Real-time predictions of seabird distribution improve oil spill risk assessments
Current knowledge of the distribution of sensitive seabirds is inadequate to safeguard seabird populations from impacts of oil spills in the Arctic. This gap is mainly driven by the fact that statistical models applied to survey data are coarse-scale and static with limited documentation of the dist...
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| Veröffentlicht in: | Marine pollution bulletin 2021-09, Vol.170, p.112625-112625, Article 112625 |
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| creator | Skov, Henrik Theophilus, Teo Zhi En Heinänen, Stefan Fauchald, Per Madsen, Mads Mortensen, Jonas Brandi Uhrenholdt, Thomas Thomsen, Frank |
| description | Current knowledge of the distribution of sensitive seabirds is inadequate to safeguard seabird populations from impacts of oil spills in the Arctic. This gap is mainly driven by the fact that statistical models applied to survey data are coarse-scale and static with limited documentation of the distributional dynamics and patchiness of seabirds relevant to risk assessments related to oil spills. This paper describes a dynamic modelling framework solution for prediction of fine-scale densities and movements of seabirds in close-to-real time using fully integrated 3-D hydrodynamic models, dynamic habitat suitability models and agent-based models. The modelling framework has been developed and validated for the swimming migration of Brünnich's Guillemot Uria lomvia in the Barents Sea. The results document that the distributional dynamics of Brünnich's Guillemot and other seabird species to a large degree can be simulated with in-situ state variables and patterns reflecting the physical meteorology and oceanography and habitat suitability.
•Oil risk assessments are impeded by static and coarse-scale models for seabirds.•Dynamic modelling frameworks can provide high-resolution information.•Solution integrates 3-D hydrodynamic, habitat suitability, and agent-based models.•Validated for the swimming migration of Brünnich's Guillemot in the Barents Sea |
| doi_str_mv | 10.1016/j.marpolbul.2021.112625 |
| format | Article |
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•Oil risk assessments are impeded by static and coarse-scale models for seabirds.•Dynamic modelling frameworks can provide high-resolution information.•Solution integrates 3-D hydrodynamic, habitat suitability, and agent-based models.•Validated for the swimming migration of Brünnich's Guillemot in the Barents Sea</description><identifier>ISSN: 0025-326X</identifier><identifier>EISSN: 1879-3363</identifier><identifier>DOI: 10.1016/j.marpolbul.2021.112625</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Agent-based modelling ; Aquatic birds ; Arctic ; Barents Sea ; Brünnich's Guillemot ; Distribution ; Dynamic habitat modelling ; Dynamic models ; Dynamics ; Fishery oceanography ; Habitats ; Hydrodynamics ; Mathematical models ; Meteorology ; Migrations ; Modelling ; Oceanography ; Oil spill risk assessments ; Oil spills ; Patchiness ; Physical oceanography ; Polar environments ; Real time ; Risk assessment ; Seabirds ; Statistical analysis ; Statistical models ; Surveying ; Swimming ; Three dimensional models</subject><ispartof>Marine pollution bulletin, 2021-09, Vol.170, p.112625-112625, Article 112625</ispartof><rights>2021</rights><rights>Copyright Elsevier BV Sep 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-e2711695266f97e719e2c7b65a9332a439a62add327ec7e751b097b2469419983</citedby><cites>FETCH-LOGICAL-c376t-e2711695266f97e719e2c7b65a9332a439a62add327ec7e751b097b2469419983</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.marpolbul.2021.112625$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Skov, Henrik</creatorcontrib><creatorcontrib>Theophilus, Teo Zhi En</creatorcontrib><creatorcontrib>Heinänen, Stefan</creatorcontrib><creatorcontrib>Fauchald, Per</creatorcontrib><creatorcontrib>Madsen, Mads</creatorcontrib><creatorcontrib>Mortensen, Jonas Brandi</creatorcontrib><creatorcontrib>Uhrenholdt, Thomas</creatorcontrib><creatorcontrib>Thomsen, Frank</creatorcontrib><title>Real-time predictions of seabird distribution improve oil spill risk assessments</title><title>Marine pollution bulletin</title><description>Current knowledge of the distribution of sensitive seabirds is inadequate to safeguard seabird populations from impacts of oil spills in the Arctic. This gap is mainly driven by the fact that statistical models applied to survey data are coarse-scale and static with limited documentation of the distributional dynamics and patchiness of seabirds relevant to risk assessments related to oil spills. This paper describes a dynamic modelling framework solution for prediction of fine-scale densities and movements of seabirds in close-to-real time using fully integrated 3-D hydrodynamic models, dynamic habitat suitability models and agent-based models. The modelling framework has been developed and validated for the swimming migration of Brünnich's Guillemot Uria lomvia in the Barents Sea. The results document that the distributional dynamics of Brünnich's Guillemot and other seabird species to a large degree can be simulated with in-situ state variables and patterns reflecting the physical meteorology and oceanography and habitat suitability.
•Oil risk assessments are impeded by static and coarse-scale models for seabirds.•Dynamic modelling frameworks can provide high-resolution information.•Solution integrates 3-D hydrodynamic, habitat suitability, and agent-based models.•Validated for the swimming migration of Brünnich's Guillemot in the Barents Sea</description><subject>Agent-based modelling</subject><subject>Aquatic birds</subject><subject>Arctic</subject><subject>Barents Sea</subject><subject>Brünnich's Guillemot</subject><subject>Distribution</subject><subject>Dynamic habitat modelling</subject><subject>Dynamic models</subject><subject>Dynamics</subject><subject>Fishery oceanography</subject><subject>Habitats</subject><subject>Hydrodynamics</subject><subject>Mathematical models</subject><subject>Meteorology</subject><subject>Migrations</subject><subject>Modelling</subject><subject>Oceanography</subject><subject>Oil spill risk assessments</subject><subject>Oil spills</subject><subject>Patchiness</subject><subject>Physical oceanography</subject><subject>Polar environments</subject><subject>Real time</subject><subject>Risk assessment</subject><subject>Seabirds</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Surveying</subject><subject>Swimming</subject><subject>Three dimensional models</subject><issn>0025-326X</issn><issn>1879-3363</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqFkMtKxDAUhoMoOF6ewYAbNx1zaROzFPEGgiIK7kKanoGMaVNzWsG3N8OICzeuzuL_zu0j5ISzJWdcna-Xvctjiu0cl4IJvuRcKNHskAW_0KaSUsldsmBMNJUU6m2fHCCuGWNaaL4gT8_gYjWFHuiYoQt-CmlAmlYUwbUhd7QLOOXQzpuAhn7M6RNoCpHiGGKkOeA7dYiA2MMw4RHZW7mIcPxTD8nrzfXL1V318Hh7f3X5UHmp1VRB2c6VaYRSK6NBcwPC61Y1zkgpXC2NU8J1nRQafMkb3jKjW1ErU3NjLuQhOdvOLQd9zICT7QN6iNENkGa0oqmbxihR1wU9_YOu05yHcl2hlFGGKWUKpbeUzwkxw8qOORS1X5YzuzFt1_bXtN2YtlvTpfNy2wnl388A2aIPMPiiM4OfbJfCvzO-AbM8it8</recordid><startdate>202109</startdate><enddate>202109</enddate><creator>Skov, Henrik</creator><creator>Theophilus, Teo Zhi En</creator><creator>Heinänen, Stefan</creator><creator>Fauchald, Per</creator><creator>Madsen, Mads</creator><creator>Mortensen, Jonas Brandi</creator><creator>Uhrenholdt, Thomas</creator><creator>Thomsen, Frank</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7T7</scope><scope>7TN</scope><scope>7TV</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope></search><sort><creationdate>202109</creationdate><title>Real-time predictions of seabird distribution improve oil spill risk assessments</title><author>Skov, Henrik ; Theophilus, Teo Zhi En ; Heinänen, Stefan ; Fauchald, Per ; Madsen, Mads ; Mortensen, Jonas Brandi ; Uhrenholdt, Thomas ; Thomsen, Frank</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-e2711695266f97e719e2c7b65a9332a439a62add327ec7e751b097b2469419983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Agent-based modelling</topic><topic>Aquatic birds</topic><topic>Arctic</topic><topic>Barents Sea</topic><topic>Brünnich's Guillemot</topic><topic>Distribution</topic><topic>Dynamic habitat modelling</topic><topic>Dynamic models</topic><topic>Dynamics</topic><topic>Fishery oceanography</topic><topic>Habitats</topic><topic>Hydrodynamics</topic><topic>Mathematical models</topic><topic>Meteorology</topic><topic>Migrations</topic><topic>Modelling</topic><topic>Oceanography</topic><topic>Oil spill risk assessments</topic><topic>Oil spills</topic><topic>Patchiness</topic><topic>Physical oceanography</topic><topic>Polar environments</topic><topic>Real time</topic><topic>Risk assessment</topic><topic>Seabirds</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Surveying</topic><topic>Swimming</topic><topic>Three dimensional models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Skov, Henrik</creatorcontrib><creatorcontrib>Theophilus, Teo Zhi En</creatorcontrib><creatorcontrib>Heinänen, Stefan</creatorcontrib><creatorcontrib>Fauchald, Per</creatorcontrib><creatorcontrib>Madsen, Mads</creatorcontrib><creatorcontrib>Mortensen, Jonas Brandi</creatorcontrib><creatorcontrib>Uhrenholdt, Thomas</creatorcontrib><creatorcontrib>Thomsen, Frank</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Oceanic Abstracts</collection><collection>Pollution Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Marine pollution bulletin</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Skov, Henrik</au><au>Theophilus, Teo Zhi En</au><au>Heinänen, Stefan</au><au>Fauchald, Per</au><au>Madsen, Mads</au><au>Mortensen, Jonas Brandi</au><au>Uhrenholdt, Thomas</au><au>Thomsen, Frank</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-time predictions of seabird distribution improve oil spill risk assessments</atitle><jtitle>Marine pollution bulletin</jtitle><date>2021-09</date><risdate>2021</risdate><volume>170</volume><spage>112625</spage><epage>112625</epage><pages>112625-112625</pages><artnum>112625</artnum><issn>0025-326X</issn><eissn>1879-3363</eissn><abstract>Current knowledge of the distribution of sensitive seabirds is inadequate to safeguard seabird populations from impacts of oil spills in the Arctic. This gap is mainly driven by the fact that statistical models applied to survey data are coarse-scale and static with limited documentation of the distributional dynamics and patchiness of seabirds relevant to risk assessments related to oil spills. This paper describes a dynamic modelling framework solution for prediction of fine-scale densities and movements of seabirds in close-to-real time using fully integrated 3-D hydrodynamic models, dynamic habitat suitability models and agent-based models. The modelling framework has been developed and validated for the swimming migration of Brünnich's Guillemot Uria lomvia in the Barents Sea. The results document that the distributional dynamics of Brünnich's Guillemot and other seabird species to a large degree can be simulated with in-situ state variables and patterns reflecting the physical meteorology and oceanography and habitat suitability.
•Oil risk assessments are impeded by static and coarse-scale models for seabirds.•Dynamic modelling frameworks can provide high-resolution information.•Solution integrates 3-D hydrodynamic, habitat suitability, and agent-based models.•Validated for the swimming migration of Brünnich's Guillemot in the Barents Sea</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.marpolbul.2021.112625</doi><tpages>1</tpages></addata></record> |
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| subjects | Agent-based modelling Aquatic birds Arctic Barents Sea Brünnich's Guillemot Distribution Dynamic habitat modelling Dynamic models Dynamics Fishery oceanography Habitats Hydrodynamics Mathematical models Meteorology Migrations Modelling Oceanography Oil spill risk assessments Oil spills Patchiness Physical oceanography Polar environments Real time Risk assessment Seabirds Statistical analysis Statistical models Surveying Swimming Three dimensional models |
| title | Real-time predictions of seabird distribution improve oil spill risk assessments |
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