The role of wind in controlling the connectivity of blue mussels (Mytilus edulis L.) populations

Abstract

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).

Predictive biophysical models of bivalve larvae dispersal in Scotland

“…Previous studies have shown the importance of circulation patterns on interannual variability of larval recruitment and dispersal (McQuaid and Phillips, 2000;Largier, 2003), and interactions between larval vertical migration and stratification have been shown to be an important driver of dispersal (Raby et al, 1994). Moreover, the water column in the West coast of Scotland remains well mixed since winter until May, which implies that stratification might not play a role in earlier spring on larval dispersal, as also shown in Figure 4; this is in addition supported by Demmer et al (2022) for the study of mussel dispersion in the northern Irish Sea.…”

“…Our results show that there is no significant difference in dispersal patterns between the three depths tested by the model. Indeed, assuming that larvae are distributed throughout the water column, performing only a limited vertical migration and in the absence of stratification, then their dispersal would be primarily controlled by tidal currents (Raby et al, 1994;McQuaid and Phillips, 2000;Demmer et al, 2022). Furthermore, assuming that bivalve larvae are mainly distributed in the near-surface waters, their dispersal would additionally be influenced by wind-driven currents.…”

“…Like most benthic organisms, bivalves spend their early life stage within the water column, which lasts from three to four weeks (Bayne, 1965;Pineda et al, 2007). We adjusted the simulation start time (1 st March, 1 st April, 1 st May), the release year (2017-2021), dispersal period (30 or 45 days; Helm et al, 2004;Pineda et al, 2007;Demmer et al, 2022), and the particle depth (2 m, 6 m or 10 m below sea level) following literature that indicates mussel pediveliger larvae are found primarily in near-surface waters (Baker and Mann, 2008;Demmer et al, 2022). The total number of released particles was 8,877,600 (20 particles 411 number of sites 24 first hours 3 different months 5 years 3 depths).…”

Genetic and particle modelling approaches to assessing population connectivity in a deep sea lobster

“…The data was used in several ways, (1) to extract the data pertaining to that of the pelagic larval phase per season (December to February-summer; March to May-Autumn; June to August-Winter; September to November-Spring), (2) to track the movement of the particles which could then provide details of the approximate number of generations needed for connectivity among dispersed locations, and (3) enumerating connectivity by calculating the total number of particles arriving at each SCI sample area from each source sample area over the modelling period (i.e., source-sink dynamics). Our use of hydrodynamic models for simulating larval dispersal draws on well-established research for a wide range of marine species e.g., [109][110][111][112] . However, our approach to the application of the modelling differs in that we are interested in multigenerational connectivity which is consistent with the biological process for migration of genes within a wide ranging population, and it ensured that inter-annual variability in hydrodynamic processes were incorporated into the modelled outcomes, whilst also ensuring there was enough time for particle dispersal to assess possible long distance multigenerational dispersal.…”

Assessment of the impact of offshore wind farm foundations in the English Channel on larval dispersal and connectivity using numerical modeling

“…Following the construction of OWFs, the presence of hard substrates and foundations attracts various benthic species, predominantly dominated by the presence of mussels, as highlighted by the authors of [16]. The establishment of offshore wind farms, recognized as stepping stones, has facilitated the broader dispersal of mussels (Mytilus edulis) in the North Sea [17][18][19][20]. Simultaneously, in the English Channel, prior to the implementation of wind farms, natural larval dispersion has been modeled.…”