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
Slide 2
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