Estimating blue mussel (Mytilus edulis) connectivity and settlement capacity in mid-latitude fjord regions.

The mussel industry faces challenges such as low and inconsistent levels of larvae settlement and poor-quality spat, leading to variable production. However, mussel farming remains a vital sustainable and environmentally responsible method for producing protein, fostering ecological responsibility in the aquaculture sector. We investigate the population connectivity and larval dispersion of blue mussels (Mytilus edulis) in Scottish waters, as a case study, using a multidisciplinary approach that combined genetic data and particle modelling. This research allows us to develop a thorough understanding of blue mussel population dynamics in mid-latitude fjord regions, to infer gene-flow patterns, and to estimate population divergence. Our findings reveal a primary south-to-north particle transport direction and the presence of five genetic clusters. We discover a significant and continuous genetic material exchange among populations within the study area, with our biophysical model's outcomes aligning with our genetic observations. Additionally, our model reveals a robust connection between the southwest coast and the rest of the west coast. This study will guide the preservation of mussel farming regions, ensuring sustainable populations that contribute to marine ecosystem health and resilience.

Benthic animal-borne sensors and citizen science combine to validate ocean modelling.

Developments in animal electronic tagging and tracking have transformed the field of movement ecology, but interest is also growing in the contributions of tagged animals to oceanography. Animal-borne sensors can address data gaps, improve ocean model skill and support model validation, but previous studies in this area have focused almost exclusively on satellite-telemetered seabirds and seals. Here, for the first time, we develop the use of benthic species as animal oceanographers by combining archival (depth and temperature) data from animal-borne tags, passive acoustic telemetry and citizen-science mark-recapture records from 2016-17 for the Critically Endangered flapper skate (Dipturus intermedius) in Scotland. By comparing temperature observations to predictions from the West Scotland Coastal Ocean Modelling System, we quantify model skill and empirically validate an independent model update. The results from bottom-temperature and temperature-depth profile validation (5,324 observations) fill a key data gap in Scotland. For predictions in 2016, we identified a consistent warm bias (mean = 0.53 °C) but a subsequent model update reduced bias by an estimated 109% and improved model skill. This study uniquely demonstrates the use of benthic animal-borne sensors and citizen-science data for ocean model validation, broadening the range of animal oceanographers in aquatic environments.

Publisher Correction: Impact of remotely generated eddies on plume dispersion at abyssal mining sites in the Pacific.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Impact of remotely generated eddies on plume dispersion at abyssal mining sites in the Pacific.

Proposed harvesting of polymetallic nodules in the Central Tropical Pacific will generate plumes of suspended sediment which are anticipated to be ecologically harmful. While the deep sea is low in energy, it also can be highly turbulent, since the vertical density gradient which suppresses turbulence is weak. The ability to predict the impact of deep plumes is limited by scarcity of in-situ observations. Our observations show that the low-energy environment more than four kilometres below the surface ultimately becomes an order of magnitude more energetic for periods of weeks in response to the passage of mesoscale eddies. The source of these eddies is remote in time and space, here identified as the Central American Gap Winds. Abyssal current variability is controlled by comparable contributions from tides, surface winds and passing eddies. During eddy-induced elevated flow periods mining-related plumes, potentially supplemented by natural sediment resuspension, are expected to spread and disperse more widely and rapidly. Predictions are given of the timing, location and scales of impact.

A high resolution hydrodynamic model system suitable for novel harmful algal bloom modelling in areas of complex coastline and topography.

Fjordic coastlines provide sheltered locations for finfish and shellfish aquaculture, and are often subject to harmful algal blooms (HABs) some of which develop offshore and are then advected to impact nearshore aquaculture. Numerical models are a potentially important tool for providing early warning of such HAB events. However, the complex topography of fjordic shelf regions is a significant challenge to modelling. This is frequently compounded by complex bathymetry and local weather patterns. Existing structured grid models do not provide the resolution needed to represent these coastlines in their wider shelf context. In a number of locations advectively transported blooms of the ichthyotoxic dinoflagellate Karenia mikimotoi are of particular concern for the finfish industry. Here were present a novel hydrodynamic model of the coastal waters to the west of Scotland that is based on unstructured finite volume methodology, providing a sufficiently high resolution hydrodynamical structure to realistically simulate the transport of particles (such as K. mikimotoi cells) within nearshore waters where aquaculture sites are sited. Model-observation comparisons reveal close correspondence of tidal elevations for major semidiurnal and diurnal tidal constituents. The thermohaline structure of the model and its current fields are also in good agreement with a number of existing observational datasets. Simulations of the transport of Lagrangian drifting buoys, along with the incorporation of an individual-based biological model, based on a bloom of K. mikimotoi, demonstrate that unstructured grid models have considerable potential for HAB prediction in Scotland and in complex topographical regions elsewhere.