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: Ocean biogeochemical (BGC) processes are fundamental for several key ocean ecosystem functions and services. For example, carbon dioxide uptake and storage by chemical uptake and biological fixation in combination with transport to depth remove anthropogenic carbon from the atmosphere and counteract global warming. Another important example would be nutrient regeneration from organic matter remineralization that sustains ocean productivity and, hence, also seafood supply. Although many BGC processes are rooted in surface waters where primary productivity and ocean / atmosphere exchange takes place, the deep ocean contributes strongly to the cycling and sequestration of elements. Without a consideration of organic particle attenuation in the deep pelagic and rates of remineralization and accumulation at the vast deep seafloor, the efficiency of the biological carbon pump and the recycling of nutrients – and potential effects of global change – can hardly be quantified. Considering the relevance of the deep ocean, the Deep Ocean Observing Strategy ‘DOOS’ is revising the Essential Ocean Variables (EOVs) specified by the Global Ocean Observing System (GOOS) with a deep ocean perspective. The DOOS community recognized, that most GOOS BGC EOVs are also relevant for deep ocean observations. However, the bottom of the sea requires more attention to assess its contribution to ocean BGC. ‘Seafloor labile organic matter’ and ‘seafloor respiration’ are candidates for additional EOVs beyond the GOOS EOVs ‘oxygen’ and ‘particulate matter’. Online questionnaires are prepared to facilitate an expert revision of GOOS BGC EOVs in order to make specific suggestions for modifications to the responsible GOOS panel. The revision includes the target phenomena and spatiotemporal scales, the observing platforms and networks considered relevant, and the requirements of observations. This contribution introduces the revision approach and invites experts to take part.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Deep Sea Research Part I: Oceanographic Research Papers 100 (2015): 21-33, doi:10.1016/j.dsr.2015.01.010.

Description: Most of our knowledge about deep-sea habitats is limited to bathyal (200–3000 m) and abyssal depths (3000–6000 m), while relatively little is known about the hadal zone (6000–11,000 m). The basic paradigm for the distribution of deep seafloor biomass suggests that the reduction in biomass and average body size of benthic animals along depth gradients is mainly related to surface productivity and remineralisation of sinking particulate organic carbon with depth. However, there is evidence that this pattern is somewhat reversed in hadal trenches by the funnelling of organic sediments, which would result in increased food availability along the axis of the trenches and towards their deeper regions. Therefore, despite the extreme hydrostatic pressure and remoteness from the pelagic food supply, it is hypothesized that biomass can increase with depth in hadal trenches. We developed a numerical model of gravitational lateral sediment transport along the seafloor as a function of slope, using the Kermadec Trench, near New Zealand, as a test environment. We propose that local topography (at a scale of tens of kilometres) and trench shape can be used to provide useful estimates of local accumulation of food and, therefore, patterns of benthic biomass. Orientation and steepness of local slopes are the drivers of organic sediment accumulation in the model, which result in higher biomass along the axis of the trench, especially in the deepest spots, and lower biomass on the slopes, from which most sediment is removed. The model outputs for the Kermadec Trench are in agreement with observations suggesting the occurrence of a funnelling effect and substantial spatial variability in biomass inside a trench. Further trench surveys will be needed to determine the degree to which seafloor currents are important compared with the gravity-driven transport modelled here. These outputs can also benefit future hadal investigations by highlighting areas of potential biological interest, on which to focus sampling effort. Comprehensive exploration of hadal trenches will, in turn, provide datasets for improving the model parameters and increasing predictive power.

Description: MCI would also like to thank the University of Southampton, the Natural Environment Research Council (NERC, grant number NEW332003) and the Institute of Marine Engineering, Science & Technology (IMarEST), for supporting his research towards a PhD. We are grateful for the support provided by the National Science Foundation (OCE-1131620 to TMS, JCD, and PHY) to the Hadal Ecosystem Studies (HADES) project to which this paper forms a contribution. Support also came from the Natural Environment Research Council (NERC) and its Marine Environmental Mapping Programme (MAREMAP).

Description: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Elementa Science of the Anthropocene 5 (2017): 4, doi:10.1525/elementa.203.

Description: The deep sea encompasses the largest ecosystems on Earth. Although poorly known, deep seafloor ecosystems provide services that are vitally important to the entire ocean and biosphere. Rising atmospheric greenhouse gases are bringing about significant changes in the environmental properties of the ocean realm in terms of water column oxygenation, temperature, pH and food supply, with concomitant impacts on deep-sea ecosystems. Projections suggest that abyssal (3000–6000 m) ocean temperatures could increase by 1°C over the next 84 years, while abyssal seafloor habitats under areas of deep-water formation may experience reductions in water column oxygen concentrations by as much as 0.03 mL L–1 by 2100. Bathyal depths (200–3000 m) worldwide will undergo the most significant reductions in pH in all oceans by the year 2100 (0.29 to 0.37 pH units). O2 concentrations will also decline in the bathyal NE Pacific and Southern Oceans, with losses up to 3.7% or more, especially at intermediate depths. Another important environmental parameter, the flux of particulate organic matter to the seafloor, is likely to decline significantly in most oceans, most notably in the abyssal and bathyal Indian Ocean where it is predicted to decrease by 40–55% by the end of the century. Unfortunately, how these major changes will affect deep-seafloor ecosystems is, in some cases, very poorly understood. In this paper, we provide a detailed overview of the impacts of these changing environmental parameters on deep-seafloor ecosystems that will most likely be seen by 2100 in continental margin, abyssal and polar settings. We also consider how these changes may combine with other anthropogenic stressors (e.g., fishing, mineral mining, oil and gas extraction) to further impact deep-seafloor ecosystems and discuss the possible societal implications. 

Description: A.K. Sweetman D.O.B. Jones and R. Danovaro acknowledge funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement 603418 (MIDAS), and the European Union Horizon 2020 research and innovation programme under grant agreement 689518 (MERCES). L.-A. Henry and J.M. Roberts acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 678760 (ATLAS).

Description: © The Author(s), 2018]. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Global Ecology and Biogeography 27 (2018): 760-786, doi:10.1111/geb.12729.

Description: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2). BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.

Description: European Research Council and EU, Grant/Award Number: AdG‐250189, PoC‐727440 and ERC‐SyG‐2013‐610028; Natural Environmental Research Council, Grant/Award Number: NE/L002531/1; National Science Foundation, Grant/Award Number: DEB‐1237733, DEB‐1456729, 9714103, 0632263, 0856516, 1432277, DEB‐9705814, BSR‐8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910, DEB 0963447, DEB‐1546686, DEB‐129764, OCE 95‐21184, OCE‐ 0099226, OCE 03‐52343, OCE‐0623874, OCE‐1031061, OCE‐1336206 and DEB‐1354563; National Science Foundation (LTER) , Grant/Award Number: DEB‐1235828, DEB‐1440297, DBI‐0620409, DEB‐9910514, DEB‐1237517, OCE‐0417412, OCE‐1026851, OCE‐1236905, OCE‐1637396, DEB 1440409, DEB‐0832652, DEB‐0936498, DEB‐0620652, DEB‐1234162 and DEB‐0823293; Fundação para a Ciência e Tecnologia, Grant/Award Number: POPH/FSE SFRH/BD/90469/2012, SFRH/BD/84030/2012, PTDC/BIA‐BIC/111184/2009; SFRH/BD/80488/2011 and PD/BD/52597/2014; Ciência sem Fronteiras/CAPES, Grant/Award Number: 1091/13‐1; Instituto Milenio de Oceanografía, Grant/Award Number: IC120019; ARC Centre of Excellence, Grant/Award Number: CE0561432; NSERC Canada; CONICYT/FONDECYT, Grant/Award Number: 1160026, ICM PO5‐002, CONICYT/FONDECYT, 11110351, 1151094, 1070808 and 1130511; RSF, Grant/Award Number: 14‐50‐00029; Gordon and Betty Moore Foundation, Grant/Award Number: GBMF4563; Catalan Government; Marie Curie Individual Fellowship, Grant/Award Number: QLK5‐CT2002‐51518 and MERG‐CT‐2004‐022065; CNPq, Grant/Award Number: 306170/2015‐9, 475434/2010‐2, 403809/2012‐6 and 561897/2010; FAPESP (São Paulo Research Foundation), Grant/Award Number: 2015/10714‐6, 2015/06743‐0, 2008/10049‐9, 2013/50714‐0 and 1999/09635‐0 e 2013/50718‐5; EU CLIMOOR, Grant/Award Number: ENV4‐CT97‐0694; VULCAN, Grant/Award Number: EVK2‐CT‐2000‐00094; Spanish, Grant/Award Number: REN2000‐0278/CCI, REN2001‐003/GLO and CGL2016‐79835‐P; Catalan, Grant/Award Number: AGAUR SGR‐2014‐453 and SGR‐2017‐1005; DFG, Grant/Award Number: 120/10‐2; Polar Continental Shelf Program; CENPES – PETROBRAS; FAPERJ, Grant/Award Number: E‐26/110.114/2013; German Academic Exchange Service; sDiv; iDiv; New Zealand Department of Conservation; Wellcome Trust, Grant/Award Number: 105621/Z/14/Z; Smithsonian Atherton Seidell Fund; Botanic Gardens and Parks Authority; Research Council of Norway; Conselleria de Innovació, Hisenda i Economia; Yukon Government Herschel Island‐Qikiqtaruk Territorial Park; UK Natural Environment Research Council ShrubTundra Grant, Grant/Award Number: NE/M016323/1; IPY; Memorial University; ArcticNet. DOI: 10.13039/50110000027. Netherlands Organization for Scientific Research in the Tropics NWO, grant W84‐194. Ciências sem Fronteiras and Coordenação de Pessoal de Nível Superior (CAPES, Brazil), Grant/Award Number: 1091/13‐1. National Science foundation (LTER), Award Number: OCE‐9982105, OCE‐0620276, OCE‐1232779. FCT ‐ SFRH / BPD / 82259 / 2011. U.S. Fish and Wildlife Service/State Wildlife federal grant number T‐15. Australian Research Council Centre of Excellence for Coral Reef Studies (CE140100020). Australian Research Council Future Fellowship FT110100609. M.B., A.J., K.P., J.S. received financial support from internal funds of University of Lódź. NSF DEB 1353139. Catalan Government fellowships (DURSI): 1998FI‐00596, 2001BEAI200208, MECD Post‐doctoral fellowship EX2002‐0022. National Science Foundation Award OPP‐1440435. FONDECYT 1141037 and FONDAP 15150003 (IDEAL). CNPq Grant 306595‐2014‐1

Description: Carbon capture and storage (CCS) is a key technology to reduce carbon dioxide (CO2) emissions from industrial processes in a feasible, substantial, and timely manner. For geological CO2 storage to be safe, reliable, and accepted by society, robust strategies for CO2 leakage detection, quantification and management are crucial. The STEMM-CCS (Strategies for Environmental Monitoring of Marine Carbon Capture and Storage) project aimed to provide techniques and understanding to enable and inform cost-effective monitoring of CCS sites in the marine environment. A controlled CO2 release experiment was carried out in the central North Sea, designed to mimic an unintended emission of CO2 from a subsurface CO2 storage site to the seafloor. A total of 675 kg of CO2 were released into the shallow sediments (~3 m below seafloor), at flow rates between 6 and 143 kg/d. A combination of novel techniques, adapted versions of existing techniques, and well-proven standard techniques were used to detect, characterise and quantify gaseous and dissolved CO2 in the sediments and the overlying seawater. This paper provides an overview of this ambitious field experiment. We describe the preparatory work prior to the release experiment, the experimental layout and procedures, the methods tested, and summarise the main results and the lessons learnt.

Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Smith, L., Cimoli, L., LaScala-Gruenewald, D., Pachiadaki, M., Phillips, B., Pillar, H., Stopa, J., Baumann-Pickering, S., Beaulieu, S., Bell, K., Harden-Davies, H., Gjerde, K., Heimbach, P., Howe, B., Janssen, F., Levin, L., Ruhl, H., Soule, A., Stocks, K., Vardaro, M.F., Wright, D. The Deep Ocean Observing Strategy: addressing global challenges in the deep sea through collaboration. Marine Technology Society Journal, 56(3), (2022): 50–66, https://doi.org/10.4031/mtsj.56.3.11.

Description: The Deep Ocean Observing Strategy (DOOS) is an international, community-driven initiative that facilitates collaboration across disciplines and fields, elevates a diverse cohort of early career researchers into future leaders, and connects scientific advancements to societal needs. DOOS represents a global network of deep-ocean observing, mapping, and modeling experts, focusing community efforts in the support of strong science, policy, and planning for sustainable oceans. Its initiatives work to propose deep-sea Essential Ocean Variables; assess technology development; develop shared best practices, standards, and cross-calibration procedures; and transfer knowledge to policy makers and deep-ocean stakeholders. Several of these efforts align with the vision of the UN Ocean Decade to generate the science we need to create the deep ocean we want. DOOS works toward (1) a healthy and resilient deep ocean by informing science-based conservation actions, including optimizing data delivery, creating habitat and ecological maps of critical areas, and developing regional demonstration projects; (2) a predicted deep ocean by strengthening collaborations within the modeling community, determining needs for interdisciplinary modeling and observing system assessment in the deep ocean; (3) an accessible deep ocean by enhancing open access to innovative low-cost sensors and open-source plans, making deep-ocean data Findable, Accessible, Interoperable, and Reusable, and focusing on capacity development in developing countries; and finally (4) an inspiring and engaging deep ocean by translating science to stakeholders/end users and informing policy and management decisions, including in international waters.

Description: Funding for the DOOS comes from the U.S. National Science Foundation AccelNet program, Award #2114717.

Description: The Deep Ocean Observing Strategy (DOOS) is an international, community-driven initiative that facilitates collaboration across disciplines and fields, elevates a diverse cohort of early career researchers into future leaders, and connects scientific advancements to societal needs. DOOS represents a global network of deep-ocean observing, mapping, and modeling experts, focusing community efforts in the support of strong science, policy, and planning for sustainable oceans. Its initiatives work to propose deep-sea Essential Ocean Variables; assess technology development; develop shared best practices, standards, and cross-calibration procedures; and transfer knowledge to policy makers and deep-ocean stakeholders. Several of these efforts align with the vision of the UN Ocean Decade to generate the science we need to create the deep ocean we want. DOOS works toward (1) a healthy and resilient deep ocean by informing science-based conservation actions, including optimizing data delivery, creating habitat and ecological maps of critical areas, and developing regional demonstration projects; (2) a predicted deep ocean by strengthening collaborations within the modeling community, determining needs for interdisciplinary modeling and observing system assessment in the deep ocean; (3) an accessible deep ocean by enhancing open access to innovative low-cost sensors and open-source plans, making deep-ocean data Findable, Accessible, Interoperable, and Reusable, and focusing on capacity development in developing countries; and finally (4) an inspiring and engaging deep ocean by translating science to stakeholders/end users and informing policy and management decisions, including in international waters.

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.