ICES Journal of Marine Science: Journal du Conseil

Sebastiaan, Mestdagh,;Tom, Ysebaert,;Tom, Moens,;Van Colen, Carl,

doi: 10.1093/icesjms/fsy122pmid: N/A

Abstract Building man-made structures in coastal seas are often preceded by dredging operations, inducing turbid plumes of suspended sediment. To study the effects of such high-concentration sediment plumes on the suspension-feeding polychaete Lanice conchilega, a laboratory experiment was performed, in which individuals of L. conchilega were exposed to natural seawater with a suspended sediment concentration (SSC) of ∼ 0.3 g l−1 and treatments with elevated SSC of 5 and 1 g l−1, representing concentrations in a dredging plume at the moment of sediment release and after initial dilution, respectively. We measured clearance rates of sediment particles, biogeochemical fluxes, and bio-irrigation. While clearance rates and nitrite efflux significantly increased in both treatments with elevated SSC compared with the control, bio-irrigation increased at 1 g l−1 but was lowest at 5 g l−1. It is suggested that piston pumping is intensified under intermediate concentrations to remove sediment, but ceases under high concentrations are due to sediment ingestion. By transporting oxygen into the sediment, bio-irrigation enhances aerobic microbial processes, among which nitrification. We conclude that short-term extreme suspended sediment concentrations can have a significant impact on the biogeochemistry of the seabed through changes in behaviour of L. conchilega. Introduction Coastal soft-sediment benthic ecosystems are often subject to short-term changes in suspended matter concentrations in the overlying water column. Episodic events such as storms can temporarily increase the suspended sediment levels (Ferré et al., 2005; Grifoll et al., 2013), and human activities, such as dredging near the coast, or even the mere presence of man-made structures, can also significantly alter the sediment load in near-shore waters (Baeye and Fettweis, 2015; Di Risio et al., 2017). In the southern North Sea, dredging is often employed for the extraction of marine aggregates and to ensure access to ports (de Groot, 1996; Plancke et al., 2008). In addition, the increased deployment of maritime man-made structures also plays its role. Structures such as offshore wind turbines or artificial reefs require dredging to accommodate the seafloor during the construction phase (Peire et al., 2009; Malhotra, 2011; Bergström et al., 2014), and dredging can be used as a means to decommission artificial reefs (Leidersdorf et al., 2011). Sediment plumes caused by dredging operations can travel along currents and expand over large areas before settling on the seafloor (Barnes et al., 2015). Furthermore, environmental characteristics can also play an important role in the persistence and spread of turbid plumes, such as water column stratification (Seo et al., 2018) or the nature of the suspended matter (Smith and Friedrichs, 2011). Turbid plumes from dredging operations usually last for only a few hours before settlement takes place (Duclos et al., 2013), but the elevated suspended sediment concentrations (SSCs) in dredging plumes, especially during extended periods of dredging, still have the potential to affect marine organisms. Corals, for instance, adapt their physiology and expel symbiotic algae as a response to the increased levels of suspended sediment and the resulting decreased light conditions (Fisher et al., 2015; Bessell-Browne et al., 2017). Furthermore, lower sperm counts have been observed in coral species and larvae manifested physiological adaptations to the increase in suspended solids (Ricardo et al., 2016). Another example of the impact of elevated turbidity are the observations of lower survival rates of fish larvae under such conditions (Ricardo et al., 2015; Suedel et al., 2017). For benthic macrofauna, the effects on suspension feeders appear to be among the most pronounced. Ellis et al. (2002) found that increased turbidity reduces the clearance rates of sediment particles from the water column and the overall physiological condition of the suspension-feeding bivalve Atrina zelandica. Reductions in feeding efficiency and prey selection have additionally been attested in the same species (Safi et al., 2007). Suspension-feeding bivalves have mechanisms to cope with indigestible particles by selective food uptake and by ejecting pseudofaeces (Widdows et al., 1979; Kiørboe et al., 1980; Ciutat et al., 2007), but under high loads of suspended sediment their capacity for food selection may fail and the sediment particles could clog the feeding apparatus and the digestive system (Penry, 2000; Lohrer et al., 2006). Since bivalve pseudofaeces deposition affects the ambient biogeochemical environment, any changes in the bivalves’ physiology can have an impact on the wider macrofaunal communities (Norkko et al., 2001). While the responses of suspension feeding bivalves on increased SSCs are relatively well studied, less research on this matter has been conducted with respect to other taxa, notably suspension feeding polychaetes. Dubois et al. (2009) observed increased clearance rates under elevated suspended sediment levels in the honeycomb worm Sabellaria alveolata, even though the tested concentrations remained within a natural range, leaving the effects of artificial (dredging plume) concentrations unknown. The terebellid polychaete Lanice conchilega (Pallas, 1766) is an important suspension feeder in intertidal and subtidal areas of the coastal seas of Europe. Lanice conchilega is an ecosystem engineer that forms dense reef structures that stabilize the sediment and in turn provide suitable habitat for many other organisms (Rabaut et al., 2009; De Smet et al., 2015), affecting the biodiversity of these soft-sediment environments. As such, these polychaetes have a substantial impact on the environment around them, affecting sedimentary processes by increasing net deposition (Borsje et al., 2014; Alves et al., 2017a). Furthermore, in order to maintain continuous oxygen availability, they increase the flow of water through the sediment in a process called bio-irrigation (Forster and Graf, 1995), with cascading effects on habitat extension for meiofauna populations (Braeckman et al., 2011). This bio-irrigation, caused by actively pumping overlying seawater into the sediment, is especially important in structuring the biogeochemical environment through shifts in the spatial occurrence of microbial communities (Yazdani Foshtomi et al., 2018). The significance of L. conchilega for biodiversity and sediment dynamics contributes to its importance for the entire ecosystem, making it a target species for legal protection (Braeckman et al., 2014). In the Belgian part of the North Sea, L. conchilega occurs in densities from low-density distributions to dense reef structures (Degraer et al., 2006) in a system with a relatively high natural turbidity, due to the well-mixed water column and the relatively high riverine sediment input (Pietrzak et al., 2011) and is also present in close vicinity of offshore wind farms (Coates et al., 2013). To study the effects of maritime infrastructure works on the behaviour of Lanice conchilega through increased suspended sediment loads, we compared the bio-irrigation of L. conchilega and the sediment biogeochemistry between natural conditions in suspended solid concentrations and conditions of elevated suspended solid concentrations observed in dredging plumes. We hypothesized that the ingestion of elevated SSCs would interfere with the worms’ pumping efficiency, reducing bio-irrigation of water into the sediment. Reduced bio-irrigation would lead to a decrease of oxygen uptake by the sediment community, and consequently affect the distribution and biogeochemical processing of other compounds. Our general aim was to contribute to the general understanding of the effects of maritime operations on the benthic soft-sediment ecosystems of the southern North Sea. Material and methods Collection of sediment and of Lanice conchilega In October 2017, 12 buckets (25 l each) of sediment were collected at the beach near the “Baai van Heist” nature reserve (51°20′42″N 3°14′05″E) at the Belgian coast, where L. conchilega were visibly abundant. The sediment was taken to the laboratory and sieved in seawater over a 1-mm mesh to remove all macrofauna. We opted for sieving as a means of defaunation since it has been demonstrated to affect microbial communities less than other defaunation techniques, and results in biogeochemical fluxes that are similar to those under natural conditions (Porter et al., 2006; Stocum and Plante, 2006). Subsequently, the buckets were refilled with the sieved sediment and stored in the laboratory with overlying filtered seawater (salinity 32) and air supply. After 2 weeks, L. conchilega were sampled with 10 cm diameter corers at the beach in Boulogne-sur-Mer (France, 50°43′58″N 1°35′16″E), on locations with high-density patches in sediments with a similar granulometry as at the “Baai van Heist” (the sediment of both sites can be characterized as fine sand and was found to have a median grain size of 180.1 ± 1.1 μm in our experiment in the Baai van Heist and 223.2 μm in Boulogne-sur-Mer; Alves et al., 2017b). The tubes were subsequently rinsed out of the sediment with seawater and tubes with live animals were collected and transplanted to the sieved sediment according to the methods described in Ziegelmeier (1969), with densities of 15 worms per bucket (119.4 ind m−2). This density is within the natural range in the Belgian part of the North Sea and similar to intertidal occurrences in the “Baai van Heist” nature reserve and polyhaline reaches of the Scheldt estuary (Degraer et al., 2006, pers. obs.), but far below the densities found in dense reefs (up to and over 10 000 ind m−2; Alves et al., 2017b). As long as the worms remained within the buckets, they were fed daily with commercial Shellfish Diet 1800 (Reed Mariculture Inc., composed of 15% Pavlova, 20% Thalassiosira weissflogii, 25% Tetraselmis, and 40% Isochrysis), diluted in the seawater. For the suspended solids, fine sediment was collected from the upper 0.5 cm of the Paulina mudflat, on the southern shore of the estuary of the river Scheldt (SW Netherlands, 51°20′58″N 3°43′35″E, polyhaline reach of the estuary). The sediment was dried for 48 h at 60°C and subsequently heated in a muffle furnace for 5 h at 450°C to remove all organic matter. The resulting dried and defaunated sediment was crushed in a mortar to a resulting median grain size of 49 μm and a mud content of 66.0%, comparable to the suspended particulate matter (SPM) in the eastern near-coastal areas of the Belgian part of the North Sea (Fettweis, 2008). Laboratory experiment The day before the start of the experiment, cylindrical Plexiglas chambers (Ø 19 cm, height 40 cm) were inserted into the sediment of three buckets, resulting in a sediment depth of approximately 15 cm in each chamber, and a water column height of approximately 25 cm. The chambers were dug out and closed at the bottom, before being filled with seawater and incubated at ambient seawater temperature (16°C) with air supply. On the day of the experiment, the overlying seawater was replaced by natural seawater (salinity 36), collected from the Belgian part of the North Sea (station 780; 51°28′17.0″N 3°03′26.3″E) and enriched with bromide (NaBr) to a final concentration of 0.01 M. The chambers were subsequently closed with a lid with a stirring disc and two luer stopcocks, to control in- and outflow of water. In each of the three chambers, a different amount of defaunated fine sediment was inserted through one of the stopcocks. The first treatment received enough sediment to arrive at suspended matter concentrations approximating a dredging plume concentration of 5 g l−1, comparable with the moment of sediment release in the water column (Dredge treatment). The second treatment received a concentration of 1 g/L−1, comparable with a dredging plume after initial dilution (Dilution treatment) (Duclos et al., 2013), while the third treatment did not receive any additional suspended sediment, containing a natural SSC of 0.34 ± 0.04 g l−1. The height of the stirring disc was fixed at 7 cm above the sediment surface and its rotation at 90 rpm to allow a continuous homogeneous mixing of the sediment in the water column, as determined from 1 min interval turbidity measurements obtained by an optical backscatter sensor (OBS-3+, Campbell Scientific, Inc., Logan, Utah), calibrated against muffled SSCs that were used in the experiment. After the addition of the defaunated sediment to the chambers, 20 ml water samples were taken from each chamber regularly with a glass syringe, while adding new seawater through the second stopcock to maintain the total water volume. The sampled water was filtered through Whatman GF/C glass microfiber filters (1.2 μm pore size), collected in a 20 ml scintillation vial and stored along with the filters at −20°C for later analysis of seawater nutrient (NO2−, NO3−, NH4+) concentrations (in μg l−1), measured via Continuous Flow Analysis (SAN++, Skalar, Breda, the Netherlands; analytical precision of 1 μg l−1). A second sample of 2 ml was taken for analysis of bromide concentration, to assess the inflow of water into the sediment, either caused biologically via bio-irrigation or physically via advection and diffusion (Meysman et al., 2007; Renz and Forster, 2014). Oxygen concentrations (in μmol l−1) were measured with a FireSting rigid O2 optode (Pyro Science GmbH, Aachen, Germany) fitted through the lid. Water samples were collected and oxygen concentration measurements recorded five times, with 1-h intervals in between, in order to calculate sediment community oxygen consumption (SCOC), nutrient fluxes and bio-irrigation rate. The experiment was repeated with new sediment and chambers, with all three treatments for four consecutive days (n = 4). At the end of each day, the worms were rinsed out of the sediment and stored at −20°C before being weighed. The total ash-free dry weight (AFDW) was determined for each replicate by calculating the difference in weight between the animals after drying (48 h at 60°C; dry weight) and subsequent burning in a muffle furnace (2 h at 450°C; ash weight). A bio-irrigation coefficient (Q, in ml min−1), encompassing both “true” bio-irrigation and potential physical water inflow, was calculated from the change in water column bromide concentration (Meysman et al., 2007; De Smet et al., 2016). Nutrient fluxes were calculated with the formula: Flux= dCdtVA where dCdt is the change in nutrient concentration over time (in mmol l−1 d−1), V is the volume of the overlying water (in l), and A is the sediment surface area (in m2). SCOC was calculated with the same formula, but with a negative concentration change. The above-mentioned Whatman GF/C filters were dried at 60°C for 48 h and burned in a muffle furnace (2 h at 450°C), and subsequently weighed to calculate the difference in suspended sediment particles between the beginning and end of the experiment. The data were used to calculate clearance rates (the amount of water the 15 individuals clear of sediment particles per hour), according to the following formula: F= VNtln⁡(C0Ct) where F is the clearance rate (in l h−1 ind−1), V is the overlying water volume (in l), N is the number of worms, t is the duration of the experiment (in h), and C0 and Ct are the concentrations of suspended sediment at the beginning and the end of the experiment (in g l−1) (Riisgård and Ivarsson, 1990). Quantifying effects of physical advection on water flow and sediment permeability The high rotational speed in closed cylindrical chambers creates pressure gradients that affect pore water transport through pure physical advective flows (Huettel and Gust, 1992; Glud et al., 1996), and potentially also by altering the sediment permeability by pumping suspended sediment into the sediment matrix. To quantify and evaluate the contribution of such physically created advection to the measured water flow, as opposed to the transport caused by L. conchilega bio-irrigation, and the possible difference in sediment permeability between treatment, a separate experiment was performed. Therefore sediment from the same location was subjected to the same experimental design as in the main experiment, but without the addition of L. conchilega. A total of 2 l samples were collected from the water column to analyse for bromide concentration, so as to quantify the advective flow of water into the sediment. Water inflow was calculated by the formula for bio-irrigation used in the main experiment. After 5 h of incubation, the water column was removed and the upper 0.5–2 cm of the sediment were sampled per 0.5 cm slice at four random locations per chamber, to analyse for permeability, based on Eggleston and Rojstaczer (1998): KH = 1.1019 × 103 m−2 s * d102 * ν, where KH is the permeability (in m2), d10 is the first decile of the grain size distribution (in m), and ν is kinematic viscosity (in m2 s−1, calculated from water temperature and salinity). Permeability of the upper 0.5 cm of sediment was not considered as deposition of suspended solids could not be avoided during removal of water from the chamber. Data analysis Before analysis, all variables were divided through the total L. conchilega AFDW for each replicate, to standardize the results. Clearance rates were standardized according to the formula Fs = (1/We)b × Fe (Bayne and Newell, 1983), where Fs is the normalized clearance rate per g of animal dry weight, We is the measured average dry weight (in g), b is an allometric coefficient (equal to 0.3159) and Fe is the individual clearance rate (in l h−1 ind−1). Effects of the experimental treatments were assessed by two-way ANOVA, with treatment (three levels) and day (four levels) as fixed factors. A Tukey test was used to test pair-wise differences. If the conditions of a normal data distribution (tested with Shapiro–Wilk’s normality test) and homogeneous variances (tested with Levene’s test) for ANOVA were not met, a fourth root transformation was performed on the data, and in case this did not result in the required conditions either, non-parametric Kruskal–Wallis tests were performed, followed by a Dunn test for pair-wise differences. Possible linear relationships between bio-irrigation and SCOC or nutrient fluxes were assessed with simple linear regression, as were potential relationships between clearance of sediment particles and bio-irrigation, SCOC or nutrient fluxes. Both the assumptions of normality of residuals and the absence of outliers were tested and met. Variability in water inflow between the treatments without Lanice (separate experiment) were tested with one-way ANOVA (treatment as factor), and differences from 0 were tested for the three treatments using a single-sample t-test. Permeability values did not meet the homoscedasticity requirement, and were therefore analysed with a two-way PERMANOVA test (treatment and depth as factors). Significant factors were tested for homogeneity of dispersions with PERMDISP analysis. All analyses were conducted with the open statistical software R (R Development Core Team, 2013), except for the PERMANOVA tests, which were performed in PRIMER v6, with PERMANOVA+ add-on (Clarke and Gorley, 2006; Anderson et al., 2008). Results Physical advection effects In the experiments without Lanice, no significant differences in water inflow were found between treatments (F = 0.0125; p = 0.9876), and flow rates were not significantly different from 0 (Control: t = −0.1577; p = 0.8776. Dilution: t = 1.0732; p = 0.3062. Dredge: t = −0.9548; p = 0.3602) (Supplementary Figure S1). Permeability was found to differ significantly between treatments, but showed a significant heterogeneity of dispersions (pseudo-F = 9.332; p = 0.003; PERMDISP p < 0.001). Depth (pseudo-F = 1.616; p = 0.226) or the treatment * depth interaction (pseudo-F = 0.415; p = 0.795) were not significant (Supplementary Figure S2). Significant pair-wise differences were found between the Control and Dredge treatment (pseudo-F = 0.007; p = 0.006), and between the Dilution and Dredge treatment (pseudo-F = 0.023; p = 0.019). The Control and Dilution treatment did not differ significantly (pseudo-F = 0.098; p = 0.102). Lanice biomass, clearance, and bio-irrigation Total AFDW of L. conchilega per chamber varied between 0.012 and 0.080 g, and did not differ significantly between treatments (Table 1). All values presented further in this article are means ± standard errors. The animals in the Dilution and Dredge treatments had ingested significant amounts of the suspended sediment (Figure 1), and the water column in those treatments was visibly less turbid at the end as compared with the start of the incubation. Indeed, clearance rates ranged from −0.004 ± 0.067 × 10−1 l h−1 g−1 (Control) to 0.091 ± 0.021 l h−1 g−1 (Dilution), and differed significantly between treatments (Table 1), with higher rates in the Dilution and Dredge treatments as compared with the Control (p = 0.007 and p = 0.012, respectively) (Figure 2). Clearance rates were not significantly different between the Dilution and the Dredge treatments. Bio-irrigation rates per AFDW of L. conchilega varied significantly between treatments, with lowest rates of 8.77 ± 5.61 ml min−1 g−1 in the Dredge treatment and highest rates of 27.72 ± 6.14 ml min−1 g−1 in the Dilution treatment (Figure 2). Table 1. Statistical factors from two-way ANOVA (F-test) or Kruskal–Wallis (χ2) tests, with Treatment (3 levels) and Day (4 levels) as factors. Variable Factor F P χ2 Total AFDW Treatment 1.428 0.311 Day 2.349 0.172 Q Treatment 5.300 0.047* Day 2.277 0.180 Fs Treatment 0.020* 7.423 SCOC Treatment 0.769 0.504 Day 0.314 0.815 Nitrite flux Treatment 0.030* 7.269 Nitrate flux Treatment 0.130 4.154 Ammonia flux Treatment 1.136 0.382 Day 1.774 0.252 Variable Factor F P χ2 Total AFDW Treatment 1.428 0.311 Day 2.349 0.172 Q Treatment 5.300 0.047* Day 2.277 0.180 Fs Treatment 0.020* 7.423 SCOC Treatment 0.769 0.504 Day 0.314 0.815 Nitrite flux Treatment 0.030* 7.269 Nitrate flux Treatment 0.130 4.154 Ammonia flux Treatment 1.136 0.382 Day 1.774 0.252 All significant (α < 0.05) results are marked with an asterisk (*). AFDW, ash-free dry weight; Q, bio-irrigation rate; Fs, clearance rate; SCOC, sediment community oxygen consumption. Bio-irrigation rate, SCOC, and the three nutrient fluxes are standardized per AFDW. Table 1. Statistical factors from two-way ANOVA (F-test) or Kruskal–Wallis (χ2) tests, with Treatment (3 levels) and Day (4 levels) as factors. Variable Factor F P χ2 Total AFDW Treatment 1.428 0.311 Day 2.349 0.172 Q Treatment 5.300 0.047* Day 2.277 0.180 Fs Treatment 0.020* 7.423 SCOC Treatment 0.769 0.504 Day 0.314 0.815 Nitrite flux Treatment 0.030* 7.269 Nitrate flux Treatment 0.130 4.154 Ammonia flux Treatment 1.136 0.382 Day 1.774 0.252 Variable Factor F P χ2 Total AFDW Treatment 1.428 0.311 Day 2.349 0.172 Q Treatment 5.300 0.047* Day 2.277 0.180 Fs Treatment 0.020* 7.423 SCOC Treatment 0.769 0.504 Day 0.314 0.815 Nitrite flux Treatment 0.030* 7.269 Nitrate flux Treatment 0.130 4.154 Ammonia flux Treatment 1.136 0.382 Day 1.774 0.252 All significant (α < 0.05) results are marked with an asterisk (*). AFDW, ash-free dry weight; Q, bio-irrigation rate; Fs, clearance rate; SCOC, sediment community oxygen consumption. Bio-irrigation rate, SCOC, and the three nutrient fluxes are standardized per AFDW. Figure 1. View largeDownload slide Photographs of Lanice conchilega individuals from (a) the Control treatment, (b) the Dilution treatment, and (c) the Dredge treatment, with the arrow indicating the ingested sediment inside the animal in the Dredge treatment. The scale bar has a length of 1 mm. Figure 1. View largeDownload slide Photographs of Lanice conchilega individuals from (a) the Control treatment, (b) the Dilution treatment, and (c) the Dredge treatment, with the arrow indicating the ingested sediment inside the animal in the Dredge treatment. The scale bar has a length of 1 mm. Figure 2. View largeDownload slide Bar charts representing (a) the clearance rates of sediment particles, and (b) the bio-irrigation rates per g AFDW in each treatment. The error bars represent means ± standard errors. Figure 2. View largeDownload slide Bar charts representing (a) the clearance rates of sediment particles, and (b) the bio-irrigation rates per g AFDW in each treatment. The error bars represent means ± standard errors. Sediment biogeochemistry The SCOC per AFDW of L. conchilega did not differ significantly between treatments and ranged from 616.05 ± 161.54 mmol m−2 d−1 g−1 (Dredge) to 1049.44 ± 80.40 mmol m−2 d−1 g−1 (Dilution). However, average values showed a pattern of increase in the Dilution treatment and a drop in the Dredge treatment, compared with the Control (Figure 3). Nitrite fluxes varied between −8.95 ± 3.40 mmol m−2 d−1 g−1 (Control) and 7.11 ± 3.57 mmol m−2 d−1 g−1 (Dilution) and differed significantly between the Control and the Dilution and Dredge treatments (p = 0.004 and p = 0.039, respectively; Table 1; Figure 3). Nitrate fluxes varied between -130.00 ± 122.30 mmol m−2 d−1 g−1 (Control) and 20.49 ± 13.55 mmol m−2 d−1 g−1 (Dilution), and ammonia fluxes between -41.29 ± 26.29 mmol m−2 d−1 g−1 (Control) and 47.53 ± 70.25 mmol m−2 d−1 g−1 (Dilution). Though no significant differences were found between treatments, nitrate fluxes showed similar patterns as nitrite fluxes, with highest effluxes in the Dilution treatment and influxes in the Control (Figure 3). Figure 3. View largeDownload slide Bar charts representing (a) sediment community oxygen consumption (SCOC) per g AFDW, (b) nitrite fluxes per g AFDW, (c) nitrate fluxes per g AFDW, and (d) ammonia fluxes per g AFDW. The error bars represent means ± standard errors. Figure 3. View largeDownload slide Bar charts representing (a) sediment community oxygen consumption (SCOC) per g AFDW, (b) nitrite fluxes per g AFDW, (c) nitrate fluxes per g AFDW, and (d) ammonia fluxes per g AFDW. The error bars represent means ± standard errors. Lanice behaviour–sediment biogeochemistry relationships All fluxes showed a positive relationship with bio-irrigation rates, but bio-irrigation rates per AFDW of L. conchilega were only significantly related to ammonia fluxes (R2 = 36.1%). Clearance rates were only significantly related to nitrite fluxes, with efflux rates increasing along with increased clearance, with an R2-value of 41.7% (Table 2). Table 2. Statistical factors for linear regressions between bio-irrigation and biogeochemical fluxes, or between clearance rate and bio-irrigation or biogeochemical fluxes. Response Predictor Slope t P R2 SCOC Q 9.774 0.884 0.398 0.072 Nitrite flux Q 0.285 1.373 0.200 0.159 Nitrate flux Q 2.363 0.647 0.532 0.040 Ammonia flux Q 4.683 2.374 0.039* 0.361 SCOC Fs −2468 −0.924 0.377 0.079 Q Fs 2.577 0.034 0.974 <0.001 Nitrite flux Fs 112.119 2.675 0.023* 0.417 Nitrate flux Fs 1478.4 1.913 0.085 0.268 Ammonia flux Fs 571.44 1.003 0.339 0.091 Response Predictor Slope t P R2 SCOC Q 9.774 0.884 0.398 0.072 Nitrite flux Q 0.285 1.373 0.200 0.159 Nitrate flux Q 2.363 0.647 0.532 0.040 Ammonia flux Q 4.683 2.374 0.039* 0.361 SCOC Fs −2468 −0.924 0.377 0.079 Q Fs 2.577 0.034 0.974 <0.001 Nitrite flux Fs 112.119 2.675 0.023* 0.417 Nitrate flux Fs 1478.4 1.913 0.085 0.268 Ammonia flux Fs 571.44 1.003 0.339 0.091 All significant (α < 0.05) results are marked with an asterisk (*). SCOC, sediment community oxygen consumption; Q, bio-irrigation rate; Fs, clearance rate. Bio-irrigation rates and biogeochemical fluxes were standardised per AFDW. Table 2. Statistical factors for linear regressions between bio-irrigation and biogeochemical fluxes, or between clearance rate and bio-irrigation or biogeochemical fluxes. Response Predictor Slope t P R2 SCOC Q 9.774 0.884 0.398 0.072 Nitrite flux Q 0.285 1.373 0.200 0.159 Nitrate flux Q 2.363 0.647 0.532 0.040 Ammonia flux Q 4.683 2.374 0.039* 0.361 SCOC Fs −2468 −0.924 0.377 0.079 Q Fs 2.577 0.034 0.974 <0.001 Nitrite flux Fs 112.119 2.675 0.023* 0.417 Nitrate flux Fs 1478.4 1.913 0.085 0.268 Ammonia flux Fs 571.44 1.003 0.339 0.091 Response Predictor Slope t P R2 SCOC Q 9.774 0.884 0.398 0.072 Nitrite flux Q 0.285 1.373 0.200 0.159 Nitrate flux Q 2.363 0.647 0.532 0.040 Ammonia flux Q 4.683 2.374 0.039* 0.361 SCOC Fs −2468 −0.924 0.377 0.079 Q Fs 2.577 0.034 0.974 <0.001 Nitrite flux Fs 112.119 2.675 0.023* 0.417 Nitrate flux Fs 1478.4 1.913 0.085 0.268 Ammonia flux Fs 571.44 1.003 0.339 0.091 All significant (α < 0.05) results are marked with an asterisk (*). SCOC, sediment community oxygen consumption; Q, bio-irrigation rate; Fs, clearance rate. Bio-irrigation rates and biogeochemical fluxes were standardised per AFDW. Discussion Experimental set-up Earlier research (e.g. Huettel and Gust, 1992; Glud et al., 1996) showed that stirring in cylindrical closed chambers can create strong pressure gradients that cause high advective pore water flows. Such flows could have created physical artefacts in our experiment by pumping overlying water and suspended solids into the sediment, thereby compromising an accurate interpretation of the governing mechanisms of the observed effects on biogeochemistry and the role of L. conchilega. We demonstrated that such artefacts were small in our experiment as sediment remained permeable (KH > 2.5 × 10−12 m2; Forster et al., 2003) and physical water flows into the sediment were shown to be small (around 0 ml min−1). As a result, the measured water flow rates and differences in biogeochemistry between treatments can be attributed to the effect of suspended solids on the activity of L. conchilega, rather than to the experimental set-up. The different outcome of this study as compared with Huettel and Gust (1992) and Glud et al. (1996) may be due to differences in the experimental set-up, such as rotation speed, height of the water column, and differences in sediment properties. Clearance of suspended sediment The duration of our experiment allowed for and even exceeded the typical duration of conditions within a sediment plume caused by dredging operations (Duclos et al., 2013). Since our experimental set-up was designed to maintain a water flow sufficient to keep the added sediment in suspension, the observed decrease in turbidity was mostly caused by the clearance activity of L. conchilega itself. This observation is supported by the ingested sediment in the worms. Suspension feeding bivalves have been shown to increase their production of pseudofaeces as a response to elevated amounts of suspended inorganic particles, thereby depositing previously suspended sediment on the seafloor (Iglesias et al., 1996; Navarro and Widdows, 1997), and a similar pseudofaeces production has been observed in polychaetes with a lifestyle resembling that of L. conchilega (i.e. the reef-building Sabellaria alveolata; Dubois et al., 2005). We were unable to determine whether L. conchilega produced pseudofaeces, but the presence of high levels of ingested sediment inside the digestive system of the worms from the Dilution and Dredge treatments (Figure 1) demonstrates that the suspended sediment was at least partially ingested by the animals. In addition to observations of ingested sediment, the calculated sediment clearance rates indicated a significant increase in clearance activity in the Dilution and Dredge treatments compared with the Control treatment without added sediment. Observations for other suspension feeders and for SSC up to ∼0.5 g l−1, however, point at drops in clearance rates with increased concentration during time periods of hours to days (Navarro and Widdows, 1997; Ellis et al., 2002; Dubois et al., 2009). In addition, the clearance rates of L. conchilega in our study were relatively low compared with the results of Denis et al. (2007), who calculated clearance rates up to 0.75 l h−1 g−1 under different current regimes from the south-eastern coasts of the English Channel, which is significantly higher than our maximal values around 0.1 l h−1 g−1. However, these authors calculated clearance rates based on decreases in chlorophyll a concentrations rather than in suspended sediment. Our observation of lower rates, calculated exclusively from inorganic sediment concentrations, might therefore be caused by a certain amount of selectivity in particle uptake by L. conchilega, resulting in inorganic particles being less likely to be captured by the animals than organic matter. Yet, particle selectivity appears to be imperfect under the elevated sediment concentrations, based on the presence of the ingested sediment inside Lanice guts, especially in the Dredge treatment. The difference in ingested sediment between the Dilution and the Dredge treatments can be explained by the clearance rates not differing significantly, but the SSC being five times higher in the Dredge treatment. Similar reductions of the particle selection efficiency were found under high sediment loads for the suspension-feeding bivalve Cerastoderma edule (Navarro and Widdows, 1997). Whether or not these inefficiencies in selecting appropriate food particles under high SSCs affect the feeding capacities of the worms could not be determined in this experiment. Sediment biogeochemistry Our results showed that short-term changes in SSCs can affect the biogeochemistry in a sediment bed with presence of L. conchilega. This polychaete has been found to contribute substantially to the biogeochemistry of its environment by increasing benthic respiration and nutrient release via its irrigating behaviour (Forster and Graf, 1995; Braeckman et al., 2010). In our experiment, bio-irrigation changed significantly between SSCs, with a twofold increase in the Dilution treatment as compared with the Control, while the lowest irrigation rates were found in the Dredge treatment. We suggest that the high sediment content in the digestive system interferes with the piston-pumping activity of L. conchilega (Riisgård, 1991; Forster and Graf, 1995). The increased irrigation found in the Dilution treatment is most likely due to increased piston-pumping activity to remove sediments from the tube and worm. Coincidental with the increased clearance rates, NO2− fluxes showed a significant increase in both treatments with elevated SSCs and switched from influx into the sediment to efflux toward the water column. L. conchilega bio-irrigation has been shown to affect the microbial communities involved in the nitrogen cycle (Yazdani Foshtomi et al., 2018). Our observations suggest that the increased irrigating activity of the worms in the Dilution treatment enhanced nitrification by transporting more oxygen into the sediment. The positive relation between bio-irrigation and ammonia release to the water column further supports the stimulatory effects of bio-irrigation on aerobic mineralization. Bio-irrigation in the Dredge treatment was however halted, and even lower than the rates in the Control treatment, likely due to reduced pumping capacity of the worms, being affected by too high amounts of ingested sediment. However, nitrifying and other aerobic microbial communities in the Dredge treatment may still have benefited from an initial increase in irrigating activity before the individuals became overly filled up with sediment. Conclusion Most maritime infrastructure projects involve dredging operations and will therefore unavoidably produce turbid plumes with high SSCs. The impact of such high-concentration plumes on marine benthic fauna is yet to be fully determined. Whether or not these plumes will affect the functioning of the benthic ecosystem likely depends on the duration of the operations, the environmental settings and the proximity and presence of suspension feeders such as L. conchilega. Our experiment proved that short-term elevated concentrations of suspended sediment can influence the behaviour of Lanice conchilega, by significantly increasing their clearance and ingestion of inorganic suspended particles, which induced non-linear effects on bio-irrigation and biogeochemistry. We did not observe mortality during this short-term experiment, but our results do not exclude increased mortality of L. conchilega under long-term exposure, when oxygen supply is limited due to reduced ventilation of tubes. We conclude that both behavioural changes and potential mortality can therefore affect Lanice’s ecosystem engineering effects, depending on the duration of exposure and the concentration of suspended sediment. Future experiments should therefore determine critical thresholds of SSCs. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Acknowledgements Sebastiaan Mestdagh enjoyed a scholarship from the UGent Special Research Fund (BOF.DOC.2014.0081.01). This research benefited from the logistic support of EMBRC Belgium—FWO project GOH3817N. We would like to thank Katja Guilini, Brecht Vanhove, An-Sofie D’Hondt, Annelien Rigaux, Inge van der Knaap and the students of the IMBRSea programme of UGent for their help during the field sampling. In addition, we want to acknowledge the useful help of laboratory technicians Bart Beuselinck for the analyses of nutrient samples and Peter Van Breugel and Yvonne van der Maas for their measurements of bromide concentrations. References Alves R. M. S. , Van Colen C. , Vincx M. , Vanaverbeke J. , De Smet B. , Guarini J. , Rabaut M. et al. 2017 . A case study on the growth of Lanice conchilega (Pallas, 1766) aggregations and their ecosystem engineering impact on sedimentary processes . Journal of Experimental Marine Biology and Ecology , 489 : 15 – 23 . 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S, Fortune, I;M, Paterson, D

doi: 10.1093/icesjms/fsy130pmid: N/A

Abstract The Oslo and Paris Commissions (OSPAR) decision 98/3 prohibits the dumping of man-made structures (MMS) offshore. However, there are regions of the world where MMS are recognized as providing an ecological and societal benefit through the provision of ecosystem goods and services. This review provides a commentary on our current understanding of the ecological influence of man-made structures, the consequences of their decommissioning and recognizes that our knowledge is far from complete. It is known that a diverse and complex ecosystem of attached organisms develops on submerged structures which supports a localized food web that could not exist without them. However, our lack of detailed information makes modelling of system response to decommissioning very tentative. Ideally, we should use the best possible scientific information to reach a consensus as to whether the blanket removal of MMS (excepting derogations) is the most environmentally supportable option. The evidence available to-date shows both benefits and some risk in leaving MMS in place and this needs to be examined without preconception. On the UKCS, MMS as artificial habitats are not considered under the Habitats Directive, irrespective of the value or rarity of the species present. We conclude that a more comprehensive regulatory process, together with the recognition of the ecology associated with man-made structures, would allow science to play a role in the decision-making rather than supporting a blanket policy ignoring ecological context. Introduction Any structure submerged in sea water quickly becomes colonized by marine biota. An ecological succession then follows, often leading to complex three dimensional and heterogeneous habitats of significant biodiversity and function. This includes man-made structures (MMS) placed within a marine environment. In the North Sea, the requirement to decommission existing MMS (OSPAR Commission Decision 98/3) introduces interesting questions around the ecological status of MMS. While technological advances have improved the planning and implementation of decommissioning, there remain more than 1 350 offshore installations in the OSPAR maritime area (OSPAR commission, 2015), many of which are mature. Despite this, there seems to be little concern over the ecology associated with MMS. The question of the best management of redundant resources is critical to the future of the offshore industry and also perhaps to the ecology of the region. Yet, management decisions are currently being made without sufficient knowledge of the potential ecological consequences. There is an urgent need to consider the purpose of decommissioning legislation in relation to ecological best practise for the future to the renewable energy sector and marine ecology. The Oslo and Paris Commissions decision 98/3 (OSPAR, 1998), prohibits the dumping of whole or partial offshore structures and states that re-use, recycling, or disposal on land is the preferred option. “Dumping” encompasses structures that might be left in place after their commercial life is over. Derogations to this requirement are very limited, requiring most MMS to be wholly or partially removed. While the intentions of these regulations are clear, namely to protect the marine environment and ensure proper management of redundant resources, there is a growing body of evidence that offshore structures themselves become part of the ecology of the system, utilized by marine biota and providing useful ecosystem function and services and habitat diversity (Todd et al., 2009; Consoli et al., 2013; Bergmark and Jørgensen, 2014; Claisse et al., 2015). Offshore man-made structures have also unintentionally served as de facto marine protected areas, providing a localized refuge from fishing activities (Fujii, 2015). Therefore, the ecology of decommissioning offshore structures requires evaluation and the implications locally, and in the wider context of the regional seas, should be acknowledged. This would allow scientific evidence to feed into a multi-criteria analysis of the optimal decommissioning outcomes for all sectors. Methodology Details of the literature search terms and search engines used are provided (Supplementary Appendix S1). The breadth of current knowledge is limited by research efforts which themselves reflect the priorities of research funding bodies, scientific expertise and policy drivers. This can create an “information bias”. For example, subject areas where there are commercial drivers (e.g. fisheries science) may be better represented and have more background information than others (seabirds). Features of the MMS environment Man-made structures in the marine environment have a number of recognizable characteristics. The most obvious is that they are composed of non-natural substrata such as steel or concrete but, despite their artificial composition and without anti-fouling treatments, MMS quickly develop a succession of marine biota (Whomersley and Picken, 2003; De Mesel et al., 2015). Both concrete and steel are suitable for settlement by invertebrate assemblages (Macreadie et al., 2011) and the substratum can be a significant variable in determining the rate and extent of settlement. For example, growth of shallow water corals was greater on painted steel than on concrete (Fitzhardinge and Bailey-Brock, 1989). In addition, steel MMS have also a more complex spatial structure than concrete and therefore provide a more three-dimensional reef habitat with greater niche variation (Pickering and Whitmarsh, 1997; inLøkkeborg et al., 2002). The side elevations of the MMS can be considered as either “intertidal”, which suggests regular wetting and drying on the basis of the tidal cycle, or fully submerged. Intertidal hard substrata are rare in the North Sea (van der Stap et al., 2016) and given the vertical nature of MMS, the “intertidal” zone is spatially restricted, akin to a marine cliff, making competition for space intense. As for any marine systems, water depth and light availability have been shown to be significant factors in the distribution of the associated biota (Jones et al., 2012; Fujii and Jamieson, 2016; van der Stap et al., 2016). The placement of the MMS can also result in localized effects, where variations in current speeds and orientation provide shelter or focus passing currents for filter feeding epifauna (Løkkeborg et al., 2002 and references therein). A feature unique to MMS is the operational legacy including maintenance and cleaning regimes, disturbance and pollution. The operational activities of offshore MMS represent a focal point of disturbance to marine biota both in terms of noise and drilling activities. The level of disturbance will vary depending on the functional stage of the MMS with the most negative impacts associated with MMS construction (Russell et al., 2016). However, there is a serious lack of data on disturbance due to decommissioning. While cessation of operations may result in the reduction of noise, the decommissioning operations involving the use of cutting equipment and/or explosives may require careful management to avoid unacceptable level of ecosystem damage. The issue of chemical pollution from operations is more relevant to the offshore oil and gas industry than the renewable sector and can result in the contamination with hydrocarbons in production water and accidental spills to the water column. The impact of contaminants on marine biota is an active area of research with several recent articles describing the mechanisms and temporal and spatial extent of impacts (Table 1). Drill cuttings piles, produced around the base of wells are often contaminated with hydrocarbons and heavy metals (Breuer et al., 2008). Air pollution also occurs due to the release of gases during operations including CO2, NOx, and small particulates (PM10). Also, grey water and ground food waste are allowable for discharge at sea (www 1) and these artificial nutrient subsidies may alter the ecological status of the locality but would not continue on decommissioning. Following cessation of operations, disturbance from noise, drilling and organic enrichment would also end. Little is known of how the MMS ecosystem would respond to this change (Fujii, 2015). Table 1. Selected peer-reviewed scientific publications examining aspects of pollution and offshore oil production (note: this is not exhaustive and is limited for brevity to the most relevant to this review, literature search methodology, and criteria detailed in Supplementary Appendix S1). Title Reference Purpose Data type Findings Environmental aspect of oil and water-based drilling muds and cuttings from Dibi and Ewan off-shore wells in the Niger Delta, Nigeria Adewole et al. (2010) Drilling muds and cuttings derived from Ewan and Dibi off-shore wells in the Niger-Delta petroleum province of Nigeria was studied to evaluate their toxicity and possible environmental impacts that may result from their indiscriminate disposal Heavy metals, THC, PAH It is likely that the drill muds and cuttings wastes will increase the pollution problems in aquatic environment, thereby causing stress for the fish and other aquatic organisms Biomarkers in natural fish populations indicate adverse biological effects of offshore oil production Balk et al. (2011) To examine samples from natural populations of haddock and cod in two areas with extensive oil production Biomarkers Exposure to and uptake of polycyclic aromatic hydrocarbons (PAHs) were demonstrated, and biomarker analyses revealed adverse biological effects, including induction of biotransformation enzymes, oxidative stress, altered fatty acid composition, and genotoxicity Assessment of metal concentrations found within a North Sea drill cuttings pile Breuer et al. (2008) The analysis of geochemical carrier substances (Mn and Fe oxyhydroxides) and metal (Ba, Co, Cr, Cu, Mo, Pb, V) concentrations from a cuttings pile Heavy metals in cuttings piles, pore water oxygen and sulfide Results show a rapid removal of oxygen within the top few millimetres of the cuttings pile along with elevated concentrations of total hydrocarbons and solid phase metal concentrations compared to the surrounding environment Historic scale and persistence of drill cuttings impacts on North Sea benthos Henry et al. (2017) To assess the temporal persistence and spatial scale of drill cutting pile impacts on benthic communities using industry survey database (UK Benthos) Industry surveys of benthic macrofauna, sediment properties, total oil, aromatic hydrocarbons, and trace metals Only 19 surveys out of 351 were standardized sufficiently to compare statistically. 12 of 19 showed significant benthic responses to drilling piles. Most effects were limited within 1 km and persisted up to 8 years post drilling. Recovery of deep-water megafaunal assemblages from hydrocarbon drilling disturbance in the Faroe–Shetland Channel Jones et al. (2012) Recovery of benthic assemblages from physical disturbance at the Laggan deep-water hydrocarbon drilling site was assessed using ROV quantitative video survey ROV video footage Sessile faunal densities and richness increased significantly with increasing distance from drilling in all years, although both metrics were significantly higher close to drilling after 3 and 10 years when compared to immediately after drilling Whole-body concentrations of elements in three fish species from offshore oil platforms and natural areas in the Southern California Bight, United States Love et al. (2013) To determine if offshore platforms are a major source of contamination by trace element in fish Whole body samples for elemental analysis None of the 21 elements measured consistently exhibited higher concentrations in fish from platforms compared to natural areas. Some elements were higher at natural sites. Some elements were found at toxic levels in both sites. Hydrocarbon contamination affects deep-sea benthic oxygen uptake and microbial community composition Main et al. (2015) To examine how crude oil affected the oxygen consumption rate of a natural, deep-sea benthic community Sediment core, microcosm, O2 consumption, phospholipid fatty acids, stable carbon isotope Sediment community oxygen consumption rates increased significantly in response to increasing levels of contamination in the overlying water of oil-treated microcosms Crude oil exposures reveal roles for intracellular calcium cycling in haddock craniofacial and cardiac development Sørhus et al. (2016) To elucidate mechanism of crude oil disruption of fish development PAH uptake, molecular expression, malformation observations These data support a unifying hypothesis whereby depletion of intracellular calcium pools by crude oil-derived PAHs disrupts several pathways critical for organogenesis in fish Title Reference Purpose Data type Findings Environmental aspect of oil and water-based drilling muds and cuttings from Dibi and Ewan off-shore wells in the Niger Delta, Nigeria Adewole et al. (2010) Drilling muds and cuttings derived from Ewan and Dibi off-shore wells in the Niger-Delta petroleum province of Nigeria was studied to evaluate their toxicity and possible environmental impacts that may result from their indiscriminate disposal Heavy metals, THC, PAH It is likely that the drill muds and cuttings wastes will increase the pollution problems in aquatic environment, thereby causing stress for the fish and other aquatic organisms Biomarkers in natural fish populations indicate adverse biological effects of offshore oil production Balk et al. (2011) To examine samples from natural populations of haddock and cod in two areas with extensive oil production Biomarkers Exposure to and uptake of polycyclic aromatic hydrocarbons (PAHs) were demonstrated, and biomarker analyses revealed adverse biological effects, including induction of biotransformation enzymes, oxidative stress, altered fatty acid composition, and genotoxicity Assessment of metal concentrations found within a North Sea drill cuttings pile Breuer et al. (2008) The analysis of geochemical carrier substances (Mn and Fe oxyhydroxides) and metal (Ba, Co, Cr, Cu, Mo, Pb, V) concentrations from a cuttings pile Heavy metals in cuttings piles, pore water oxygen and sulfide Results show a rapid removal of oxygen within the top few millimetres of the cuttings pile along with elevated concentrations of total hydrocarbons and solid phase metal concentrations compared to the surrounding environment Historic scale and persistence of drill cuttings impacts on North Sea benthos Henry et al. (2017) To assess the temporal persistence and spatial scale of drill cutting pile impacts on benthic communities using industry survey database (UK Benthos) Industry surveys of benthic macrofauna, sediment properties, total oil, aromatic hydrocarbons, and trace metals Only 19 surveys out of 351 were standardized sufficiently to compare statistically. 12 of 19 showed significant benthic responses to drilling piles. Most effects were limited within 1 km and persisted up to 8 years post drilling. Recovery of deep-water megafaunal assemblages from hydrocarbon drilling disturbance in the Faroe–Shetland Channel Jones et al. (2012) Recovery of benthic assemblages from physical disturbance at the Laggan deep-water hydrocarbon drilling site was assessed using ROV quantitative video survey ROV video footage Sessile faunal densities and richness increased significantly with increasing distance from drilling in all years, although both metrics were significantly higher close to drilling after 3 and 10 years when compared to immediately after drilling Whole-body concentrations of elements in three fish species from offshore oil platforms and natural areas in the Southern California Bight, United States Love et al. (2013) To determine if offshore platforms are a major source of contamination by trace element in fish Whole body samples for elemental analysis None of the 21 elements measured consistently exhibited higher concentrations in fish from platforms compared to natural areas. Some elements were higher at natural sites. Some elements were found at toxic levels in both sites. Hydrocarbon contamination affects deep-sea benthic oxygen uptake and microbial community composition Main et al. (2015) To examine how crude oil affected the oxygen consumption rate of a natural, deep-sea benthic community Sediment core, microcosm, O2 consumption, phospholipid fatty acids, stable carbon isotope Sediment community oxygen consumption rates increased significantly in response to increasing levels of contamination in the overlying water of oil-treated microcosms Crude oil exposures reveal roles for intracellular calcium cycling in haddock craniofacial and cardiac development Sørhus et al. (2016) To elucidate mechanism of crude oil disruption of fish development PAH uptake, molecular expression, malformation observations These data support a unifying hypothesis whereby depletion of intracellular calcium pools by crude oil-derived PAHs disrupts several pathways critical for organogenesis in fish Table 1. Selected peer-reviewed scientific publications examining aspects of pollution and offshore oil production (note: this is not exhaustive and is limited for brevity to the most relevant to this review, literature search methodology, and criteria detailed in Supplementary Appendix S1). Title Reference Purpose Data type Findings Environmental aspect of oil and water-based drilling muds and cuttings from Dibi and Ewan off-shore wells in the Niger Delta, Nigeria Adewole et al. (2010) Drilling muds and cuttings derived from Ewan and Dibi off-shore wells in the Niger-Delta petroleum province of Nigeria was studied to evaluate their toxicity and possible environmental impacts that may result from their indiscriminate disposal Heavy metals, THC, PAH It is likely that the drill muds and cuttings wastes will increase the pollution problems in aquatic environment, thereby causing stress for the fish and other aquatic organisms Biomarkers in natural fish populations indicate adverse biological effects of offshore oil production Balk et al. (2011) To examine samples from natural populations of haddock and cod in two areas with extensive oil production Biomarkers Exposure to and uptake of polycyclic aromatic hydrocarbons (PAHs) were demonstrated, and biomarker analyses revealed adverse biological effects, including induction of biotransformation enzymes, oxidative stress, altered fatty acid composition, and genotoxicity Assessment of metal concentrations found within a North Sea drill cuttings pile Breuer et al. (2008) The analysis of geochemical carrier substances (Mn and Fe oxyhydroxides) and metal (Ba, Co, Cr, Cu, Mo, Pb, V) concentrations from a cuttings pile Heavy metals in cuttings piles, pore water oxygen and sulfide Results show a rapid removal of oxygen within the top few millimetres of the cuttings pile along with elevated concentrations of total hydrocarbons and solid phase metal concentrations compared to the surrounding environment Historic scale and persistence of drill cuttings impacts on North Sea benthos Henry et al. (2017) To assess the temporal persistence and spatial scale of drill cutting pile impacts on benthic communities using industry survey database (UK Benthos) Industry surveys of benthic macrofauna, sediment properties, total oil, aromatic hydrocarbons, and trace metals Only 19 surveys out of 351 were standardized sufficiently to compare statistically. 12 of 19 showed significant benthic responses to drilling piles. Most effects were limited within 1 km and persisted up to 8 years post drilling. Recovery of deep-water megafaunal assemblages from hydrocarbon drilling disturbance in the Faroe–Shetland Channel Jones et al. (2012) Recovery of benthic assemblages from physical disturbance at the Laggan deep-water hydrocarbon drilling site was assessed using ROV quantitative video survey ROV video footage Sessile faunal densities and richness increased significantly with increasing distance from drilling in all years, although both metrics were significantly higher close to drilling after 3 and 10 years when compared to immediately after drilling Whole-body concentrations of elements in three fish species from offshore oil platforms and natural areas in the Southern California Bight, United States Love et al. (2013) To determine if offshore platforms are a major source of contamination by trace element in fish Whole body samples for elemental analysis None of the 21 elements measured consistently exhibited higher concentrations in fish from platforms compared to natural areas. Some elements were higher at natural sites. Some elements were found at toxic levels in both sites. Hydrocarbon contamination affects deep-sea benthic oxygen uptake and microbial community composition Main et al. (2015) To examine how crude oil affected the oxygen consumption rate of a natural, deep-sea benthic community Sediment core, microcosm, O2 consumption, phospholipid fatty acids, stable carbon isotope Sediment community oxygen consumption rates increased significantly in response to increasing levels of contamination in the overlying water of oil-treated microcosms Crude oil exposures reveal roles for intracellular calcium cycling in haddock craniofacial and cardiac development Sørhus et al. (2016) To elucidate mechanism of crude oil disruption of fish development PAH uptake, molecular expression, malformation observations These data support a unifying hypothesis whereby depletion of intracellular calcium pools by crude oil-derived PAHs disrupts several pathways critical for organogenesis in fish Title Reference Purpose Data type Findings Environmental aspect of oil and water-based drilling muds and cuttings from Dibi and Ewan off-shore wells in the Niger Delta, Nigeria Adewole et al. (2010) Drilling muds and cuttings derived from Ewan and Dibi off-shore wells in the Niger-Delta petroleum province of Nigeria was studied to evaluate their toxicity and possible environmental impacts that may result from their indiscriminate disposal Heavy metals, THC, PAH It is likely that the drill muds and cuttings wastes will increase the pollution problems in aquatic environment, thereby causing stress for the fish and other aquatic organisms Biomarkers in natural fish populations indicate adverse biological effects of offshore oil production Balk et al. (2011) To examine samples from natural populations of haddock and cod in two areas with extensive oil production Biomarkers Exposure to and uptake of polycyclic aromatic hydrocarbons (PAHs) were demonstrated, and biomarker analyses revealed adverse biological effects, including induction of biotransformation enzymes, oxidative stress, altered fatty acid composition, and genotoxicity Assessment of metal concentrations found within a North Sea drill cuttings pile Breuer et al. (2008) The analysis of geochemical carrier substances (Mn and Fe oxyhydroxides) and metal (Ba, Co, Cr, Cu, Mo, Pb, V) concentrations from a cuttings pile Heavy metals in cuttings piles, pore water oxygen and sulfide Results show a rapid removal of oxygen within the top few millimetres of the cuttings pile along with elevated concentrations of total hydrocarbons and solid phase metal concentrations compared to the surrounding environment Historic scale and persistence of drill cuttings impacts on North Sea benthos Henry et al. (2017) To assess the temporal persistence and spatial scale of drill cutting pile impacts on benthic communities using industry survey database (UK Benthos) Industry surveys of benthic macrofauna, sediment properties, total oil, aromatic hydrocarbons, and trace metals Only 19 surveys out of 351 were standardized sufficiently to compare statistically. 12 of 19 showed significant benthic responses to drilling piles. Most effects were limited within 1 km and persisted up to 8 years post drilling. Recovery of deep-water megafaunal assemblages from hydrocarbon drilling disturbance in the Faroe–Shetland Channel Jones et al. (2012) Recovery of benthic assemblages from physical disturbance at the Laggan deep-water hydrocarbon drilling site was assessed using ROV quantitative video survey ROV video footage Sessile faunal densities and richness increased significantly with increasing distance from drilling in all years, although both metrics were significantly higher close to drilling after 3 and 10 years when compared to immediately after drilling Whole-body concentrations of elements in three fish species from offshore oil platforms and natural areas in the Southern California Bight, United States Love et al. (2013) To determine if offshore platforms are a major source of contamination by trace element in fish Whole body samples for elemental analysis None of the 21 elements measured consistently exhibited higher concentrations in fish from platforms compared to natural areas. Some elements were higher at natural sites. Some elements were found at toxic levels in both sites. Hydrocarbon contamination affects deep-sea benthic oxygen uptake and microbial community composition Main et al. (2015) To examine how crude oil affected the oxygen consumption rate of a natural, deep-sea benthic community Sediment core, microcosm, O2 consumption, phospholipid fatty acids, stable carbon isotope Sediment community oxygen consumption rates increased significantly in response to increasing levels of contamination in the overlying water of oil-treated microcosms Crude oil exposures reveal roles for intracellular calcium cycling in haddock craniofacial and cardiac development Sørhus et al. (2016) To elucidate mechanism of crude oil disruption of fish development PAH uptake, molecular expression, malformation observations These data support a unifying hypothesis whereby depletion of intracellular calcium pools by crude oil-derived PAHs disrupts several pathways critical for organogenesis in fish Understanding MMS ecosystems MMS are challenging environments to study, often with additional limitations to research. As privately owned assets, permission must be sought to carry out ecological surveys (as well as to publish findings). Even with permission, access may be restricted due to operational, safety and weather factors. The assessment of fish abundance is important yet difficult at MMS locations as trawl-surveys are limited for safety reasons. Consequently, techniques have been developed and adapted for use at MMS including; underwater visual census, hydro-acoustic monitoring, photography/video footage, gill net surveys, and fish bait traps (Figure 1). The data derived from such surveys are difficult to compare as the methods may be selective for different species or sizes, depending on fish behaviour. For example, baited traps are not attractive for all species and may under-report fish diversity. A study of fish assemblages at the decommissioned Miller platform (Central North Sea) used baited fish traps and recorded relatively high numbers of saithe which were absent or rare at open water sites as assessed using bottom trawl surveys (International bottom trawl survey, IBTS, Fujii, 2015). Also, trawling vessels may have variable gears and selective mesh sizes (Løkkeborg et al., 2002). Conversely, non-selective trawls, passive gill-netting, and ROV video footage may suggest other fish species such as ling (Molva molva) and flatfish (Fujii, 2015) are important. Smaller species that dwell in cavities may be underreported due to their secretive behaviour and non-commercial status. Thus, while all survey data is helpful, it is unclear what influence variable methodology has on the findings. Figure 1. View largeDownload slide (a) A visual census of platform Gina showing anemones and kelp bass, Santa Barbara Channel, California (© James Forte, courtesy of Dr Milton Love, University of California at Santa Barbara). (b) Brambling, one of many migrating birds visiting oil platforms (courtesy of North Sea Bird Club). (c) Longeared owl at Murdoch Platform (image courtesy of NSBC). (d) Young-of-year Vermilion Rockfish (Sebastes miniatus) at platform Gilda. Figure 1. View largeDownload slide (a) A visual census of platform Gina showing anemones and kelp bass, Santa Barbara Channel, California (© James Forte, courtesy of Dr Milton Love, University of California at Santa Barbara). (b) Brambling, one of many migrating birds visiting oil platforms (courtesy of North Sea Bird Club). (c) Longeared owl at Murdoch Platform (image courtesy of NSBC). (d) Young-of-year Vermilion Rockfish (Sebastes miniatus) at platform Gilda. Monitoring the movement of fish over time is similarly limited by access and by weather conditions and studies are often conducted during summer months (Løkkeborg et al., 2002; Soldal et al., 2002). A longer period of monitoring has been achieved around the decommissioned Miller platform (2 years, Fujii, 2015) and a pilot study indicated diurnal movements of fish and their prey species (Fujii and Jamieson, 2016). Despite technical challenges, data from the baited fish traps do suggest turnovers of individual fish using MMS, perhaps regulated at seasonal scales (Fujii, 2015). The ecological baseline A considerable difficulty in assessing the impact of MMS decommissioning is the lack of ecological information on the state of the marine environment prior to the MMS installation. The absence of baseline data makes it very difficult, if not impossible, to accurately assess the impact of the MMS on the host system or to provide a “target” for restoration post-decommissioning. Background or control sites some distance from oil and gas production are often used as a comparison (Jones et al., 2012; Main et al., 2015). However, under the Marine Strategy Framework Directive (MSFD) this implies that any selected comparative site represents “Good Ecologic Status” (GES, EU MSFD) which is often arguably not the case. The problem of a valid, or at least representative baseline, is a recurring one in environmental impact assessment. While there are no easy solutions, it is important to recognize this baseline problem and seek pragmatic answers. This is dealt with in some detail by Borja et al. (2013) highlighting four ways of determining the baseline or reference condition for the assessment of GES, namely: Find an area similar to the one under study but without the pressures (control area) Hind-cast conditions to a time before pressures were exerted Numerically model an “un-impacted” (control) condition Use expert judgement to gauge expected ecology. With respect to decommissioning in the North Sea OSPAR region, these problems become very apparent. For (1), an area without an MMS may not reach GES where there are other pressures that affect the ecosystem, such as pollution, transport, noise, or fishing. The MMS area may have higher biodiversity and functionality and be closer to GES than an open region so that the baseline is confounded. For (2), how far do we have to hind cast to consider an untouched marine environment, such as the North Sea? This is hard to say and would this be correct, in any case, given that all systems change and adapt with time? For (3), while modelling is advancing there is still reason to require validation and in this context, that would be difficult though the modelling exercise may be valuable in increasing our understanding. Often, we revert to (4) as a workable solution. Therefore, the best pragmatic environment practise may be to aim to improve ecosystem functioning above the status quo and certainly do no damage. This approach suggests that the aim of decommissioning management could be to achieve an improving environmental trajectory. Therefore, a clear question for environmental management is whether or not the local habitat would be improved by removing the MMS during decommissioning? However, any environmental change may benefit some species whilst being detrimental to others, such as those adapted to the “industrialized” conditions (e.g. Capitella spp.) and this would have to be recognized as part of the assessment process. Given the variability of geographical and hydrodynamic context between MMS, it seems only sensible to assess the individual circumstances of each platform and their ecology on a “case by case” basis. MMS ecosystem structure The most obvious MMS ecosystem components are attached to the structure. This highly visible epifauna can be identified from video footage or physical samples (Whomersley and Picken, 2003; Coolen et al., 2016; van der Stap et al., 2016). Less is known about other element of the ecosystem including mobile and cryptic species, meiofauna, plankton, and the microbial elements, including surface biofilms. In the Southern North Sea, sessile species richness (S) at offshore gas platforms increased from the surface to a depth of 15–20 m then decreased (van der Stap et al., 2016). The lower S in shallow water may reflect the harshness of the intertidal system with periods of wetting and drying, the force of breaking waves and salinity change from rainfall, all factors observed in intertidal zonation. The lower S at greater depth may be related to competition from dominant taxa such as the plumose anemone (Metridium dianthus, synonym senile). This depth effect is in line with the “intermediate disturbance hypothesis” that at low levels of disturbance, strong competitors exclude inferior species, whereas at higher rates of disturbance, recruitment cannot compensate for high mortality (van der Stap et al., 2016). Depth is therefore an important factor in the distribution of epifauna as well as variation in species richness on MMS (De Mesel et al., 2015). It is clear that the food webs associated with hard substrata are very different from those of open water. The question of how food webs on natural hard substrata vary from MMS is more subtle (Figure 2). The basics are similar; both are dominated by sessile life stages of a varied assemblage of marine forms. Differences in the community assemblages between natural and artificial systems will be driven by the physical nature of the surface, the three-dimensional conformation and the local environmental context. This does not account for deliberate measures to prevent or minimize colonization (anti-biofouling rings, anti-fouling treatments, materials, etc.) or local pollution. Differences will arise since settlement can be affected but direct comparative studies are lacking, however, it is clear a complex three-dimensional system develops on MMS and this habitat provisioning is an important ecosystem service. Scientists agree that there is a link between biodiversity and ecosystem function and services but also recognize the variability and context dependency of that link (Bulling et al., 2010). Therefore, a local increase in biodiversity would be expected to alter the function of the ecosystem. This is clearly the case for MMS where new ecosystems that could not existed are now supported (Figure 2, De Mesel et al., 2015). Considerable research effort has focused on evaluating the role of MMS for habitat provision, primarily for fish also as a substratum for epifauna such as cold water corals (Gass and Roberts, 2006; Fowler et al., 2015). Figure 2. View largeDownload slide North Sea food web from both natural hard substratum and MMS are similar however there are impacts on the distribution and diversity of assemblages from operational activities including; pollution, noise, anti-fouling, and the presence of drill cuttings piles. Figure 2. View largeDownload slide North Sea food web from both natural hard substratum and MMS are similar however there are impacts on the distribution and diversity of assemblages from operational activities including; pollution, noise, anti-fouling, and the presence of drill cuttings piles. MMS may serve as a refuge from fishing and higher levels of fish biomass are found at the MMS than in surrounding waters (Løkkeborg et al., 2002; Claisse et al., 2015). The quantification of fish is often assessed in conjunction with environmental variables such as depth, to gain an understanding of the dynamic relationships at play. For example, in the central North Sea, most fish caught at a “semi-cold” platform were at the lowest depth of 100 m compared to 10 and 50 m (Fujii, 2015). MMS size and orientation are important factors in determining the fish community present (Bartholomew et al., 2008) with small artificial reefs having greater fish densities than larger artificial reefs, while larger reefs show higher fish biomass but fewer individuals. Soldal et al. (2002), noted cod (Gadus morhua) size increased in proximity to a decommissioned platform compared to those caught further away (1.25–5 nautical miles). While numbers of haddock (Melanogrammus aeglefinus) decreased in proximity to the platform, possibly as smaller fish were eaten or repelled by larger fish at the platform. Orientation of the MMS has been shown to be an important variable for fish abundance (Soldal et al., 2002) perhaps affecting foraging and shelter. Also MMS may provide orientation cues and there is a need to understand the fish-habitat dependency in relation to changes in the number and distribution of MMS through decommissioning (Fujii, 2015). Attraction vs. production debate There is an ongoing debate about whether the higher fish biomass found at MMS results from an attraction to the MMS from the background area or whether the MMS facilitates the production of new biomass, through food provision and survivorship (Pickering and Whitmarsh, 1997; Osenberg et al., 2002). The aggregation of fish at MMS has been attributed to a lower risk of predation, higher prey densities, and shelter from currents (Løkkeborg et al., 2002). Some authors state that MMS attract and aggregate fish that would otherwise be widely dispersed, citing numerous studies finding higher levels of species richness and abundance associated with MMS (Consoli et al., 2013). This has implications for the ecological management of MMS since attraction may concentrate fish stocks making them more vulnerable to predation or exploitation. One study examining the attraction vs. production debate estimated the time spent by a fish species at an artificial reef (Smith et al., 2016). These authors distinguish between “local” and “new” fish biomass production with “local” defined as fish attracted to the site and “new production” biomass that would not exist without the site. While the site was highly productive (211 kg y−1), only 4-5% represented new production. Hence, the presence of MMS may not add significantly to net fish production. In the context of the North Sea, it has been suggested that the present areas closed to fishing (MPA, Platforms, etc.) would maintain the status quo of fish stocks and the conversion of existing structures into reefs is unlikely to further enhance fish stocks (Sayer and Baine, 2002). The attraction/production debate is not relevant to “biofouling” organisms (anemones, bivalves, corals) as these settle on surfaces and fulfil a trophic function, facilitating the presence of carnivorous fish and larger predators. MMS may act as plankton accumulators through hydrodynamic and illumination effects (Keenan et al., 2007) although there is uncertainty over the temporal significance of this impact. The presence of plankton is exploited by filter-feeding invertebrates, thus promoting biomass production. So, MMS are productive environments, especially for lower trophic levels, and attractive to other marine biota. The increased densities of fish and other marine life on MMS are exploited by sea mammals (Todd et al., 2009; Russell et al., 2014). A very small proportion of Grey and Harbour seals were found to follow subsea pipelines and to navigate between structures, presumably as a behavioural response to improve foraging success. These authors noted that burial or removal of such pipelines during decommissioning would remove these foraging opportunities (Russell et al., 2014). The presence of top predators, such as the harbour porpoise (Phocoena p. phocoena), around North Sea MMS was shown to vary throughout a 24-h period with more encounters detected at night and indications that these visits were associated with hunting and feeding (Todd et al., 2009). Bird and MMS interactions The effect of MMS on birds is not well understood with little peer-reviewed research (Ronconi et al., 2015). The most commonly described direct effects are collision and attraction to lights and flares. This occurs in an unpredictable manner but often coincides with poor weather and limited visibility. MMS may be visited by significant numbers of migrating birds in the spring and autumn, especially if they are exhausted. Many of these migratory birds will be in poor condition and use MMS as an opportunistic resting site (Figure 1) and without a site they may have “ditched” into the ocean and died. Thus, MMS may increase the survivorship for some birds but others may perish after arrival. Starvation was the most common cause of mortality observed at offshore platforms in the Gulf of Mexico (Ronconi et al., 2015). However, the initial benefits such as rest sites may be offset by disruption to the natural functioning of the ecosystem (Ronconi et al., 2015). Other MMS effects on birds include; the provision of foraging and roosting sites, exposure to contaminants, and physical hazards. Light attraction is believed to be the most important factor driving nocturnal circulation of birds around offshore platforms and contributing to the mortality of high numbers of birds annually in the North Sea (Ronconi et al., 2015). Mitigation strategies such as shielding and light reduction may be helpful. Weather conditions are also an important factor in the success or otherwise of migration flights. Anecdotally, poor visibility can increase the number of land birds visiting platforms but there is a lack of systematic data. One study on a wind energy platform found a correlation in call rates of migratory birds and fog, drizzle, and rain with 50% of strikes recorded occurred over a 2-day period of poor visibility (Ronconi et al., 2015, and references therein). Foraging activities by seabirds appear to be most notable in darkness when the lights and flares attract prey to surface waters (Ronconi et al., 2015). Visiting corvids and raptors may be able to reside for some time if they prey on smaller migrants. Thus MMS provide a source of prey while increasing the exposure of smaller birds to predation (Figure 1). Information regarding the bird species and numbers is usually collected by observer-based measurements. This is time-consuming, expensive and of variable quality due to the lack of standard protocols. Also, access to MMS for systematic monitoring may be restricted and vessel-based observations are biased towards summer months. There is scope for improving data collected using improved technology including radar, camera, acoustics, and telemetry. Many small seabirds and most passerines are too small to carry satellite or GPS tags so VHF tags can be used and platforms fitted with receiver stations to record the presence/absence of tagged birds. This method requires an intervention to fit the tag and analysis will be limited by sample size. However, instruments operate continually and automatically in most conditions and could complement observer-based recording. Data from different sensors could be linked, validated and economical if integrated with existing technology (radar on platforms) (Ronconi et al., 2015). A long-term continuous monitoring program is necessary if bird–MMS interactions and the full impacts of decommissioning are to be understood. Connectivity and invasive species Most species do not reside on platforms for their entire lives and may use the surrounding regions (seabed and water column), as well as parts of the platform at different life stages. Therefore, they are ecologically part of a wider regional community of interconnected populations (Schroeder and Love, 2004). Understanding the connections, both biological and physical, is important for determining the implications of the removal of structures. Connectivity information could be a useful addition to the environmental impact assessment of individual decommissioning plans. Only integrated regional-scale assessments can provide a complete insight of decommissioning impact. The biological traits of an organism such as mobility, planktonic larval stages, duration of larval viability (PLD), spawning timing, vertical migration behaviour, and life cycle length will influence the range of connectivity between MMS. Connectivity is also affected by abiotic factors such as water currents, wind speed and direction, density of MMS (available hard substrata), and anthropogenic vectors (vessel movements). The spatial isolation of an MMS may influence the ratio of resident to visitor fish. Structures which support large resident populations are more likely to offer value as a habitat than structures which support small or transient populations, and would therefore be more valuable in ecological terms for decommissioning options which involve leaving all or part of the structure in place (Fowler et al., 2015). However, assessments of each individual MMS ecosystem is important to take account of particular geographic importance that even small structures may have where populations of rare or endangered species are involved. A negative impact of connectivity is the potential spread of invasive non-native species (INNS). The dispersal of INNS is considered one of the greatest threats to ecological functioning of “native” ecosystems (Page et al., 2006; Cloern and Jassby, 2012). In addition, the consequences of INNS can be severe for fisheries and aquaculture. Although more commonly associated with marinas and inshore infrastructure, INNS have been found on renewable infrastructure in the Orkney Island Archipelago and in the southern North Sea (Coolen et al., 2016; Want et al., 2017). In deeper waters, the lack of baseline data on species distribution hampers evaluation of the occurrences and impact of INNS. It is positive that the International Ballast Waters Directive has finally been ratified and action on the control of ballast water is more prominent and entered into law in 2017 (www 2). The occurrence and potential spread of exotic invertebrates on offshore platforms in the pacific offshore continental shelf (POCS) was explored using biophysical models (Simons et al., 2016). INNS were present in inverse proportion to native species, demonstrating competition for space. A finding of note was the enhanced dispersal of planktonic larvae from offshore structures compared to near shore sites (travelling up to 10 km cf. 100 m) due to high and sustained offshore advection. The presence of INNS on MMS would reduce the ecological value of the MMS and introduce risk. Monitoring for INNS is advisable and methods include rapid assessment surveys or settlement panels with scrape sampling (Cook et al., 2015). A high risk of INNS occurring and dispersing could be an important factor in the decommissioning decision for MMS in that area. Environmental aspects of decommissioning options The EIA process The offshore industry undertakes environmental impact assessments (EIA) and produces an environmental statement as part of decommissioning to identify and assess likely impacts. However, in the United Kingdom, the BEIS “streamlined decommissioning programme template” specifically excludes consideration of all marine biota adhering to the structure. Instead, the EIA lists “environmental receptors” including; seabed, fish, fisheries, marine mammals, birds. So, for example, a marine worm living in the seabed is counted and the decommissioning impact upon it assessed, a marine worm attached to the structure is not. BEIS guidance states: “regulations do not apply to artificial habitats created by the infrastructure that is the subject of the decommissioning programme, and it will therefore be unnecessary to justify the removal of structures that have been colonized by protected or rare species”. For example, the EIA for Ninian North Platform with respect to the protected coral species, Lophelia pertusa, states Lophelia covered 5 to 100% (in places) within the depth range 53 m to the seabed in 2011…CNRI have undertaken consultation with JNCC regarding the presence of L. pertusa on the legs of the NNP…JNCC advised, that under the Habitats Directive, it is clear that the habitats listed for protection should be natural, and therefore the marine growth on the infrastructure does not need to be considered by itself under the Habitats Directive… It is found that mortality as a result of decommissioning operations would not be considered as an issue of significant concern for the ES’ (NNP, ES, CNR UK). (CNR International, 2017, decommissioning program p. 49) Equally one could argue that the presence of rare, protected marine life or indeed an active and functioning ecosystem could be a valid reason for advocating that the MMS remain in place, particularly if the MMS is a candidate for derogation. There is no clear scientific or ecological basis for exclusion of epifauna and other adhering marine biota in the EIA, rather this is policy driven and is based on the assertion that any artificial substratum is not protected by legislation (i.e. ‘Offshore Petroleum Activities—Conservation of Habitats 2001’ regulations). Thus, a pragmatic route is to ignore biota living on the infrastructure. One may compare this to the protection given to rare plants whether they grow upon a stony outcrop or upon a man-made monument (The Wildlife and Countryside Act 1981, schedule 8). While a natural or artificial substratum in the marine environment might be considered not ecologically relevant, it is politically sensitive. As it stands, the regulatory framework does not provide provision for the protection of the whole ecosystem of MMS. This is an interesting potential contradiction to the EU Marine Framework Directive which seek to promote of Good Environmental Status (GES) and the use of the “ecosystem approach”. The aftermath of decommissioning Currently, adhering biota termed, “marine growth” is categorized as a “waste stream” and is removed from decommissioned MMS at the onshore disposal facilities and land-filled (MT Cordah, 2013). As well as the loss of a marine ecosystem, this incurs additional transport and labour costs. Land-fill is the least preferable option of the waste management hierarchy and landfill of biodegradable waste such as this is increasingly restricted. However, there is a lack of capacity for composting/land spreading, resulting in operational challenges for disposal facilities (MT Cordah, 2013). Depending on the composition of the “marine growth”, there may be scope for exploring potential re-use for example, as a soil conditioner, fuel for anaerobic digestion, or fish feed. For example, the gelatinous carcasses of marine organisms are known to form part of the diet of the economically important species, Norway lobster, Nephrops norvegicus (Dunlop et al., 2017). A comparative assessment of waste management found onshore disposal of marine growth to be preferable, in expenditure and safety terms, than dumping at sea. In environmental and societal terms, on shore disposal was the least preferable option (MT Cordah, 2013). Note, this did not include a scenario for leaving the marine growth with the MMS (or part of it) in place. The environmental aspects of marine growth management scenarios are illustrated (Figure 3). Figure 3. View largeDownload slide Schematic diagram showing environmental aspects of marine growth management during decommissioning including “leave in place” scenario. Figure 3. View largeDownload slide Schematic diagram showing environmental aspects of marine growth management during decommissioning including “leave in place” scenario. Whole removal Whole removal is the option permitted and preferred through current OSPAR commission regulations and is also the default option in the Gulf of Mexico (GOM, Schroeder and Love, 2004). MMS removal may also cause damage to the environment through noise and disturbance of cuttings piles. There is a lack of quantitative assessments on the loss of biotic productivity during whole removal and the use of explosives can be contentious (Lakhal et al., 2009). In general, all local demersal fish and many pelagic fish will be killed by the shock waves of explosive deconstruction. Those more likely to survive are species without swim bladders (i.e. gobies, blennies) but they would face considerable mortality as they relocate (Schroeder and Love, 2004). Attached invertebrates would have complete mortality when the structure is removed to shore. Biological surveys of the MMS, with estimates of biomass, would inform decision makers on the balance of benefits and losses of decommissioning options. Also, there are notable emissions to air during decommissioning. For example, the removal of a large platform off California (Harmony, 365m depth) has been estimated to incur the release of 29 400 tonnes of CO2, 600 tonnes NOx, and 21 tonnes of fine particulates (PM10, Henrion et al., 2015). Consideration of emissions is relevant in a holistic (ecosystem) approach to decommissioning. Indeed, an OSPAR strategic policy document states “where necessary, revise existing measures and/or develop and adopt new measures, taking climate change impacts into account”. Although decommissioning generates general, special and clinical wastes, scrap metal, explosives and asbestos, 95% of wastes from decommissioning on the UKCS were re-used or recycled in 2015 (www 3. Oil & Gas Environmental Report, 2016). The material and flows of decommissioning case studies were analysed along with the financial costs, to highlight the economic, social and environmental concerns (Ekins et al., 2006). The conclusions of the authors remain pertinent in the present context. Namely that the advantages of whole removal are; a clear seabed and the conservation of material stocks (as recycling onshore avoids the extraction of virgin material i.e. steel). The disadvantages are; impacts on the marine environment including fish, health and safety concerns, the use of landfill for non-recyclables and expenditure (Ekins et al., 2006). The removal of the footings is deemed to be particularly negative in terms of adverse impacts on the marine environment, technical effort and expense. The authors caution that the findings are based on a limited number of case studies available at the time of publication (Ekins et al., 2006). Efforts are being made to develop more efficient and cost effective mechanisms of complete removal, by lifting larger sections of infrastructure to shore. Less attention has been paid to researching re-use as reefing programs (Lakhal et al., 2009), presumably as they are not legally permissible in the present regulatory framework. After whole removal is it expected that the marine biota will gradually shift towards a community typical of a soft sediment bottom and recovery rates (as defined by their likeness to ecosystems in similar substrates at a distance from the impact site) will depend on the ecological status at the point of decommissioning, disturbance rates (i.e. trawling), species migration rates (both larval and benthic stages) and the degree of contamination (Schroeder and Love, 2004). Related studies of ecosystem recovery from dredging sites indicate that it may take a decade or more for an impacted site to recover their normal functionality after the cessation of active dredging (Wan Hussin et al., 2012). Partial removal/leave in place In decision 98/3, the definition of a disused offshore installation does not include an installation serving another legitimate purpose. While an artificial reef could be classed as such it would be subject to the OSPAR convention 1992 and article 8 therein (Sayer and Baine, 2002) and ‘Guidelines on Artificial Reefs in Relation to Living Marine Resources’ (OSPAR commission) which only allow virgin materials for artificial reef construction. These guidelines serve as a potential obstacle with respect to leaving MMS in place (Bergmark and Jørgensen, 2014). There is general consensus that partial removal of the topsides to shore for recycling and disposal is the only “leave in place” option worthy of consideration (Ekins et al., 2006) unless a viable re-use for the topsides is proposed (i.e. emergency bad weather shelter, night club!). The expense of cathodic protection and maintenance can be prohibitive for re-use (Schroeder and Love, 2004). Partial removal in the North Sea would require 55 m depth clearance between the structure and the sea surface (LAT, BEIS). In California, the clearance requirement is only 26 m (Claisse et al., 2015). The remaining structure is then marked on navigation charts and/or with buoys. Partial removal of rigs has been used in the Gulf of Mexico (GOM) to leave sites for recreational fishing. When well-conductors are retained to the same depth as the jacket, additional complexity improves habitat quality. Partial removal offers a compromise as the lower part of the platform is retained for artificial reefing and whole removal costs are reduced, although this saving may be small since the plugging of the wells is a significant proportion of the decommissioning cost. Also the use of explosives is unnecessary while access to maritime vessels is generally accommodated. However, there are implications for some commercial fishing such as bottom trawling which carries the risk of snagging. Indeed, safe navigation is enshrined in international legislation through the UN convention on the law of the sea (UNCLOS) and guidelines of the International Maritime Organisation (IMO). If MMS are left in situ, there are obvious benefits for the marine ecosystems associated with them. Claisse et al. (2015) estimated that while total removal would result in the total loss of fish associated with the MMS, partial removal would retain 80% of the biomass. This is because most of the fish were associated with depths that would remain after partial decommissioning. A model of fish production at offshore oil platforms in Southern California was used to predict the outcome of the two decommissioning scenarios; namely whole removal or partial removal. Transect biometric data was used to estimate standing stock (total biomass), recruitment and production per species, per platform. While whole removal was predicted to result in the loss of most of the fish biomass, partial cutting would retain more that 90% of fish biomass at deep water platforms, due to the depth preference of the local species (predominantly rockfishes, Pondella et al., 2015). For most platforms in this study, there was no significant effect on fish recruitment with partial removal but a 100% loss of young-of-year recruitment predicted for whole MMS removal (Figure 1, note, there was significant variation in fish standing stock between MMS due to location and depth). Furthermore, the ecological benefits may extend to non-resident (transient) biota, for example, as a feeding location for porpoise (Todd et al., 2009). A cautionary note: the use of the MMS as artificial reefs for spawning or fish nursery grounds would require remaining levels of hydrocarbon contamination to be very low given the development abnormalities shown of some species (Sørhus et al., 2016). In partial removal, species associated with the intertidal portion of the MMS are lost and the input of detritus from this layer (i.e. mussel shells, faecal pellets) to the seabed would cease which may cause alterations to the benthic community (Schroeder and Love, 2004). However, it’s important to note that several studies have shown the benthic community (protists, meio-, macro-, and megafauna) associated with MMS have changed in terms of biodiversity and density and biomass relative to reference sites (Cordes et al., 2016 and references therein). Repositioning of MMS Re-positioning of MMS is defined as the removal and towing of a whole MMS to a new location or the toppling over of a MMS in its current location (Claisse et al., 2015). The advantages of re-positioning are that the MMS can be moved to a region deemed optimal for reef success, commercial re-use (lobster fishery) or away from contaminated drill cuttings piles, or to allow continued oil/gas production with a new structure at the original site (Schroeder and Love, 2004, Bergmark and Jørgensen, 2014). The negative aspects are the initial removal impacts noted above and the risk of introducing disturbance and invasive species to a new or pristine area. Long distance wet tows used in decommissioning could provide a marine pathway for INNS (Wanless et al., 2010). Toppling the platform is considered to be less favourable than partial cutting due to the change in habitat depth and orientation of cross beams relative to the seabed (Pondella et al., 2015). Drill cuttings In the context of the Central and Northern North Sea, relatively weak tidal currents allow the formation of drill cuttings piles (dcp, Breuer et al., 2008; Henry et al., 2017). On the UKCS, water-based fluid drill cuttings are usually permitted to be discharged to sea. However, there is a legacy of oil-based dcp and their constituents may include; barite and bentonite, heavy metals and hydrocarbons including polycyclic aromatic hydrocarbons (PAHs, Adewole et al., 2010). Synergistic effects of multiple contaminants may be possible and toxicity data are often incomplete (Lakhal et al., 2009). Microbially-mediated diagenetic reactions result in the removal of oxygen in the upper millimetres of the dcp. This creates an anoxic state within the pile that restricts the degradation of hydrocarbons. Metals that are released into the pore water then migrate into overlying water or they diffuse downward forming a potentially toxic sink (Breuer et al., 2008). The marine diversity most acutely impacted by dcp is the benthos (Cordes et al., 2016). This occurs initially through the physical smothering of the seabed (Jones et al., 2012). Tidal pumping and faunal ventilation may also draw oil beneath the sediment surface (Main et al., 2015). In sediments contaminated with hydrocarbons, an increase in benthic respiration have been measured, reflecting the up-regulation of compensatory mechanisms (Olsen et al., 2007). Effects can be locally severe, leading to depauperated sediment dominated by anaerobic bacterial assemblages. However, the wider effects of contamination have also been shown in the pelagic environment; cod (G.morhua) and haddock (M.aeglefinus) from the North Sea showed evidence of the uptake of PAHs and adverse biological consequences including oxidative stress and genotoxicity. The responses were highest in the vicinity of intensive production but were also noted in an area of decommissioned infrastructure indicating background contamination (Balk et al., 2011). There are established on-shore methods for cleaning dcp however this is limited to situations when treatment rates and potential reuse of recovered oil are economically viable. Treatments include thermal separation of oil and re-use of cuttings as road and construction materials (Lakhal et al., 2009). Opinion among scientists is divided on the best management for dcp with some advocating removal to shore for cleaning and re-use during decommissioning or reefing and others of the view that they should be left undisturbed (Henry et al., 2017). Environmental monitoring of the dredging, and hence disturbance, of cuttings piles in Norway reported a decline in water and sediment quality at the dredging site and dispersal of fine particles up to 1 km but recovery to a state prior to dredging was expected within a few years (OSPAR Commission, 2016). This concludes that leaching from dcp and disturbance from over-trawling are unlikely to have significant impact on marine biota and that cuttings should be left in place. This is partly due to low levels of contaminant in the upper 10 cm of the piles and the already poor condition of the seabed in the vicinity of the cutting piles. A major concern for the scenario of leaving MMS is that they may be a source of contamination to the surrounding environment perhaps from leaching of hydrocarbons, heavy metals, drill cuttings piles or degradation of the structure. To assess this risk, Love et al. (2013), measured concentrations of 21 trace elements in 3 fish species caught at oil platforms and at natural reefs in California. Statistical comparison found the concentrations of trace elements were not significantly greater at platforms than at natural reefs. A recent study using benthic survey data suggested that any decommissioning activity that causes disturbance to a drill cuttings pile should be monitored for at least 8 years (Henry et al., 2017). Given the variability in MMS, operational legacy and local conditions, the risk of residual contamination of potential artificial reefs must be assessed on an individual basis. Lifetime stewardship, ongoing monitoring, and obligations In the GOM, the state of Louisiana assumes long-term liability for rigs-to-reefs and operators donate the structure to a reefing program and also 50% of the cost savings for removal are paid to the state to be added to a rigs-to-reef trust fund. Although the state (via a fisheries agency) pays for navigational aid/buoys this comes from the reefing account and is not funded by the state or federal government (Schroeder and Love, 2004). On the UKCS, derogations to OSPAR 98/3 (OSPAR, 1998) represent oil and gas infrastructure decommissioned in situ and all the licensees relating to it are subject to the provisions of The Petroleum Act 1998. It is clear that great variability exists in the habitat value of MMS and individual assessments must be carried out to establish the specific ecosystem qualities and context. The current environmental status of the MMS and of the associated ecosystem will play a role in the potential ecological value of MMS. The location of the MMS and connectivity to other natural or artificial reefs is also important for the capacity to sustain mobile fauna without producing an overlap of depleted prey zones (prey “halos”, Campbell et al., 2011). In California, rigs-to-reef guidelines call for enhancement of MMS reef habitat, for example, by adding rocks to increase niche complexity (Schroeder and Love, 2004; Ajemian et al., 2015). Knowledge gaps and future research Future research should be driven by the need to complete basic biological insight that restrict our knowledge of MMS ecology. Limited access to MMS locations and a lack of baseline environmental data restrict our understanding of the impacts of decommissioning on the marine environment. Nevertheless, future decommissioning projects could provide important opportunities for research, as has been shown with the BP Miller platform (Fujii, 2015). A qualitative review of the scientific literature indicates little published information on; the impact of decommissioning on the ecology associated systems, the benthic habitat of MMS, foodwebs associated with MMS and systematic data for birds/MMS interactions. 156 decommissioning projects have been completed (as of 2015) in the OSPAR maritime area in the absence of this understanding and there is a risk that individual EIA do not consider the full impact on the ecology of MMS on the wider system, for example system connectivity and interaction with MPAs. Variability in marine biodiversity associated with MMS prevents a general prediction of the consequences of the different decommissioning scenarios and requires each decommissioning program to be considered individually. The oil and gas industry are improving autonomous underwater vehicle (AUV) technology (such as autonaut passive acoustic monitoring) and there is an opportunity for this technology to be better utilized for understanding MMS ecology. For example, using AUV with smart technology, such as real-time biochemical sensors and eDNA samplers and the potential installation of “smart buoys” that are relatively cheap to maintain and can return pre-processed data via satellite. Finally, oil and gas exploration and production is expanding into deeper waters and environmental assessments must be improved and better baseline data collected to assess potential impacts under more difficult conditions. Ecosystems here are particularly sensitive to disturbance and a precautionary approach is recommended in the management of deep water resources (Cordes et al., 2016) with limitations recommended on the type and timing of operations and appropriate spatial buffer zones such as an ‘ecologically or biologically significant area’ (ESBA, United Nations Convention on Biological Diversity). Gathering information on benthic biota and processes remains a challenge, the most promising emerging approach uses multi-beam echo sounder to provide acoustic data. However, acoustic data alone will not be sufficient unless supported by significant in situ sampling or imagery (photographic or video) to characterize the biological structures and assemblages of the MMS benthos compared to reference sites. Conclusions This review highlights our knowledge of the ecology of MMS and recognizes that this is far from complete. The loss of platforms due to decommissioning may be positive or negative in ecological terms and should be examined without preconception. The quality of the individual MMS as a reef habitat determines whether the removal of the structure would improve or degrade the marine environment. In the UKCS, The BEIS decommissioning EIA protocols excludes the inclusion of organisms or ecosystems on platforms, making a clear distinction between biological life on natural and artificial substrates. Comparisons can be made with other post-industrial man-made sites, such as shale-oil bings colonized by unique biological assemblages. This marine exclusion prevents a balanced debate on the biological costs and benefits of decommissioning and the exclusion has no ecological validity. Thus, there is an urgent need to quantify the ecosystem services that they provide. A more comprehensive EIA process together with the recognition of the ecology associated with man-made structures would allow science to play a role in the decision-making process as opposed to a blanket policy ignoring the ecological context. Thus a policy review may be warranted for management of ecosystems of MMS by OSPAR and its’ signatories. Acknowledgements The authors of this review acknowledge that the contents are derived from an independent report commissioned by Oil and Gas UK. DMP acknowledges the support of the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) in the completion of this study. Funding MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions”. The independent report was funded by Oil and Gas UK. References Adewole G. M. , Adewale T. M. , Ufuoma E. 2010 . 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Google Scholar Crossref Search ADS www 1 . https://oilandgasukenvironmentallegislation.co.uk/contents/topicfiles/offshore/ waste.htm www 2 . http://www.imo.org/en/About/Conventions/ListOfConventions/Pages/International-Convention-for-the-Control-and-Management-of-Ships%27-Ballast-Water-and-Sediments-(BWM).aspx www 3 . https://oilandgasuk.co.uk/wp-content/uploads/2016/11/Environment-Report-2016-Oil-Gas-UK.pdf © International Council for the Exploration of the Sea 2018. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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

doi: 10.1093/icesjms/fsy125pmid: N/A

Abstract The French government is planning the construction of offshore wind farms (OWF) in the next decade (around 2900 MW). Following the European Environmental Impact Assessment Directive 85/337/EEC, several studies have been undertaken to identify the environmental conditions and ecosystem functioning at selected sites prior to OWF construction. However, these studies are generally focused on the conservation of some species and there is no holistic approach for analysing the effects arising from OWF construction and operation. The objective of this article is to promote a sampling strategy to collect data on the different ecosystem compartments of the future Dieppe-Le Tréport (DLT) wind farm site, adopting an ecosystem approach, which could be applied to other OWFs for the implementation of a trophic network analysis. For that purpose, an Ecopath model is used here to derive indices from Ecological Network Analysis (ENA) to investigate the ecosystem structure and functioning. The results show that the ecosystem is most likely detritus-based, associated with a biomass dominated by bivalves, which could act as a dead end for a classic trophic food web since their consumption by top predators is low in comparison to their biomass. The systemic approach developed for DLT OWF site should be applied for other French and European installations of Offshore Wind Farm. Introduction The worldwide demand for renewable energy development is increasing rapidly, motivated by the challenge to reduce fossil fuel emissions in accordance with political imperatives to combat global climate change (Raoux et al., 2017). For instance, the European Union (EU) has set a target of 20% of energy consumption to be derived from renewable energy sources by 2020 (Directive 2009/28/EC). In accordance with these political ambitions, many Marine Renewable Energies (MRE) (i.e. wind farms, tidal energy farms) are being developed to facilitate the energy transition. At present, wind represents one of the most cost-effective offshore sources of exploitable renewable energy and is by far the most technically advanced of all MRE sources (Leung and Yang, 2012). The first OWFs (called Horn Rev 1 and 2) were built in 2002–2003 in Denmark, followed by the Netherlands in 2007, the United Kingdom and Belgium in 2008, and Germany in 2010 (Petersen and Malm, 2006; Leonhard et al., 2011; Wilding et al., 2017). For more than 15 years, Offshore Wind Farms (OWF) have been built in European waters (Raoux, 2017). This development of MRE raises many technical and social issues. Moreover, concerns have been expressed about the potential environmental impacts of these new structures on marine ecosystems, and their potential impacts need to be carefully assessed (Lindeboom et al., 2011; Bailey et al., 2014). The site exploration, construction, operation, and decommissioning of OWFs could indeed lead to temporary and/or permanent effects on marine ecosystems such as local damage of the seabed, or the disturbance of fish and marine mammal populations (OSPAR, 2008; Mueller-Blenkle et al., 2010; Shields and Payne, 2014). All OWFs that have been built in Europe are subject to environmental monitoring programmes (which is a regulatory requirement of several authorities) to investigate the impacts of these new structures on the surrounding marine ecosystems (Wilding et al., 2017). All these previous studies provide a large amount of data on environmental effects at the species level. However, one of the main issues linked to these environmental monitoring programmes is that they are focused on certain ecosystem components such as: marine mammals, birds, fish, and benthos. Additionally, a particular emphasis was placed on iconic or flagship species not only due to their endangered status (Boehlert and Gill, 2010) but also their highly popular image among the public (Borger et al., 2014). Thus, even if the monitoring of top predators is accepted, other biological compartments, particularly within the benthic community, have not yet been taken into account. However, several studies have stressed the need to include the benthos within ecosystem monitoring of MREs (Villnäs and Norkko, 2011; Wilding et al., 2017). Although the benthos is a core ecosystem component, Wilding et al. (2017) highlighted our poor understanding of its interaction with MRE technology. Moreover, environmental monitoring programmes have so far only attempted to consider the sensitivity to potential disruptions of a number of ecological compartments (plankton, benthos, suprabenthos, fish, marine mammals, and birds), but in a disparate manner without taking into account the trophic links between the compartments (Raoux, 2017; Raoux et al., 2017). Thus, the environmental impacts of OWF construction and operation remain unclear at the ecosystem scale, particularly as regards the trophic web structure and functioning (Bailey et al., 2014). As highlighted by Raoux et al. (2017), there is a need to adopt a holistic approach to the impact of OWFs on ecosystem functioning through the use of trophic web modelling tools. In fact, 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. For instance, the Ecopath with Ecosim trophic web model (Polovina, 1984; Christensen and Walters, 2004) considers all the biotic components of a system simultaneously, and is useful for gaining a better understanding of the system structure and functioning, as well as in predicting ecosystem changes in response to the construction and operation of MRE projects. Thus, the Ecopath with Ecosim modelling approach contributes to the estimation of anthropogenic effects on ecosystems (Raoux et al., 2017). However, a common bias in Ecopath with Ecosim applications arises from the use of non-representative dietary data (i.e. diet composition data for a different time period without taking into account differences in the relative prey species abundances between two time periods) (Plaganyi and Butterworth, 2004). Such models based on poorly representative data will compromise the results of Ecopath mass balance calculations (Plaganyi and Butterworth, 2004). Thus, there is a need to obtain robust estimates of consumption through stomacal content analysis taking into account the chosen time and space-scales of the model under construction (Plaganyi and Butterworth, 2004; Pezy, 2017; Raoux, 2017). Ecosystem management requires a clear understanding of marine ecosystem structure and functioning. Thus, the objective of the present study is to promote a sampling strategy to collect local data for future OWF environmental monitoring programmes, and to build an Ecopath model based on robust estimates of energy transfer. This holistic view of the impact of OWFs on the ecosystem through trophic web modelling could be replicated on the other site in the English Channel (EC) and could be useful to analyse the long-term reef and reserve effects in the context of climate change. Indeed, using quantitative modelling to assess the impacts of OWF on the whole ecosystem would allow new knowledge to be bought to the attention of policy makers. It would also facilitate a better integration of ecological considerations in managing decisions, and for planning maritime space. Material and methods In France, no OWFs have yet been constructed. However, three successive calls for tenders related to OWF development have been issued and seven sites have been selected for future OWF construction. Among these OWF projects, three are planned in the Eastern part of the EC: Courseulles-sur-Mer (50 km2, 75 wind turbines), Fécamp (65 km2, 83 wind turbines), and Dieppe-Le Tréport (DLT) (67 km2, 62 wind turbines) (Raoux et al., 2017). Beyond the fact that the EC is the current hotspot for OWF development in France (Raoux et al., 2017, 2018; Figure 1), this maritime space is also subject to a large panel of anthropogenic disturbances including pollution, transport, fishing, aquaculture, aggregate extraction, or sediment dredging and deposition (Halpern et al., 2008; Dauvin, 2012). Figure 1. View largeDownload slide Human activities (without fishing) along the EC. For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. Figure 1. View largeDownload slide Human activities (without fishing) along the EC. For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. Dieppe-Le Tréport (DLT) offshore wind farm project The prime contractor of the project is “Eoliennes en mer Dieppe-Le Tréport”, a subsidiary of Engie (formerly named GDF Suez). The proposed wind farm will be located at distances of 15.5 and 17 km offshore from the coast of Le Tréport and Dieppe, respectively. The water depth ranges from 12 to 25 m. The wind farm will cover a total area of approximately 92 km2, and will comprise 62 turbines with a capacity of 8 MW each giving a combined nameplate capacity of 496 MW. 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. The foundations are composed of jacket structures. Trophic modelling approach The Ecopath with Ecosim (EwE) software (Polovina, 1984; Christensen and Walters, 2004; Christensen et al., 2008) is used here to model the food web flows at the DLT OWF site. This approach uses functional groups or species biomasses which are connected together through a predator–prey diet matrix (DC). This approach allows us to take into account a large number of species in ecological functional groups. Ecopath is designed to build a snapshot of the ecosystem functioning while Ecosim provides a simulation of its dynamic evolution through time. Ecopath is a mass balance (i.e. ignoring year-on-year changes in biomass compared with flows), single-solution model (i.e. yielding only one value per flow) for estimating fluxes between a set of established trophic compartments. Each compartment corresponds to a single species or a group of species similar in terms of predators, prey, and metabolic rates (i.e. trophic group). It 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 DC which represents the interactions between predators and prey in the ecosystem. The parameterization of an Ecopath model is based on satisfying two equations. The first equation [Equation (1)] describes the production of each compartment in the system as a function of the consumption ratio (Q/B) of its predators (j), the fishing mortality (Yi, gC m−2), the net migration (Ei; emigration – immigration, year−1), the biomass accumulation (BAi, year−1), and its natural mortality (1 − EEi). EE corresponds to the Ecotrophic Efficiency or the proportion of biomass consumed in the system for each compartment in the system. BPBi=∑jBjQBjDCij+Yi+Ei+BAi+BiPBi1-EEi (1) The second equation [Equation (2)] describes the energy balance within a compartment. Qi=Pi+Ri+Ui (2) The energy balance of each group in Equation (2) is maintained by assuming that consumption of the ith group Qi) is equal to the sum of its production (Pi), respiration (Ri, gC m−2), and excretion of unassimilated food (Ui). Towards an ecosystem approach concerning offshore wind farms Sampling and analytical procedures From 2014 to 2016, sampling was carried out during four surveys. The benthic invertebrates, suprabenthos (or hyperbenthos), demersal fishes, meiofauna, and zooplankton were sampled at two seasons: in March during the winter period and in September during the summer. The different compartments were sampled during the same week, to limit temporal variability. This sampling strategy was aimed at assessing the initial state of the ecosystem before the installation of the OWF and the status of the different biological compartments taking into account seasonal changes (Figure 2). Figure 2. View largeDownload slide Sampling strategy on the future DLT OWF located in the eastern basin of the EC. For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. Figure 2. View largeDownload slide Sampling strategy on the future DLT OWF located in the eastern basin of the EC. For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. Benthic macrofauna The sampling plan was carried out on 25 stations, 20 located inside and 5 located outside the future OWF, to characterize the benthic communities in its proximity. Five replicates were collected at each station for benthic fauna analysis. Benthic invertebrates were sampled with a 0.1 m2 Van Veen grab. Sieving was performed on board using a circular 1 mm mesh. The retained material was preserved in 10% formalin until the final treatments in laboratory. Rose Bengal solution was used to facilitate the sorting of organisms from the sediments. The samples were then sorted, and the organisms were identified at the species taxonomic level when feasible. The biomass of each species was determined and expressed in terms of g of Ash-Free Dry Weight (AFDW) per m2 (loss of weight of dry organisms after 5 h at 500°C). Demersal fish species and cephalopods Abundance and biomass data for the demersal fish were collected by sampling with a 3-m beam trawl (utilized by IFREMER). A total of 10 hauls were operated (5 inside and 5 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). At each sampling station, large-size invertebrates (mega invertebrates), cephalopods, and fish were identified, measured, weighed, and fixed with 10% formaldehyde solution for future analysis. The analysis of fish stomach contents is the only way to provide accurate information about the composition of prey species. This analysis allows prey determination at the species level, using hard parts that resist digestion such as crustacean exoskeletons, fish otoliths, and bones (Jackson et al., 2007). Thus, fish stomach content analyses were made at the DLT OWF site to quantify the contribution of benthic prey to the diet of demersal fish species and thus identify the benthic species playing a key role in the trophic web. The biomass of demersal fish individuals was determined based on g of AFDW (loss of weight of dry organisms after 5 h at 500°C). The biomass is given for the area covered by the trawl, which corresponds to the haul length multiplied by the trawl width (3 m). Meiofauna The sampling plan was carried out on 25 stations corresponding to the same stations used for the macrofauna. The meiofauna was sampled with a 0.1 m2 Van Veen grab and a 3.6-cm diameter sediment core via a hatch on the top of the grab. Sieving was performed using a 1-mm mesh and then a 38-µm mesh. The samples were preserved in 4% formalin prepared with boiling water and then stored until final treatments in the laboratory. The biomass of each sample corresponds to all the meiobenthic organisms comprised between 1 mm and 38 µm. Owing to the absence of mud, these organisms could be separated from the sediment by elutriation. The biomass was determined based on g of AFDW per m2 (loss of weight of dry organisms after 5 h at 500°C). Suprabenthos The suprabenthos is defined as organisms living in the water layer immediately above the seabed, which perform daily vertical migrations and/or seasonal movements at varying distances from the bottom (Brunel et al., 1978). In this study, we consider the suprabenthos sensus stricto: peracarids (amphipods, cumaceans, mysids, tanaids, isopods, pycnogonids, leptostracea) and decapods (Dauvin and Vallet, 2006). Here, the suprabenthos is divided into two groups: (i) holosuprabenthos, which corresponds to peracarids, present in the water column adjacent to the seabed at both seasons (summer and winter); and (ii) the merosuprabenthos, which corresponds to individuals present in the column water adjacent to the seabed during a given season (such as decapods larvae, etc.). Sampling was carried out inside and outside the future wind farm area according to two different sedimentary types. For each sedimentary type, samples were taken during the night and day to study nycthemeral migration. The suprabenthos was sampled using a Macer-GIROQ sledge (Dauvin and Lorgeré, 1989), which consists of four 0.18 m2 boxes (0.6 × 0.3 m), designed to filter the water column in four layers above the sea bottom: 0.10–0.40 m (box 1), 0.45–0.75 m (box 2), 0.80–1.10 m (box 3), and 1.15–1.45 m (box 4) (Dauvin et al., 2000). Each box was equipped with a WP2 zooplankton net (500 µm), including a Tsurimi-Seiki-Kosakusho (TSK) flow meter at its centre to measure the volume of water filtered. The sampling period (i.e. the period during which the sledge was in contact with the seabed) was 15 min at a sledge speed of approximately 1.5 knots. All sampled organisms were washed, fixed with 10% neutralized formaldehyde, and then transferred to a 70% ethanol solution. All specimens were sorted, identified and counted under a dissecting microscope to the species level. The species richness corresponds to the total number of taxa found in the four nets, while the abundance and biomass values (AFDW) are standardized to a mean volume of 100 m3 per haul or surface area (m2) corresponding to sledge length multiplied by the sledge width (0.5 m) and the total area of the four nets. Holozooplankton The sampling plan was carried out inside and outside the future wind farm at the same stations used for the suprabenthos. The holozooplankton was sampled using a WP2 net (200 µm) with a flow meter at its centre to measure the volume of water filtered. The holozooplankton corresponds to pelagic organisms present during both seasons (winter and summer) in the water column. At each station, four diagonal lines were operated for sampling at a speed of approximately 1 m s−1, two during the day and two during the night. All sampled organisms were washed and sieved on a 100-µm mesh. Then, organisms were identified and sorted into two permanent groups: chaetognaths and copepods. Biomass of each group and sample were determined based on g of AFDW per m2. Defining the model compartments The functional groups used in this study were defined according to the biological and ecological characteristics of the species, such as their food preference, size, and commercial importance, while also considering data availability. On this basis, we selected 28 groups, 5 of which comprise fish, 15 invertebrates, 1 holozooplankton, 1 merosuprabenthos, 1 primary producer, 1 bacteria, and 1 detritus group. Out of 27 alive functional groups (including consumed and non-consumed categories), 21 were obtained from sampling operations. Demersal and pelagic fish compartments Fish were grouped into five functional groups: (i) whiting (Merlangius merlangus), which is considered as a piscivore, (ii) the Ammodytidae (e.g. greater sand eel, Hyperoplus lanceolatus, (iii) fish benthos feeders, (iv) demersal flatfish, and (v) pelagic planktivorous fish. AFDW biomass was converted to carbon contents using a conversion factor of 0.4 (Elliott and Hemingway, 2002). The consumption to biomass ratio (Q/B) and the production to biomass ratio (P/B) ratios were taken from Mackinson and Daskalov (2007). The DC was constructed using stomach content analyses. Invertebrate compartments Cephalopods Cuttlefish were grouped into a single functional group: cephalopods. Abundance data (in t km−2) for cephalopods were derived from another study focused on the EC (Carpentier et al., 2009). Conversion factors of 0.192 and 0.402 were used to convert wet weights into dry weights and then into carbon content, respectively (Brey, 2001). Benthic invertebrates Species were grouped into 15 functional groups, with a special subdivision for 6 of these groups (consumed and non-consumed): 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), and meiofauna. AFDW biomass was converted to carbon content using a conversion factor of 0.518 (Salonen et al., 1976 in Brey, 2001). P/B, Q/B, and dietary data were derived from another study focused also on the EC (Garcia, 2010). Suprabenthos Species were grouped into two groups: holosuprabenthos and merosuprabenthos. AFDW biomass was converted to carbon contents using a conversion factor of 0.518 (Salonen et al., 1976 in Brey, 2001). P/B and Q/B and the diet were obtained from another study focused also on the EC (Garcia, 2010). Zooplankton Only the holozooplankton were taken into account here, so the merozooplankton corresponds to the merosuprabenthos. AFDW biomass was converted to carbon contents using a conversion factor of 0.4 (Feller and Warwick, 1988). P/B ratios were obtained from another study also focused on the EC (Garcia, 2010). Primary producers, bacteria, and detritus Data on primary production, bacteria, and detritus were obtained from another study focused also on the EC (Garcia, 2010). Trophic structure and Ecological Network Analysis The ENA results presented here were used to characterize the ecosystem state and functioning. The Total System Throughput (T.) measures the size of the ecosystem (Latham, 2006), while Finn’s cycling index (FCI) corresponds to the ratio between flows generated by cycling divided by the total system throughput (Finn, 1976). The System Omnivory Index (SOI) measures how the interactions are distributed among trophic levels (Libralato, 2008). The Ascendency (A) integrates system activity (Total System Throughput) with its degree of organization (Average Mutual Information, AMI) (Ulanowicz and Abarca-Arenas, 1997; Ortiz and Wolff, 2002). These above indices are calculated using the network analysis plug-in included in EwE (Christensen and Walters, 2004). Results The calculated Pedigree index for the model is 0.73. The initial model is not balanced, since there are some ecotrophic efficiencies greater than 1. For instance, biomass and production estimates for the Ammodytidae group are insufficient to support consumption by the whiting. Thus, the biomass of the Ammodytidae is estimated by the model after setting a value of 0.97 for the Ecotrophic Efficiency (biomass proportion consumed in the system for each compartment in the system). The estimated biomass is higher than the input data first entered during model construction. This can be partly explained by the fact that Ammodytidae biomass data were acquired during the day and not during the night. Biomass and trophic level The functional groups dominating the biomass are the benthic invertebrates, filter feeders NC (mostly composed of Glycymeris glycymeris and the clam Polititapes rhomboides), which represent 70% of the total living biomass of the ecosystem (Table 1). The other major groups of the system are phytoplankton and benthic invertebrates, non-selective deposit feeders NC (mostly composed of the sea urchin Echinocardium cordatum and the polychaete Polygordius lacteus). Table 1. Biomass values and trophic levels (TL) for different compartments at the DLT OWF site. Compartments Biomass, gC m−2 TL Cetaceans 0.0016 4.45 Seals 0.0004 4.33 Cephalopods 0.0161 4.07 Whiting, piscivorous 0.0313 4.16 Fish, planktivorous 0.5570 3.16 Greater sand eel Hyperoplus lanceolatus 0.0511 3.16 Fish, benthos feeders 0.0091 3.79 Fish, flat fish 0.0212 3.34 Benthic inv., predator consumed 0.0789 3.16 Benthic inv., scavenger consumed 0.2369 3.41 Benthic inv., filter feeders consumed 0.2794 2.25 Benthic inv., sDF consumed 0.4249 2.19 Benthic inv., ssDF consumed 0.0761 2.19 Benthic inv., grazer consumed 0.0011 2.00 Amphioxus 0.8340 2.18 Benthic inv., predator not consumed 0.9337 3.10 Benthic inv., scavenger not consumed 0.2899 3.37 Benthic inv., filter feeders not consumed 31.9298 2.23 Benthic inv., sDF not consumed 1.8946 2.19 Benthic inv., ssDF not consumed 2.3412 2.19 Benthic inv., grazer not consumed 0.0129 2.00 Meiofauna 1.3720 2.22 Merosuprabenthos 0.4120 2.51 Holosuprabenthos 0.1131 2.51 Holozooplankton 1.1954 2.15 Bacteria 0.8244 2.00 Phytoplankton 3.1000 Compartments Biomass, gC m−2 TL Cetaceans 0.0016 4.45 Seals 0.0004 4.33 Cephalopods 0.0161 4.07 Whiting, piscivorous 0.0313 4.16 Fish, planktivorous 0.5570 3.16 Greater sand eel Hyperoplus lanceolatus 0.0511 3.16 Fish, benthos feeders 0.0091 3.79 Fish, flat fish 0.0212 3.34 Benthic inv., predator consumed 0.0789 3.16 Benthic inv., scavenger consumed 0.2369 3.41 Benthic inv., filter feeders consumed 0.2794 2.25 Benthic inv., sDF consumed 0.4249 2.19 Benthic inv., ssDF consumed 0.0761 2.19 Benthic inv., grazer consumed 0.0011 2.00 Amphioxus 0.8340 2.18 Benthic inv., predator not consumed 0.9337 3.10 Benthic inv., scavenger not consumed 0.2899 3.37 Benthic inv., filter feeders not consumed 31.9298 2.23 Benthic inv., sDF not consumed 1.8946 2.19 Benthic inv., ssDF not consumed 2.3412 2.19 Benthic inv., grazer not consumed 0.0129 2.00 Meiofauna 1.3720 2.22 Merosuprabenthos 0.4120 2.51 Holosuprabenthos 0.1131 2.51 Holozooplankton 1.1954 2.15 Bacteria 0.8244 2.00 Phytoplankton 3.1000 Benthic inv., benthic invertebrate; sDF, surface deposit feeders; ssDF, sub-surface deposit feeders. Table 1. Biomass values and trophic levels (TL) for different compartments at the DLT OWF site. Compartments Biomass, gC m−2 TL Cetaceans 0.0016 4.45 Seals 0.0004 4.33 Cephalopods 0.0161 4.07 Whiting, piscivorous 0.0313 4.16 Fish, planktivorous 0.5570 3.16 Greater sand eel Hyperoplus lanceolatus 0.0511 3.16 Fish, benthos feeders 0.0091 3.79 Fish, flat fish 0.0212 3.34 Benthic inv., predator consumed 0.0789 3.16 Benthic inv., scavenger consumed 0.2369 3.41 Benthic inv., filter feeders consumed 0.2794 2.25 Benthic inv., sDF consumed 0.4249 2.19 Benthic inv., ssDF consumed 0.0761 2.19 Benthic inv., grazer consumed 0.0011 2.00 Amphioxus 0.8340 2.18 Benthic inv., predator not consumed 0.9337 3.10 Benthic inv., scavenger not consumed 0.2899 3.37 Benthic inv., filter feeders not consumed 31.9298 2.23 Benthic inv., sDF not consumed 1.8946 2.19 Benthic inv., ssDF not consumed 2.3412 2.19 Benthic inv., grazer not consumed 0.0129 2.00 Meiofauna 1.3720 2.22 Merosuprabenthos 0.4120 2.51 Holosuprabenthos 0.1131 2.51 Holozooplankton 1.1954 2.15 Bacteria 0.8244 2.00 Phytoplankton 3.1000 Compartments Biomass, gC m−2 TL Cetaceans 0.0016 4.45 Seals 0.0004 4.33 Cephalopods 0.0161 4.07 Whiting, piscivorous 0.0313 4.16 Fish, planktivorous 0.5570 3.16 Greater sand eel Hyperoplus lanceolatus 0.0511 3.16 Fish, benthos feeders 0.0091 3.79 Fish, flat fish 0.0212 3.34 Benthic inv., predator consumed 0.0789 3.16 Benthic inv., scavenger consumed 0.2369 3.41 Benthic inv., filter feeders consumed 0.2794 2.25 Benthic inv., sDF consumed 0.4249 2.19 Benthic inv., ssDF consumed 0.0761 2.19 Benthic inv., grazer consumed 0.0011 2.00 Amphioxus 0.8340 2.18 Benthic inv., predator not consumed 0.9337 3.10 Benthic inv., scavenger not consumed 0.2899 3.37 Benthic inv., filter feeders not consumed 31.9298 2.23 Benthic inv., sDF not consumed 1.8946 2.19 Benthic inv., ssDF not consumed 2.3412 2.19 Benthic inv., grazer not consumed 0.0129 2.00 Meiofauna 1.3720 2.22 Merosuprabenthos 0.4120 2.51 Holosuprabenthos 0.1131 2.51 Holozooplankton 1.1954 2.15 Bacteria 0.8244 2.00 Phytoplankton 3.1000 Benthic inv., benthic invertebrate; sDF, surface deposit feeders; ssDF, sub-surface deposit feeders. The Trophic Level of functional groups ranged from TL = 1 for primary producers and detritus to a maximum of 4.45 represented by cetaceans (Figure 3; Table 1). The other marine mammals (seals) were ranked just below as top predators in trophic webs with a TL of 4.33. Cephalopods and whiting rank just below with a trophic level of 4.1 (Figure 3; Table 1). Figure 3. View largeDownload slide Functional groups of the DLT OWF ecosystem, with trophic level indicated on the y-axis and benthic/pelagic partitioning on the x-axis. White rectangles represent the biomass compartments from the literature and the coloured rectangles represent the biomass compartments from this study. Blue rectangles represent the pelagic invertebrate compartments, orange rectangles represent the benthic invertebrate compartments and the grey rectangles represent the cephalopod and fish compartments. (NC, non-consumed; C, consumed). Figure 3. View largeDownload slide Functional groups of the DLT OWF ecosystem, with trophic level indicated on the y-axis and benthic/pelagic partitioning on the x-axis. White rectangles represent the biomass compartments from the literature and the coloured rectangles represent the biomass compartments from this study. Blue rectangles represent the pelagic invertebrate compartments, orange rectangles represent the benthic invertebrate compartments and the grey rectangles represent the cephalopod and fish compartments. (NC, non-consumed; C, consumed). ENA indices The system is estimated to process 951.3 gC m−2 year−1 (T.), with 10.8% of the total throughput being recycled (FCI) (Table 2). In addition, the EE (proportion of biomass consumed for each compartment within the system) of detritus is estimated to be 0.6, indicating that more or less all the energy entering this compartment is re-used in the system. The SOI which is a proxy of the food web complexity, yields a value close to 0.20. Finally, the DLT site ecosystem has an Asendency (A) of 1005.8 gC m−2 an−1. Table 2. ENA indices for the DLT OWF model. ENA DLT OWF site T. 951.3 A 1005.8 FCI 10.3 SOI 0.2 ENA DLT OWF site T. 951.3 A 1005.8 FCI 10.3 SOI 0.2 The Total System Throughput (T., gC m − 2 year−1) is calculated as the sum of all flows in the food web. FCI gives the percentage of all flows generated by cycling. The Ascendency (A) is a measure of the system activity (Total System Throughput) linked to its degree of organization (AMI) and is expressed in gC m−2 year−1. The SOI is a proxy of the trophic web complexity. Table 2. ENA indices for the DLT OWF model. ENA DLT OWF site T. 951.3 A 1005.8 FCI 10.3 SOI 0.2 ENA DLT OWF site T. 951.3 A 1005.8 FCI 10.3 SOI 0.2 The Total System Throughput (T., gC m − 2 year−1) is calculated as the sum of all flows in the food web. FCI gives the percentage of all flows generated by cycling. The Ascendency (A) is a measure of the system activity (Total System Throughput) linked to its degree of organization (AMI) and is expressed in gC m−2 year−1. The SOI is a proxy of the trophic web complexity. Discussion To assess changes in ecosystem structure and functioning in both space and time, this study was undertaken to improve our understanding on the biological compartments prior to construction at the DLT OWF site in order to set up a sampling framework based on Before After Control Impact (BACI) (Underwood, 1991, 1994; Magurran et al., 2010). Importance of site-associated data From a methodological point of view, the model is based on high-quality source data as shown by the high value of the pedigree index compared with the distribution of indices obtained from previous models (Morissette, 2007). In fact, the pedigree index (0.7) is situated at the maximum of the range (0.16 to 0.7) reported in Morissette (2007). This result can be explained by the fact that the biomass data for 21 groups out of 27 were obtained from local, highly replicated and detailed sampling. In addition, the diet compositions of the model fish species are derived from stomach content studies on fish caught at the DLT site. In most cases, Ecopath models are built with biomass data not collected from the study site (same sediment type, depth, and season), using literature data that can induce a bias in the model. In addition, the DC used to build Ecopath models is not always based on the stomach contents of the species of the study site in question, which can also induce a bias in the model and so compromise the Ecopath mass balance results (Plaganyi and Butterworth, 2004). In fact, the diet of marine organisms can vary significantly between individuals of a given species in different areas (Kopp et al., 2015). Such models based on poor data cannot be used for management purposes. Thus, the main strength of the present study is that it is based on the development of an EMR ecosystem approach: (1) using local biomass data from the following compartments: zooplankton, meiofauna, benthos, and demersal fish and 2) taking into account the link between demersal fish and the benthos through stomach content studies. Moreover, stomach content studies allow us to identify the benthos species that are either consumed or not consumed. The results show that the group with the highest biomass is the “benthic invertebrates, Non-Consumed filter feeders”, suggesting that this group could act as a trophic dead end (cul-de-sac) for the fish but participate in the recycling of energy flow as suspension feeders. In fact, there is a dual problem of accessibility and bivalve size in comparison with the sizes of fish living on the study site. Nevertheless, certain bivalves can be consumed by predators such as Asterias rubens. In addition, the Non-Consumed filter feeders play a role in the trophic web through their consumption of phytoplankton. ENA explanation The values of FCI and the EE (the percentage of production consumed by a predator) for detritus suggests a detritus-based trophic web with detritus acting as a source of food for the bivalves. Concerning the SOI, which is an indicator of the food web complexity (Libralato, 2008), the result obtained can be considered as an intermediate value when compared with the distribution of indices for pre-existing models of Northern Europe (0.14–0.36) (Mackinson and Daskalov, 2007). The need to adopt an ecosystem approach for MRE projects Environmental impact assessments for the future OWF in France, which consider the sensitivity of each ecological compartments to potential pressures, are still under development. In addition, these studies are conducted compartment by compartment, which does not allow taking into account the ecosystems complexity and dynamics (Raoux et al., 2017, 2018). Thus, OWF construction effects on the ecosystem structure and function remain unclear (Raoux et al., 2017). In addition, OWFs will integrate into ecosystems already subject to a growing number of natural and anthropogenic disturbances such as granulate extraction, and dumping of spoil sediments (Dauvin, 2012). These can cause changes in the ecosystems functioning and resilience, making them susceptible to changes from one state to another if the cumulative pressures become too frequent and abundant (Knowlton, 1992). Understanding the behaviour of these complex systems is essential in order to anticipate potential changes of states (Hughes et al., 2005) and facilitate the implementation of conservation actions with sustainable development scopes. According to Rosenberg and McLeod (2005), only an ecosystem approach would enable efficient management of the ecosystem. In this context, and as a complementary approach to the traditional impact assessments, the objective of our study was to develop an integrated ecosystem approach using trophic web modelling tools that consider the ecosystem as a whole. Indeed, holistic approaches such as trophic web models are needed as they allow considering, at the same time, the full range of biota size classes, from prokaryote to large top predators. The quantification of the energetic flows between all living organisms in the ecosystem can allow the calculation of numerical indices necessary for the characterization of a system’s functional properties (ENA indices). In fact, as it was illustrated by our case study, ENA indices enable the characterization, among others, of the recycling, the Omnivory, and the Ascendency (Latham, 2006). In addition, some of these indices have been related to ecological theories about stability, maturity, and stress (Saint-Béat et al. 2015) and have been proposed as ecosystem health indicators for describing the food web functioning in different contexts, including the implementation of the Marine Strategy Framework Directive in Europe (Niquil et al., 2014). Our modelling approach can be applied in other OWF implementations and others human activities in European waters. It underlined also the need to have detailed biomass data on a maximum of biological compartment as well as on fish stomach content useful to the modelling of food webs. To do that, it is necessary to persuade the wind farm developers to ensure a long-term monitoring of the new infrastructures impact on the coastal ecosystem taking into account a maximum of biological compartments and estimation of their biomasses, from microbial to top predators and to promote a homogenous ecosystem approach for MRE developments. The long-term exploitation of wind energy will require long-term monitoring for the different OWFs present along the EC coast. With five OWFs (one in the western basin and four in the eastern basin of the EC), this new activity could provide an observatory at the regional scale that can detect the potential global changes or introduction and/or geographic dispersal of marine species. The development of OWFs corresponds to a new human activity along the French coast, for a period of operation of 30 years. Thus, it provides an occasion to promote a holistic approach to MREs. The foundation of the wind turbine, as well as the presence of scour protection and unburied cables, will favour the colonization of hard substrates by many species (Wilhelmsson et al., 2006). This reef effect will create a habitat heterogeneity with the creation of a hard substrate on soft substrates, which may lead to species competition for space and resources (Wilhelmsson et al., 2006, 2010; Wilhelmsson and Malm, 2008). OWF development may locally threaten sessile species with a small geographic range, low turnover or recolonization capacity (OSPAR, 2008), as well as engineering species (Di Carlo and Kenworthy, 2008). Thus, the baseline of the DLT OWF site will allow us to monitor the evolution of the ecosystem (functioning, structure, and resilience) after construction of the OWF. In fact, it is necessary to maintain this ecosystem approach during operational phases to improve our understanding of the behaviour of a given ecosystem. This would allow us to anticipate potential changes of ecosystem states, and implement conservation actions in a sustainable manner. Conclusion In the context of OWF development in France, an Ecopath model of the future DLT OWF site was built to characterize the structure and functionning of this ecosystem. The main ecosystem characteristics reflected by ENA indices show that the trophic web is most likely detritus based and that the ecosytem biomass is dominated by “Non-Consumed benthic invertebrates”, which could act as a trophic dead end or cul-de-sac for fish due to the size of these filter feeders against size of sampling fish in the DLT area. Our study highlights the importance of adopting an ecosystem approach for MRE based on local data taking into account the link between demersal fish and the benthos through fish stomach content studies. Moreover, an ecosystem approach needs to be maintained throughout the operational phases of the OWF. Such an approach should be adopted for all future wind farms along the French coast. Acknowledgments This study forms part of the doctoral research work of J.P. 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Lise, Klunder,;S, Lavaleye, Marc S;Amalia, Filippidi,;van Bleijswijk, Judith D L, ;Gert-Jan, Reichart,;van der Veer, Henk W, ;A, Duineveld, Gerard C;Furu, Mienis,

doi: 10.1093/icesjms/fsy114pmid: N/A

Abstract Man-made structures in the North Sea are known to act as artificial reefs by providing a habitat for sessile epifauna in a predominantly soft sediment environment. This epifauna is hypothesized to cast a so-called “shadow” over the soft sediment ecosystem by altering the nutrient composition in the overlying water column. In addition, the structure itself could alter currents and thereby influence the deposition and erosion of the sediments in the wake of the platform. This study aims to assess the long-term effects of a gas platform in the southern North Sea on the surrounding benthic community by both morphological and molecular identification of benthic species. The species composition and a set of abiotic factors of the sediment around a gas platform were assessed along four transects. Differences for the abiotic factors were found in the closer vicinity of the platform in the direction corresponding to the predominant currents. The number of benthic fauna families found in the molecular approach were on average three times higher than for the morphological approach. Both approaches showed that small differences occurred primarily due to changes in sedimentary organic matter content. Differences in species composition were more pronounced between transects rather than between distances from the platform. Introduction Over the last decades man-made structures such as oil and gas platforms have become a widespread phenomenon in the North Sea. Through time, these structures often have become artificial reefs as they provide a solid substrate for sessile epifauna in areas that are mainly characterized by soft sediment habitat (Whomersley and Picken, 2003; Krone et al., 2013; Stap et al., 2016). So far it is unknown whether artificial structures are beneficial for ecosystem functioning and biodiversity in a wider area surrounding these structures. This question is becoming increasingly important as many platforms in the southern North Sea are about to be decommissioned in the coming decades. The contracted fate of offshore platforms at the end of their productive life is complete removal. However, arguments have been raised to leave parts or whole platforms in place as artificial reefs (Jørgensen, 2012) similarly as in the Gulf of Mexico and other parts of the United States (Fowler et al., 2014; Claisse et al., 2015). The biomass of epifauna on submerged artificial structures may reach up to 500-fold the biomass found in the soft sediments (Picken et al., 2000). Since the epifauna on new artificial substrates mainly consists of filter–feeders (e.g. mussels) this community may act as a biofilter, depleting primary organic matter in the water column while enriching it by producing faeces, nutrients, dissolved organics, and larvae (Krone et al., 2013; Coates et al., 2014). Consequently, water chemistry and particle composition are altered in the close vicinity (up to 100 m) of an artificial structure (Maar et al., 2009). It is hence hypothesized that epifauna on oil and gas structures will cast a so-called “shadow”, over the soft sediment ecosystem near the structures, which influences food availability for surrounding benthic assemblages and impact species composition. Model calculations of the effect of mussels growing on a wind turbine (Maar et al., 2009) demonstrated significant effects on the water column including depletion of water column chlorophyll in the wake of the structure which was subsequently confirmed by actual measurements. Beside these biogeochemical impacts, the physical presence of a structure will alter local hydrodynamics, which may influence sedimentological characteristics, changing erosion and deposition (Vanhellemont and Ruddick, 2014; Baeye and Fettweis, 2015; Carpenter et al., 2016). Field studies on the impact of offshore gas platforms in the Adriatic Sea revealed a widespread effect (up to 1000 m) on the surrounding nematode community which was ascribed to physical alteration of the habitat (Fraschetti et al., 2016), while the growth of a mussel population on the structure led in one case to the development of a mound of dead mussel shells close to the platform with consequences for the resident bottom fauna (Bomkamp et al., 2004; Manoukian et al., 2010). A growing number of studies on the introduction of artificial hard substrate and their biological effects on the surrounding soft sediment environment is being published (Lindeboom et al., 2011; Degraer and Brabant, 2012; Vandendriessche et al., 2015; Coolen, 2017). So far, studies have mostly dealt with short-term effects (up to 5 years). Fouling communities on artificial structures, however, have been shown to change in composition when the structure remains intact long enough (Whomersley and Picken, 2003; Vandendriessche et al., 2015). To aid the discussion of the effects of decommissioning of artificial structures, knowledge on the actual effects of a platform on its surroundings is necessary, for which a longer time-scale (multi-decadal) perspective is needed. Therefore, this study targets an >40-year-old offshore gas platform (L7A) in the southern North Sea and investigates whether this platform casts a “shadow” on the composition of the soft-sediment benthic communities. It was hypothesized that species composition in the wake of the platform, in the residual current direction, will differ from the species composition in reference areas. Besides species composition, a set of abiotic factors which are known to influence the benthic community composition were measured. Most impact studies involving benthos by means of morphological identification only deal with macrofauna (Coates et al., 2014; Coolen, 2017). Only when highly specialist knowledge is available, meiofauna is considered. Yet meiofauna organisms are, next to macrofauna, key indicators for ecosystem health (Balsamo et al., 2012; Spilmont, 2013; Lallias et al., 2014; Fraschetti et al., 2016). This study applies both a classic morphological identification approach, via identification of macrofaunal specimens by a highly experienced taxonomist, and a molecular identification approach, via metabarcoding of DNA extracted from the entire benthic assemblage (including meiofauna and macrofauna), using a combination of next-generation sequencing and taxonomic affiliation based on reference libraries. The latter approach provides the opportunity to assess marine metazoan benthos in a new holistic and replicable manner (Chariton et al., 2010; Taberlet et al., 2012; Cowart et al., 2015). Methods Study site and sampling design The platform L7A in the southern North Sea was installed over 40 years ago. It was selected as study site, since non-toxic substances were used during past drilling operations and long-lasting effects due to toxic wastes can be excluded (Duineveld et al., 2007). The platform is situated in a fauna-rich area with fine muddy sand (median grainsize between 106 and 113 μm) at a water depth of 35 m (Duineveld et al., 2007) (Figure 1). Based on the dominant residual current directions the shadow area was defined as the sector between 0° and 90° compass direction and the opposite direction between 180° and 270°. Figure 1. View largeDownload slide Sample locations were based around the L-7A gas-platform in the southern North Sea (left panel). Sample locations were distributed along four different transects; north-east (45°), south-east (135°), south-west (225°) and north-west (315°). Along each transect a box core was taken at 150, 300, 450 and 600 m from the platform. The right panel shows the sum of current vectors in the different directions as measured during the scientific cruise in 2017. The dominant current direction was between 0-90° (flood) and 180-270° (ebb), which were designated as shadow areas. Areas between 90-180 and 270-360° were designated as reference areas. Figure 1. View largeDownload slide Sample locations were based around the L-7A gas-platform in the southern North Sea (left panel). Sample locations were distributed along four different transects; north-east (45°), south-east (135°), south-west (225°) and north-west (315°). Along each transect a box core was taken at 150, 300, 450 and 600 m from the platform. The right panel shows the sum of current vectors in the different directions as measured during the scientific cruise in 2017. The dominant current direction was between 0-90° (flood) and 180-270° (ebb), which were designated as shadow areas. Areas between 90-180 and 270-360° were designated as reference areas. Samples and measurements were collected during a cruise with RV Pelagia (7–12 May 2016). Sediment samples were collected at 150, 300, 450, and 600 m, respectively, from the platform along four transects in four perpendicular directions (SW, SE, NW, and NE) using the NIOZ box corer with a 706 cm2 surface area and 55 cm sampling depth (equivalent to roughly 38 litres of sediment). From each box core one sub core (78 cm2 surface) was retrieved for sedimentological analysis and stored at 4° C. Two surface samples for organic content measurements, taken of the surface of the box core with a spatula, were stored at −20° C. Samples for molecular identification (environmental DNA samples) were collected from the box core using a smaller core with a 5.60 cm2 surface area and 10 cm sampling depth (equivalent to 56 ml of sediment) and immediately stored at −80° C. The remaining sediment in the box core was used for morphological identification and was sieved over a 1 mm round mesh sieve. Living organisms were sorted manually and identified up to the family level with a stereomicroscope by an experienced taxonomist (ML) according to Hartmann–Schröder (1996) and Hayward and Ryland (1995). Identified species were stored in 96% ethanol at ambient temperatures as reference samples for molecular methods. Environmental variables The sub cores taken for sedimentological analysis were opened in the laboratory and the top 10 cm was sliced into 1 cm slices. A subsample of each slice was cryodesiccated and subsequently left overnight in a water bath (100 ml) consisting of 0.1 M dispersing agent (Na4P2O7) to prevent flocculation. Grain sizes were measured three times with a Beckman Coulter LS Particle Size Analyzer which measures a grain size distribution from 0.04 to 2000 μm for 117 size classes. Thereafter, measurements were divided into three size fractions: clay (<0.002 mm), silt (0.002–0.05 mm), and sand (0.05–2 mm). Total organic carbon (TOC) was determined from the surface samples after removing inorganic carbonates by shaking samples twice in 2 M HCl (respectively for 4 and 12 h) followed by rinsing them twice with Milli-Q water. Subsequently, the sediments were cryodesiccated and thoroughly ground in an agate mortar. TOC and nitrogen (N) contents were measured on an Organic Elemental Analyser (Flash 2000, Thermo Scientific Bremen, Germany). Reference library and mock sample Genomic DNA from a subset of the morphological identified specimens (Supplementary Table S1) was extracted using the GenElute™ Mammalian Genomic DNA miniprep kit (Sigma-Aldrich Inc.) following the manufacturer’s protocol. A 450 base pair (bp) part of nuclear small ribosomal subunit (18S) was amplified using the oligo-nucleotides F04 and R22 as primer pair (Sinniger et al., 2016). The 18S gene was amplified in a 50 μl volume reaction, containing 0.6 μM of each primer, 0.2 μM dNTP, 800 ng/μL BSA, 1 U Phusion® High-Fidelity DNA Polymerase (Thermo Scientific Inc.), 1× Phusion® HF buffer (Thermo Scientific Inc.) and 5 μl of DNA extract. The thermal cycle conditions were as follows: an initial cycle of 30 s at 98°C; followed by 27 cycles, each comprised of 10 s at 98°C, 20 s at 60°C, and 30 s at 72°C, followed by a single cycle of 5 min at 72°C. The PCR products were Sanger sequenced in both directions on the ABI3730XL sequencer from Life Technologies by BaseClear (Leiden, Netherlands). Consensus sequences were complemented with their taxonomic data and stored as a local reference database. One mock test sample was generated by combining DNA extractions from 21 species, representing 7 different phyla (Supplementary Table S1). The DNA extracts of the selected species were quantified on a Qubit 3.0 fluorimeter (Qiagen, Inc.) and were pooled in equimolar quantities. The mock sample served as a positive control throughout the 18S species identification process. Molecular analysis A subsample (10 g) was taken from each eDNA subcore at the following depth intervals: 0–2, 2–3, and 5–6 cm. DNA was extracted from these subsamples using the Powermax Soil™ DNA isolation kit (MoBio Inc.) following the manufacturer’s instructions. DNA from all extractions, as well as a mock sample, were used as template to amplify, in triplicate, the 18S gene fragment as described in Methods section. All forward and reverse primers were extended with 12 nt unique barcodes. The PCR products were visually inspected after electrophoresis through a 1% agarose gel at 75 V for 50 min, excised from the gel, purified using the Qiaquick Gel Extraction Kit (Qiagen, Inc.) and quantified with a Qubit™ 3.0 fluorometer (Qiagen, Inc.). Samples were pooled in equimolar quantities together with blank PCR controls. The pooled sample was then subjected to a final purification using MinElute™ PCR Purification columns (Qiagen Inc.) as described by the manufacturer. The pooled sample was submitted for sequencing at Useq (Utrecht, Netherlands) on an Illumina MiSeq using the 2× 300 bp V3 kit. Bioinformatics Raw sequences were quality filtered using the fastq_quality_filter script in the FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). Reads with a quality score ≤30 over 75% of the positions were discarded. Quality filtered reads were de-multiplexed using the split_libraries.py script in QIIME (Caporaso et al., 2010), allowing zero mismatches in both the forward and reverse primer. De-multiplexed sequences were then front, and end clipped to remove the primers. Reads were first de-replicated at a 100% similarity and sequences with less than 10 copies were discarded. The remaining unique sequences were clustered using a 95% similarity cut-off in VSEARCH (Rognes et al., 2016). Taxonomic assignments were performed against the SILVA 18S rRNA database (release 119, www.arb-silva.de; Pruesse et al., 2007) using the RDP Classifier (Wang et al., 2007) with a minimum confidence of 0.5. At a confidence level of 0.5 all OTUs found for the mock sample could be assigned at the family level to one of the species added to this sample. Data analysis For the morphological approach, count data were merged into taxonomic families when possible, resulting in a total number of individuals per taxonomic family. As the number of reads for the molecular approach have a weak relationship with biomass or abundance (Elbrecht and Leese, 2015), all OTU data were converted to presence or absence prior to further analysis (Chariton et al., 2015). OTUs were first merged per location (i.e. depth-subsamples were averaged at each location) and subsequently combined into taxonomic families. The correlation between the number of metazoan OTUs and read depth was tested to avoid a bias introduced by sampling effort and no correlations were found (Pearson, r = −0.176, p = 0.515). Also, rarefaction curves (using R package vegan/rarecurve, step = 20) showed a saturation for all samples (Supplementary Figure S2). Therefore, no transformation of OTU abundances was necessary and the number of OTUs per taxonomic families were used throughout subsequent analyses. Shannon–Wiener diversity estimates were calculated using the count data for the morphological approach and the number of OTUs per taxonomic family for the molecular approach. Since it was hypothesized that the platform could exert an effect on the benthic community via a change in sediment composition, the metazoan data were screened for potential indicators of a change in grain size, TOC, and N. Taxa were designated as potential indicator taxa if the number of individuals (morphological approach) or the number of OTUs (molecular approach) within a taxonomic family were correlated to either the percentage of silt, TOC, or N. A taxonomic family was designated as potential indicator taxa if the correlation coefficient rho was found to be higher than 0.4 or lower than −0.4 and at least 10 species or OTUs were found within this family for all samples combined. The number of individuals for the morphological approach and the number of OTUs for the molecular approach within a taxonomic family were subjected to Hellinger transformation (Legendre and Gallagher, 2001) using the vegan function “decostand”. A screeplot was made to check for variances of the ordination components and to determine the optimal number of dimensions or axis in the multidimensional scaling. Non-metric multidimensional scaling using Bray–Curtis dissimilarity distances at two dimensions for the morphological approach and three dimensions for the molecular approach were performed. The Bray–Curtis dissimilarity matrix was further used for analysis of variance between the transects (permanova) using the adonis2 function and for the simper analysis to discriminate the effect of each species. All data were analysed in R v3.4.3. Results Validation of taxonomic assignment After sequence quality control, a total of 85 923 reads were assigned to the mock sample. These reads resulted in 490 OTUs after clustering at a threshold at >95%. Only 1 out of the 21 species added to the mock sample was not recovered, i.e. Ampelisca brevicornis. False positives were found below a relative read abundance of 0.001%. This relative read abundance was then used as cut-off in the further data analysis of the 16 environmental samples. Taxonomic composition Classic morphological analysis of the box core samples, sieved over a 1-mm sieve and identified by stereo microscope, yielded a total of 1264 specimens belonging to 43 taxonomic families for the 16 samples. In total, seven metazoan phyla were found, of which only four were found at all locations. Most individuals belonged to the echinoderms (52%), and annelids (20%) (Figure 2, upper panel). The most abundant families were the echinoderm family Amphiuridae, the annelid family Lumbrineridae, the phoronid family Phoronidae, and the arthropod family Callianassidae. Figure 2. View largeDownload slide Taxonomic composition per location (Distance in m from the platform). For each of the samples grouped per transect the proportions of phyla identified through the morphological approach (upper panel) or the molecular approach (lower panel). The number of individuals per phylum was used for the morphological approach and number of OTUs within a phylum was calculated for the molecular approach. Figure 2. View largeDownload slide Taxonomic composition per location (Distance in m from the platform). For each of the samples grouped per transect the proportions of phyla identified through the morphological approach (upper panel) or the molecular approach (lower panel). The number of individuals per phylum was used for the morphological approach and number of OTUs within a phylum was calculated for the molecular approach. The Illumina sequencing of the molecular samples generated 6.4 million reads after quality filtering for the 16 samples, of which 22% could be confidently assigned to metazoans. Clustering resulted in 544 metazoan operational taxonomic units (OTUs), the number of OTUs ranged from 121 to 323 for individual samples. The molecular approach recovered many more metazoan phyla than the morphological approach; 16 in the combined samples. Ten of these phyla were recovered from each location (Figure 2, lower panel). The metazoan diversity derived from the molecular approach was largely dominated by nematodes, which formed the most diverse group (11% of all OTUs). The next most diverse phyla were the arthropods (5%), and annelids (2%). Of all metazoan OTUs, 60% could be taxonomically classified at the family level. The most abundant metazoan families based on the number of OTUs were the nematode families Comesomatidae, Oxystominidae, and Loxoconchidae, followed by the arthropod family Temoridae and the annelid family Lumbrineridae. Biodiversity and environmental variables Percentage clay was very low (between 0% and 0.2%) for all locations, therefore, instead percentage silt was used as a measure for further grain size analyses. Percentage silt was highest in the NE transect with a mean percentage of 30.43 ± 1.9% (Figure 3; Supplementary Table S3) and lowest in the SE transect (24.88 ± 4.78%). The percentage silt tended to increase with increasing distance from the platform. However, only for the SW transect this trend was significant (Pearson, r = 0.965, p = 0.035). The levels of TOC and N were on average highest in the NE transect (TOC: 0.566 ± 0.19%; N: 0.047 ± 0.01%) (Figure 3; Supplementary Table S3). Similarly as the percentage of silt, TOC and N levels showed an increase with increasing distance from the platform, however, correlations were not statistically significant. Figure 3. View largeDownload slide Environmental variables for the various sampling locations. The percentage of carbon and nitrogen from surface sediments and the average percentage of silt from the top 10 cm of sediment is shown for each sample location. Figure 3. View largeDownload slide Environmental variables for the various sampling locations. The percentage of carbon and nitrogen from surface sediments and the average percentage of silt from the top 10 cm of sediment is shown for each sample location. The Shannon–Wiener index based on the numbers of specimen per family found from the morphological identification approach was negatively correlated with the percentages of TOC, N, and silt (Figure 4, upper panel). However, only the relationship with the percentage silt was statistically significant (Pearson, r = −0.626, p = 0.010). The Shannon–Wiener index based on the number of OTUs per family found in the molecular approach was twice as high as the index derived from the morphological approach. The correlations between Shannon–Wiener index values from the molecular approach and the environmental variables were less strong than the correlations with index values based on the morphological approach and none of these were significant (Figure 4, lower panel). The Shannon–Wiener index based on the molecular approach increased slightly with increasing percentages of silt as opposed to the decreasing trend found with values based on the morphological approach. Figure 4. View largeDownload slide Relationships between the biodiversity and environmental variables. The Shannon–Wiener index was calculated from number of individuals within a taxonomic family for the morphological approach (top panel) or the number of OTUs within a taxonomic family for the molecular approach (bottom panel) and was plotted against the percentage of carbon, nitrogen and silt found at the sample location. Figure 4. View largeDownload slide Relationships between the biodiversity and environmental variables. The Shannon–Wiener index was calculated from number of individuals within a taxonomic family for the morphological approach (top panel) or the number of OTUs within a taxonomic family for the molecular approach (bottom panel) and was plotted against the percentage of carbon, nitrogen and silt found at the sample location. Potential indicator taxa Within the dataset based on the morphological approach, the arthropod family Upogebiidae was significantly negatively correlated with the percentage of N (Pearson r = −0.525, p = 0.037) and was therefore designated as potential indicator taxa (Table 1). The echinoderm family Amphiuridae, the annelid family Lumbrineridae, and the Phoronidae were found in high abundances with still a moderate correlation with one or multiple of the environmental variables (r > 0.4). The Amphiuridae and Lumbrineridae were positively affected by higher organic content, either TOC or N. Whereas the Amphiuridea were found in higher abundances with higher percentages of silt, the Phoronida were found in lower abundances in relation to higher silt contents. Table 1. Potential indicator taxa. Approach Family N TOC N Silt Rho P Rho P Rho P Morpho Upogebiidae (Ar) 23 –0.489 0.055 –0.525 0.037* Nuculidae (M) 26 –0.479 0.061 –0.477 0.062 Amphiuridae (E) 641 0.458 0.074 0.449 0.093 Lumbrineridae (An) 116 0.411 0.113 Phoronida (P) 102 –0.405 0.134 Molecular Camacoloimidae (N) 14 –0.644 0.007* –0.495 0.051 –0.483 0.058 Thoracostomopsidae (N) 108 0.538 0.031* – – – – Cyantholaimidae (N) 179 0.538 0.032* – – – – Scalibregmatidae (An) 25 – – – – –0.532 0.034* Loxoconchidae (Ar) 211 0.519 0.039* 0.546 0.029* 0.459 0.074 Corbulidae (M) 33 – – 0.510 0.044* – – Linhomoeidae (N) 64 – – – – –0.504 0.046* Amphiuridae (E) 89 0.496 0.051 – – – – Miraciidae (Ar) 17 – – – – 0.466 0.069 Semelidae (M) 25 – – –0.420 0.105 – – Calanidae (Ar) 12 –0.411 0.114 –0.418 0.107 – – Xyalidae (N) 135 – – –0.417 0.108 – – Approach Family N TOC N Silt Rho P Rho P Rho P Morpho Upogebiidae (Ar) 23 –0.489 0.055 –0.525 0.037* Nuculidae (M) 26 –0.479 0.061 –0.477 0.062 Amphiuridae (E) 641 0.458 0.074 0.449 0.093 Lumbrineridae (An) 116 0.411 0.113 Phoronida (P) 102 –0.405 0.134 Molecular Camacoloimidae (N) 14 –0.644 0.007* –0.495 0.051 –0.483 0.058 Thoracostomopsidae (N) 108 0.538 0.031* – – – – Cyantholaimidae (N) 179 0.538 0.032* – – – – Scalibregmatidae (An) 25 – – – – –0.532 0.034* Loxoconchidae (Ar) 211 0.519 0.039* 0.546 0.029* 0.459 0.074 Corbulidae (M) 33 – – 0.510 0.044* – – Linhomoeidae (N) 64 – – – – –0.504 0.046* Amphiuridae (E) 89 0.496 0.051 – – – – Miraciidae (Ar) 17 – – – – 0.466 0.069 Semelidae (M) 25 – – –0.420 0.105 – – Calanidae (Ar) 12 –0.411 0.114 –0.418 0.107 – – Xyalidae (N) 135 – – –0.417 0.108 – – Correlation tests were performed for the abundance of specimens within a taxonomic family for the morphological approach and the number of OTUs within a taxonomic family for the molecular approach against the environmental variables. Only families showing a strong or moderate correlation (r > 4) and an abundance (N) above 10 are presented here. An = Annelida, Ar = Arthropoda, E = Echinodermata, M= Mollusca, N = Nematoda, P = Phoronida. Table 1. Potential indicator taxa. Approach Family N TOC N Silt Rho P Rho P Rho P Morpho Upogebiidae (Ar) 23 –0.489 0.055 –0.525 0.037* Nuculidae (M) 26 –0.479 0.061 –0.477 0.062 Amphiuridae (E) 641 0.458 0.074 0.449 0.093 Lumbrineridae (An) 116 0.411 0.113 Phoronida (P) 102 –0.405 0.134 Molecular Camacoloimidae (N) 14 –0.644 0.007* –0.495 0.051 –0.483 0.058 Thoracostomopsidae (N) 108 0.538 0.031* – – – – Cyantholaimidae (N) 179 0.538 0.032* – – – – Scalibregmatidae (An) 25 – – – – –0.532 0.034* Loxoconchidae (Ar) 211 0.519 0.039* 0.546 0.029* 0.459 0.074 Corbulidae (M) 33 – – 0.510 0.044* – – Linhomoeidae (N) 64 – – – – –0.504 0.046* Amphiuridae (E) 89 0.496 0.051 – – – – Miraciidae (Ar) 17 – – – – 0.466 0.069 Semelidae (M) 25 – – –0.420 0.105 – – Calanidae (Ar) 12 –0.411 0.114 –0.418 0.107 – – Xyalidae (N) 135 – – –0.417 0.108 – – Approach Family N TOC N Silt Rho P Rho P Rho P Morpho Upogebiidae (Ar) 23 –0.489 0.055 –0.525 0.037* Nuculidae (M) 26 –0.479 0.061 –0.477 0.062 Amphiuridae (E) 641 0.458 0.074 0.449 0.093 Lumbrineridae (An) 116 0.411 0.113 Phoronida (P) 102 –0.405 0.134 Molecular Camacoloimidae (N) 14 –0.644 0.007* –0.495 0.051 –0.483 0.058 Thoracostomopsidae (N) 108 0.538 0.031* – – – – Cyantholaimidae (N) 179 0.538 0.032* – – – – Scalibregmatidae (An) 25 – – – – –0.532 0.034* Loxoconchidae (Ar) 211 0.519 0.039* 0.546 0.029* 0.459 0.074 Corbulidae (M) 33 – – 0.510 0.044* – – Linhomoeidae (N) 64 – – – – –0.504 0.046* Amphiuridae (E) 89 0.496 0.051 – – – – Miraciidae (Ar) 17 – – – – 0.466 0.069 Semelidae (M) 25 – – –0.420 0.105 – – Calanidae (Ar) 12 –0.411 0.114 –0.418 0.107 – – Xyalidae (N) 135 – – –0.417 0.108 – – Correlation tests were performed for the abundance of specimens within a taxonomic family for the morphological approach and the number of OTUs within a taxonomic family for the molecular approach against the environmental variables. Only families showing a strong or moderate correlation (r > 4) and an abundance (N) above 10 are presented here. An = Annelida, Ar = Arthropoda, E = Echinodermata, M= Mollusca, N = Nematoda, P = Phoronida. The Amphiuridae family was the only family designated as indicator taxon by both the morphological and molecular approach. The abundance of Amphiuridae in the molecular approach was positively correlated with the percentage of TOC. The molecular approach also identified several meiofauna families as potential indicator taxa. The number of OTUs within the nematode families Thoracostomopsidae and Cyantholaimidae showed a positive correlation with TOC levels (Pearson, r = 0.538, p = 0.031 and r = 0.538, p = 0.032, respectively), whereas nematodes from the family Camacoloimidae were negatively correlated with TOC levels (Pearson, r = −0.644, p = 0.007). For the other phyla, the arthopod family Loxoconchidae was positively correlated to both TOC and N levels (Pearson, r = 0.519, p = 0.039 and r = 0.546, p = 0.029) and the mollusc family Corbulidae was positively correlated to the levels of nitrogen (Pearson, r = 0.510, p = 0.044). The annelid family Scalibregmatidae and the nematod family Linhomoeidae were negatively correlated to the percentage silt (Pearson, r = −0.532, p = 0.034 and r = −0.504, p = 0.046, respectively). Comparison between sample locations The values of the Shannon–Wiener index varied between sample locations for both the morphological and molecular approach (Figure 5; Supplementary Table S4). The Shannon values based on the morphological approach showed the strongest trend along the NW transect, for which diversity decreased with increasing distance from the platform (Pearson, r = −0.956, p = 0.044). In contrast, diversity was stable with increasing distances on the NE transect (Pearson, r = −0.156, p = 0.844) and diversity in this transect showed the lowest variation along the distances (μ  =  1.520 ± 0.121). The Shannon values based on the molecular approach showed a negative correlation with distance on all transects, however, none of these were statistically significant. The MDS ordination for the morphological approach showed a deviating composition of macrofauna on the SW transect with all its data points separated from the remaining samples (Figure 6, left panel). A permanova analysis indicated a statistically significant difference between the transects (F3,12 = 1.547, p = 0.026). Simper analysis showed that this deviation was mainly due to a lower abundance of the echinoderm family Amphiuridae and the phylum Phoronida on the SW transect. An MDS ordination for the molecular approach combined with a permanova analysis resulted in a significant different between the benthic communities for the different transects (F3,12 = 1.497, p = 0.040) but not between the distances from the platform (F3,12 = 1.064, p = 0.366) (Figure 6, right panel). Figure 5. View largeDownload slide Biodiversity per sample location. The Shannon–Wiener index was calculated from the number of individuals within a taxonomic family for the morphological approach (left panel) or the number of OTUs within a taxonomic family for the molecular approach (right panel) and was plotted for each sample location per transect. Figure 5. View largeDownload slide Biodiversity per sample location. The Shannon–Wiener index was calculated from the number of individuals within a taxonomic family for the morphological approach (left panel) or the number of OTUs within a taxonomic family for the molecular approach (right panel) and was plotted for each sample location per transect. Figure 6. View largeDownload slide Nonmetric multidimensional scaling (nMDS) plot for community composition. The nMDS is based on Bray–Curtis dissimilarities of community composition. Composition was based on the number of individuals per taxonomic family for the morphological approach (left panel) and the number OTUs per taxonomic family for the molecular approach (right panel). Figure 6. View largeDownload slide Nonmetric multidimensional scaling (nMDS) plot for community composition. The nMDS is based on Bray–Curtis dissimilarities of community composition. Composition was based on the number of individuals per taxonomic family for the morphological approach (left panel) and the number OTUs per taxonomic family for the molecular approach (right panel). Discussion The aim of the present study was to seek evidence for an effect of an offshore gas platform on the composition of the surrounding metazoan communities. The supposed effect of the platform was hypothesized to be primarily due to the long established epifaunal community which acts as a biofilter and casts a shadow over its surroundings (Van der Stap et al., 2016). The methodology that was applied consisted of a classical morphological approach targeting only macrofauna and a molecular approach which also include the smaller meiofauna. Prominent variation was found in the grain size characteristics in the environment surrounding the gas platform. A higher percentage of silt was found on the transect in the residual current direction, i.e. in the “shadow” area of the structure, while coarser sediment was observed in the close vicinity of the artificial structure. Similar grain size effects have been found around other artificial structures and were interpreted to reflect changes in velocity and direction of water movement (Mendoza and Henkel, 2017). The strongest correlation between distance from the platform and grain sizes were found in the SW and NE transects, aligning with the directions of the dominant residual currents. TOC and nitrogen (N) levels showed equivocal trends around the platform. The organic content of the surface sediment was on average highest in the NE transect, which is in line with the distribution of silt. The combined data suggests a redistribution of silt and associated organic matter in the direction of the residual current from SW to NE (Heery et al., 2017). For the low percentages of TOC and N in the vicinity of the platform two explanations are proposed: the first is in line with the original hypothesis of this project, i.e. depletion of the organic content of water due to the biofilter effect of the epifauna (Davis et al., 1982; Maar et al., 2009). The other (mechanistic) explanation evokes scouring of the sediment caused by the acceleration of the flow by the platform structure and deposition of fine particles further from the platform (Rudolph et al., 2004). The Shannon–Wiener index is a commonly used index for comparing benthic communities analysed by classical morphology approaches (Gray, 2000; Patrício et al., 2009). More recently it has also been used in connection with molecular approaches (Lanzén et al., 2016). However, the outcomes of these indices are not necessarily comparable. First, there is a difference between the sampling procedure in the two approaches in terms of sampling volume and size fraction of the fauna. In the morphological approach, a fixed surface area is sampled and a cut-off size selection is applied by sieving. Moreover, only organisms of this size class that are present at the time of sampling will be collected. Environmental DNA, on the contrary, can persist in the environment over time and therefore will reflect present and past presences of fauna, and possibly even presences from organisms in the wider area (Dell’Anno et al., 2002). In addition, the molecular approach includes fauna from all size classes, which likely increases taxonomic richness. Additionally, abundance estimates used for the calculation of the Shannon–Wiener index are different in the morphological and molecular approach and hence also the meaning of the index differs. Abundance estimates in the morphological approach are counts of specimens belonging to a specific taxon, while the molecular approach in this study uses OTU abundance within a taxon (here family). OTU abundance has been shown to increase with increasing numbers of specimens analysed within a taxonomic group due to polymorphism (Dell’Anno et al., 2015). However, this genetic diversity is not necessarily similar for all taxa. Whilst the Shannon–Wiener index is sensitive to the number of taxa (richness) in both approaches, it expresses evenness in the distribution of specimens across taxa in the classical approach as opposed to differential genetic diversity within taxa in the molecular approach. Despite the different meaning of the Shannon–Wiener index, Lejzerowicz et al. (2015) already showed the applicability of biotic indices, and in particular the Shannon–Wiener index for the molecular approach. An extensive review of the use of biotic indices for molecular approaches has been performed by Pawlowski et al. (2018). Here, we only touched upon the essentials relevant to this study. Potential indicator taxa for the effects of changing organic content or silt levels could be indicated. For both the molecular and morphological approach more taxa correlated positively than negatively to the organic content. For both approaches, the abundance of the macrofaunal family Amphiuridae increased with increasing organic content levels. Species in the Amphiuridae family are rapid growing with a high metabolic rate and hence have high food requirements and thrive under high food conditions (Buchanan, 1964; Josefson and Jensen, 1992). In this study, lowest abundances for the Amphiuridae were found in the SW transect and highest in the NE transect, which is in line with the distribution of organic carbon and silt. Although macrofauna species have been used as key indicators for environmental health for decades, recent developments in terms of molecular techniques have made it easier to also assess the function of meiofauna as bio-indicators (Fonseca et al., 2014; Lallias et al., 2014; Chariton et al., 2015; Lanzén et al., 2016). Meiofauna species are considered to be suitable indicators for marine ecosystem monitoring due to their relatively high abundance and their complex interplay with other faunal species (Sutherland et al., 2007; Balsamo et al., 2012). Abundances of certain nematode species have been shown to increase at slightly elevated levels of organic content (Gee et al., 1985) and also in this study, two nematode families showed a positive correlation with TOC and N levels. Relations between arthropods and environmental variables in the study were less clear. One meiofaunal arthropod family of ostracods, the Loxoconchidae, showed a positive relationship between OTU numbers and increasing TOC and N levels, whereas the copepod family Calanidae showed a negative relationship with organic content levels. Previous studies have investigated the influences of man-made structures on the surrounding benthic environment by either a morphological identification approach (Danovaro et al., 2002; Terlizzi et al., 2008; Manoukian et al., 2010; Coates et al., 2014; Fraschetti et al., 2016) or a molecular identification approach (Lanzén et al., 2016). This study is the first to date to encompass both approaches. An obvious advantage of the morphological approach is the ability to provide actual species abundance data, whereas metabarcoding datasets are still limited to presence/absence data of OTUs (Deagle et al., 2013; Cowart et al., 2015; Ficetola et al., 2015; Piñol et al., 2015). A good example is the phylum Phoronida. This phylum consists of only one family Phoronidae (WoRMS, 2018) with few species and thus molecular diversity within this phylum is nihil. Even though the morphological approach found high numbers of Phoronidae, in this study, the abundance of different OTU’s within the Phoronidae in the molecular approach was low. On the other hand, the most noticeable disadvantage of the morphological approach is the taxonomic expertise needed for fauna identification. Because of this, meiofauna species are often excluded from environmental impact studies. Since molecular methods, such as metabarcoding, include macrofauna and meiofauna, they provide a more holistic view of the benthic community composition (Taberlet et al., 2012; Chariton et al., 2015; Lanzén et al., 2016; Sinniger et al., 2016). In this study, the number of families found in the molecular approach were on average three times higher compared with the morphological approach. The additional families found were mainly meiofauna families, taxa which are potential bio-indicators of changing environmental conditions. The analysis of indicator taxa and the analysis of biotic indices from both the morphological and molecular approach showed that differences in abundance within taxonomic families can occur due to abiotic changes. As the particular platform has been established several decades ago, species composition on the platform was supposed to represent a mature community and therefore is a good measure of the long-term effects of artificial structures on their immediate surroundings. Differences within the surrounding communities and abiotic factors were most noticeable between the NE and SW transect, which are the downstream and upstream directions of the strongest currents around the platform, respectively. Differences in community composition were most pronounced between the transects rather than within a transect at varying distances from the platform. This would suggest that the presence of the platform has evoked changes in soft bottom communities. However, it was not possible to disentangle the biological effects of epifauna from the physical effect of the platform itself. Likely both factors are involved. Either way, the complete removal of the platform as part of the decommissioning process will alter the current benthic species composition. Acknowledgements We are grateful to captain, crew and technicians for their help on board RV Pelagia. We thank the INSITE programme for funding the scientific SHADOW project. Royal NIOZ and INSITE have funded the scientific cruise. We thank Total for granting permission to work around platform L7A. The morphological identification on board greatly benefited from the efforts of Ulrike Hanz, whereas the work in the molecular laboratory could not have succeeded without the help of Harry Witte. A. Filippidi was financially supported by the INSITE programme. 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