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Remotely sensing phytoplankton size structure in the Red Sea
Phytoplankton size structure impacts ocean food-web dynamics and biogeochemical cycling, and is thus an important ecological indicator that can be utilised to quantitatively evaluate the state of marine ecosystems. Potential alterations to size structure are predicted to occur in tropical regions un...
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Published in: | Remote sensing of environment 2019-12, Vol.234, p.111387, Article 111387 |
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description | Phytoplankton size structure impacts ocean food-web dynamics and biogeochemical cycling, and is thus an important ecological indicator that can be utilised to quantitatively evaluate the state of marine ecosystems. Potential alterations to size structure are predicted to occur in tropical regions under future scenarios of climate change. Therefore, there is an increasing requirement for the synoptic monitoring of phytoplankton size structure in marine systems. The Red Sea remains a comparatively unexplored tropical marine ecosystem, particularly with regards to its large-scale biological dynamics. Using an in situ pigment dataset acquired in the Red Sea, we parameterise a two-component, abundance-based phytoplankton size model and apply it to remotely-sensed observations of chlorophyll-a (Chl-a) concentration, to infer Chl-a in two size classes of phytoplankton, small cells 2 μm in size. Satellite-derived estimates of phytoplankton size structure are in good agreement with corresponding in situ measurements and also capture the spatial variability related to regional mesoscale dynamics. Our analysis reveals that, for the estimation of Chl-a in the two size classes, the model performs comparably or in some cases better, to validations in other oceanic regions. Our model parameterisation will be useful for future studies on the seasonal and interannual variability of phytoplankton size classes in the Red Sea, which may ultimately be relevant for understanding trophic linkages between phytoplankton size structure and fisheries, and the development of marine management strategies.
•First validation of satellite-derived phytoplankton size structure in the Red Sea•We re-parameterise a two-component, abundance-based phytoplankton size model.•The model performs comparably, or better, to validations in other oceanic regions.•Our re-parameterisation will enable future work on interannual variability and trophic linkages. |
doi_str_mv | 10.1016/j.rse.2019.111387 |
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•First validation of satellite-derived phytoplankton size structure in the Red Sea•We re-parameterise a two-component, abundance-based phytoplankton size model.•The model performs comparably, or better, to validations in other oceanic regions.•Our re-parameterisation will enable future work on interannual variability and trophic linkages.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2019.111387</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Cell size ; Chlorophyll ; Climate change ; Dynamic structural analysis ; Environmental changes ; Fisheries ; Fishery development ; Food chains ; Food webs ; In situ measurement ; Interannual variability ; Marine ecosystems ; Marine systems ; Ocean colour ; Parameterization ; Phytoplankton ; Plankton ; Red Sea ; Regional analysis ; Remote sensing ; Seasonal variability ; Size structure ; Spatial variability ; Tropical environment ; Tropical environments</subject><ispartof>Remote sensing of environment, 2019-12, Vol.234, p.111387, Article 111387</ispartof><rights>2019 Elsevier Inc.</rights><rights>Copyright Elsevier BV Dec 1, 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-414fe37127ff46a3edebdb0e8df39b34a0254b6cc8e769f87bbcdd99c05fda703</citedby><cites>FETCH-LOGICAL-c405t-414fe37127ff46a3edebdb0e8df39b34a0254b6cc8e769f87bbcdd99c05fda703</cites><orcidid>0000-0002-3751-4393 ; 0000-0003-4183-3482</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Gittings, John A.</creatorcontrib><creatorcontrib>Brewin, Robert J.W.</creatorcontrib><creatorcontrib>Raitsos, Dionysios E.</creatorcontrib><creatorcontrib>Kheireddine, Malika</creatorcontrib><creatorcontrib>Ouhssain, Mustapha</creatorcontrib><creatorcontrib>Jones, Burton H.</creatorcontrib><creatorcontrib>Hoteit, Ibrahim</creatorcontrib><title>Remotely sensing phytoplankton size structure in the Red Sea</title><title>Remote sensing of environment</title><description>Phytoplankton size structure impacts ocean food-web dynamics and biogeochemical cycling, and is thus an important ecological indicator that can be utilised to quantitatively evaluate the state of marine ecosystems. Potential alterations to size structure are predicted to occur in tropical regions under future scenarios of climate change. Therefore, there is an increasing requirement for the synoptic monitoring of phytoplankton size structure in marine systems. The Red Sea remains a comparatively unexplored tropical marine ecosystem, particularly with regards to its large-scale biological dynamics. Using an in situ pigment dataset acquired in the Red Sea, we parameterise a two-component, abundance-based phytoplankton size model and apply it to remotely-sensed observations of chlorophyll-a (Chl-a) concentration, to infer Chl-a in two size classes of phytoplankton, small cells <2 μm in size (picophytoplankton) and large cells >2 μm in size. Satellite-derived estimates of phytoplankton size structure are in good agreement with corresponding in situ measurements and also capture the spatial variability related to regional mesoscale dynamics. Our analysis reveals that, for the estimation of Chl-a in the two size classes, the model performs comparably or in some cases better, to validations in other oceanic regions. Our model parameterisation will be useful for future studies on the seasonal and interannual variability of phytoplankton size classes in the Red Sea, which may ultimately be relevant for understanding trophic linkages between phytoplankton size structure and fisheries, and the development of marine management strategies.
•First validation of satellite-derived phytoplankton size structure in the Red Sea•We re-parameterise a two-component, abundance-based phytoplankton size model.•The model performs comparably, or better, to validations in other oceanic regions.•Our re-parameterisation will enable future work on interannual variability and trophic linkages.</description><subject>Cell size</subject><subject>Chlorophyll</subject><subject>Climate change</subject><subject>Dynamic structural analysis</subject><subject>Environmental changes</subject><subject>Fisheries</subject><subject>Fishery development</subject><subject>Food chains</subject><subject>Food webs</subject><subject>In situ measurement</subject><subject>Interannual variability</subject><subject>Marine ecosystems</subject><subject>Marine systems</subject><subject>Ocean colour</subject><subject>Parameterization</subject><subject>Phytoplankton</subject><subject>Plankton</subject><subject>Red Sea</subject><subject>Regional analysis</subject><subject>Remote sensing</subject><subject>Seasonal variability</subject><subject>Size structure</subject><subject>Spatial variability</subject><subject>Tropical environment</subject><subject>Tropical environments</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAURYMoOI7-AHcB1x3zmrRp0Y0MfsGAMOo6tMmLkzrT1iQVxl9vh7p29Tb33Ps4hFwCWwCD_LpZ-ICLlEG5AABeyCMyg0KWCZNMHJMZY1wkIs3kKTkLoWEMskLCjNyucddF3O5pwDa49oP2m33s-m3VfsaupcH9IA3RDzoOHqlradwgXaOhr1idkxNbbQNe_N05eX-4f1s-JauXx-fl3SrRgmUxESAscgmptFbkFUeDtakZFsbysuaiYmkm6lzrAmVe2kLWtTamLDXLrKkk43NyNfX2vvsaMETVdINvx0mV8jRLy7EcxhRMKe27EDxa1Xu3q_xeAVMHSapRoyR1kKQmSSNzMzE4vv_t0KugHbYajfOoozKd-4f-BfTFb7c</recordid><startdate>20191201</startdate><enddate>20191201</enddate><creator>Gittings, John A.</creator><creator>Brewin, Robert J.W.</creator><creator>Raitsos, Dionysios E.</creator><creator>Kheireddine, Malika</creator><creator>Ouhssain, Mustapha</creator><creator>Jones, Burton H.</creator><creator>Hoteit, Ibrahim</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0002-3751-4393</orcidid><orcidid>https://orcid.org/0000-0003-4183-3482</orcidid></search><sort><creationdate>20191201</creationdate><title>Remotely sensing phytoplankton size structure in the Red Sea</title><author>Gittings, John A. ; Brewin, Robert J.W. ; Raitsos, Dionysios E. ; Kheireddine, Malika ; Ouhssain, Mustapha ; Jones, Burton H. ; Hoteit, Ibrahim</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-414fe37127ff46a3edebdb0e8df39b34a0254b6cc8e769f87bbcdd99c05fda703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Cell size</topic><topic>Chlorophyll</topic><topic>Climate change</topic><topic>Dynamic structural analysis</topic><topic>Environmental changes</topic><topic>Fisheries</topic><topic>Fishery development</topic><topic>Food chains</topic><topic>Food webs</topic><topic>In situ measurement</topic><topic>Interannual variability</topic><topic>Marine ecosystems</topic><topic>Marine systems</topic><topic>Ocean colour</topic><topic>Parameterization</topic><topic>Phytoplankton</topic><topic>Plankton</topic><topic>Red Sea</topic><topic>Regional analysis</topic><topic>Remote sensing</topic><topic>Seasonal variability</topic><topic>Size structure</topic><topic>Spatial variability</topic><topic>Tropical environment</topic><topic>Tropical environments</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gittings, John A.</creatorcontrib><creatorcontrib>Brewin, Robert J.W.</creatorcontrib><creatorcontrib>Raitsos, Dionysios E.</creatorcontrib><creatorcontrib>Kheireddine, Malika</creatorcontrib><creatorcontrib>Ouhssain, Mustapha</creatorcontrib><creatorcontrib>Jones, Burton H.</creatorcontrib><creatorcontrib>Hoteit, Ibrahim</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gittings, John A.</au><au>Brewin, Robert J.W.</au><au>Raitsos, Dionysios E.</au><au>Kheireddine, Malika</au><au>Ouhssain, Mustapha</au><au>Jones, Burton H.</au><au>Hoteit, Ibrahim</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Remotely sensing phytoplankton size structure in the Red Sea</atitle><jtitle>Remote sensing of environment</jtitle><date>2019-12-01</date><risdate>2019</risdate><volume>234</volume><spage>111387</spage><pages>111387-</pages><artnum>111387</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Phytoplankton size structure impacts ocean food-web dynamics and biogeochemical cycling, and is thus an important ecological indicator that can be utilised to quantitatively evaluate the state of marine ecosystems. Potential alterations to size structure are predicted to occur in tropical regions under future scenarios of climate change. Therefore, there is an increasing requirement for the synoptic monitoring of phytoplankton size structure in marine systems. The Red Sea remains a comparatively unexplored tropical marine ecosystem, particularly with regards to its large-scale biological dynamics. Using an in situ pigment dataset acquired in the Red Sea, we parameterise a two-component, abundance-based phytoplankton size model and apply it to remotely-sensed observations of chlorophyll-a (Chl-a) concentration, to infer Chl-a in two size classes of phytoplankton, small cells <2 μm in size (picophytoplankton) and large cells >2 μm in size. Satellite-derived estimates of phytoplankton size structure are in good agreement with corresponding in situ measurements and also capture the spatial variability related to regional mesoscale dynamics. Our analysis reveals that, for the estimation of Chl-a in the two size classes, the model performs comparably or in some cases better, to validations in other oceanic regions. Our model parameterisation will be useful for future studies on the seasonal and interannual variability of phytoplankton size classes in the Red Sea, which may ultimately be relevant for understanding trophic linkages between phytoplankton size structure and fisheries, and the development of marine management strategies.
•First validation of satellite-derived phytoplankton size structure in the Red Sea•We re-parameterise a two-component, abundance-based phytoplankton size model.•The model performs comparably, or better, to validations in other oceanic regions.•Our re-parameterisation will enable future work on interannual variability and trophic linkages.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2019.111387</doi><orcidid>https://orcid.org/0000-0002-3751-4393</orcidid><orcidid>https://orcid.org/0000-0003-4183-3482</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Cell size Chlorophyll Climate change Dynamic structural analysis Environmental changes Fisheries Fishery development Food chains Food webs In situ measurement Interannual variability Marine ecosystems Marine systems Ocean colour Parameterization Phytoplankton Plankton Red Sea Regional analysis Remote sensing Seasonal variability Size structure Spatial variability Tropical environment Tropical environments |
title | Remotely sensing phytoplankton size structure in the Red Sea |
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