Jonathan Demmer

Accurate ocean data (i.e., tide, current velocity and direction, wave) are essential for numerous environmental studies: 1) development of marine renewable energy (e.g., maximize the efficiency of energy conversion); 2) aquaculture (e.g., offshore development); 3) anthropic pollution (e.g., plastic/pollutant dispersal); and 4) ecology (e.g., spread of invasive species).The exponential increase of computational power has made numerical models, such as Eularian hydrodynamic models and Lagrangian particle tracking models (PTM) useful tools to characterize physical oceanographic parameters. However, methods to validate PTMs appear less developed due the complexity of biophysical process interactions; for example, uncertainty remains on the impact of wind on surface currents and how the effects of wind are propagated through the water columnHere, we use a novel set of data representing the travel of drifters in the Irish Sea during two consecutive years (summer 2021 and 2022). The experiment aim is to reduce the near surface flow uncertainty influencing particle dispersal. Data were collecting using a range of drifters released in coastal and offshore locations of a tidally dominate shelf-sea (Irish Sea): 1) variation of drogue depth between 1m and 5m; 2) variation of period from tidal cycles to spring-neap cycles; and 3) some with reduced “windage” designs (no drogue and minimal exposure above surface).Preliminary results show the importance of wind driven current between the surface and 5 m depth, which should be take into account when considering the development of PTM. Furthermore, we find some scales of oceanographic processes that affect transport, such as turbulent eddies and waves, were not resolved - and yet our predictions broadly matched observations.

Complex coastal currents control early-stage larval dispersal from intertidal populations, and late-stage settlement patterns, but are often poorly resolved in larval dispersal models. Generally, there is high uncertainty in the timing of larval spawning, which markedly affects larval dispersal. In this study, we describe the physical parameters that induce spawning events in the blue mussel, Mytilus edulis, using a variation of the Condition Index (which relates the mass of meat to the mass of the shell) as a proxy. We developed a high-resolution Eulerian coastal hydrodynamic model, coupled with a Lagrangian particle tracking model, to quantify the potential dispersal of early-stage mussel larvae based on differing spawning dates obtained from field data. Our results showed that (1) the timings of larval spawning cannot be explained solely by ‘thermal shocks’ in the sea or air temperatures (i.e. fluctuations in temperature causing stress); (2) larger spawning events generally occurred during neap tides; (3) the simulated larval dispersal was largely but not always predicted by averaged current pathways (calculated over two weeks period); and (4) simulated self-recruitment was low at sites associated with strong tidal currents. These results have important implications for shellfisheries stock management and sustainability. Specific to this study, simulated mussels from shellfishery beds off North Wales dispersed more than 25 km in one week and so could feasibly contribute to the wider population throughout the northern part of the Irish Sea.

Background: Larval connectivity between distinct benthic populations is essential for their persistence. Although connectivity is difficult to measure in situ, it can be predicted via models that simulate biophysical interactions between larval behaviour and ocean currents. The blue mussel (Mytilus Edulis L.) is widespread throughout the northern hemisphere and extensively commercialised worldwide. In the Irish Sea, this industry represents ~ 50% of Welsh shellfisheries, where cultivation is mainly based on wild spat. However, the main sources and amount of spat varied interannually (1100 tonnes harvest in 2014 against zero in 2018). The aim of this study is to characterise the structure and dynamics of the blue mussel metapopulation within the northern part of the Irish Sea. Methods: We develop a Lagrangian particle tracking model, driven by a high-resolution (from 30 to 5000 m) validated unstructured coastal hydrodynamic model of the Irish Sea, to simulate spatial and temporal variability of larval dispersal and connectivity between distinct mussel populations and potential settlement areas. Results: Our results showed that: (1) larvae positioned near the surface were strongly influenced by wind-driven currents suggesting that connectivity networks had the potential to span hundreds of kilometres; (2) in contrast, larvae positioned deeper in the water column were driven by tidal currents, producing intricate spatial patterns of connectivity between mussel beds over tens of kilometres that were consistent over time. Conclusions: Dispersal of mussel larvae in the tidally energetic Irish Sea during the April–May spawning season is potentially driven by wind-driven surface currents, as confirmed by fisherman observations of inter-annual variability in wild spat collection. These results have important implications for metapopulation dynamics within the context of climate change and sustainable shellfisheries management (i.e. gain and loss of populations and harvest areas according to wind conditions).