Airflow modelling predicts seabird breeding habitat across islands
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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ftzenodo:oai:zenodo.org:5563258 2024-09-15T18:40:04+00:00 Airflow modelling predicts seabird breeding habitat across islands Lempidakis, Emmanouil Ross, Andrew Börger, Luca Shepard, Emily 2021-10-17 https://doi.org/10.5061/dryad.h9w0vt4jk unknown Zenodo https://doi.org/10.5281/zenodo.5557126 https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.h9w0vt4jk oai:zenodo.org:5563258 info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode wind species' spatial distribution computational fluid dynamics habitat use seabird info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5061/dryad.h9w0vt4jk10.5281/zenodo.5557126 2024-07-25T11:56:18Z 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. Variable list for files: SW wind - Section table on Skomer (Standardised).csv / NW wind - Section table on Skomer (Standardised).csv / SE wind - Section table on Skomer (Standardised).csv /NE wind - Section table on Skomer (Standardised).csv and SW wind - Sections on Skokholm (Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanUMedian; MeanUIQR, MeanUSkewness, MeanUCV: Median, interquartile range,skewness and coefficient of variation of mean wind speed per section HorizontalMedian;HorizontalIQR,HorizontalSkewness,HorizontalCV: Median, interquartile range,skewness and coefficient of variation of horizontal wind speed per ... Other/Unknown Material Uria aalge uria Zenodo |
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unknown |
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wind species' spatial distribution computational fluid dynamics habitat use seabird |
spellingShingle |
wind species' spatial distribution computational fluid dynamics habitat use seabird Lempidakis, Emmanouil Ross, Andrew Börger, Luca Shepard, Emily Airflow modelling predicts seabird breeding habitat across islands |
topic_facet |
wind species' spatial distribution computational fluid dynamics habitat use seabird |
description |
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. Variable list for files: SW wind - Section table on Skomer (Standardised).csv / NW wind - Section table on Skomer (Standardised).csv / SE wind - Section table on Skomer (Standardised).csv /NE wind - Section table on Skomer (Standardised).csv and SW wind - Sections on Skokholm (Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanUMedian; MeanUIQR, MeanUSkewness, MeanUCV: Median, interquartile range,skewness and coefficient of variation of mean wind speed per section HorizontalMedian;HorizontalIQR,HorizontalSkewness,HorizontalCV: Median, interquartile range,skewness and coefficient of variation of horizontal wind speed per ... |
format |
Other/Unknown Material |
author |
Lempidakis, Emmanouil Ross, Andrew Börger, Luca Shepard, Emily |
author_facet |
Lempidakis, Emmanouil Ross, Andrew Börger, Luca Shepard, Emily |
author_sort |
Lempidakis, Emmanouil |
title |
Airflow modelling predicts seabird breeding habitat across islands |
title_short |
Airflow modelling predicts seabird breeding habitat across islands |
title_full |
Airflow modelling predicts seabird breeding habitat across islands |
title_fullStr |
Airflow modelling predicts seabird breeding habitat across islands |
title_full_unstemmed |
Airflow modelling predicts seabird breeding habitat across islands |
title_sort |
airflow modelling predicts seabird breeding habitat across islands |
publisher |
Zenodo |
publishDate |
2021 |
url |
https://doi.org/10.5061/dryad.h9w0vt4jk |
genre |
Uria aalge uria |
genre_facet |
Uria aalge uria |
op_relation |
https://doi.org/10.5281/zenodo.5557126 https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.h9w0vt4jk oai:zenodo.org:5563258 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode |
op_doi |
https://doi.org/10.5061/dryad.h9w0vt4jk10.5281/zenodo.5557126 |
_version_ |
1810484392143880192 |