Estimating trans-seasonal variability in water column biomass for a highly migratory, deep diving predator.
Identifieur interne : 003326 ( PubMed/Corpus ); précédent : 003325; suivant : 003327Estimating trans-seasonal variability in water column biomass for a highly migratory, deep diving predator.
Auteurs : Malcolm D. O'Toole ; Mary-Anne Lea ; Christophe Guinet ; Mark A. HindellSource :
- PloS one [ 1932-6203 ] ; 2014.
English descriptors
- KwdEn :
- Animal Migration (physiology), Animals, Carnivory (physiology), Chlorophyll (biosynthesis), Diving (physiology), Ecosystem, Female, Food Chain, Light, Mobile Applications, Phytoplankton (growth & development), Predatory Behavior (physiology), Seals, Earless (physiology), Seasons, Seawater (analysis), Seawater (chemistry), Spatio-Temporal Analysis, Temperature.
- MESH :
- chemical , biosynthesis : Chlorophyll.
- analysis : Seawater.
- chemistry : Seawater.
- growth & development : Phytoplankton.
- physiology : Animal Migration, Carnivory, Diving, Predatory Behavior, Seals, Earless.
- Animals, Ecosystem, Female, Food Chain, Light, Mobile Applications, Seasons, Spatio-Temporal Analysis, Temperature.
Abstract
The deployment of animal-borne electronic tags is revolutionizing our understanding of how pelagic species respond to their environment by providing in situ oceanographic information such as temperature, salinity, and light measurements. These tags, deployed on pelagic animals, provide data that can be used to study the ecological context of their foraging behaviour and surrounding environment. Satellite-derived measures of ocean colour reveal temporal and spatial variability of surface chlorophyll-a (a useful proxy for phytoplankton distribution). However, this information can be patchy in space and time resulting in poor correspondence with marine animal behaviour. Alternatively, light data collected by animal-borne tag sensors can be used to estimate chlorophyll-a distribution. Here, we use light level and depth data to generate a phytoplankton index that matches daily seal movements. Time-depth-light recorders (TDLRs) were deployed on 89 southern elephant seals (Mirounga leonina) over a period of 6 years (1999-2005). TDLR data were used to calculate integrated light attenuation of the top 250 m of the water column (LA(250)), which provided an index of phytoplankton density at the daily scale that was concurrent with the movement and behaviour of seals throughout their entire foraging trip. These index values were consistent with typical seasonal chl-a patterns as measured from 8-daySea-viewing Wide Field-of-view Sensor (SeaWiFs) images. The availability of data recorded by the TDLRs was far greater than concurrent remotely sensed chl-a at higher latitudes and during winter months. Improving the spatial and temporal availability of phytoplankton information concurrent with animal behaviour has ecological implications for understanding the movement of deep diving predators in relation to lower trophic levels in the Southern Ocean. Light attenuation profiles recorded by animal-borne electronic tags can be used more broadly and routinely to estimate lower trophic distribution at sea in relation to deep diving predator foraging behaviour.
DOI: 10.1371/journal.pone.0113171
PubMed: 25427104
Links to Exploration step
pubmed:25427104Le document en format XML
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O'Toole</name><affiliation><nlm:affiliation>Institute of Marine and Antarctic Studies, University of Tasmania, Hobart, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Lea, Mary Anne" sort="Lea, Mary Anne" uniqKey="Lea M" first="Mary-Anne" last="Lea">Mary-Anne Lea</name><affiliation><nlm:affiliation>Institute of Marine and Antarctic Studies, University of Tasmania, Hobart, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Guinet, Christophe" sort="Guinet, Christophe" uniqKey="Guinet C" first="Christophe" last="Guinet">Christophe Guinet</name><affiliation><nlm:affiliation>Marine Predator Department, Centre détudes biologiques de Chizé, Villiers-en-Bois, France.</nlm:affiliation></affiliation></author><author><name sortKey="Hindell, Mark A" sort="Hindell, Mark A" uniqKey="Hindell M" first="Mark A" last="Hindell">Mark A. 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These tags, deployed on pelagic animals, provide data that can be used to study the ecological context of their foraging behaviour and surrounding environment. Satellite-derived measures of ocean colour reveal temporal and spatial variability of surface chlorophyll-a (a useful proxy for phytoplankton distribution). However, this information can be patchy in space and time resulting in poor correspondence with marine animal behaviour. Alternatively, light data collected by animal-borne tag sensors can be used to estimate chlorophyll-a distribution. Here, we use light level and depth data to generate a phytoplankton index that matches daily seal movements. Time-depth-light recorders (TDLRs) were deployed on 89 southern elephant seals (Mirounga leonina) over a period of 6 years (1999-2005). TDLR data were used to calculate integrated light attenuation of the top 250 m of the water column (LA(250)), which provided an index of phytoplankton density at the daily scale that was concurrent with the movement and behaviour of seals throughout their entire foraging trip. These index values were consistent with typical seasonal chl-a patterns as measured from 8-daySea-viewing Wide Field-of-view Sensor (SeaWiFs) images. The availability of data recorded by the TDLRs was far greater than concurrent remotely sensed chl-a at higher latitudes and during winter months. Improving the spatial and temporal availability of phytoplankton information concurrent with animal behaviour has ecological implications for understanding the movement of deep diving predators in relation to lower trophic levels in the Southern Ocean. Light attenuation profiles recorded by animal-borne electronic tags can be used more broadly and routinely to estimate lower trophic distribution at sea in relation to deep diving predator foraging behaviour.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25427104</PMID><DateCreated><Year>2014</Year><Month>11</Month><Day>27</Day></DateCreated><DateCompleted><Year>2015</Year><Month>07</Month><Day>13</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><Issue>11</Issue><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Estimating trans-seasonal variability in water column biomass for a highly migratory, deep diving predator.</ArticleTitle><Pagination><MedlinePgn>e113171</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0113171</ELocationID><Abstract><AbstractText>The deployment of animal-borne electronic tags is revolutionizing our understanding of how pelagic species respond to their environment by providing in situ oceanographic information such as temperature, salinity, and light measurements. These tags, deployed on pelagic animals, provide data that can be used to study the ecological context of their foraging behaviour and surrounding environment. Satellite-derived measures of ocean colour reveal temporal and spatial variability of surface chlorophyll-a (a useful proxy for phytoplankton distribution). However, this information can be patchy in space and time resulting in poor correspondence with marine animal behaviour. Alternatively, light data collected by animal-borne tag sensors can be used to estimate chlorophyll-a distribution. Here, we use light level and depth data to generate a phytoplankton index that matches daily seal movements. Time-depth-light recorders (TDLRs) were deployed on 89 southern elephant seals (Mirounga leonina) over a period of 6 years (1999-2005). TDLR data were used to calculate integrated light attenuation of the top 250 m of the water column (LA(250)), which provided an index of phytoplankton density at the daily scale that was concurrent with the movement and behaviour of seals throughout their entire foraging trip. These index values were consistent with typical seasonal chl-a patterns as measured from 8-daySea-viewing Wide Field-of-view Sensor (SeaWiFs) images. The availability of data recorded by the TDLRs was far greater than concurrent remotely sensed chl-a at higher latitudes and during winter months. Improving the spatial and temporal availability of phytoplankton information concurrent with animal behaviour has ecological implications for understanding the movement of deep diving predators in relation to lower trophic levels in the Southern Ocean. 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Version="1">23082166</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Vet Rec. 2002 Aug 24;151(8):235-40</RefSource><PMID Version="1">12219901</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Vet Rec. 2000 Feb 26;146(9):251-4</RefSource><PMID Version="1">10737295</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2007 Aug 21;104(34):13705-10</RefSource><PMID Version="1">17693555</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Integr Comp Biol. 2010 Dec;50(6):1018-30</RefSource><PMID Version="1">21558256</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Biol Lett. 2009 Aug 23;5(4):473-6</RefSource><PMID Version="1">19447814</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2010 Oct 26;107(43):18366-70</RefSource><PMID Version="1">20974927</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D025041" MajorTopicYN="N">Animal Migration</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D060435" MajorTopicYN="N">Carnivory</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002734" MajorTopicYN="N">Chlorophyll</DescriptorName><QualifierName UI="Q000096" MajorTopicYN="N">biosynthesis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004242" MajorTopicYN="N">Diving</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017753" 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MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012621" MajorTopicYN="N">Seasons</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012623" MajorTopicYN="N">Seawater</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062211" MajorTopicYN="Y">Spatio-Temporal Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013696" MajorTopicYN="N">Temperature</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4245103</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>01</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>10</Month><Day>20</Day></PubMedPubDate><PubMedPubDate 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