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dc.contributor.authorPeltomaa, E.
dc.contributor.authorAsikainen, H.
dc.contributor.authorBlomster, J.
dc.contributor.authorPakkanen, H.
dc.contributor.authorRigaud, C.
dc.contributor.authorSalmi, P.
dc.contributor.authorTaipale, S.
dc.date.accessioned2023-03-20T08:03:34Z
dc.date.available2023-03-20T08:03:34Z
dc.date.issued2023
dc.identifier.citationPeltomaa, E., Asikainen, H., Blomster, J., Pakkanen, H., Rigaud, C., Salmi, P., & Taipale, S. (2023). Phytoplankton group identification with chemotaxonomic biomarkers : In combination they do better. <i>Phytochemistry</i>, <i>209</i>, Article 113624. <a href="https://doi.org/10.1016/j.phytochem.2023.113624" target="_blank">https://doi.org/10.1016/j.phytochem.2023.113624</a>
dc.identifier.otherCONVID_177377409
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/86039
dc.description.abstractChemotaxonomic biomarkers are needed to monitor and evaluate the nutritional quality of phytoplankton communities. The biomolecules produced by different phytoplankton species do not always follow genetic phylogeny. Therefore, we analyzed fatty acids, sterols, and carotenoids from 57 freshwater phytoplankton strains to evaluate the usability of these biomolecules as chemotaxonomic biomarkers. We found 29 fatty acids, 34 sterols, and 26 carotenoids in our samples. The strains were grouped into cryptomonads, cyanobacteria, diatoms, dinoflagellates, golden algae, green algae, and raphidophytes, and the phytoplankton group explained 61%, 54%, and 89% of the variability of fatty acids, sterols, and carotenoids, respectively. Fatty acid and carotenoid profiles distinguished most phytoplankton groups, but not flawlessly. For example, fatty acids could not distinguish golden algae and cryptomonads, whereas carotenoids did not separate diatoms and golden algae. The sterol composition was heterogeneous but seemed to be useful for distinguishing different genera within a phytoplankton group. The chemotaxonomy biomarkers yielded optimal genetic phylogeny when the fatty acids, sterols, and carotenoids were used together in multivariate statistical analysis. Our results suggest that the accuracy of phytoplankton composition modeling could be enhanced by combining these three biomolecule groups.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesPhytochemistry
dc.rightsCC BY 4.0
dc.subject.otherkemotaksonomia
dc.subject.otherchemotaxonomy
dc.subject.othereukaryotic algae
dc.subject.othercyanobacteria
dc.subject.otherfatty acid
dc.subject.othersterol
dc.subject.othercarotenoid
dc.titlePhytoplankton group identification with chemotaxonomic biomarkers : In combination they do better
dc.typeresearch article
dc.identifier.urnURN:NBN:fi:jyu-202303202191
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineSoveltava kemiafi
dc.contributor.oppiaineAkvaattiset tieteetfi
dc.contributor.oppiaineComputing, Information Technology and Mathematicsfi
dc.contributor.oppiaineResurssiviisausyhteisöfi
dc.contributor.oppiaineEkologia ja evoluutiobiologiafi
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineApplied Chemistryen
dc.contributor.oppiaineAquatic Sciencesen
dc.contributor.oppiaineComputing, Information Technology and Mathematicsen
dc.contributor.oppiaineSchool of Resource Wisdomen
dc.contributor.oppiaineEcology and Evolutionary Biologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn0031-9422
dc.relation.volume209
dc.type.versionpublishedVersion
dc.rights.copyright© 2023 The Authors. Published by Elsevier Ltd.
dc.rights.accesslevelopenAccessfi
dc.type.publicationarticle
dc.relation.grantnumber333564
dc.subject.ysosyanobakteerit
dc.subject.ysoplankton
dc.subject.ysosterolit
dc.subject.ysokarotenoidit
dc.subject.ysorasvahapot
dc.subject.ysomikrolevät
dc.subject.ysobiomarkkerit
dc.subject.ysomonitorointi
dc.subject.ysosystematiikka (biologia)
dc.subject.ysoeliöyhteisöt
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p3324
jyx.subject.urihttp://www.yso.fi/onto/yso/p3053
jyx.subject.urihttp://www.yso.fi/onto/yso/p4360
jyx.subject.urihttp://www.yso.fi/onto/yso/p6692
jyx.subject.urihttp://www.yso.fi/onto/yso/p4800
jyx.subject.urihttp://www.yso.fi/onto/yso/p26977
jyx.subject.urihttp://www.yso.fi/onto/yso/p12288
jyx.subject.urihttp://www.yso.fi/onto/yso/p3628
jyx.subject.urihttp://www.yso.fi/onto/yso/p19946
jyx.subject.urihttp://www.yso.fi/onto/yso/p4636
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.phytochem.2023.113624
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationThis work was supported by the Academy of Finland [grant nos. 276,268 and 333,564]; the Maj and Tor Nessling Foundation [grant no. 202140014]; and the Helsinki University Three-Year Research Grants [grant awarded to J.B.].
dc.type.okmA1


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