Spatial dynamics of eukaryotic microbial communities in the German Bight

Pim.Sprong [ at ]


Monitoring changes in eukaryotic microbial communities is critical for understanding ecosystem dynamics, trophic interactions and the impacts of climate change. Long-term time series are an important tool for monitoring changes in ecological communities, but time series from a single location may not be representative of regional dynamics. In the German Bight, the Helgoland Roads time series is such a long-term series. Here, we consider the spatial dynamics of the eukaryotic microbes as an indicator of the representativeness of the Helgoland Roads site for the coastal German Bight, which is located in the North Sea. The eukaryotic microbial community in the German Bight was analysed at Helgoland Roads and two coastal stations (Cuxhaven and Wilhelmshaven) between March and October 2016 using metabarcoding. In addition, an oceanographical model was used to check for potential hydrological connectivity between the stations during the sampling period. Our results showed that the communities were different at the three stations. Helgoland was dominated by dinoflagellates, whereas the coastal stations had more diverse communities. Furthermore, differences were observed in the dinoflagellate and diatom communities between the three stations. Lagrangian particle tracking applied to the model results, showed limited connectivity between Helgoland and the coastal stations in 2016. The differences between Helgoland and the coastal stations were correlated with the different hydrological regimes and associated nutrient contents. Our observations suggest the presence of different eukaryotic microbial communities separated by complex hydrological conditions in the coastal German Bight.

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DOI 10.1016/j.seares.2020.101914

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Sprong, P. , Fofonova, V. , Wiltshire, K. H. , Neuhaus, S. , Ludwichowski, K. U. , Käse, L. , Androsov, A. and Metfies, K. (2020): Spatial dynamics of eukaryotic microbial communities in the German Bight , Journal of Sea Research . doi: 10.1016/j.seares.2020.101914

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