Plankton, consisting of phyto-, zoo- and bacterio/virioplankton, form the basis of the food chain in many marine ecosystems. Phytoplankton, represented by photoautotrophic plankton organisms, are the main primary producers of the oceans. Zooplankton, consisting of heterotrophic organisms, play a pivotal role in aquatic ecosystems. Zooplankton transfer the energy and matter synthesised by phytoplankton to higher trophic levels in the food chain. Many planktonic organisms actually play several roles in the food chain simultaneously: they are mixotrophic.
Both zooplankton and phytoplankton are good indicators of global change. Indeed, they respond to changes in the environment very quickly by variations in abundance and/or specific composition and biomass. Moreover, phytoplankton are capable of forming blooms (rapid and exponential multiplication) which are sometimes toxic and which, by the anoxia of the environment at night, or by the synthesis of toxins, can have a negative impact on the whole ecosystem. For all these reasons, these highly dynamic communities are studied extensively and provide valuable information on the functioning of the oceans.
Currently, the organisms that make up plankton are still frequently measured and counted manually under a microscope by specialised taxonomists. The complete analysis of a sample can take from 1 to 3 days depending on the desired taxonomic detail, and this therefore limits the number of samples that can be analysed in a reasonable time. This manual method is therefore not adapted to high spatial and temporal resolution, which is the only way to highlight planktonic variations in these highly dynamic, patchy communities.
Since the 1980s, image analysis coupled with supervised classification has been used to automate (at least partially) and accelerate the processing of samples. From plankton images, various parameters are measured and used by supervised classification methods to automatically determine the taxonomic group of each particle imaged.
In the Numerical Ecology Unit, we are developing a software called Zoo/PhytoImage for the elaboration of spatio-temporal plankton series by automating the sample processing. This open-source software allows us to analyse various kinds of digital images of plankton, and to measure, count and classify the different planktonic organisms “digitally” fixed on these images. It then calculates ecologically important variables, such as abundances, biomasses and size spectra by taxonomic group or for the whole sample.
Our scientific publications related to this research theme are referenced in the institutional database DI-Fusion UMONS.