Hydrokinetic Energy As an Ecological Factor – How Might Wave and Tidal Energy Extraction Affect the Distribution of Marine Organisms?

NOT TO BE CITED WITHOUT PRIOR REFERENCE TO THE AUTHORS

ICES Annual Science Conference 2011 ICES CM 2011 / S:14 Theme Session S: Extracting energy from waves and tides – what are the consequences for ecosystems, physical processes and other sea users?

Hydrokinetic energy as an ecological factor – how might wave and tidal energy extraction affect the distribution of marine organisms?

Michael C. Bell1, Eric P. M. Grist2, Susana Baston1, Sally Rouse3,4, Mary Spencer Jones3, Joanne S. Porter4, Andrew Want1, Robert E. Harris1, and Jonathan C. Side1

1 International Centre for Island Technology, Institute of Petroleum Engineering, Heriot‐Watt University, Old Academy, Back Road, Stromness, Orkney, KW16 3AW, United Kingdom. Tel. +44 (0)1856 850605, Fax +44 (0)1856 851349, Web www.icit.hw.ac.uk, Email [email protected] 2 WCA Environment Ltd, Brunel House, Faringdon, Oxfordshire, SN7 7YR, United Kingdom. 3 Department of Zoology, Natural History Museum, Cromwell Road, London, SW7 5DB, United Kingdom. 4 Centre for Marine Biodiversity and Biotechnology, School of Life Sciences, Heriot‐Watt University, John Muir Building, Gait 1, Edinburgh, EH14 4AS, United Kingdom.

ABSTRACT Water movements define some of the most important ecological factors determining the distribution of organisms in marine environments. This is true both at large spatial scales, where ecological connectivity and trophic coupling are defined by circulation patterns and vertical mixing structure, and at the much smaller scales at which individual organisms experience flow, turbulence and shear forces. Moving water possesses energy, and this is increasingly regarded as a resource for power generation, potentially meeting 15% of energy demands at a European level by the middle of this century. Conversion of hydrokinetic energy into other forms of energy that are useful for human purposes inevitably involves diversion of physical processes from their ‘natural’ pathways, with possible consequences also for ecological processes. In simple terms, extraction of energy from water flow involves reducing the average velocity of flow and hence changing the conditions experienced by an organism living in the flowing water. In reality, the hydrodynamic consequences of extracting energy are likely to be complex and site‐specific, with changes in turbulence as well as both increases and decreases in local flow velocities. We use statistical models applied to incidence records for marine bryozoan species in Scottish waters to examine the extent to which their distribution may be governed by the same wave and tidal energy variables that influence the location of marine renewable energy developments, and address the question of whether it is possible to predict what might be the consequences of energy extraction for species distribution.

KEYWORDS: wave energy, tidal energy, marine biogeography, ecological impacts

INTRODUCTION The wave and tidal energy industry is emerging as an important new user of space and resources in the marine environment and must co‐exist with a complex and dynamic mix of environmental and ecological features and other maritime activities and values. Extraction of energy from waves and tides involves the interception of hydrokinetic energy that would otherwise be expended elsewhere in the marine environment. In simple terms, extraction of energy will result in reductions in the velocity of water flow (Bryden et al., 2004; Bryden & Couch, 2006), although in practice the effects are likely to be complex involving turbulence, wave‐current interactions and boundary‐layer effects dependent upon seabed characteristics (see review by Bell & Side, 2011). Hydrodynamic features are defining environmental factors of the ecological niches of many if not all marine species (e.g. see review by Shields at al., 2011). It is thus pertinent to ask: what are the likely consequences for marine organisms of hydrodynamic changes resulting from energy extraction? A number of recent reviews have drawn together much relevant information for a qualitative appreciation of the likely potential for environmental and ecological interactions involving marine renewable energy developments (e.g. Gill, 2005; Inger et al., 2009; ICES, 2010a, 2010b; Shields et al., 2011), but little direct research attention has so far been paid to the potential for alteration of hydrodynamic processes by energy extraction to have ecological consequences at systemic or local scales. Recent research, driven by the immediate needs of regulators to address statutory requirements in relation to protected species and habitats, has tended to focus on the potential for direct effects of energy extraction devices on marine wildlife, such as noise impacts and collisions (e.g. Wilson et al., 2007; OSPAR Commission, 2009). Some commentators have suggested more serious systemic consequences of energy extraction (van Haren, 2010). Whilst not all scientists would agree about the seriousness of such concerns, this does highlight the need for research to improve our understanding at a whole system level of the role of hydrodynamics in marine ecology and the consequences of disrupting hydrodynamic processes by energy extraction. This paper uses an example data set on the incidence of marine bryozoan species in Scottish waters (Rouse, 2010) to examine the extent to which hydrodynamic variables relating to the resource targeted by wave and tidal energy developers are also ecological factors determining the distribution of marine organisms. Realistic scenarios for hydrodynamic changes consequent on energy extraction are not yet available (and are an urgent research need), but we use our models of species incidence to consider how sensitive the distribution of selected bryozoan species might be to changes in hydrokinetic energy. We also highlight the need to judge any responses against a background of concurrent climate change.

MATERIALS AND METHODS Sources of species distribution data As an example data set for examining the extent to which hydrokinetic energy is an ecological factor determining the geographical incidence of marine species we used records for Bryozoa in Scottish waters collated by Rouse (2010). Ecological niches occupied by marine bryozoan species are known to encompass a range of conditions with respect to current velocities and exposure to wave energy. The likelihood of different species showing differential responses, coupled with the existence of extensive records for Scottish waters,

2 makes this group particularly suitable for providing examples of species sensitive to changes in hydrodynamic conditions. Scottish waters were defined for this collation as lying between 54°38’2” N – 60°51’38” N and 0°46’50” W – 13°40’13” W. Historical records were obtained from the Bryozoa collection at the Natural History Museum, London. Contemporary records were sourced primarily from the reports of the Marine Nature Conservation Review (MNCR), which sampled the marine fauna of the UK between 1987 and 1998 (Hiscock, 1998). Additional records were sourced from selected literature, a field survey conducted in Orkney during 26‐30 June 2010 and the National Biodiversity Network gateway (http://data.nbn.org.uk/, which includes data collected by the Joint Nature Conservancy Council (JNCC), Scottish Natural Heritage (SNH), MarLin (http://www.marlin.ac.uk/); Seasearch (http://www.seasearch.co.uk/) and private contract surveys). Records from north Liverpool Bay were included owing to their proximity to the Scottish border. Full details of data sources, sampling methods, taxonomy and data quality criteria are given by Rouse (2010). A total of 22,376 records were collated for 249 taxa in Scottish waters (Figure . 1) There were 508 records for 49 taxa within the spatial domain considered for the Pentland Firth and Scapa Flow (see below).

Sources of environmental data An implementation of the SUNTANS hydrodynamic model (Fringer et al., 2006) for the Pentland Firth and Scapa Flow (hereinafter referred to as Pentland Firth)s wa used to generate information on tidal flow conditions in these waters over a 10‐day period including spring tides (see Baston & Harris, in press, for further information on this implementation). SUNTANS is a 3‐D model running on an unstructured grid, and was run for the Pentland Firth using 20 depth bands at a resolution of 4.9 m (Figure 2). We used maximum velocities for the deepest band at each grid node to represent near‐bed peak current flow, and for the purposes of modelling species distributions we calculated averages of these peak values for a regular grid of 0.01° latitude by 0.01° longitude over the spatial domain of the model. Following Gross et al. (2011) we then calculated bed shear stress (τ in units of Pa) as:

2  ρτ uC bd ‐3 where ρ is average water density (1025 kg.m ), Cd is the drag coefficient and ub is the near‐ ‐1 bed velocity (in m.s ). A drag coefficient of Cd = 0.005 (dimensionless) was used, determined by Baston & Harris (in press) to be the most appropriate value for the Pentland Firth based on validation of the SUNTANS model. A wave model for the Pentland Firth is not yet available, so we used large‐scale modelled data on annual mean significant wave heights in UK waters obtained from the UK Department of Trade and Industry (DTI, 2004) interpolated onto the same 0.01° grid as for tidal current data. We used the same wave data for UK waters as a whole, interpolated onto a 0.05° grid. The same source was used for average tidal velocity data for spring tides, again interpolated onto a 0.05° grid for UK waters. Shear stress was calculated as for the Pentland Firth data. In principle, Cd is likely to vary between areas according to seabed type, but in the absence of better information no attempt was made to adjust for different Cd values at different locations. Also, given that these tidal velocity data stemmed from a 2‐D hydrodynamic model, no adjustment was made for depth to derive near‐bed values. As a

3 matter of convenience, the DTI data sets were also used as the main source of bathymetry data. Thirty‐year (1971‐2000) means for surface and near‐bed sea temperature and salinity in northwest European shelf waters have been collated by Berx & Hughes (2009) and gridded at a resolution of 0.1667° longitude by 0.1° latitude. These data were downloaded from http://www.ices.dk/ocean/oceanclimate/oceanclimate.asp and bilinear interpolation was used to obtain grids of 0.05° latitude and longitude for UK waters and 0.01° for Pentland Firth. The data were summarised according to annual and monthly means, minima and maxima, and after consideration of cross‐correlations the following variables were taken forward into analyses of species distribution: annual minimum and maximum temperature and salinity values at each grid node; the maximum difference between surface and near‐ bed values of temperature and salinity. Broad‐scale data on seabed types were obtained from the EUSeaMap data set (Cameron & Askew, 2011, http://jncc.defra.gov.uk/page‐5020), with classification into shallow seabed, mud and sandy mud, sand and muddy sand, coarse sediment, mixed sediment and rock or other hard substrata. Classifications were summarized onto 0.01° and 0.05° grids for Pentland Firth and UK waters respectively, under the assumption that the type registered at the centre of a grid square represents the grid square as a whole.

Analysis of species distribution data The maximum entropy method (MAXENT) was chosen for modelling the geographic distributions of bryozoan species in relation to hydrokinetic energy and other aspects of environmental variation. MAXENT is a machine‐learning method used for the predictive modelling of species environmental requirements using presence‐only data (Phillips et al., 2006; Philips & Dudík, 2008). The underlying idea is that MAXENT estimates the probability distribution of maximum entropy (closest to uniformity) for the incidence of a species subject to the constraint that the expected values of a set of environmental features for the predicted distribution should match the averages seen for the sample represented by the observed incidence records. Elith et al. (2011) describe the MAXENT model as minimising “the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space.” The method has been widely applied in predicting the distributions of organisms at a variety of scales (e.g. Ready et al., 2010; Davies & Guinotte, 2011). We used version 3.3.1 of the MAXENT software, and accepted the default settings for parameters of the analysis, including automatic selection mof the for of response curves for the environmental features (see documentation available along with the software at http://www.cs.princeton.edu/~schapire/maxent/). Interpretations of the roles of environmental variables in the MAXENT models were based on heuristic estimates of their relative contributions, with jackknife analyses performed for confirmation. Overall model performance was assessed using the area under the curve (AUC) for the receiver operating characteristic (ROC) curve (see Phillips et al., 2006). Values approaching the maximum value of 1 were interpreted as indicating good discrimination of suitable areas for a species. We applied MAXENT to bryozoan incidence data at two spatial scales. First, we used a grid of environmental data at a resolution of 0.01° latitude and longitude for the Pentland Firth over a spatial domain defined by the coverage of the SUNTANS hydrodynamic model (Figure 2). Second, we used a grid of environmental data at a resolution of 0.05° latitude and

4 longitude for Scottish waters as a whole, defined as the spatial domain of the bryozoan records of the species selected for analysis (decimal limits 54.35 – 61.00°N and 0.45 – 8.7°W). Given that the range of environmental variation seen in Scottish waters is much greater than in the Pentland Firth (especially sin term of temperature and salinity), the MAXENT model for Scottish waters was also applied to the Pentland Firth grid for comparison with predictions based on Pentland Firth data only. Given that tidal shear stress values for the Pentland Firth were derived from the SUNTANS hydrodynamic model and relate to dnear‐be tidal flow, whereas those for Scottish waters were derived from the 2‐D model used by DTI (2004) (i.e. effectively depth‐averaged), for the purposes of projecting the Scottish model onto the Pentland Firth grid we re‐scaled the SUNTANS‐derived shear stress values for this grid to have ethe sam mean value as DTI‐derived shear stresses interpolated over the same spatial domain. This approach ensured applicability of the Scottish model whilst retaining the high spatial resolution allowed by the use of SUNTANS. Two sets of analyses were performed: (i) using all the environmental variables listed in Table dX; an (ii) omitting depth and sediment type from the environmental grid. These variables were omitted because it was suspected that they were acting as proxies for other environmental effects, obscuring the contributions of hydrokinetic energy and sea temperature. Given the predominance of recording effort in near‐shore waters, it is also likely that sampling biases are related to depth, hence omission of this variable may reduce the probability of spurious inferences about the preference of a species for shallow water. Salinity variables were also omitted from the Pentland Firth models because of near‐ uniformity in their values over this region. Model projections were all made using models without depth and sediment type. In the absence of models for hydrokinetic energy extraction applied to Scottish waters or the Pentland Firth, realistic scenarios for future geographical patterns in wave heights and tidal velocities are not available. However, in order to explore the sensitivity of predicted species incidence to changes in hydrokinetic energy we projected the MAXENT models using environmental grids for which wave heights and tidal current velocities (before conversion to shear stress values) were uniformly reduced by 10% or 50%. Given that interpreting any future species’ responses to energy extraction will need to take account of concurrent climate change, we also made additional projections using sea temperatures uniformly increased by 1°C. Again, it should be stressed that these projections do not represent realistic future scenarios. They should instead be seen as sensitivity analyses. Bryozoan species were selected for analysis based on the number of records available for the Pentland Firth. Twelve species were selected, having 18‐46 records for this area (Table 2). When considered at the spatial resolution of the environmental grid (0.01°), some of these records were treated as duplicates, reducing the number of records to 5‐23. These species were represented by 50‐3,013 records for Scottish waters as a whole, reducing to 8‐ 215 records after elimination of duplicates on the 0.05° environmental grid.

RESULTS AND DISCUSSION MAXENT models at different spatial scales MAXENT models for the Pentland Firth showed depth to be a dominant variable for most of the twelve bryozoan species considered, with sediment type also showing sizeable contributions for some species. AUC values for the ROC curves averaged 0.90 (range 0.78 to

0.99), indicating that the models performed well in discriminating between areas suitable and unsuitable for the species. For most species the reduction in AUC on omitting depth and sediment type was small (3‐6%), indicating that environmental determinants of their distribution are well represented by temperature and hydrodynamic variables, without the need for depth or sediment type as proxies. For Aetea anguina there was a 26% reduction (from 0.98 to 0.72) and for Membranipora membranacea and Escharella immersa the reduction was around 10%. The first two species occur in shallow waters up to the low water mark on macroalgae, hence depth may genuinely be an important ecological factor. E. immersa, however, is known to occur offshore, which could suggest that the apparent dependence on depth may be an artefact of sampling bias. Predicted distributions in the Pentland Firth for models with and without depth and sediment type are shown in Figures 3 and 4. For some species, such as Scrupocellaria scruposa and Electra pilosa, the predicted distributions are broadly comparable between the two types of model. In other cases the reduced models tend to show predicted distributions ‘smeared’ towards offshore areas. In the case of some shallow water species such as A. anguina this may not be realistic, but is worth noticing that in some cases, such as Flustrellidra hispida, a species associated with macroalgae in shallow waters, an inshore distribution is predicted even without depth included in the model. For further inference about the roles of temperature and hydrokinetic energy, models omitting depth and sediment type were used for all species. Contributions of environmental variables to the Pentland Firth models are illustrated in Figure 5. Bed shear stress from tidal currents appears to be particularly important for S. scruposa and F. hispida; response curves for these two species show that they are expected to occur only at the lowest shear stress levels (Figure 6). Significant wave height appears important for A. anguina and E. immersa, but in this case the response curve is positive (Figure 7). Temperature variables appear important for several species, notably the two Alcyonidium species and Celleporella hyalina. In these three species there is a declining probability of occurrence with increasing maximum near‐bed temperature, which is the commonest pattern across all species (Figure 8). Cribrilina punctata is the one exception in this regard, showing a weak positive relationship. This feature, replicated in the model for Scottish waters as a whole (see below), is somewhat puzzling given that this is a species of northern distribution, reaching the southern limits of its distribution in the south of the UK. It is obviously hazardous, however, to interpret environmental relationships based on analyses over a very small part of a species’ distribution. Trial analyses (not shown) indicated a negative relationship with maximum near‐bed sea temperature at a UK scale for C. punctata. MAXENT models fitted to incidence data at the scale of Scottish waters ought to provide a better representation of the role of temperature, salinity and hydrodynamics in determining the ecological niches of these twelve bryozoan species. Predicted distributions at this scale are shown in Figure 9. These predictions from models omitting depth and sediment type are broadly comparable to those from models including these variables (not shown), but show greater offshore extensions for some species, notably A. anguina (probably not realistic) and E. immersa. AUC values for these models were high (0.86 to 0.96, average 0.93), indicating that the models performed well in discriminating between areas suitable and unsuitable for each species.

There was no close resemblance of environmental variable contributions between MAXENT models fitted at scale of Scottish waters and those fitted at the scale of the Pentland Firth (compare Figures 5 and 10). Large contributions of hydrodynamic and temperature variables identified at the scale of the Pentland Firth did not follow through to similarly important contributions at a national scale (Figure 11). This could be because the factors governing distributions at a local scale are different from those acting at larger scales. For example, temperature may play a role in determining whether a particular species can exist in the Pentland Firth as compared with the Solway Firth, but may be irrelevant in defining the suitability of different sites within the Pentland Firth. Conversely, bed shear stress from tidal currents may determine exactly where in the Pentland Firth a species may exist, whilst playing little part in determining the overall geographic distribution of eth species. Thus it is possible that the lack of correspondence between the two analyses indicates the different scales at which environmental factors are acting rather than any real conflict of interpretation. At the national scale, shear stress from tidal currents appears to be most relevant for C. hyalina and Microporella ciliata. Response curves show a largely negative association with current shear stress for these species, although the former species appears to persist at higher levels (Figure 12). Several species show optima at higher shear stress levels, notably Parasmittina trispinosa, Alcyonidium diaphanum, Electra pilosa and Membranipora membranacea. Responses to significant wave height (Figure 13) similarly show patterns ranging from largely negative (e.g. M. membranacea, Escharella immersa) to having optima (e.g. A. diaphanum, P. trispinosa). Responses to temperature variables are more complex (Figure 14), with optima shown for several species (e.g. E. immersa, C. hyalina).

Model projections Once we are able to predict species incidence based on environmental features, it becomes possible for us to project beyond the environment we observe to explore what might be the consequences of any environmental changes. Figures 15‐18 show, for four selected bryozoan species, predicted distributions if sea temperatures were to increase uniformly by 1°C (‘climate change’), if tidal current velocities and significant wave heights were both reduced by 50% (‘energy extraction’) and if all these changes occurred together. Projections were also performed for 10% energy extraction, but the changes from base conditions were too subtle to show clear patterns on these national scale maps. Likely levels of energy extraction at any given location are uncertain. Two points are worth emphasising: (i) these are not intended to represent realistic future scenarios – instead, they indicate the gross sensitivity of the predictions to environmental changes; and (ii) even if the environmental changes were realistic, this does not mean that the predicted changes in distribution would necessarily be realised, even in the long‐term, since it is possible for there to be barriers to dispersal and other limiting factors. Increased temperatures would appear to make Pentland Firth and Orkney waters less favourable for the four bryozoan species, and this is also true for Scottish waters as a whole. In the case of Celleporella hyalina, for example, currently favourable areas around Orkney and on the west coast of Scotland would no longer be suitable in the event of a 1°C temperature rise (Figure 16). It appears possible that reductions in wave height and tidal current velocities might have the capacity to mitigate some of these changes – more extensive areas appear favourable for the four species, and when ‘energy extraction’ is combined with ‘climate change’ the predictions are more similar to the baseline than under either type of change alone. It is not possible to

7 generalise from this finding that such compensation would always occur; the analyses only indicate that such is possible. It is equally possible for the combined effect to be ‘worse’ (in terms of reductions in suitable area). Again, it is worth emphasising that these projections are not intended to be predictions of what will happen in any possible future, they merely indicate the expected directions of change in response to uniform modifications of particular environmental features. Figures 19‐22 show predictions for four bryozoan species at the scale of the Pentland Firth for 50% ‘energy extraction’. Application of the Scottish model (estimated for a 0.05° environmental grid) to the Pentland Firth grid (0.01° resolution) shows somewhat different results from applying the Pentland Firth model to its own spatial domain. Predictions for baseline conditions show higher probabilities of species occurrence under the Scottish models, presumably because the larger scale models are better at describing the overall suitability of the area for each species than at representing the fine‐scale response to local variations in environmental conditions. The Scottish models indicate that decreases in wave height and tidal current velocities would result in overall increases in the suitability of the Pentland Firth area for all four species, grossly so in the case of Scrupocellaria scruposa. Projected changes using the Pentland Firth models are much more subtle. There are likely to be two reasons for this: (i) the Pentland Firth models were estimated with a restricted range of environmental variation, thus may underestimate the capacity for response to changes; and (ii) the Pentland Firth models allow description of more subtle local scale responses to local scale environmental variations. These analyses thus highlight the balance that needs to be struck between, on the one hand, measuring responses to environment at the right spatial scale to be useful for predictions and, on the other hand, including a sufficiently larger geographical scope to allow responses to be measured across as wide a range of environmental variation as possible. For three of the four species, predictions based on the Pentland Firth models generally confirm the outcomes of the Scottish models in that suitable areas would be expected to increase under 50% ‘energy extraction’; the two sets of models differ in the scale of increases and the overall probability levels. For Celleporella hyalina, however, 50% ‘energy extraction’ would be expected to result in a slight but discernable decrease in suitable area within the Pentland Firth (Figure 19). This is likely to result from the slight positive relationship with significant wave height (Figure 7), but even without any estimates of statistical uncertainty around these predictions it is obvious that we would have low confidence in this outcome.

CONCLUSIONS The species incidence models and projections presented in this paper are neither definitive nor comprehensive, but they do illustrate how we might approach the problem of predicting large‐scale, long‐term responses of marine organisms to extraction of hydrokinetic energy by the wave and tidal energy industry. There are two elements to these predictions. First, there is a need for a quantitative understanding of how the distribution and abundance of marine organisms are related to environmental factors. In this paper we have addressed this question using the MAXENT modelling framework applied to presence records for species of marine Bryozoa in Scottish waters. This framework is easy to apply, very flexible in the way responses to environmental factors can be defined and very powerful as a tool for exploring scenarios of possible future environments. Other approaches are available (e.g. Elith et al., 2006; Ready et al., 2009), and methods such as generalised additive

8 modelling may be particularly valuable for exploring patterns of abundance as well as incidence. Using the MAXENT approach we have been able to demonstrate that the incidence of bryozoan species can be related to environmental features including those that are likely to affected by energy extraction and climate change. We have also demonstrated that the expected distribution of these organisms is likely to be responsive to future changes in environmental features, which relates to the second element of predictions – realistic scenarios for the future. Such scenarios do not currently exist, although we do have the capacity to construct them. Scenarios for climate change have existed for some time (e.g. UK Climate Impacts Programme, 2002), including factors such as temperature and large‐scale circulation patterns. Limited scenarios also exist for hydrokinetic energy extraction in some areas (e.g. Sutherland et al., 2007; Walkington & Burrows, 2009; Karsten et al., 2008). If we are to understand the systemic ecological consequences of extracting energy from waves and tides, we need to construct integrated hydrodynamic models with coupling between large‐scale systemic processes and processes operating at the local scales at which energy extraction is likely to occur. Much research remains to be done in determining the systemic ecological consequences of extracting energy from wave and tides. In this paper we have demonstrated one modelling approach to taking this forward. Further progress is needed in the following areas: (i) construction of integrated hydrodynamic models with coupling between large‐scale systemic processes and processes operating at the local scales at which energy extraction will occur; (ii) use of the models to demonstrate the consequences of hydrokinetic energy extraction for circulation patterns, tidal currents and heights and wave regimes at both local and whole system scales; (iii) collation of data on species incidence and abundance alongside environmental data, with both sufficient spatial resolution and wide enough geographical scope for meaningful quantification of environmental determinants of ecological niches; (iv) ecological projections using realistic scenarios of energy extraction and taking into account the concurrent effects of climate change.

REFERENCES Baston, S. & Harris, R.E., in press. Modelling the hydrodynamic characteristics of tidal flow in the Pentland Firth. Manuscript for the 9th European Wave and Tidal Energy Conference, Southampton, 5‐9 September 2011. 7 pp. Bell, M.C. & Side, J.C., 2011. Environmental impacts of tidal and wave power developments and key issues for consideration by environment agencies. Background Report on Task 2 of Sniffer Contract ER20: Tidal Technologies: Key issues across planning and development for environmental regulators. International Centre for Island Technology, Heriot‐Watt University, Stromness. Berx, B. & Hughes, S.L., 2009. Climatology of surface and near‐bed temperature and salinity on the north‐west European continental shelf for 1971‐2000. Continental Shelf Research, 29, 2286‐2292.

Bryden, I.G. & Couch, S.J., 2006. ME1 – marine energy extraction: tidal resource analysis. Renewable Energy, 31, 133‐139. Bryden, I.G., Grinsted, T. & Melville, G.T., 2004. Assessing the potential of a simple tidal channel to deliver useful energy. Applied Ocean Research, 26, 198‐204. Cameron, A. & Askew, N. (eds.), 2011. EUSeaMap – Preparatory Action for development and assessment of a European broad‐scale seabed habitat map. Final Report. http://jncc.gov.uk/euseamap. Davies, A.J. & Guinotte, J.M., 2011. Global habitat suitability for framework‐forming cold‐ water corals. PLoS ONE, 6(4), e18483. doi:10.1371/journal.pone.0018483. DTI, 2004. Atlas of UK marine renewable energy resources: technical report. ABP Marine Environmental Research Ltd, Report No. R.1106. Department of Trade and Industry. 154 pp. Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.McC., Peterson, A.T., Phillips, S.J., Richardson, K.S., Scachetti‐Pereira, R., Schapire, R.E., Soberón, J., Williams, S., Wisz, M.S. & Zimmermann, N.E., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129‐151. Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E. & Yates, C.J., 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43‐57. Fringer, O.B., Gerritsen, M. & Street, R.L., 2006. An unstructured‐grid, finite‐volume, nonhydrostatic, parallel coastal ocean simulator. Ocean Modelling, 14, 139‐173. Gill, A.B., 2005. Offshore renewable energy: ecological implications of generating electricity in the coastal zone. Journal of Applied Ecology, 42, 605‐615. Gross, E.S., Chang, S. & Holleman, R., 2011. Preliminary simulations of sediment dynamics in south San Francisco Bay. The San Francisco Estuary Institute. 33 pp. Hiscock, K., ed. 1998. Marine Nature Conservation Review. Benthic marine ecosystems of Great Britain and the north‐east Atlantic. Peterborough, Joint Nature Conservation Committee. (Coasts and seas of the United Kingdom. MNCR series.) ICES, 2010a. Report of the Working Group on Ecosystem Effects of Fishing Activities (WGECO). ICES CM 2010/ACOM:23. ICES, 2010b. Report of the Working Group on Integrated Coastal Zone Management (WGICZM). ICES CM 2010/SSGHIE:05. Inger, R., Attrill, M.J., Bearhop, S., Broderick, A.C., Grecian, W.J., Hodgson, D.J., Mills, C., Sheehan, E., Votier, S.C., Witt, M.J. & Godley, B.J., 2009. Marine renewable energy: potential benefits to biodiversity? An urgent call for research. Journal of Applied Ecology, 46, 1145‐1153 Karsten, R.H., McMillan, J.M., Lickley, M.J. & Haynes, R.D., 2008. Assessment of tidal current energy in the Minas Passage, Bay of Fundy. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 222(A), 493‐507.

OSPAR Commission, 2009. Overview of the impacts of anthropogenic underwater sound in the marine environment. Report 441/2009, OSPAR Commission, London. 133 pp. Phillips, S.J., Anderson, R.P. & Schapire, R.E., 2006. Maximum entropy modelling of species geographic distributions. Ecological Modelling, 190, 231‐259. Phillips, S.J. & Dudík, M., 2008. Modeling ofs specie distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161‐175. Ready, J., Kaschner, K., South, A.B., Eastwood, P.D., Rees, T., Rius, J., Agbayani, E., Kullander, S. & Froese, R., 2010. Predicting the distributions of marine organisms at the global scale. Ecological Modelling, 221, 467‐478. Rouse, S. 2010. The Distribution and Biodiversity of Bryozoans in Scotland. Unpublished MSc Thesis, Heriot Watt University. Shields, M.A., Woolf, D.K., Grist, E.P.M., Kerr, S.A., Jackson, A.C., Harris, R.E., Bell, M.C., Beharie, R., Want, A., Osalusi, E., Gibb, S.W. & Side, J., 2011. Marine renewable energy: The ecological implications of altering the hydrodynamics of the marine environment. Ocean & Coastal Management, 54, 2‐9. Sutherland, G., Foreman, M. & Garrett, C., 2007. Tidal current energy assessment for Johnstone Strait, Vancouver Island. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 221(A2), 147‐157. UK Climate Impacts Programme, 2002. Climate Change Scenarios for the United Kingdom. The UKCIP02 Briefing Report. van Haren, H., 2010. Tidal power? No thanks. New Scientist, 206 (Issue No. 2754), 20‐21. Walkington, I. & Burrows, R., 2009. Modelling tidal stream power potential. Applied Ocean Research, 31, 239‐245. Wilson, B., Batty, R.S., Daunt, F. & Carter, C., 2007. Collision risks between marine renewable energy devices and mammals, fish and diving birds. Report to the Scottish Executive. Scottish Association for Marine Science, Scotland.

TABLE 1. Environmental variables included in MAXENT models for bryozoan species. ShearMaxB and ShearSpring are alternative variables, used for Pentland Firth and Scottish waters respectively.

Variable Short Name Source

Bathymetry data from SUNTANS model (Pentland Depth (m) Depth Firth) or DTI (2004) (Scottish waters)

Sediment types from EUSeaMap (Cameron & Askew, Sediment type Sediment 2011)

Maximum near‐bed sea MaxTB temperature (°C) Interpolated from 30‐year averages (1971‐2000) for Minimum near‐bed sea MinTB sea surface and near‐bed sea temperature data temperature (°C) collated by Berx & Hughes (2009) for the NW European continental shelf Maximum difference in surface and near‐bed sea StratT temperatures (°C)

Maximum near‐bed MaxSB seawater salinity (‰) Interpolated from 30‐year averages (1971‐2000) for Minimum near‐bed MinSB sea surface and near‐bed seawater salinity data seawater salinity (‰) collated by Berx & Hughes (2009) for the NW European continental shelf Maximum difference in surface and near‐bed StratS seawater salinities (‰)

Maximum near‐bed shear Calculated from maximum current velocities from the stress from tidal currents ShearMaxB deepest layer of a SUNTANS model run for the (Pa) Pentland Firth

Spring tide current shear Calculated from spring tide velocity data for Scottish ShearSpring stress (Pa) waters from DTI (2004)

Annual mean significant Interpolated from modelled wave data presented by Hs wave height (m) DTI (2004)

TABLE 2. Bryozoan species used in analyses of distribution in relation to hydrokinetic energy and other environmental variables.

Number of Records Species Pentland Firth Scotland Notes on ecology and distribution Primarily on algae and hydroids, occasionally stones and shells. Colonies form creeping stolons adhering to Aetea anguina 20 50 the substrate with free erect portions. Ranges from the lower shore to approximately 50 m depth. Present off all British coasts. An erect species that colonises rock in areas of good tidal flow. Distributed around the whole of the British Alcyonidium diaphanum 25 1,521 Isles. An erect species that colonises algal substrates in areas of good tidal flow and shelter from wave exposure. Alcyonidium hirsutum 23 868 Distributed all around the British Isles. Encrusting on algae in sheltered waters, and occasionally stones and shells. Ranges from the intertidal to Celleporella hyalina 19 148 approximately 55 m. Precise geographic range is uncertain, recorded from south and west coasts of the British Isles and around to the Northern North Sea. An encrusting species that colonises a range of substrates including rock and algae, from the lower shore to Cribrilina punctata 18 104 approximately 200 m. Distributed from the Faroe Isles, through the North Sea to the English Channel. An encrusting species common on sheltered shores and in sublittoral waters. Widely distributed throughout Electra pilosa 46 2,438 Britain and generally abundant. Colonises a range of substrates including rock and algae. Encrusting on hard substrates and occasionally Laminaria holdfasts, frequently found in offshore waters, but Escharella immersa 18 110 also known from the lower intertidal zone. Widespread and abundant throughout Britain, but does not seem to range any further south. Encrusting on algae from mid to lower intertidal zone. Very rarely found on hard substrates. Abundant on Flustrellidra hispida 19 685 sheltered to moderately exposed rocky shores. Widespread and abundant off all British coasts. Distributed off all British coasts, common on sheltered rocky shores. Colonies grow best in areas of fast Membranipora membranacea 34 3,013 flowing water or high tidal influx. Found on kelp and occasionally Fucus spp. Microporella ciliata 30 176 Common and widespread off all British coasts. Encrusting on stones, shells and algae. An encrusting species that colonises hard substrates from shallow sublittoral to the continental shelf. Parasmittina trispinosa 26 1,555 Distributed off all British coasts and often abundant on offshore shell banks. Tolerates high energy environments. An erect branching species ranging from shallow water to approximately 600 m. Colonises algae, stones, Scrupocellaria scruposa 18 484 shells and other bryozoans. Widely distributed off all British coasts. Tolerates moderate to high energy conditions.

60.5

59.5

58.5

57.5

56.5

55.5

54.5

54 -10-9-8-7-6-5-4-3-2-10

FIGURE 1. Distribution of bryozoan records in Scottish waters. A small number of records for locations west of 10°W are omitted from this map.

FIGURE 2. The spatial domain of the SUNTANS model for the Pentland Firth and Scapa Flow, showing bathymetry in metres.

Aetea anguina Alcyonidium diaphanum Alcyonidium hirsutum

Celleporella hyalina Cribrilina punctata Electra pilosa

Escharella immersa Flustrellidra hispida Membranipora membranacea

Microporella ciliata Parasmittina trispinosa Scrupocellaria scruposa

FIGURE 3. Predicted distribution maps for bryozoan species in the Pentland Firth from MAXENT models including all environmental variables (see Table 1). Warmer colours represent higher probabilities of occurrence; white dots indicate incidence records; black represents areas excluded from the prediction grid (mostly land).

Aetea anguina Alcyonidium diaphanum Alcyonidium hirsutum

Celleporella hyalina Cribrilina punctata Electra pilosa

Escharella immersa Flustrellidra hispida Membranipora membranacea

Microporella ciliata Parasmittina trispinosa Scrupocellaria scruposa

FIGURE 4. Predicted distribution maps for bryozoan species in the Pentland Firth and Scapa Flow from MAXENT models omitting depth and sediment type from the list of environmental variables (see Table 1). Warmer colours represent higher probabilities of occurrence; white dots indicate incidence records; black represents areas excluded from the prediction grid (mostly land).

100 Aetea anguina 100 Alcyonidium diaphanum 100 Alcyonidium hirsutum

75 75 75

50 50 50 contribution contribution contribution

25 25 25 % % %

0 0 0 Hs Hs Hs StratT StratT StratT MinTB MinTB MinTB MaxTB MaxTB MaxTB ShearMaxB ShearMaxB ShearMaxB

100 Celleporella hyalina 100 Cribrilina punctata 100 Electra pilosa

75 75 75

50 50 50 contribution contribution contribution 25 25 25 % % %

0 0 0 Hs Hs Hs StratT StratT StratT MinTB MinTB MinTB MaxTB MaxTB MaxTB ShearMaxB ShearMaxB ShearMaxB

100 Escharella immersa 100 Flustrellidra hispida 100 Membanipora mebranacea

75 75 75

50 50 50 contribution contribution contribution 25 25 25 % % %

0 0 0 Hs Hs Hs StratT StratT StratT MinTB MinTB MinTB MaxTB MaxTB MaxTB ShearMaxB ShearMaxB ShearMaxB

100 Microporella ciliata 100 Parasmittina trispinosa 100 Scrupocellaria scruposa

75 75 75

50 50 50 contribution contribution contribution 25 25 25 % % %

0 0 0 Hs Hs Hs StratT StratT StratT MinTB MinTB MinTB MaxTB MaxTB MaxTB

ShearMaxB ShearMaxB ShearMaxB

FIGURE 5. Contributions of environmental variables to MAXENT models for bryozoan species in the Pentland Firth and Scapa Flow, depth and sediment type omitted. See Table 1 for explanation of variables.

Aetea anguina Alcyonidium diaphanum Alcyonidium hirsutum

Celleporella hyalina Cribrilina punctata Electra pilosa

Escharella immersa Flustrellidra hispida Membranipora membranacea

Microporella ciliata Parasmittina trispinosa Scrupocellaria scruposa

FIGURE 6. MAXENT response curves for bryozoan species in the Pentland Firth and Scapa Flow in relation to maximum bottom sshear stres (Pa). The vertical axis on each plot is the probability of occurrence for the species. MAXENT models omitted depth and sediment type from the environmental variables (see Table 1).

19 Aetea anguina Alcyonidium diaphanum Alcyonidium hirsutum

Celleporella hyalina Cribrilina punctata Electra pilosa

Escharella immersa Flustrellidra hispida Membranipora membranacea

Microporella ciliata Parasmittina trispinosa Scrupocellaria scruposa

FIGURE 7. MAXENT response curves for bryozoan species in the Pentland Firth and Scapa Flow in relation to significant wave height (m). The vertical axis on each plot is the probability of occurrence for the species. MAXENT models omitted depth and sediment type from the environmental variables (see Table 1).

20 Aetea anguina Alcyonidium diaphanum Alcyonidium hirsutum

Celleporella hyalina Cribrilina punctata Electra pilosa

Escharella immersa Flustrellidra hispida Membranipora membranacea

Microporella ciliata Parasmittina trispinosa Scrupocellaria scruposa

FIGURE 8. MAXENT response curves for bryozoan species in the Pentland Firth and Scapa Flow in relation to maximum bottom temperature (°C). The vertical axis on each plot is the probability of occurrence for the species. MAXENT models omitted depth and sediment type from the environmental variables (see Table 1).

21 Aetea anguina Alcyonidium diaphanum Alcyonidium hirsutum

Celleporella hyalina Cribrilina punctata Electra pilosa

Escharella immersa Flustrellidra hispida Membranipora membranacea

Microporella ciliata Parasmittina trispinosa Scrupocellaria scruposa

FIGURE 9. Predicted distribution maps for bryozoan species in Scottish waters from MAXENT models omitting depth and sediment type from the list of environmental variables (see Table 1). Warmer colours represent higher probabilities of occurrence; white dots indicate incidence records; black represents areas excluded from the prediction grid (including land).

100 Aetea anguina 100 Alcyonidium diaphanum 100 Alcyonidium hirsutum

75 75 75

50 50 50 contribution contribution contribution

25 25 25 % % %

0 0 0 Hs Hs Hs StratS StratS StratS StratT StratT StratT MinSB MinSB MinSB MinTB MinTB MinTB MaxSB MaxSB MaxSB MaxTB MaxTB MaxTB ShearSpring ShearSpring ShearSpring

100 Celleporella hyalina 100 Cribrilina punctata 100 Electra pilosa

75 75 75

50 50 50 contribution contribution contribution 25 25 25 % % %

0 0 0 Hs Hs Hs StratS StratS StratS StratT StratT StratT MinSB MinSB MinSB MinTB MinTB MinTB MaxSB MaxSB MaxSB MaxTB MaxTB MaxTB ShearSpring ShearSpring ShearSpring

100 Escharella immersa 100 Flustrellidra hispida 100 Membanipora mebranacea

75 75 75

50 50 50 contribution contribution contribution 25 25 25 % % %

0 0 0 Hs Hs Hs StratS StratS StratS StratT StratT StratT MinSB MinSB MinSB MinTB MinTB MinTB MaxSB MaxSB MaxSB MaxTB MaxTB MaxTB ShearSpring ShearSpring ShearSpring

100 Microporella ciliata 100 Parasmittina trispinosa 100 Scrupocellaria scruposa

75 75 75

50 50 50 contribution contribution contribution 25 25 25 % % %

0 0 0 Hs Hs Hs StratS StratS StratS StratT StratT StratT MinSB MinSB MinSB MinTB MinTB MinTB MaxSB MaxSB MaxSB MaxTB MaxTB MaxTB

ShearSpring ShearSpring ShearSpring

FIGURE 10. Contributions of environmental variables to MAXENT models for bryozoan species in Scottish waters, depth and sediment type omitted. See Table 1 for explanation of variables.

Shear stress from tidal currents 100 model

75 Firth

50 Pentland ‐

25 contribution

% 0 0 255075100 % contribution ‐ Scottish model

Significant wave height 100 model

75 Firth

50 Pentland ‐

25 contribution

% 0 0 255075100 % contribution ‐ Scottish model

Maximum near‐bed temperature 100 model

75 Firth

50 Pentland ‐

25 contribution

% 0 0 255075100 % contribution ‐ Scottish model

FIGURE 11. Comparison of contributions of temperature and hydrodynamic variables to MAXENT models for bryozoan species between Pentland Firth and Scottish waters as a whole. Contributions to Scottish models were adjusted for inclusion of salinity variables.

Aetea anguina Alcyonidium diaphanum Alcyonidium hirsutum

Celleporella hyalina Cribrilina punctata Electra pilosa

Escharella immersa Flustrellidra hispida Membranipora membranacea

Microporella ciliata Parasmittina trispinosa Scrupocellaria scruposa

FIGURE 12. MAXENT response curves for bryozoan species in Scottish waters in relation to shear stress at spring tides (Pa). The vertical axis on each plot is the probability of occurrence for the species. MAXENT models omitted depth and sediment type from the environmental variables (see Table 1).

25 Aetea anguina Alcyonidium diaphanum Alcyonidium hirsutum

Celleporella hyalina Cribrilina punctata Electra pilosa

Escharella immersa Flustrellidra hispida Membranipora membranacea

Microporella ciliata Parasmittina trispinosa Scrupocellaria scruposa

FIGURE 13. MAXENT response curves for bryozoan species in Scottish waters in relation to significant wave height (m). The vertical axis on each plot is the probability of occurrence for the species. MAXENT models omitted depth and sediment type from the environmental variables (see Table 1).

26 Aetea anguina Alcyonidium diaphanum Alcyonidium hirsutum

Celleporella hyalina Cribrilina punctata Electra pilosa

Escharella immersa Flustrellidra hispida Membranipora membranacea

Microporella ciliata Parasmittina trispinosa Scrupocellaria scruposa

FIGURE 14. MAXENT response curves for bryozoan species in Scottish waters in relation to maximum bottom temperature (°C). The vertical haxis on eac plot is the probability of occurrence for the species. MAXENT models omitted depth and sediment type from the environmental variables (see Table 1).

27 Present conditions ‘Climate change’

‘Energy extraction’ ‘Climate change’ + ‘Energy extraction’

FIGURE 15. Predicted distribution maps for Alcyonidium hirsutum in Scottish waters under present conditions and projections of ‘climate change’ (1°C increase in sea temperature) and ‘energy extraction’ (50% reduction in significant wave height and current speed at spring tide), based on MAXENT models omitting depth and sediment type.

Present conditions ‘Climate change’

‘Energy extraction’ ‘Climate change’ + ‘Energy extraction’

FIGURE 16. Predicted distribution maps for Celleporella hyalina in Scottish waters under present conditions and projections of ‘climate change’ (1°C increase in sea temperature) and ‘energy extraction’ (50% reduction in significant wave height and current speed at spring tide), based on MAXENT models omitting depth and sediment type.

Present conditions ‘Climate change’

‘Energy extraction’ ‘Climate change’ + ‘Energy extraction’

FIGURE 17. Predicted distribution maps for Parasmittina trispinosa in Scottish waters under present conditions and projections of ‘climate change’ (1°C increase in sea temperature) and ‘energy extraction’ (50% reduction in significant wave height and current speed at spring tide), based on MAXENT models omitting depth and sediment type.

Present conditions ‘Climate change’

‘Energy extraction’ ‘Climate change’ + ‘Energy extraction’

FIGURE 18. Predicted distribution maps for Scrupocellaria scruposa in Scottish waters under present conditions and projections of ‘climate change’ (1°C increase in sea temperature) and ‘energy extraction’ (50% reduction in significant wave height and current speed at spring tide), based on MAXENT models omitting depth and sediment type.

Present conditions – Scottish model Present conditions – Pentland Firth model

‘Energy extraction’ – Scottish model ‘Energy extraction’ – Pentland Firth model

FIGURE 19. Predicted distribution maps for Alcyonidium hirsutum in the Pentland Firth and Scapa Flow under present conditions and projections of ‘energy extraction’ (50% reduction in significant wave height and current speed at spring tide), based on MAXENT models omitting depth and sediment type.

Present conditions – Scottish model Present conditions – Pentland Firth model

‘Energy extraction’ – Scottish model ‘Energy extraction’ – Pentland Firth model

FIGURE 20. Predicted distribution maps for Celleporella hyalina in the Pentland Firth and Scapa Flow under present conditions and projections of ‘energy extraction’ (50% reduction in significant wave height and current speed at spring tide), based on MAXENT models omitting depth and sediment type.

Present conditions – Scottish model Present conditions – Pentland Firth model

‘Energy extraction’ – Scottish model ‘Energy extraction’ – Pentland Firth model

FIGURE 21. Predicted distribution maps for Parasmittina trispinosa in the Pentland Firth and Scapa Flow under present conditions and projections of ‘energy extraction’ (50% reduction in significant wave height and current speed at spring tide), based on MAXENT models omitting depth and sediment type.

Present conditions – Scottish model Present conditions – Pentland Firth model

‘Energy extraction’ – Scottish model ‘Energy extraction’ – Pentland Firth model

FIGURE 22. Predicted distribution maps for Scrupocellaria scruposa in the Pentland Firth and Scapa Flow under present conditions and projections of ‘energy extraction’ (50% reduction in significant wave height and current speed at spring tide), based on MAXENT models omitting depth and sediment type.