Because the seafloor is a complex ecosystem, a multidisciplinary approach must be adopted in order to produce comprehensive habitat maps. Such multidisciplinary projects have been lacking for the Dutch area of the North Sea. To address this lack, the Distribution, structure and functioning of low resilience seafloor communities and habitats of the Dutch North Sea (DISCLOSE) project, funded by the Gieskes- Strijbis Fonds, was initiated. The consortium for the project included three research institutes, as well as the North Sea Foundation. The first of the research institutes was the Delft University of Technology, tasked with the large-scale mapping of the seafloor, using acoustic systems such as the multibeam echosounder (MBES). The second research institute, the University of Groningen (UG), focused on the use of photography and videography to study the seafloor and the epifauna at a smaller, yet more detailed, spatial scale. Finally, the Royal Netherlands Institute for Sea Research (NIOZ), studied the seafloor from both the perspective of particle size and macrofauna using grab-sample data. All of these measurement methods were utilized for the same research areas, in order to maximize the possibility to established links between the sampling methods, and thereby create detailed habitat maps. The work in this thesis focuses specifically on the acoustic results generated within the DISCLOSE project. In recent years the MBES has become the standard tool for the large-scale mapping of the ocean floor. With the MBES, large swaths of the seafloor can be covered in short periods of time. The use of the two-way travel time to measure the bathymetry of the ocean has become very standardized. In addition to measuring the bathymetry, the MBES can also deliver the collocated backscatter product. The appropriate use of backscatter for the classification of seafloor properties and habitats is much less well understood than bathymetry. As such, this is an active field of research. Within Dutch waters, most research has taken place using datasets from the area of the Cleaverbank. Other areas have not been well studied, for example, the southern sandy area. Utilizing MBES backscatter-based seafloor classification in sandy areas is a major focus in this thesis. A dataset from the Brown Bank area of the North Sea was used in order to study seafloor classification over mega ripple structures. A big part of the Southern North Sea is covered in nested sand waves of different sizes. The largest of these is the tidal ridge, with some ten kilometers from crest to crest. The second largest is the sand wave, and the smallest is the mega ripple. Obviously, the main sediment type in this area is sand. Previous research suggests that a difference in grain size is to be expected between the crest of the tidal ridge to the trough. It was not known if a difference in grain size from the crest to the trough of the sand wave or the mega ripple is present, or detectable using MBES backscatter. As such, for this research a few things were very important. Firstly, it was necessary to accurately correct the backscatter for the seafloor slopes in the research area. Next, it was important to have a high spatial resolution for the final classification results. Additionally, a high geo-acoustic resolution was also needed. This final resolution is needed because it is expected that the difference in sediment properties from the trough to crest of a mega-ripple may be just slightly coarser or finer sand. From our research, it was found that it is possible to use MBES backscatter in order to classify the sediment types at the scale of mega ripples. It was found that the coarsest sediments were in the troughs, finer sediments on the stoss side slopes, and a mixture of sediments on the lee side slopes of the mega ripples...@en

Biological traits of benthic macroinvertebrates from a large area of the North Sea soft sediments were used to explore habitat occupancy within seascapes of contrasting hydrodynamics. The area, the Dutch sector of the North Sea, is mainly composed of 2 habitats: shallow dynamic bottoms of heterogeneous geomorphologies and deep homogeneous muddy bottoms. Higher within-habitat heterogeneity was hypothesized to more specifically select benthic life strategies according to environmental filtering, i.e. through the action of abiotic forces. Functional community patterns were explored through the RLQ method, which relates habitat and trait variables, at different spatial scales of specific seascape heterogeneity, and functional diversity indices were used to shed light on community assembly mechanisms. Locally, 3 associations between habitat characteristics and biological traits were shown to correspond with predictions of life history theories, whereas only 2 emerged when considering all types of seascapes. This spatial scaledependence was explained by abiotic alternations masked over the larger scale at which all the existing strategies could not be properly disentangled. The relative composition in strategies obeyed specific assembly rules as identified by functional diversity indices. Seascape geomorphology was locally discriminant of functional patterns, and could account for biodiversification, much beyond basic taxonomic counts. Whereas habitats of higher physical stability hosted the taxonomically richest communities, stress or disturbance frequency increased functional variations within communities due to different strategist habitat occupancies. This study proposes a generic mechanism of benthic community structuring in soft sediment shelves.

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Conflicts of interests between economic and nature conservation stakeholders are increasingly common in coastal seas, inducing a growing need for evidence-based marine spatial planning. This requires accurate, high-resolution habitat maps showing the spatial distribution of benthic assemblages and enabling intersections of habitats and anthropogenic activities. However, such detailed maps are often not available because relevant biological data are scarce or poorly integrated. Instead, physiotope maps, solely based on abiotic variables, are now often used in marine spatial planning. Here, we investigated how pointwise, relatively sparse biological data can be integrated with gridded, high-resolution environmental data into informative habitat maps, using the intensively used southern North Sea as a case-study. We first conducted hierarchical clustering to identify discrete biological assemblages for three faunal groups: demersal fish, epifauna, and endobenthos. Using Random Forest models with high-resolution abiotic predictors, we then interpolated the distribution of these assemblages to high resolution grids. Finally, we quantified different anthropogenic pressures for each habitat. Habitat maps comprised a different number of habitats between faunal groups (6, 13, and 10 for demersal fish, epifauna, and endobenthos respectively) but showed similar spatial patterns for each group. Several of these ‘fauna-inclusive’ habitats resembled physiotopes, but substantial differences were also observed, especially when few (6; demersal fish) or most (13; epifauna) physiotopes were delineated. Demersal fishing and offshore wind farms (OWFs) were clearly associated with specific habitats, resulting in unequal anthropogenic pressure between different habitats. Natura-2000 areas were not specifically associated with demersal fishing, but OWFs were situated mostly inside these protected areas. We thus conclude that habitat maps derived from biological datasets that cover relevant faunal groups should be included more in ecology-inclusive marine spatial planning, instead of only using physiotope maps based on abiotic variables. This allows better balancing of nature conservation and socio-economic interests in continental shelf seas.

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Biogenic reefs form biodiversity hotspots and are key components of marine ecosystems, making them priority habitats for nature conservation. However, the conservation status of biogenic reefs generally depends on their size and stability. Dynamic, patchy reefs may therefore be excluded from protection. Here, we studied epibenthos and epifauna density, richness, and community composition of patchy, dynamic Sabellaria spinulosa (ross worm) reefs in the North Sea. This study was conducted by comparing boxcore (endobenthos) and video transect (epifauna) data from two research campaigns in 2017 and 2019 to the Brown Bank area on the Dutch Continental Shelf, where S. spinulosa reefs were first discovered in 2017. The Brown Bank area is characterized by dynamic, migratory bedforms at multiple scales which potentially affect biogenic reef stability. We showed that S. spinulosa habitats had a patchy distribution and alternated with habitats comprised of plain sand. Average S. spinulosa habitat patch size was 5.57 ± 0.99 m and 3.94 ± 0.22 m in 2017 and 2019, respectively (mean ± SE), which especially in 2019 closely resembled the small-scale megaripple bedforms. Contrary to the endobenthos communities that were unaffected by S. spinulosa, epifauna density and species richness were at least two times higher in S. spinulosa habitats compared to sandy habitats, resulting in different community compositions between the two habitat types. We showed that S. spinulosa persisted in the area for almost 2 years. Although the stability of individual patches remained unclear, we demonstrated that even patchy biogenic reefs may promote density and local biodiversity of mobile, epibenthic species, very likely as a result of increased habitat heterogeneity provided by reef habitat patches. This indicates that patchy biogenic reefs that occur in dynamic environments may also have high ecological value and their conservation status should be (re)considered to ensure their protection.

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In this paper, object-based image analysis classification methods are developed that do not rely on backscatter in order to classify the seafloor. Instead, these methods make use of bathymetry, bathymetric derivatives, and grab samples for classification. The classification is performed on image object statistics. One of the methods utilizes only texture-based features, that is, features that are related to the spatial arrangement of image characteristics. The second method is similar, but relies on a wider set of image object features. The methods were developed and tested using a dataset from Norwegian waters, specifically the Røstbanken area off the coast of Lofoten. The classification results were compared to backscatter-based classification and to grab sample ground-reference data. The algorithm that performed the best was then also applied to a dataset from the Borkumer Stones area close to the island of Schiermonnikoog in Dutch waters. This allowed testing the applicability of the algorithm for different datasets. Because the algorithms that were developed do not require backscatter, the availability of which is much more scarce than bathymetry, and because of the low computational requirements, they could be applied to any area where high-resolution bathymetry and grab samples are available.

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High-resolution surveying techniques of subtidal soft-bottom seafloor habitats show higher small-scale variation in topography and sediment type than previously thought, but the ecological relevance of this variation remains unclear. In addition, high-resolution surveys of benthic fauna show a large spatial variability in community composition, but this has yet poorly been linked to seafloor morphology and sediment composition. For instance, on soft-bottom coastal shelves, hydrodynamic forces from winds and tidal currents can cause nested multiscale morphological features ranging from metre-scale (mega)ripples, to sand waves and kilometre-scale linear sandbanks. This multiscale habitat heterogeneity is generally disregarded in the ecological assessments of benthic habitats. We therefore developed and tested a novel multiscale assessment toolbox that combines standard bathymetry, multibeam backscatter classification, video surveying of epibenthos and box core samples of sediment and macrobenthos. In a study on the Brown Bank, a sandbank in the southern North Sea, we found that these methods are greatly complementary and allow for more detail in the interpretation of benthic surveys. Acoustic and video data characterised the seafloor surface and subsurface, and macrobenthos communities were found to be structured by both sandbank and sand wave topography. We found indications that acoustic techniques can be used to determine the location of epibenthic reefs. The multiscale assessment toolbox furthermore allows formulating recommendations for conservation management related to the impact of sea floor disturbances through dredging and trawling.

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Backscatter data from multibeam echosounders are commonly used to classify seafloor sediment composition. Previously, it was found that the survey azimuth affects backscatter when small organized seafloor structures, such as sand ripples, are present. These sand ripples are too small to be detected in the multibeam bathymetry. Here, we show that such azimuth effects are time dependent and are useful to examine the orientation of sand ripples in relation to the flow direction of the tide. To this end, multibeam echosounder data at four different frequencies were gathered from the area of the Brown Bank in the North Sea. The acoustic results were compared to video and tide-flow data for validation. The sand ripples affected the backscatter at all frequencies, but for the lowest frequencies the effect was spread over more beam angles. Using the acoustic data made it possible to deduce the orientations of the sand ripples over areas of multiple square kilometers. We found that the top centimeter(s) of the seafloor undergoes a complete transformation every six hours, as the orientation of the sand ripples changes with the changing tide. Our methodology allows for morphology change detection at larger scales and higher resolutions than previously achieved.@en

Seafloor characterization using multibeam echosounder (MBES) backscatter data is an active field of research. The observed backscatter curve (OBC) is used in an inversion algorithm with available physics-based models to determine the seafloor geoacoustic parameters. A complication is that the OBC cannot directly be coupled to the modeled backscatter curve (MBC) due to the correction of uncalibrated sonars. Grab samples at reference areas are usually required to estimate the angular calibration curve (ACC) prior to the inversion. We first attempt to estimate the MBES ACC without grab sampling by using the least squares cubic spline approximation method implemented in a differential evolution optimization algorithm. The geoacoustic parameters are then inverted over the entire area using the OBCs corrected for the estimated ACC. The results indicate that a search for at least three geoacoustic parameters is required, which includes the sediment mean grain size, roughness parameter, and volume scattering parameter. The inverted mean grain sizes are in agreement with grab samples, indicating reliability and stability of the proposed method. Furthermore, the interaction between the geoacoustic parameters and Bayesian acoustic classes is investigated. It is observed that higher backscatter values, and thereby higher acoustic classes, should not only be attributed to (slightly) coarser sediment, especially in a homogeneous sedimentary environment such as the Brown Bank, North Sea. Higher acoustic classes should also be attributed to larger seafloor roughness and volume scattering parameters, which are not likely intrinsic to only sediment characteristics but also to other contributing factors.@en

High resolution maps of sandy seafloors are valuable to understand seafloor dynamics, plan engineering projects, and create detailed benthic habitat maps. This paper presents multibeam echosounder backscatter classification results of the Brown Bank area of the North Sea. We apply the Bayesian classification method in a megaripple and sand wave area with significant slopes. Prior to the classification, corrections are implemented to account for the slopes. This includes corrections on the backscatter value and its corresponding incident angle. A trade-off in classification resolutions is found. A higher geo-acoustic resolution is obtained at the price of losing spatial resolution, however, the Bayesian classification method remains robust with respect to these trade-off decisions. The classification results are compared to grab sample particle size analysis and classified video footage. In non-distinctive sedimentary environments, the acoustic classes are not attributed to only the mean grain size of the grab samples but to the full spectrum of the grain sizes. Finally, we show the Bayesian classification results can be used to characterize the sedimentary composition of megaripples. Coarser sediments were found in the troughs and on the crests, finer sediments on the stoss slopes and a mixture of sediments on the lee slopes.

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The tube-building polychaete Sabellaria spinulosa (Ross worm) can form conspicuous biogenic reefs that stabilize the seabed and increase biodiversity by providing a habitat for a multitude of other species. These reefs, however, are assumed to be vulnerable to human-induced physical disturbances of the seabed. In the Greater North Sea, S. spinulosa reefs are recognized to be under threat and worthy of protection. In August 2017, three S. spinulosa reefs with a minimum extent of 1016 m² were discovered in the Dutch Brown Bank area. This area comprises a large-scale sandbank and adjacent troughs. The reefs were found within the sandbank troughs, which have proven to be subject to high demersal fishing intensities (fished >5 times a year). Detailed bathymetry measurements showed that S. spinulosa reefs were mainly located within valleys of smaller-scaled sand waves, which have a perpendicular orientation compared to the large-scale sandbank structure of the Brown Bank. We hypothesize that the valleys in between sand waves offer suitable substrate for settlement and refuge from abrasion by fishing activities, enabling the S. spinulosa reefs to persist despite high fishing intensities. ROV footage of the reefs showed higher estimates of species abundances on the reefs compared with adjacent habitats, with some species present that are typical for hard substrate (rock gunnel, Pholis gunnellus; edible crab, Cancer pagurus; and velvet swimming crab, Necora puber). The information presented could be used for drafting management policies to protect these reefs, as Contracting Parties of the OSPAR Convention are committed to take measures and protect biodiversity.

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In the last two decades, the use of multibeam echosounders has been growing for seafloor mapping and characterization. The former uses bathymetry data whereas the latter makes use of backscatter data. The use of backscatter data has been the subject of intensive research to gain insight into seafloor composition using either empirical or model-based methods. Model-based methods employ the available physical models for predicting the backscatter strength and determine the seafloor geoacoustic parameters in an inversion algorithm using optimization methods. These methods allow for direct coupling between the backscatter curve and sediment characteristics. But the methods usually suffer from a shortcoming associated to uncalibrated sonars, which is referred to as calibration curve. Grab samples at reference areas are required to estimate the calibration curve. A question may arise as to whether, or to what extent, the calibration curve can be estimated without grab sampling. Knowing that the calibration curve is an unknown function of incident angle, in principle, one can approximate it using the available estimation and optimization theories. This is elaborated in this paper and its opportunities and challenges will be addressed. The potential benefit is twofold. 1) The huge amount of MBES backscatter currently available in many hydrographic organizations can directly be used for seafloor characterization. 2) The available multiple-frequency MBESs can further improve the performance of the inversion process. There are also challenges to be addressed. 1) Estimation of the calibration curve is an unstable process because it is merely based on observed backscatter data without using grab samples. 2) The physical models, and component parts thereof, are not usually wellbehaved functions, possibly due to their discontinuities or discontinuity of their derivatives. These issues will be elaborated in this paper. @en

Obtaining an overview of the spatial and temporal distribution of seabed sediments is of high interest for multiple research disciplines. Multibeam echosounders allow for the mapping of seabed sediments with high area coverage. In this paper, the repeatability of acoustic classification derived from multibeam echosounder backscatter is addressed. To this end, multibeam echosounder backscatter data acquired on the Cleaver Bank (North Sea) during five different surveys is employed using two different classification methods, i.e., a method based on the principal component analyses and the Bayesian technique. Different vessels were used for the different surveys. The comparison of the classification results between the different surveys indicates good repeatability. This repeatability demonstrates the potential of using backscatter for long-term environmental monitoring. However, the use of different classification methods results in somewhat different classification maps. Monitoring, therefore, requires the consistent use of a single method. Furthermore, it is found that the statistical characteristics of backscatter is such that clustering algorithms are less suited to discern the number of sediment types present in the study area. The Bayesian technique accounting for backscatter statistics is therefore recommended. A strong positive correlation between backscatter and median grain size for finer sediments (<0.5 mm) using a frequency of 300 kHz is observed within the study area, but an ambiguity is found for sediments with median grain sizes >0.5 mm. Consequently, for the situation considered a unique assignment of sediment type to acoustic class is not possible for these coarser sediments.@en

The sandy seabed of shallow coastal shelf seas displays morphological patterns of various dimensions. The seabed also harbors a rich ecosystem. Increasing pressure from human-induced disturbances necessitates further study on drivers of benthic community distributions over morphological patterns. Moreover, a greater understanding of the sand ripple distribution over tidal sand waves may improve morphological model predictions. Here we analyzed the biotic abundance and ripple morphology in sand wave troughs and crests using video transects. We found that both the epibenthos and endobenthos are significantly more abundant in sand wave troughs, where ripples are less abundant and more irregularly shaped. Finally, we show that camera systems are relatively quick and effective tools to study biotic spatial patterns in relation to seabed morphology.

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Knowing the morphology and sediment composition of the seabed is of high importance for various applications. In this contribution, the repeatability of acoustic seafloor classification (ASC) results obtained from MBES backscatter value is investigated. The unsupervised classification algorithm based on Principal Component Analysis has been applied to the MBES backscatter acquired in the Cleaver Bank, Netherlands Continental Shelf, during five different surveys with two vessels. In general, there is good repeatability between surveys demonstrating the potential of using backscatter for marine environmental monitoring. To increase the discrimination performance the so-called depth residuals can be used. These are derived from the bathymetric measurements and considered to be representative for the sediment roughness. The challenge is that the small-scale depth variations are not solely dependent on the sediment roughness but also on the intrinsic uncertainties inherent to the MBES system. An A-Priori Multibeam Uncertainty Simulation Tool (AMUST) has been developed to predict the depth errors induced by various contributors. Correcting the measured depths for these uncertainties, as predicted by AMUST, theoretically provides information about the actual sediment roughness and this should improve the ASC algorithms. This was first tested on a MBES data set from Shallow Survey Conference Plymouth, 2015. It was shown that for the water depth of 20 m the standard deviation of the depth measurements was in agreement with AMUST predictions indicating a smooth seafloor, however, discrepancies between the predictions and real measurements occurred for the water depth of 8 m which is an indication of roughness or morphological features. This indicates the necessity of knowledge about the uncertainties when the objective is to derive the sediment roughness from MBES measurements. @en