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FlowCAM

The FlowCAM (Flow Cytometer And Microscope) is and automated technique for particle enumeration that uses flow cytometry and microscopy, enabling near to automatic classification and quantification of phytoplankton in a water sample.

General

Phytoplankton forms the basis of the food web, contains species that are toxic and is a good bio-indicator of environmental changes. Variations in phytoplankton communities may impact a wide range of species, ranging from zooplankton and fish to seabirds and marine mammals. However, studies of plankton are difficult to carry out because of their high diversity and spatiotemporal variability. In addition, traditional identification techniques are time-consuming and require highly qualified taxonomists.

Infrastructure

The Flanders Marine Institute (VLIZ) installed a FlowCAM as part of the Belgian LifeWatch observatory.

From May 2017 onwards, phytoplankton is sampled at 9 onshore stations every month; and every season, the sampling occurs at 17 on and offshore stations. The samples are fixed with Lugol and stored for its posterior analysis. Once in the Marine Station Ostend (MSO), the samples are processed by the FlowCAM at 4X magnification to obtain good resolution images of size ranges of interest, primarily micro-phytoplankton from 55 to 300 µm. A gray-scale camera is used because it offers higher resolution quality for species identification and because of the fixed samples do not present fluorescent phytoplankton pigments. However, with the FlowCAM it would be possible to process the samples in real time without preservation based on the fluorescence of the pigments.

     

Left: FlowCAM laboratory set-up at MSO (©VLIZ) - Middle: Phytoplankton sample fixed with Lugol (©VLIZ) - Right: Sample entry in FlowCAM (©VLIZ)

Once the pictures are obtained, they are processed with a software included in the FlowCAM (VisualSpreadsheet). This software makes automatic classifications into taxonomical groups by building statistical filters based on the particles properties of libraries. Those libraries are created by the operator to identify specific taxa with similar properties. After running the automatic classification in the software, the operator needs to verify the classification.

In spite of a sometimes lower taxonomic resolution versus more traditional methodologies, this high-throughput approach allows for a highly efficient analysis of pythoplankton samples. In combination with more advanced Artificial Intelligence algorithms currently under research we envisage to accelerate phytoplankton processing by factor 10 or more.

 

          
Left: Processing of a sample with VisualSpreadsheet (©VLIZ) - Right: FlowCAM collage from a sample at 4x magnification (©VLIZ)

Map of the sampling stations in the Belgian Part of the North Sea (©VLIZ)

 

Data

Data can be accessed via the LifeWatch Data Explorer for flowcam data.

Metadata is available at:

  • LifeWatch observatory data: phytoplankton observations by imaging flow cytometry (FlowCam) in the Belgian Part of the North Sea, More

 

Useful links

 

Most recent publications

  • Amadei Martínez, L.; Mortelmans, J.; Dillen, N.; Debusschere, E.; Deneudt, K. (2020). LifeWatch observatory data: phytoplankton observations in the Belgian Part of the North Sea. Biodiversity Data Journal 8: e57236. https://hdl.handle.net/10.3897/bdj.8.e57236 [link to IMIS record]
  • Amadei Martínez, L.; Debusschere, E.; Mortelmans, J.; Deneudt, K.; Hernandez, F. (2019). Semi-automatic identification of phytoplankton using image classification techniques, in: Mees, J. et al. (Ed.) Book of abstracts – VLIZ Marine Science Day. Bredene, Belgium, 13 March 2019. VLIZ Special Publication, 83: pp. 62 [link to IMIS record]
  • Lloret, L.; Heredia, I.; Aguilar, F.; Debusschere, E.; Deneudt, K.; Hernandez, F. (2018). Convolutional Neural Networks for Phytoplankton identification and classification. Biodiversity Information Science and Standards 2: e25762. https://hdl.handle.net/10.3897/biss.2.25762 [link to IMIS record]
  • Debusschere, E.; Deneudt, K.; Oset Garcia, P.; Mortelmans, J.; Claus, S. (2018). Biological data integration: Flanders Marine Institute (VLIZ). Ppt presented at Third JERICO-NEXT Workshop on Phytoplankton Automated Observation, France, 19th - 21st March 2018. Flanders Marine Institute: Oostende. 33 pp. [link to IMIS record]
  • Deneudt, K.; Oset Garcia, P.; Mortelmans, J.; Debusschere, E.; Claus, S. (2018). Jerico Next – Biological data management. Ppt presented at Third Jerico-Next Workshop on Phytoplankton Automated Observation, France, 19th - 21st March 2018. Flanders Marine Institute (VLIZ): Oostende. 21 pp. [link to IMIS record]

  • Puillat, I.; Artigas, L.F.; Creach, V.; Debusschere, E.; Rijkeboer, M.; Marrec, P.; Thyssen, M.; Karlson, B. (2018). JERICO-RI: Progress toward an automated detection of phytoplankton in Europe coastal areas, in: 4th Geo Blue Planet Symposium, July 4-6, 2018, Toulouse, France: Abstracts. [link to IMIS record]

  • Debusschere, E.; Deneudt, K.; de Blok, R.; Vyverman, W.; Louchart, A.; Lizon, F.; Mortelmans, J.; Tyberghein, L.; Beauchard, O.; Rijkeboer, M. (2018). Results from campaign in the Channel-North Sea and Belgian Coastal Zone – RV Simon Stevin. Ppt presented at Third JERICO-NEXT Workshop on Phytoplankton Automated Observation, France, 19th - 21st March 2018. VLIZ/CNRS/NIOZ/RWS: Oostende. 28 pp. [link to IMIS record]