Can marine renewable installations provide a new
niche for priority habitats?
Rebecca Grieve¹, Bill Sanderson¹, Mike Bell², Hamish Mair¹, and John Baxter³
¹Centre for Marine Biodiversity and Biotechnology, Heriot-Watt University, Edinburgh; ²ICIT, Heriot-Watt University, Stromness, Orkney; ³Marine
Ecology, Scottish Natural Heritage, Edinburgh
Introduction
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 support¹’²’³. Both habitats
exist in areas of moderate to strong tidal flows and
wave-exposed near shore coastal environments¹’
⁴.Currently the renewable industry is testing
devices in high flow and wave environments
highlighting possible conflict between installation,
use and biogenic structures.
Horse mussel beds
thrive in dynamic
conditions and
provide habitat
heterogeneity which
helps to make them
‘biodiversity hotspots’.
Film by Flora Kent
1
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.
Map showing the potenital conflict
between renewables and study priority
habitats.
© CrownEstate
The tidal turbine array which will be
installed in the Pentland Firth over the
next ten years.
2
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.
Preliminary Results
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.
*
Variable
% Contribution
Landscape 36
Bathym.
24.2
Salinity
Current
speed
Bot. temp
Slope
Fig 3. Infaunal diversity index (Shan.Wein
Hlogₑ) in different tidal flow categories
(m/s) ( * is sig dif. from 0.7m/s p<0.05)
23.9
10.1
4.9
1
Table 1. Imporatnce of envir.
factors in predicting Horse
mussel bed distribution
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 0.101m/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
Discussion & Next Steps
The current results obtained with publically available
physical data and biological data from SNH are helping to
clarify the just the complex environmental characteristics
that are needed to support such habitats and what
makes them ‘biodiversity hotspots’.
A survey being
carried out on a maerl
bed on the west coast
of Scotland;
investigating what
physical and biological
factors drive
biodiversity.
Understanding the link between the benthic community and
hydrodynamics, as well as the response of biogenic reefs to changes in
hydrodynamics will help to inform the most appropriate areas where
marine renewable structures could potentially have a positive impact.
The intention of this research is to provide evidence to support marine
spatial planning, serving to enhance biodiversity and therefore help to
achieve various national and European environmental and climate
change targets.
Further Acknowledgements
To Kate Gormley for data and
MAXENT/GIS expertise, to MASTS
and ETP for recent funding
awards which supported training
and aspects of my research , to
the HW dive team for their
invaluable help and videography
/photography skills, SNH for data.
Photos – Rob cook
3
The environments requirements of priority habitats are
complex and unlikely to be static at a small scale,
therefore modelling with large low resolution data sets
should be taken with caution and can only give us an
indication of trends rather. More fine-scale physical data
is currently being collected at specific maerl and horse
mussel beds. Similarly habitats are being surveyed and
material collected to enhance our knowledge of
associated diversity and community. This data will be of a
higher resolution than what is currently available,
although at a much smaller scale and will make
modelling more powerful.
The beautiful maerl beds of Orkney, supporting a wide
range of life in dynamic conditions which in the future
could be protected alongside marine renewable
developments.
References
¹Birkett, D.A., Maggs, C.A., & Dring, M.J. 1998. Maërl (volume V). An overview of dynamic and sensitivity characteristics for
conservation management of marine SACs. Scottish Association for Marine Science (UK Marine SACs Project). 116 pp.
²Gormley, K. S. G., Porter, J., Bell, M., Hull, A., & Sanderson, W. (2013). Predictive habitat modelling as a tool to assess the
distribution and extent of an OSPAR priority habitat under a climate change scenario: Informing marine protected area
designation. PLoS ONE, 8(7): e68263.doi:10.1371/journal.pone.0068263.
³Howson, C. M., Steel. L., Carruthers, M. & Gillham, K. 2012. Identification of Priority Marine Features in Scottish territorial waters
Scottish Natural Heritage Commissioned Report No. 388
⁴Rees I (2009) Assessment of Modiolus modiolus beds in the OSPAR area. Prepared on behalf of Joint Nature Conservation
Committee.