Integrating phytoplankton phenology, traits, and model-data fusion to advance bloom prediction

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dc.contributor.authorHipsey, M.R.
dc.contributor.authorCarey, C.C.
dc.contributor.authorBrookes, J.D.
dc.contributor.authorBurford, M.A.
dc.contributor.authorDang, H.V.
dc.contributor.authorIbelings, B.W.
dc.contributor.authorHamilton, D.P.
dc.date.issued2025
dc.descriptionOnlinePubl. Available online 13 August 2025
dc.description.abstractWhile there is a diversity of approaches for modeling phytoplankton blooms, their accuracy in predicting the onset and manifestation of a bloom is still lagging behind what is needed to support effective management. We outline a framework that integrates trait theory and ecosystem modeling to improve bloom prediction. This framework builds on the concept that the phenology of blooms is determined by the dynamic interaction between the environment and traits within the phytoplankton community. Phytoplankton groups exhibit a collection of traits that govern the interplay of processes that ultimately control the phases of bloom initiation, maintenance, and collapse. An example of process-trait mapping is used to demonstrate a more consistent approach to bloom model parameterization that allows better alignment with models and laboratory- and ecosystem-scale datasets. Further approaches linking statistical-mechanistic models to trait parameter databases are discussed as a way to help optimize models to better simulate bloom phenology and allow them to support a wider range of management needs.
dc.description.statementofresponsibilityMatthew R. Hipsey, Cayelan C. Carey, Justin D. Brookes, Michele A. Burford, Hoang V. Dang, Bas W. Ibelings, David P. Hamilton
dc.identifier.citationLimnology and Oceanography Letters, 2025; 10(6):1-20
dc.identifier.doi10.1002/lol2.70052
dc.identifier.issn2378-2242
dc.identifier.issn2378-2242
dc.identifier.orcidBrookes, J.D. [0000-0001-8408-9142]
dc.identifier.urihttps://hdl.handle.net/2440/147885
dc.language.isoen
dc.publisherWiley
dc.relation.granthttp://purl.org/au-research/grants/arc/DP190101848
dc.relation.granthttp://purl.org/au-research/grants/arc/LP200200910
dc.relation.granthttp://purl.org/au-research/grants/arc/LP150100451
dc.rights© 2025 The Author(s). Limnology and Oceanography Letters published by Wiley Periodicals LLC on behalf of Association for the Sciences of Limnology and Oceanography. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.source.urihttps://doi.org/10.1002/lol2.70052
dc.subjectIntegrating phytoplankton phenology; advance bloom prediction
dc.titleIntegrating phytoplankton phenology, traits, and model-data fusion to advance bloom prediction
dc.typeJournal article
pubs.publication-statusPublished

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