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A Sustainability Index of potential co-location of offshore wind farmsand open water aquaculture
G. Benassai a,*, P. Mariani b,c, C. Stenberg b, M. Christoffersen b
aDepartment of Engineering, University Parthenope, Naples, ItalybNational Institute of Aquatic Resources, Technical University of Denmark, Lyngby, DenmarkcCentre for the Ocean Life, Technical University of Denmark, Charlottenlund, Denmark
a r t i c l e i n f o
Article history:Available online 9 May 2014
a b s t r a c t
This paper presents the definition of a Sustainability Index for the co-location in marine areas of offshorewind farms and aquaculture plans. The development of the index is focused on the application of MCEtechnique based on physical constraints and biological parameters that are directly linked to the primaryproduction. The relevant physical factors considered are wind velocity and depth range (which directlygoverns the choice of the site for energy production and for offshore technology), the relevant biologicalparameters are SST, SST anomaly and CHL-a concentration (as a measurement of the productivity). Thefurther development of the technique, already used in open water aquaculture localization, consists inconverting raw data into sustainability scores, which have been combined using additive models, inorder to define the overall sustainability. The study area used to implement the computation of theSustainability Index (SI) was identified in the Danish portion of the Baltic Sea and in the western part ofthe Danish North Sea. Results on the spatial distribution of the SI underline different responses as afunction of the physical and biological main influencing parameters.
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1. Introduction
Offshore wind farms allow for increased availability of windpower and wind persistence, as well as lower visual impact of theturbines (EWEA, 2009). Compared to onshore wind, offshore windfarms are more costly to install and maintain but also have anumber of key advantages: stronger and more stable wind; largerwind turbines; less conflict with neighbouring citizens and otherstakeholders unless they interfere with competing maritime ac-tivities or impact negatively on important marine environmentalinterests (Bilgili et al., 2011). Moreover, enhanced current velocitydue to the presence of the piles and to the air fluxes of the turbinesmay increase the environmental sustainability of aquaculture plansin these areas (Moland et al., 2013).
On the other hand, the expansion of the offshore wind in-dustries in recent years is likely to result in high spatial competition(Buck et al., 2008; Mee, 2006). The focus for co-location has inev-itably been on wind farms and aquaculture as these activities has
some common traits and possible synergies. They both claim largeareas at sea in relatively shallow areas, have restrictions for othertypes of activities (e.g. ship traffic and fishing) and have logisticsand infrastructures that to some degree can benefit from a co-use(Benassai et al., 2011). Furthermore, they may provide an alterna-tive livelihood for fishermen faced with losing their traditionalfishing grounds (Michler-Cieluk and Krause, 2008).
The advantage of moving aquaculture further away from coastalwaters consists in an enhanced water quality. Open ocean watersare in general less exposed to anthropogenic impacts and mightprovide a continuous supply of clean water having satisfactorylevels of dissolved oxygen and less pollutants like pesticides andnear-surface agents (Buck, 2004). On the other hand, significantalterations to the technologies may be required, e.g., to the turbinefoundations to withstand the additional mechanical load ofaquaculture equipment. In addition, open ocean hydrodynamicconditions can represent further challenges for aquaculture in-frastructures and require resistant species that can withstandstrong currents and large wave heights.
The identification of sustainable aquaculture sites among thosealready devoted to offshore wind farms requires therefore a deepknowledge of the marine environment in view of the mooringoptimization (Benassai et al., 2014) as well as an understanding ofthe numerous conflicting uses and constraints (Longdill et al.,
* Corresponding author. Tel.: þ39 (0)81 5476590; fax: þ39 (0)81 5476414.E-mail addresses: [email protected], [email protected] (G. Benassai),
[email protected] (P. Mariani), [email protected] (C. Stenberg), [email protected](M. Christoffersen).
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2008). The spatial decision making process begins with therecognition and definition of the problem, e.g., identifying suitableand sustainable sites for the open coastal culture of shellfish. Oncedefined, the Multi-Criteria Evaluation (MCE) technique (Nath et al.,2000), focuses on specifying, creating and aggregating compre-hensive sets of evaluation criteria.
The development of the technique, already used in open wateraquaculture localization (Longdill et al., 2008), consists in con-verting raw data of physical and biological parameters into sus-tainability scores, combined using additive models, in order todefine the overall sustainability.
In order to overcome the complications arising from the varietyof scales and units involved, the MCE technique requires each cri-terion to be transformed to comparable units (Longdill et al., 2008).Data are generally converted to standardised sustainability scores(normalised values between 0 and 1) through the use of ParameterSpecific Sustainability Functions (PSSFs) (Vincenzi et al., 2006).
In Denmark several sites were used or planned to be used foroffshore wind farms installations (Stenberg et al., 2010). In thispaper we have computed a spatial distribution of the SI, and thenwe have verified if some of those sites appear to be suitable loca-tions for a multi-use purpose combining wind energy and aqua-culture activities.
The paper is structured as follows: in Section 2, the factorsinfluencing the sustainability of offshore aquaculture are described;in Section 3 the analytic framework of MCE and of PSSF is pre-sented, while in Section 4 the study area and data source areillustrated. Finally, the experimental results and the discussion aregiven in Sections 5 and 6, together with some conclusions on therobustness and the limits of the method in Section 7.
2. Factors influencing the sustainability of aquaculture inoffshore wind farms installations
The first step of the MCE technique is the identification of therelevant factors influencing aquaculture sustainability, amongthem the main physical and biological factors are wind velocity andpersistence, current velocity as a function of water depth, depthrange (which directly governs the choice of offshore technology)and both SST and SST anomaly, dissolved oxygen, CHL-a concen-tration, (which govern the productivity).
The wind climate information is the most important part of thedata base. The wind speed has to be related to the rated power ofthe wind mill, which is reported in Fig. 1 for a 3.6 MW turbine,particularly suited for offshore sites. It is noted that the majority ofthe power percentage is obtained for a wind speed range between8 and 14 m/s.
The sensitivity of the economic performance of thewind farm toreliable wind speed data claims for precise windmeasurement. The
approach followed in this paper is the use of real measured data,which is obtained by remote sensing techniques.
Current speed is another important parameter, because in-creased current speed can act to decrease flushing times throughaquaculture developments, thus enabling the support of denserpopulations than if water exchange was more limited. Further,several authors have correlated bivalve growth directly to currentspeeds (Longdill et al., 2008).
The depth range is crucial for offshore wind farm installations,because of the robustness and economy of the monopile solution,which is suitable for depths not higher than 30e40 m. For higherdepths, the structures are totally different and much more expen-sive. So the most important factor influencing the feasibility of anoffshore wind farm is the favourable depth condition (lower than40 m).
Optimal sites for a sustainable offshore aquaculture are char-acterized by conditions leading to relatively enhanced growth rates,which are largely controlled by food availability and phytoplanctondynamics (Winter, 1978; Soniat and Ray,1985). Several studies haveidentified strong direct linkage (R2 ¼ 0.77) between upwellingindices and cultured shellfish production and quality (Espinosa-Carreon et al., 2004). In fact upwelling typically provides a richsource of nutrients to enhance phytoplankton growth and largevolumes of shellfish are cultured in areas of high phytoplanktonconcentrations.
An upwelling index, associated to high productivity areas, isconsidered an increase in sea surface CHL-a concentrations(Valavanis et al., 2004). In fact areas with high CHL-a concentra-tions are associated with an increase of available nutrients tophoto-synthesisers. The oceanographic processes such as upwell-ing, gyres or eddies, which can transport cold, nutrient-rich waterfrom below the pycnocline to the euphotic zone where photosyn-thesis of autotrophs organisms takes place are typically associatedwith low SSTs. The spatial integration of normalized SST and CHL-aanomalies indicates areas of productive processes such as upwell-ing, gyres, etc. (Valavanis et al., 2004). The use of climatological(long-term) datasets allow the identification of persistently pro-ductive regions, independent of short-term variability.
Coastal monthly mean SSTs were obtained for each coastalsegment as the mean temperature of the entire coastal segmentFig. 1. Rated power of turbine as a function of the wind speed.
Fig. 2. Distribution of offshore wind farms (red ¼ operational/blue ¼ proposed) inDenmark. (For interpretation of the references to colour in this figure legend, thereader is referred to the web version of this article.)
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obtained from the climatological monthly mean data set. Theclimatological monthly mean data were compared with the cor-responding monthly data mean in order to identify temperatureanomalies and areas of persistently higher or lower temperaturethan that of the coastal mean.
Long-term coastal CHL-a anomalies were generated for eachcoastal segment in an identical manner to that of SST anomalies,that is through the subtraction of the monthly mean data to theclimatological monthly mean data.
Normalised (0e1) climatological SST and CHL-a anomalies weremultiplied together to identify regions with persistently low SSTand high CHL-a, which correspond to areas of productive processesalready mentioned.
3. Analytic framework
The usage of MCE techniques requires the transformation of thevaluesof eachparameter in comparableunits through theParameter-Specific Sustainability Functions (PSSFs), which convert the raw datato standardised sustainability scores with reference to the specificphysical parameter. The sustainability scores are defined on an arbi-trary scale between0 (non-suitable parameter) and1 (most suitable).
MCE techniques are used to aggregate contributing factors into aspatially variable Sustainability Index for co-location of open wateraquaculture in OWF areas.
Thus, we defined the Sustainability Index (SI) as the geometricmean of all environmental indexes relevant to our study. Thoseenvironmental indexes were based on climatological values of: SeaSurface Temperature (SST), chlorophyll concentration, topography,wind power and SST anomalies (Becker and Pauly, 1996) which arethen modified by their Parameter Specific Sustainability Functionsand subsequent restrictions by the Boolean constraints layer:
SI ¼Yn
i¼1
PSSFi (1)
This one allows an element to belong to a ‘crisp’ set (0 or 1)taking into account the existing technology and environmentalrestrictions.
4. Study area and data source
4.1. Study area
The study area used as a test site to implement the computationof the Sustainability Index (SI) was identified in the Danish portionof the Baltic Sea and in the western part of the Danish North Sea,where many offshore wind farms are already installed and manyprojects are in construction or in the planning stage (Fig. 2).
4.2. Wind speed
Wind speed data were obtained from Scatterometer Clima-tology of Ocean Winds (SCOW) (Risien and Chelton, 2008) andwere based on a yearly average of climatological monthly values(122 months included) at w26 Km resolution in the study area(Fig. 3). This relatively low resolution did not allow a detaileddescription of the Kattegat area. In order to have a completecoverage in the definition of the PSSF of the study area we thenextrapolated the wind speed values in the Skagerrak into the Kat-tegat area. The resulting map showed average wind speed rangingbetween 7 and 10 m/s with higher values in the Northern zones.
4.3. Water depth
Depth data in the North Sea area were derived from ETOPO2seabed topography (Fig. 4) which showed a quite shallow anduniform depth in the southern part of the basin and along the entirecoastline. In particular close (<30 Km) to the Danish coast, depthwas always lower than 20e25 m.
4.4. SST anomalies
The Sea Surface Temperature (SST) and SST anomalies in theNorth Sea (Figs. 5 and 6) were obtained from the Advanced VeryHigh Resolution Radiometer (AVHRR) Pathfinder data produced byNOAA and the University of Miami’s Rosenstiel School of Marineand Atmospheric Science (http://pathfinder.nodc.noaa.gov). SSTcould have ecological consequences on the growth of organisms
Fig. 3. Map of seabed topography in the study area.
Fig. 4. Map of the distribution of Sea Surface Temperature (SST) in the study area.
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with higher temperatures allowing faster growth of the aquacul-ture species. Moreover, additional potential environmental changesin response to changes in SSTcould bemeasured as the frequency oftemperature anomalies (Halpern et al., 2008).
Data for SST anomalies included anomaly frequency between2000e2005 and 1985e1990. Those data were derived fromHalpern et al. (2008) and provided a baseline for determiningwhen temperatures were unusually warm having negative con-sequences on aquaculture species. Lower anomalies occurred inthe South-Eastern part of the North Sea and further East, into theSkagerrak and Kattegat. The Southern and Eastern North Sea, fromthe German Bight into the Skagerrak, were under the influence ofthe Jutland current which in the Northern part of Denmarkbranches into two currents, one staying in the North Sea and theother entering the Skagerrak. In the Kattegat the influence of
oceanic circulation was weaker than in the North Sea sincedominating factors were river run-off and larger atmosphericvariability above land (Becker and Pauly, 1996; EC-FP5 REVAMP,2005).
4.5. CHL-a anomalies
The CHL-a climatology in the North Sea (Fig. 7), exhibited asimilar variation: in the South-Eastern part of the North Sea CHL-awas influenced by the shallow water and the strong tidal currentswhich transported high sediment loads. In general the North Seacirculation was anti-clockwise, so that German Bight waters, whichwere nutrient-rich due to river discharge in relatively low depth,were transported up along the Jutland west coast, into the Ska-gerrak and even spilled into the Kattegat (EC-FP5 REVAMP, 2005).
5. Computation of PSSF for offshore wind farms and openaquaculture plans
Raw data of the environmental conditions described abovewerere-binned in the study area using relatively high horizontal reso-lution (1 Km) and nearest values interpolation method. An ad-hocnormalization procedure was then applied to compute the PSSFranging between 0 and 1.
Seabed topography and wind speed were first considered toderive PSSF of the wind farm activities in the North Sea. Withreference to water depth, due to the different cost percentage be-tween the turbine and the monopile structure in relatively lowdepths (<40 m) a step cost function for the depth was assigned,with four steps ranging from below 20 m (most favourable), tohigher than 40 m (least favourable).
With reference to the wind speed, a conversion between windspeed and energy was obtained using the standard power curve forthe Vestas V112, 3 MW turbine which was particularly suited foroffshore sites. Then a normalization procedure (values/max value)ensured PSSF varying between 0 and 1. The maximum value of 1(most favourable) was given to the mean wind speed correspond-ing to 100% of the power transfer function (12 m/s), while the otherPSSF values were assigned consistently. Thus the PSSF was not
Fig. 5. Map of the distribution of SST anomaly in the study area.
Fig. 6. Map of the distribution of CHL-a concentration (in log scale).
Fig. 7. Map of the distribution of wind speed in the study area. Data are lost betweenDenmark and Sweden.
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linear with wind speed but shaped accordingly to the physicalenergy transfer from the wind.
SST, SST anomaly and CHL-a were the three environmentalfactors assumed to affect the PSSF of aquaculture activities. Thecalculation of PSSF for the SST and CHL-awas made on the basis of anormalization technique over the environmental data (values/maxvalue). Chlorophyll values changed over many orders of magnitude,so in order to weight similarly all the environmental parameters anadditional rescaling was applied to the logarithmic (log10) of CHL-ain order to obtain the PSSF ranging from 0 to 1. The SST anomalieswere transformed in anomaly indexes suitable for aquaculture,attributing 1 to low values of temperature anomaly and 0 tomaximum positive anomaly, because large SST anomalies aredetrimental for aquaculture activities. A synthesis of the variablesdirectly considered for SI and their ranking for SI computation wasgiven in Table 1.
A constrain was assigned to the benthic sediment type, whichcan be only constituted by sand or gravel, due to the limits of themonopile technology. A similar constrain was assigned to the cur-rent speed, with a maximum allowable value of 2 m/s (Table 2).
The final ranking of the Sustainability Index was classified infour distinct classes (good, medium, poor, unsuitable) on the basisof the following equation:
SI ¼
8>><>>:
unsuitable if SI < 0;25poor if 0;25 � SI < 0;50medium if 0;50 � SI < 0;75good if SI � 0;75
9>>=>>;
(2)
6. Results
The spatial distribution of the Sustainability Index (SI) in thestudy area shows that the coastal areas in the South and East NorthSea are thosewith relatively higher SI (Fig. 8). The index varies fromSI w 0 in the open ocean northern North Sea to SI w 0.6 in thecoastal zones.
Numerical values of the SI for several sites already elected orplanned to be used for offshore wind farms are reported in Fig. 9.
The highest indexes are located in the Horns Rev windfarms:Horns Rev3 (7.9E 55.7N) and Horns Rev1,2 (7.8E 55.5N) in the NorthSea. But also the windfarm Nysted (11.2E 55.0N) in the Baltic Seahas a relative high score (SI ¼ 0.62), while the existing windfarmAnholt in the Kattegat (11.3E 56.5N) has a relatively low rank (22ndposition).
We note that depth is largely controlling the distribution and itis very low for Horns Rev3 and Horns Rev 1,2 (2e9 m), low forNysted (6e9.5 m) and intermediate for Anholt (14e20 m).
On the other hand SSTandwind speed are higher for Horns Rev3and Horns Rev 1, while SST anomaly is lower. CHL-a values arecomparable for all cited sites.
The analysis therefore suggests that Horns Rev installation arethe most suitable sites for running aquaculture activities.
7. Discussion
The suitable values of the Sustainability Index (SI) are mainlyassociated, on the basis of the ranking range of Table 1, with higherwinds, lower depths, lower SST anomalies and higher CHL-aanomalies. Due to low resolution wind and CHL-a data, the SI dis-tribution is mainly influenced by the SST and CHL-a anomaly spatialdistribution. This is the reasonwhy Fig. 8 shows the higher values ofSI for a wide area offshore the German Bight and the Kattegat, andfor a much more restricted area offshore the north eastern coast ofDenmark. The Northern deepest zones of the study area showunsuitable values of SI. As shown in Figs. 8 and 9, medium-good SIvalues were also evident in the Central North Sea close to theDogger bank.
Table 1Ranking of Sustainability Index (SI) variables.
Variable 1 2 3 4
a) Wind speed (m/s) <8 8 O 10 10 O 12 >12b) Water depth (m) >40 30 O 40 20 O 30 <20c) SST anomaly (�C) >0.6 0.5 O 0.6 0.4 O 0.5 <0.4d) CHL-a anomaly log10 (mg/m3) <�0.5 �0.5 O 0 0 O þ0.5 >þ0.5Sustainability Index (SI) <0.25 0.25 O 0.50 0.50 O 0.75 >0.75
Table 2Other variables used to set the Sustainability Index (SI).
Variable 0 1
a) Sediment type Rock Sand/gravelb) Current speed (m/s) >2 0 O 2
Fig. 8. Map of the Sustainability Index for the North Sea and adjacent seas.
Fig. 9. Sustainable Index values at the locations (proposed ¼ blue, operating ¼ red) ofDanish offshore wind farms. The highest value is for the proposed location Horns Rev 3(7.9E 55.7N), the 2nd and 3rd highest are the Horns Rev 1, 2 (7.8E 55.5N) while the 4this the Nysted (11.2E 55.0N). The Anholt site (11.3E 56.5N) is at position 22. (Forinterpretation of the references to colour in this figure legend, the reader is referred tothe web version of this article.)
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The SI appeared to be consistent with shallow areas having highSST and low SST anomalies while CHL-a and wind speed appearedto have a secondary effect due to their relatively uniformity in thestudy area.
The distribution of SI appears to be rather homogeneous, for anumber of reasons. First, the present results rely heavily on thedata resolution, which could be probably increased for the windvelocity and the CHL-a which should be extended to values otherthan the surface. Second, other relevant information could not beadded at this stage because it needs a different approach, based onlocally measured data other than remotely sensed. In this frame-work, also societal constraints can be added to the first SIevaluation.
8. Conclusions
Co-location is an essential consideration in marine planning,because the increasing pressure onmarine resources from increasedactivity levels will lead to spatial conflicts if effective enablingmanagement measures are not in place. In this framework, thisstudy was focused on the application of MCE technique for co-location of offshore wind farms and open water mussel cultivationbased mainly on physical constraints. The applicationwas collectedin a definition of a Sustainability Index (SI), which is a possibleresponse to the actual and future need of a smart tool for multiplemarine spaceutilization. This SI is easily implemented inGIS inorderto do a first order selection of the most promising areas to be morespecifically studied in a second order approach, to be based on localfield data.
The Sustainability Index can be considered as a standard sitingoptimization approach providing a convenient macro-siting tool,which can be used within a marine spatial planning context toaddress physical and biological constraints at the first level. In otherwords it provides a first and large scale evaluation of the feasibilityof aquaculture activities in offshore wind farm sites.
It is, however, recommended that a more in-depth approach isrequired in order to more fully inform consideration of co-locationpotential for offshore wind farms and aquaculture. Such anapproach will need to consider other relevant aspects, such as thewave and current loads on the long-lines of mussels, whichmust beassessed on the basis of further on-site detailed studies.
Inotherwords, there is theneedofan integrationof amicro-sitingoptimization approach into the earliermacro-siting protocol alreadydefined, which should provide an optimal localization of openwateraquaculture co-location inside the area of the offshore wind farmlayout already identified as feasible at the macro-siting level.
References
Becker, G.A., Pauly, M., 1996. Sea Surface Temperature changes in the North Sea andtheir causes. Ices J. Mar. Sci. 53, 887e898.
Benassai, G., Stenberg, C., Christoffersen, M., Mariani, P., 2011. A Sustainability Indexfor offshore wind farms and open water aquaculture. In: Coastal Processes IIedn WIT Press, pp. 3e14.
Benassai, G., Campanile, A., Piscopo, V., Scamardella, A., 2014. Mooring control ofsemi-submersible structures for wind turbines. Procedia Eng 70, 132e141.
Bilgili, M., Yasar, A., Simsek, E., 2011. Offshore wind power development in Europeand its comparison with onshore counterpart. Renew. Sustain. Energy Rev. 15(2), 905e915.
Buck, B., 2004. Extensive open ocean aquaculture development within wind farmsin Germany: the prospect of offshore co-management and legal constraints.Ocean. Coast. Manag. 47, 95e122.
Buck, B.H., Krause, G., Michler-Cieluch, T., Brenner, M., Buchholz, C.M., Busch, C.M.,Fisch, R., Geisen, M., Zielinski, 2008. Meeting the quest for spatial efficiency:progress and prospects of extensive aquaculture within offshore wind farms.Helgol. Mar. Res. 62, 269e281.
EC-FP5 REVAMP, 2005. Atlas of Chlorophyll-a Concentration for the North Sea basedon MERIS Imagery of 2003. Project Report 1e121.
Espinosa-Carreon, T.L., Strub, P.T., Beier, E., Ocampo-Torres, F., Gaxiola-Castro, G., 2004.Seasonal and interannual variability of satellite-derived chlorophyll pigment,surface height, and temperature off Baja California. J. Geophys. Res. Oceans 109.
EWEA, 2009. Annual Report. http://www.ewea.org.Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F., et al., 2008.
A global map of human impact on marine ecosystems. Science 319, 948e952.Longdill, P.C., Healy, T.R., Black, K.P., 2008. An integrated GIS approach for sus-
tainable aquaculture management area site selection. Ocean. Coast. Manag. 51(2008), 612e624.
Mee, L., 2006. Complementary Benefits of Alternative Energy: Suitability of OWFs asAquaculture Sites. Inshore Fisheries andAquaculture Technology, Innovation andDevelopment, Seafish project, Ref. 10517. Univ. of Plymouth Marine Institute.
Michler-Cieluch, T., Krause, G., 2008. Perceive concerns and possible managementstrategies for governing ‘wind farm e mariculture integration’. Ocean. Coast.Manag. 51 (1), 57e68.
Moland, E., Olsen, E.M., Knutsen, H., Garrigou, P., Espeland, S.H., Kleiven, A.R.,André, C., Knutsen, J.A., 2013. Lobster and cod benefit from small-scale northernmarine protected areas: inference from an empirical beforeeafter control-impact study. Proc. Royal Soc. B: Biol. Sci. 280 (1754).
Nath, S.S., Bolte, J.P., Ross, L.G., Aguilar Manjerrez, J., 2000. Applications ofgeographic information systems for spatial decision support in aquaculture.Aquac. Eng. 23, 233e278.
Risien, C.M., Chelton, D.B., 2008. A global climatology of surface wind and windstress Fields from eight years of QuikSCAT scatterometer data. J. Phys. Oceanogr.38, 2379e2413.
Soniat, T.R., Ray, S.M., 1985. Relationship between possible available food and thecomposition, condition and reproductive state oysters from Galveston Bay,Texas. Contrib. Mar. Sci. 28, 109e121.
Stenberg, C., Christoffersen, M., Mariani, P., Krog, C., Dolmer, P., Maar, M., Tørring, D.,2010. Offshore Wind Farms and Their Potential for Shellfish Aquaculture andRestocking. ICES Conference 2010 ICES CM 2010, O-12.
Valavanis, V.D., Kapantagakis, A., Katara, I., Palialexis, A., 2004. Critical regions: aGIS-based model of marine productivity hotspots. Aquat. Sci. 66, 139e148.
Vincenzi, S., Caramari, G., Rossi, R., De Leo, G.A., 2006. A GIS-based habitat suit-ability model for commercial yeld estimation of Tapas Philippinarum in amediterranean coastal lagoon (Sacca di Goro, Italy). Ecol. Model. 193, 90e104.
Winter, J.E., 1978. A review on the knowledge of suspension feeding in lamelli-branchiate bivalves, with special reference to artificial aquaculture systems.Aquaculture 13, 1e33.
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