A geophysical, geochemical and microbiological study of a newly discovered pockmark with active gas seepage and submarine groundwater discharge (MET1-BH, central Gulf of Gdańsk, southern Baltic Sea)

Pockmarks and associated fresh submarine groundwater discharge in the seafloor of Puck Bay, southern Baltic Sea

This is the first well-documented report on the occurrence of pockmarks in Puck Bay. Pockmarks in the seafloor of Puck Bay were discovered during a hydroacoustic survey carried out in 2020. They are located at a depth of 25-27 m in the southwestern part of the bay. Significant depletion of chloride (Cl-) concentrations in sediment pore water was found within the depressions. Most likely, the formation of pockmarks was due to groundwater flow through the Miocene-Pleistocene system of aquifers, which extends from land to the bay area. One-dimensional modeling of vertical Cl- concentration profiles in pore water revealed the upward flow of freshened groundwater within the pockmarks. The magnitude of submarine groundwater discharge (SGD) was estimated to vary from 1.53·10-2 to 18·10-2 L·m-2·h-1. The effect of groundwater seepage was also observed at 3 cm above the seafloor within the pockmarks, which was identified as a decrease in salinity of approximately 0.12 PSU compared to reference sites. Furthermore, due to the effect of water advection, SGD can be detected even several meters above the seafloor as a decrease in salinity values within the thermocline layer.

Assessment of prokaryotic communities in Southwestern Atlantic deep-sea sediments reveals prevalent methanol-oxidising Methylomirabilales

Continental slopes can play a significant contribution to marine productivity and carbon cycling. These regions can harbour distinct geological features, such as salt diapirs and pockmarks, in which their depressions may serve as natural sediment traps where different compounds can accumulate. We investigated the prokaryotic communities in surface (0-2 cm) and subsurface (18-20 or 22-24 cm) sediments from a salt diapir and pockmark field in Santos Basin, Southwest Atlantic Ocean. Metabarcoding of 16 samples revealed that surface sediments were dominated by the archaeal class Nitrososphaeria, while the bacterial class Dehalococcoidia was the most prevalent in subsurface samples. Sediment strata were found to be a significant factor explaining 27% of the variability in community composition. However, no significant difference was observed among geomorphological features. We also performed a metagenomic analysis of three surface samples and analysed the highest quality metagenome-assembled genome retrieved, which belonged to the family CSP1-5, phylum Methylomirabilota. This non-methanotrophic methylotroph contains genes encoding for methanol oxidation and Calvin Cycle pathways, along with diverse functions that may contribute to its adaptation to deep-sea habitats and to oscillating environmental conditions. By integrating metabarcoding and metagenomic approaches, we reported that CSP1-5 is prevalent in the sediment samples from Santos Basin slope, indicating the potential importance of methanol metabolism in this region. Finally, using a phylogenetic approach integrating 16S rRNA sequences assigned to Methylomirabilota in this study with those from a public database, we argued that CSP1-5 public sequences might be misclassified as Methylomirabilaceae (the methanotrophic clade) and, therefore, the role of these organisms and the methanol cycling could also be neglected in other environments.

The Baltic Sea methane pockmark microbiome: The new insights into the patterns of relative abundance and ANME niche separation

Pockmarks are important "pumps", which are believed to play a significant role in the global methane cycling and harboring a unique assemblage of very diverse prokaryotes. This study reports the results of massive sequencing of the 16S rRNA gene V4 hypervariable regions for the samples from thirteen pockmark horizons (the Baltic Sea) collected at depths from 0 to 280 cm below seafloor (cmbsf) and the rates of microbially mediated anaerobic oxidation of methane (AOM) and sulfate reduction (SR). Altogether, 76 bacterial and 12 archaeal phyla were identified, 23 of which were candidate divisions. Of the total obtained in the pockmark sequences, 84.3% of them were classified as Bacteria and 12.4% as Archaea; 3.3% of the sequences were assigned to unknown operational taxonomic units (OTUs). Members of the phyla Planctomycetota, Chloroflexota, Desulfobacterota, Caldatribacteriota, Acidobacteriota and Proteobacteria predominated across all horizons, comprising 58.5% of the total prokaryotic community. These phyla showed different types of patterns of relative abundance. Analysis of AOM-SR-mediated prokaryotes abundance and biogeochemical measurements revealed that ANME-2a-2b subcluster was predominant in sulfate-rich upper horizons (including sulfate-methane transition zone (SMTZ)) and together with sulfate-reducing bacterial group SEEP-SRB1 had a primary role in AOM coupled to SR. At deeper sulfate-depleted horizons ANME-2a-2b shifted to ANME-1a and ANME-1b which alone mediated AOM or switch to methanogenic metabolism. Shifting of the ANME subclusters depending on depth reflect a tendency for niche separation in these groups. It was shown that the abundance of Caldatribacteriota and organohalide-respiring Dehalococcoidia (Chloroflexota) exhibited a strong correlation with AOM rates. This is the first detailed study of depth profiles of prokaryotic diversity, patterns of relative abundance, and ANME niche separation in the Baltic Sea pockmark microbiomes sheds light on assembly of prokaryotes in a pockmark.

Semi-Automated Data Processing and Semi-Supervised Machine Learning for the Detection and Classification of Water-Column Fish Schools and Gas Seeps with a Multibeam Echosounder

Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model.

Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn.