Beschreibung: These data list the fish counts and densities observed using time-lapse cameras at the two DELOS observatory platforms, located at 1400 m water depth on the Angolan continental margin between February 2009 and July 2016. Timelapse photographs were captured from both the "Near Field" (NF; 7.90°S, 12.14°E) and "Far Field" (FF; 7.95°S, 12.28°E) DELOS observatories using a Kongsberg OE14-208 5.1 megapixel digital camera and a Kongsberg OE11-242 flash. Where appropriate: Fish counts are listed as no. individuals observed per photograph. Fish densities are listed as no. individuals observed per photograph, per calendar month, and multiplied by 1000. The DELOS platforms (DELOS A and DELOS B) are under Angolan jurisdiction and all activities must abide by Angolan law. As such, any person intending to publish DELOS data in any form is required to obtain prior permission from the National Concessionaire (Sonangol). Permission can be requested via Robert O'Brien at BP UK (Robert.OBrien@uk.bp.com) or the DELOS PI Dr. David Bailey (David.Bailey@glasgow.ac.uk). This process is not intended as a deterrent and applications to use DELOS data are welcomed. Participating Institutions: BP Exploration, BP Angola, University of Aberdeen, MBARI, National Oceanography Centre, INIP - Angola Instituto Nacional de Investigação Pesqueira (Angolan National Institute of Fisheries), Texas A&M University, Glasgow University

Beschreibung: The excel file has two spread-sheets: (i) "Microsatellites-4Areas" including the information of the 11 microsatellites used in the paper for the four different areas we investigated; (ii) "Microsatellites-30Populations" including the information of the 11 microsatellites used in the paper for the 30 different populations we investigated. In the two spread-sheets we include the following columns: "Number of individual" from 1 to 168; "Area" with the names of the areas or populations for every individual; "Sample Code" with the name of the sample used in the paper; "1Ple, 3Ple, 11Ple, 13Ple, 12Ple, 14Ple, 16Ple, 5Ple, 19Ple, 10Ple, and 2Ple" the name of each of the 11 microsatellites gentotyped in our study. In addition to that, we provide a small summary of the "Number of microsatellites", "Number of Individuals", "Number of Areas", "Number of Populations", and "N of individuals per area and population".

Beschreibung: The data are counts of megafaunal specimens in seabed photographs captured with a Teledyne Gavia autonomous underwater vehicle deployed from the RRS James Cook in May 2019 at a site in UK sector of the Central North Sea (Connelly, 2019), as part of the Strategies for Environmental Monitoring of Marine Carbon Capture and Storage (STEMM-CCS) project. The seabed photographs were captured using a GRAS-14S5M-C camera with a Tamron TAM 23FM08-L lens mounted to the Gavia autonomous underwater vehicle. The camera captured photographs at a temporal frequency of 1.875 frames per second, a resolution of 1280 x 960 pixels, and at a target altitude of 2 m above the seafloor. Overlapping photos were removed. Megafaunal specimens (〉1 cm) in the non-overlapping images were detected using the MAIA machine learning algorithm in BIIGLE. The potential specimens detected using this method were reviewed to remove false positives and classified into morphotypes manually. Counts by morphotype, latitude and longitude (in degrees), camera altitude (m above seafloor) and seabed area (m2) are provided for each photo. The following additional unchecked raw data are also provided: date, time, AUV mission number, and AUV heading, pitch, and roll. Acknowledgements We thank the crew and operators of the RRS James Cook and the Gavia autonomous underwater vehicle. The project was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 654462.

Beschreibung: The deep ocean below 200 m water depth is the least observed, but largest habitat on our planet by volume and area. Over 150 years of exploration has revealed that this dynamic system provides critical climate regulation, houses a wealth of energy, mineral, and biological resources, and represents a vast repository of biological diversity. A long history of deep-ocean exploration and observation led to the initial concept for the Deep-Ocean Observing Strategy (DOOS), under the auspices of the Global Ocean Observing System (GOOS). Here we discuss the scientific need for globally integrated deep-ocean observing, its status, and the key scientific questions and societal mandates driving observing requirements over the next decade. We consider the Essential Ocean Variables (EOVs) needed to address deep-ocean challenges within the physical, biogeochemical, and biological/ecosystem sciences according to the Framework for Ocean Observing (FOO), and map these onto scientific questions. Opportunities for new and expanded synergies among deep-ocean stakeholders are discussed, including academic-industry partnerships with the oil and gas, mining, cable and fishing industries, the ocean exploration and mapping community, and biodiversity conservation initiatives. Future deep-ocean observing will benefit from the greater integration across traditional disciplines and sectors, achieved through demonstration projects and facilitated reuse and repurposing of existing deep-sea data efforts. We highlight examples of existing and emerging deep-sea methods and technologies, noting key challenges associated with data volume, preservation, standardization, and accessibility. Emerging technologies relevant to deep-ocean sustainability and the blue economy include novel genomics approaches, imaging technologies, and ultra-deep hydrographic measurements. Capacity building will be necessary to integrate capabilities into programs and projects at a global scale. Progress can be facilitated by Open Science and Findable, Accessible, Interoperable, Reusable (FAIR) data principles and converge on agreed to data standards, practices, vocabularies, and registries. We envision expansion of the deep-ocean observing community to embrace the participation of academia, industry, NGOs, national governments, international governmental organizations, and the public at large in order to unlock critical knowledge contained in the deep ocean over coming decades, and to realize the mutual benefits of thoughtful deep-ocean observing for all elements of a sustainable ocean.

Beschreibung: 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.

Beschreibung: Future deep-sea mining for polymetallic nodules in abyssal plains will negatively impact the benthic ecosystem, but it is largely unclear whether this ecosystem will be able to recover from mining disturbance and if so, to what extent and at what timescale. During the "DISturbance and reCOLonization" (DISCOL) experiment, a total of 22% of the seafloor within a 10.8km2 circular area of the nodule-rich seafloor in the Peru Basin (SE Pacific) was ploughed in 1989 to bury nodules and mix the surface sediment. This area was revisited 0.1, 0.5, 3, 7, and 26 years after the disturbance to assess macrofauna, invertebrate megafauna and fish density and diversity. We used this unique abyssal faunal time series to develop carbon-based food web models for each point in the time series using the linear inverse modeling approach for sediments subjected to two disturbance levels: (1) outside the plough tracks; not directly disturbed by plough, but probably suffered from additional sedimentation; and (2) inside the plough tracks. Total faunal carbon stock was always higher outside plough tracks compared with inside plough tracks. After 26 years, the carbon stock inside the plough tracks was 54% of the carbon stock outside plough tracks. Deposit feeders were least affected by the disturbance, with modeled respiration, external predation, and excretion rates being reduced by only 2.6% inside plough tracks compared with outside plough tracks after 26 years. In contrast, the respiration rate of filter and suspension feeders was 79.5% lower in the plough tracks after 26 years. The "total system throughput" (T..), i.e., the total sum of modeled carbon flows in the food web, was higher throughout the time series outside plough tracks compared with the corresponding inside plough tracks area and was lowest inside plough tracks directly after the disturbance (8.63 × 10−3±1.58 × 10−5mmolCm−2d−1). Even 26 years after the DISCOL disturbance, the discrepancy of T.. between outside and inside plough tracks was still 56%. Hence, C cycling within the faunal compartments of an abyssal plain ecosystem remains reduced 26 years after physical disturbance, and a longer period is required for the system to recover from such a small-scale sediment disturbance experiment.