Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data.
Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be availab...
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ftdoajarticles:oai:doaj.org/article:d660771ae66a45f28b7c046da6684afc 2023-05-15T13:42:00+02:00 Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data. Grant Richard Woodrow Humphries 2015-01-01T00:00:00Z https://doi.org/10.1371/journal.pone.0137241 https://doaj.org/article/d660771ae66a45f28b7c046da6684afc EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC4557983?pdf=render https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0137241 https://doaj.org/article/d660771ae66a45f28b7c046da6684afc PLoS ONE, Vol 10, Iss 9, p e0137241 (2015) Medicine R Science Q article 2015 ftdoajarticles https://doi.org/10.1371/journal.pone.0137241 2023-01-08T01:33:45Z Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be available; however, long term population monitoring data may exist. In this study, a method to infer important oceanographic regions for seabirds will be presented using breeding sooty shearwaters as a case study. This method combines a popular machine learning algorithm (generalized boosted regression modeling), geographic information systems, long-term ecological data and open access oceanographic datasets. Time series of chick size and harvest index data derived from a long term dataset of Maori 'muttonbirder' diaries were obtained and used as response variables in a gridded spatial model. It was found that areas of the sub-Antarctic water region best capture the variation in the chick size data. Oceanographic features including wind speed and charnock (a derived variable representing ocean surface roughness) came out as top predictor variables in these models. Previously collected GPS data demonstrates that these regions are used as "flyways" by sooty shearwaters during the breeding season. It is therefore likely that wind speeds in these flyways affect the ability of sooty shearwaters to provision for their chicks due to changes in flight dynamics. This approach was designed to utilize machine learning methodology but can also be implemented with other statistical algorithms. Furthermore, these methods can be applied to any long term time series of population data to identify important regions for a species of interest. Article in Journal/Newspaper Antarc* Antarctic Directory of Open Access Journals: DOAJ Articles Antarctic PLOS ONE 10 9 e0137241 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
topic |
Medicine R Science Q |
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Medicine R Science Q Grant Richard Woodrow Humphries Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data. |
topic_facet |
Medicine R Science Q |
description |
Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be available; however, long term population monitoring data may exist. In this study, a method to infer important oceanographic regions for seabirds will be presented using breeding sooty shearwaters as a case study. This method combines a popular machine learning algorithm (generalized boosted regression modeling), geographic information systems, long-term ecological data and open access oceanographic datasets. Time series of chick size and harvest index data derived from a long term dataset of Maori 'muttonbirder' diaries were obtained and used as response variables in a gridded spatial model. It was found that areas of the sub-Antarctic water region best capture the variation in the chick size data. Oceanographic features including wind speed and charnock (a derived variable representing ocean surface roughness) came out as top predictor variables in these models. Previously collected GPS data demonstrates that these regions are used as "flyways" by sooty shearwaters during the breeding season. It is therefore likely that wind speeds in these flyways affect the ability of sooty shearwaters to provision for their chicks due to changes in flight dynamics. This approach was designed to utilize machine learning methodology but can also be implemented with other statistical algorithms. Furthermore, these methods can be applied to any long term time series of population data to identify important regions for a species of interest. |
format |
Article in Journal/Newspaper |
author |
Grant Richard Woodrow Humphries |
author_facet |
Grant Richard Woodrow Humphries |
author_sort |
Grant Richard Woodrow Humphries |
title |
Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data. |
title_short |
Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data. |
title_full |
Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data. |
title_fullStr |
Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data. |
title_full_unstemmed |
Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data. |
title_sort |
estimating regions of oceanographic importance for seabirds using a-spatial data. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2015 |
url |
https://doi.org/10.1371/journal.pone.0137241 https://doaj.org/article/d660771ae66a45f28b7c046da6684afc |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
PLoS ONE, Vol 10, Iss 9, p e0137241 (2015) |
op_relation |
http://europepmc.org/articles/PMC4557983?pdf=render https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0137241 https://doaj.org/article/d660771ae66a45f28b7c046da6684afc |
op_doi |
https://doi.org/10.1371/journal.pone.0137241 |
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PLOS ONE |
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10 |
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9 |
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