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  • 1
    Publication Date: 2024-06-15
    Description: The objective of this study is to determine the effects of ocean acidification (OA) on the survival, development and swimming behaviour of embryos of the deep-sea coral Desmophyllum pertusum (syn. Lophelia pertusa). Upon spawning, fertilized embryos were collected and exposed to two pCO2 treatments corresponding to present pCO2 conditions (400 ppm) and future pCO2 conditions predicted under scenario IPCC RCP8.5 for the end of the century (1000 ppm). We monitored survival daily and we measured swimming velocity on day 9 after spawning. Temperature and pH were measured every 24h, salinity was measured every other day, and water samples were collected during the first and last day of the experiment to determine total alkalinity (TA). This dataset includes data on the effects of ocean acidification on swimming velocity of larvae of the deep-sea coral Desmophyllum pertusum. Embryos were exposed to two acidification (pCO2) treatments: present pCO2 conditions (400 ppm) and future pCO2 conditions predicted under RCP8.5 for the end of the century (1000 ppm). After rearing the embryos in the respective treatments for nine days, we recorded the swimming behaviour of larvae with a video camera. Videos were analyzed with manual particle tracking, and here we report the swimming velocity and total traveled distance of larvae in each experimental treatment.
    Keywords: ASSEMBLE_Plus; Association of European Marine Biological Laboratories Expanded; Climate change; cold-water coral; DATE/TIME; Deep sea; early life history; early life stages; iAtlantic; Image analysis, NIH ImageJ, MTrackJ plugin; Integrated Assessment of Atlantic Marine Ecosystems in Space and Time; Laboratory experiment; Larvae; larval behaviour; physiology; Remote operated vehicle; ROV; Sample ID; Species; Species, unique identification (Semantic URI); Species, unique identification (URI); Speed, swimming; Swim distance; Time in hours; Tisler_Sampling_Lophelia_pertusa_4; Tisler_Sampling_Lophelia_pertusa_6; Tisler Reef, Skagerrak; Treatment
    Type: Dataset
    Format: text/tab-separated-values, 480 data points
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  • 2
    Publication Date: 2024-06-15
    Description: The objective of this study is to determine the effects of ocean acidification (OA) on the survival, development and swimming behaviour of embryos of the deep-sea coral Desmophyllum pertusum (syn. Lophelia pertusa). Upon spawning, fertilized embryos were collected and exposed to two pCO2 treatments corresponding to present pCO2 conditions (400 ppm) and future pCO2 conditions predicted under scenario IPCC RCP8.5 for the end of the century (1000 ppm). We monitored survival daily and we measured swimming velocity on day 9 after spawning. Temperature and pH were measured every 24h, salinity was measured every other day, and water samples were collected during the first and last day of the experiment to determine total alkalinity (TA). This dataset includes data on the effects of OA on embryo and larval survival of the deep-sea coral Desmophyllum pertusum. Embryos (age: first cleavage and 2 cell stage) were exposed to two acidification (pCO2) treatments: present pCO2 conditions (400 ppm) and future pCO2 conditions predicted under RCP8.5 for the end of the century (1000 ppm) and reared for a total of nine days. We counted embryos and larvae daily to determine larval survival under the two treatments.
    Keywords: ASSEMBLE_Plus; Association of European Marine Biological Laboratories Expanded; Climate change; cold-water coral; DATE/TIME; Deep sea; early life history; early life stages; iAtlantic; Integrated Assessment of Atlantic Marine Ecosystems in Space and Time; Laboratory experiment; Larvae; larval behaviour; pH; pH meter, Mettler Toledo, Seven2Go pH /Ion meter S8; physiology; Remote operated vehicle; Replicate; ROV; Salinity; Sample ID; Species; Species, unique identification (Semantic URI); Species, unique identification (URI); Temperature, water; Time in hours; Tisler_Sampling_Lophelia_pertusa_4; Tisler_Sampling_Lophelia_pertusa_6; Tisler Reef, Skagerrak; Treatment; Visual counts
    Type: Dataset
    Format: text/tab-separated-values, 1116 data points
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  • 3
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    PANGAEA
    In:  Supplement to: Pignet, G; Martin, G; Bougault, Henri (1979): Les nodules polymétalliques et la dépollution des eaux (Polymetalic nodules and water depollution). Journal Français d'Hydrologie, 10(3), 179-188, https://archimer.ifremer.fr/doc/00000/5396/4850.pdf
    Publication Date: 2024-06-14
    Description: Manganese rich polymetallic nodules are used for trace element removal from fresh-water. The theory of kinetics and mechanisms of reactions are discussed for manganese removal. The efficiency of nodules is higher than efficiency related to other manganese removal methods. Pilot units involving continuous processes are discussed.
    Keywords: Atomic absorption spectrometry (AAS); DEPTH, sediment/rock; Description; Dredge; DRG; Elevation of event; Iron; Jean Charcot; Latitude of event; Longitude of event; Manganese; Method/Device of event; Nickel; NOAA and MMS Marine Minerals Geochemical Database; NOAA-MMS; Sample ID; South Atlantic Ocean; WALDA-003_CH20; WALDA-DS17
    Type: Dataset
    Format: text/tab-separated-values, 5 data points
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  • 4
    Publication Date: 2024-06-14
    Description: The shallow meridional overturning cells of the Atlantic Ocean, the Subtropical Cells (STCs), consist of poleward Ekman transport at the surface, subduction in the subtropics, equatorward flow at thermocline level and upwelling along the equator and at the eastern boundary. In this study we provide the first observational estimate of transport variability associated with the horizontal branches of the Atlantic STCs in both hemispheres based on Argo float data and supplemented by reanalysis products. Thermocline layer transport convergence and surface layer transport divergence between 10°N and 10°S are dominated by seasonal variability. Meridional thermocline layer transport anomalies at the western boundary and in the interior basin are anti-correlated and partially compensate each other at all resolved time scales. It is suggested that the seesaw-like relation is forced by the large-scale off-equatorial wind stress changes through low-baroclinic-mode Rossby wave adjustment. We further show that anomalies of the thermocline layer interior transport convergence modulate sea surface temperature (SST) variability in the upwelling regions along the equator and at the eastern boundary at time scales longer than 5 years. Phases of weaker (stronger) interior transport are associated with phases of higher (lower) equatorial SST. At these time scales, STC transport variability is forced by off-equatorial wind stress changes, especially by those in the southern hemisphere. At shorter time scales, equatorial SST anomalies are, instead, mainly forced by local changes of zonal wind stress.
    Keywords: BANINO; Benguela Niños: Physikalische Prozesse und langperiodische Variabilität; Climate - Biogeochemistry Interactions in the Tropical Ocean; RACE; Regional Atlantic Circulation and global Change; SFB754; TRIATLAS; Tropical and South Atlantic climate-based marine ecosystem predictions for sustainable management
    Type: Dataset
    Format: 16 datasets
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  • 5
    Publication Date: 2024-06-14
    Description: The upper-ocean circulation of the tropical Atlantic is a complex superposition of thermohaline and wind-driven flow components. The resulting zonally- and vertically-integrated upper-ocean meridional flow is referred to as the upper branch of the Atlantic Meridional Overturning Circulation (AMOC) - a major component and potential tipping element of the global climate system. We investigate the tropical part of the northward AMOC branch, i.e. the return flow covering the upper 1,200 m, based on Argo data and repeated shipboard velocity measurements. The western boundary mean circulation at 11°S is realistically reproduced from high-resolution Argo data showing a remarkably good representation of the vertical structure of meridional velocity and the volume transport of water mass layers when compared to results from direct velocity measurements along a repeated ship section. Thus, we extend the analysis to the inner tropical Atlantic. Within the AMOC return flow, a diapycnal upwelling of central water into the thermocline layer of ~2 Sv is derived between 11°S and 10°N which is about half the magnitude of previous estimates, likely due to improved horizontal resolution. The mean strength of the AMOC return flow is ~16 Sv across 11°S and 10°N. At 11°S, northward transport is concentrated at the western boundary where the AMOC return flow enters the tropics at all vertical layers above 1,200 m. At 10°N, northward transport is observed both at the western boundary and in the interior predominantly in the surface and intermediate layer indicating recirculation and transformation of thermocline and central water within the tropics.
    Keywords: BANINO; Benguela Niños: Physikalische Prozesse und langperiodische Variabilität; currents; physical oceanography; TRIATLAS; Tropical and South Atlantic climate-based marine ecosystem predictions for sustainable management; tropical Atlantic
    Type: Dataset
    Format: 46 datasets
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  • 6
    Publication Date: 2024-06-14
    Description: Physical oceanography variables and carbon remineralisation (juveniles/adults of Cyclothone species and Argyropelecus hemigymnus) were analysed during the BATHYPELAGIC cruise (North Atlantic, June 2018). This dataset contains the depth, temperature, and conductivity which were recorded from surface to a maximum depth of 2000 m using a SeaBird SBE 25plus CTD equipped with a Seabird-43 Dissolved Oxygen sensor and a Seapoint Fluorometer. Values of numerical abundance, biomass, specific ETS activity, specific respiraton and respiration flux data analyzed from Northwest Africa (20° N, 20° W) to the South of Iceland are presented. A. hemigymnus specimens were collected using a ''Mesopelagos” net (5 x7 m mouth opening, 58 m total length) equipped with graded-mesh netting (starting with 30 mm and ending with 4 mm) and a multi-sampler for collecting samples from 5 different depth layers. However, Cyclothone specimens were collected using the Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS-1 m²) zooplankton net with a 0.2 mm mesh size and with several nets for collecting samples from 8 different depth layers. The Mesopelagos catches were sorted out and identified on board to the lowest possible taxon, and specimens selected for Electron Transfer System (ETS) analyses were immediately frozen in liquid nitrogen for later analysis in the laboratory. MOCNESS samples were preserved in 5% buffered formalin, and specimens were sorted out later in the laboratory. Stomiiforms respiration in the meso- and bathypelagic zones of the ocean were estimated along the transect. Abundance, biomass, specific ETS activity, specific respiration and respiration are given by layer between e.g. 100 m and 1000 m depth (MOCNESS net, 1900–1600 m, 1600–1300 m, 1300–1000 m, 1000–700 m, 700–400 m, 400–200 m, 200–100 m and 100–0 m; Mesopelagos, 1900–1200 m, 1200–800 m, 800–500 m, 500–200 m and 200–0 m).
    Keywords: bathypelagic; BATHYPELAGIC; biological carbon pump; Biomass and Active Flux in the Bathypelagic Zone; Carbon; fish; ICM_Excellence_Centre; mesopelagic; Northeast Atlantic; remineralization; respiration flux; Severo Ochoa Centre of Excellence; SUMMER; Sustainable Management of Mesopelagic Resources; TRIATLAS; Tropical and South Atlantic climate-based marine ecosystem predictions for sustainable management
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 7
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    PANGAEA
    Publication Date: 2024-06-14
    Keywords: Acer; Aesculus; Alnus; Anabaena; Apiaceae; Artemisia; Asteraceae Liguliflorae; Asteraceae Tubuliflorae; Azolla/Salvinia; Betula; Boraginaceae; Botryococcus braunii; Brassicaceae; Calligonum; Campanula; Cannabaceae; Carpinus betulus; Caryophyllaceae; Castanea; Cedrus; Centaurea; Cerealia-type; Chenopodiaceae/Amaranthaceae; Convolvulaceae; Cornus; Corylus; Counting, palynology; Cousinia; Cyperaceae; DEPTH, sediment/rock; Dryopteris filix-mas; Echium; Elaeagnus; Ephedra; Ephedra alata-type; Ephedra major-type; Eremurus; Ericaceae; Erodium; Euphorbia; Fabaceae; Fagus; Filipendula; Fraxinus excelsior; Gloeotrichia; GS05; Haplophyllum; Heliotropium; Hippophae; Incertae sedis; Indeterminable: unknown; Juglans; Juniperus; KULC; KULLENBERG corer; Lamiaceae; Ligustrum; Liliaceae; Littorella; Menyanthes; Mercurialis annua; Monolete non-psilate; Monolete psilate; Mougeotia; Nitraria; Olea; Ophioglossum; Parrotia persica; Pediastrum boryanum; Picea; Pinaceae; Pinus; Pinus haploxylon; Pistacia; Plantago; Plumbaginaceae; Poaceae; Pollen, reworked; Pollen and spores per unit sediment mass; Pollen indeterminata; Polygonum; Polygonum bistorta-type; Polypodium vulgare; Potamogeton; Pteridium aquilinum; Pterocarya; Pterosperma; Quercus; Quercus sempervirens; Radiosperma; Ranunculaceae; Ranunculus acris-type; Rhamnaceae; Rhus; Riccia; Rosaceae; Rubiaceae; Rumex sp.; Ruppia; Salix; Sambucus; Sanguisorba minor; Sanguisorba officinalis; Sphagnum; Tamarix; Taxus; Teucrium; Thalictrum; Thymelaeaceae; Tilia; Trilete non-psilate; Trilete psilate; Typha/Sparganium; Typha latifolia-type; Ulmus/Zelkova; Urticaceae/Moraceae; Vitis; Zygnema
    Type: Dataset
    Format: text/tab-separated-values, 7181 data points
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  • 8
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    Unknown
    PANGAEA
    In:  European Pollen Database (EPD)
    Publication Date: 2024-06-14
    Keywords: Acer; AGE; Alnus; Alternanthera; Armeria; Artemisia; Asteroideae; Betula; Boraginaceae; Botrychium; Calligonum; Calluna; Carpinus betulus; Caryophyllaceae; Caspian Sea CP 14; Castanea; Centaurea; Centaurea collina; Centaurea cyanus; Centaurea montana; Centaurea sinaica; Cerealia; Chenopodiaceae/Amaranthaceae; Cichorioideae; Corylus; Counting, palynology; CP14; Cruciferae; Cupressaceae; Cyperaceae; DEPTH, sediment/rock; Dipsacus; Dryopteris filix-mas; Echium; Elaeagnus; EPD; Ephedra alata-type; Ephedra major-type; Ephedra undifferentiated; Eremurus; Ericaceae; Euphorbia; Fagus; Filipendula; Fraxinus; Gramineae; Haplophyllum; Hippophae; Indeterminable: undifferentiated; Indeterminable: unknown; Juglans; KULC; KULLENBERG corer; Labiatae (6); Legousia; Liliaceae; Littorella; Lycopodium annotinum; Lycopodium selago; Monolete non-psilate; Monolete psilate; Myrtaceae; Papilionaceae; Parrotia persica; Picea; Pinus; Pistacia; Plantago; Plantago lanceolata; Pollen; Pollen, reworked; Polypodium vulgare; Potamogeton; Pteridium aquilinum; Pterocarya; Quercus; Quercus sempervirens; Rhamnaceae; Riccia; Rosaceae; Rubiaceae; Rumex; Salix; Sanguisorba minor; Spergularia; Sphagnum; Tamarix; Thalictrum; Thelypteris palustris; Tilia; Trifolium; Trilete non-psilate; Trilete psilate; Tsuga; Typha/Sparganium; Ulmus/Zelkova; Umbelliferae; Urticaceae/Moraceae; Viscum; Vitis
    Type: Dataset
    Format: text/tab-separated-values, 2668 data points
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  • 9
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    Unknown
    PANGAEA
    In:  European Pollen Database (EPD)
    Publication Date: 2024-06-14
    Keywords: Abies; Acer; Aesculus; AGE; Alnus; Alternanthera; Anabaena; Anthoceros; Apiaceae; Artemisia; Asteroideae; Astragalus; Azolla/Salvinia; Betula; Boraginaceae; Botrychium; Botryococcus braunii; Brassicaceae; Buxus; Calligonum; Calluna; Campanulaceae; Cannabaceae; Carpinus betulus; Carpinus orientalis; Caryophyllaceae; Caspian Sea; Castanea; Cedrus; Centaurea; Ceratophyllum; Cerealia-type; Chenopodiaceae/Amaranthaceae; Cichorioideae; Concentricystes circulus; Convolvulaceae; Cornus; Corylus; Counting, palynology; Crassulaceae; Cyperaceae; Cystopteris; Debarya; DEPTH, sediment/rock; Dipsacaceae; Dryopteris filix-mas; Echium; Elaeagnus; Ephedra alata-type; Ephedra major-type; Ephedra undifferentiated; Eremurus; Ericaceae; Euphorbiaceae; Fabaceae; Fagus; Filipendula; Fraxinus excelsior; Gentianaceae; Gloeotrichia; GS18; Haplophyllum; Helianthemum; Heliotropium; Hippophae; Ilex; Incertae sedis; Indeterminable: undifferentiated; Indeterminable: unknown; Jasminum; Juglans; Juniperus; KULC; KULLENBERG corer; Lamiaceae; Liliaceae; Lilium; Liquidambar; Littorella; Lotus; Lycopodium clavatum; Melampyrum; Monolete non-psilate; Monolete psilate; Myrica; Myriophyllum alterniflorum; Myriophyllum verticillatum; Nitraria; Olea; Oleaceae; Orchidaceae; Parrotia persica; Pediastrum boryanum; Pediastrum simplex; Picea; Pinaceae; Pinus; Pinus haplostellate; Pistacia; Plantago; Plumbaginaceae; Poaceae; Pollen; Pollen, reworked; Polygonum aviculare; Polygonum bistorta-type; Polygonum undifferentiated; Potamogeton; Potentilla-type; Primulaceae; Pteridium aquilinum; Pteris; Pterocarya; Pterosperma; Quercus, deciduous; Quercus sempervirens; Radiosperma; Ranunculaceae; Ranunculus acris-type; Rhamnaceae; Riccia; Rosaceae; Rubiaceae; Rumex; Ruppia; Salix; Salvinia/Azolla massulae; Sanguisorba minor; Sanguisorba officinalis; Saxifraga; Scenedesmus; Scrophulariaceae; Secale; Selaginella selaginoides; Sphagnum; Spirogyra; Tamarix; Taxus; Tetraedron; Thalictrum; Thelypteris palustris; Tilia; Trilete non-psilate; Trilete psilate; Typha/Sparganium; Typha latifolia-type; Ulmus/Zelkova; Umbilicus; Urticaceae/Moraceae; Valerianaceae; Vitis
    Type: Dataset
    Format: text/tab-separated-values, 16530 data points
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  • 10
    facet.materialart.
    Unknown
    PANGAEA
    In:  European Pollen Database (EPD)
    Publication Date: 2024-06-14
    Keywords: Abies; Acer; Alnus; Artemisia; Asteroideae; Batrachium; Betula; Botrychium; Buxus; Calligonum; Calystegia; Campanulaceae; Carpinus betulus; Caryophyllaceae; Caspian Sea CP 18; Celtis; Centaurea; Cerealia; Chenopodiaceae/Amaranthaceae; Chrozophora; Cichorioideae; Corylus; Counting, palynology; CP18; Cruciferae; Cupressaceae; Cyperaceae; DEPTH, sediment/rock; Dryopteris filix-mas; Echium; Emex; EPD; Ephedra alata-type; Ephedra major-type; Equisetum; Ericaceae; Euphorbia; Fagus; Filipendula; Fraxinus; Gramineae; Hippophae; Ilex; Indeterminable: undifferentiated; Indeterminable: unknown; Juglans; KULC; KULLENBERG corer; Labiatae; Labiatae (6); Ligustrum; Liliaceae; Littorella; Mercurialis; Monolete non-psilate; Monolete psilate; Myriophyllum verticillatum; Nymphaea; Nymphoides peltata; Parrotia persica; Picea; Pinaceae; Pinus; Pistacia; Plantago; Plantago lanceolata; Plantago major-type; Plumbaginaceae; Pollen; Pollen, reworked; Polygonum bistorta-type; Polygonum persicaria-type; Polygonum undifferentiated; Polypodium vulgare; Potamogeton; Pteridium aquilinum; Pterocarya; Quercus; Quercus sempervirens; Ranunculaceae; Riccia; Rubiaceae; Rumex; Rumex acetosa; Rumex acetosella; Salix; Sanguisorba minor; Sphagnum; Tamarix; Thalictrum; Thelypteris palustris; Thymelaeaceae; Tilia; Trilete non-psilate; Trilete psilate; Typha/Sparganium; Typha latifolia-type; Ulmus/Zelkova; Umbelliferae; Urticaceae/Moraceae; Valerianaceae; Vitis
    Type: Dataset
    Format: text/tab-separated-values, 3420 data points
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