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A demographic model to forecast Dinophysis acuminata harmful algal blooms

2024-03-11Artigo científico Acesso aberto
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datacite.subject.sdg14:Proteger a Vida Marinha
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg02:Erradicar a Fome
dc.contributor.authorVieira, Vasco Manuel Nobre de Carvalho da Silva
dc.contributor.authorLeal Rosa, Teresa
dc.contributor.authorSobrinho-Gonçalves, Luís
dc.contributor.authorMateus, Marcos Duarte
dc.contributor.authorMota, Bernardo
dc.date.accessioned2026-05-19T15:03:14Z
dc.date.available2026-05-19T15:03:14Z
dc.date.issued2024-03-11
dc.description.abstractHarmful algal blooms (HABs) in marine environments have significant adverse effects on public health, aquaculture and recreational activities. Surges of certain phytoplanktonic toxin-producing microalgae (mostly dinoflagellates or diatoms species) can induce Amnesic, Diarrhetic or Paralytic Shellfish Poisoning (ASP, DSP and PSP). Among HAB species, the genus Dinophysis leads to DSP in human consumers; this being the most recurrent problem in the Iberian Peninsula with the biggest economic impact on clam production and harvesting. While complete elimination of HABs is not feasible, timely implementation of appropriate measures can prevent their negative consequences. This is critical for aquaculture. Research on D. acuminata (dominant Dinophysis species in the North Atlantic) has been focused on ecophysiology and population dynamics, although with few modelling attempts. Weekly monitoring along the Portuguese coast since 2006 has revealed that D. acuminata thrives under spring/summer photosynthetically active radiation (PAR) coupled with water temperatures below 20°C, which typically coincide with the local upwelling regime. In order to advance this knowledge numerically, we developed a demographic model linking D. acuminata growth rate to PAR and sea surface temperature (SST). The 13-year (1-Jan-2006 to 31-Dec-2018) time-series of observations was closely fit by model forecasts. However, the model demonstrated limitations in issuing timely warnings of harmful proliferation of D. acuminata, failing to do so in 50% of cases, and issuing incorrect warnings in 5% of the cases. Furthermore, improving the odds of emitting timely warnings always worsened the odds of emitting false warnings, and vice-versa. To simultaneously improve both aspects, the modelling results clearly indicated the need of implementing both census/ projection intervals smaller than 7 days and a laboratory detection limit below 20 cell/L. The time resolution of the census and of the model proved to be the most limiting factor that must be addressed in order to improve numerical forecasting of HABs.eng
dc.description.sponsorshipIPMA-2018-04-BI
dc.identifier.doi10.3389/fmars.2024.1355706
dc.identifier.issn2296-7745
dc.identifier.urihttp://hdl.handle.net/10400.1/29001
dc.language.isoeng
dc.peerreviewedyes
dc.publisherFrontiers Media
dc.relationAlgarve Centre for Marine Sciences
dc.relationAlgarve Centre for Marine Sciences
dc.relationCentre for Marine and Environmental Research
dc.relation.ispartofFrontiers in Marine Science
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectHarmful algal blooms
dc.subjectHAB
dc.subjectDSP
dc.subjectDinophysis
dc.subjectModelling
dc.subjectForecasting
dc.titleA demographic model to forecast Dinophysis acuminata harmful algal bloomseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/04326/2020
oaire.awardNumberUIDP/04326/2020
oaire.awardNumberLA/P/0101/2020
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleCentre for Marine and Environmental Research
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0101%2F2020/PT
oaire.citation.titleFrontiers in Marine Science
oaire.citation.volume11
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameLeal Rosa
person.givenNameTeresa
person.identifier.orcid0000-0001-9797-8977
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication9f5e9dd7-de76-4f00-acc7-e9c041612f43
relation.isAuthorOfPublication.latestForDiscovery9f5e9dd7-de76-4f00-acc7-e9c041612f43
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