Description: It is increasingly recognised that deep-sea mining of seafloor massive sulphides (SMS) could become an important source of mineral resources. These operations will remove the targeted substrate and produce potentially sediment toxic plumes from in situ seabed excavation and from the return water pumped back down to the seafloor. However, the spatial extent of the impacts of deep-sea mining plumes is still uncertain because few field experiments and models of plumes dispersion have been conducted. Morato et al. (2022) used three-dimensional hydrodynamic models of the Azores region together with a theoretical commercial mining operation of polymetallic SMS to simulate the potential dispersal of sediment plumes originating from different phases of mining operations and to assess the magnitude of potential impacts. The areas used in the modelling work were (from North to South): Cavala seamount (38.265, -30.710), Lucky Strike Hole (37.503, -31.955), Menez Hom (37.109, -32.618), Famous (37.001, -33.039), Saldanha (36.658, -33.420), and Rainbow (36.262 -33.824). The datasets published here contain all the model outputs, namely for 1) the in situ excavation sediment plume, 2) the return water discharge plume, and 3) the return sediments discharge plume: 1) The concentration of solids and of the discharge water in each horizontal 2-dimensional space cell is calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during each trimester and during a 12-months simulation. 1.1) Concentration of sediments produced during the in situ excavation sediment plume calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation. Sediments were composed of six classes of different particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and 〉2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1. 1.2) Concentration of return water discharge plume (shown in dilution folds) in six study areas calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation and assuming a control temperature as the annual minimum temperature of each location (T1). The salinity of discharge was calculated assuming the MOHID salinity of 83.3% surface water and 16.7% of seafloor water. 1.3) Concentration of sediments in the return sediment discharge plume, calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation. The average particle diameter was assumed to be 4 µm with an average particle density of 3,780 kg·m-3 and a resultant settling velocity of 0.002 cm·s-1. 2) The proportion of simulated time (temporal frequency) that a specific 2-dimensional space contained plume concentrations higher than the adopted thresholds; 1.2 mg·L-1 for sediment solids and 5,000 fold dilution for discharge water. Those cells whose temporal frequency above the thresholds was greater than 50%, i.e. 6 months out of 12 months, were considered as cells with persistent plumes. 2.1) Proportion of simulated time (temporal frequency) that a specific a 2-dimensional space cell, in six study areas, contained in situ excavation sediment plume above a 1.2 mg·L-1 concentration threshold, during a 12-months simulation, assuming six classes of particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and 〉2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1. 2.2) Proportion of simulated time (temporal frequency) that a specific 2-dimensional space, in six study areas, contained return water discharge plume concentrations higher than the adopted thresholds (i.e., 5,000 fold dilution), during a 12-months simulation and assuming a control temperature as the annual minimum temperature of each location (T1). The salinity of discharge was calculated assuming the MOHID salinity of 83.3% surface water and 16.7% of seafloor water. 2.3) Proportion of simulated time (temporal frequency) that a specific 2-dimensional space cell, in six study areas, contained return sediments discharge plume above a 1.2 mg·L-1 concentration threshold, during a 12-months simulation, assuming an average particle diameter of 4 µm, an average particle density of 3,780 kg·m-3, and a resultant settling velocity of 0.002 cm·s-1. 3) In addition to the thresholds and targets described above, the datasets also present the model results for Cavala seamount and Lucky Strike Hole against other thresholds: 5 mg·L-1, 10 mg·L-1 and 25 mg·L-1 for sediments and 1,000, 600, 300 and 200 fold dilution for discharge water. 4) Seasonal variations in the model outputs for plumes dispersal are also presented for Cavala seamount and Lucky Strike Hole by computing the probability of concentration above thresholds for four periods of three months (January-March, April-June, July-September, and October-December). In these scenarios, the model run duration was approximately 90 days. 5) The sediment thickness of the settled sediments from the discharge sediment and excavation. 5.1) Bottom thickness of settled sediments produced during the in situ excavation sediment plume assuming six classes of particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and 〉2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1. The duration of the simulation is one year. 5.2) Bottom thickness of settled sediments from the return sediment discharge plume modelled assuming an average particle diameter of 4 µm, an average particle density of 3,780 kg·m-3, and a resultant settling velocity of 0.002 cm·s-1. The duration of the simulation is one year.

Description: We report the results of an aquaria-based experiment testing the effects of suspended particles generated during potential mining activities, on a common habitat-building coral species in the Azores, Dentomuricea aff. meteor. Corals were collected from the summit of Condor Seamount (Azores, NE Atlantic) at depths between 185-210 m in August 2014. Coral fragments were maintained in 10-L aquaria and exposed to three experimental treatments for a period of four weeks at the DeepSeaLab aquaria facilities (Okeanos-University of the Azores): (1) control conditions (no added sediments); (2) suspended polymetallic sulphide (PMS) particles; (3) suspended quartz particles. PMS particles were obtained by grinding PMS inactive chimney rocks collected at the hydrothermal vent field Lucky Strike. Both particle types were delivered at a concentration of 25 mg L-1. The putative effects of PMS particles were evaluated through measurements of the coral physiological responses at the levels of the organism (oxygen consumption, ammonium excretion), tissue (bioaccumulation of metals) and cell (enzyme activity and gene expression).

Description: In 2014, two experimental Agassiz trawls were conducted on the Schulz Bank; one at the summit (568–670 m depth) and one on the southwestern flank (1,464 m depth). The 3-m-wide Agassiz trawl, with a 1 cm mesh size in the cod-end, was towed along the seafloor for 676 m on the summit and 441 m on the flank, resulting in a disturbed area of 2,028 and 1,323 m², for the summit and flank, respectively. Towing speed was maintained at around 2 knots and the initial and final position were recorded. In August 2018, the two trawl marks and four additional control transects (one on either side of the trawl mark) were surveyed while onboard the R/V G.O.Sars. Video recordings were taken along the trawl marks and control transects with the ROV AEGIR6000. Control transects were located 50 m to the east and west of each trawl mark and were performed parallel to the mark.

Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Levin, L. A., Bett, B. J., Gates, A. R., Heimbach, P., Howe, B. M., Janssen, F., McCurdy, A., Ruhl, H. A., Snelgrove, P., Stocks, K., I., Bailey, D., Baumann-Pickering, S., Beaverson, C., Benfield, M. C., Booth, D. J., Carreiro-Silva, M., Colaco, A., Eble, M. C., Fowler, A. M., Gjerde, K. M., Jones, D. O. B., Katsumata, K., Kelley, D., Le Bris, N., Leonardi, A. P., Lejzerowicz, F., Macreadie, P., I., McLean, D., Meitz, F., Morato, T., Netburn, A., Pawlowski, J., Smith, C. R., Sun, S., Uchida, H., Vardaro, M. F., Venkatesan, R., & Weller, R. A. Global observing needs in the deep ocean. Frontiers in Marine Science, 6, (2019):241, doi: 10.3389/fmars.2019.00241.

Description: 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.

Description: Preparation of this manuscript was supported by NNX16AJ87A (NASA) Consortium for Ocean Leadership, Sub-Award No. SA16-33. AC was supported by FCT-Investigador contract (IF/00029/2014/CP1230/CT0002). LL was supported by a NASA subaward from the Consortium for Ocean Leadership. AG and HR were supported by Horizon 2020, EU Project “EMSO Link” grant ID 731036. AG, BB, DJ, and HR contributions were supported by the UK Natural Environment Research Council Climate Linked Atlantic Section Science project (NE/R015953/1). JP was funded by the Swiss Network for International Studies, and the Swiss National Science Foundation (grant 31003A_179125). TM was supported by Program Investigador FCT (IF/01194/2013), IFCT Exploratory Project (IF/01194/2013/CP1199/CT0002), H2020 Atlas project (GA 678760), and the H2020 MERCES project (GA 689518). This is PMEL contribution number 4965.

Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Howell, K. L., Hilario, A., Allcock, A. L., Bailey, D. M., Baker, M., Clark, M. R., Colaco, A., Copley, J., Cordes, E. E., Danovaro, R., Dissanayake, A., Escobar, E., Esquete, P., Gallagher, A. J., Gates, A. R., Gaudron, S. M., German, C. R., Gjerde, K. M., Higgs, N. D., Le Bris, N., Levin, L. A., Manea, E., McClain, C., Menot, L., Mestre, N. C., Metaxas, A., Milligan, R. J., Muthumbi, A. W. N., Narayanaswamy, B. E., Ramalho, S. P., Ramirez-Llodra, E., Robson, L. M., Rogers, A. D., Sellanes, J., Sigwart, J. D., Sink, K., Snelgrove, P. V. R., Stefanoudis, P., V., Sumida, P. Y., Taylor, M. L., Thurber, A. R., Vieira, R. P., Watanabe, H. K., Woodall, L. C., & Xavier, J. R. A blueprint for an inclusive, global deep-sea ocean decade field program. Frontiers in Marine Science, 7, (2020): 584861, doi:10.3389/fmars.2020.584861.

Description: The ocean plays a crucial role in the functioning of the Earth System and in the provision of vital goods and services. The United Nations (UN) declared 2021–2030 as the UN Decade of Ocean Science for Sustainable Development. The Roadmap for the Ocean Decade aims to achieve six critical societal outcomes (SOs) by 2030, through the pursuit of four objectives (Os). It specifically recognizes the scarcity of biological data for deep-sea biomes, and challenges the global scientific community to conduct research to advance understanding of deep-sea ecosystems to inform sustainable management. In this paper, we map four key scientific questions identified by the academic community to the Ocean Decade SOs: (i) What is the diversity of life in the deep ocean? (ii) How are populations and habitats connected? (iii) What is the role of living organisms in ecosystem function and service provision? and (iv) How do species, communities, and ecosystems respond to disturbance? We then consider the design of a global-scale program to address these questions by reviewing key drivers of ecological pattern and process. We recommend using the following criteria to stratify a global survey design: biogeographic region, depth, horizontal distance, substrate type, high and low climate hazard, fished/unfished, near/far from sources of pollution, licensed/protected from industry activities. We consider both spatial and temporal surveys, and emphasize new biological data collection that prioritizes southern and polar latitudes, deeper (〉 2000 m) depths, and midwater environments. We provide guidance on observational, experimental, and monitoring needs for different benthic and pelagic ecosystems. We then review recent efforts to standardize biological data and specimen collection and archiving, making “sampling design to knowledge application” recommendations in the context of a new global program. We also review and comment on needs, and recommend actions, to develop capacity in deep-sea research; and the role of inclusivity - from accessing indigenous and local knowledge to the sharing of technologies - as part of such a global program. We discuss the concept of a new global deep-sea biological research program ‘Challenger 150,’ highlighting what it could deliver for the Ocean Decade and UN Sustainable Development Goal 14.

Description: Development of this paper was supported by funding from the Scientific Committee on Oceanic Research (SCOR) awarded to KH and AH as working group 159 co-chairs. KH, BN, and KS are supported by the UKRI funded One Ocean Hub NE/S008950/1. AH work is supported by the CESAM (UIDP/50017/2020 + 1432 UIDB/50017/2020) that is funded by Fundação para a Ciência e a Tecnologia (FCT)/MCTES through national funds. AA is supported by Science Foundation Ireland and the Marine Institute under the Investigators Program Grant Number SFI/15/IA/3100 co-funded under the European Regional Development Fund 2014–2020. AC is supported through the FunAzores -ACORES 01-0145-FEDER-000123 grant and by FCT through strategic project UID/05634/2020 and FCT and Direção-Geral de Politica do Mar (DGPM) through the project Mining2/2017/005. PE is funded by national funds (OE), through FCT in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. SG research is supported by CNRS funds. CG is supported by an Independent Study Award and the Investment in Science Fund at WHOI. KG gratefully acknowledges support from Synchronicity Earth. LL is funded by the NOAA Office of Ocean Exploration and Research (NA19OAR0110305) and the US National Science Foundation (OCE 1634172). NM is supported by FCT and DGPM, through the project Mining2/2017/001 and the FCT grants CEECIND/00526/2017, UIDB/00350/2020 + UIDP/00350/2020. SR is funded by the FCTgrant CEECIND/00758/2017. JS is supported by ANID FONDECYT #1181153 and ANID Millennium Science Initiative Program #NC120030. JX research is funded by the European Union’s Horizon 2020 research and innovation program through the SponGES project (grant agreement no. 679849) and further supported by national funds through FCT within the scope of UIDB/04423/2020 and UIDP/04423/2020. The Natural Sciences and Engineering Council of Canada supports AM and PVRS. MB and the Deep-Ocean Stewardship Initiative are supported by Arcadia - A charitable fund of Lisbet Rausing and Peter Baldwin. BN work is supported by the NERC funded Arctic PRIZE NE/P006302/1.