Lempidakis, E, Ross, AN orcid.org/0000-0002-8631-3512, Borger, L et al. (1 more author) (2021) Airflow modelling predicts seabird breeding habitat across islands. Ecography: pattern and diversity in ecology. ISSN 0906-7590
Abstract
Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here, we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots Uria aalge breeding on Skomer Island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high-quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos. This is an open access article under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
Keywords: | climate change; computational fluid dynamics; distribution; flight; habitat use; seabird; wind |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst for Climate & Atmos Science (ICAS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Nov 2021 16:46 |
Last Modified: | 30 Nov 2021 16:46 |
Status: | Published online |
Publisher: | Wiley |
Identification Number: | 10.1111/ecog.05733 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180730 |