Description: Acoustic backscatter data were collected at five frequencies (18, 38, 70, 120 and 200 kHz) across two visits to site P3 (P3A, P3B), South Georgia, aboard the RRS Discovery during DY086. Acoustic backscatter was measured with a Simrad EK60. The data consistently shows no evidence of synchronised diel vertical migration (Cook et al. 2023).

Description: Data derived from net catches for zooplankton and micronekton during the COMICS cruise DY086 in November to December, 2017. Raw catch counts and biomass measurements have been used alongside published values to provide biomass, respiration and ingestion data between 0 and 500 metres depth (Belcher et al. 2022, Cook et al. 2023, Stowasser et al. 2020). Data values are from multiple net deployments and the number of deployments for each value are provided in the dataset. Bongo, Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) and Rectangular Midwater Trawl (RMT) nets collected small (100 μm mesh; day only), medium (330 μm mesh; day and night) and large (4000 μm mesh; day and night) samples, respectively.

Description: Physical, chemical and biogeochemical measurements derived from CTD-rosette deployments during three visits to site P3 (November to December, 2017) in the South Atlantic. Measurements were made during COMICS cruise DY086 on the RRS Discovery using a trace metal free Titanium Rosette (events 4, 7, 15, 19, 24, 26, 29) and a Stainless Steel Rosette (all other events). Physical parameters include temperature, salinity, density, photosynthetically active radiation and turbulence; chemical parameters include dissolved oxygen, dissolved oxygen saturation, nitrate, phosphate and silicate; biogeochemical parameters include turbidity, beam transmittance, beam attenuation, fluorescence, particulate organic carbon (POC), dissolved organic carbon (DOC), chlorophyll-a, net primary productivity (NPP), ambient leucine assimilation and bacterial cell count. To determine turbulence, a downward facing lowered acoustic doppler current profiler (LADCP, Teledyne Workhorse Monitor 300 kHz ADCP) was attached to the CTD frame. Shear and strain, which are obtained from velocity and density measurements, were used to estimate the dissipation rate of turbulent kinetic energy and the diapycnal eddy diffusivity from a fine-scale parameterisation. Estimates are calculated by parameterising internal wave-wave interactions and assuming that wave breaking modulates turbulent mixing. A detailed description of the method for calculating diffusivity from LADCP and CTD can be found in Kunze et al. (2006). Two datasets with different vertical resolutions were produced: one in which the shear is integrated from 150 to 300 m and the strain over 20-150 m, and one in which the shear is integrated from 70 to 200 m and the strain over 30-200 m. Nutrients (nitrate, phosphate, silicate) were determined via colourimetric analysis (see cruise report, Giering and Sanders, 2019), POC was determined as described in Giering et al. (2023), DOC and DOC flux were determined as described in Lovecchio et al. (2023), NPP was determined as described in Poulton et al. (2019), and ambient leucine assimilation and bacterial cell count were determined as described in Rayne et al. (2024). Bacterial abundance and leucine assimilation were made from bottle samples of six CTD casts of the stainless-steel rosette. Water was collected at six depths (6 m, deep-chlorophyll maximum, mixed layer depth + 10, 100, 250 and 500 m). Acid-cleaned HDPE carboys and tubing were used for sampling. Samples were then stored in the dark and at in-situ temperature prior to on-board laboratory sample preparation or analysis. Flow cytometry was used to measure bacterial abundance. Room temperature paraformaldehyde was used to fix 1.6 ml samples for 30 minutes. Then, using liquid nitrogen, the samples were flash frozen and stored at -80°C. Samples were then defrosted before being stained using SYBR Green I and run through the flow cytometer (BD FACSort™). The method of Hill et al. (2013) was applied to determine prokaryotic leucine assimilation using L-[4,5-³H] leucine which has a specific activity of 89.3 Ci/mmol­. In the mixed and upper layers of the water column, the protocol in Zubkov et al. (2007) was followed. Below the mixed layer, adaptions to the method included reducing the concentration of ³H-Leucine to 0.005, 0.01, 0.025, 0.04 and 0.05 nM; increasing experimental volumes to 30 ml; enhancing incubation times to 30, 60, 90 and 120 min. These adaptions were made to improve accuracy where lower rates of leucine assimilation were expected. Data were provided by the British Oceanographic Data Centre and funded by the National Environment Research Council.

Description: The island of South Georgia is situated in the iron (Fe) depleted Antarctic Circumpolar Current of the Southern Ocean. Iron emanating from its shelf system fuels large phytoplankton blooms downstream of the island, but the actual supply mechanisms are unclear. To address this we present the first inventory of Fe, manganese (Mn) and aluminium (Al) in shelf sediments, pore waters and the water column in the vicinity of South Georgia, alongside data on zooplankton-mediated Fe cycling processes. The seafloor sediments were the main particulate Fe source to shelf bottom waters as indicated by Fe / Mn and Fe / Al ratios for shelf sediments and suspended particles in the water column. Less than 1 % of the total particulate Fe pool was leachable surface adsorbed (labile) Fe, and therefore potentially available to organisms. Pore waters formed the primary dissolved Fe (DFe) source to shelf bottom waters supplying 0.1–4 μmol DFe m−2 d−1. However, only 0.41 ± 0.26 μmol DFe m−2 d−1 was transferred to the surface mixed layer by vertical diffusive and advective mixing. Other trace metal sources to surface waters included glacial flour released by melting glaciers and zooplankton excretion processes. On average 6.5 ± 8.2 μmol m−2 d−1 of labile particulate Fe was supplied to the surface mixed layer via krill faecal pellets, with further DFe released by krill at around 1.1 ± 2.2 μmol m−2 d−1. The faecal pellets released by krill constituted of seafloor derived lithogenic material and settled algae debris, in addition to freshly ingested suspended phytoplankton specimen. The phytoplankton Fe requirement in the blooms ca. 1250 km downstream the island of South Georgia was 0.33 ± 0.11 μmol m−2 d−1, with the DFe supply by horizontal/vertical mixing, deep winter mixing and via aeolian dust estimated as ~ 0.12 μmol m−2 d−1. We suggest that additionally required DFe was provided through recycling of biogenically stored Fe following luxury Fe uptake by phytoplankton on the Fe rich shelf. This process would allow Fe to be retained in the surface mixed layer of waters downstream of South Georgia through continuous recycling and biological uptake, and facilitate the large scale blooms.

Description: Highlights • Iron content in krill muscle rises with the amount of ingested lithogenic particles • Krill feces have ∼5-fold higher proportions of labile iron than intact diatoms • Lithogenic iron mobilized by krill can enter the dissolved pool via multiple pathways • The prevailing foodweb structure plays an important role in ocean iron fertilization Iron is an essential nutrient for phytoplankton, but low concentrations limit primary production and associated atmospheric carbon drawdown in large parts of the world's oceans [1, 2]. Lithogenic particles deriving from aeolian dust deposition, glacial runoff, or river discharges can form an important source if the attached iron becomes dissolved and therefore bioavailable [3–5]. Acidic digestion by zooplankton is a potential mechanism for iron mobilization [6], but evidence is lacking. Here we show that Antarctic krill sampled near glacial outlets at the island of South Georgia (Southern Ocean) ingest large amounts of lithogenic particles and contain 3-fold higher iron concentrations in their muscle than specimens from offshore, which confirms mineral dissolution in their guts. About 90% of the lithogenic and biogenic iron ingested by krill is passed into their fecal pellets, which contain ∼5-fold higher proportions of labile (reactive) iron than intact diatoms. The mobilized iron can be released in dissolved form directly from krill or via multiple pathways involving microbes, other zooplankton, and krill predators. This can deliver substantial amounts of bioavailable iron and contribute to the fertilization of coastal waters and the ocean beyond. In line with our findings, phytoplankton blooms downstream of South Georgia are more intensive and longer lasting during years with high krill abundance on-shelf. Thus, krill crop phytoplankton but boost new production via their nutrient supply. Understanding and quantifying iron mobilization by zooplankton is essential to predict ocean productivity in a warming climate where lithogenic iron inputs from deserts, glaciers, and rivers are increasing [7–10].

Description: The OceanGliders program started in 2016 to support active coordination and enhancement of global glider activity. OceanGliders contributes to the international efforts of the Global Ocean Observation System (GOOS) for Climate, Ocean Health, and Operational Services. It brings together marine scientists and engineers operating gliders around the world: (1) to observe the long-term physical, biogeochemical, and biological ocean processes and phenomena that are relevant for societal applications; and, (2) to contribute to the GOOS through real-time and delayed mode data dissemination. The OceanGliders program is distributed across national and regional observing systems and significantly contributes to integrated, multi-scale and multi-platform sampling strategies. OceanGliders shares best practices, requirements, and scientific knowledge needed for glider operations, data collection and analysis. It also monitors global glider activity and supports the dissemination of glider data through regional and global databases, in real-time and delayed modes, facilitating data access to the wider community. OceanGliders currently supports national, regional and global initiatives to maintain and expand the capabilities and application of gliders to meet key global challenges such as improved measurement of ocean boundary currents, water transformation and storm forecast.

Description: The ocean's biological carbon pump plays a central role in regulating atmospheric CO2 levels. In particular, the depth at which sinking organic carbon is broken down and respired in the mesopelagic zone is critical, with deeper remineralization resulting in greater carbon storage. Until recently, however, a balanced budget of the supply and consumption of organic carbon in the mesopelagic had not been constructed in any region of the ocean, and the processes controlling organic carbon turnover are still poorly understood. Large-scale data syntheses suggest that a wide range of factors can influence remineralization depth including upper-ocean ecological interactions, and interior dissolved oxygen concentration and temperature. However, these analyses do not provide a mechanistic understanding of remineralization, which increases the challenge of appropriately modeling the mesopelagic carbon dynamics. In light of this, the UK Natural Environment Research Council has funded a programme with this mechanistic understanding as its aim, drawing targeted fieldwork right through to implementation of a new parameterization for mesopelagic remineralization within an IPCC class global biogeochemical model. The Controls over Ocean Mesopelagic Interior Carbon Storage (COMICS) programme will deliver new insights into the processes of carbon cycling in the mesopelagic zone and how these influence ocean carbon storage. Here we outline the programme's rationale, its goals, planned fieldwork, and modeling activities, with the aim of stimulating international collaboration.

Description: Dataset: pteropod shell dissolution

Description: This dataset contains data from a study of pteropod shell dissolution on individuals exposed to CO2-enriched seawater. The data include the amount of dissolution as well as the physical and chemical parameters on which carbonate chemistry parameters were calculated. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/489471

Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1041106, United Kingdom Natural Environmental Research Council (NERC) NE/H017267/1, European Commission Marie Curie Actions Program (EC - Marie Curie Actions) MEST-CT-2004-514159

Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Testor, P., de Young, B., Rudnick, D. L., Glenn, S., Hayes, D., Lee, C. M., Pattiaratchi, C., Hill, K., Heslop, E., Turpin, V., Alenius, P., Barrera, C., Barth, J. A., Beaird, N., Becu, G., Bosse, A., Bourrin, F., Brearley, J. A., Chao, Y., Chen, S., Chiggiato, J., Coppola, L., Crout, R., Cummings, J., Curry, B., Curry, R., Davis, R., Desai, K., DiMarco, S., Edwards, C., Fielding, S., Fer, I., Frajka-Williams, E., Gildor, H., Goni, G., Gutierrez, D., Haugan, P., Hebert, D., Heiderich, J., Henson, S., Heywood, K., Hogan, P., Houpert, L., Huh, S., Inall, M. E., Ishii, M., Ito, S., Itoh, S., Jan, S., Kaiser, J., Karstensen, J., Kirkpatrick, B., Klymak, J., Kohut, J., Krahmann, G., Krug, M., McClatchie, S., Marin, F., Mauri, E., Mehra, A., Meredith, M. P., Meunier, T., Miles, T., Morell, J. M., Mortier, L., Nicholson, S., O'Callaghan, J., O'Conchubhair, D., Oke, P., Pallas-Sanz, E., Palmer, M., Park, J., Perivoliotis, L., Poulain, P., Perry, R., Queste, B., Rainville, L., Rehm, E., Roughan, M., Rome, N., Ross, T., Ruiz, S., Saba, G., Schaeffer, A., Schonau, M., Schroeder, K., Shimizu, Y., Sloyan, B. M., Smeed, D., Snowden, D., Song, Y., Swart, S., Tenreiro, M., Thompson, A., Tintore, J., Todd, R. E., Toro, C., Venables, H., Wagawa, T., Waterman, S., Watlington, R. A., & Wilson, D. OceanGliders: A component of the integrated GOOS. Frontiers in Marine Science, 6, (2019): 422, doi:10.3389/fmars.2019.00422.

Description: The OceanGliders program started in 2016 to support active coordination and enhancement of global glider activity. OceanGliders contributes to the international efforts of the Global Ocean Observation System (GOOS) for Climate, Ocean Health, and Operational Services. It brings together marine scientists and engineers operating gliders around the world: (1) to observe the long-term physical, biogeochemical, and biological ocean processes and phenomena that are relevant for societal applications; and, (2) to contribute to the GOOS through real-time and delayed mode data dissemination. The OceanGliders program is distributed across national and regional observing systems and significantly contributes to integrated, multi-scale and multi-platform sampling strategies. OceanGliders shares best practices, requirements, and scientific knowledge needed for glider operations, data collection and analysis. It also monitors global glider activity and supports the dissemination of glider data through regional and global databases, in real-time and delayed modes, facilitating data access to the wider community. OceanGliders currently supports national, regional and global initiatives to maintain and expand the capabilities and application of gliders to meet key global challenges such as improved measurement of ocean boundary currents, water transformation and storm forecast.

Description: The editorial team would like to recognize the support of the global glider community to this paper. Our requests for data and information were met with enthusiasm and welcome contributions from around the globe, clearly demonstrating to us a point made in this paper that there are many active and dedicated teams of glider operators and users. We should also acknowledge the support that OceanGliders has received from the WMO/IOC JCOMM-OCG and JCOMMOPS that have allowed this program to develop, encouraging us to articulate a vision for the role of gliders in the GOOS. We acknowledge support from the EU Horizon 2020 AtlantOS project funded under grant agreement No. 633211 and gratefully acknowledge the many agencies and programs that have supported underwater gliders: AlterEco, ANR, CFI, CIGOM, CLASS Ellet Array, CNES, CNRS/INSU, CONACyT, CSIRO, DEFRA, DFG/SFB-754, DFO, DGA, DSTL, ERC, FCO, FP7, and H2020 Europen Commission, HIMIOFoTS, Ifremer, IMOS, IMS, IOOS, IPEV, IRD, Israel MOST, JSPS, MEOPAR, NASA, NAVOCEANO (Navy), NERC, NFR, NJDEP, NOAA, NRC, NRL, NSF, NSERC, ONR, OSNAP, Taiwan MOST, SANAP-NRF, SENER, SIMS, Shell Exploration and Production Company, Sorbonne Université, SSB, UKRI, UNSW, Vettleson, Wallenberg Academy Fellowship, and WWF.