Deep-sea Data Research Articles

Towards an Integrated Legal Regime for Data and Sample Regulation under China’s Deep Seabed Mining Law

Abstract China enacted its Deep Seabed Mining Law in 2016 and promulgated the provisional measures for deep-sea data and sample management under Article 18 of the Deep Seabed Mining Law in 2017. After the institutional reforms in 2018, China entered a new era of ocean governance. This article presents a critical assessment of the provisional measures issued by the former State Oceanic Administration and proposes recommendations for the revisions of the provisional measures. Part one of this article introduces the background and briefly describes the purpose of this article. Part two provides an overview of the provisional measures for deep-sea data and sample management. Part three delivers a critical analysis of these provisional measures, demonstrating the flaws that the current provisional measures suffer. Part four proposes recommendations for the revision of the provisional measures, suggesting that an integrated data and sample management measure with a higher legal hierarchy should be promulgated by the Ministry of Natural Resources and that a streamlined regulatory framework for deep-sea data and sample management should be adopted.

Data Analysis in Deep-Sea Mineral and Organism Interplay: A Review

The deep sea, a realm characterized by extreme environmental conditions and remarkable biodiversity, has become a focal point for both scientific exploration and industrial exploitation. This comprehensive review aims to elucidate the data analysis techniques that are instrumental in studying the complex relationship between deep-sea mineral resources and the organisms that inhabit these extreme environments. The paper begins by outlining the types of deep-sea mineral resources and their distribution, followed by an overview of the methods and technologies used for exploration and extraction. It then delves into the diversity and classification of deep-sea organisms, their unique survival mechanisms, and their ecological roles. The core of the review focuses on data analysis techniques, covering traditional methods, advancements in machine learning and AI, and the inherent challenges and limitations in analyzing intricate deep-sea data. The paper concludes by discussing the direct and indirect impacts of mineral extraction on deep-sea ecosystems, supported by case studies and potential long-term effects on biodiversity and ecosystem health.

Preliminary deep-sea data analysis collected at Gloria seamount, Azores-Biscay Rise

The present study provides the first results of the exploratory research campaign to Gloria seamount (summit at 45.03°N, 15.54°W), a newly identified seamount in the NE Atlantic. New multibeam bathymetry and an 8 h remote operated vehicle footage and photography data were compiled and analyzed to give the first insights on the geological nature and benthic megafauna assemblages of Gloria seamount. Footage evidenced three distinct seafloor coverage areas, ranging from unconsolidated fine sediment with ripple marks, to increasingly coarser-grain sediment with large blocks, to outcrops with steep slopes along successive ridges, locally exhibiting volcanic structures. The deep-sea benthic assemblages differed with depth (2700−2450 m) and seafloor substrate type. In sediment habitats, pink holothurians (Elpidiidae) and brittle stars (Ophiuroidea) were recorded, whereas in hard substrates, sponges (Hexactinellida and Demospongiae) and stalked crinoids (Crinoidea) were mixed with distinct coral specimens, such as bamboo corals (Isidiidae) and Coralliidae (Scleralcyonacea). The integration of these data contribute to the geo-habitats knowledge of an unexplored seamount.

Distribution and Species Richness of Benthic Polychaeta and Sipuncula in the Northwestern Pacific

Polychaeta and Sipuncula are abundant inhabitants of benthic marine habitats and have been increasingly sampled in the Northwest Pacific (NWP). However, polychaete and sipunculan species richness, composition, and distribution patterns still require further investigation, despite previous studies due to increasing deep-sea data flow. Using occurrence records for Polychaeta and Sipuncula from the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility (GBIF), we analyzed sampling effort (the number of distribution records), alpha (the number of species per 700,000 km2 hexagon cells) and gamma (the number of species per 5° latitudinal band) species diversity, and estimated species richness along latitudinal and bathymetric gradients. The species richness estimations were also correlated with multiple environmental and topographic variables, including depth, temperature, dissolved oxygen, chlorophyll, primary production, phytoplankton, current velocity, light, iron, nitrate, phosphate, silicate, and salinity. The dataset included over 30,000 distribution records belonging to polychaete (31,114 records, 98%) and sipunculan (690 records, 2%) species. Half of the distribution records were reported at a species level. The area around the island of Hainan and South Korea showed the highest alpha species richness (the number of species per 700,000 km2 hexagon cell), yet the estimated species richness (ES50) indicated that there might be many unknown, unsampled, or non-digitized species throughout the whole NWP. Correspondingly, most distribution records (sampling effort) and gamma species richness were found between latitudes 20 and 40° and decreased towards higher latitudes. Sipuncula were reported relatively more frequently from the deep sea than Polychaeta (62.8% vs. 12%). Overall, the number of species and records decreased with increasing depth, with a peak at about 5000 m. The alpha species richness had the strongest positive correlations with temperature, chlorophyll, primary production, and phytoplankton concentration. Here, we provide an overview of the species richness and distribution of Polychaeta in comparison with Sipuncula in the NWP, in both shallow and deep environments. This study demonstrates where further sampling efforts are needed to fill our knowledge gaps on annelids’ distribution and diversity along the NWP. This could improve the analyses of the distribution and diversity of annelids to better understand the current environmental drivers of biodiversity, as well as predicting potential future drivers. The outcome of the environmental correlation provides thus valuable knowledge for predicting the future impacts of global warming on potential distribution shifts of annelids into new environments such as the Arctic Sea, possibly resulting in biological invasions.

Deep-sea biology in undergraduate classrooms: Open access data from remotely operated vehicles provide impactful research experiences

Opportunities for students to conduct research in courses increase feelings of belonging in science, retention in STEM majors, and sense of ownership in a student’s educational experience. However, many research fields are challenging to bring to students: for example, deep-sea biology often requires expensive expeditions, restricting participation and accessibility. Access to deep-sea systems has been expanded by programs such as the National Oceanic and Atmospheric Administration's Ocean Exploration (NOAA Ocean Exploration), which uses telepresence to bring deep-sea exploration to a global audience. Here, we present one example of how remotely operated vehicles can engage students in original research. Students in an undergraduate Marine Biology lab at at the State University of New York at Geneseo investigated the relationship between substrate rugosity and biodiversity of cold-water coral communities and associate fauna. The study site in the Pacific Remote Islands Marine National Monument was explored by NOAA Ocean Exploration using NOAA Ship Okeanos Explorer as part of the 2017 Mountains in the Deep Expedition, EX1705. Organism density was greater at higher rugosity levels, suggesting that complex substrates support higher abundances in deep-sea communities. This research experience enhanced student’s understanding of the scientific process, appreciation for deep-sea communities, and engagement in the course. Open access deep-sea data provide impactful opportunities for students to participate in original research, increasing the accessibility and reach of deep-sea science.

Low-Cost, Deep-Sea Imaging and Analysis Tools for Deep-Sea Exploration: A Collaborative Design Study

A minuscule fraction of the deep sea has been scientifically explored and characterized due to several constraints, including expense, inefficiency, exclusion, and the resulting inequitable access to tools and resources around the world. To meet the demand for understanding the largest biosphere on our planet, we must accelerate the pace and broaden the scope of exploration by adding low-cost, scalable tools to the traditional suite of research assets. Exploration strategies should increasingly employ collaborative, inclusive, and innovative research methods to promote inclusion, accessibility, and equity to ocean discovery globally. Here, we present an important step toward this new paradigm: a collaborative design study on technical capacity needs for equitable deep-sea exploration. The study focuses on opportunities and challenges related to low-cost, scalable tools for deep-sea data collection and artificial intelligence-driven data analysis. It was conducted in partnership with twenty marine professionals worldwide, covering a broad representation of geography, demographics, and domain knowledge within the ocean space. The results of the study include a set of technical requirements for low-cost deep-sea imaging and sensing systems and automated image and data analysis systems. As a result of the study, a camera system called Maka Niu was prototyped and is being field-tested by thirteen interviewees and an online AI-driven video analysis platform is in development. We also identified six categories of open design and implementation questions highlighting participant concerns and potential trade-offs that have not yet been addressed within the scope of the current projects but are identified as important considerations for future work. Finally, we offer recommendations for collaborative design projects related to the deep sea and outline our future work in this space.

A deep dive into the ecology of fear

The largest migration on Earth occurs on a daily basis in the ocean as creatures move up near the surface during the night to feed and descend to the relative safety of the dark depths during the day. This phenomenon is thought to reflect feeding opportunities and predator avoidance according to light levels, but the dynamics of this have rarely been studied. Samuel Urmy and Kelly Benoit-Bird from the Monterey Bay Aquarium Research Institute, USA, inspired by the originally terrestrial concept of ‘landscapes of fear’, sought to investigate if the fear of predation shapes the ecological process of daily migrations.Urmy and Benoit-Bird used an upwards-facing echosounder, which produces sonar pulses and records the returning echoes to detect and visualize schools of fish, squid and plankton, to provide information about their daily vertical movement patterns. Additionally, an underwater microphone next to the echosounder listened for the hunting sounds made by nearby toothed whale predators, such as Risso's dolphins (Grampus griseus) and Pacific white-sided dolphins (Lagenorhynchus obliquidens). Recording the echolocation clicks gave the researchers an indication of the presence of a predator, allowing them to investigate the interactions between the predators and the prey in their vicinity. Successfully collecting data beneath almost 1 km of seawater for more than a year is no easy feat, so the team sent down an unmanned robot to survey the area with video to visually confirm the identities of the different species that they were picking up with the echosounder.Scrutinizing a whole year of deep-sea data, the researchers observed many examples of prey taking evasive action to avoid predators. In one example, a bout of dolphin echolocation clicks triggered a school of fish to cluster together and dive for safety moments later. While such a diving response physically increases the distance between the fish and the predator, the fish's vertical plunge also results in weaker echoes returning from the vulnerable fish, reducing the opportunity for the hunting predator to successfully home in on their fishy prey. In addition, the team could use the distinctive echo signatures produced by different species – such as larger Pacific hake (Merluccius productus) and smaller anchovies (Engraulis mordax) – to provide a best guess for which species were appearing in the echosounder traces when video data were not available. This was especially useful for identifying instances of predation that did not involve an echolocating species, such as an occasion when a school of predatory fish approached a cloud of zooplankton and the tiny creatures dived to avoid the hunters. The scientists also discovered that it sometimes took clusters of zooplankton weeks to return to their usual depth after an encounter with a hungry predator. This shaping of a prey animal's behaviour and movement, even in the absence of a predator, provides evidence that the concept of the ‘ecology of fear’ is indeed applicable in the 3D marine realm and complements recent research on marine ‘soundscapes of fear’, where prey fall silent to avoid detection by predators that could be nearby.The vertical movement of animals in the ocean plays a major part in nutrient recycling, and Urmy and Benoit-Bird demonstrate that the lasting behavioural responses of prey to hunting predators potentially has a major influence on this flow of nutrients. The effect of predators on the behaviour of their prey can thus be more lingering than we had thought. Zooplankton layers in the open ocean are therefore not just governed by forces of nature or their particular lifestyles, but by the ecology of fear too.

Performance studies of MIMO based DCO-OFDM in underwater wireless optical communication systems

The research in underwater communication system is focused towards wireless networks in view of recent developments in wireless technology and increasing need for deep sea data mining. The underwater wireless optical communication (UWOC) have more advantages for short-range wireless links due to very high bandwidth and data rate than acoustic communication. Although UWOC offers high capacity links at low latencies, they suffer from limited communication range due to various distinctive characteristics of underwater channel. A major degrading effect associated with the underwater channel is turbulence-induced fading. To mitigate this fading and to extend the viable communication range, spatial multipath diversity techniques can be adopted over UWOC links. The proposed work evaluates the performance of a multicarrier modulation scheme, i.e., MIMO based DC biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) for underwater wireless optical channel. The simulation of MIMO-DC biased optical OFDM technique employing spatial diversity in underwater optical communication is a novel approach towards the determination of an optimal modulation technique for underwater optical wireless channels. The performance is evaluated based on the various parameters such as Signal to Interference plus Noise Ratio (SINR), Throughput and Bit Error Rate (BER). A simple experiment is conducted to demonstrate the BER performance assessment for different link distance of 0.45 m and 1.5 m with different transmitter and receiver configurations (SIMO and MIMO) for both clear water and coastal water using MIMO DCO OFDM technique.

Global Observing Needs in the Deep Ocean

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.

An acquisition, curation and management workflow for sustainable, terabyte-scale marine image analysis

Optical imaging is a common technique in ocean research. Diving robots, towed cameras, drop-cameras and TV-guided sampling gear: all produce image data of the underwater environment. Technological advances like 4K cameras, autonomous robots, high-capacity batteries and LED lighting now allow systematic optical monitoring at large spatial scale and shorter time but with increased data volume and velocity. Volume and velocity are further increased by growing fleets and emerging swarms of autonomous vehicles creating big data sets in parallel. This generates a need for automated data processing to harvest maximum information. Systematic data analysis benefits from calibrated, geo-referenced data with clear metadata description, particularly for machine vision and machine learning. Hence, the expensive data acquisition must be documented, data should be curated as soon as possible, backed up and made publicly available. Here, we present a workflow towards sustainable marine image analysis. We describe guidelines for data acquisition, curation and management and apply it to the use case of a multi-terabyte deep-sea data set acquired by an autonomous underwater vehicle.

Revealing higher than expected meiofaunal diversity in Antarctic sediments: a metabarcoding approach

An increasing number of studies are showing that Antarctic mega- and macrofauna are highly diverse, however, little is known about meiofaunal biodiversity in sediment communities, which are a vital part of a healthy and functional ecosystem. This is the first study to analyse community DNA (targeting meiofauna) using metabarcoding to investigate biodiversity levels in sediment communities of the Antarctic Peninsula. The results show that almost all of the meiofaunal biodiversity in the benthic habitat has yet to be characterised, levels of biodiversity were higher than expected and similar to temperate regions, albeit with the existence of potentially new and locally adapted species never described before at the molecular level. The Rothera meiofaunal sample sites showed four dominant eukaryotic groups, the nematodes, arthropods, platyhelminthes, and the annelids; some of which could comprise species complexes. Comparisons with deep-sea data from the same region suggest little exchange of Operational Taxonomic Units (OTUs) between depths with the nematodes prevalent at all depths, but sharing the shallow water benthos with the copepods. This study provides a preliminary analysis of benthic Antarctic Peninsula meiofauna using high throughput sequencing which substantiates how little is known on the biodiversity of one of the most diverse, yet underexplored communities of the Antarctic: the benthos.

Application of 3D stereotomography to the deep-sea data acquired in the South China Sea: a tomography inversion case

A 3D stereotomography algorithm, which is derived from the 3D Cartesian coordinate, is applied for the first time to the deep-sea data acquired in the LH area, South China Sea, to invert a macro velocity model for pre-stack depth migration. The successful implementation of stereotomography is highly dependent on the correct extraction of slowness components and the proper application of regularization terms. With the help of the structure tensor algorithm, a high-quality 3D stereotomography data space is achieved in a very efficient manner. Then, considering that the horizontal slowness in cross-line direction is usually unavailable for 3D narrow-azimuth data, the regularization terms must be enhanced to guarantee a stable convergence of the presented algorithm. The inverted model serves as a good model for the 3D pre-stack depth migration. The synthetic and real data examples demonstrated the robustness and effectiveness of the presented algorithm and the related schemes.

Minimizing deep sea data collection delay with autonomous underwater vehicles

As a special application of delay tolerant networks (DTNs), efficient data collection in the deep sea poses some unique challenges, due to the need for timely data reporting and the delay of acoustic transmission in the ocean. Autonomous underwater vehicles (AUVs) are deployed in the deep sea to surface frequently to transmit collected data from sensors (in a 2-dimensional or 3-dimensional search space) to the surface stations. However, additional delay occurs at each resurfacing. In this paper, we want to minimize the average data reporting delay, through optimizing the number and locations of AUV resurfacing events. We also study the AUV trajectory planning using an extended Euler circuit, where the search space is a set of segments (e.g., oil pipes) in the deep sea. To further reduce the data reporting delay, several schemes, which schedules multiple AUVs cooperatively, are also explored. Finally, experiments in both the synthetic and real traces validate the efficiency and effectiveness of the proposed algorithms.

Using fisheries by‐catch data to predict octocoral habitat suitability around South Georgia

AbstractAimFor many deep‐sea fisheries, there is an urgent management requirement for information on the presence of vulnerable marine ecosystems (VMEs). Gathering deep‐sea data using conventional techniques can be expensive and time‐consuming. One way to provide a relatively rapid assessment of VME presence is to use data from fisheries by‐catch and historical scientific observations. Our aim was to predict suitable habitat for octocorals around South Georgia and to estimate the extent to which octocoral habitat is currently protected by fisheries management measures. In addition, we attempted to determine the types of terrain in which octocorals and fishing activities occur.LocationSouth Georgia, sub‐Antarctic.MethodsA terrain map of South Georgia was created using Benthic Terrain Modeler (BTM). Georeferenced octocoral data were combined with environmental layers to create an octocoral habitat suitability map using ecological niche factor analysis (ENFA).ResultsMost octocoral by‐catch samples originated from narrow crest (an area representing shelf break and moraines) and steep (continental) slope terrains. Calcite saturation state and apparent oxygen utilization (AOU) were the most influential environmental parameters in determining highly suitable octocoral habitat. The ENFA model highlighted shelf‐break areas to be highly suitable habitat for octocorals and that 38% of this habitat around South Georgia lies within areas currently protected by fisheries management; a further 20% is below the 2000‐m fishing depth, meaning effectively that 58% of predicted highly suitable octocoral habitat is currently protected. Although these results indicate protection levels for octocoral habitat well above international standards/targets, the fishery remains active within a relatively concentrated shelf area at 700–2000 m, potentially having a large impact on the 42% of highly suitable octocoral habitat predicted to lie at these depths.Main conclusionsThis research demonstrates the potential for using fisheries by‐catch data to create habitat suitability maps that can inform fisheries management and future research.

Dispatches

Dispatches

Pacman profiling: a simple procedure to identify stratigraphic outliers in high-density deep-sea microfossil data

The deep-sea microfossil record is characterized by an extraordinarily high density and abundance of fossil specimens, and by a very high degree of spatial and temporal continuity of sedimentation. This record provides a unique opportunity to study evolution at the species level for entire clades of organisms. Compilations of deep-sea microfossil species occurrences are, however, affected by reworking of material, age model errors, and taxonomic uncertainties, all of which combine to displace a small fraction of the recorded occurrence data both forward and backwards in time, extending total stratigraphic ranges for taxa. These data outliers introduce substantial errors into both biostratigraphic and evolutionary analyses of species occurrences over time. We propose a simple method—Pacman—to identify and remove outliers from such data, and to identify problematic samples or sections from which the outlier data have derived. The method consists of, for a large group of species, compiling species occurrences by time and marking as outliers calibrated fractions of the youngest and oldest occurrence data for each species. A subset of biostratigraphic marker species whose ranges have been previously documented is used to calibrate the fraction of occurrences to mark as outliers. These outlier occurrences are compiled for samples, and profiles of outlier frequency are made from the sections used to compile the data; the profiles can then identify samples and sections with problematic data caused, for example, by taxonomic errors, incorrect age models, or reworking of sediment. These samples/sections can then be targeted for re-study.

Matching Mind and Method with Material: John Imbrie and Quantitative Facies Analysis

John Imbrie (b. 1925) always had deep mathematical insight and facility. At Yale University he completed his PhD (1951) under Carl Dunbar working on Middle Devonian brachiopods where he employed a statistical technique—'reduced major axis regression'—to differentiate several subspecies. Later, in a study with Edwin Colbert at the American Museum of Natural History, he used the same technique to determine subtle, yet significant, variations in the growth patterns of Triassic Metoposaurid amphibians (1956). At about the same time as sedimentary facies analysis was becoming of increased interest, Imbrie sought to test what one might do with quantitative facies analysis by undertaking a decade-long study of the Lower Permian Florena Shale (Kansas) using multivariate cluster analysis to characterize different litho- and biofacies. Despite much hard work in the field and with a highdecibel desk calculator, the hoped for results were lackluster. But neither the man nor the methods were wanting. The materials—fragmented, scattered invertebrate fossils imbedded in shales and limestones—permitted no more understanding than qualitative, eye-ball analysis. Even a late stage attack with the IBM computer at Columbia University merely groaned and brought forth similar mousey results. What was needed was a problem whose material components (abundant planktonic microfossils) within well-characterized stratigraphic sequences (deep-sea Pleistocene cores) were suitably matched to the man's mind and his quantitative procedures. And, of course, the result was phenomenal: his empirical demonstration of the deep-sea data for the validity of Milankovitch Cycles as the forcing factors for large-scale global climate change. His scientific success was duly honored by awards, prizes, medals, and elections to distinguished honorary societies. How did this happen?

Source estimation and direct wave reconstruction in Laplace-domain waveform inversion for deep-sea seismic data

SUMMARY We propose a strategy to overcome the high sensitivity to early-time noise of the Laplace-domain waveform inversion. In deep-sea seismic data, this problem is particularly crucial to obtaining accurate velocity structures. To this end, rather than simply filtering or muting early-time data, we propose replacing the original, noise-contaminated direct waves with analytically computed noise-free waves. To reconstruct the noise-free direct waves, we compute Green's functions for half-space media, estimate the source wavelet from the original direct waves using the full Newton method in the frequency domain and then convolve the Green's functions with the estimated source wavelet. The data obtained by merging the reconstructed direct waves with the original late-time data set can then be used for Laplace- and Laplace-Fourier-domain waveform inversions. To verify the source estimation and direct wave reconstruction strategy, we applied it to field data acquired in a deep-sea environment and obtained a realistic 2-D velocity model. The source estimation and direct wave reconstruction methods can also be applied to 3-D Laplace-domain waveform inversion.

Observations of mount etna seismicity during the 2002–2003 eruption based on deep-sea gravimeter data

This paper concerns observations made by a broadband deep-sea gravimeter installed on the plat-form of the SN-1 multiparameter seafloor observatory. The observatory was deployed at a distance of 25 km from the east coast of Sicily in southern Italy at a depth of 2105 m and was operated in a self-contained mode from October 2002 to February 2003 (134 days). The proximity to Mount Etna and the period of eruptive activity starting in late October 2002 lent added interest to this experiment. The seismic activity of Mount Etna, as recorded by the gravimeter, is characterized by the presence of two signal types, viz., a long-period volcanic tremor of variable amplitude and volcano-tectonic earthquakes. The bulk of energy in the long-period tremor occurs in the spectral interval between 2 and 5 s. The long-period seismic signals due to volcanic and global earthquakes were used to estimate resonant characteristics for Etna’s heterogeneous structures.

Vertical distribution of picoeukaryotic diversity in the Sargasso Sea

Eukaryotic molecular diversity within the picoplanktonic size-fraction has primarily been studied in marine surface waters. Here, the vertical distribution of picoeukaryotic diversity was investigated in the Sargasso Sea from euphotic to abyssal waters, using size-fractionated samples (< 2 microm). 18S rRNA gene clone libraries were used to generate sequences from euphotic zone samples (deep chlorophyll maximum to the surface); the permanent thermocline (500 m); and the pelagic deep-sea (3000 m). Euphotic zone and deep-sea data contrasted strongly, the former displaying greater diversity at the first-rank taxon level, based on 232 nearly full-length sequences. Deep-sea sequences belonged almost exclusively to the Alveolata and Radiolaria, while surface samples also contained known and putative photosynthetic groups, such as unique Chlorarachniophyta and Chrysophyceae sequences. Phylogenetic analyses placed most Alveolata and Stramenopile sequences within previously reported 'environmental' clades, i.e. clades within the Novel Alveolate groups I and II (NAI and NAII), or the novel Marine Stramenopiles (MAST). However, some deep-sea NAII formed distinct, bootstrap supported clades. Stramenopiles were recovered from the euphotic zone only, although many MAST are reportedly heterotrophic, making the observed distribution a point for further investigation. An unexpectedly high proportion of radiolarian sequences were recovered. From these, five environmental radiolarian clades, RAD-I to RAD-V, were identified. RAD-IV and RAD-V were composed of Taxopodida-like sequences, with the former solely containing Sargasso Sea sequences, although from all depth zones sampled. Our findings highlight the vast diversity of these protists, most of which remain uncultured and of unknown ecological function.