Microplanktonic community structure in a coastal system relative to a Phaeocystis bloom inferred from morphological...

Massive phytoplankton blooms, like the recurrent Phaeocystis proliferation observed every year in the Eastern English Channel (EEC), have a significant influence on the overall planktonic community structure and their food web dynamics. As well as being an important area for local fisheries, the EEC is an ideal ecosystem for work on microbial diversity. This is because, although its environmental context is relatively complex, it is reasonably well understood due to several years of monitoring and morphological observations of its planktonic organisms. The objective of our study was to better understand the under-explored microbial eukaryotic diversity relative to the Phaeocystis bloom.

The community structure of microplankton (diatoms, haptophytes, ciliates and dinoflagellates) was studied through morphological observations and tag pyrosequencing. During the annual Phaeocystis spring bloom, the phytoplankton biomass increased by 34-fold, while the microzooplankton biomass showed a 4-fold increase, representing on average about 4.6% of the biomass of their phytoplankton prey. Tag pyrosequencing unveiled an extensive diversity of Gymnodiniaceae, with G. spirale and G. fusiformis representing the most abundant reads. An extended diversity of Phaeocystales, with partial 18S rDNA genes sequence identity as low as 85% was found, with taxa corresponding to P. globosa, but also to unknown Phaeocystaceae.

Morphological analyses and pyrosequencing were generally in accordance with capturing frequency shifts of abundant taxa. Tag pyrosequencing allowed highlighting the maintenance of microplankton diversity during the Phaeocystis bloom and the increase of the taxa presenting low number of reads (minor taxa) along with the dominant ones in response to biotic and/or abiotic changing conditions. Although molecular approaches have enhanced our perception on diversity, it has come to light that the challenge of modelling and predicting ecological change requires the use of different complementary approaches, to link taxonomic data with the functional roles of microbes in biogeochemical cycles.