The Influence of Changing Nutrients and Warming on Phytoplankton and its Interactions with Zooplankton in the North Sea

Coastal zones are increasingly stressed by human activities and climate change. In the German Bight (North Sea), nitrogen (N) and phosphorus (P) inputs have declined markedly while sea-surface temperature (SST) has risen. This thesis examines how these changes affect phytoplankton (Chlorophyll-a, Chl-a) and phytoplankton–zooplankton interactions using long-term satellite and in situ data. Satellite ocean-color analyses show predominantly declining Chl-a anomalies across the German Bight, with localized increases near the Elbe estuary. SST explains part of the long-term Chl-a variability, but responses differ strongly in space. In situ analyses indicate that spring Chl-a peaked in the 1980s–1990s and has since declined, though higher values persist in southern inner coastal waters. Statistical models identify decreasing winter dissolved inorganic nitrogen as the primary driver of reduced spring Chl-a; winter P and N:P are not significant predictors, and spring SST has a weak positive effect. Information-theoretic network analysis indicates that plankton interactions at Helgoland Roads are highly sensitive to environmental conditions. Hybrid interaction strengths increase under low nutrient and light availability. Bottom-up strength (from primary producers to grazers) is highest under high light availability, N:P between 20 and 25, and intermediate SST; whereas top-down strength (from grazers to primary producers) dominates under lower light availability and N:P below 16. Other network properties respond nonlinearly to environmental drivers, suggesting that small nutrient shifts can reorganize interaction structure. Overall, the thesis links nutrient-reduction policies to improved water quality while highlighting possible trade-offs, such as elevated N:P under strong light affecting food quality for higher trophic levels. Future work should target Chl-a trends near the Elbe mouth and expand network approaches to improve ecosystem prediction.