Marine Scotland Offshore Renewables Research Programme
Policy Review: Seabird Research - Work Package G – Effects of displacement
from marine renewable developments on seabirds breeding at the Isle of May
1.
About the project
The project has produced a model which estimates the consequences of
displacement and barrier effects on the time/energy budget of breeding seabirds.
For the purposes of this “proof and concept” report, common guillemot breeding on
the Isle of May were used in order to demonstrate the feasibility of this approach to
modelling consequences of displacement and barrier effects from offshore wind farm
development.
Within the offshore renewables research policy context, this project is providing
information to inform (Phase 2) Prediction of Impact.
Offshore Renewables Research Policy is set out in 6 distinct phases:
Phase 1:
Phase 2:
Phase 3:
Phase 4:
Phase 5:
Phase 6:
2.
Species and Habitats Characterisation
Prediction of Impact
Methodology to assess significance
Effectiveness of Mitigation and changes to impact significance
Ability to advise on granting / refusing licensing / consents
Monitoring requirements
Aim of the project
The aim of the project is to develop a displacement model for adult Common
guillemots Uria aalge rearing chicks on the Isle of May (part of Forth Islands SPA) in
relation to the proposed offshore wind farm at Neart na Gaoithe. The model
estimates the consequences of displacement and barrier effects on the time/energy
budget of breeding birds.
The project will provide valuable information on the applicability of this modelling
approach to other species of seabird and additional offshore wind developments.
The outputs of this project will assist developers and regulators to undertake
modelling work for predicting potential effects from offshore energy developments as
part of the Licensing processes for offshore energy projects. The project was
undertaken by the Centre for Ecology and Hydrology (CEH) on behalf of Marine
Scotland.
3.
Project Method
The model simulates the feeding location of 1000 Common guillemots (ca 6% of the
SPA population) over a 24 hour period during chick rearing. The model allows the
Guillemots to choose the most suitable location for feeding during one trip from the
colony. The model predicts the prey density for each 1 Km 2 location (total modelled
area was 6,532 Km2). Within the model, the most suitable location was less than 50
km and greater than 0 km from the Isle of May, the depth location was greater than 0
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metres and included one or more prey individuals. If more than one location met
these criteria, they were ranked to find the location with the least dive depth, smallest
distance from Isle of May and with the highest prey density. The model then updates
after each Guillemot had chosen a location, to account for eaten / dispersed prey.
The project has modelled 3 different scenarios:
o Random prey distribution
o Clustered prey distribution
o Increased interference co-efficient
Results of the modelling under each scenario is show below in section 7.
4.
Species Covered
The model has used the Common guillemot as the proof of concept subject. The
Common guillemot is a diving bird which preys on small fish (e.g. sandeels) and
breeds in dense colonies. Foraging behaviour indicates that one Guillemot from a
pair will normally stay with the chicks while the other forages. The current estimated
breeding population of Common guillemot at May Island is ca 18,000 pairs.
When flying away from a colony, the Guillemots typically do so on a single bearing
until they reach the foraging ground. Guillemots depart the colony either singly or in
flocks. Within the model, it has been assumed that they would have to fly around the
wind farm, thereby increasing their flight distance to feeding grounds further offshore.
5.
SPA Site Covered
The Isle of May was used within the model as there is a good background of
historical data on the resident seabirds. The Isle of May is a component of the Forth
Islands SPA and is located in the eastern part of the Firth of Forth, covering 105.06
hectares. The SPA comprises island and cliff habitats suitable for a variety of cliff
nesting species and smaller islands for breeding gulls and terns, together with
extensions into the surrounding sea area.
6.
Potential Interactions with Development Sites
Displacement from an offshore renewable energy development has the potential to
impact on body condition and therefore the survival prospects of breeding birds and
therefore reduce breeding success because of a change in provisioning rates. If
total foraging time is greater than 12 hours for one trip, birds would not be able to
fulfil daily energy requirements.
The prediction of the impact of displacement is driven by two main processes: 1) the
increased travelling costs for the subset of the population that is displaced or for
which the wind farm forms a barrier to movement, and 2) the reduction in average
prey densities in the remaining habitat due to intensified intra-specific competition,
affecting not just displaced birds but the population as a whole.
7.
Modelled Scenarios
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Scenario 1 – Random prey distribution
The model produced a foraging location map of 1000 birds and was repeated 50
times to obtain mean and standard deviations of guillemot locations.
On the colony side of Neart na Gaiothe, the model showed an increase in foraging
costs due to an increase in density of birds in one location.
On the far side of Neart na Gaiothe, the model showed an increase in flight and
foraging costs due to the longer distance travelled.
When the wind farm was not present, the number of birds that did not meet energy
requirements was 1.82 individuals (0.18%).
When the wind farm was present, the number of birds that did not meet energy
requirements was 4.46 individuals (0.46%).
When the wind farm was present, the number of birds displaced was 100.66
individuals (10.07%).
When the wind farm was present, the number of birds displaced on the outward
journey was 43.22 individuals (4.33%) and 45.18 individuals (4.52%) on the return
journey.
Overall the scenario 1 modelling has shown an increase in the number of birds
sharing each feeding location. The presence of Neart na Gaiothe resulted in a
higher density of birds per location particularly on the colony side of Neart na
Gaiothe, thereby indicating the displacement of birds away from the farm and into
adjacent feeding areas.
Scenario 2 – Clustered prey distribution
The model produced a foraging location map of 1000 birds and was repeated 50
times to obtain a mean and standard deviation guillemot locations.
The clustered prey density distribution mimics the shoaling behaviour of fish targeted
by guillemots within the model.
When the wind farm was not present, the number of birds that did not meet energy
requirements was 6.76 individuals (0.68%).
When the wind farm was present, the number of birds that did not meet energy
requirements was 7.66 individuals (0.77%).
When the wind farm was present, the number of birds displaced was 103.34
individuals (10.33%).
When the wind farm was present, the number of birds displaced on the outward and
return journey was 15.10 individuals (1.51%).
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Overall, the mean flight and foraging costs were marginally lower using the clustered
prey density distribution, compared to the model for a random prey density
distribution.
The presence of Neart na Gaiothe caused an increase in mean flight and foraging
cost within the clustered prey modelled scenario.
Scenario 3 – Increased Interference Co-efficient
This scenario simulates more intense interference competition between foraging
guillemots that might occur if the prey availability decreased.
The model produced a foraging location map of 1000 birds and was repeated 50
times to obtain a mean and standard deviation guillemot locations.
When the wind farm was not present, the number of birds that did not meet energy
requirements was 2.40 individuals (0.24%).
When the wind farm was present, the number of birds that did not meet energy
requirements was 5.86 individuals (0.59%).
When the wind farm was present, the number of birds displaced was 83.84
individuals (8.38%).
When the wind farm was present, the number of birds displaced on the outward
journey was 38.02 individuals (3.80%) and 39.40 individuals (3.94%) on the return
journey.
Overall the scenario 3 modelling has shown an increase in the number of birds
sharing each feeding location. The presence of Neart na Gaiothe resulted in a
higher density of birds per location, particularly on the colony side of Neart na
Gaiothe, thereby indicating the displacement of birds away from the farm and into
adjacent feeding areas.
Interestingly though, within this scenario, without the presence of Neart na Gaiothe
the mean number of hours foraging is greater, with Neart na Gaiothe present there is
no difference in the mean number of hours foraging.
The report is available from:
http://www.scotland.gov.uk/Topics/marine/marineenergy/Research
8.
Consequences of the Model Outputs
In all three scenarios, the presence of Neart na Gaiothe resulted in an increase in
average costs of foraging. Displacement effect was apparent at different levels of
prey aggregation and degrees of interference between individual guillemots,
especially on the colony side of Neart na Gaiothe.
However, in all cases, the number of guillemots that did not achieve their daily
energy requirements was comparatively small. It is worth noting though, that this
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project was to provide a proof of concept for the modelling approach and not assess
the consequences of the displacement effects on the breeding population of
Common Guillemots on May Island.
9.
Mitigation Recommendations
Marine Scotland and/or Developers should undertake a joint working project on the
recommended projects outlined in section 11.
The models may suggest effective mitigation methods, for example developers could
be required to schedule or design their work to mitigate and/or avoid effects as a
result of disturbance from the wind farms.
Marine Scotland and/or SNH could design a monitoring programme of particular SPA
colonies and species to assess effect of development through construction and
operation.
10.
Risks to Successful Licensing
The current worst case scenario used in the assessment of the consequences of
disturbance is to assume that displacement results in 100% mortality of the
displaced birds. There is a need to model more realistic consequences of
displacement in order to provide greater confidence in prediction of the effects of
development within the licensing process.
Modelling should be extended to cover the course of a SPA breeding season to
assess the impact from development on the whole population of an SPA. Therefore
it is recommended that the further work identified below is taken forward
immediately.
11.
Future Work
The “proof of concept” model developed in this project has been shown to be a
feasible approach to justifiably reducing the degree of precaution in assessments of
the impact of displacement on sea birds. The concept and modelling method should
be adapted to other case studies (current wind farm development proposals). It has
been designed so that cumulative effects from multiple developments can be
estimated.
Changes in the time/energy budgets of breeding seabirds can have important
population consequences. This is because such changes may impact on the body
condition of adult breeders which, in turn, can affect breeding success (through
abandonment of young), adult survival and, ultimately, population size. Additionally,
breeding success may be affected directly if provisioning rates alter significantly.
There is an urgent need to estimate these more realistic population consequences of
displacement, to provide improved assessments of likely adverse effects on SPA
populations.
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Future modelling should focus on developing the approach to explore patterns
across a whole breeding season for the SPA population as a whole, extending the
species to cover a wider range of protected species, and extending the development
scenario to include all the proposed marine wind farms in the Froth and Tay area.
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