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Airflow modelling predicts seabird breeding habitat across islands

Emmanouil Lempidakis, Andrew N. Ross, Luca Borger Orcid Logo, Emily Shepard Orcid Logo

Ecography, Volume: 2022, Issue: 1

Swansea University Authors: Luca Borger Orcid Logo, Emily Shepard Orcid Logo

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DOI (Published version): 10.1111/ecog.05733

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...

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Published in: Ecography
ISSN: 0906-7590 1600-0587
Published: Wiley 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58173
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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.
Keywords: climate change; computational fluid dynamics; distribution; flight; habitat use; seabird; wind
College: College of Science
Funders: European Research Council under the European Union’s Horizon 2020 research and innovation program Grant 715874 (to ELCS).
Issue: 1