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Continental shelf sediments contain some of the largest stocks of organic carbon (OC) on Earth and play a vital role in influencing the global carbon cycle. Quantifying how much OC is stored in shelf sediments and determining its residence time is key to assessing how human activities can accelerate the process of OC remineralization into carbon dioxide. Spatial variations in terrestrial carbon stocks are well studied and mapped at high resolution, but our knowledge of the distribution of marine OC in different seafloor settings is still very limited, particularly in the highly dynamic and spatially variable shelf environments. The lack of knowledge reduces our ability to understand and predict how much and for how long oceans sequester CO2. In this study, we use high-resolution multibeam echosounder (MBES) data from the Eastern Shore Islands offshore Nova Scotia (Canada), combined with OC measurements from discrete samples, to assess the distribution of OC content in seafloor sediments. We derive three different spatial estimates of organic carbon: i) assuming a homogenous seafloor the carbon stock estimates were scaled to the entire study region; ii) using a high-resolution substrate map, the estimates were scaled to the areas of soft substrate only, and, finally, iii) using Empirical Bayesian Regression Kriging (EBRK) regression prediction within the area of soft substrate, carbon stock estimates in areas of soft substrate were refined to account for spatial variability in the concentration of OC. These three distinct spatial models yielded dramatically different estimates of average standing stock of OC in our study area, 1275, 259 and 203 Mt of OC respectively. Our study demonstrates that high-resolution mapping is critically important for improved estimates of OC stocks on continental shelves, and to the identification of carbon hotspots that need to be considered in seabed management and climate mitigation strategies. [ABSTRACT FROM AUTHOR]

This editorial published in the journal Frontiers in Marine Science discusses the field of marine geomorphometry, which involves the quantitative characterization of the seabed terrain. The editorial highlights advancements in seabed mapping technologies and the increasing availability of data, which provide opportunities for innovation in marine geomorphometry. The document introduces a research topic on Frontiers in Marine Geomorphometry, aiming to disseminate novel approaches and applications of quantitative analysis of seafloor mapping datasets. The editorial emphasizes the importance of understanding, monitoring, and managing the oceans, especially in the context of the United Nations' Decade of Ocean Science for Sustainable Development. The document concludes by discussing various applications of marine geomorphometry, including habitat mapping, ecological analysis, and geohazard assessment, and mentions state-of-the-art approaches such as deep learning for automating marine geomorphometric and morphological analyses. [Extracted from the article]

In soft‐bottom marine ecosystems, bedform variation is induced by wind‐ and tidal‐driven hydrodynamics. The resulting megaripples, sand waves and sandbanks form a spatially and temporally heterogeneous seafloor landscape. The strong physical forces imposed by the migration of these bedforms are important determinants for the occurrence of different macrozoobenthic species. Quantifying the effect of these forces can help in differentiating natural‐ and anthropogenically induced physical stressors. However, large‐scale mapping of seabed morphology at high resolution using multibeam echosounder is challenging, costly and time‐consuming, especially in shallow seas, prohibiting wide swaths. Instead, their bathymetry is typically studied using single‐beam transects that are interpolated to bathymetric grids with a relatively coarse resolution (20 m). However, this leaves out information on smaller scale (<20 m) bedforms that can be ecologically relevant. In the Dutch Wadden sea, a shallow tidal system, we characterized bedform variation at high resolution using single‐beam data for the first time. We calculated a 2‐D Terrain Ruggedness Index (TRI) at sub‐meter resolution along the single‐beam transects and interpolated the results to a full 3‐D grid. We then validated the result by relating TRI to independently modeled hydrodynamic parameters and to the distribution of macrozoobenthic species. We found that TRI successfully integrates the variation of tidal‐driven bed shear stress and wave‐driven orbital velocity. In addition, we found TRI to be a good predictor of the occurrence of macrozoobenthic species. The inferred small‐scale bedforms provide valuable information for separating the relative importance of natural dynamics versus anthropogenic disturbances such as dredging and bottom trawling activities. We discuss that by repurposing already available single‐beam data in this way, bedforms can be characterized at high resolution without the need for additional equipment or mapping campaigns, yielding novel input to decision‐making on marine management and conservation. [ABSTRACT FROM AUTHOR]

Advances in sonar technology have revolutionized our ability to map the seafloor, however, differences between legacy and modern data pose challenges when analysing multi-source datasets. Acoustic backscatter recorded via multibeam echosounder is commonly used to characterize the seafloor, but a lack of standardized calibration often yields relative rather than absolute backscatter measurements, hindering comparison between surveys. ‘Bulk shift’ methods have been developed for harmonizing legacy backscatter datasets using overlapping survey areas for relative statistical calibration. This becomes increasingly difficult, though, given many datasets collected over extensive time periods. Backscatter data were collected in the Bay of Fundy, Canada, using multiple sonar systems and vessels over an 18-year period. Here, we propose a reproduceable strategy for harmonizing this large volume of disparate backscatter data using the bulk shift method. A final, harmonized map is presented for the entire Bay of Fundy and is validated using in situ observations from seafloor imagery. [ABSTRACT FROM AUTHOR]

Mapping seafloor surficial geology provides information necessary for effective marine spatial planning, assessing natural and anthropogenic disturbances to the environment, and science-based fisheries and natural resource management. Traditional methods for seafloor geological mapping depend on expert by eye interpretation and delineation into seafloor classes (e.g., substrate type or geomorphic classes). However, these methods are generally subjective, non-repeatable, and lacking in statistical validation. Recent advances in computing technology and modelling techniques, such as machine learning (ML), have allowed scientists to use spatial predictive modelling to efficiently produce statistically accurate and spatially continuous map products that characterize various aspects of seafloor geology. This research applied common machine learning algorithms to generate a variety of thematic seafloor maps across the Northwest Atlantic Shelf using coarse resolution (>400 m) open-source datasets. Seafloor substrate maps (hard substrate occurrence, modified Folk class, mean grain size, and % mud/sand/gravel) were modelled using random forest, trained with observations from Natural Resources Canada (NRCan) Seabed grainsize analysis and seafloor photograph databases for offshore Canada. A semi-automated approach was developed to classify seafloor morphology from the GEBCO 2020 bathymetric grid, using a k-means clustering algorithm and manual assignment of feature names based on standardized feature definitions. The standardized workflow from this study enables the integration of datasets from a variety of sources and provides output maps that are comparable over large regions and across a variety of ocean governance boundaries. [ABSTRACT FROM AUTHOR]