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Effects of displacement from marine renewable developments on seabirds breeding at the Isle of May

Description
ISBN9781782560432
Official Print Publication Date
Website Publication DateOctober 25, 2012

Listen

Final report to MSS
Claire McDonald, Kate Searle, Sarah Wanless & Francis Daunt
CEH Edinburgh

ISBN 978 1 78256 043 2 (Web only publication)
DPPAS 13316

This document is also available in pdf format (1.5mb)

Contents

Executive Summary

1. Introduction
1.1. Background
1.2. Aims & Objectives

2. Methods
2.1. Study Area and Species
2.2. Simulation Model
2.2.1. Input layers
2.2.2. Guillemot behaviour
2.2.3. Wind farm presence
2.2.4. Model Output
2.3. Cost Model
2.3.1. Flight Cost
2.3.2. Foraging Cost
2.3.3. Wind farm specific costs
2.4. Scenarios

3. Results
3.1. Random prey density
3.2. Clustered prey
3.3. Increased Interference Coefficient

4. Discussion
4.1. Displacement model
4.2. Population consequences of displacement

5. Conclusions

6. Acknowledgements

7. References

8. Appendix 1

List of Figures

Figure 1. The three simulation model input layers required. a) Prey density distribution - scale bar is number of prey individuals.
Figure 2. The 11 sectors (blue polygons) used in the simulation model to represent the distribution of guillemot flight directions from the Isle of May.
Figure 3. The prey density map used in the simulation model with a halo of reduced prey density shown in the circled area.
Figure 4. The location of the Neart na Gaoithe wind farm (red polygon) and the 5km buffer zone (white polygon) used in the simulation model.
Figure 5. The clustered prey distribution used in Scenario 2 to represent prey items as a shoal. Scale bar is the number of individuals in each cell.
Figure 6. The mean number of guillemots within each cell of the simulation model using a random prey density distribution from 50 simulations with a) no wind farm and b) with a wind farm.
Figure 7. The standard deviation of the mean number of guillemots within each cell from 50 simulations with a random prey density distribution: a) no wind farm and b) with a wind farm.
Figure 8. The mean flight cost incurred at each cell after 50 simulations of the model with a random prey density distribution layer and with a) no wind farm and b) with a wind farm present.
Figure 9. The mean foraging cost incurred at each cell after 50 simulations of the model with a random prey distribution layer and with a) no wind farm and b) with a wind farm present.
Figure 10. The distribution of flight costs incurred for 50 simulations of 1000 birds with a random prey density layer: a) without a wind farm present and b) with a wind farm present.
Figure 11. The distribution of foraging costs incurred for 50 simulations of 1000 guillemots with a random prey density layer: a) without a wind farm present and b) with a wind farm present.
Figure 12. The mean number of guillemots within each cell of the simulation model using a clustered prey density map from 50 simulations with a) no wind farm and b) with a wind farm.
Figure 13. The standard deviation of the mean number of guillemots within each cell from 50 simulations with a clustered prey density distribution: a) no wind farm and b) with a wind farm.
Figure 14. The mean flight cost incurred at each cell after 50 simulations of The model with a clustered prey density distribution layer and with a) no wind farm and b) with a wind farm present.
Figure 15. The mean foraging cost incurred at each cell after 50 simulations of the model with a clustered prey distribution layer and with a) no wind farm and b) with a wind farm present.
Figure 16. The distribution of flight costs incurred for 50 simulations of 1000 birds with a clustered prey density layer: a) without a wind farm present and b) with a wind farm present.
Figure 17. The distribution of foraging costs incurred for 50 simulations of 1000 guillemots with a clustered prey density layer: a) without a wind farm present and b) with a wind farm present.
Figure 18. The mean number of guillemots within each cell of the simulation model using a random prey density map and a high interference coefficient from 50 simulations with a) no wind farm and b) with a wind farm.
Figure 19. The standard deviation of the mean number of guillemots within each cell from 50 simulations with a random prey density distribution and an increased interference coefficient: a) no wind farm and b) with a wind farm.
Figure 20. The mean flight cost incurred at each cell after 50 simulations of the model with a random prey density distribution layer, an increased interference coefficient and with a) no wind farm and b) with a wind farm present.
Figure 21. The mean foraging cost incurred at each cell after 50 simulations of the model with a clustered prey distribution layer and with a) no wind farm and b) with a wind farm present.
Figure 22. The distribution of flight costs incurred for 50 simulations of 1000 birds with a random prey distribution layer and an increased interference coefficient: a) without a wind farm present and b) with a wind farm present.
Figure 23. The distribution of foraging costs incurred for 50 simulations of 1000 guillemots with a random prey distribution layer and an increased interference coefficient: a) without a wind farm present and b) with a wind farm present.
Figure 24. Flow diagram illustrating the linking of time-energy budget and population models to estimate population consequences of displacement.