Joint spatiotemporal models to predict seabird densities at sea
IntroductionSeabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability...
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Online Access: | https://doi.org/10.3389/fmars.2023.1078042 https://doaj.org/article/dbfbf20256e4403ab6b6df16b8365b2c |
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ftdoajarticles:oai:doaj.org/article:dbfbf20256e4403ab6b6df16b8365b2c 2023-05-15T16:18:18+02:00 Joint spatiotemporal models to predict seabird densities at sea Mayumi L. Arimitsu John F. Piatt James T. Thorson Katherine J. Kuletz Gary S. Drew Sarah K. Schoen Daniel A. Cushing Caitlin Kroeger William J. Sydeman 2023-01-01T00:00:00Z https://doi.org/10.3389/fmars.2023.1078042 https://doaj.org/article/dbfbf20256e4403ab6b6df16b8365b2c EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/fmars.2023.1078042/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2023.1078042 https://doaj.org/article/dbfbf20256e4403ab6b6df16b8365b2c Frontiers in Marine Science, Vol 10 (2023) species distribution models (SDM) marine bird distribution marine bird surveys detection factor decadal scale change Cook Inlet Science Q General. Including nature conservation geographical distribution QH1-199.5 article 2023 ftdoajarticles https://doi.org/10.3389/fmars.2023.1078042 2023-02-05T01:32:04Z IntroductionSeabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability in dispersion of the birds, sampling effort over time and space, and differences in bird detection rates associated with survey vessel type.MethodsUsing a novel approach for modeling seabirds at sea, we applied joint dynamic species distribution models (JDSDM) with a vector-autoregressive spatiotemporal framework to survey data collected over nearly five decades and archived in the North Pacific Pelagic Seabird Database. We produced monthly gridded density predictions and abundance estimates for 8 species groups (77% of all birds observed) within Cook Inlet, Alaska. JDSDMs included habitat covariates to inform density predictions in unsampled areas and accounted for changes in observed densities due to differing survey methods and decadal-scale variation in ocean conditions. ResultsThe best fit model provided a high level of explanatory power (86% of deviance explained). Abundance estimates were reasonably precise, and consistent with limited historical studies. Modeled densities identified seasonal variability in abundance with peak numbers of all species groups in July or August. Seabirds were largely absent from the study region in either fall (e.g., murrelets) or spring (e.g., puffins) months, or both periods (shearwaters).DiscussionOur results indicated that pelagic shearwaters (Ardenna spp.) and tufted puffin (Fratercula cirrhata) have declined over the past four decades and these taxa warrant further investigation into underlying mechanisms explaining these trends. JDSDMs provide a useful tool to estimate seabird distribution and seasonal trends that will facilitate risk assessments and planning in areas affected by human activities such as oil and gas development, shipping, and offshore wind and renewable ... Article in Journal/Newspaper fratercula Alaska Directory of Open Access Journals: DOAJ Articles Pacific Frontiers in Marine Science 10 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
species distribution models (SDM) marine bird distribution marine bird surveys detection factor decadal scale change Cook Inlet Science Q General. Including nature conservation geographical distribution QH1-199.5 |
spellingShingle |
species distribution models (SDM) marine bird distribution marine bird surveys detection factor decadal scale change Cook Inlet Science Q General. Including nature conservation geographical distribution QH1-199.5 Mayumi L. Arimitsu John F. Piatt James T. Thorson Katherine J. Kuletz Gary S. Drew Sarah K. Schoen Daniel A. Cushing Caitlin Kroeger William J. Sydeman Joint spatiotemporal models to predict seabird densities at sea |
topic_facet |
species distribution models (SDM) marine bird distribution marine bird surveys detection factor decadal scale change Cook Inlet Science Q General. Including nature conservation geographical distribution QH1-199.5 |
description |
IntroductionSeabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability in dispersion of the birds, sampling effort over time and space, and differences in bird detection rates associated with survey vessel type.MethodsUsing a novel approach for modeling seabirds at sea, we applied joint dynamic species distribution models (JDSDM) with a vector-autoregressive spatiotemporal framework to survey data collected over nearly five decades and archived in the North Pacific Pelagic Seabird Database. We produced monthly gridded density predictions and abundance estimates for 8 species groups (77% of all birds observed) within Cook Inlet, Alaska. JDSDMs included habitat covariates to inform density predictions in unsampled areas and accounted for changes in observed densities due to differing survey methods and decadal-scale variation in ocean conditions. ResultsThe best fit model provided a high level of explanatory power (86% of deviance explained). Abundance estimates were reasonably precise, and consistent with limited historical studies. Modeled densities identified seasonal variability in abundance with peak numbers of all species groups in July or August. Seabirds were largely absent from the study region in either fall (e.g., murrelets) or spring (e.g., puffins) months, or both periods (shearwaters).DiscussionOur results indicated that pelagic shearwaters (Ardenna spp.) and tufted puffin (Fratercula cirrhata) have declined over the past four decades and these taxa warrant further investigation into underlying mechanisms explaining these trends. JDSDMs provide a useful tool to estimate seabird distribution and seasonal trends that will facilitate risk assessments and planning in areas affected by human activities such as oil and gas development, shipping, and offshore wind and renewable ... |
format |
Article in Journal/Newspaper |
author |
Mayumi L. Arimitsu John F. Piatt James T. Thorson Katherine J. Kuletz Gary S. Drew Sarah K. Schoen Daniel A. Cushing Caitlin Kroeger William J. Sydeman |
author_facet |
Mayumi L. Arimitsu John F. Piatt James T. Thorson Katherine J. Kuletz Gary S. Drew Sarah K. Schoen Daniel A. Cushing Caitlin Kroeger William J. Sydeman |
author_sort |
Mayumi L. Arimitsu |
title |
Joint spatiotemporal models to predict seabird densities at sea |
title_short |
Joint spatiotemporal models to predict seabird densities at sea |
title_full |
Joint spatiotemporal models to predict seabird densities at sea |
title_fullStr |
Joint spatiotemporal models to predict seabird densities at sea |
title_full_unstemmed |
Joint spatiotemporal models to predict seabird densities at sea |
title_sort |
joint spatiotemporal models to predict seabird densities at sea |
publisher |
Frontiers Media S.A. |
publishDate |
2023 |
url |
https://doi.org/10.3389/fmars.2023.1078042 https://doaj.org/article/dbfbf20256e4403ab6b6df16b8365b2c |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
fratercula Alaska |
genre_facet |
fratercula Alaska |
op_source |
Frontiers in Marine Science, Vol 10 (2023) |
op_relation |
https://www.frontiersin.org/articles/10.3389/fmars.2023.1078042/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2023.1078042 https://doaj.org/article/dbfbf20256e4403ab6b6df16b8365b2c |
op_doi |
https://doi.org/10.3389/fmars.2023.1078042 |
container_title |
Frontiers in Marine Science |
container_volume |
10 |
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1766004455756529664 |