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  • Oxford University Press  (83,816)
  • PANGAEA  (48,452)
  • 2015-2019  (132,268)
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  • 1
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    Microcharcoal concentration and elongation, and age model of sediment core MD96-2098 (2015)
    PANGAEA
    In:  Supplement to: Daniau, Anne-Laure; Sanchez Goñi, Maria Fernanda; Martinez, Philippe; Urrego, Dunia H; Bout-Roumazeilles, Viviane; Desprat, Stéphanie; Marlon, Jennifer R (2013): Orbital-scale climate forcing of grassland burning in southern Africa. Proceedings of the National Academy of Sciences, 110(13), 5069-5073, https://doi.org/10.1073/pnas.1214292110
    Details
    Publication Date: 2024-05-27
    Description: Although grassland and savanna occupy only a quarter of the world's vegetation, burning in these ecosystems accounts for roughly half the global carbon emissions from fire. However, the processes that govern changes in grassland burning are poorly understood, particularly on time scales beyond satellite records. We analyzed microcharcoal, sediments, and geochemistry in a high-resolution marine sediment core off Namibia to identify the processes that have controlled biomass burning in southern African grassland ecosystems under large, multimillennial-scale climate changes. Six fire cycles occurred during the past 170,000 y in southern Africa that correspond both in timing and magnitude to the precessional forcing of north-south shifts in the Intertropical Convergence Zone. Contrary to the conventional expectation that fire increases with higher temperatures and increased drought, we found that wetter and cooler climates cause increased burning in the study region, owing to a shift in rainfall amount and seasonality (and thus vegetation flammability). We also show that charcoal morphology (i.e., the particle's length-to-width ratio) can be used to reconstruct changes in fire activity as well as biome shifts over time. Our results provide essential context for understanding current and future grassland-fire dynamics and their associated carbon emissions.
    Keywords: CALYPSO; Calypso Corer; IMAGES; IMAGES II; International Marine Global Change Study; Lüderitz Transect; Marion Dufresne (1995); MD105; MD962098; MD96-2098
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 2
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    Organic-walled dinoflagellate cyst analysis of sediment core GeoB8331-4 and from surface sediment samples off western South Africa (2017)
    PANGAEA
    In:  Supplement to: Zhao, Xueqin; Dupont, Lydie M; Schefuß, Enno; Bouimetarhan, Ilham; Wefer, Gerold (2017): Palynological evidence for Holocene climatic and oceanographic changes off western South Africa. Quaternary Science Reviews, 165, 88-101, https://doi.org/10.1016/j.quascirev.2017.04.022
    Details
    Publication Date: 2024-05-27
    Description: Atmospheric and oceanographic interactions between the Atlantic and Indian Oceans influence upwelling in the southern Benguela upwelling system. In order to obtain a better knowledge of paleoceanographic and paleoenvironmental changes in the southern Benguela region during the Holocene, 12 marine surface sediment samples and one gravity core GeoB8331-4 from the Namaqualand mudbelt off the west coast of South Africa have been studied for organic-walled dinoflagellate cysts in high temporal resolution. The results are compared with pollen and geochemical records from the same samples. Our study emphasizes significantly distinct histories in upwelling intensity as well as the influence of fluvial input during the Holocene. Three main phases were identified for the Holocene. High percentages of cysts produced by autotrophic taxa like Operculodinium centrocarpum and Spiniferites spp. indicate warmer and stratified conditions during the early Holocene (9900-8400 cal. yr BP), suggesting reduced upwelling likely due to a northward shift of the southern westerlies. In contrast, the middle Holocene (8400-3100 cal. yr BP) is characterized by a strong increase in heterotrophic taxa in particular Lejeunecysta paratenella and Echinidinium spp. at the expense of autotrophic taxa. This indicates cool and nutrient-rich waters with active upwelling probably caused by a southward shift of the southern westerlies. During the late Holocene (3100 cal. yr BP to modern), Brigantedinium spp. and other abundant taxa interpreted to indicate fluvial nutrient input such as cyst of Protoperidinium americanum and Lejeunecysta oliva imply strong river discharge with high nutrient supply between 3100 and 640 cal. yr BP.
    Keywords: Center for Marine Environmental Sciences; MARUM; RAiN; Regional Archives for Integrated iNvestigations
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 3
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    Temperature affects the morphology and calcification of Emiliania huxleyi strains (2016)
    PANGAEA
    In:  Supplement to: Rosas-Navarro, Anaid; Langer, Gerald; Ziveri, Patrizia (2016): Temperature affects the morphology and calcification of Emiliania huxleyi strains. Biogeosciences, 13(10), 2913-2926, https://doi.org/10.5194/bg-13-2913-2016
    Details
    Publication Date: 2024-05-27
    Description: The global warming debate has sparked an unprecedented interest in temperature effects on coccolithophores. The calcification response to temperature changes reported in the literature, however, is ambiguous. The two main sources of this ambiguity are putatively differences in experimental setup and strain-specificity. In this study we therefore compare three strains isolated in the North Pacific under identical experimental conditions. Three strains of Emiliania huxleyi type A were grown under non-limiting nutrient and light conditions, at 10, 15, 20 and 25 ºC. All three strains displayed similar growth rate versus temperature relationships, with an optimum at 20-25 ºC. Elemental production (particulate inorganic carbon (PIC), particulate organic carbon (POC), total particulate nitrogen (TPN)), coccolith mass, coccolith size, and width of the tube elements cycle were positively correlated with temperature over the sub-optimum to optimum temperature range. The correlation between PIC production and coccolith mass/size supports the notion that coccolith mass can be used as a proxy for PIC production in sediment samples. Increasing PIC production was significantly positively correlated with the percentage of incomplete coccoliths in one strain only. Generally, coccoliths were heavier when PIC production was higher. This shows that incompleteness of coccoliths is not due to time shortage at high PIC production. Sub-optimal growth temperatures lead to an increase in the percentage of malformed coccoliths in a strain-specific fashion. Since in total only six strains have been tested thus far, it is presently difficult to say whether sub-optimal temperature is an important factor causing malformations in the field. The most important parameter in biogeochemical terms, the PIC:POC, shows a minimum at optimum growth temperature in all investigated strains. This clarifies the ambiguous picture featuring in the literature, i.e. discrepancies between PIC:POC-temperature relationships reported in different studies using different strains and different experimental setups. In summary, global warming might cause a decline in coccolithophore's PIC contribution to the rain ratio, as well as improved fitness in some genotypes due to less coccolith malformations.
    Keywords: -; Alkalinity, total; Bicarbonate ion; Bottle number; Calcite saturation state; Calculated; Calculated using CO2SYS; Carbon, inorganic, dissolved; Carbon, inorganic, particulate, per cell; Carbon, inorganic, particulate, production per cell; Carbon, organic, particulate, per cell; Carbon, organic, particulate, production per cell; Carbon, total, particulate, per cell; Carbon, total, particulate, production per cell; Carbonate ion; Carbon dioxide; Coccoliths, incomplete; Concentration per cell; Estimated by measuring brightness in cross-polarized light (birefringence); Growth rate; Identification; Length; Malformation rate; Mass; Mediterranean Sea Acidification in a Changing Climate; MedSeA; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Particulate inorganic carbon/particulate organic carbon ratio; pH; Potentiometric titration; Ratio; Scanning electron microscope (SEM); Slope; Species; Strain; Temperature, water; TOC analyzer (Shimadzu); Width
    Type: Dataset
    Format: text/tab-separated-values, 1130 data points
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  • 4
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    Nitrate limitation and ocean acidification interact with UV-B to reduce photosynthetic performance in the diatom (2015)
    PANGAEA
    In:  Supplement to: Li, Wei; Gao, Kunshan; Beardall, John (2015): Nitrate limitation and ocean acidification interact with UV-B to reduce photosynthetic performance in the diatom Phaeodactylum tricornutum. Biogeosciences, 12(8), 2383-2393, https://doi.org/10.5194/bg-12-2383-2015
    Details
    Publication Date: 2024-05-27
    Description: It has been proposed that ocean acidification (OA) will interact with other environmental factors to influence the overall impact of global change on biological systems. Accordingly we investigated the influence of nitrogen limitation and OA on the physiology of diatoms by growing the diatom Phaeodactylum tricornutum Bohlin under elevated (1000 µatm; high CO2- HC) or ambient (390 µatm; low CO2-LC) levels of CO2 with replete (110 µmol/L; high nitrate-HN) or reduced (10 ?mol/L; low nitrate-LN) levels of NO3- and subjecting the cells to solar radiation with or without UV irradiance to determine their susceptibility to UV radiation (UVR, 280-400 nm). Our results indicate that OA and UVB induced significantly higher inhibition of both the photosynthetic rate and quantum yield under LN than under HN conditions. UVA or/and UVB increased the cells' non-photochemical quenching (NPQ) regardless of the CO2 levels. Under LN and OA conditions, activity of superoxide dismutase and catalase activities were enhanced, along with the highest sensitivity to UVB and the lowest ratio of repair to damage of PSII. HC-grown cells showed a faster recovery rate of yield under HN but not under LN conditions. We conclude therefore that nutrient limitation makes cells more prone to the deleterious effects of UV radiation and that HC conditions (ocean acidification) exacerbate this effect. The finding that nitrate limitation and ocean acidification interact with UV-B to reduce photosynthetic performance of the diatom P. tricornutum implies that ocean primary production and the marine biological C pump will be affected by OA under multiple stressors.
    Keywords: Alkalinity, total; Alkalinity, total, standard deviation; Aragonite saturation state; Bicarbonate ion; Bicarbonate ion, standard deviation; Bottles or small containers/Aquaria (〈20 L); Calcite saturation state; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbonate ion; Carbonate ion, standard deviation; Carbonate system computation flag; Carbon dioxide; Carbon dioxide, standard deviation; Catalase activity, standard deviation; Catalase activity, unit per cell; Catalase activity, unit per protein mass; Charophyta; Chromista; Coulometric titration; Damage/repair ratio; Damage/repair ratio, standard deviation; Damage rate; Damage rate, standard deviation; Effective quantum yield; Effective quantum yield, standard deviation; Exponential rate constant for recovery; Exponential rate constant for recovery, standard deviation; Figure; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Laboratory experiment; Laboratory strains; Light; Macro-nutrients; Non photochemical quenching; Non photochemical quenching, standard deviation; North Atlantic; OA-ICC; Ocean Acidification International Coordination Centre; Ochrophyta; Partial pressure of carbon dioxide, standard deviation; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Pelagos; pH; pH, standard deviation; Phaeodactylum tricornutum; Photosynthetic carbon fixation rate, per cell; Photosynthetic carbon fixation rate, per chlorophyll a; Photosynthetic carbon fixation rate, standard deviation; Phytoplankton; Potentiometric; Primary production/Photosynthesis; Protein per cell; Proteins, standard deviation; Repair rate; Repair rate, standard deviation; Salinity; Single species; Species; Superoxide dismutase activity, standard deviation; Superoxide dismutase activity, unit per cell; Superoxide dismutase activity, unit per protein mass; Table; Temperature, water; Time, standard deviation; Time in minutes; Treatment; Ultraviolet-a radiation-induced inhibition of carbon fixation; Ultraviolet-a radiation-induced inhibition of carbon fixation, standard deviation; Ultraviolet-a radiation-induced inhibition of effective photochemical quantum yield; Ultraviolet-a radiation-induced inhibition of effective photochemical quantum yield, standard deviation; Ultraviolet-b radiation-induced inhibition of carbon fixation; Ultraviolet-b radiation-induced inhibition of carbon fixation, standard deviation; Ultraviolet-b radiation-induced inhibition of effective photochemical quantum yield; Ultraviolet-b radiation-induced inhibition of effective photochemical quantum yield, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 7864 data points
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  • 5
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    Effects of high CO2 levels on the ecophysiology of the diatom Thalassiosira weissflogii differ depending on the iron nutritional status (2016)
    PANGAEA
    In:  Supplement to: Sugie, Koji; Yoshimura, T (2016): Effects of high CO2 levels on the ecophysiology of the diatom Thalassiosira weissflogii differ depending on the iron nutritional status. ICES Journal of Marine Science, 73(3), 680-692, https://doi.org/10.1093/icesjms/fsv259
    Details
    Publication Date: 2024-05-27
    Description: Iron availability in seawater, namely the concentration of dissolved inorganic iron ([Fe']), is affected by changes in pH. Such changes in the availability of iron should be taken into account when investigating the effects of ocean acidification on phytoplankton ecophysiology because iron plays a key role in phytoplankton metabolism. However, changes in iron availability in response to changes in ocean acidity are difficult to quantify specifically using natural seawater because these factors change simultaneously. In the present study, the availability of iron and carbonate chemistry were manipulated individually and simultaneously in the laboratory to examine the effect of each factor on phytoplankton ecophysiology. The effects of various pCO2 conditions (390, 600, and 800 µatm) on the growth, cell size, and elemental stoichiometry (carbon [C], nitrogen [N], phosphorus [P], and silicon [Si]) of the diatom Thalassiosira weissflogii under high iron ([Fe'] = 240 pmol/l) and low iron ([Fe'] = 24 pmol/l) conditions were investigated. Cell volume decreased with increasing pCO2, whereas intracellular C, N, and P concentrations increased with increasing pCO2 only under high iron conditions. Si:C, Si:N, and Si:P ratios decreased with increasing pCO2. It reflects higher production of net C, N, and P with no corresponding change in net Si production under high pCO2 and high iron conditions. In contrast, significant linear relationships between measured parameters and pCO2 were rarely detected under low iron conditions. We conclude that the increasing CO2 levels could affect on the biogeochemical cycling of bioelements selectively under the iron-replete conditions in the coastal ecosystems.
    Keywords: Alkalinity, total; Aragonite saturation state; Bicarbonate ion; Biogenic silica; Biogenic silica, per cell; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcite saturation state; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, intracellular; Carbon, organic, particulate; Carbon, organic, particulate, per cell; Carbon, organic, particulate, production per cell; Carbon/Nitrogen ratio; Carbon/Phosphorus ratio; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Cell biovolume; Cell density; Chlorophyll a; Chlorophyll a, intracellular; Chlorophyll a per cell; Chlorophyll a production per cell; Chromista; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Growth rate; Iron, dissolved, inorganic; Laboratory experiment; Laboratory strains; Micro-nutrients; Net nitrogen production rate; Net phosphorus production; Net silicon production; Nitrogen, intracellular; Nitrogen, particulate; Nitrogen, particulate, per cell; Nitrogen/Phosphorus ratio; North Pacific; OA-ICC; Ocean Acidification International Coordination Centre; Ochrophyta; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Pelagos; pH; Phosphorus, intracellular; Phosphorus, organic, particulate, per cell; Phosphorus, particulate; Phytoplankton; Primary production/Photosynthesis; Registration number of species; Salinity; Silicon/Carbon, molar ratio; Silicon/Nitrogen, molar ratio; Silicon/Phosphorus ratio; Silicon per surface area; Single species; Species; Surface area; Temperature, water; Thalassiosira weissflogii; Time in days; Treatment; Type; Uniform resource locator/link to reference
    Type: Dataset
    Format: text/tab-separated-values, 1179 data points
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  • 6
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    The modulating effect of light intensity on the response of the coccolithophore Gephyrocapsa oceanica to ocean acidification (2016)
    PANGAEA
    In:  GEOMAR - Helmholtz Centre for Ocean Research Kiel | Supplement to: Zhang, Yong; Bach, Lennart Thomas; Schulz, Kai Georg; Riebesell, Ulf (2015): The modulating effect of light intensity on the response of the coccolithophore Gephyrocapsa oceanica to ocean acidification. Limnology and Oceanography, 60(6), 2145-2157, https://doi.org/10.1002/lno.10161
    Details
    Publication Date: 2024-05-27
    Description: Global change leads to a multitude of simultaneous modifications in the marine realm among which shoaling of the upper mixed layer, leading to enhanced surface layer light intensities, as well as increased carbon dioxide (CO2) concentration are some of the most critical environmental alterations for phytoplankton. In this study, we investigated the responses of growth, photosynthetic carbon fixation and calcification of the coccolithophore Gephyrocapsa oceanica to elevated inline image (51 Pa, 105 Pa, and 152 Pa) (1 Pa ~ 10 µatm) at a variety of light intensities (50-800 µmol photons/m**2/s). By fitting the light response curve, our results showed that rising inline image reduced the maximum rates for growth, photosynthetic carbon fixation and calcification. Increasing light intensity enhanced the sensitivity of these rate responses to inline image, and shifted the inline image optima toward lower levels. Combining the results of this and a previous study (Sett et al. 2014) on the same strain indicates that both limiting low inline image and inhibiting high inline image levels (this study) induce similar responses, reducing growth, carbon fixation and calcification rates of G. oceanica. At limiting low light intensities the inline image optima for maximum growth, carbon fixation and calcification are shifted toward higher levels. Interacting effects of simultaneously occurring environmental changes, such as increasing light intensity and ocean acidification, need to be considered when trying to assess metabolic rates of marine phytoplankton under future ocean scenarios.
    Keywords: BIOACID; Biological Impacts of Ocean Acidification; Carbon, inorganic, particulate, production per cell; Carbon, organic, particulate, production per cell; Carbon, organic, particulate/Nitrogen, organic, particulate ratio; Carbon, organic, particulate/Nitrogen, organic, particulate ratio, standard deviation; Carbon dioxide, partial pressure; Electron transport rate, relative; Electron transport rate, relative, standard deviation; Experimental treatment; Growth rate; Growth rate, standard deviation; Initial slope of rapid light curve; Initial slope of rapid light curve, standard deviation; Light:Dark cycle; Light saturation point; Light saturation point, standard deviation; Particulate inorganic carbon, production, standard deviation; Particulate inorganic carbon/particulate organic carbon ratio; Particulate inorganic carbon/particulate organic carbon ratio, standard deviation; Particulate organic carbon, production, standard deviation; Radiation, photosynthetically active; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 378 data points
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  • 7
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    Reduced resilience of a globally distributed coccolithophore to ocean acidification: Confirmed up to 2000 generations (2016)
    PANGAEA
    In:  Supplement to: Jin, Peng; Gao, Kunshan (2016): Reduced resilience of a globally distributed coccolithophore to ocean acidification: Confirmed up to 2000 generations. Marine Pollution Bulletin, 103(1-2), 101-108, https://doi.org/10.1016/j.marpolbul.2015.12.039
    Details
    Publication Date: 2024-05-27
    Description: Ocean acidification (OA), induced by rapid anthropogenic CO2 rise and its dissolution in seawater, is known to have consequences for marine organisms. However, knowledge on the evolutionary responses of phytoplankton to OA has been poorly studied. Here we examined the coccolithophore Gephyrocapsa oceanica, while growing it for 2000 generations under ambient and elevated CO2 levels. While OA stimulated growth in the earlier selection period (from generations 700 to 1550), it reduced it in the later selection period up to 2000 generations. Similarly, stimulated production of particulate organic carbon and nitrogen reduced with increasing selection period and decreased under OA up to 2000 generations. The specific adaptation of growth to OA disappeared in generations 1700 to 2000 when compared with that at 1000 generations. Both phenotypic plasticity and fitness decreased within selection time, suggesting that the species' resilience to OA decreased after 2000 generations under high CO2 selection.
    Keywords: Alkalinity, total; Alkalinity, total, standard deviation; Aragonite saturation state; Bicarbonate, standard deviation; Bicarbonate ion; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcite saturation state; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbon, organic, particulate, per cell; Carbon, organic, particulate, production per cell; Carbon/Nitrogen ratio; Carbonate ion; Carbonate ion, standard deviation; Carbonate system computation flag; Carbon dioxide; Chromista; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Generation; Gephyrocapsa oceanica; Growth/Morphology; Growth rate; Haptophyta; Incubation duration; Laboratory experiment; Laboratory strains; Nitrogen, organic, particulate, per cell; North Pacific; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Pelagos; pH; pH, standard deviation; Phytoplankton; Plasticity; Potentiometric; Primary production/Photosynthesis; Production of particulate organic nitrogen; Registration number of species; Response, direct; Responses, correlated; Salinity; Single species; Species; Temperature, water; Treatment; Type; Uniform resource locator/link to reference
    Type: Dataset
    Format: text/tab-separated-values, 25329 data points
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  • 8
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    The modulating effect of light intensity on the response of the coccolithophore Gephyrocapsa oceanica to ocean acidification (2015)
    PANGAEA
    In:  Supplement to: Zhang, Yong; Bach, Lennart Thomas; Schulz, Kai Georg; Riebesell, Ulf (2015): The modulating effect of light intensity on the response of the coccolithophore Gephyrocapsa oceanica to ocean acidification. Limnology and Oceanography, 60(6), 2145-2157, https://doi.org/10.1002/lno.10161
    Details
    Publication Date: 2024-05-27
    Description: Global change leads to a multitude of simultaneous modifications in the marine realm among which shoaling of the upper mixed layer, leading to enhanced surface layer light intensities, as well as increased carbon dioxide (CO2) concentration are some of the most critical environmental alterations for phytoplankton. In this study, we investigated the responses of growth, photosynthetic carbon fixation and calcification of the coccolithophore Gephyrocapsa oceanica to elevated inline image (51 Pa, 105 Pa, and 152 Pa) (1 Pa = 10 µatm) at a variety of light intensities (50-800 µmol photons/m**2/s). By fitting the light response curve, our results showed that rising inline image reduced the maximum rates for growth, photosynthetic carbon fixation and calcification. Increasing light intensity enhanced the sensitivity of these rate responses to inline image, and shifted the inline image optima toward lower levels. Combining the results of this and a previous study (Sett et al. 2014) on the same strain indicates that both limiting low inline image and inhibiting high inline image levels (this study) induce similar responses, reducing growth, carbon fixation and calcification rates of G. oceanica. At limiting low light intensities the inline image optima for maximum growth, carbon fixation and calcification are shifted toward higher levels. Interacting effects of simultaneously occurring environmental changes, such as increasing light intensity and ocean acidification, need to be considered when trying to assess metabolic rates of marine phytoplankton under future ocean scenarios.
    Keywords: Alkalinity, total; Alkalinity, total, standard deviation; Aragonite saturation state; Bicarbonate ion; Bicarbonate ion, standard deviation; Bottles or small containers/Aquaria (〈20 L); Calcification/Dissolution; Calcite saturation state; Calcite saturation state, standard deviation; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbon, inorganic, particulate, production per cell; Carbon, organic, particulate, production per cell; Carbon, organic, particulate/Nitrogen, organic, particulate ratio; Carbon, organic, particulate/Nitrogen, organic, particulate ratio, standard deviation; Carbonate ion; Carbonate ion, standard deviation; Carbonate system computation flag; Carbon dioxide; Carbon dioxide, partial pressure; Carbon dioxide, partial pressure, standard deviation; Carbon dioxide, standard deviation; Chromista; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Gephyrocapsa oceanica; Growth/Morphology; Growth rate; Growth rate, standard deviation; Haptophyta; Initial slope of rapid light curve; Initial slope of rapid light curve, standard deviation; Laboratory experiment; Laboratory strains; Light; Light intensity; Light saturation point; Light saturation point, standard deviation; Maximal electron transport rate, relative; Maximal electron transport rate, relative, standard deviation; North Atlantic; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Particulate inorganic carbon, production, standard deviation; Particulate inorganic carbon/particulate organic carbon ratio; Particulate inorganic carbon/particulate organic carbon ratio, standard deviation; Particulate organic carbon, production, standard deviation; Pelagos; pH; pH, standard deviation; Phosphate; Phytoplankton; Potentiometric titration; Primary production/Photosynthesis; Registration number of species; Salinity; Single species; Species; Temperature, water; Type; Uniform resource locator/link to reference
    Type: Dataset
    Format: text/tab-separated-values, 882 data points
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  • 9
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    Differing responses of three Southern Ocean Emiliania huxleyi ecotypes to changing seawater carbonate chemistry (2015)
    PANGAEA
    In:  Supplement to: Müller, Marius N; Trull, Tom W; Hallegraeff, Gustaaf M (2015): Differing responses of three Southern Ocean Emiliania huxleyi ecotypes to changing seawater carbonate chemistry. Marine Ecology Progress Series, 531, 81-90, https://doi.org/10.3354/meps11309
    Details
    Publication Date: 2024-05-27
    Description: The invasion of anthropogenic carbon dioxide into the surface ocean is altering seawater carbonate speciation, a process commonly called ocean acidification. The high latitude waters of the Southern Ocean are one of the primary and most severely affected regions. Coccolithophores are an important phytoplankton group, responsible for the majority of pelagic calcium carbonate production in the world's oceans, with a distribution that ranges from tropical to polar waters. Emiliania huxleyi is numerically the most abundant coccolithophore species and appears in several different ecotypes. We tested the effects of ocean acidification on 3 carefully selected E. huxleyi ecotypes isolated from the Southern Ocean. Their responses were measured in terms of growth, photosynthesis, calcification, cellular geometry, and stoichiometry. The 3 ecotypes exhibited differing sensitivities in regards to seawater carbonate chemistry when cultured at the same temperature (14°C) and continuous light (110 µmol photons/m2/s). Under future ocean acidification scenarios, particulate inorganic to organic carbon ratios (PIC:POC) decreased by 38-44, 47-51 and 71-98% in morphotype A 'over-calcified' (A o/c), A and B/C, respectively. All ecotypes reduced their rate of calcification, but the cold-water adapted ecotype (morphotype B/C) was by far the most sensitive, and almost ceased calcification at partial pressure of carbon dioxide ( pCO2) levels above 1000 µatm. We recommend that future surveys for E. huxleyi cells in the Southern Ocean should include the capability of recognising 'naked cells' by molecular and microscopic tools. The distinct differences in the physiological responses of these 3 dominant Southern Ocean coccolithophore ecotypes are likely to have consequences for future coccolithophore community structures and thereby the Southern Ocean carbon cycle.
    Keywords: Alkalinity, total; Alkalinity, total, standard deviation; Aragonite saturation state; Bicarbonate ion; Bicarbonate ion, standard deviation; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcification/Dissolution; Calcite saturation state; Calcite saturation state, standard deviation; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbon, inorganic, particulate, per cell; Carbon, inorganic, particulate, production per cell; Carbon, organic, particulate, per cell; Carbon, organic, particulate, production per cell; Carbon, organic, particulate/Nitrogen, organic, particulate ratio; Carbon, organic, particulate/Nitrogen, organic, particulate ratio, standard deviation; Carbonate ion; Carbonate ion, standard deviation; Carbonate system computation flag; Carbon dioxide; Carbon dioxide, standard deviation; Cell, diameter; Cell, diameter, standard deviation; Cell biovolume; Cell biovolume, standard deviation; Chromista; Coccoliths, diameter; Coccoliths, diameter, standard deviation; Coccoliths, volume; Coccoliths, volume, standard deviation; Emiliania huxleyi; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Growth rate; Growth rate, standard deviation; Haptophyta; Laboratory experiment; Laboratory strains; Nitrogen, organic, particulate, per cell; Not applicable; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide, standard deviation; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Particulate inorganic carbon, production, standard deviation; Particulate inorganic carbon/particulate organic carbon ratio; Particulate inorganic carbon/particulate organic carbon ratio, standard deviation; Particulate inorganic carbon per cell, standard deviation; Particulate organic carbon, production, standard deviation; Particulate organic carbon content per cell, standard deviation; Particulate organic nitrogen per cell, standard deviation; Particulate organic nitrogen production, standard deviation; Pelagos; pH; pH, standard deviation; Phytoplankton; Potentiometric titration; Primary production/Photosynthesis; Production of particulate organic nitrogen; Registration number of species; Salinity; Single species; Species; Strain; Temperature, water; Type; Uniform resource locator/link to reference
    Type: Dataset
    Format: text/tab-separated-values, 2082 data points
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  • 10
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    CLAM age model and pollen profile of sediment core W8709A-8 (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abies; Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; Alnus; Asteraceae; Betula; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Chenopodiaceae; Classical age-modeling approach, CLAM (Blaauw, 2010); Corylus; Counting, palynology; Cupressaceae/Taxaceae/Taxodiaceae; Cyperaceae; DEPTH, sediment/rock; Myrica; PC; Picea; Pinus; Piston corer; Poaceae; Pseudotsuga; Quercus; Sample ID; Sequoia; Tsuga heterophylla; Tsuga mertensiana; Type of age model; W8709A; W8709A-8; Wecoma
    Type: Dataset
    Format: text/tab-separated-values, 2712 data points
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  • 11
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    Seawater carbonate chemistry and particulate organic and inorganic carbon, growth of Emiliania huxleyi (2018)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Description: Coccolithophore responses to changes in carbonate chemistry speciation such as CO2 and H+ are highly modulated by light intensity and temperature. Here, we fit an analytical equation, accounting for simultaneous changes in carbonate chemistry speciation, light and temperature, to published and original data for Emiliania huxleyi, and compare the projections with those for Gephyrocapsa oceanica. Based on our analysis, the two most common bloom-forming species in present-day coccolithophore communities appear to be adapted for a similar fundamental light niche but slightly different ones for temperature and CO2, with E. huxleyi having a tolerance to lower temperatures and higher CO2 levels than G. oceanica. Based on growth rates, a dominance of E. huxleyi over G. oceanica is projected below temperatures of 22 °C at current atmospheric CO2 levels. This is similar to a global surface sediment compilation of E. huxleyi and G. oceanica coccolith abundances suggesting temperature-dependent dominance shifts. For a future Representative Concentration Pathway (RCP) 8.5 climate change scenario (1000 µatm fCO2), we project a CO2 driven niche contraction for G. oceanica to regions of even higher temperatures. However, the greater sensitivity of G. oceanica to increasing CO2 is partially mitigated by increasing temperatures. Finally, we compare satellite-derived particulate inorganic carbon estimates in the surface ocean with a recently proposed metric for potential coccolithophore success on the community level, i.e. the temperature-, light- and carbonate-chemistry-dependent CaCO3 production potential (CCPP). Based on E. huxleyi alone, as there was interestingly a better correlation than when in combination with G. oceanica, and excluding the Antarctic province from the analysis, we found a good correlation between CCPP and satellite-derived particulate inorganic carbon (PIC) with an R2 of 0.73, p 〈 0.01 and a slope of 1.03 for austral winter/boreal summer and an R2 of 0.85, p 〈 0.01 and a slope of 0.32 for austral summer/boreal winter.
    Keywords: Alkalinity, total; Aragonite saturation state; Bicarbonate ion; Bottles or small containers/Aquaria (〈20 L); Calcification/Dissolution; Calcite saturation state; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, particulate, per cell; Carbon, inorganic, particulate, production per cell; Carbon, organic, particulate, per cell; Carbon, organic, particulate, production per cell; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Chromista; Coast and continental shelf; Emiliania huxleyi; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Growth rate; Haptophyta; Hydrogen ion concentration; Irradiance; Laboratory experiment; Light; North Atlantic; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Pelagos; pH; Phytoplankton; Primary production/Photosynthesis; Registration number of species; Salinity; Single species; Species; Temperate; Temperature, water; Type; Uniform resource locator/link to reference
    Type: Dataset
    Format: text/tab-separated-values, 1392 data points
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  • 12
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    CLAM age model and pollen profile of sediment core Kalaloch (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abies amabilis/A. grandis; Abies lasiocarpa; Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; Alnus sinuata; Apiaceae; Artemisia; Asteraceae; Boraginaceae; Botrychium; Brassicaceae; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Camassia; Caryophyllaceae; Chamaecyparis/Thuja; Chenopodiaceae; Classical age-modeling approach, CLAM (Blaauw, 2010); Counting, palynology; Cyperaceae; DEPTH, sediment/rock; Empetrum/Ericaceae; Epilobium; Gentiana; Kalaloch; Lonicera; Lycopodium; Lysichitum; Malvaceae; Mimulus; Myrica; Picea sitchensis; Pinus contorta; Pinus monticola; Poaceae; Polemonium; Polygonum bistortoides; Polypodiaceae; Polypodium; Ranunculaceae; Rosaceae; Rubiaceae; Salix; Sample ID; Sanguisorba; Sphagnum; Tsuga heterophylla; Tsuga mertensiana; Type of age model; Valeriana
    Type: Dataset
    Format: text/tab-separated-values, 20075 data points
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  • 13
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    CLAM age model and pollen profile of sediment core Kashiru_Bog (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Acacia; Acalypha; Acanthaceae; Accumulation model; ACER; Achyranthes-type; Adina rubrostipulata-type; Aeschynomene baumii-type; Afrocrania volkensii; Alchemilla; Alchornea; Allophylus; Aloe-type; Amannia prieureana-type; Amaranthaceae/Chenopodiaceae; Anthocerotaceae; Anthocleista; Anthospermum; Apiaceae; Apodytes dimidiata; Araliaceae; Artemisia; Ascolepis; Asteraceae; Basella alba-type; Basilicum-type; Begonia; Brachystegia; Brassicaceae; Brucea antidysenterica; Caesalpinioideae; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Canthium gueinzii-type; Canthium schimperianum; Capitanya ostostegioides-type; Carduus-type; Caryophyllaceae; Celosia trigyna-type; Celtis; Cerastium afromontanum-type; Cerastium octandrum-type; Chamaecrista mimosoides-type; Chenopodiaceae; Classical age-modeling approach, CLAM (Blaauw, 2010); Clematis-type; Cliffortia nitidula; Clutia; Combretaceae; Commelina benghalensis-type; Commiphora; Commiphora boiviniana-type; Counting, palynology; Crassocephalum montuosum-type; Croton-type; Cucurbitaceae; Cyathula orthacantha-type; Cyperaceae; DEPTH, sediment/rock; Dichondra micrantha-type; Diospyros; Dipsacaceae; Dodonaea viscosa; Dombeya-type; Ekebergia-type; Embelia; Entada-type; Ericaceae; Eriocaulaceae; Erythrococca-type; Euclea; Euphorbia; Euphorbia hirta-type; Ficalhoa/Nuxia; Ficus; Flabellaria paniculata-type; Galiniera coffeoides; Garcinia volkensii-type; Gentianaceae; Geranium; Hagenia abyssinica; Harungana; Heliotropium steudneri-type; Hydrocotyle; Hymenodictyon floribundum-type; Hypericum; Hypoestes-type; Ilex mitis; Impatiens; Ipomoea-type; Isoglossa; Jasminum; Juniperus procera; Kashiru_Bog; Kotschya-type; Lamiaceae; Lannea-type; Laurembergia tetrandra; Leucas-type; Liliopsida; Lobelia; Loranthaceae; Lythrum; Macaranga; Maerua-type; Maesa lanceolata-type; Margaritaria discoidea-type; Melastomataceae; Mimulopsis-type; Moraceae; Myrica; Myrsine africana; Nymphaea lotus-type; Oldenlandia-type; Olea capensis; Olea europaea; Onagraceae; Ormocarpum trichocarpum-type; Papilionoideae; Parinari-type; Phoenix reclinata; Phyllanthus muellerianus-type; Phyllanthus niruri-type; Phyllanthus nummulariifolius-type; Phyllanthus reticulatus-type; Phyllanthus rivae-type; Pilea bambuseti-type; Plantago africana-type; Poaceae; Podocarpus; Pollen indeterminata; Polygala-type; Polygonum nepalense-type; Polygonum senegalense-type; Polypodiales; Polyscias fulva-type; Potamogeton thunbergii-type; Primulaceae; Protea-type; Prunus africana; Ranunculus; Rapanea melanophloeos; Resedaceae; Restio; Rhamnaceae; Ricinus communis; Rubiaceae; Rubia-type; Rubus pinnatus-type; Rumex; Salix subserrata; Sample ID; Sapotaceae; Schefflera abyssinica-type; Schefflera myriantha-type; Senecio mannii-type; Sericostachys scandens-type; Silene burchellii-type; Solanum; Sphagnum; Stellaria mannii-type; Stephania abyssinica-type; Sterculia-type; Stoebe kilimandscharica-type; Swertia kilimandscharica-type; Swertia usambarensis-type; Syzygium-type; Tarenna graveolens-type; Teclea-type; Tetrorchidium; Thymelaeaceae; Tiliaceae; Trema orientale-type; Type of age model; Typha; Uapaca; Uebelinia abyssinica-type; Urticaceae; Vangueria acutiloba-type; Vernonia perrottetii-type; Vernonieae; Virectaria; Xyris; Zanthoxylum chalybeum-type; Zanthoxylum usambarense-type
    Type: Dataset
    Format: text/tab-separated-values, 24882 data points
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  • 14
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    (Table S1) Chronostratigraphic age model for sediment core MD96-2098 (2015)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Age, 14C AMS; Age, 14C calibrated; Age, dated; Age, dated material; Age, dated standard error; Age, maximum/old; Age, minimum/young; Age model; CALYPSO; Calypso Corer; Depth, corrected; DEPTH, sediment/rock; IMAGES; IMAGES II; Intercore correlation; International Marine Global Change Study; Isotopic event; Laboratory code/label; Lüderitz Transect; Marion Dufresne (1995); MD105; MD962098; MD96-2098; Reference/source
    Type: Dataset
    Format: text/tab-separated-values, 120 data points
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  • 15
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    Harmonized names for the pollen taxa, link to a list in different formats (zipped) (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; ACER
    Type: Dataset
    Format: application/zip, 207.3 kBytes
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  • 16
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    Inorganic geochemistry of surface sediment samples from southeast Africa (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: 1; 21; 22; 23; 3; AFRIDEEP; Aluminium; BC; Box corer; Bromine; Calcium; Center for Marine Environmental Sciences; Chromium; Copper; DEPTH, sediment/rock; ECT-10-1; ECT-10-2; ECT-1-1; ECT-11-2; ECT-1-2; ECT-12-2; ECT-12-3; ECT-12-4; ECT-1-3; ECT-14-1; ECT-14-2; ECT-15-3; ECT-15-4; ECT-16-1; ECT-16-3; ECT-17-2; ECT-17-3; ECT-18-1; ECT-19-2; ECT-20-1; ECT-2-1; ECT-21-1; ECT-2-2; ECT-22-2; ECT-23-2; ECT-23-3; ECT-24-1; ECT-25-1; ECT-26-1; ECT-27-2; ECT-27-3; ECT-3-1; ECT-5-1; ECT-5-2; ECT-6-1; ECT-6-2; ECT-7-2; ECT-8-1; ECT-9-1; ECT-9-2; Event label; GeoB20602-1; GeoB20604-1; GeoB20607-1; GeoB20608-2; GeoB20609-1; GeoB20610-1; GeoB20611-1; GeoB20613-1; GeoB20615-1; GeoB20619-1; GeoB20624-2; GeoB20625-1; GeoB20628-1; GeoB9301-1; GeoB9302-5; GeoB9312-2; GeoB9313-3; GeoB9314-1; Gourits River; Iron; Limpopo Fan; M123; M123_161-1; M123_163-1; M123_166-1; M123_167-2; M123_168-1; M123_169-1; M123_170-1; M123_172-1; M123_174-1; M123_178-1; M123_183-2; M123_184-1; M123_187-1; M63/1; Magnesium; Manganese; MARUM; Meteor (1986); MUC; MultiCorer; Nickel; North of Tugela Cone; Potassium; Rubidium; Sample ID; Silicon; South of Limpopo Fan; South of Tugela Cone; Strontium; Sulfur, total; Titanium; VC; Vibro corer; Zinc; Zirconium
    Type: Dataset
    Format: text/tab-separated-values, 1062 data points
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  • 17
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    Sites versus references, link to a list in different formats (zipped) (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; ACER
    Type: Dataset
    Format: application/zip, 49.6 kBytes
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  • 18
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    River surface pollen data in southwestern Morocco (2018)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Acacia; Acanthaceae; Aizoaceae; Amaranthaceae/Chenopodiaceae; Apiaceae; Argania spinosa; Artemisia; Aruncus-type; Asphodelus-type; Asteroideae; Brassicaceae; Caryophyllaceae; Cedrus; Centaurea-type; Center for Marine Environmental Sciences; Cichorioideae; Combretaceae; Concentricystes; Convolvulaceae; Corylus; Cyperaceae; ECHo1-1a2; ECHo1-1a3; ECHo1-1a4; ECHo1-2a1; ECHo1-2a2; ECHo1-2a3; ECHo2-1a1; ECHo2-1a2; ECHo2-1a3; ECHo2-2a2; ECHo2-2a3; ECHo2-3a1; ECHo2-4a1; ECHo2-4a2; ECHo2-4a3; ECHo3-1a1; ECHo3-1a2; ECHo3-1a3; ECHo3-3a1; ECHo3-4a1; ECHo3-4a2; Ephedra; Ericaceae; Euphorbia; Euphorbiaceae; Event label; Fabaceae; Indeterminata; Jasminum; Juglans; Juniperus/Tetraclinis; Justicia-type; Labiatae; Leguminosae; Lotus-type; Marker, added; Marker, found; MARUM; Mass; Myrtaceae; Olea; Pentzia-type; Phillyrea; Pinus; Plantago; Poaceae; Pollen, total; Polygalaceae; Polygonaceae; Polygonum; Quercus ilex-type; Quercus robur-type; Rhamnus; Rhus; Rumex; Salix; Selaginella; Solanaceae; Spores; Spores, monolete; Spores, trilete; Tamarix; Tribulus; Typha; Ulmus; Urticaceae; Xanthium-type
    Type: Dataset
    Format: text/tab-separated-values, 1344 data points
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  • 19
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    MAAT reconstruction of sediment core Colonia_CO14 (2019)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: AGE; Araucaria; Atlantic forest; Colonia_CO14; DEPTH, sediment/rock; Glacial; Interglacial; peat-lake; SEDCO; Sediment corer; Temperature, air, annual mean; Temperature anomaly
    Type: Dataset
    Format: text/tab-separated-values, 182 data points
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  • 20
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    CLAM age model and pollen profile of sediment core BIW95-4 (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abies; Abrupt Climate Changes and Environmental Responses; Accumulation model; Acer; ACER; Aesculus; Alisma; Alnus; Amaranthaceae/Chenopodiaceae; Anemone; Apiaceae; Araliaceae; Artemisia; Asteraceae; Betula; BIW95-4; Brassicaceae; Buxus; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Carpinus/Ostrya; Carpinus tschonoskii; Caryophyllaceae; Castanea/Castanopsis; Celastraceae; Celtis/Aphananthe; Cercidiphyllum; Classical age-modeling approach, CLAM (Blaauw, 2010); Cornus; Corylus; Counting, palynology; Cryptomeria; Cupressaceae-type; Cyperaceae; DEPTH, sediment/rock; Elaeagnaceae; Equisetum; Ericaceae; Eriocaulon; Fagus crenata; Fagus japonica; Fraxinus; Gentiana; Haloragis/Myriophyllum; Hemiptelea; Ilex; Impatiens; Isoetes; Juglans/Pterocarya; Lake Biwa; Lamiaceae; Larix; Ligustrum; Liliaceae; Lonicera; Lycopodium clavatum-type; Lycopodium inundatum-type; Lycopodium serratum-type; Lysichiton; Lythrum; Menyanthes; Myrica; Nuphar; Nymphoides; Nymphoides indica; Osmundaceae; Parthenocissus; Phellodendron; Picea; Pinus; Poaceae; Pollen indeterminata; Polygonum persicaria-type; Polygonum reynoutria-type; Polypodiales; Potamogeton; Pteridium; Pteridophyta; Quercus subgen. Cyclobalanopsis; Quercus subgen. Lepidobalanus; Ranunculus; Rhamnaceae; Rhus; Rosaceae; Rutaceae; Sagittaria; Salix; Sample ID; Sanguisorba; Sciadopitys; Sparganium/Trapa; Sparganium/Typha; Sphagnum; Styracaceae; Symplocos; Thalictrum; Tilia; Tsuga; Type of age model; Ulmus/Zelkova; Viburnum; Vitis
    Type: Dataset
    Format: text/tab-separated-values, 11013 data points
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  • 21
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    CLAM age model and pollen profile of sediment core Caco (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Acalypha; Accumulation model; ACER; Aegiphila; Alchornea; Alismataceae; Alternanthera; Amaouia; Ambrosia; Anacardium; Andira-type; Apeiba; Apiaceae; Apocynaceae; Apuleia; Araceae; Ardisia; Arecaceae; Arrabidea; Aspidosperma; Asteraceae; Astrocaryum; Astronium; Balfourodendron; Banara-type; Bauhinia; Bauhinia guianensis; Begonia; Bertiera; Borreria; Bowdichia; Bromeliaceae; Byrsonima; Cabomba; Caco; Caesalpinia; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Caryocar; Caryophyllaceae; Casearia; Cassia; Cecropia; Cedrela; Celtis; Cestrum; Chamaecrista; Chamaesyce; Cheiloclinium; Chenopodiaceae; Chrysophyllum; Cissampelos; Cissus; Citrus; Classical age-modeling approach, CLAM (Blaauw, 2010); Clitoria-type; Clusia; Cochlospermum; Colubrina; Copaifera; Cordia; Couepia; Counting, palynology; Coussapoa; Croton; Crudia; Cupania; Cuphea; Curatella; Cyathea; Cybianthus; Cybistax; Cydista; Cyperaceae; Dalbergia; Dalbergia/Machaerium; Davilla; DEPTH, sediment/rock; Desmodium; Dialium; Dichapetalum; Didymopanax; Dioclea; Diospyros; Doliocarpus; Drosera; Elaeocarpaceae; Ephedra; Eriocaulon; Eriotheca; Erythrina; Erythrochiton; Erythroxylum; Evolvulus; Fabaceae; Fagara; Faramea-type; Gallesia; Gilia; Gomphrena; Gouania; Guazuma; Hedyosmum; Heisteria; Heliotropium; Heteropteris; Hirtella; Hymenia; Hymenolobium; Ilex; Indigofera-type; Iryanthera; Isoetes; Jacaranda; Julocroton; Juncaceae; Justicia; Lamiaceae; lecythidaceae; Licania; Linaceae; Lithraea; Loranthaceae; Luehea; Lycopodiaceae; Mabea; Macrolobium; Malanea; Malvaceae; Manettia; Manilkara; Marcgravia; Maripa; Matayba; Mauritia; Maytenus; Melastomataceae; Meliaceae/Sapotaceae; Mesechites-type; Metrodorea; Mikania; Mimosa; Mimosa scabrella; Minquartia; Moraceae; Mouriri-type; Myriophyllum; Myroxylon-type; Myrsine; Myrtaceae; Norantea; Nymphaeaceae; Nymphoides; Onagraceae; Ormosia; Ouratea; Paullinia; Peltogyne; Phyllanthus; Piper; Pisonia; Poaceae; Podocarpus; Poiretia (Fabaceae); Polygala; Polygonum; Polypodiales; Polypodium; Pouteria; Protium; Pseudobombax; Psychotria; Pteridaceae; Pterodon-type; Pterogyne-type; Qualea; Ranunculaceae; Rhamnaceae; Rhizophora-type; Rhynchosia-type; Richardia; Ricinus; Roucheria; Roupala; Rudgea; Ruellia; Sabicea; Sample ID; Sapium; Sauvagesia; Schinus; Scrophulariaceae; Sebastiania; Serjania; Simarouba; Sloanea; Solanum; Spermacoce; Spondias; Sterculiaceae; Styrax; Swartzia; Symphonia; Symplocos; Tabebuia; Talisia; Tapirira; Tecoma; Ternstroemia; Tontelea; Tournefortia; Trema; Triplaris; Trixis; Turneraceae; Type of age model; Typha; Unknown; Urticales; Utricularia; Vantanea; Vismia; Vochysia; Xylosma; Xyris
    Type: Dataset
    Format: text/tab-separated-values, 31113 data points
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  • 22
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    CLAM age model and pollen profile of sediment core Khoe (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abies; Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; Alnus; Amaranthaceae/Chenopodiaceae; Apiaceae; Artemisia; Asteraceae; Betula; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Campanula; Carpinus/Ostrya; Caryophyllaceae; Classical age-modeling approach, CLAM (Blaauw, 2010); Coptis; Corylus; Counting, palynology; Cyperaceae; DEPTH, sediment/rock; Drosera; Epilobium; Equisetum; Ericaceae; Fraxinus; Geranium; Ilex; Iris; Juglans/Pterocarya; Khoe; Lamiaceae; Larix; Lycopodium; Menyanthes; Myrica; Osmundaceae; Persicaria; Picea; Pinus; Poaceae; Polemonium; Polygonum; Polypodiales; Quercus subgen. Lepidobalanus; Ranunculus; Rosaceae; Rubus chamaemorus; Salix; Sample ID; Sanguisorba; Saxifraga; Selaginella selaginoides; Sorbus; Sparganium/Typha; Sphagnum; Thalictrum; Tilia; Tsuga; Type of age model; Ulmus/Zelkova; Valerianaceae
    Type: Dataset
    Format: text/tab-separated-values, 3127 data points
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  • 23
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    CLAM age model and pollen profile of sediment core La_Laguna (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Acalypha; Accumulation model; ACER; Alchornea; Alnus; Apiaceae; Aragoa; Arecaceae-type; Arenaria-type; Asteraceae; Bocconia; Brassicaceae; Calceolaria; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Cerastium/Stellaria; Classical age-modeling approach, CLAM (Blaauw, 2010); Clethra-type; Clusia-type; Cordia lanata; Coriaria; Counting, palynology; Cyatheaceae; Cyperaceae; Daphnopsis; DEPTH, sediment/rock; Drimys; Ericaceae; Eryngium; Escallonia-type; Eucalyptus; Eugenia; Gentianaceae; Geranium; Grammitis; Guarea; Hedyosmum; Hesperomeles; Hydrocotyle; Hymenophyllum; Hypericum; Ilex; Isoetes; Jamesonia; Juglans; La_Laguna; Lachemilla; Leguminosae; Lycopodium; Macrolobium; Melastomataceae; Myrica; Myrteola; Oreopanax-type; Pinus; Plantago; Poaceae; Podocarpus; Polylepis; Polypodiales; Potamogeton; Puya; Quercus; Ranunculus; Ranunculus-type; Rapanea; Rhus; Rumex; Rumex acetocella-type; Salix-type; Sample ID; Satureja; Sericotheca; Solanaceae; Styloceras; Symplocos; Thalictrum; Type of age model; Urticales; Valeriana; Vallea; Viburnum; Vismia-type; Weinmannia; Zanthoxylum; Zea mays
    Type: Dataset
    Format: text/tab-separated-values, 4925 data points
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  • 24
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    Pollen and spore counts of sediment core GeoB12624 (2015)
    PANGAEA
    In:  Supplement to: Bouimetarhan, Ilham; Dupont, Lydie M; Kuhlmann, Holger; Pätzold, Jürgen; Prange, Matthias; Schefuß, Enno; Zonneveld, Karin A F (2015): Northern Hemisphere control of deglacial vegetation changes in the Rufiji uplands (Tanzania). Climate of the Past, 11(5), 751-764, https://doi.org/10.5194/cp-11-751-2015
    Details
    Publication Date: 2024-05-27
    Description: In tropical eastern Africa, vegetation distribution is largely controlled by regional hydrology, which has varied over the past 20 000 years. Therefore, accurate reconstructions of past vegetation and hydrological changes are crucial for a better understanding of climate variability in the tropical southeastern African region. We present high-resolution pollen records from a marine sediment core recovered offshore of the Rufiji River delta. Our data document significant shifts in pollen assemblages during the last deglaciation, identifying, through changes in both upland and lowland vegetation, specific responses of plant communities to atmospheric (precipitation) and coastal (coastal dynamics and sea-level changes) alterations. Specifically, arid conditions reflected by a maximum pollen representation of dry and open vegetation occurred during the Northern Hemisphere cold Heinrich event 1 (H1), suggesting that the expansion of drier upland vegetation was synchronous with cold Northern Hemisphere conditions. This arid period is followed by an interval in which forest and humid woodlands expanded, indicating a hydrologic shift towards more humid conditions. Droughts during H1 and the shift to humid conditions around 14.8 kyr BP in the uplands are consistent with latitudinal shifts of the intertropical convergence zone (ITCZ) driven by high-latitude Northern Hemisphere climatic fluctuations. Additionally, our results show that the lowland vegetation, consisting of well-developed salt marshes and mangroves in a successional pattern typical for vegetation occurring in intertidal habitats, has responded mainly to local coastal dynamics related to marine inundation frequencies and soil salinity in the Rufiji Delta as well as to the local moisture availability. Lowland vegetation shows a substantial expansion of mangrove trees after ~ 14.8 kyr BP, suggesting an increased moisture availability and river runoff in the coastal area. The results of this study highlight the decoupled climatic and environmental processes to which the vegetation in the uplands and the Rufiji Delta has responded during the last deglaciation.
    Keywords: Acacia; AGE; Alchornea; Algae; Amaranthaceae/Chenopodiaceae; Area South of Mafia Island; Artemisia; Asteroideae; Borreria; Boscia-type; Butyrospermum; Caryophyllaceae; Cassia-type; Celtis; Center for Marine Environmental Sciences; Cleome; Combretaceae; Counting, palynology; Cyperaceae; DEPTH, sediment/rock; Euphorbia-type; Galium; GeoB12624-1; Gramineae; Gravity corer (Kiel type); Hymenocardia; Indigofera; Isoberlinia; Lycopodium spores added; Lycopodium spores counted; M75/2; M75/2_115-1; MARUM; Meteor (1986); Mimosa-type; Olea; Phyllanthus; Piliostigma; Plantago; Podocarpus; Pollen, total; Psydrax-type subcordata; Pterocarpus-type; Rhizophora; Rhus-type; SL; Spores; Stereospermum-type; Tamarindus-type indica; Typha; Uapaca; Vernonia-type; Ziziphus
    Type: Dataset
    Format: text/tab-separated-values, 1621 data points
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  • 25
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    Pollen and spores of ODP Hole 175-1081A, Miocene-Pliocene, Walvis Ridge (2015)
    PANGAEA
    In:  Supplement to: Hötzel, Sebastian; Dupont, Lydie M; Wefer, Gerold (2015): Miocene-Pliocene Vegetation change in south-western Africa (ODP Site 1081, offshore Namibia). Palaeogeography, Palaeoclimatology, Palaeoecology, 423, 102-108, https://doi.org/10.1016/j.palaeo.2015.02.002
    Details
    Publication Date: 2024-05-27
    Description: Aridification is an important component of Late Neogene climate change in south-western Africa probably caused by modifications in the atmospheric circulation in relation to the initiation and intensification of the Benguela Upwelling System due to globally steepening of the meridional pressure gradient. Intensification of the meridional pressure gradient influenced the climate intensively which had then an impact on the vegetation. However, vegetation changes of south-western Africa from the Miocene to Pliocene have not yet been reported and only indirectly investigated by sedimentological data. Here, we present a pollen record of marine ODP Site 1081 retrieved 160 km offshore Namibia covering the time between 9 and 2.7 Ma. Using an endmember unmixing model we distinguished three vegetation phases: a relative wet phase, during the Tortonian, showing higher representations of Cyperaceae, a transition phase during the Messinian, when especially grasses expanded, and a dry one covering the Pliocene with a strong representation of desert and semi-desert plants. The three phases indicate ongoing aridification probably caused by intensified meridional pressure gradients. Additionally, aquatic vegetation indicators appear in our pollen record from around 5 Ma on, which we attribute to a relocation of the lower course of the Cunene River to its modern outlet in the Atlantic Ocean. Redirection of the Cunene River toward the Atlantic would have deprived the palaeolake Cunene of an important source of fresh-water ultimately resulting in desiccation of the lake and the formation of the Etosha Pan.
    Keywords: 175-1081A; Abutilon; Acacia; Acanthaceae; Adenium; AGE; Aizoaceae; Amanoa; Amaranthaceae/Chenopodiaceae; Aniseia; Arecaceae; Artemisia-type; Asteraceae tubiliflorae; Asystasia gangetica; Barleria; Basilicum; Benguela Current, South Atlantic Ocean; Berkheya-type; Blepharis; Borassus-type; Brachystegia; Cassia-type; Casuarina; Celtis; Cephalaria; Clausena; Cliffortia; Coccinia; Colophospermum mopane; Combretaceae; Commiphora; Cotula-type; Counting, palynology; Cyperaceae; Delonix; DEPTH, sediment/rock; Detarium; Dichrostachys cinerea; Dicoma-type; Diodia-type; Dombeya-type; DRILL; Drilling/drill rig; Ecbolium; Ephedra; Ericaceae; Euphorbia; Euphorbiaceae undifferentiated; Evolvulus-type; Gardenia; Gazania-type; Gerbera-type; Grewia; Gunnera perpensa; Heritiera-type; Hevea; Hildebrandtia; Hypoestes type; Ipomoea; Isoberlinia-type; Jasminum; Jatropha; Joides Resolution; Justicia-type; Kedrostis; Leg175; Luffa; Lycopodium; Mallotus; Malvaceae (Africa); Marker, added; Marker, found; Meliaceae; Merremia; Mesembryanthenum-type; Mimosaceae undifferentiated; Mohria; Monsonia; Myrica; Myrsine africana; Neurada/Grielum; Nyctaginaceae; Nymphaea; Ocean Drilling Program; ODP; Oleaceae; Osmunda-type; Passerina; Pavonia-type; Pelargonium; Pentas; Pentzia-type; Peristrophe; Petalidium; Phaeoceros; Phyllanthus; Picris-type; Piliostigma; Poaceae; Podocarpus; Polygala; Polygonum; Polypodiaceae; Proteaceae; Pteris; Rapanea; Restionaceae; Riccia; Rothmannia; Rubiaceae tetrade; Rubiaceae undifferentiated; Ruellia; Sample code/label; Selaginella; Senecio-type; Sesamum; Sorindeia-type; Spathodea; Spores, varia; Sporomorphes, total; Sterculia-type; Stoebe-type; Tetrorchidium; Thymelaeaceae; Tiliaceae; Tribulus; Trichotomosulcate reticulate; Typha; undetermined; Vernonia-type; Vigna; Volume; Welwitschia; Zanthoxylum
    Type: Dataset
    Format: text/tab-separated-values, 8946 data points
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  • 26
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    Negative effects of ocean acidification on calcification vary within the coccolithophore genus Calcidiscus (2015)
    PANGAEA
    In:  Supplement to: Diner, Rachel E; Benner, Ina; Passow, Uta; Komada, Tomoko; Carpenter, E J; Stillman, Jonathon H (2015): Negative effects of ocean acidification on calcification vary within the coccolithophore genus Calcidiscus. Marine Biology, 162(6), 1287-1305, https://doi.org/10.1007/s00227-015-2669-x
    Details
    Publication Date: 2024-05-27
    Description: A large percentage of CO2 emitted into the atmosphere is absorbed by the oceans, causing chemical changes in surface waters known as ocean acidification (OA). Despite the high interest and increased pace of OA research to understand the effects of OA on marine organisms, many ecologically important organisms remain unstudied. Calcidiscus is a heavily calcified coccolithophore genus that is widespread and genetically and morphologically diverse. It contributes substantially to global calcium carbonate production, organic carbon production, oceanic carbon burial, and ocean-atmosphere CO2 exchange. Despite the importance of this genus, relatively little work has examined its responses to OA. We examined changes in growth, morphology, and carbon allocation in multiple strains of Calcidiscus leptoporus in response to ocean acidification. We also, for the first time, examined the OA response of Calcidiscus quadriperforatus, a larger and more heavily calcified Calcidiscus congener. All Calcidiscus coccolithophores responded negatively to OA with impaired coccolith morphology and a decreased ratio of particulate inorganic to organic carbon (PIC:POC). However, strains responded variably; C. quadriperforatus showed the most sensitivity, while the most lightly calcified strain of C. leptoporus showed little response to OA. Our findings suggest that calcium carbonate production relative to organic carbon production by Calcidiscus coccolithophores may decrease in future oceans and that Calcidiscus distributions may shift if more resilient strains and species become dominant in assemblages. This study demonstrates that variable responses to OA may be strain or species specific in a way that is closely linked to physiological traits, such as cellular calcite quota.
    Keywords: Alkalinity, total; Alkalinity, total, standard error; Aragonite saturation state; Bicarbonate ion; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcidiscus leptoporus; Calcidiscus quadriperforatus; Calcification/Dissolution; Calcite saturation state; Calculated; Calculated using CO2calc; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard error; Carbon, inorganic, particulate, per cell; Carbon, inorganic, particulate, production per cell; Carbon, organic, particulate, per cell; Carbon, organic, particulate, production per cell; Carbon, total, particulate, per cell; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Change; Change, standard error; Chromista; Coccoliths; Coulometric titration; Figure; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Growth rate; Haptophyta; Laboratory experiment; Laboratory strains; Not applicable; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Partial pressure of carbon dioxide (water) at sea surface temperature (wet air), standard error; Particulate alcian blue-stainable material, per cell; Particulate inorganic carbon/particulate organic carbon ratio; Pelagos; Percentage; Percentage, standard deviation; pH; pH, standard error; Phytoplankton; Potentiometric titration; Primary production/Photosynthesis; Replicate; Salinity; Salinity, standard error; Single species; Species; Strain; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 4298 data points
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  • 27
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    Ocean acidification decreases the light-use efficiency in an Antarctic diatom under dynamic but not constant light (2015)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Alkalinity, total; Alkalinity, total, standard deviation; Antarctic; Aragonite saturation state; Bicarbonate ion; Biogenic silica, per cell; Biogenic silica production per cell; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcite saturation state; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbon, organic, particulate, per cell; Carbon, organic, particulate, production per cell; Carbon, organic, particulate/Nitrogen, organic, particulate ratio; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Chaetoceros debilis; Chlorophyll a/particulate organic carbon ratio; Chlorophyll a per cell; Chlorophyll a production per cell; Chromista; Coulometric titration; Electron transport rate per chlorophyll a; Electron transport rate per chlorophyll a per day; Energy transfer efficiency per carbon; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Growth rate; Laboratory experiment; Laboratory strains; Light; Light mode; Light saturation point; Net primary production of carbon per chlorophyll a; Nitrogen, organic, particulate, per cell; Non photochemical quenching; OA-ICC; Ocean Acidification International Coordination Centre; Ochrophyta; Partial pressure of carbon dioxide, standard deviation; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Pelagos; pH; pH, standard deviation; Photosynthetic efficiency per chlorophyll a; Phytoplankton; Potentiometric; Potentiometric titration; Primary production/Photosynthesis; Production of particulate organic nitrogen; Replicate; Salinity; Single species; Species; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 780 data points
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  • 28
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    Solar UV irradiances modulate effects of ocean acidification on the Coccolithophorid Emiliania huxleyi (2015)
    PANGAEA
    In:  Supplement to: Xu, Kai; Gao, Kunshan (2015): Solar UV Irradiances Modulate Effects of Ocean Acidification on the Coccolithophorid Emiliania huxleyi. Photochemistry and Photobiology, 91(1), 92-101, https://doi.org/10.1111/php.12363
    Details
    Publication Date: 2024-05-27
    Description: Emiliania huxleyi, the most abundant coccolithophorid in the oceans, is naturally exposed to solar UV radiation (UVR, 280-400 nm) in addition to photosynthetically active radiation (PAR). We investigated the physiological responses of E. huxleyi to the present day and elevated CO2 (390 vs 1000 µatm; with pH(NBS) 8.20 vs 7.86) under indoor constant PAR and fluctuating solar radiation with or without UVR. Enrichment of CO2 stimulated the production rate of particulate organic carbon (POC) under constant PAR, but led to unchanged POC production under incident fluctuating solar radiation. The production rates of particulate inorganic carbon (PIC) as well as PIC/POC ratios were reduced under the elevated CO2, ocean acidification (OA) condition, regardless of PAR levels, and the presence of UVR. However, moderate levels of UVR increased PIC production rates and PIC/POC ratios. OA treatment interacted with UVR to influence the alga's physiological performance, leading to reduced specific growth rate in the presence of UVA (315-400 nm) and decreased quantum yield, along with enhanced nonphotochemical quenching, with addition of UVB (280-315 nm). The results clearly indicate that UV radiation needs to be invoked as a key stressor when considering the impacts of ocean acidification on E. huxleyi.
    Keywords: Alkalinity, total; Alkalinity, total, standard deviation; Aragonite saturation state; Bicarbonate ion; Bottles or small containers/Aquaria (〈20 L); Calcification/Dissolution; Calcite saturation state; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, particulate, production per cell; Carbon, organic, particulate, production per cell; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Chromista; Coccosphere, diameter; Duration, number of days; Emiliania huxleyi; Experiment; Figure; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Growth rate; Growth rate, standard deviation; Haptophyta; Irradiance; Laboratory experiment; Laboratory strains; Light; Non photochemical quenching; Non photochemical quenching, standard deviation; Not applicable; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Particulate inorganic carbon, production, standard deviation; Particulate inorganic carbon/particulate organic carbon ratio; Particulate inorganic carbon/particulate organic carbon ratio, standard deviation; Particulate organic carbon, production, standard deviation; Pelagos; pH; pH, standard deviation; Photochemical efficiency; Photochemical efficiency, standard deviation; Phytoplankton; Potentiometric; Potentiometric titration; Primary production/Photosynthesis; Radiation, photosynthetically active; Radiation, photosynthetically active, dose daily; Salinity; Single species; Species; Temperature, water; Time of day; Treatment; Ultraviolet-a radiation, dose daily; Ultraviolet-b radiation, dose daily; Ultraviolet radiation
    Type: Dataset
    Format: text/tab-separated-values, 73874 data points
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  • 29
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    Negative effects of ocean acidification on calcification vary within the coccolithophore genus Calcidiscus (2015)
    PANGAEA
    In:  Supplement to: Diner, Rachel E; Benner, Ina; Passow, Uta; Komada, Tomoko; Carpenter, E J; Stillman, Jonathon H (2015): Negative effects of ocean acidification on calcification vary within the coccolithophore genus Calcidiscus. Marine Biology, 162(6), 1287-1305, https://doi.org/10.1007/s00227-015-2669-x
    Details
    Publication Date: 2024-05-27
    Description: A large percentage of CO2 emitted into the atmosphere is absorbed by the oceans, causing chemical changes in surface waters known as ocean acidification (OA). Despite the high interest and increased pace of OA research to understand the effects of OA on marine organisms, many ecologically important organisms remain unstudied. Calcidiscus is a heavily calcified coccolithophore genus that is widespread and genetically and morphologically diverse. It contributes substantially to global calcium carbonate production, organic carbon production, oceanic carbon burial, and ocean-atmosphere CO2 exchange. Despite the importance of this genus, relatively little work has examined its responses to OA. We examined changes in growth, morphology, and carbon allocation in multiple strains of Calcidiscus leptoporus in response to ocean acidification. We also, for the first time, examined the OA response of Calcidiscus quadriperforatus, a larger and more heavily calcified Calcidiscus congener. All Calcidiscus coccolithophores responded negatively to OA with impaired coccolith morphology and a decreased ratio of particulate inorganic to organic carbon (PIC:POC). However, strains responded variably; C. quadriperforatus showed the most sensitivity, while the most lightly calcified strain of C. leptoporus showed little response to OA. Our findings suggest that calcium carbonate production relative to organic carbon production by Calcidiscus coccolithophores may decrease in future oceans and that Calcidiscus distributions may shift if more resilient strains and species become dominant in assemblages. This study demonstrates that variable responses to OA may be strain or species specific in a way that is closely linked to physiological traits, such as cellular calcite quota.
    Keywords: Alkalinity, total; Alkalinity, total, standard error; Aragonite saturation state; Bicarbonate ion; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcidiscus leptoporus; Calcidiscus quadriperforatus; Calcification/Dissolution; Calcite saturation state; Calculated; Calculated using CO2calc; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard error; Carbon, inorganic, particulate, per cell; Carbon, inorganic, particulate, production per cell; Carbon, organic, particulate, per cell; Carbon, organic, particulate, production per cell; Carbon, total, particulate, per cell; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Change; Change, standard error; Chromista; Coccoliths; Coulometric titration; Figure; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Growth rate; Haptophyta; Laboratory experiment; Laboratory strains; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Partial pressure of carbon dioxide (water) at sea surface temperature (wet air), standard error; Particulate alcian blue-stainable material, per cell; Particulate inorganic carbon/particulate organic carbon ratio; Pelagos; Percentage; Percentage, standard deviation; pH; pH, standard error; Phytoplankton; Potentiometric titration; Primary production/Photosynthesis; Replicate; Salinity; Salinity, standard error; Single species; Species; Strain; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 4298 data points
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  • 30
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    Phytoplankton calcification as an effective mechanism to alleviate cellular calcium poisoning (2015)
    PANGAEA
    In:  Supplement to: Müller, Marius N; Barcelos e Ramos, Joana; Schulz, Kai Georg; Riebesell, Ulf; Kaźmierczak, J; Gallo, F; Mackinder, Luke C M; Li, Y; Nesterenko, P N; Trull, Tom W; Hallegraeff, Gustaaf M (2015): Phytoplankton calcification as an effective mechanism to alleviate cellular calcium poisoning. Biogeosciences, 12(21), 6493-6501, https://doi.org/10.5194/bg-12-6493-2015
    Details
    Publication Date: 2024-05-27
    Description: Marine phytoplankton has developed the remarkable ability to tightly regulate the concentration of free calcium ions in the intracellular cytosol at a level of ~ 0.1 µmol /l in the presence of seawater Ca2+ concentrations of 10 mmol/1. The low cytosolic calcium ion concentration is of utmost importance for proper cell signalling function. While the regulatory mechanisms responsible for the tight control of intracellular Ca2+ concentration are not completely understood, phytoplankton taxonomic groups appear to have evolved different strategies, which may affect their ability to cope with changes in seawater Ca2+ concentrations in their environment on geological time scales. For example, the Cretaceous (145 to 66 Ma ago), an era known for the high abundance of coccolithophores and the production of enormous calcium carbonate deposits, exhibited seawater calcium concentrations up to four times present-day levels. We show that calcifying coccolithophore species (Emiliania huxleyi, Gephyrocapsa oceanica and Coccolithus braarudii) are able to maintain their relative fitness (in terms of growth rate and photosynthesis) at simulated Cretaceous seawater calcium concentrations, whereas these rates are severely reduced under these conditions in some non-calcareous phytoplankton species (Chaetoceros sp., Ceratoneis closterium and Heterosigma akashiwo). Most notably, this also applies to a non-calcifying strain of E. huxleyi which displays a calcium-sensitivity similar to the non-calcareous species. We hypothesize that the process of calcification in coccolithophores provides an efficient mechanism to alleviate cellular calcium poisoning and thereby offered a potential key evolutionary advantage, responsible for the proliferation of coccolithophores during times of high seawater calcium concentrations. The exact function of calcification and the reason behind the highly-ornate physical structures of coccoliths remain elusive.
    Keywords: Alkalinity, total; Alkalinity, total, standard deviation; Calcite saturation state; Calcite saturation state, standard deviation; Calcium; Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbon, inorganic, particulate, per cell; Carbon, inorganic, particulate, production per cell; Carbon, organic, particulate, per cell; Carbon, organic, particulate, production per cell; Carbon, organic, particulate, standard deviation; Growth rate; Growth rate, standard deviation; Partial pressure of carbon dioxide, standard deviation; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); pH; Photosynthetic competence; Photosynthetic efficiency, standard deviation; Species; Standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 714 data points
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  • 31
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    CLAM age model and microcharcoal of sediment core Megali_Limni (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Charcoal; Classical age-modeling approach, CLAM (Blaauw, 2010); DEPTH, sediment/rock; Megali_Limni; Sample ID; Type of age model; Unit
    Type: Dataset
    Format: text/tab-separated-values, 676 data points
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  • 32
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    CLAM age model and microcharcoal of sediment core Pacucha (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Charcoal; Classical age-modeling approach, CLAM (Blaauw, 2010); DEPTH, sediment/rock; Pacucha; Sample ID; Type of age model; Unit
    Type: Dataset
    Format: text/tab-separated-values, 687 data points
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  • 33
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    CLAM age model and biomes of sediment core 161-976 (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: 161-976; Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; AGE; Alboran Sea; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Classical age-modeling approach, CLAM (Blaauw, 2010); COMPCORE; Composite Core; DEPTH, sediment/rock; Joides Resolution; Leg161; Pollen, temperate forest; Sample ID; Type of age model
    Type: Dataset
    Format: text/tab-separated-values, 1742 data points
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  • 34
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    CLAM age model and biomes of sediment core Caco (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; AGE; Caco; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Classical age-modeling approach, CLAM (Blaauw, 2010); DEPTH, sediment/rock; Pollen, temperate mountain forest; Pollen, tropical forest; Pollen, warm-temperate forest; Sample ID; Type of age model
    Type: Dataset
    Format: text/tab-separated-values, 867 data points
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  • 35
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    CLAM age model and biomes of sediment core Caledonia_Fen (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; AGE; Caledonia_Fen; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Classical age-modeling approach, CLAM (Blaauw, 2010); DEPTH, sediment/rock; Pollen, savanah; Pollen, warm-temperate forest; Sample ID; Type of age model
    Type: Dataset
    Format: text/tab-separated-values, 3637 data points
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  • 36
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    CLAM age model and microcharcoal of sediment core Rice_Lake_79 (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Charcoal; Classical age-modeling approach, CLAM (Blaauw, 2010); DEPTH, sediment/rock; Rice_Lake_79; RL79; Sample ID; Type of age model; Unit
    Type: Dataset
    Format: text/tab-separated-values, 767 data points
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  • 37
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    CLAM age model and microcharcoal of sediment core Valle_di_Castiglione (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Charcoal; Classical age-modeling approach, CLAM (Blaauw, 2010); DEPTH, sediment/rock; Sample ID; Type of age model; Unit; Valle_di_Castiglione
    Type: Dataset
    Format: text/tab-separated-values, 360 data points
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  • 38
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    CLAM age model and microcharcoal of sediment core Wonderkrater_borehole_3 (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; ACER; Charcoal; DEPTH, sediment/rock; Sample ID; Unit; Wonderkrater_borehole_3
    Type: Dataset
    Format: text/tab-separated-values, 165 data points
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  • 39
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    CLAM age model and microcharcoal of sediment core Wonderkrater_borehole_4 (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Charcoal; Classical age-modeling approach, CLAM (Blaauw, 2010); DEPTH, sediment/rock; Sample ID; Type of age model; Unit; Wonderkrater_borehole_4
    Type: Dataset
    Format: text/tab-separated-values, 243 data points
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  • 40
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    CLAM age model and biomes of sediment core Azzano_Decimo (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; AGE; Azzano_Decimo; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Classical age-modeling approach, CLAM (Blaauw, 2010); DEPTH, sediment/rock; Pollen, temperate forest; Sample ID; Type of age model
    Type: Dataset
    Format: text/tab-separated-values, 1063 data points
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  • 41
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    CLAM age model and pollen profile of sediment core Okarito_Pakihi (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Acaena; ACER; Adiantum; Alepis; Apiaceae; Aristotelia; Ascarina; Asplenium; Astelia; Asteraceae; Baumea-type; Blechnum; Bulbinella; Callitriche; Calocedrus; Caryophyllaceae; Casuarina; Centrolepidaceae; cf. Podocarpus; Chenopodiaceae; Coprosma; Coriaria; Counting, palynology; Cyathea dealbata; Cyathea medullaris; Cyathea smithii; Cyperaceae; Dacrycarpus; Dacrydium cupressinum; DEPTH, sediment/rock; Dicksonia; Dicksonia lanata; Discaria; Dracophyllum; Drosera; Elaeocarpus; Empodisma; Euphrasia; Forstera; Fuchsia; Gaultheria; Gentiana; Gleichenia; Gonocarpus; Grammitis; Griselinia; Gunnera; Halocarpus; HAND; Hebe; Histiopteris; Hoheria; Hymenophyllum; Hypolepis; Isoetes; Lagarostrobus; Lepidothamnus; Leptospermum-type; Leucopogon fasciculatus; Lycopodium fastigiatum; Lycopodium scariosum; Lycopodium-type; Lycopodium varium; Melicytus; Metrosideros; Muehlenbeckia; Myriophyllum; Myrsine; Neomyrtus; Nestegis; Nothofagus; Nothofagus menziesii; Okarito_Pakihi; Ophioglossum; Peraxilla; Phyllocladus; Phymatosorus; Pinus; Plagianthus; Plantaginaceae; Poaceae; Podocarpus; Polypodiales; Prumnopitys ferruginea; Prumnopitys taxifolia; Pseudopanax; Pseudowintera; Pteris; Pteris tremula; Quintinia; Ranunculus; Rubus; Sample ID; Sampling by hand; Sphagnum; Typha; Weinmannia
    Type: Dataset
    Format: text/tab-separated-values, 15015 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 42
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    CLAM age model and pollen profile of sediment core Tyrrendara_Swamp (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Acacia; Accumulation model; ACER; Amperea-type; Apiaceae; Aquatics; Asteraceae; Azolla; Banksia; Boraginaceae; Brassicaceae; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Callitris; Caryophyllaceae; Casuarinaceae; cf. Cyathea; cf. Dicksonia; cf. Geraniaceae; cf. Linaria; cf. Scrophulariaceae; Chenopodiaceae; Classical age-modeling approach, CLAM (Blaauw, 2010); Counting, palynology; Cyperaceae; DEPTH, sediment/rock; Epacridaceae; Eucalyptus; Euphorbiaceae; Haloragis; Herbs; Hydrocharitaceae-type; Hydrocotyle; Leptospermum; Melaleuca; Myriophyllum; Myriophyllum muelleri; Pinus; Plantago; Poaceae; Polygalaceae; Polypodiales; Pomaderris; Primulaceae; Prostanthera; Pteridaceae; Ranunculaceae; Ranunculus; Restionaceae; Rhamnaceae; Rumex-type; Sample ID; Triglochin; Type of age model; Typha; Tyrrendara_Swamp; Villarsia; Woody taxa
    Type: Dataset
    Format: text/tab-separated-values, 1745 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 43
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    CLAM age model and pollen profile of sediment core Walker_Lake (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abies; Abies/Picea; Abrupt Climate Changes and Environmental Responses; Accumulation model; Acer; ACER; Alnus; Amaranthaceae/Chenopodiaceae; Ambrosia; Aquatics; Artemisia; Asteraceae; Berberis; Betula; Brassicaceae; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Classical age-modeling approach, CLAM (Blaauw, 2010); Counting, palynology; Cyperaceae; DEPTH, sediment/rock; Ephedra; Fraxinus; Gentiana; Juglans; Juniperus; Leguminosae; Malva; Mirabilis; Nyctaginaceae; Picea; Pinus; Plantago; Poaceae; Pollen indeterminata; Polygonaceae; Polygonum; Polygonum bistoides; Populus; Pseudotsuga; Quercus; Rhamnus; Rosaceae; Sample ID; Sarcobatus; Saxifragaceae; Tsuga; Type of age model; Walker_Lake
    Type: Dataset
    Format: text/tab-separated-values, 21199 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 44
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    CLAM age model and pollen profile of sediment core 146-893A (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: 146-893A; Abies; Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; Alnus; Anacardiaceae/Rhamnaceae/Rosaceae; Artemisia; Asteraceae; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Chenopodiaceae; Classical age-modeling approach, CLAM (Blaauw, 2010); Counting, palynology; Cupressus; Cyperaceae; DEPTH, sediment/rock; DRILL; Drilling/drill rig; Ephedra; Eriogonum; Ilex; Joides Resolution; Juglans; Leg146; Myrica; North Pacific Ocean; Picea; Pinus; Poaceae; Pseudotsuga; Quercus; Salix; Sample ID; Tsuga; Type of age model
    Type: Dataset
    Format: text/tab-separated-values, 19576 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 45
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    CLAM age model and pollen profile of sediment core 133-820 (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: 133-820; Abrupt Climate Changes and Environmental Responses; Acacia; Accumulation model; ACER; Agathis; Apiaceae; Araucaria; Arecaceae; Asteraceae; Avicennia marina; Balanops; Bruguiera/Ceriops; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Callitris; Camptostemon; Casuarinaceae; Celtis; Chenopodiaceae; Classical age-modeling approach, CLAM (Blaauw, 2010); COMPCORE; Composite Core; Coral Sea; Counting, palynology; Cunoniaceae; Cyathea; Cyperaceae; Dacrydium guillauminii; DEPTH, sediment/rock; Dodonaea; Elaeocarpus; Epacridaceae; Eucalyptus; Euphorbiaceae; Fabaceae; Faradaya; Ficus; Flindersia; Gleichenia; Gyrostemonaceae; Iridaceae; Joides Resolution; Leg133; Lonchocarpus; Lycopodium; Macaranga/Mallotus; Malvaceae; Melaleuca; Myriophyllum; Nothofagus brassii; Olea paniculata; Ophioglossum; Pandanus; Pellaea falcata; Pellaea paradoxa; Plantago; Poaceae; Podocarpus; Polypodiales; Potamogeton; Proteaceae; Rhizophora; Sample ID; Sapindaceae; Sapotaceae; Syzygium; Trema; Triglochin; Type of age model
    Type: Dataset
    Format: text/tab-separated-values, 4171 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 46
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    CLAM age model and pollen profile of sediment core Carp_Lake (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abies; Abrupt Climate Changes and Environmental Responses; Accumulation model; Acer; ACER; Alnus; Alnus rubra-type; Alnus sinuata-type; Ambrosia-type; Amelanchier-type; Apiaceae; Arceuthobium; Artemisia; Asteraceae; Betula; Bidens-type; Botrychium; Brasenia; Brassicaceae; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Camassia-type; Carp_Lake; Caryophyllaceae; Ceanothus/Rhamnus; Chenopodiaceae; Classical age-modeling approach, CLAM (Blaauw, 2010); Corylus; Counting, palynology; Cupressaceae; Cyperaceae; DEPTH, sediment/rock; Dodecatheon-type; Dryopteris; Elaeagnus; Ephedra; Equisetum; Ericaceae; Eriogonum; Fabaceae; Fraxinus; Galium; Gilia-type; Herbs; Isoetes; Lamiaceae; Larix/Pseudotsuga; Lemna; Liliaceae; Myriophyllum; Nuphar; Onagraceae; Phlox-type; Picea; Pinus; Plantago; Plectritis-type; Poaceae; Pollen indeterminata; Polygonum; Polygonum californicum-type; Populus; Populus balsamifera-type; Populus tremuloides-type; Potamogeton; Potentilla-type; Poylgonum amphibium; Prunus-type; Pteridium; Quercus; Ranunculus; Rhus; Rosaceae; Rumex; Sagittaria; Salix; Sambucus; Sample ID; Sarcobatus; Saxifragaceae; Selaginella densa-type; Shepherdia canadensis; Sparganium; Sphaeralcea; Spiraea-type; Taxus; Thalictrum; Tsuga heterophylla; Tsuga mertensiana; Type of age model; Typha latifolia-type; Unknown; Urtica-type; Valeriana
    Type: Dataset
    Format: text/tab-separated-values, 17978 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 47
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    CLAM age model and pollen profile of sediment core Kenbuchi_Basin (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abies; Abrupt Climate Changes and Environmental Responses; Accumulation model; Acer; ACER; Alnus; Amaranthaceae/Chenopodiaceae; Apiaceae; Araliaceae; Artemisia; Asteraceae; Betula; Brassicaceae; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Campanula; Carpinus/Ostrya; Caryophyllaceae; Castanea/Castanopsis; Celtis/Aphananthe; Classical age-modeling approach, CLAM (Blaauw, 2010); Corydalis; Corylus; Counting, palynology; Cryptomeria; Cyperaceae; DEPTH, sediment/rock; Drosera; Elaeagnus; Epilobium; Ericaceae; Euonymus; Fagus crenata; Fraxinus; Galium; Geranium; Geum; Hydrangea; Ilex; Juglans/Pterocarya; Kenbuchi_Basin; Lamiaceae; Larix; Leguminosae; Ligustrum; Liliaceae; Lonicera; Lycopodium; Lysichiton; Menyanthes; Morus; Myrica; Osmundaceae; Papaveraceae; Persicaria; Phellodendron; Picea; Pinus; Poaceae; Polemonium; Polygonum; Polygonum bistorta; Polypodiales; Quercus subgen. Lepidobalanus; Ranunculus; Rhamnaceae; Rhus; Rosaceae; Rumex; Salix; Sample ID; Sanguisorba; Saxifraga; Selaginella selaginoides; Sorbus; Sparganium/Typha; Sphagnum; Styrax; Thalictrum; Tilia; Tsuga; Type of age model; Ulmus/Zelkova; Valerianaceae; Viburnum
    Type: Dataset
    Format: text/tab-separated-values, 5830 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 48
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    CLAM age model and pollen profile of sediment core Kurota_Lowland (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abies; Abrupt Climate Changes and Environmental Responses; Accumulation model; Acer; ACER; Aesculus; Alnus; Amaranthaceae/Chenopodiaceae; Apiaceae; Araliaceae; Artemisia; Asteraceae; Betula; Buxus; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Carpinus/Ostrya; Carpinus tschonoskii; Caryophyllaceae; Castanea/Castanopsis; Celtis/Aphananthe; Classical age-modeling approach, CLAM (Blaauw, 2010); Corylus; Counting, palynology; Cryptomeria; Cupressaceae-type; Cyperaceae; DEPTH, sediment/rock; Ericaceae; Eriocaulon; Fagus crenata; Fagus japonica; Fraxinus; Geranium; Haloragis/Myriophyllum; Ilex; Impatiens; Juglans; Kurota_Lowland; Lagerstroemia; Lamiaceae; Ligustrum; Liliaceae; Lonicera; Lycopodium clavatum-type; Lycopodium inundatum-type; Lycopodium serratum-type; Lysichiton; Menyanthes; Myrica; Nuphar; Osmundaceae; Parthenocissus; Phellodendron; Picea; Pinus; Poaceae; Polygonum bistorta-type; Polygonum persicaria-type; Polypodiales; Pteridophyta; Pterocarya; Quercus subgen. Cyclobalanopsis; Quercus subgen. Lepidobalanus; Ranunculus; Rhus; Rosaceae; Sagittaria; Salix; Sample ID; Sanguisorba; Sciadopitys; Sparganium/Trapa; Sparganium/Typha; Sphagnum; Symplocos; Thalictrum; Tilia; Tsuga; Type of age model; Ulmus/Zelkova; Weigela
    Type: Dataset
    Format: text/tab-separated-values, 3195 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 49
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    CLAM age model and pollen profile of sediment core Lagaccione (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abies; Abrupt Climate Changes and Environmental Responses; Accumulation model; Acer; ACER; Alisma; Alnus; Apiaceae; Arbutus; Armeria; Artemisia; Asteraceae; Betula; Boraginaceae; Brassicaceae; Buxus; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Calligonum-type; Calluna; Campanulaceae; Cannabaceae; Carpinus betulus; Caryophyllaceae; Castanea; Cedrus; Centaurea; Chenopodiaceae; Cistus; Classical age-modeling approach, CLAM (Blaauw, 2010); Convolvulaceae; Corylus; Counting, palynology; Cyperaceae; Daphne; DEPTH, sediment/rock; Dipsacaceae; Ephedra distachya-type; Ephedra fragilis-type; Epilobium; Ericaceae; Euphorbia; Fagus; Filipendula; Fraxinus; Hedera; Helianthemum; Hippophae; Ilex; Juglans; Juniperus; Lagaccione; Lamiaceae; Leguminosae; Liliaceae; Lythraceae; Malvaceae; Myriophyllum alterniflorum; Myriophyllum spicatum; Nuphar; Nymphaeaceae; Olea; Osmunda; Ostrya; Phillyrea; Picea; Pinus; Pistacia; Plantago; Poaceae; Pollen indeterminata; Polygonum amphibium; Polygonum bistorta-type; Polypodiales; Polypodium; Populus; Potamogeton; Quercus; Quercus cerris/Quercus suber; Ranunculaceae; Rhamnus; Rosaceae; Rubiaceae; Rumex; Salix; Sambucus; Sample ID; Sanguisorba; Sparganium; Thalictrum; Tilia; Type of age model; Typha; Ulmus; Urticaceae; Utricularia; Valerianaceae; Viburnum; Viscum; Vitis; Zelkova
    Type: Dataset
    Format: text/tab-separated-values, 22710 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 50
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    CLAM age model and biomes of sediment core Khoe (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-27
    Keywords: Abrupt Climate Changes and Environmental Responses; Accumulation model; ACER; AGE; Calendar age; Calendar age, maximum/old; Calendar age, minimum/young; Classical age-modeling approach, CLAM (Blaauw, 2010); DEPTH, sediment/rock; Khoe; Pollen, boreal forest; Pollen, grassland; Pollen, subtropical forest; Pollen, temperate forest; Pollen, warm-temperate forest; Sample ID; Type of age model
    Type: Dataset
    Format: text/tab-separated-values, 551 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
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