Dr. Ellen Oldenburg
Research Focus
Microbial communities are key players in marine ecosystems, influencing biogeochemical cycles and ecosystem stability. In polar regions, their seasonal dynamics and interactions remain insufficiently understood, yet they are crucial for predicting ecosystem responses to environmental change. My research focuses on integrating long-term time-series data with network analyses and causal inference methods to unravel microbial community dynamics in the Arctic Ocean. By applying advanced computational approaches such as co-occurrence networks, convergent cross mapping (CCM), and energy landscape analysis (ELA), I investigate how microbial interactions shift across seasons and for example how winter conditions act as an ecological reset.

Each node in the CCM network represents an ASV, and each edge represents the causal influences. The edge weight corresponds to the Normalized Mutual Information determined from the comparison of the individual ASV and their predicted representation in the shadow manifold. ASVs are connected if the smoothed p value of the weight is p < 0.05 (Supplementary Information Fig. S3).
A: Node color reflects the month in which the ASV exhibits maximal abundance, calculated from the maximum abundance mode for each year ranging from January to December.
B: Nodes are colored based on community membership determined by the Louvain algorithm. “HS” = high light summer, “LW” = low light winter, “TS” = transition spring, “TA” = transition autumn.
C: Aggregated Normalized Mutual Information between clusters; arrow thickness corresponds to interaction strength.
D: Interaction analysis between taxonomic clusters at the class level (“Syndiniales”, “Dinophyceae”, “Bacillariophyta”, “MAST”). Arrow thickness indicates interaction strength; node shapes represent taxonomic groups.
Dr. Ellen Oldenburg

Telephone+49 211 81 10175
Institute for Quantitative and Theoretical Biology
Heinrich Heine University Düsseldorf