Andrew James Wheeler

Suspended and slow‐sinking particles, especially those concentrated within nepheloid layers, can act as pivotal mediators of oceanic carbon transport and benthic‐pelagic coupling. However, identifying nepheloid layers and distinguishing their composition (i.e., inorganic particles, phytoplankton, zooplankton, and fish) using only acoustic systems remains challenging due to limited multidisciplinary observations. Here, multi‐frequency acoustic data, multisensory biogeochemical observations, and optical cameras are integrated to identify suspended and sinking particles, plankton, and fish off NW Ireland, Rockall Trough (NE Atlantic Ocean). Observed nepheloid layers exhibited high values in turbidity, particle abundance, buoyancy frequency, and synthetic seismograms, which are clearly distinct from plankton and fish shoals, as validated by direct camera observations. They mainly developed at 200–800 m depths and extended at least 40 km across the continental slope. Maximum thickness and echo intensity occurred on the upper slope, where internal waves break, and M ₂ internal tide generation rates are high. Internal waves can cause particle resuspension, inducing nepheloid layers. Internal waves laterally transported particles and lifted them to the upper water column. During the neap‐to‐spring tidal transition, intensified tides enhanced bottom currents and turbulent mixing, vertically diffusing and thickening the nepheloid layers. A cyclonic mesoscale eddy transported particles from the slope to the deeper ocean, thereby further expanding nepheloid layers laterally. This study characterizes the spatiotemporal variability of nepheloid layers, explores their hydrodynamic responses in the NE Rockall Trough, and highlights their role as efficient particle transport highways.

The collection of a beep-sea brachiopod specimen exhibiting peculiar morphological characters led to the definition of a new genus and species, Inequilateria tropica sp. nov.. This specimen present sub-oval, (weakly) rectimarginate, and planoconvex calcitic shell. The valves are asymmetrical with respect to the plane orthogonal to the commissure plane (the typical symmetry plane for brachiopods); on one side they are significantly longer than on the other, with a Latin sail-like shape in the anterior region, tapering to the left side in its posterior region. The pedicle valve’s anterior region is broken, and it’s wider and less tapered in the posterior region. The specimen exhibits no foramen; the pedicle emerge from between the valves. The pedicle is short (3 mm) and has a hard texture. No soft tissues of the specimen were preserved. The presence of these peculiarities, together with the composition of the specimen’s shell, complicate the higher taxonomical positioning of this proposed new species. The deep-sea environment of collection of the specimen is characterised by a soft, unconsolidated sandy to silty sandy substrate, suggesting a semi-infaunal mode of life although the peculiar texture of the pedicle might look more suitable for an epifaunal mode of life.

The siting of maritime developments, such as new offshore wind farms, should be data driven and balance diverse stakeholder priorities from different stakeholder groups to maximise benefits whilst minimising conflict and opposition. Here, we present a GIS-based Multi-Criteria Decision Analysis (MCDA) driven by the priorities and opinions of different stakeholder groups, integrating 186 spatial datasets relevant to floating offshore wind farm development. These datasets were consolidated into six expert-weighted or modelled “combined data layers” (reflecting relevant environmental, economic, and societal data types). The combined data layers were further weighted using the Analytic Hierarchy Process (AHP) by stakeholder groups including coastal residents, offshore wind energy developers, environmental NGOs, fisheries representatives, government agencies, harbour services and others. Model outputs reveal strong consensus across groups, with a shared preference for sites further offshore. Nearshore areas, identified as least preferable for development, are also identified as areas where opinions diverged the most showcasing the model’s ability to identify potential zones of disagreement between groups. By explicitly identifying both areas of alignment and conflict, this approach provides a transparent decision-support tool to guide offshore wind farm siting and foster more inclusive marine spatial planning.

Detailed methodologies for a geographic information system (GIS)-based multi-criteria decision support tool based on 186 spatial datasets spanning geological, oceanographic, meteorological, biological, social and economic parameters for an offshore floating wind farm developed in NW Ireland are presented. These are aggregated into six Combined Data Layers weighted by expert input. Two modelling approaches were applied: (1) a soft constraint analysis, which is an objective approach with the 50% suitability threshold replicating the Irish government's Designated Maritime Area Plan for Offshore Renewable Energy (DMAP) methodology; and (2) a stakeholder-weighted GIS-based multi-criteria decision analysis using the Analytic Hierarchy Process (AHP). Both approaches produced broadly consistent spatial outputs, with increasing suitability further offshore. Stakeholders rated some areas as more suitable than the objective model, indicating greater tolerance than is strictly necessary. The highest levels of stakeholder disagreement occurred in areas with the lowest suitability, highlighting that the least suitable sites are more controversial among stakeholder groups, signifying zones of potential conflict. Conversely, areas with high suitability showed strong cross-group consensus, suggesting widespread support. This methodology provides a replicable framework for integrating technical constraints and stakeholder values in marine spatial planning. By identifying zones of alignment and contention, it offers a powerful tool to improve site selection, reduce conflict and accelerate offshore wind development through enhanced social acceptance.

Multibeam echosounders provide ideal data for the semi-automated seabed feature extraction and accurate morphometric measurements. In this study, bathymetric and raw backscatter data were initially used to manually delimit the reef morphologies found in an insular semi-enclosed gulf in the northern Aegean Sea (Gera Gulf, Lesvos Island, Greece). The complexity of this environment makes it an ideal area to “crash test” (test to the limit) and compare the results of the delineation methods. A large number of (more than 7000) small but prominent reefs were detected, which made manual mapping extremely time-consuming. Three semi-automated tools were also employed to map the reefs: the Benthic Terrain Modeler (BTM), Confined Morphologies Mapping (CoMMa), and eCognition Multiresolution Segmentation. BTM did not function properly with irregular reef footprints, but by modifying both the bathymetry and slope, the outcome was improved, producing accurate results that appeared to exceed the accuracy of manual mapping. CoMMa, a new GIS morphometric toolbox, was a “one-stop shop” that, besides generating satisfactory reef delineation results (i.e., detecting the same total reef area as the manual method), was also used to extract the morphometric characteristics of the polygons resulting from all the methods. Lastly, the Multiresolution Segmentation also gave satisfactory results with the highest precision. To compare the final maps with the distribution of the reefs, mapcurves were created to estimate the goodness-of-fit (GOF) with the Precision, Recall, and F1 Scores producing values higher than 0.78, suggesting a good detection accuracy for the semi-automated methods. The analysis reveals that the semi-automated methods provided more efficient results in comparison with the time-consuming manual mapping. Overall, for this case study, the modification of the bathymetry and slope enabled the results’ accuracy to be further enhanced. This study asserts that the use of semi-automated mapping is an effective method for delineating the geomorphometry of intricate relief and serves as a powerful tool for habitat mapping and decision-making.

Cold-water coral (CWC) reefs are considered “hotspots” of biodiversity in deep-sea environments. Like tropical coral reefs, these habitats are subject to climate and anthropogenic threats. The use of remotely operated vehicles (ROVSs) in combination with three-dimensional (3D) modelling and augmented reality (AR) has enabled detailed visualisation of terrestrial and marine environments while promoting data accessibility and scientific outreach. However, remote environments such as CWC reefs still present challenges with data acquisition, which impacts the further understanding of these environments. This study aims to develop a mobile application using structure-from-motion (SfM) 3D photogrammetric data and AR for the visualisation of CWC reefs. The mobile application was developed to display 3D models of CWC reefs from the Piddington Mound area, southwest of Ireland. The 3D models were tested at different resolutions to analyse the visualisation experience and trade-off between resolution and application size. The results from the 3D reconstructions with higher resolution indicate that the combination of SfM, AR, and mobile phones is a promising tool for raising awareness and literacy regarding CWC and deep-water habitats. This study is the first of its kind to showcase CWC habitats accessible to anyone, anywhere with a mobile phone and internet connectivity.

Seabed sedimentary bedforms (SSBs) are strong indicators of current flow (direction and velocity) and can be mapped in high resolution using multibeam echosounders. Many approaches have been designed to automate the classification of such SSBs imaged in multibeam echosounder data. However, these classification systems only apply a geomorphological contextualisation to the data without making direct assertions on the velocities of benthic currents that form these SSBs. Here, we apply an object-based image analysis (OBIA) workflow to derive a geomorphological classification of SSBs in the Moira Mounds area of the Belgica Mound Province, NE Atlantic through k-means clustering. Cold-water coral reefs as sessile filter-feeders benefit from strong currents are often found in close association with sediment wave fields. This OBIA provided the framework to derive SSB wavelength and wave height, these SSB attributes were used as predictor variables for a multiple linear regression to estimate current velocities. Results show a bimodal distribution of current flow directions and current speed. Furthermore, a 5 k-means classification of the SSB geomorphology exhibited an imprinting of current flow consistency which altered throughout the study site due to the interaction of regional, local, and micro scale topographic steering forces. This study is proof-of-concept for an assessment tool applied to vulnerable marine ecosystems but has wider applications for applied seabed appraisals and can inform management and monitoring practice across a variety of spatial and temporal scales. Deriving spatial patterns of hydrodynamic processes from widely available multibeam echosounder maps is pertinent to many avenues of research including scour predictions for offshore structures such as wind turbines, sediment transport modelling, benthic fisheries, e.g., scallops, cable route and pipeline risk assessment and habitat mapping.

National mapping programs (e.g., INFOMAR and MAREANO) and global efforts (Seabed 2030) acquire large volumes of multibeam echosounder data to map large areas of the seafloor. Developing an objective, automated and repeatable approach to extract meaningful information from such vast quantities of data is now essential. Many automated or semi-automated approaches have been defined to achieve this goal. However, such efforts have resulted in classification schemes that are isolated or bespoke, and therefore it is necessary to form a standardised classification method. Sediment wave fields are the ideal platform for this as they maintain consistent morphologies across various spatial scales and influence the distribution of biological assemblages. Here, we apply an object-based image analysis (OBIA) workflow to multibeam bathymetry to compare the accuracy of four classifiers (two multilayer perceptrons, support vector machine, and voting ensemble) in identifying seabed sediment waves across three separate study sites. The classifiers are trained on high-spatial-resolution (0.5 m) multibeam bathymetric data from Cork Harbour, Ireland and are then applied to lower-spatial-resolution EMODnet data (25 m) from the Hemptons Turbot Bank SAC and offshore of County Wexford, Ireland. A stratified 10-fold cross-validation was enacted to assess overfitting to the sample data. Samples were taken from the lower-resolution sites and examined separately to determine the efficacy of classification. Results showed that the voting ensemble classifier achieved the most consistent accuracy scores across the high-resolution and low-resolution sites. This is the first object-based image analysis classification of bathymetric data able to cope with significant disparity in spatial resolution. Applications for this approach include benthic current speed assessments, a geomorphological classification framework for benthic biota, and a baseline for monitoring of marine protected areas.