Description: We present a comprehensive study showing new results from a shallow gas seep area in approximate to 40 m water depth located in the North Sea, Netherlands sector B13 that we call Dutch Dogger Bank seep area. It has been postulated that methane presumably originating from a gas reservoir in approximate to 600 m depth below the seafloor is naturally leaking to the seafloor. Our ship-based subbottom echosounder data indicate that the migrating gas is trapped in numerous gas pockets in the shallow sediments. The gas pockets are located at the boundary between the top of the Late Pliocene section and overlying fine-grained sediments, which were deposited during the early Holocene marine transgression after the last glaciation. We mapped gas emissions during three R/V Heincke cruises in 2014, 2015, and 2016 and repeatedly observed up to 850 flares in the study area. Most of them (approximate to 80%) were concentrated at five flare clusters. Our repeated analysis revealed spatial similarities of seep clusters, but also heterogeneities in emission intensities. A first calculation of the methane released from these clusters into the water column revealed a flow rate of 277 L/min (SD=140), with two clusters emitting 132 and 142 L/min representing the most significant seepage sites. Above these two flare clusters, elevated methane concentrations were recorded in atmospheric measurements. Our results illustrate the effective transport of methane via gas bubbles through a approximate to 40 m water column, and furthermore provide an estimate of the emission rate needed to allow for a contribution to the atmospheric methane concentration.

Description: In this study ship- and AUV-based multibeam data from the German Mn-nodule license area in the Clarion-Clipperton Zone (CCZ; eastern Pacific) are linked to ground truth data from optical imaging. Photographs obtained by an AUV enable semi-quantitative assessments of nodule coverage at a spatial resolution in the range of meters. Together with high resolution AUV bathymetry this revealed a correlation of small-scale terrain variations (〈 5 m horizontally, 〈 1 m vertically) with nodule abundance. In the presented data set, increased nodule coverage could be correlated with slopes 〉 1.8° and concave terrain. On a more regional scale, factors such as the geological setting (existence of horst and graben structures, sediment thickness, outcropping basement) and influence of bottom currents seem to play an essential role for the spatial variation of nodule abundance and the related hard substrate habitat. AUV imagery was also successfully employed to map the distribution of re-settled sediment following a disturbance and sediment cloud generation during a sampling deployment of an Epibenthic Sledge. Data from before and after the "disturbance" allows a direct assessment of the impact. Automated image processing analyzed the nodule coverage at the seafloor, revealing nodule blanketing by resettling of suspended sediment within 16 hours after the disturbance. The visually detectable impact was spatially limited to a maximum of 100m distance from the disturbance track, downstream of the bottom water current. A correlation with high resolution AUV bathymetry reveals that the blanketing pattern varies in extent by tens of meters, strictly following the bathymetry, even in areas of only slightly undulating seafloor (〈 1 m vertical change). These results highlight the importance of detailed terrain knowledge when engaging in resource assessment studies for nodule abundance estimates and defining minable areas. At the same time, it shows the importance of high resolution mapping for detailed benthic habitat studies that show a heterogeneity at scales of 10 m to 100 m. Terrain knowledge is also needed to determine the scale of the impact by seafloor sediment blanketing during mining-operations.

Description: In this study, high-resolution bathymetric multibeam and optical image data, both obtained within the Belgian manganese (Mn) nodule mining license area by the autonomous underwater vehicle (AUV) Abyss, were combined in order to create a predictive random forests (RF) machine learning model. AUV bathymetry reveals small-scale terrain variations, allowing slope estimations and calculation of bathymetric derivatives such as slope, curvature, and ruggedness. Optical AUV imagery provides quantitative information regarding the distribution (number and median size) of Mn nodules. Within the area considered in this study, Mn nodules show a heterogeneous and spatially clustered pattern, and their number per square meter is negatively correlated with their median size. A prediction of the number of Mn nodules was achieved by combining information derived from the acoustic and optical data using a RF model. This model was tuned by examining the influence of the training set size, the number of growing trees (ntree), and the number of predictor variables to be randomly selected at each node (mtry) on the RF prediction accuracy. The use of larger training data sets with higher ntree and mtry values increases the accuracy. To estimate the Mn-nodule abundance, these predictions were linked to ground-truth data acquired by box coring. Linking optical and hydroacoustic data revealed a nonlinear relationship between the Mn-nodule distribution and topographic characteristics. This highlights the importance of a detailed terrain reconstruction for a predictive modeling of Mn-nodule abundance. In addition, this study underlines the necessity of a sufficient spatial distribution of the optical data to provide reliable modeling input for the RF.

Description: The Vestnesa Ridge comprises a 〉100 km long sediment drift located between the western continental slope of Svalbard and the Arctic mid-ocean ridges. It hosts a deep water (〉1000 m) gas hydrate and associated seafloor seepage system. Near-seafloor headspace gas compositions and its methane carbon isotopic signature along the ridge indicate a predominance of thermogenic gas sources feeding the system. Prediction of the base of the gas hydrate stability zone for theoretical pressure and temperature conditions and measured gas compositions results in an unusual underestimation of the observed bottom-simulating reflector (BSR) depth. The BSR is up to 60 m deeper than predicted for pure methane and measured gas compositions with 〉99% methane. Models for measured gas compositions with 〉4% higher-order hydrocarbons result in a better BSR approximation. However, the BSR remains 〉20 m deeper than predicted in a region without active seepage. A BSR deeper than predicted is primarily explained by unaccounted spatial variations in the geothermal gradient and by larger amounts of thermogenic gas at the base of the gas hydrate stability zone. Hydrates containing higher-order hydrocarbons form at greater depths and higher temperatures and contribute with larger amounts of carbons than pure methane hydrates. In thermogenic provinces, this may imply a significant upward revision (up to 50% in the case of Vestnesa Ridge) of the amount of carbon in gas hydrates.

Description: Estimating the spatial position of organisms is essential to quantify interactions between the organism and the characteristics of its surroundings, for example, predator–prey interactions, habitat selection, and social associations. Because marine mammals spend most of their time under water and may appear at the surface only briefly, determining their exact geographic location can be challenging. Here, we developed a photogrammetric method to accurately estimate the spatial position of marine mammals or birds at the sea surface. Digital recordings containing landscape features with known geographic coordinates can be used to estimate the distance and bearing of each sighting relative to the observation point. The method can correct for frame rotation, estimates pixel size based on the reference points, and can be applied to scenarios with and without a visible horizon. A set of R functions was written to process the images and obtain accurate geographic coordinates for each sighting. The method is applied to estimate the spatiotemporal fine-scale distribution of harbour porpoises in a tidal inlet. Video recordings of harbour porpoises were made from land, using a standard digital single-lens reflex (DSLR) camera, positioned at a height of 9.59 m above mean sea level. Porpoises were detected up to a distance of ~3136 m (mean 596 m), with a mean location error of 12 m. The method presented here allows for multiple detections of different individuals within a single video frame and for tracking movements of individuals based on repeated sightings. In comparison with traditional methods, this method only requires a digital camera to provide accurate location estimates. It especially has great potential in regions with ample data on local (a)biotic conditions, to help resolve functional mechanisms underlying habitat selection and other behaviors in marine mammals in coastal areas.

Description: Large internal wave breaking is observed exceeding a vertical array of 61 high-resolution temperature sensors at 1 m intervals between 7 and 67 m above the bottom. The array was moored for 5 days at 969 m of Opouawe Bank, New Zealand, a known methane seep area. As breaking internal waves dominate sediment resuspension above sloping topography in other ocean areas, they are expected to also influence methane transport. Despite being visible in single beam echosounder data, indications for turbulence due to rising gas bubbles are not found in the present 1 Hz sampled temperature records. Likely, the mooring was too far away from the very localized bubble release spot. Instead, the temperature sensors show detailed internal wave-turbulence transitions. Every tidal cycle, a solibore (a frontal turbulent bore with a train of trailing solitary waves) changes shape and intensity. These solibores are highly turbulent and they restratify the bottom boundary layer, thereby maintaining efficient mixing. Details of different turbulent bore developments are discussed. Averaged over a few tidal cycles and over the sensors range, mean vertical eddy diffusivity amounts to 3 ± 1 × 10−3 m2 s−1 and mean turbulent kinetic energy dissipation to 1.6 ± 0.7 × 10−7 W kg−1, with variations over 4 orders of magnitude. Such turbulence will affect the distribution of dissolved methane and other geochemical species in the lower 100–150 m above the bottom and their release from the bottom. The above mean values are remarkably similar to those found at various other sites in the NE Atlantic Ocean.

Description: We use new gas-hydrate geochemistry analyses, echosounder data, and three-dimensional P-Cable seismic data to study a gas-hydrate and free-gas system in 1200 m water depth at the Vestnesa Ridge offshore NW Svalbard. Geochemical measurements of gas from hydrates collected at the ridge revealed a thermogenic source. The presence of thermogenic gas and temperatures of similar to 3.3 degrees C result in a shallow top of the hydrate stability zone (THSZ) at similar to 340 m below sea level (mbsl). Therefore, hydrate-skinned gas bubbles, which inhibit gas-dissolution processes, are thermodynamically stable to this shallow water depth. This was confirmed by hydroacoustic observations of flares in 2010 and 2012 reaching water depths between 210 and 480 mbsl. At the seafloor, bubbles are released from acoustically transparent zones in the seismic data, which we interpret as regions where free gas is migrating through the hydrate stability zone (HSZ). These intrusions result in vertical variations in the base of the HSZ (BHSZ) of up to similar to 150 m, possibly making the shallow hydrate reservoir more susceptible to warming. Such Arctic gas-hydrate and free-gas systems are important because of their potential role in climate change and in fueling marine life, but remain largely understudied due to limited data coverage in seasonally ice-covered Arctic environments.

Description: We find that summer methane (CH4) release from seabed sediments west of Svalbard substantially increases CH4 concentrations in the ocean but has limited influence on the atmospheric CH4 levels. Our conclusion stems from complementary measurements at the seafloor, in the ocean, and in the atmosphere from land-based, ship and aircraft platforms during a summer campaign in 2014. We detected high concentrations of dissolved CH4 in the ocean above the seafloor with a sharp decrease above the pycnocline. Model approaches taking potential CH4 emissions from both dissolved and bubble-released CH4 from a larger region into account reveal a maximum flux compatible with the observed atmospheric CH4 mixing ratios of 2.4–3.8 nmol m−2 s−1. This is too low to have an impact on the atmospheric summer CH4 budget in the year 2014. Long-term ocean observatories may shed light on the complex variations of Arctic CH4 cycles throughout the year.

Description: Takahe seep, located on the Opouawe Bank, Hikurangi Margin, is characterized by a well-defined subsurface seismic chimney structure ca. 80,500 m2 in area. Sub-seafloor geophysical data based on acoustic anomaly layers indicated the presence of gas hydrate and free gas layers within the chimney structure. Reaction-transport modeling was applied to porewater data from 11 gravity cores to constrain methane turnover rates and benthic methane fluxes in the upper 10 m. Model results show that methane dynamics were highly variable due to transport and dissolution of ascending gas. The dissolution of gas (up to 3761 mmol m−2 yr−1) dwarfed the rate of methanogenesis within the simulated sediment column (2.6 mmol m−2 yr−1). Dissolved methane is mainly consumed by anaerobic oxidation of methane (AOM) at the base of the sulfate reduction zone and trapped by methane hydrate formation below it, with maximum rates in the central part of the chimney (946 and 2420 mmol m−2 yr−1, respectively). A seep-wide methane budget was constrained by combining the biogeochemical model results with geophysical data and led to estimates of AOM rates, gas hydrate formation and benthic dissolved methane fluxes of 3.68 × 104 mol yr−1, 73.85 × 104 mol yr−1and 1.19 × 104 mol yr−1, respectively. A much larger flux of methane probably escapes in gaseous form through focused bubble vents. The approach of linking geochemical model results with spatial geophysical data put forward here can be applied elsewhere to improve benthic methane turnover rates from limited single spot measurements to larger spatial scales.

Description: Autonomous underwater vehicles (AUVs) offer unique possibilities for exploring the deep seafloor in high resolution over large areas. We highlight the results from AUV-based multibeam echosounder (MBES) bathymetry / backscatter and digital optical imagery from the DISCOL area acquired during research cruise SO242 in 2015. AUV bathymetry reveals a morphologically complex seafloor with rough terrain in seamount areas and low-relief variations in sedimentary abyssal plains which are covered in Mn-nodules. Backscatter provides valuable information about the seafloor type and particularly about the influence of Mn-nodules on the response of the transmitted acoustic signal. Primarily, Mn-nodule abundances were determined by means of automated nodule detection on AUV seafloor imagery and nodule metrics such as nodules m−2 were calculated automatically for each image allowing further spatial analysis within GIS in conjunction with the acoustic data. AUV-based backscatter was clustered using both raw data and corrected backscatter mosaics. In total, two unsupervised methods and one machine learning approach were utilized for backscatter classification and Mn-nodule predictive mapping. Bayesian statistical analysis was applied to the raw backscatter values resulting in six acoustic classes. In addition, Iterative Self-Organizing Data Analysis (ISODATA) clustering was applied to the backscatter mosaic and its statistics (mean, mode, 10th, and 90th quantiles) suggesting an optimum of six clusters as well. Part of the nodule metrics data was combined with bathymetry, bathymetric derivatives and backscatter statistics for predictive mapping of the Mn-nodule density using a Random Forest classifier. Results indicate that acoustic classes, predictions from Random Forest model and image-based nodule metrics show very similar spatial distribution patterns with acoustic classes hence capturing most of the fine-scale Mn-nodule variability. Backscatter classes reflect areas with homogeneous nodule density. A strong influence of mean backscatter, fine scale BPI and concavity of the bathymetry on nodule prediction is seen. These observations imply that nodule densities are generally affected by local micro-bathymetry in a way that is not yet fully understood. However, it can be concluded that the spatial occurrence of Mn-covered areas can be sufficiently analysed by means of acoustic classification and multivariate predictive mapping allowing to determine the spatial nodule density in a much more robust way than previously possible.