Greening Blue Energy - BioTools For Business

depending on the species (Dierschke et al. 2006).
Another study suggested that in a worst case scenario,
where 18 wind farms are constructed simultaneously
in the German North Sea, 39% of the harbour
poises in the region could show behavioural
responses to this (Gilles et al. 2009).
When baseline studies are conducted (according to
e.g. EIA and SEA requirements), ecological integrity
and ecological risks should be assessed in order to
understand how the provision of ecosystem services
is affected by one or several wind farms in
a region (Nunneri et al. 2008). Furthermore, with
the focus on large-scale patterns and processes
using an ecosystem or a seascape as focal unit,
spatial variability, landscape complexity and temporal
dynamics need to be integrated and analysed
across scales. To achieve such quite complex analyses,
the most suitable tools would be geographical
information systems (GIS), spatial statistics, remote
sensing techniques and global positioning systems
(GPS) (Turner et al. 2001, Farina 2006).
Using empirical information of temporal (seasonal
and interannual) and spatial variability in the distribution
and movement of organisms as well as the
distribution and fragmentation of coastal habitats a
conceptual GIS model could be created.
Examples of parameters could be:
• Relevant daily movement/migration and
ontogenetic migration of fish
• Distance from fish spawning sites
• Larval dispersal /supply/connectivity
• Daily and seasonal migration of birds
• Cetacean behaviour focusing on foraging,
nursery and areas used for reproduction
Such a model would typically be conducted using
raster GIS modelling on generalized and continuous
spatial data where geographic spaces of interest
are divided into regular cells, each with a specific
attribute digital value, and subsequently utilized as
input to mathematic equation models. For application
in the chosen GIS program (e.g. ArcGIS) all ecological
parameters are put in a dynamic predictive
model where the suitability of a locality is analysed
(quantitatively and qualitatively). The proposed GIS
model should be useful to simulate future scenario
dynamics of biophysical characteristics and ecological
patterns. If possible, the model should also take
into account unexpected large-scale processes such
as potential future environmental changes/events
(e.g. elevated temperature, increased runoff and/
or alterations in nutrient input).
In order to turn GIS modelling into firm implementation
recommendations, information of the aforementioned
ecological parameters will be stored
as different layers and used in the analyzing process.
All components are included in the model as
continuous data, put as layers upon each other in
maps, and analyzed by automated selection procedures,
e.g. stepwise regression and cross-validation
techniques. To evaluate suitable locations for wind
60 GREENING BLUE ENERGY - Identifying and managing biodiversity risks and opportunities of offshore renewable energy
farms within a certain area a graded scale, based
on risk of environmental disturbances, can be used.
This approach does not only promote due precautionary
approaches, but may also facilitate more
cost-and time-effective application, consenting and
permitting processes for offshore wind power projects.
12 Variability across latitudes,
regions and localities
Variability, not only between groups of organisms
but also among related species, makes predictions
of impacts of offshore wind farms on marine ecology
and the marine environment difficult. Apart
from the obvious variation in species composition
between regions, general findings from one
geographic area may not be applicable to another
since regional ecological and environmental factors
strongly influence the ecological performance
of marine organisms (Bohnsack et al. 1991, Baine
2001). Major regulatory factors on fish communities,
for example, differ at larger geographic scales
(e.g. Bohnsack et al. 1991). Trophic interactions,
mobility and spatial use of marine biota also vary
along latitudinal gradients (e.g. Floeter et al. 2004,
Laurel & Bradbury 2009).
At regional and local levels, bio-geographic and
oceanographic factors influence the marine ecological
settings. Available habitats and the connectivity
between them are of key importance.