Novel interactions between phytoplankton and bacteria shape microbial seasonal dynamics in coastal ocean waters

Trophic interactions between marine phytoplankton and heterotrophic bacteria are at the base of the biogeochemical carbon cycling in the ocean. However, the specific interactions taking place between phytoplankton and bacterial taxa remain largely unexplored, particularly out of phytoplankton blooming events. Here, we applied network analysis to a 3.5-year time-series dataset to assess the specific associations between different phytoplankton and bacterial taxa along the seasonal scale, distinguishing between free-living and particle-attached bacteria. Using a newly developed network post-analysis technique we removed bacteria-phytoplankton correlations that were primarily driven by environmental parameters, to detect potential biotic interactions. Our results indicate that phytoplankton dynamics may be a strong driver of the inter-annual variability in bacterial community composition. We found the highest abundance of specific bacteria-phytoplankton associations in the particle-attached fraction, indicating a tighter bacteria-phytoplankton association than in the free-living fraction. In the particle-associated fraction we unveiled novel potential associations such as the one between Planctomycetes taxa and the diatom Leptocylindrus spp. Consistent correlations were also found between free-living bacterial taxa and different diatoms, including novel associations such as those between SAR11 with Naviculales diatom order, and between Actinobacteria and Cylindrotheca spp. We also confirmed previously known associations between Rhodobacteraceae and Thalassiosira spp. Our results expand our view on bacteria-phytoplankton associations, suggesting that taxa-specific interactions may largely impact the seasonal dynamics of heterotrophic bacterial communities

Disentangling environmental effects in microbial association networks

Ecolocial interctions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions by associations across time and space, which can be represented as association networks. Links in these networks could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally-driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not.S

Quantifying long-term recurrence in planktonic microbial eukaryotes

How much temporal recurrence is present in microbial assemblages is still an unanswered ecological question. Even though marked seasonal changes have been reported for whole microbial communities, less is known on the dynamics and seasonality of individual taxa. Here, we aim at understanding microbial recurrence at three different levels: community, taxonomic group and operational taxonomic units (OTUs). For that, we focused on a model microbial eukaryotic community populating a long-term marine microbial observatory using 18S rRNA gene data from two organismal size fractions: the picoplankton (0.2–3 µm) and the nanoplankton (3–20 µm). We have developed an index to quantify recurrence in particular taxa. We found that community structure oscillated systematically between two main configurations corresponding to winter and summer over the 10 years studied. A few taxonomic groups such as Mamiellophyceae or MALV-III presented clear recurrence (i.e., seasonality), whereas 13%–19% of the OTUs in both size fractions, accounting for ~40% of the relative abundance, featured recurrent dynamics. Altogether, our work links long-term whole community dynamics with that of individual OTUs and taxonomic groups, indicating that recurrent and non-recurrent changes characterize the dynamics of microbial assemblages