</td><td>1.226</td><td>0.015</td><td>0.031</td></tr><tr><td>N.C.</td><td>0.098</td><td>0.133</td><td>0.176</td><td>0.081</td><td>6.210</td><td>4.670</td><td>0.089</td><td>3.171</td><td>2.423</td></tr><tr><td>Benthic inv, C.</td><td>0.009</td><td>0.142</td><td>0.176</td><td>0.016</td><td>0.037</td><td>-</td><td>0.012</td><td>0.089</td><td>0.088</td></tr><tr><td>N.C.</td><td>4.700</td><td>1.058</td><td>0.974</td><td>3.400</td><td>2.110</td><td>1.810</td><td>4.050</td><td>1.580</td><td>1.390</td></tr><tr><td>Benthic inv, Grazer C.</td><td>0.0004</td><td>0.002</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.0002</td><td>0.0010</td><td>-</td></tr><tr><td>N.C.</td><td>0.0001</td><td>0.012</td><td>0.030</td><td>0.003</td><td>0.007</td><td>0.026</td><td>0.0015</td><td>0.0095</td><td>0.0280</td></tr><tr><td>Meiofauna</td><td>1.210</td><td>1.670</td><td>1.370</td><td>0.970</td><td>1.660</td><td>1.360</td><td>1.090</td><td>1.660</td><td>1.360</td></tr><tr><td>Holosuprabenthos</td><td>0.035</td><td>0.027</td><td>0.470</td><td>0.047</td><td>0.043</td><td>0.057</td><td>0.041</td><td>0.035</td><td>0.263</td></tr><tr><td>Merosuprabenthos</td><td>0.244</td><td>0.146</td><td>0.146</td><td>0.611</td><td>0.664</td><td>0.660</td><td>0.427</td><td>0.405</td><td>0.403</td></tr><tr><td>Zooplankton</td><td>1.223</td><td>0.471</td><td>1.966</td><td>1.966</td><td>0.758</td><td>0.788</td><td>1.594</td><td>0.614</td><td>1.377</td></tr><tr><td>Phytoplankton</td><td>3.100</td><td>3.100</td><td>3.100</td><td>3.100</td><td>3.100</td><td>3.100</td><td>3.100</td><td>3.100</td><td>3.100</td></tr><tr><td>Bacteria</td><td>0.750</td><td>0.750</td><td>0.750</td><td>0.750</td><td>0.750</td><td>0.750</td><td>0.750</td><td>0.750</td><td>0.750</td></tr><tr><td>Detritus</td><td>6.000</td><td>6.000</td><td>6.000</td><td>6.000</td><td>6.000</td><td>6.000</td><td>6.000</td><td>6.000</td><td>6.000</td></tr></tbody></table>

564 (Benthic inv., benthic invertebrate; sDF, surface deposit feeders; ssDF, sub-sur-face deposit feeders;

565 C.: consumed; N.C.: not consumed; sG: sandy gravel; gS: gravelly sand; S: medium sand)
<table><caption>**Table 3:** Trophic levels (TL) for the six Ecopath models (Benthic inv., benthic invertebrate; sDF, surface deposit feeders; ssDF, sub-sur-face deposit feeders; sG: sandy gravel, gS: gravelly sand, S: medium sand).</caption><thead><tr><th rowspan="2">Compartments</th><th rowspan="2"></th><th colspan="3">Winter</th><th colspan="3">Summer</th><th colspan="3">Annual</th></tr><tr><th>S</th><th>gS</th><th>sG</th><th>S</th><th>gS</th><th>sG</th><th>S</th><th>gS</th><th>sG</th></tr></thead><tbody><tr><td>Cetaceans</td><td></td><td>4.46</td><td>4.36</td><td>4.33</td><td>4.62</td><td>4.66</td><td>4.29</td><td>4.54</td><td>4.51</td><td>4.31</td></tr><tr><td>Seals</td><td></td><td>4.38</td><td>4.44</td><td>4.44</td><td>4.21</td><td>4.33</td><td>4.21</td><td>4.29</td><td>4.38</td><td>4.32</td></tr><tr><td>Cephalopods (Sepia officinalis)</td><td></td><td>4.20</td><td>3.95</td><td>4.09</td><td>4.03</td><td>4.00</td><td>4.13</td><td>4.11</td><td>3.97</td><td>4.11</td></tr><tr><td>Piscivorous (Merlangius merlangius)</td><td></td><td>4.15</td><td>4.16</td><td>4.16</td><td>-</td><td>-</td><td>-</td><td>4.15</td><td>4.16</td><td>4.16</td></tr><tr><td>Hyperoplus lanceolatus</td><td></td><td>3.16</td><td>3.16</td><td>3.16</td><td>3.16</td><td>3.16</td><td>3.17</td><td>3.16</td><td>3.16</td><td>3.16</td></tr><tr><td>Fish, benthos feeders</td><td></td><td>3.98</td><td>3.57</td><td>3.45</td><td>4.09</td><td>3.71</td><td>3.96</td><td>4.03</td><td>3.64</td><td>3.70</td></tr><tr><td>Fish, flat fish</td><td></td><td>3.32</td><td>3.04</td><td>3.74</td><td>3.30</td><td>3.30</td><td>-</td><td>3.31</td><td>3.17</td><td>3.74</td></tr><tr><td>Fish, planctivorous</td><td></td><td>3.15</td><td>3.15</td><td>3.15</td><td>3.18</td><td>3.15</td><td>3.17</td><td>3.16</td><td>3.15</td><td>3.16</td></tr><tr><td>Branchiostoma lanceolatum</td><td></td><td>2.18</td><td>2.18</td><td>2.18</td><td>2.18</td><td>2.18</td><td>2.18</td><td>2.18</td><td>2.18</td><td>2.18</td></tr><tr><td rowspan="2">Benthic inv, Predator</td><td>C.</td><td>3.12</td><td>3.08</td><td>3.21</td><td>3.21</td><td>3.16</td><td></td><td>3.16</td><td>3.12</td><td>3.21</td></tr><tr><td>N.C.</td><td>3.15</td><td>3.10</td><td>3.09</td><td>3.09</td><td>3.09</td><td>3.06</td><td>3.12</td><td>3.09</td><td>3.07</td></tr><tr><td rowspan="2">Benthic inv, Scavenger</td><td>C.</td><td>3.52</td><td>3.66</td><td>3.48</td><td>3.18</td><td>3.38</td><td>3.25</td><td>3.35</td><td>3.52</td><td>3.36</td></tr><tr><td>N.C.</td><td>3.39</td><td>3.33</td><td>3.50</td><td>3.41</td><td>3.37</td><td>3.21</td><td>3.40</td><td>3.35</td><td>3.35</td></tr><tr><td rowspan="2">Benthic inv, Filter feeders</td><td>C.</td><td>2.25</td><td>2.25</td><td>2.25</td><td>2.25</td><td>2.25</td><td>2.25</td><td>2.25</td><td>2.25</td><td>2.25</td></tr><tr><td>N.C.</td><td>2.25</td><td>2.25</td><td>2.25</td><td>2.25</td><td>2.20</td><td>2.19</td><td>2.25</td><td>2.22</td><td>2.22</td></tr><tr><td rowspan="2">Benthic inv, sDF</td><td>C.</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td></tr><tr><td>N.C.</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td></tr><tr><td rowspan="2">Benthic inv, ssDF</td><td>C.</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>-</td><td>2.19</td><td>2.19</td><td>2.19</td></tr><tr><td>N.C.</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td><td>2.19</td></tr><tr><td rowspan="2">Benthic inv, Grazer</td><td>C.</td><td>2.00</td><td>2.00</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.00</td><td>2.00</td><td>-</td></tr><tr><td>N.C.</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td></tr><tr><td>Meiofauna</td><td></td><td>2.25</td><td>2.20</td><td>2.20</td><td>2.20</td><td>2.25</td><td>2.20</td><td>2.22</td><td>2.22</td><td>2.20</td></tr><tr><td>Holosuprabenthos</td><td></td><td>2.51</td><td>2.51</td><td>2.51</td><td>2.51</td><td>2.51</td><td>2.50</td><td>2.51</td><td>2.51</td><td>2.50</td></tr><tr><td>Merosuprabenthos</td><td></td><td>2.52</td><td>2.51</td><td>2.56</td><td>2.52</td><td>2.50</td><td>2.46</td><td>2.52</td><td>2.50</td><td>2.51</td></tr><tr><td>Zooplankton</td><td></td><td>2.15</td><td>2.15</td><td>2.15</td><td>2.15</td><td>2.15</td><td>2.15</td><td>2.15</td><td>2.15</td><td>2.15</td></tr><tr><td>Bacteria</td><td></td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td><td>2.00</td></tr></tbody></table>
<table><caption>**Table 4:** ENA indices and maturity ratios for the nine models. Total System Throughput (T.., gC. m⁻². Year⁻¹); Ascendency (A, gC. m⁻². Year⁻¹); Finn's Cycling Index (FCI, %) and System Omnivory Index (SOI, %); the total living biomass (Bt, gC. m⁻²). PP/R: total primary production/total system respiration; PP/B: total primary production/total biomass; Bt/T..: Total Biomass/ Total System Throughputs; sG: sandy gravel, gS: gravelly sand, S: medium sand).</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="5">ENA</th><th colspan="3">Maturity ratios</th></tr><tr><th>T..</th><th>A</th><th>FCI</th><th>SOI</th><th>Bt</th><th>PPt/R</th><th>PPt/Bt</th><th>Bt/T..</th></tr></thead><tbody><tr><td rowspan="3">S</td><td>Winter</td><td>832.9</td><td>928.8</td><td>9.5</td><td>0.3</td><td>15.5</td><td>2.81</td><td>15.30</td><td>0.02</td></tr><tr><td>Summer</td><td>892.7</td><td>957.2</td><td>9.9</td><td>0.2</td><td>20.3</td><td>1.75</td><td>11.72</td><td>0.02</td></tr><tr><td>Annual</td><td>862.8</td><td>943.0</td><td>9.7</td><td>0.2</td><td>17.9</td><td>2.28</td><td>13.50</td><td>0.02</td></tr><tr><td rowspan="3">gS</td><td>Winter</td><td>875.3</td><td>968.9</td><td>10.4</td><td>0.2</td><td>37.3</td><td>1.85</td><td>6.36</td><td>0.04</td></tr><tr><td>Summer</td><td>1,182.9</td><td>1,080.0</td><td>12.2</td><td>0.2</td><td>84.1</td><td>0.86</td><td>2.89</td><td>0.07</td></tr><tr><td>Annual</td><td>1,029.1</td><td>1,024.4</td><td>11.3</td><td>0.2</td><td>60.7</td><td>1.35</td><td>4.62</td><td>0.05</td></tr><tr><td rowspan="3">sG</td><td>Winter</td><td>923.2</td><td>1,021</td><td>10.5</td><td>0.2</td><td>48.8</td><td>1.49</td><td>4.87</td><td>0.05</td></tr><tr><td>Summer</td><td>1,000.8</td><td>1,079</td><td>12.2</td><td>0.2</td><td>75.6</td><td>1.04</td><td>3.14</td><td>0.08</td></tr><tr><td>Annual</td><td>962.0</td><td>1,050</td><td>11.35</td><td>0.2</td><td>86.6</td><td>1.26</td><td>4.00</td><td>0.06</td></tr></tbody></table>

580

581

582

583

584

585

586

587

588
<table><caption>**Table 5:** ENA indices for the current study compared to values reported for similar sedimentary types in other research projects. Total System Throughput (T.., gC. m⁻². Year⁻¹); Ascendency (A, gC. m⁻². Year⁻¹); Finn's Cycling Index (FCI, %) and System Omnivory Index (SOI, %); the total living biomass (Bt, gC. m⁻².)</caption><thead><tr><th>Sediment type</th><th>Site</th><th>T..</th><th>A</th><th>FCI</th><th>SOI</th><th>Reference for Ecopath models</th></tr></thead><tbody><tr><td rowspan="2">gS</td><td>DLT OWF</td><td>1029</td><td>1024</td><td>11.3</td><td>0.20</td><td>This study</td></tr><tr><td>CSM OWF</td><td>1608</td><td>1869</td><td>9.16</td><td>0.17</td><td>Raoux et al. (2017)</td></tr><tr><td rowspan="2">S</td><td>DLT OWF</td><td>863</td><td>943</td><td>9.7</td><td>0.2</td><td>This study</td></tr><tr><td>MACHU</td><td>1130</td><td>1130</td><td>11.8</td><td>0.22</td><td>Pezy et al. (2017)</td></tr></tbody></table>

(gS: gravelly sand, S: medium sand).
<figure><img src="image_2.png" /><figcaption>597 Figure 1.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>601 Figure 2.</figcaption></figure>