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DNA-metabarcoding of marine phytoplankton

Research project Analysis of phytoplankton is an important part of the marine environmental monitoring. The analyses are normally based on microscopy, which is time-consuming and requires personnel with high taxonomic skills. DNA-analysis has been discussed as a compliment to microscopy, but there is a need for optimization and validation of this method to be able to use it in the monitoring programmes. The goal of this project is to do a full comparison between microscopy- and DNA analysis of phytoplankton.

Head of project

Project overview

Project period:

2019-01-01 2024-12-31

Participating departments and units at Umeå University

Department of Ecology and Environmental Science, Umeå Marine Sciences Centre (UMF)

Research area

Biological sciences, Environmental sciences, Marine science, Molecular biology and genetics

External funding

Swedish Environmental Protection Agency, Swedish Agency for Marine and Water Management

Project description

Participating departments

The project is performed by a team of researchers at Umeå University, the Royal Institute of Technology KTH and SMHI with complementary expertise in plankton ecology, marine monitoring, molecular biology and bioinformatics.

Umeå university, Department of Ecology and Environmental Sciences

KTH, SciLifeLab


Umeå University, Umeå Marine Sciences Centre

Reference group

Professor Bente Edvardsen, Oslo university, Norway

Dr Uwe John, Alfred Wegener Institute, Bremerhaven , Germany

Dr Elisabeth Sahlsten, Swedish Agency for Marine and Water Management, Sweden

Environment ecologist Anna Dimming, County Administrative Board of Västra Götaland, Sweden

Background, plans and goals

Microscopy analysis of phytoplankton is time-consuming and requires personnel with high taxonomic skills. Furthermore, microscopy has some limitations, such as the fact that it is impossible to identify the smallest cells with microscope analysis. Therefore DNA analysis has been discussed as a complement to the microscope analysis, or as a way to partly replace the time-consuming microscopy.

DNA-analysis has the potential to become a relatively exact and reproducible method, but just like microscopy it has its limitations. To implement DNA analysis as a method in monitoring requires careful evaluation and comparative studies of the different methods.

Quantitative assessment of marine phytoplankton communities is an important part of the environmental monitoring. Phytoplankton play a key role in global biogeochemical cycles and form the basis of marine food webs. Changes in phytoplankton community composition, non-indigenous species and harmful algae can be important signals of environmental problems and climate change.

The main goal of the project is to evaluate high-throughput sequencing (DNA analysis) as a tool to investigate the diversity of phytoplankton in the major sea areas surrounding Sweden, with a special focus on non-indigenous species and harmful algae. The tests will be conducted on water samples collected in the Swedish National Marine Monitoring program. This will enable assembly of a large data set covering all major sea areas surrounding Sweden, and allow us to compare the DNA results with taxonomic identifications obtained using microscopy.

We are planning to:

  • optimize sampling and sample treatment, and establish standard protocols,

  • develop and evaluate an improved method for identifying phytoplankton, based on sequencing a longer region of the rRNA operons,

  • harmonize reference data in DNA databases with traditional phytoplankton taxonomic classifications,

  • compare results from metabarcoding with results from microscope-based methods.


Andersson A, Zhao L, Brugel S, Figueroa D, Huseby S (2023) Metabarcoding vs Microscopy: Comparison of Methods To Monitor Phytoplankton Communities. ACS ES&T Water 3, 2671−2680.

Latz, Meike A. C.; Andersson, Agneta; Brugel, Sonia; et al. (2024)
A comprehensive dataset on spatiotemporal variation of microbial plankton communities in the Baltic Sea. Scientific Data, Springer Nature 2024, Vol. 11, (1).

External funding

Latest update: 2024-01-16