High-Resolution Vertical Habitat Mapping of a Deep-Sea Cliff offshore Greenland

Recent advances in deep-sea exploration with underwater vehicles have led to the discovery of vertical environments inhabited by a diverse sessile fauna. However, despite their ecological importance, vertical habitats remain poorly characterized by conventional downward-looking survey techniques. Here we present a high-resolution 3-dimensional habitat map of a vertical cliff hosting a suspension-feeding community at the flank of an underwater glacial trough in the Greenland waters of the Labrador Sea. Using a forward-looking set-up on a Remotely Operated Vehicle (ROV), a high-resolution multibeam echosounder was used to map out the topography of the deep-sea terrain, including, for the first time, the backscatter intensity. Navigational accuracy was improved through a combination of the USBL and the DVL navigation of the ROV. Multi-scale terrain descriptors were derived and assigned to the 3D point cloud of the terrain. Following an unsupervised habitat mapping approach, the application of a K-means clustering revealed four potential habitat types, driven by geomorphology, backscatter and fine-scale features. Using groundtruthing seabed images, the ecological significance of the four habitat clusters was assessed in order to evaluate the benefit of unsupervised habitat mapping for further fine-scale ecological studies of vertical environments. This study demonstrates the importance of a priori knowledge of the terrain around habitats that are rarely explored for ecological investigations. It also emphasizes the importance of remote characterization of habitat distribution for assessing the representativeness of benthic faunal studies often constrained by time-limited sampling activities. This case study further identifies current limitations (e.g., navigation accuracy, irregular terrain acquisition difficulties) that can potentially limit the use of deep-sea terrain models for fine-scale investigations

High-Resolution Vertical Habitat Mapping of a Deep-Sea Cliff Offshore Greenland

Recent advances in deep-sea exploration with underwater vehicles have led to the discovery of vertical environments inhabited by a diverse sessile fauna. However, despite their ecological importance, vertical habitats remain poorly characterized by conventional downward-looking survey techniques. Here we present a high-resolution 3-dimensional habitat map of a vertical cliff hosting a suspension-feeding community at the flank of an underwater glacial trough in the Greenland waters of the Labrador Sea. Using a forward-looking set-up on a Remotely Operated Vehicle (ROV), a high-resolution multibeam echosounder was used to map out the topography of the deep-sea terrain, including, for the first time, the backscatter intensity. Navigational accuracy was improved through a combination of the USBL and the DVL navigation of the ROV. Multi-scale terrain descriptors were derived and assigned to the 3D point cloud of the terrain. Following an unsupervised habitat mapping approach, the application of a K-means clustering revealed four potential habitat types, driven by geomorphology, backscatter and fine-scale features. Using groundtruthing seabed images, the ecological significance of the four habitat clusters was assessed in order to evaluate the benefit of unsupervised habitat mapping for further fine-scale ecological studies of vertical environments. This study demonstrates the importance of a priori knowledge of the terrain around habitats that are rarely explored for ecological investigations. It also emphasizes the importance of remote characterization of habitat distribution for assessing the representativeness of benthic faunal studies often constrained by time-limited sampling activities. This case study further identifies current limitations (e.g., navigation accuracy, irregular terrain acquisition difficulties) that can potentially limit the use of deep-sea terrain models for fine-scale investigations

South Coast Fjords 2023: Bay d’Espoir – White Bear Bay, 18th to the 26th of November 2023, RV Connor Murphy, Cruise ID: CM231118

Cruise report for the CM231118 expedition to Bay d’Espoir and White Bear Bay, NL, onboard the marine vessel Connor Murphy. This expedition (18th - 26th November 2023) funded by Fisheries and Ocean Canada under the “Coastal Environmental Baseline Program” collected baseline data to characterize the deep-water fauna of two fjords on the South Coast of the island of Newfoundland. This expedition consisted of 66 stations where scientific activities such as Van Veen grabs, drop video camera, CTD casts and multibeam surveys were conducted. All samples collected are also listed

High-Resolution Vertical of a Deep-Sea Cliff offshore Greenland and the Associated Epibenthic Fauna

We used a multibeam echosounder (Reson7125) front-mounted onto the ROV Isis (Dive D333, DY081 expedition) to map the terrain of a vertical feature marking the edge of a deep-sea glacial trough (Labrador Sea, [63°51.9'N, 53°16.9'W, depth: 650 to 800 m]). After correction of the ROV navigation (i.e. merging of USBL and DVL), bathymetry [m] and backscatter [nominal unit] were extracted at a resolution of 0.3 m and different terrain descriptors were computed: Slope, Bathymetric Position Index (BPI), Terrain Ruggedness Index, Roughness, Mean and Gaussian curvatures and orientations (Northness and Eastness), at scales of 0.9, 3 and 9 m. Using a Principal Component Analysis (PCA), the terrain descriptors enabled to retrieve 4 terrain clusters and their associated confusion index, to investigate the spatial heterogeneity of the terrain. This approach also underlined the presence of geomorphic features in the wall terrain. The extraction of the backscatter intensity for the first time considering vertical terrains, opens space for further acquisition and processing development. Using photographs collected by the ROV Isis (Dive D334, DY081 expedition), epibenthic fauna was annotated. Each image was linked to a terrain cluster in the 3D space and pooled into 20-m² bins of images. A Bray-Curtis dissimilarity matrix was constructed from morphospecies abundances. This enabled to test for differences of assemblage composition among clusters. Few species appeared more abundant in particular clusters such as L. pertusa in high-roughness cluster. However, nMDS suggested differences in assemblage composition but these dissimilarities were not strongly delineated. Whereas the design of this study may have limited distinctive differences among assemblages, this shows the potential of this cost-effective method of top-down habitat mapping to be applied in undersampled benthic habitat in order to provide a priori knwoledge for defining appropriate sampling design

Terrain Point Cloud data of a Deep-Sea Cliff offshore Greenland and the Associated Epibenthic Fauna

Terrain data retrieved with a forward-looking sonar (Reson7125) the ROV Isis (NOC, UK) used to map a deep-sea wall located offshore the West coast of Greenland (Labrador Sea, [63°51.9'N, 53°16.9'W, depth: 703 to 803 m]) with a high resolution. The file contains a 3D point cloud storing terrain variables derived from the bathymetry [m] and backscatter [nominal unit] extracted at a resolution of 0.3 m. Different terrain descriptors were computed: Slope, Bathymetric Position Index (BPI), Terrain Ruggedness Index, Roughness, Mean and Gaussian curvatures and orientations (Northness and Eastness), at scales of 0.9, 3 and 9 m. Using a Principal Component Analysis (PCA), the terrain descriptors enabled to retrieve 4 terrain clusters and their associated confusion index, to investigate the spatial heterogeneity of the terrain

Abundance of epibenthic fauna on a Deep-Sea Cliff offshore Greenland and the Associated Epibenthic Fauna

Abundance data of the epibenthic fauna annotated on each photograph taken with the ROV Isis (NOC, UK) at a deep-sea wall located offshore the West coast of Greenland (Labrador Sea, [63°51.9'N, 53°16.9'W, depth: 703 to 803 m]). Abundances were standardized to 1 m² using the images' total area. Screenshots of the morphospecies are presented in the morphospecies guide pdf. These data were used to assess for differences in community composition analysis pooled in 20 m² samples defined according to high-resolution terrain abiotic clusters computed in an unsupervised way