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Journal article

Bio-informed blade patterns for mitigating bird collisions with wind turbines

Abstract:

Although the rapid expansion of wind energy is essential for achieving carbon net zero targets, it may conflict with ecological objectives aimed at conserving and enhancing biodiversity. Birds in general are susceptible to colliding with wind turbines and the impact of these structures may be especially significant for certain species. By examining how birds acquire, process, and respond to environmental information, we propose a new collision mitigation approach that integrates sensory ecology with gaze strategy, flight behaviour, and natural avoidance mechanisms. To enhance collision avoidance, we propose investigating blade patterns that alter the optic flow perceived by birds – the dynamic visual contrast resulting from their movement through the environment. Empirical behavioural research is now needed to assess the effectiveness of these designs. Given the diversity of affected species and environments there may be no universal solution, and mitigations must also take account of the practical implications for developers and manufacturers. Our aim is to inspire and guide new research towards bio-informed solutions that protect wildlife without inhibiting clean energy generation.

Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1098/rsif.2025.0719

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Oxford college:
St Hugh's College
Role:
Author
ORCID:
0000-0002-2438-2352
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Role:
Author


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Funder identifier:
https://ror.org/00cwqg982
Grant:
549


Publisher:
Royal Society
Journal:
Journal of the Royal Society Interface More from this journal
Volume:
23
Issue:
238
Article number:
20250719
Publication date:
2026-05-06
Acceptance date:
2026-03-18
DOI:
EISSN:
1742-5662
ISSN:
1742-5689


Language:
English
Keywords:
Pubs id:
2389655
Local pid:
pubs:2389655
Deposit date:
2026-03-15
ARK identifier:

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