Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior
Abstract Detailed information acquired using tracking technology has the potential to provide accurate pictures of the types of movements and behaviors performed by animals. To date, such data have not been widely exploited to provide inferred information about the foraging habitat. We collected dat...
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Open Polar |
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Original Research animal movements foraging strategies patch profitability predators prey availability envir geo |
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Original Research animal movements foraging strategies patch profitability predators prey availability envir geo Thomas Cornulier Mark Bolton Ian M. Davies Beth E. Scott Ellie Owen Marianna Chimienti Justin M. J. Travis Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior |
topic_facet |
Original Research animal movements foraging strategies patch profitability predators prey availability envir geo |
description |
Abstract Detailed information acquired using tracking technology has the potential to provide accurate pictures of the types of movements and behaviors performed by animals. To date, such data have not been widely exploited to provide inferred information about the foraging habitat. We collected data using multiple sensors (GPS, time depth recorders, and accelerometers) from two species of diving seabirds, razorbills (Alca torda, N = 5, from Fair Isle, UK) and common guillemots (Uria aalge, N = 2 from Fair Isle and N = 2 from Colonsay, UK). We used a clustering algorithm to identify pursuit and catching events and the time spent pursuing and catching underwater, which we then used as indicators for inferring prey encounters throughout the water column and responses to changes in prey availability of the areas visited at two levels: individual dives and groups of dives. For each individual dive (N = 661 for guillemots, 6214 for razorbills), we modeled the number of pursuit and catching events, in relation to dive depth, duration, and type of dive performed (benthic vs. pelagic). For groups of dives (N = 58 for guillemots, 156 for razorbills), we modeled the total time spent pursuing and catching in relation to time spent underwater. Razorbills performed only pelagic dives, most likely exploiting prey available at shallow depths as indicated by the vertical distribution of pursuit and catching events. In contrast, guillemots were more flexible in their behavior, switching between benthic and pelagic dives. Capture attempt rates indicated that they were exploiting deep prey aggregations. The study highlights how novel analysis of movement data can give new insights into how animals exploit food patches, offering a unique opportunity to comprehend the behavioral ecology behind different movement patterns and understand how animals might respond to changes in prey distributions. |
format |
Article in Journal/Newspaper |
author |
Thomas Cornulier Mark Bolton Ian M. Davies Beth E. Scott Ellie Owen Marianna Chimienti Justin M. J. Travis |
author_facet |
Thomas Cornulier Mark Bolton Ian M. Davies Beth E. Scott Ellie Owen Marianna Chimienti Justin M. J. Travis |
author_sort |
Thomas Cornulier |
title |
Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior |
title_short |
Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior |
title_full |
Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior |
title_fullStr |
Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior |
title_full_unstemmed |
Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior |
title_sort |
taking movement data to new depths: inferring prey availability and patch profitability from seabird foraging behavior |
publisher |
Wiley |
publishDate |
2017 |
url |
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fece3.3551 http://onlinelibrary.wiley.com/wol1/doi/10.1002/ece3.3551/fullpdf https://doi.org/10.1002/ece3.3551 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.3551 https://onlinelibrary.wiley.com/doi/10.1002/ece3.3551 https://www.ncbi.nlm.nih.gov/pubmed/29238552 https://pubmed.ncbi.nlm.nih.gov/29238552/ https://abdn.pure.elsevier.com/en/publications/taking-movement-data-to-new-depths-inferring-prey-availability-an https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.3551 http://aura.abdn.ac.uk/handle/2164/9514 https://core.ac.uk/display/131024893 https://academic.microsoft.com/#/detail/2765918182 http://europepmc.org/articles/PMC5723613 |
genre |
Alca torda Uria aalge uria |
genre_facet |
Alca torda Uria aalge uria |
op_source |
10.1002/ece3.3551 2765918182 oai:pubmedcentral.nih.gov:5723613 29238552 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 10|doajarticles::13ae4a9d2a75f5bb322f19d8ef599c7c 10|openaire____::8ac8380272269217cb09a928c8caa993 10|openaire____::5f532a3fc4f1ea403f37070f59a7a53a 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|openaire____::55045bd2a65019fd8e6741a755395c8c 10|opendoar____::eda80a3d5b344bc40f3bc04f65b7a357 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c 10|openaire____::806360c771262b4d6770e7cdf04b5c5a |
op_relation |
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fece3.3551 http://onlinelibrary.wiley.com/wol1/doi/10.1002/ece3.3551/fullpdf http://dx.doi.org/10.1002/ece3.3551 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.3551 https://onlinelibrary.wiley.com/doi/10.1002/ece3.3551 https://www.ncbi.nlm.nih.gov/pubmed/29238552 https://pubmed.ncbi.nlm.nih.gov/29238552/ https://abdn.pure.elsevier.com/en/publications/taking-movement-data-to-new-depths-inferring-prey-availability-an https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.3551 http://aura.abdn.ac.uk/handle/2164/9514 https://core.ac.uk/display/131024893 https://academic.microsoft.com/#/detail/2765918182 https://dx.doi.org/10.1002/ece3.3551 http://europepmc.org/articles/PMC5723613 |
op_rights |
lic_creative-commons |
op_doi |
https://doi.org/10.1002/ece3.3551 |
container_title |
Ecology and Evolution |
container_volume |
7 |
container_issue |
23 |
container_start_page |
10252 |
op_container_end_page |
10265 |
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1766251425883488256 |
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fttriple:oai:gotriple.eu:50|dedup_wf_001::b26490c00f0b309c0ec73cccebd5276f 2023-05-15T13:12:20+02:00 Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior Thomas Cornulier Mark Bolton Ian M. Davies Beth E. Scott Ellie Owen Marianna Chimienti Justin M. J. Travis 2017-12-01 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fece3.3551 http://onlinelibrary.wiley.com/wol1/doi/10.1002/ece3.3551/fullpdf https://doi.org/10.1002/ece3.3551 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.3551 https://onlinelibrary.wiley.com/doi/10.1002/ece3.3551 https://www.ncbi.nlm.nih.gov/pubmed/29238552 https://pubmed.ncbi.nlm.nih.gov/29238552/ https://abdn.pure.elsevier.com/en/publications/taking-movement-data-to-new-depths-inferring-prey-availability-an https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.3551 http://aura.abdn.ac.uk/handle/2164/9514 https://core.ac.uk/display/131024893 https://academic.microsoft.com/#/detail/2765918182 http://europepmc.org/articles/PMC5723613 undefined unknown Wiley https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fece3.3551 http://onlinelibrary.wiley.com/wol1/doi/10.1002/ece3.3551/fullpdf http://dx.doi.org/10.1002/ece3.3551 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.3551 https://onlinelibrary.wiley.com/doi/10.1002/ece3.3551 https://www.ncbi.nlm.nih.gov/pubmed/29238552 https://pubmed.ncbi.nlm.nih.gov/29238552/ https://abdn.pure.elsevier.com/en/publications/taking-movement-data-to-new-depths-inferring-prey-availability-an https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.3551 http://aura.abdn.ac.uk/handle/2164/9514 https://core.ac.uk/display/131024893 https://academic.microsoft.com/#/detail/2765918182 https://dx.doi.org/10.1002/ece3.3551 http://europepmc.org/articles/PMC5723613 lic_creative-commons 10.1002/ece3.3551 2765918182 oai:pubmedcentral.nih.gov:5723613 29238552 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 10|doajarticles::13ae4a9d2a75f5bb322f19d8ef599c7c 10|openaire____::8ac8380272269217cb09a928c8caa993 10|openaire____::5f532a3fc4f1ea403f37070f59a7a53a 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|openaire____::55045bd2a65019fd8e6741a755395c8c 10|opendoar____::eda80a3d5b344bc40f3bc04f65b7a357 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c 10|openaire____::806360c771262b4d6770e7cdf04b5c5a Original Research animal movements foraging strategies patch profitability predators prey availability envir geo Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2017 fttriple https://doi.org/10.1002/ece3.3551 2023-01-22T17:15:34Z Abstract Detailed information acquired using tracking technology has the potential to provide accurate pictures of the types of movements and behaviors performed by animals. To date, such data have not been widely exploited to provide inferred information about the foraging habitat. We collected data using multiple sensors (GPS, time depth recorders, and accelerometers) from two species of diving seabirds, razorbills (Alca torda, N = 5, from Fair Isle, UK) and common guillemots (Uria aalge, N = 2 from Fair Isle and N = 2 from Colonsay, UK). We used a clustering algorithm to identify pursuit and catching events and the time spent pursuing and catching underwater, which we then used as indicators for inferring prey encounters throughout the water column and responses to changes in prey availability of the areas visited at two levels: individual dives and groups of dives. For each individual dive (N = 661 for guillemots, 6214 for razorbills), we modeled the number of pursuit and catching events, in relation to dive depth, duration, and type of dive performed (benthic vs. pelagic). For groups of dives (N = 58 for guillemots, 156 for razorbills), we modeled the total time spent pursuing and catching in relation to time spent underwater. Razorbills performed only pelagic dives, most likely exploiting prey available at shallow depths as indicated by the vertical distribution of pursuit and catching events. In contrast, guillemots were more flexible in their behavior, switching between benthic and pelagic dives. Capture attempt rates indicated that they were exploiting deep prey aggregations. The study highlights how novel analysis of movement data can give new insights into how animals exploit food patches, offering a unique opportunity to comprehend the behavioral ecology behind different movement patterns and understand how animals might respond to changes in prey distributions. Article in Journal/Newspaper Alca torda Uria aalge uria Unknown Ecology and Evolution 7 23 10252 10265 |