The Large Scale Impact of Offshore Wind Farm Structures on Pelagic

Hydrobiologia manuscript No. (will be inserted by the editor)

The large scale impact of oﬀshore wind farm structures on pelagic primary productivity in the southern North Sea

Kaela Slavik · Carsten Lemmen · Wenyan Zhang · Onur Kerimoglu · Knut Klingbeil · Kai W. Wirtz

Received: date / Accepted: date

Abstract The increasing demand for renewable energy is projected to result in a 40-fold increase in offshore This research is funded by the Marine, Coastal and Po- lar Systems (PACES II) of the Hermann von Helmholtz- wind electricity in the European Union by 2030. De- Gemeinschaft Deutscher Forschungszentren e.V.. K.S. is spite a great number of local impact studies for selected funded by the European Commission Erasmus Mundus Mas- marine populations, the regional ecosystem impacts of ters Course in Environmental Sciences, Policy and Man- offshore wind farm structures are not yet well assessed agement (MESPOM). C.L., O.K. and K.K. received support from the “Modular System for Shelves and Coasts” nor understood. Our study investigates whether the ac- (MOSSCO) grant provided by the Bundesministerium für cumulation of epifauna, dominated by the filter feeder Bildung und Forschung under agreements 03F0667A and Mytilus edulis (blue mussel), on turbine structures af- 03F0667B; O.K. and K.W. are also supported by the DFG fects pelagic primary productivity and ecosystem func- priority programme 1704 “Flexibility matters: Interplay between trait diversity and ecological dynamics using aquatic tioning in the southern North Sea. We estimate the an- communities as model system” (DynaTrait) under grant thropogenically increased potential distribution based agreement KE 1970/1-1. K.K. is furthermore supported by on the current projections of turbine locations and re- the DFG Collaborative Research Center “Energy Transfers in ported patterns of M. edulis settlement. This distribu- Atmosphere and Ocean” TRR181. We thank all co-developers of the model coupling framework MOSSCO, foremost M. tion is integrated through the Modular Coupling Sys- Hassan Nasermoaddeli and Richard Hofmeister. The authors tem for Shelves and Coasts to state-of-the-art hydro- gratefully acknowledge the computing time granted by the dynamic and ecosystem models. Our simulations reveal John von Neumann Institute for Computing (NIC) and pro- non-negligible potential changes in regional annual pri- vided on the supercomputer JURECA at Forschungszentrum Jülich. We are grateful to the open source community that mary productivity of up to 8% within the offshore wind provided many of the tools used in this study, including but farm area, and induced maximal increases of the same not limited to the communities developing ESMF, FABM, magnitude in daily productivity also far from the wind GETM and GOTM. farms. Our setup and modular coupling are effective K. Slavik tools for system scale studies of other environmental Helmholtz Zentrum Geesthacht Zentrum fürMaterial- und changes arising from large-scale offshore wind-farming Küstenforschung, Germany such as ocean physics and distributions of pelagic top

arXiv:1709.02386v3 [q-bio.PE] 9 May 2018 Present address: Future Earth Paris Global Hub, Université Pierre et Marie Curie, France predators. C. Lemmen Helmholtz Zentrum Geesthacht Zentrum fürMaterial- und Küstenforschung, Germany Keywords Offshore wind farm primary produc- Corresponding author · tivity North Sea MOSSCO modular coupling Tel.: +49 4152 87-2013 · · · · Fax: +49 4152 87-2020 biofouling E-mail: [email protected] W. Zhang, O. Kerimoglu, K.W. Wirtz Helmholtz Zentrum Geesthacht Zentrum fürMaterial- und Küstenforschung, Germany UniversitätHamburg, Germany and Leibniz-Institut fürOst- K. Klingbeil seeforschung Warnemünde,Germany 2 Slavik et al.

Study area in deeper waters, with the average water depth increas- Operational OWF ing three-fold and the average distance from shore five- Construction fold between 1991 and 2010 (Kaldellis & Kapsali 2013). Consented Planned The large additional build-up of OWFs by 2030 is evi- Operational OWF Construction dent from the spatial distribution of operational, under Consented construction, consented and planned OWFs in the SNS Planned (Fig. 1). The increasing number of OWFs alters the functioning of the surrounding pelagic ecosystem by re- structuring the biological communities at and around the submerged foundations and pile structures (Joschko et al 2008; Krone et al 2013). Specifically, they increase the biomass and distribution of filter feeders (Krone 2012; Lindeboom et al 2011), because OWFs provide the hard substrate needed for colonisation by a variety of epistructural species. This colonisation is also referred to as biofouling. Among the colonisers, the bivalve Mytilus edulis (L. 1758, Bivalvia: Mytilidae) is the dominant species near the water surface (Table 1) 2 52105 1015152020 3030 4040 70 70 (Freire & Gonzalez-Gurriaran 1995; Riis & Dolmer 2003; Water depth (m) Water depth (m) Wilhelmsson & Malm 2008; Joschko et al 2008; Krone Fig.Figure 1 1:Study Study (and (and model) model) domain domain in in the the Southern Southern North North Sea Sea et al 2013). For example, piles of the OWF research with distribution distribution of of offshore offshore wind wind farms farms (OWF, (OWFs, data data from from Ho et platform FINO 1 (Forschungsplattformen in Nord- und al.Ho 2016). et al 2016). Color indicates Color indicates the planning the planning status of status each OWF of each as of Ostsee) are covered by an average of 4300 kg of M. September 2015 where parks in operation and under construction OWF as of September 2015 where parks in operation and edulis, with a turnover rate of more than 50% of the areunder shown construction in magenta are and shown red color,in magenta and foreseen and red constructions color, and withforeseen consented constructions or planned with status consented in orange or planned and gray status color. in For stock per year (Krone et al 2013). ourorange scenario and gray analysis, color. we For consider our scenario a maximum analysis, exploitation we consider that Biofouling not only generates habitat for a new epi- assumesa maximum that exploitation all shown OWF that are assumes in operation that all by shown 2030. OWFs structural community, i.e. biota that live on and at- are in operation by 2030. tach to a structure, but it has further consequences for the benthic community underneath and the surround- 1 Introduction ing pelagic zone (Krone 2012; Maar et al 2009). Fil- ter feeders have been shown to significantly reduce the Recognition of the role of burning fossil fuels in anthro- ambient concentration of phytoplankton and of micro- pogenic climate change has led to increased investment and mesozooplankton (Dolmer 2000; Maar et al 2007), in renewable energy such as wind farming (Edenhofer which to some extent likely applies to epistructural M. et al 2011). In particular, offshore wind energy has pro- edulis as well (Maar et al 2009). By changing phyto- liferated over the past decade and will be integral in plankton biomass, epistructural filtration can be ex- the transition to renewable energy systems. In the Eu- pected to affect primary productivity and thus the very ropean Union (EU), offshore wind farms (OWFs) are basis of the marine food web and biogeochemical cy- predicted to increase 13-fold between 2015 and 2020, cling locally above mussel beds and around the offshore and 40-fold by 2030, in order to meet 4.2% of EU total wind turbine. electricity consumption (Global Wind Energy Council Our study aims to assess the sensitivity of pelagic 2015). primary productivity to changed abundance and distri- Currently, 63% of OWFs in EU waters are concen- bution of M. edulis on OWFs for an entire regional- trated in the southern North Sea (SNS), with the re- scale ecosystem. It is the first study to investigate the mainder located in the Atlantic Ocean and Baltic Sea. accumulated effects on primary productivity at the sys- The SNS is expected to remain a hotspot for EU OWF tems scale, beyond the local impacts of individual off- development, where 85% of OWFs are currently un- shore wind turbines. Prerequisites for such an assess- ≈ der construction and 75% of OWFs have received ment are (i) the reconstruction ofM. edulis abundance ≈ consent (Ho et al 2016). Offshore wind turbines are solid both for their natural, epibenthic habitat and for the structures, typically larger than their onshore counter- new epistructural niches; (ii) the functional coupling parts, built of steel or concrete, with monopiles account- of the lateral and vertical distribution of reconstructed ing for 80%. OWFs are being built further offshore and mussels to phytoplankton prey fields in a realistic hy- Offshore wind farm impact on North Sea primary productivity 3

Table 1 Oﬀshore wind farms in the Southern North Sea where M. edulis are the dominant species.

Country Location Reference Germany FINO 1 research platform Krone et al 2013 Belgium C-Power OWF Kerckhof et al 2012 Netherlands Egmond aan Zee OWF Bouma & Lengkeek 2012; Lindeboom et al 2011 Denmark Horns Rev OWF Leonhard et al 2006 Sweden West coast of Sweden Langhamer et al 2009 drodynamic and biogeochemical representation of the land, before turning back towards and flowing north SNS. along the Danish coastline (Carpenter et al 2016). For the integrated modelling of benthic and epistructural filtration, water physics and pelagic biogeo- 2.2 Reconstruction of spatial distribution of epibenthic chemistry, we use the recently introduced modular frame- M. edulis work by Lemmen et al (2018), which contains a novel ecosystem model recently applied to and verified for the Open access spatial data on the abundance and dis- SNS by Kerimoglu et al (2017). Multi-annual simula- tribution of M. edulis were obtained from the Joint tions run with and without epistructural mussels allow Nature Conservation Committee (JNCC), the Ocean a first estimate of the sensitivity of pelagic primary pro- Biogeographic Information System (OBIS), the Archive ductivity to the projected OWFs in this regional sea. for Marine Species and Habitats Data (DASSH), the Global Biodiversity Information Facility (GBIF) and the Belgian Marine Data Centre (BMDB). Most of the 2 Materials and Methods data (43%, rounded) was from JNCC, 26% and 23% from BMDC and GBIF (containing presence only data), 2.1 Study location and 9% from OBIS. Only few data points came from DASSH < 1%. Removing duplicate locations, in total The southern North Sea (SNS) is located between 51◦ N 4074 count observations and 37 214 presence only data and 56◦ N and is bordered by the United Kingdom, Bel- were used for the reconstruction of the spatial distribu- gium, the Netherlands, Germany and Denmark (Fig. 1). tion of epibenthic M. edulis. The water is fairly shallow with an average depth of To extrapolate and interpolate the count and occur- 30 m and comprises an extended area of intertidal flats rence data to the entire domain of the SNS, we used em- and several major estuaries (Eisma & Kalf 1987). The pirical relationships between mussel abundance, sedi- seabed is composed predominantly of sand and, in the ment grain size and depth. We added to this a low abun- deeper and more coastal parts, of mud (Walday & Krog- dance random distribution for deep water and a con- lund 2002). The SNS experiences strong seasonal vari- stant high abundance for mussel beds. As M. edulis are ability, with winter storms often generating large sur- tolerant to large variations in temperature (0–29 ◦C) face waves and suspending greater amounts of sedi- and salinity (Seed & Suchanek 1992), such factors were ments (Groll & Weisse 2017; Nasermoaddeli et al 2017). not considered in the reconstruction. Taking the aver- Currents in the North Sea are generated by tides and age adult M. edulis individual biomass as 600 mg dry wind forcing, with the latter especially important dur- weight (DW) (Bayne & Worrall 1980), which equals ing storm events (Howarth 2001). The North Sea obeys 64.5 mg ash-free dry weight (AFDW, Ricciardi & Bour- a general cyclonic circulation. This is driven by prevail- get 1998, Table 2), the abundance and distribution of ing westerly winds, residual tidal currents and the baro- M. edulis in the SNS was spatially reconstructed using clinic pressure gradient set up by coastal river discharge the median sediment grain size map that is publicly (e.g., Otto et al 1990). The residual circulation within available from the NOAH habitat atlas (www.noah- the basin flows southward along the east coast of the project.de/habitatatlas/). UK, before turning west in the East Anglia plume and M. edulis prefers larger sediment grain sizes and then continuing westward along the West Frisian bar- hard substrate (OSPAR Commission 2010), thus an in- rier islands. Part of the residual current then continues crease in abundance density (n) with increasing sedi- 2 northward towards Norway. The other part continues ment grain size, ranging from an abundance of 1 m− along the East Frisian barrier islands and joins the Elbe in muddy areas (median grain size d50 < 0.06 mm) to 2 and Weser River inflows. It then turns northwest again 40 m− in areas of coarse gravel, at locations where mus- towards the central North Sea, bypassing Helgoland Is- sels are found. We employed a Random Forest model 4 Slavik et al.

(Liaw & Wiener 2002) to create a predictor of abun- Table 2 M. edulis biomass with depth, averaged over all dance density from median grain size. Comparison to years 2005–2007 sampled by Krone et al (2013, , pp. 4–5). Wadden Sea field data compiled by Compton et al (2013), Depth (m) Distribution range biomass mean biomass however, indicated that predicted shallow-water mus- (%) density (kg total (kg) sel abundance was greatly overestimated, which can be m−2) attributed to a positive sampling bias in the citizen- 0.0 – 2.5 95 22.3–43 3258.08 science data set. We thus interpreted the count data as 2.5 – 7.5 3 0.5–3.9 58.58 relative, i.e. as a probability of occurrence that needs to 7.5 – 15 2 n/a 19.29 be rescaled to conform to the Compton et al (2013) es- 15.0 – 30.0 n/a 0 1.63 timate where it borders the Wadden Sea, and rescaled the data accordingly. M. edulis is the dominant macrofauna species at shal- The abundance–sediment grain size relationship is lower depths, while at greater depths Anthozoa and applied up to a 10 m natural depth limitation (Reise Jassa spp. are more prolific; other major taxa such & Schubert 1987; Suchanek 1978). Outside the depth as green algae, Asterias rubens (Asteroidea), Bryozoa, limitation, M. edulis still occur, however at a much re- Porifera and Tubularia spp. are also present (Krone et al duced density, and are often completely absent: A ran- 2 2 2013). M. edulis is the most abundant and ecologically dom density between 0 m− and 0.5 m− is assigned. In important species at OWF epistructrual communities the Wadden Sea, no sediment data is available in the 2 in the North Sea (Table 1 and Borthagaray & Carranza NOAH data set, and a constant value of 2 m− is as- 2007), contributing up to 90% of epistructural biomass signed on the Wadden flats consistent with Compton in some locations. It is therefore also the main driver et al (2013). Mussel beds were incorporated as point of ecological change around offshore structures (Krone data using the OSPAR Biodiversity Committee habi- 2 et al 2013; Maar et al 2009). tat classification, where a constant density of 3911 m− 2 The additional settlement of M. edulis as a result of (Nielsen & Maar 2007) is downscaled to 170 m− to ac- OWFs is considered by incorporating the vertical dis- count for the patchiness of the beds. tribution observed by Krone et al (2013) at the FINO 1 Presence only data is not a preferred estimator for OWF. M. edulis abundance (n) at an offshore wind tur- species distribution modelling, especially when there is bine is a function of its radius (r) and its base depth a sampling bias. Many of the GBIF-reported M. edulis (z), with the radius assumed to be 3 m at all OWFs observations are opportunistic finds reported by citi- (Orbis Energy Centre 2013). The influence of M. edulis zen scientist divers, with a bias towards more acces- on water properties is assumed to be equal around the sible near-coast areas and towards summer tempera- entire circumference, without consideration of current ture. This bias may be overcome by environmental con- direction. Multiplying the abundance density by the cir- straints that can serve as proximate absence (Phillips cumference gives the vertical distribution of M. edulis et al 2009), such as water depth for M. edulis. We note with depth at offshore wind turbines (Table 2). The that the epibenthic reconstruction of abundance pre- abundance density over depth at each offshore wind sented here is preliminary. As it serves as a baseline turbine was calculated by converting the wet weight only, the uncertainty in this epibenthic reconstruction reported by Krone et al (2013) to DW using a factor does not harm the results obtained for the ecosystem of 6.6% and assuming 600 mg DW ind 1 (Ricciardi & sensitivity (see Sect. 2.3). We are currently working − Bourget 1998; Bayne & Worrall 1980). We did not con- on a refined epibenthic reconstruction that address the sider annual variation despite observed seasonal vari- effect of this uncertainty on the baseline itself (Lem- ations in the data set by Krone et al (2013) because men, North Sea ecosystem-scale quantification of pri- mussel biomass sampled at different seasons over the mary productivity changes by the benthic filter feeder years 2005–2007 were not found to be significantly dif- Mytilus edulis, unpublished manuscript). ferent.

2.3 Epistructural M. edulis 2.4 Spatial subgrid distribution The biomass and species diversity of epistructrual communities at OWFs are much higher than would be found The spatial distribution of current and projected OWFs on natural hard substrate (Wilson & Elliott 2009), with in the southern North Sea (Fig. 1) was overlaid on species composition varying with both depth and time, a curvilinear grid later used for the numerical model. as recorded at both FINO 1 (Krone et al 2013; Joschko Epibenthic areal abundance of M. edulis was considered et al 2008), and the Kentish Flats OWF (Bessel 2008). to be (vertically) equally distributed within the lower- Oﬀshore wind farm impact on North Sea primary productivity 5

Vertical and horizontal reconstructed mussel abundance (a) (b) Epibenthic (c) Epistructural

Epistructural Epistructural mussels biomass

2.5 m Water depth (below msl) Wind turbine pile Model representation

- Level above sea floor Epibenthic mussels Epistructural 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0 2 4 6 8 10 biomass Count (m 2) Count (m 2)

Fig.Figure 2 (a) 2: Vertical (a) Vertical representation representation of epibenthic of and epibenthic epistructural andM. epistructural edulis as observedMytilus (left) and edulis in theas model observed space (right). (left) andEpibenthic in the mussels model are space homogeneously (right). distributed Epibenthic in the mussels lowermost are model homogeneously layer. Epistructural distributed mussels are in equally the lowermost distributed modelin all model layer. layers Epistructural above 2.5 m depth, mussels and are proportionally equally distributed in the model in level all modelencompassing layers the above 2.5 m 2.5 depth m depth, contour. and No mussels are considered in the intermediate layers. (b) Reconstructed abundance of M. edulis at the sea floor, estimated from presenceproportionally and count in data the and model sediment level habitat encompassing mapping; mussel the beds 2.5 withm depth uniform contour. abundance No 170 mussels m−2 shown are as considered black dots. in(c) Abundancethe intermediate on wind turbine layers. piles, (b) estimated Reconstructed from scaling abundance individual pile of monitoringM. edulis observationsat the to sea the floor, coarse modelestimated grid. from presence and count data and sediment habitat mapping, mussel beds with uniform abundance 2 most170 m representable− shown as physical black dots. layer in (c) the Abundance grid of the hy-on windcoastal turbine ocean piles, focus estimated on processes from at the scaling benthic–pelagic individual drodynamicpile monitoring model. observations Epistructural to areal the abundance coarse model was grid.interface and, among others, explain spatio-temporal vertically distributed in all simulation layers represent- patterns in coastal nutrient concentration (Hofmeister ing the upper 2.5 m of the water column in the hydro- et al 2017; Kerimoglu et al 2017), primary productivity dynamic model (Fig. 2a). (Kerimoglu et al 2017), macrobenthic biomass and com- Estimating abundance density at OWFs from the munity dynamics (Zhang & Wirtz 2017) and suspended abundance at individual turbines requires consideration sediment concentration as affected by macrobenthic ac- of the turbine density at OWFs. Offshore wind turbines tivities (Nasermoaddeli et al 2017). are often spaced between five and eight times the rotor MOSSCO features generic output and input com- diameter (E.ON Climate & Renewables 2011), which ponents that can be used to integrate, e.g., river nu- tend to range from 80 to 100 m (International Renew- trient fluxes, open ocean boundary conditions and fau- able Energy Agency 2012). Assuming a rotor diameter nal abundance. As a physical driver within MOSSCO, of 100 m and a spacing of five times this distance, this we employed the coastal ocean model GETM (Gen- means that each offshore wind turbine requires 500 m eral Estuarine Transport Model, Burchard et al 2002; spacing or 0.25 km2 area, giving an average wind tur- 2 Klingbeil & Burchard 2013) to calculate sea level, cur- bine density of 4 km− . rents, temperature and salinity distributions, and to transport the biogeochemical and ecological quantities. GETM obtains state-of-the-art turbulence closure from 2.5 Coupled model system the General Ocean Turbulence Model (GOTM, Umlauf & Burchard 2005), and has been shown to have high Extrapolation from the compiled OWF locations to the skill in various studies for the North Sea and SNS (e.g. entire SNS area and the description of M. edulis influ- Gräwe et al 2016; Purkiani et al 2016). ence on the pelagic ecosystem requires a spatially ex- plicit, coupled model approach, for which we employ the Pelagic ecology was described by the Model for Adap- recently developed open source software infrastructure tive Ecosystems in Coastal Seas (MAECS, Wirtz & Ke- Modular System for Shelves and Coasts (MOSSCO, rimoglu 2016) implemented as a FABM module; MAECS www.mossco.de, Lemmen et al 2018). MOSSCO facil- simulates pelagic nutrient, phytoplankton, zooplankton itates the exchangeable coupling of models and data and detritus dynamics and accounts for the acclima- sets and enables the integration of modules describ- tion of intracellular composition in phytoplankton. In ing physical, chemical, geological, ecological and biogeo- our application, MAECS resolves the elements carbon chemical processes. MOSSCO applications for the 3D (C), nitrogen (N), and phosphorus (P), and features 6 Slavik et al. adaptive shifts in phytoplankton ecophysiology as de- (1993). They formulated the relationship in terms of scribed by, e.g., variable chlorophyll a (Chl-a) and Ru- phytoplankton carbon amount concentration ([C]) and BisCO contents. The underlying scheme for these adap- total particulate matter (TPM) relative to an assumed tive shifts has been derived as an optimality theory and individual DW of 300 mg. was first applied to phytoplankton growth and succes- FR = 0.05 [C]0.983, (1) sion by Wirtz & Eckhardt (1996). Pelagic element fluxes TPM,300 · are described similar to other ecosystem models in- The following assumptions for the conversion of coef- cluding nutrient uptake during phytoplankton growth, ficients and carbon units were used: we (i) take the transformation through phytoplankton mortality includ- experimentally-determined organic matter fraction of ing herbivorous grazing, and stoichiometrically controlled 56% (average over all experiments in Bayne et al 1993 of turnover of detritus and dissolved organic matter in measured particulate organic matter (POM) to TPM); terms of C, N, and P. (ii) use carbon mass to molar ratio with of 12.011 mg 1 A full description and an extensive performance as- mmol− ; (iii) use dry weight (DW), ash free dry weight sessment of the model for a decadal hindcast of the SNS (AFDW) and wet weight conversions from Ricciardi & has been provided by Kerimoglu et al (2017). Our cou- Bourget (1998); (iv) apply molar mass conversion in pled setup differs in two respects: (i) we resolve filtra- Redfield stoichiometry (molar ratio 106:16:1 C:N:P) to tion (see Sect. 2.5.1), and (ii) we used the full 3D bio- express the DW to amount carbon ratio as 32.43 mg per geochemical model OmexDia based on Soetaert et al mmol C; (v) scale all rates to individual mass 600 mg (1996) instead of the the single layer soil parameter- with the experimentally confirmed metabolic scaling ization by Kerimoglu et al (2017). There, top-down exponent of 0.67 (Bayne & Worrall 1980; Bayne et al mortality of zooplankton is uniform, while we prescribe 1993). As a lower threshold for filtration, a phytoplank- 3 higher zooplankton mortality near the coast. Further- ton carbon concentration of [C]min = 0.7 mmol m− was more, the ecosystem model MAECS has since evolved chosen, consistent with the threshold suggested by Ri- 3 and now includes a parameterization for viral loss of isg˚ardet al (2003) of 0.5 mg Chl-a m− . Filtration of phytoplankton (Wirtz 2018, Physics or biology? Per- phytoplankton biomass by M. edulis removes particu- sistent chlorophyll accumulations in a shallow coastal late carbon, nitrogen, and phosphorus from the pelagic sea explained by pathogens and carnivorous grazing, phytoplankton compartment, in the same stoichiomet- submitted manuscript, hereinafter referred to as Wirtz, ric proportion as the food, and with it also reduces de- submitted). pendent phytoplankton properties like Chl-a. The phytoplankton compartment is converted to detritus, rep- 2.5.1 Filtration model resenting faeces and pseudofaeces, in carbon, nitrogen and phosphorous. We assume that 20% of the carbon M. edulis actively passes water over a specialized filter- is lost to respiration, leading to higher quality ejected ing structure (the gill), thereby removing a significant detritus compared to the food source; direct DIN (e.g., proportion of both organic (i.e., mainly phytoplankton) urea, see Cockcroft e.g., 1990) release by mussels is not and inorganic particles with high efficiency (Widdows considered. et al 1979). The volume of water passed over the gill The filtration model is technically realised as an area per unit of time and individual body volume is Earth System Modeling Framework (ESMF, Hill et al referred to as the clearance rate (CR). CR has been ob- 2004) component and coupled with MOSSCO (Lemmen served to increase with rising current velocity (Cranford et al 2018) to the Framework for Aquatic Biogeochem- & Hill 1999). At very low ambient Chl-a concentration ical Models (FABM, Bruggeman & Bolding 2014) with 3 the MAECS biogeochemical model in the pelagic and below about 0.5 mg m− , however, CR ceases for energetic reasons (Riisg˚ardet al 2003). The removal of OmexDia (Soetaert et al 1996) with added phosphorous particles from the cleared water, termed the filtration cycle (Hofmeister et al 2014) in the soil FABM domains. rate (FR), depends, among others, on the concentration and organic quality of particles. A physiological regula- 2.5.2 Model setup and scenarios tion of filtration rate is, however, debated and has been studied for high ambient food concentrations only. At The SNS was represented on a curvilinear grid with cell the concentrations typically found in the SNS, full ex- size between 2 and 64 km2, with the highest resolution ploitation of the ambient concentration can be expected in the German Bight. Vertically, the water column was (Clausen & Riisg˚ard1996; Asmus & Asmus 1991). represented by 20 terrain-following σ-layers (Kerimoglu Our model implementation of M. edulis FR is based et al 2017). The model setup accounts for the discharge on the empirical relations identified by Bayne et al of freshwater, phosphorous and nitrogen from major Offshore wind farm impact on North Sea primary productivity 7

Table 3 Scenarios contrasted in this study 2.6 Data for model evaluation Scenario Description Total biomass No observational data is available for primary produc- REF Presence of epibenthic mus- 16 · 1011 in- tivity at the scale of the SNS. Rather than productiv- sels. This represents the refer- div. 96 tons ity as a rate, the stock of phytoplankton is readily ob- ence state against which the addition of artificial hard sub- served with in situ methods or by remote sensing. We strate by OWFs is compared. evaluate Chl-a as simulated by the model against sta- OWF As REF, but with additional 7 · 1010 in- tion observations of chlorophyll fluorescence along three presence of epistructural mus- div. 42 tons transects and against synoptic satellite observations of sels in pelagic surface layers. ocean color. Time series of near-surface Chl-a concentration were obtained from the Dutch authority Rijkswaterstaat through the OpenEarth portal (Rijkswaterstaat 2017). From all available station data, we selected three transects that rivers into the southern North Sea, including the Elbe, cross the coastal nutrient gradient from nearshore No- Weser, Ems, Rhine, Meuse, Scheldt and Humber (see ordwijk, Terschelling and Rottumerplat to up to 235 km Kerimoglu et al 2017). Tidal sea surface elevation was offshore. Satellite observations were obtained from the forced at the open ocean boundary. Open ocean bound- European Space Agency Ocean Color Climate Change ary conditions for nutrients in dissolved and particulate Initiative (ESA-CCI version 3.1), a multi-platform com- forms were obtained from a North Atlantic shelf sim- bined product of Chl-a concentration. ulation with ECOHAM (Ecosystem Model Hamburg, Große et al 2016) and provided as a 10 year climatology Kerimoglu et al (2017). Phytoplankton and zooplank- 3 Results ton were assumed to be at zero-gradient at the boundaries. The meteorological forcing was obtained from the The reconstructed abundance of M. edulis in the SNS long-term Climate Limited area Model reconstruction suggests 1.6 1011 individuals on natural (benthic) sub- · available in the CoastDat database (Geyer 2014). strate and within mussel beds (Fig. 2b) The reconstructed accumulated biomass of benthic M. edulis in Simulations were run for the duration for 14 con- the SNS amounts to total mussel mass of 96 Mt DW secutive years 2000-2013, with the first three years dis- (or 10 Mt AFDW). For the potential “artificial” stock carded to allow for a model spin-up, especially for the at offshore wind turbines, the reconstructed abundance equilibration of winter nutrient storage in the sediment. (Fig. 2c) in the entire SNS amounts to 7.0 1010 indi- · As we are evaluating a sensitivity for a projected year viduals, or 42 Mt DW (4.5 Mt AFDW). Once all the 2030 scenario, the choice of this period is arbitrary planned wind farms are in operation, they will provide and reflects availability of station and satellite data for habitat for mussels that are equal to 44% of the stock model evaluation. Two different scenarios were com- of benthic mussels. pared, (1) presence of only epibenthic mussels (scenario “REF”), and (2) additional presence of epistructural M. edulis at OWFs, focussed within the upper pelagic lay- 3.1 Uncertainty estimates of reconstruction ers (scenario “OWF”) (Table 3). The reconstruction of mussel abundance in the south- The filtration model was configured with phytoplank- ern North Sea is based on analysis of field data (in total ton carbon as the main species to filter, with co-filtration 4074 count observations and 37 214 presence only data, of phytoplankton nitrogen, phosphorous, Chl-a and ru- which reveals a positive correlation (r = 0.78) between bisco. The model diagnostic rates of relative carbon up- abundance and sediment grain size. The 10 m water take were multiplied by phytoplankton carbon concen- depth line is introduced to provide a pseudo-absence tration and subsequently integrated for the entire year criterion. To test a sensitivity of the reconstruction re- to obtain the annual net primary productivity. The 3D sult to the water depth limitation, we also calculated time step of the hydrodynamic model was 6 minutes. the abundance using 25 m water depth contour line Data exchange between the different components of the ( 95% of observed presence occurs within this water ≈ model system was performed every 30 minutes. The depth) as an alternative constraint, which leads to an bottom roughness length was constant at z = 0.002 m, increase in abundance by 1.4 1010 compared to that 0 ≈ · wave forcing was disabled. A Jerlov Type III water class using the 10 m. This amounts to 9% of the total bud- ≈ was used for the radiation scheme. get estimated using the 10 m water depth constraint, 8 Slavik et al. Chlorophyll model–data comparison 25 25 80 a) Noordwijk N10 80 b) Noordwijk N70 ) ) 20 20 3 60 3 60 15 15 m m

g g ( 40 ( 40

10 10 n n o o i i t t a 20 a 20 r r 5 5 t t n n e e c c n n o o C C

0 00 0 30 c) Rottumerplate R50 2530 d) Rottumerplate R70 25 25 25 ) ) 20 20 3 3

20 2004-Jan 2005-Jan 2006-Jan 2007-Jan 2008-Jan 2009-Jan 2010-Jan 2011-Jan 2012-Jan 2013-Jan 20 2004-Jan 2005-Jan 2006-Jan 2007-Jan 2008-Jan 2009-Jan 2010-Jan 2011-Jan 2012-Jan 2013-Jan 15 15 m m

15 15 g g ( (

10 10 n 10 n 10 o o i i t t a a r r 5 5 t 5 t 5 n n e e c c n n o o C C

0 00 0 40 1040 10 35 35 Model 8 8 ) 30 e) Terschelling T10 ) 30 f) Terschelling 235 In situ 3 3 25 2004-Jan 2005-Jan 2006-Jan 2007-Jan 2008-Jan 2009-Jan 2010-Jan 2011-Jan 2012-Jan 2013-Jan 256 2004-Jan 2005-Jan 2006-Jan 2007-Jan 2008-Jan 2009-Jan 2010-Jan 2011-Jan 2012-Jan Satellite2013-Jan 6 m 20 20 g ( 15 154 4 n o i

10 t 10 a

r 2 2 t

5 n 5 e c n o Concentration (g m C

0 00 0

40 g)25 Terschelling T4 h) Terschelling T50 40 25 ) ) 3 3 30 20 30 20

2004-Jan 2005-Jan 2006-Jan 2007-Jan 2009-Jan 2010-Jan 2011-Jan 2012-Jan 2013-Jan 2004-Jan 2005-Jan 2006-Jan 2007-Jan 2008-Jan 2009-Jan 2010-Jan 2011-Jan 2012-Jan 2013-Jan m m

15 15 g g 20 20 ( (

Terschelling transect n n 10 10 o o i i t

t T235 a

a 10 10 r r t t 5 5 n n Rottumerplate e e transect c c i) Locations R70 n n T70 R50 o o T50 C C T10

Noordwijk T4 0 0 0 0 transect

N70

N10

2004-Jan 2004-Jan2005-Jan 2005-Jan2006-Jan 2006-Jan2007-Jan 2007-Jan2008-Jan 2008-Jan2009-Jan 2009-Jan2010-Jan 2010-Jan2011-Jan 2011-Jan2012-Jan 2012-Jan2013-Jan 2013-Jan

Fig.Figure 3 Comparison 3: Comparison between betweenin situ measurements in situ measurements (diamonds), satellite (diamonds), observations satellite (gray line) observations and simulations (gray (black line) of surface chlorophyll for three coastal transects “Noordwijk” (a,b), “Terschelling” (e–h) and “Rottumerplate” (c,d), where numbersand simulations denote distance (black from line)coast (e.g., of surface R10 = 10 chlorophyll km off Rottumerplate). for three Stations coastal T4 and transects T50 did not “Noordwijk” provide in-situ (a,b), data for“Terschelling” part of the time (e–h) series. Theand satellite “Rottumerplate” gridded observation (c,d), and where the simulation numbers from denote scenario distance REF were sampled from coast in a 5000 (e.g., m radiusR10 = around 10 km the off respective Rottumerplate). station location Stations (i). Data from T4 andRijkswaterstaat T50 did and not European provide Space in-situ Agency data (CCI for v3.1 part product). of the time series. The satellite gridded observation and the simulation from scenario REF were sampled in a 5000 m radius around the respective station location (i). Data from Rijkswaterstaat and European Space Agency (CCI v3.1 product). Offshore wind farm impact on North Sea primary productivity 9 and thus does not affect our estimation to a large de- 3.3 Net primary productivity gree. The area covered by mussel beds in the Wadden Sea oscillated annually between 6.2 107 and 3 107 m2 The simulated annual vertically integrated net primary · · since 1998 (Nehls et al 2009). Since our estimation of productivity (NPP, expressed as carbon productivity) the stock in the Wadden Sea is based on a maximum in the SNS, as well as the climatological average over value, the annual oscillation of mussel beds would affect the years 2003–2013, broadly separates the model do- the total estimated budget in the SNS by 10% at most: main into three regions (Fig. 4): (1) the coastal area in- our reconstruction and the annual fluctuation should be cluding the Wadden Sea, of highly variable and low ver- 2 1 within 30% of the total estimated budget, taking into tically integrated carbon production (< 50 g m− a− , account oscillations of the mussel beds and the impact very shallow and turbid water), (2) the near-coast tran- 2 1 of extreme wind waves on offshore wind turbines, which sition zone with a high productivity above 180 g m− a− 2 1 might occasionally clear all mussels from a wind turbine up to 400 g m− a− , and (3) the offshore SNS, again ≈ 2 1 (Krone et al 2013). with relatively low productivity around 90 g m− a− . This pattern is consistent across all simulation years. Maximum productivity in this simulation occurs in an elongated coast-following area 20 km north and east of the West Frisian and East Frisian islands, in the central Southern Bight, and off the East coast of England. Within the period 2003–2013, the year 2010 exhibits the 3.2 Simulated chlorophyll and comparison to 2 1 lowest productivity with 118 47 g m− a− , and the it station/statellite data ± 2 1 is highest in 2003 (142 52 g m− a− ). Most OWF are ± located in the transition zone between the maximum Simulated surface Chl-a for the years 2003–2013 ex- productivity band and the low productivity areas off- hibits a typical annual phytoplankton cycle with a large shore. spring bloom and a smaller summer bloom. At the bloom There is less primary productivity locally in the 3 peak, the Chl-a concentration reaches 20–50 mg m− OWF than in the REF scenario in all years (Fig. 5). The 3 at coastal and below 5 mg m− at far offshore locations maximum loss occurs within the OWF areas (up to 8%), (Fig. 3). The simulation reproduces in situ time series of and is on average 3.7 1.5%, with a maximum in 2005 ± near-surface Chl-a concentration along the three tran- and 2010 (4.1%) and a minimum in 2008 (3.3%). Vari- sects from Noordwijk, Terschelling and Rottumerplate. ability is high between the different OWF areas with a The peak spring bloom Chl-a concentrations are well climatological standard deviations of 1.5%). Loss out- matched across the entire coastal gradient; overall the side the OWF areas is much smaller, but the change 3 simulation has a small positive bias below 4 mg m− , is consistently negative and 0.4 2.5%) in the long- 3 ± with a larger overestimation of 9 mg m− at Noord- term mean. This outside-OWF loss also has a typical wijk 10. The variability of Chl-a concentrations is also distribution with largest losses in the maximum pro- well represented. At most stations, the simulated and ductivity band along the East and West Frisian barrier the observed standard deviation agree to within 1.2 mg islands and in the vicinity of the OWF. In many years, 3 m− , with the exception of Terschelling 10 and Noord- productivity is increased (a very small increase below wijk 10, where the model standard deviation is 3 mg 1%) along the North Frisian barrier islands. m 3 higher than the observed variability. − To identify a regional effect outside the OWFs, we The comparison against satellite observations shows identified the maximum increase and maximum decrease that both model and in situ observations have a wider of daily NPP between the scenarios for each year (Fig. 3.3, temporal variability, while the mean Chl-a concentra- shown for 2006). The maximum daily decrease of NPP 3 1 tion is again well represented. The model surface Chl-a is 11 9 mmol C m− d− , with the largest decreases − ± 3 1 climatology, i.e. the multi-annual average over all years (below 20 mmol C m− d− ) occurring within the two − 2003–2013, has a small positive bias compared to satel- large clusters of OWF areas in the eastern SNS. The 3 lite observations: it is below or near 1.0 mg m− in fall spatial distribution of the maximum daily increase of and winter, and largest during May, when simulated NPP shows changes of the same order of magnitude 3 3 1 Chl-a is 3.6 mg m− larger on average. This difference throughout the SNS (11 12 mmol C m− d− ). In con- 3 ± is smallest (below 1 mg m− ) offshore and where most trast, however, maximum increases also occur outside 3 3 1 of the OWF are located. It is largest (up to 15 mg m− ) the OWF areas, with the largest increases (above 20 mmol C m− d− ) in the near-shore high-productivity zone along the East east of the central eastern SNS OWF cluster and also and West Frisian barrier islands. bear the East Frisian and North Frisian barrier islands. 10 Slavik et al.

Annual net primary productivity (scenario OWF) 2003 2004 2005 2006

142.2±51.6 130.0±51.6 120.5±47.2 127.7±50.7

64.5 222.1 52.6 217.4 47.0 200.9 48.3 214.2

200760 80 100 120 140 160 180 200 200860 80 100 120 140 160 180 200 200960 80 100 120 140 160 180 200 201060 80 100 120 140 160 180 200 (g m 2 a 1) (g m 2 a 1) (g m 2 a 1) (g m 2 a 1)

127.8±50.7 132.5±52.0 122.8±51.2 118.1±46.5

50.5 214.5 53.4 222.2 42.7 209.4 49.2 196.4

201160 80 100 120 140 160 180 200 201260 80 100 120 140 160 180 200 201360 80 100 120 140 160 180 200 2003–201360 80 100 120 140 160 180 200 (g m 2 a 1) (g m 2 a 1) (g m 2 a 1) (g m 2 a 1)

127.6±49.0

125.7±51.3 128.5±49.9 51.9 211.2127.3±50.0 127.6±49.0 46.8 213.9 51.6 215.2 52.5 208.3 51.9 211.2

60 80 100 120 140 160 180 200 60 80 100 120 140 160 180 200 60 80 100 120 140 160 180 200 60 80 100 120 140 160 180 200 (g m 2 a 1) (g m 2 a 1) (g m 2 a 1) (g m 2 a 1) 60 80 100 120 140 160 180 200 (g m 2 a 1)

FigureFig. 4 Simulated 4: Simulated annual netannual primary net productivity primary productivity2003–2013. Contours 2003–2013. denote areas Contours with OWF denote epistructuralareasM. with edulis OWF. epistructural Mytilus edulis. The maximum increase is seen up to 50 km away from should note that they averaged over the much higher the farms. trophic state period 1985–2005, such that lower productivity should be expected for the period 2003–2013. Already Emeis et al (2015) report values around 2 1 4 Discussion 200–270 g m− a− , for an area corresponding to our coastal high productivity region in the year 2002, based Net primary productivity calculated by our coupled on the Ecosystem Model Hamburg (ECOHAM, Pätsch model shows low productivity in the Wadden Sea area & Kühn2008). The comparison by van Leeuwen et al (Fig. 4). Simulated Chl-a concentrations in this area (2013) with in situ observation derived NPP estimates are also lower than those estimated by the satellite im- by Weston (2005), however, also showed that their model agery (e.g., Kerimoglu et al 2017; Ford et al 2017), under- or overestimates observations by a factor of two while in the open SNS, our model modestly overesti- depending on the area type (stratified, frontal bank), mates Chl-a and probably also NPP. NPP simulated and overestimated surface mixed layer productivity by by van Leeuwen et al (2013) with the same hydrody- up to a factor of five (van Leeuwen et al 2013, Table 1). namic (GETM) but with a different ecosystem model Given these considerations the simulated productivity (ERSEM, Baretta et al 1995) is much higher (on aver- in the coastal and open SNS (ranging between 50 and 2 1 2 1 age 318 29 g m− a− ) than NPP simulated here for 400 g m− a− ) is plausible. Its skill needs to be assessed ± their region termed “SNS”, referring to a small area of against observational data in forthcoming studies, such the Southern Bight of the North Sea. While this is not as Wirtz (submitted) . a good choice of region for comparison, because the ex- In contrast to productivity, biomass related vari- act location of this maximum productivity region varies ables are readily observable from a variety of platforms: between the years (Fig. 4), also their entire North Sea the agreement between the in situ measured, the re- 2 1 estimate of 180 10 g m− a− is higher than our calcu- mote sensing observed, and the simulated Chl-a concen- ± lation. When comparing the two studies, however, one trations (Fig. 3) suggests that phytoplankton dynamics Offshore wind farm impact on North Sea primary productivity 11

Annual net primary productivity (relative diﬀerence OWF - REF) 2003 2004 2005 2006

-3.8±2.0 -0.4±0.8 -3.4±1.5 -0.4±0.9 -4.1±2.0 -0.5±1.0 -3.9±1.6 -0.7±1.7

8 7 6 5 4 3 2 1 0 1 8 7 6 5 4 3 2 1 0 1 8 7 6 5 4 3 2 1 0 1 8 7 6 5 4 3 2 1 0 1 2007 Relative change (%) 2008 Relative change (%) 2009 Relative change (%) 2010 Relative change (%)

-3.1±1.5 -0.3±1.2 -3.3±1.3 -0.4±1.6 -3.6±1.4 -0.5±1.2 -4.1±1.6 -0.2±25.7

8 7 6 5 4 3 2 1 0 1 8 7 6 5 4 3 2 1 0 1 8 7 6 5 4 3 2 1 0 1 8 7 6 5 4 3 2 1 0 1 2011 Relative change (%) 2012 Relative change (%) 2013 Relative change (%) 2003–2013Relative change (%)

-3.7±1.5 -0.4±2.5 -4.0±1.7 -0.5±2.6 -4.0±1.8 -0.4±2.1 -3.4±1.4 -0.5±3.2 -3.7±1.5 -0.4±2.5

8 7 6 5 4 3 2 1 0 1 8 7 6 5 4 3 2 1 0 1 8 7 6 5 4 3 2 1 0 1 8 7 6 5 4 3 2 1 0 1 Relative change (%) Relative change (%) Relative change (%) Relative change (%) 8 7 6 5 4 3 2 1 0 1 Relative change (%)

FigureFig. 5 Simulated 5: Simulated relative difference relative of difference annual net primary of annual productivity net primary 2003–2013 product and climatologicalivity 2003–2013 between simulations and cli- with and without epistructural mussels, calculated as 100 · (OWF − REF) /REF. matological between simulations with and without epistructural mussels, calculated as 100 (OWF REF) /REF. · is well reproduced,− which builds confidence in the rep- into the entire water column. After filtration, nutrients resentation of primary productivity by the model. Mod- that were bound in phytoplankton are readily made erate discrepancies in the cross-coastal distribution of available by pelagic remineralization of the ejected high- NPP and Chl-a are in part due to the simplified de- quality detritus. By this mechanism, it is to be expected scription of water attenuation by high concentrations that filtration sustains a longer bloom through faster of suspended particles close to the coast (Kerimoglu nutrient recycling and also supports higher productiv- et al 2017) and the prescribed mortality gradient of ity in regions that receive nutrient-enriched and phy- zooplankton. However, given that the concentrations toplankton reduced water masses from OWF areas by are in the range where mussel filtration exhibits a lin- currents. ear functional response to food concentration, the mod- The maximum daily NPP changes (Fig. 3.3) indeed erate mismatch should not affect our estimates of the demonstrate that the ecosystem effect of epistructu- relative effects of additional epistructural filtration. ral filtration is not a local one, but a regional one, with a decrease of phytoplankton carbon throughout many parts of the SNS (albeit concentrated up to 20 km 4.1 Non-local spatial effects around the OWF) and a strong increase up to 50 km outside the OWF area. It can be argued that the mag- During a bloom, phytoplankton will usually first appear nitude of several percent per year in overall draw-down at the surface and then sink down through the water is well within the uncertainty range of state-of-the-art column (Cloern 1996), producing a delay between sur- ecosystem models. The effect is, however, regionally face phytoplankton concentrations and those at depths. very different and thus changes horizontal gradients in Similarly, the phytoplankton loss from epistructural fil- productivity that have not been discussed before: there tration is first and clearly visible at the surface, where is a notable impact of projected epistructural suspen- M. edulis are concentrated, before being transmitted sion feeders on the ecosystem functions of a regional 12 Slavik et al.

available primary productivity, especially at the local scale, as a result of a higher abundance of filter feed- Maximum effect on NPP ers (Prins et al 1997). Filtration activity transforms the carbon, nutrient and energy flows at OWFs from (a) Decrease which the benthic food-web benefits, with faeces, pseudofaeces and dead mussels enriching the benthic organic pool, as observed in many shellfish aquaculture facilities worldwide (e.g. Cranford et al 2007). Notably, filter feeding much accelerates the transformation of living biomass into partially dissolved and bio-available nutrients, which may again fuel productivity. The effects on autotrophic standing stock investigated in our study hence do not provide a full account of processes relevant for assessing primary productivity. Our results suggest that the increased abundance of M. edulis at OWFs only moderately affects ecosystem functioning. They emphasize M. edulis’s role as (a) Increase an ecosystem engineer (Borthagaray & Carranza 2007), not just locally but on a scale of greater than 50 km. Pelagic primary productivity is just one of many facets of ecosystem functioning, which have been hypothesized to be altered by OWFs (amongst others): 1. Networks of OWFs are beneficial for the conservation of threatened species by acting as de facto marine protected areas (Inger et al 2009). Access to most areas designated as OWFs is limited, primarily for safety reasons, which limits anthropogenic pressures such as fishing, including bottom trawling, potentially leading to an increased level of biodiversity at OWFs as compared to unprotected areas (Kaiser et al 2006; Tillin et al 2006; Inger et al 2009). 2. Mussels such as M. edulis play a significant role in modifying the natural substrate and increasing species Fig. 6 Maximum daily net primary productivity effect of epi- richness (Borthagaray & Carranza 2007). M. edulis bio- structural M. edulis (exemplary for year 2006). (a) Maximum decrease in 2006, and (b) maximum increase in 2006. engineers its surrounding environment through shell lit- ter fall (White & Pickett 1985), water filtration and clarification (Newell 2004), concentration of nutrients shelf sea. Even though the decrease in primary produc- (van Broekhoven et al 2014), ultimately increasing the tivity is relatively small, it extends over a large area and species diversity and landscape heterogeneity as a result intensifies in close proximity to OWFs, reaching a max- of structural and functional effects (Norling & Kaut- imum reduction in annual net primary productivity of sky 2007). Shells, both dead or living, increase the de- 8%. Despite the dilution of meso-scale mussel density gree of habitat complexity, encouraging a higher level due to the low area density of offshore wind turbines, of species richness (White & Pickett 1985). Bivalve and massive biofouling accumulates to an effect size which gastropod shells are persistent and abundant physical is only one order of magnitude lower than the 60% re- structures which provide substrata for attachment and duction within shellfish aquacultures (Waite 1989). refuge from predation as well as physical or physiological stress (Gutiérrezet al 2003). 3. Mediated through the associated epibenthic com- 4.2 Altered ecological functioning munity, OWF constructions can act as stepping stones for the dispersal of exotic species (Glasby et al 2007). Primary productivity represents the major energy source The artificial habitat is open for colonization by new for ecosystems globally (Imhoff et al 2004). Our model species assemblages (Wilson & Elliott 2009), which could results indicate that the construction of OWFs reduces not quickly establish in soft-bottom sea regions. One Offshore wind farm impact on North Sea primary productivity 13 example of this is Telmatogeton japonicus, the marine up-scaling of data sampled from single piles. And the splash midge, which is native to Australasian waters. benthic reconstruction suffers from the sampling bias Known to be transported on the hulls of ships, it has introduced by opportunistic observations. These data been observed at OWFs in Denmark and along the issues will likely ameliorate in the future with moni- Swedish Baltic coast (Wilhelmsson & Malm 2008). The toring programs and systematic surveys. The filtration projected density of offshore constructions will likely model is very simple and does not include, for example, facilitate immigration by non-native species (Bulleri & age structured population dynamics or nutrient recy- Airoldi 2005), such as the leathery sea squirt (Styela cling: this study shows how essential it is to improve fil- clava), slipper limpet (Crepidula fornicate), pacific oys- tration models, which so far are often neglected in shelf ter (Crassostrea gigas) and Japanese skeleton shrimp ecosystem models. For studies adressing the forthcom- (Caprella mutica) (Buschbaum & Gutow 2005; Diederich ing decades, a more accurate quantification of the epi- et al 2005; Lützen1999; Thieltges et al 2003). Through structural filtration effect seems to be required. Physi- these changes in biodiversity, OWFs could shape the cal effects of wind farm on atmospheric boundary layer marine ecosystem beyond their physical boundaries. circulation and ocean currents and vertical mixing (e.g. 4. M. edulis are a highly diverse prey source capa- McCombs et al 2014; Carpenter et al 2016) have not ble of supporting higher trophic levels, especially vagile been considered by our coupled model: there is still a demersal megafauna (e.g. fish like Trisopterus luscus scale problem that needs to be addressed in physical and crabs like Cancer pagurus) (Langhamer et al 2009; modelling to bridge the wind pile (order of 10 m) to Reubens et al 2011). Their abundance and distribution ecosystem (order of 100 km) scales. Recent develop- at OWFs is driven by changes in attraction, produc- ments in nested or unstructured models seem promis- tivity and redistribution (Bohnsack 1989). An increase ing here. Last but not least, the uncertainties of the in the population of vagile demersal megafauna fur- simulation result are difficult to quantify: estimates of ther impact the local community, leading to increased productivity in the literature have large uncertainties species diversity (Wilhelmsson & Malm 2008). Larger themselves. There are no regional studies to which the megafauna may also benefit from increased food avail- filtration rates can be compared. Our findings of a 8% 3 1 ability (e.g., Brasseur et al 2012), with seals extending annual productivity and up to 30mmol C m− d− daily their distribution towards an OWF after construction productivity justify further research into the large-scale in Denmark (Tougaard et al 2006). impact of OWFs. Remote sensing observations might Many of the ecosystem feedbacks and hence changes provide the first regional direct observations of OWF to ecosystem services are yet unknown and need to be effects as the resolution of the sensors now allows the studied both in situ and in future system-wide synoptic identification of single wind turbines, as has already studies. Mostly for supporting the planning process, a been done for physical parameters (Platis et al 2018). high number of often not published studies were con- ducted, almost always considering individual offshore 5 Conclusions wind turbines and focused on selected potential effects such as on birds, megafauna, or hydrodynamics (Bailey Construction of offshore wind farms (OWFs) in the et al 2014, e.g.). southern North Sea is predicted to continue into the future, highlighting the importance of understanding 4.3 Limitations and outlook the potential nature and magnitude of the environmental impact of the epistructural biomass known to ac- This study is the first large scale assessment of epi- cumulate on their subsurface structures. Epistructural structural M. edulis filtration at OWFs. The level of communities at OWFs in the southern North Sea are quantification achieved in our study clearly shows that dominated by M. edulis, a filter feeder capable of in- at least with respect to primary productivity there is ducing extensive ecological change through filtration, a non-negligible regional ecosystem effect originating amongst other processes. The construction of all cur- from epistructural M. edulis filtration. Modular model rently planned, consented and under construction OWFs, systems are needed to integrate effects and feed-backs in addition to those already in operation, is likely to in- across trophic levels and up to the regional scale, as pro- crease the overall abundance of M. edulis in the south- posed and to a large but not complete degree realised ern North Sea by more than 40%. In addition to pro- here. viding an additional food source and a new habitat, M. There are still large uncertainties related to simulat- edulis also remove phytoplankton from the water col- ing complex ecosystem interactions. The reconstruction umn through filtration, which impacts ecosystem func- of filter feeder epistructural abundance is yet a simple tioning. 14 Slavik et al.

Reconstructing and analysing the impact of epistruc- marine ecosystem model. Netherlands Journal of Sea Re- tural biomass at OWFs on pelagic primary productivity search 33(3/4):233–246 at a larger spatial scale, in this case the southern North Bayne BL, Worrall CM (1980) Growth and Production of Mussels Mytilus edulis from Two Populations. Ma- Sea, provides valuable insights into ecosystem function- rine Ecology Progress Series 3:317–328, DOI 10.3354/ ing which are not visible at the scale of a single offshore meps003317 wind turbine or OWF. The impact of OWFs on annual Bayne BL, Iglesias J, Hawkins AJS (1993) Feeding behaviour primary productivity is predominately local, at short of the mussel, Mytilus edulis: responses to variations in quantity and organic content of the seston. 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