Faunal activity rhythms influencing early community succession of an implanted whale carcass offshore Sagami Bay, Japan

Occurrence and levels of polybrominated diphenyl ethers (PBDEs) in deep-sea sharks from Suruga Bay, Japan

Few studies have investigated the prevalence of polybrominated diphenyl ethers (PBDEs) in deep-sea sharks. In this study, the levels and profiles of PBDEs were determined in liver samples of eight different species of deep-sea sharks collected in Suruga Bay, Japan. Widespread contamination of PBDEs in the deep-sea environment was reconfirmed in this study as these persistent organic pollutants (POPs) were detected in all specimens analyzed. Mean ΣPBDE levels in the deep-sea sharks ranged from 7 to 517 ng/g of lipid weight. The distribution patterns of BDE homologues were similar in all species where tetra-BDEs provided the dominant contribution to total PBDEs (46%). PBDEs levels were similar to, or higher than, those seen in other deep-sea sharks from different regions. The levels of PBDEs were likely to reflect their feeding preferences as higher PBDE levels were seen in species with higher trophic positions.

Constructing a Defined Starter for Multispecies Vinegar Fermentation via Evaluating the Vitality and Dominance of Functional Microbes in Autochthonous Starter

Mature vinegar culture has usually been used as a type of autochthonous starter for rapidly initiate initiating the next batch of acetic acid fermentation (AAF) and maintaining the batch-to-batch uniformity of AAF in the production of traditional cereal vinegar. However, the vitality and dominance of functional microbes in autochthonous starters remain unclear, which hinders further improvement of fermentation yield and production. Here, based on metagenomic (MG), metatranscriptomic (MT), and 16S rRNA gene sequencings, 11 bacterial operational taxonomic units (OTUs) with significant metabolic activity (MT/MG ratio >1) and dominance (relative abundance >1%) were targeted in the autochthonous vinegar starter, all of which were assigned to 4 species (Acetobacter pasteurianus, Lactobacillus acetotolerans, L. helveticus, Acetilactobacillus jinshanensis). Then, we evaluated the successions and interactions of these 11 bacterial OTUs at different AAF stages. Last, a defined starter was constructed with 4 core species isolated from the autochthonous starter (A. pasteurianus, L. acetotolerans, L. helveticus, Ac. jinshanensis). The defined starter culture could rapidly initiate the AAF in a sterile or unsterilized environment and similar dynamics of metabolites (ethanol, titratable acidity, acetic acid, lactic acid, and volatile compounds) and environmental indexes (temperature, pH) of fermentation were observed as compared with that of autochthonous starter (P > 0.05). This work provides a method to construct a defined microbiota from a complex system while preserving its metabolic function. IMPORTANCE Complex microorganisms are beneficial to the flavor formation in natural food fermentation, but they also pose challenges to the mass production of standardized products. It is attractive to construct a defined starter to rapidly initiate fermentation process and significantly improve fermentation yield. This study provides a comprehensive understanding of vital and dominant species in the autochthonous vinegar starter via multi-omics, and designs a defined microbial community for the efficient fermentation of cereal vinegar.

Can whale-fall studies inform human forensics?

Experimental knowledge of human body decomposition in the deep ocean is very limited, partly due to the logistical challenges of deep-sea research. The literature on ecological responses to the arrival of naturally sunk and implanted whale carcasses on the seafloor represents a potential source of information relevant to questions of human body survival and recovery in the deep ocean. Whale falls trigger the formation of complex, localized, and dense biological communities that have become a point of interest for marine biologists for the past 2-3 decades. Researchers have documented whale falls by whale type, size, geographic location, water depth and water chemistry, and there have been some comparative analyses of decomposition rates and faunal presence on carcasses. We undertook a review and meta-analysis of the whale-fall literature to identify and statistically model trends relevant to human forensics. Results from studies using deep-sea cameras baited with pig carcasses and simulated carrion provided further validation of noted trends. The stages of whale carcass decomposition most relevant to human forensics are those characterised by mobile scavengers that strip the soft tissues from carcasses, and to a lesser degree, other biota that degrade skeletal material. Our statistical models used the number of faunal taxa attracted to the whale carcasses as a measure of the ecological response and the potential rate of decomposition. Negative binomial models identified significant influences of carcass age and dissolved oxygen concentration on the ecological response (taxon numbers). The strongest environmental effects were identified in data from experimental studies that implanted whale carcasses across a broad range of dissolved-oxygen conditions. We propose directions for further experimental research to refine models of environmental controls on decomposition in the deep sea. Our results also highlight the potential use of publicly available global databases on environmental conditions in the deep ocean for informing body scavenging activity and thus body survival. Applying a forensic lens to whale-fall studies provides a window into an otherwise unseen world from the standpoint of human forensic taphonomy.

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

Towards Naples Ecological REsearch for Augmented Observatories (NEREA): The NEREA-Fix Module, a Stand-Alone Platform for Long-Term Deep-Sea Ecosystem Monitoring

Deep-sea ecological monitoring is increasingly recognized as indispensable for the comprehension of the largest biome on Earth, but at the same time it is subjected to growing human impacts for the exploitation of biotic and abiotic resources. Here, we present the Naples Ecological REsearch (NEREA) stand-alone observatory concept (NEREA-fix), an integrated observatory with a modular, adaptive structure, characterized by a multiparametric video-platform to be deployed in the Dohrn canyon (Gulf of Naples, Tyrrhenian Sea) at ca. 650 m depth. The observatory integrates a seabed platform with optoacoustic and oceanographic/geochemical sensors connected to a surface transmission buoy, plus a mooring line (also equipped with depth-staged environmental sensors). This reinforced high-frequency and long-lasting ecological monitoring will integrate the historical data conducted over 40 years for the Long-Term Ecological Research (LTER) at the station “Mare Chiara”, and ongoing vessel-assisted plankton (and future environmental DNA-eDNA) sampling. NEREA aims at expanding the observational capacity in a key area of the Mediterranean Sea, representing a first step towards the establishment of a bentho-pelagic network to enforce an end-to-end transdisciplinary approach for the monitoring of marine ecosystems across a wide range of animal sizes (from bacteria to megafauna).

Ecological variables for developing a global deep-ocean monitoring and conservation strategy

The deep sea (>200 m depth) encompasses >95% of the world's ocean volume and represents the largest and least explored biome on Earth (<0.0001% of ocean surface), yet is increasingly under threat from multiple direct and indirect anthropogenic pressures. Our ability to preserve both benthic and pelagic deep-sea ecosystems depends upon effective ecosystem-based management strategies and monitoring based on widely agreed deep-sea ecological variables. Here, we identify a set of deep-sea essential ecological variables among five scientific areas of the deep ocean: (1) biodiversity; (2) ecosystem functions; (3) impacts and risk assessment; (4) climate change, adaptation and evolution; and (5) ecosystem conservation. Conducting an expert elicitation (1,155 deep-sea scientists consulted and 112 respondents), our analysis indicates a wide consensus amongst deep-sea experts that monitoring should prioritize large organisms (that is, macro- and megafauna) living in deep waters and in benthic habitats, whereas monitoring of ecosystem functioning should focus on trophic structure and biomass production. Habitat degradation and recovery rates are identified as crucial features for monitoring deep-sea ecosystem health, while global climate change will likely shift bathymetric distributions and cause local extinction in deep-sea species. Finally, deep-sea conservation efforts should focus primarily on vulnerable marine ecosystems and habitat-forming species. Deep-sea observation efforts that prioritize these variables will help to support the implementation of effective management strategies on a global scale.

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

New High-Tech Flexible Networks for the Monitoring of Deep-Sea Ecosystems

Increasing interest in the acquisition of biotic and abiotic resources from within the deep sea (e.g., fisheries, oil-gas extraction, and mining) urgently imposes the development of novel monitoring technologies, beyond the traditional vessel-assisted, time-consuming, high-cost sampling surveys. The implementation of permanent networks of seabed and water-column-cabled (fixed) and docked mobile platforms is presently enforced, to cooperatively measure biological features and environmental (physicochemical) parameters. Video and acoustic (i.e., optoacoustic) imaging are becoming central approaches for studying benthic fauna (e.g., quantifying species presence, behavior, and trophic interactions) in a remote, continuous, and prolonged fashion. Imaging is also being complemented by in situ environmental-DNA sequencing technologies, allowing the traceability of a wide range of organisms (including prokaryotes) beyond the reach of optoacoustic tools. Here, we describe the different fixed and mobile platforms of those benthic and pelagic monitoring networks, proposing at the same time an innovative roadmap for the automated computing of hierarchical ecological information on deep-sea ecosystems (i.e., from single species' abundance and life traits to community composition, and overall biodiversity).