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A novel three-step biologically informed ocean partitioning strategy: insights from toxigenic phytoplankton in a coastal upwelling system

2026-07Artigo científico Acesso aberto
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datacite.subject.sdg14:Proteger a Vida Marinha
datacite.subject.sdg13:Ação Climática
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorBorlido Oliveira Lima, Maria João
dc.contributor.authorCaballero, I.
dc.contributor.authorBarbosa, Ana
dc.date.accessioned2026-06-26T09:34:58Z
dc.date.available2026-06-26T09:34:58Z
dc.date.issued2026-07
dc.description.abstractOcean partitions are often based on readily accessible variables, such as abiotic factors and chlorophyll-a concentration, but provide limited insight into biological patterns. This study developed a three-step partitioning strategy prioritizing environmental factors that best described the variability patterns of harmful algal bloom (HAB)-forming taxa off SW Iberia. These included the producers of amnesic shellfish poisoning (ASP), diarrhetic shellfish poisoning (DSP), and paralytic shellfish poisoning (PSP). First, dimensionality reduction and unsupervised classification were applied to three environmental datasets, derived from remote sensing and model outputs, covering a 19-year period. Second, different empirical-statistical models were used to determine which datasets best explained the abundance of HAB-producers, available for an 8-year period in different classified coastal production areas. Finally, the best datasets were used to derive partitions prioritizing the variability of different HAB groups, at a pixel level. The first classifications identified up to 12 regions, with four to five located in the coastal-slope domain, with a variable configuration depending on the dataset. The best predictor datasets and models identified five regions (two inner-shelf, two outer-shelf/slope, and one transitional coastal-ocean region), representative of HAB groups. No clear distinctive partitions were identified for different groups, namely for ASP- and DSP-producers, likely due to the combined influence of upwelling and freshwater discharges, along with submarine topographic features. Our partitioning strategy can be applied to other marine systems and taxonomic groups. Future improvements, including more complete environmental and biological datasets, could enhance the value of biologically informed environmental partitions as proxies for species abundance.eng
dc.description.sponsorshipUIDP/00350/2025
dc.identifier.doi10.1016/j.marenvres.2026.108101
dc.identifier.issn0141-1136
dc.identifier.urihttp://hdl.handle.net/10400.1/29149
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationAquatic Research Infrastructure Network
dc.relation.ispartofMarine Environmental Research
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectNovel partitioning strategy
dc.subjectGroup-specific
dc.subjectHarmful algal blooms
dc.subjectEnvironmental variability
dc.subjectRemote sensing
dc.subjectEmpirical-statistical models
dc.titleA novel three-step biologically informed ocean partitioning strategy: insights from toxigenic phytoplankton in a coastal upwelling systemeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberLA/P/0069/2020
oaire.awardTitleAquatic Research Infrastructure Network
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0069%2F2020/PT
oaire.citation.startPage108101
oaire.citation.titleMarine Environmental Research
oaire.citation.volume219
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBorlido Oliveira Lima
person.familyNameBarbosa
person.givenNameMaria João
person.givenNameAna
person.identifier.ciencia-idDE10-78F7-6E02
person.identifier.ciencia-idF514-2180-4CF8
person.identifier.orcid0000-0001-9401-0661
person.identifier.orcid0000-0002-7402-246X
person.identifier.scopus-author-id7103244778
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication9f0228f8-33f1-4a44-8c60-5d8bce4b5b39
relation.isAuthorOfPublication5b72648c-41b5-46c0-9a7f-7fa2f514b973
relation.isAuthorOfPublication.latestForDiscovery9f0228f8-33f1-4a44-8c60-5d8bce4b5b39
relation.isProjectOfPublication5af011f9-3888-449a-a18c-d08b59e87091
relation.isProjectOfPublication.latestForDiscovery5af011f9-3888-449a-a18c-d08b59e87091

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