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1 Can marine renewable installations provide a new niche for priority habitats? Rebecca Grieve, Bill Sanderson, Mike Bell, Hamish Mair, and John Baxter Centre for Marine Biodiversity and Biotechnology, Heriot-Watt University, Edinburgh; ICIT, Heriot-Watt University, Stromness, Orkney; Marine Ecology, Scottish Natural Heritage, Edinburgh Maerl beds and horse mussel beds are both classified as priority habitats in the NE Atlantic, PMFs in Scotland and are included in the EC Habitats Directive due to their longevity, sensitivity to anthropogenic pressures and the high biodiversity they support. Both habitats exist in areas of moderate to strong tidal flows and wave-exposed near shore coastal environments .Currently the renewable industry is testing devices in high flow and wave environments highlighting possible conflict between installation, use and biogenic structures. Large scale changes in tidal, circulation and wave patterns could potentially affect the availability and distribution of biogenic reef habitats, but equally could provide new niches. The installation of marine renewable devices will inevitably increase in the coming years therefore, understanding the change in habitat condition in response to a change in hydrodynamics is crucial in the management of sensitive habitats. With so much competition for marine space, knowing where the change in conditions by MREI is just right for priority habitats will help us reach a win-win situation and couple MREI and MPAs in the future. It is hoped that a combination of predictive habitat modelling, environmental envelope analysis and site specific 3D flow modelling, will identify where the installation of marine renewables could create new environmental niches for these priority habitats. The tidal turbine array which will be installed in the Pentland Firth over the next ten years. Horse mussel beds thrive in dynamic conditions and provide habitat heterogeneity which helps to make them biodiversity hotspots . Film by Flora Kent Introduction Map showing the potenital conflict between renewables and study priority habitats. CrownEstate

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2 Preliminary Results Predictive modelling can be used to identify how the distribution of habitats will change under different conditions such as in a changing climate and which environmental variables contribute most to the distribution of both maerl and horse mussel beds. Such a model, MAXENT, the maximum entropy predictive habitat model, uses presence data alongside a range of biophysical data to predict where a habitat or species is most likely to occur. It also indicates which variables contribute most to the distribution. Physical data such as temperature, salinity, current speed for the UK marine area is near impossible to collect as an individual therefore a collation of publically available data has been used in preliminary modelling attempts although this is of a low resolution. Similarly historical SNH diversity data has been collated for use in analysis. MAXENT model output showed that bathymetry contributed most to maerl distribution, followed by landscape or substrate type with current speed as the factor which contributes least (AUC values 1-0.9, 0.9-0.7, 0.7-0.5 are high, moderate and low predictive power respectively) (Figure 1). A closer look shows that 0.10- 1m/s is the most probable current speed range for maerl peaking at 0.2m/s (Fig2). A GLM indicated that there was no significant relationship between infaunal diversity and tidal flow across a range of maerl beds at varying depths around Scotland. Depth was more closely related to infaunal diversity. 0.2m/s had significantly higher infaunal diversity, number and abundance of species than the highest tidal values 0.7m/s (Figure 3). The predictive model output for Horse mussel beds indicated that current speed was a much more important factor in determining distribution (Table 1) than with maerl. However, substrate and bathymetry are both highest contributing factors Figure 1. Model output showing the variable contribution of factors in prediction of maerl beds. Figure 2. Modelled response of maerl in relation to current speed. Shaded area most powerful in prediction. Fig 3. Infaunal diversity index (Shan.Wein Hlog) in different tidal flow categories (m/s) ( * is sig dif. from 0.7m/s p