ALBERT

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: 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.

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: Industrial interest in deep-sea mineral extraction began decades ago and today it is at an all-time high, accelerated by global demand for metals. Several seafloor ecosystem disturbance experiments were performed beginning in the 1970’s, including the DISturbance and reCOLonization experiment (DISCOL) conducted in the Peru Basin in 1989. A large seafloor disturbance was created by repeatedly plowing the seafloor over an area of ~ 10.8 km2. Though a number of studies in abyssal mining regions have evaluated megafaunal biodiversity and ecosystem responses, few have included quantitative and detailed data on fishes or scavengers despite their ecological importance as top predators. We used towed camera transects and baited camera data to evaluate the fish community at the DISCOL site. The abyssal fish community was relatively diverse with 16 taxa dominated by Ipnops meadi. Fish density was lower in ploughed habitat during the several years following disturbance but thereafter increased over time in part due to changes in regional environmental conditions. 26 years post disturbance there were no differences in overall total fish densities between reference and experimental areas, but the dominant fish, I. meadi, still exhibited much lower densities in ploughed habitat suggesting only partial fish community recovery. The scavenging community was dominated by eelpouts (Pachycara spp), hermit crabs (Probeebei mirabilis) and shrimp. The large contribution of hermit crabs appears unique amongst abyssal scavenger studies worldwide. The abyssal fish community at DISCOL was similar to that in the more northerly Clarion Clipperton Zone, though some species have only been observed at DISCOL thus far. Also, further species level identifications are required to refine this assessment. Additional studies across the polymetallic nodule provinces of the Pacific are required to further evaluate the environmental drivers of fish density and diversity and species biogeographies, which will be important for the development of appropriate management plans aimed at minimizing human impact from deep-sea mining.

Description: High-resolution optical and hydroacoustic seafloor data acquired in 2015 enabled the reconstruction of disturbance tracks of a past Benthic Impact Experiment that was conducted in 1989 in the Peru Basin in the course of former German environmental impact studies associated with manganese nodule mining. Based on this information, the disturbance level of the experiment regarding the plough impact and distribution and re-deposition of sediment from the evolving sediment plume was assessed qualitatively. Through this, the evolution over the 26 years of a number of the total 78 disturbance tracks could be analyzed which highlights the considerable difference between natural sedimentation in the deep-sea and sedimentation of a resettled sediment plume. Such plumes are seen as one of the most concerning impact associated with potential Mn-nodule mining. Problems in data processing became eminent while dealing with old data from the late 80s, at a time when GPS was just invented and underwater navigation was in an infant stage. However, even today the uncertainties of underwater navigation and the use of a variety of acoustical and optical sensors at different resolutions require detailed post-processing in terms of absolute geographic positioning to improve the overall accuracy of the data. In this study, a ship-based bathymetric map of the survey area was used as absolute geographic reference and a workflow was applied successfully resulting in the most accurate geo-referenced dataset of the DISCOL Experimental Area to date. The new field data were acquired with sensors attached to GEOMARs AUV Abyss and the 0.5 × 1° EM122 multibeam system of RV SONNE during cruise SO242 -1 while the old data first needed to be found and compiled before they could be digitized and properly georeferenced for the presented joined analyses.

Description: With the mining of polymetallic nodules from the deep-sea seafloor once more evoking commercial interest, decisions must be taken on how to most efficiently regulate and monitor physical and community disturbance in these remote ecosystems. Image-based approaches allow non-destructive assessment of the abundance of larger fauna to be derived from survey data, with repeat surveys of areas possible to allow time series data collection. At the time of writing, key underwater imaging platforms commonly used to map seafloor fauna abundances are autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs) and towed camera “ocean floor observation systems” (OFOSs). These systems are highly customisable, with cameras, illumination sources and deployment protocols changing rapidly, even during a survey cruise. In this study, eight image datasets were collected from a discrete area of polymetallic-nodule-rich seafloor by an AUV and several OFOSs deployed at various altitudes above the seafloor. A fauna identification catalogue was used by five annotators to estimate the abundances of 20 fauna categories from the different datasets. Results show that, for many categories of megafauna, differences in image resolution greatly influenced the estimations of fauna abundance determined by the annotators. This is an important finding for the development of future monitoring legislation for these areas. When and if commercial exploitation of these marine resources commences, robust and verifiable standards which incorporate developing technological advances in camera-based monitoring surveys should be key to developing appropriate management regulations for these regions.

Description: Temperature extremes not only directly affect the marine environment and ecosystems but also indirectly influence hydrodynamics and marine life. In this study, the role of heat wave events in the occurrence and persistence of thermal stratification was analysed by simulating the water temperature of the North Sea from 2011 to 2018 using a fully coupled hydrodynamic and wave model within the framework of the Geesthacht Coupled cOAstal model SysTem (GCOAST). The model results were assessed against reprocessed satellite data and in situ observations from field campaigns and fixed Marine Environmental Monitoring Network (MARNET) stations. To quantify the degree of stratification, the potential energy anomaly throughout the water column was calculated. The air temperatures and potential energy anomalies in the North Sea (excluding the Norwegian Trench and the area south of 54∘ N) were linearly correlated. Different from the northern North Sea, where the water column is stratified in the warm season each year, the southern North Sea is seasonally stratified in years when a heat wave occurs. The influences of heat waves on the occurrence of summer stratification in the southern North Sea are mainly in the form of two aspects, i.e. a rapid rise in sea surface temperature at the early stage of the heat wave period and a higher water temperature during summer than the multiyear mean. Another factor that enhances the thermal stratification in summer is the memory of the water column to cold spells earlier in the year. Differences between the seasonally stratified northern North Sea and the heat wave-induced stratified southern North Sea were ultimately attributed to changes in water depth.