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  • 1
    Publication Date: 2023-12-14
    Description: Estimates of Last Glacial Maximum annual mean seawater temperatures at 50 m depth. The temperature estimates are derived using the Modern Analogue Technique using the ForCenS synthesis (https://doi.org/10.1594/PANGAEA.873570; https://doi.org/10.1038/sdata.2017.109) and World Ocean Atlas 1998 temperature (http://www.nodc.noaa.gov/oc5/woa98.html) for calibration. Dissimilarity to the core top data was calculated using the square-chord distance and the temperatures are the weighted averages of the 10 closest analogues. Estimates were averaged for sites where more than a single sample is available. This data set contains ensembles of the reconstructed temperature with spatially correlated noise with characteristics of the transfer function model residuals. Please refer to https://doi.pangaea.de/10.1594/PANGAEA.962957 for details on linking IDs to sediment cores.
    Keywords: Center for Marine Environmental Sciences; Group; Identification; LATITUDE; LGM; LONGITUDE; MARUM; Planktonic foraminifera; Seawater temperature; Temperature anomaly; transfer function
    Type: Dataset
    Format: text/tab-separated-values, 1941000 data points
    Location Call Number Limitation Availability
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  • 2
    Publication Date: 2024-05-21
    Description: Estimates of Last Glacial Maximum annual mean seawater temperatures at 50 m depth. The temperature estimates are derived using the Modern Analogue Technique using the ForCenS synthesis (https://doi.org/10.1594/PANGAEA.873570; https://doi.org/10.1038/sdata.2017.109) and World Ocean Atlas 1998 temperature (http://www.nodc.noaa.gov/oc5/woa98.html) for calibration. Dissimilarity to the core top data was calculated using the square-chord distance and the temperatures are the weighted averages of the 10 closest analogues. Estimates were averaged for sites where more than a single sample is available. The data contain a unique ID for each site, a core name, longitude and latitude, temperature in degree Celsius, the temperature anomaly with respect to the World Ocean Atlas climatology, an estimate of species turnover with respect to the nearest core top sample (Bray-Curtis dissimilarity), the number of data points and the uncertainty of the temperature estimate. This uncertainty considers spatial autocorrelation in the training set. It is reduced by the square root of n whenever multiple samples were available for a site.
    Keywords: 06MT15_2; 06MT41_3; 087-1; 101; 108-658C; 111-677B; 117-723A; 122-760A; 122-762B; 134-828; 138-846B; 145-883D; 160-969; 160-973; 161-975; 161-977; 165-999A; 181-1123; 202-1240; 2065N; 21SL; 31-KL; 39KL; 41; 47KL; 509-1; 63F/NL; 6706-2; 70-506B; 7SL; 80KB11; 90-588; 90-591; 90-592; 90-593; 90-594; 94a; 99; A150/180; A15558; A156-4; A164-24; A164-5; A164-6; A164-61; A167-13; A167-14; A172-1; A172-2; A173-4; A179-15; A180-15; A180-16; A180-32; A180-39; A180-47; A180-48; A180-56; A180-72; A180-73; A180-74; A180-76; A180-9; Aegean Sea; After Bray & Curtis (1957); AGSO Cruise 147; Agulhas Current; Akademik M.A. Lavrentiev; Alboran Sea; ALIENOR; also published as VM28-122; AMADEUS; Amazon Fan; Angola Basin; Antarctic Ocean; ANT-IV/1c; APSARA4; Arabian Sea; Arctic Ocean; Argentine Basin; ARK-II/4; ARK-IV/3; ARK-IX/4; ARK-V/3b; ARK-VII/3b; ARK-VIII/2; ARK-X/2; ARK-XIII/2; ARK-XIII/3; Atlantic Ocean; AUSCAN; B0914c/53; Barents Sea; Baruna Jaya I; Bay of Biscay; BC; BC15; Bear Island Fan; Belgica; BG09/14c; Biscaya; BOFS11882#4; BOFS11886#2; BOFS11896#1; BOFS11902#1; BOFS11905#1; BOFS14K; BOFS16K; BOFS17K; BOFS31/1K; BOFS31#1; BOFS5K; BOFS8K; Box corer; Brazil Basin; BS79-22; BS79-33; BS79-37; BS79-38; CALYPSO; CALYPSO2; Calypso Corer; Calypso Corer II; Calypso square corer; Canarias Sea; Cape Basin; Caribbean Sea; CASQ; CD154; CD154-17-17K; CD53; Center for Marine Environmental Sciences; CEPAG; CH82-24; CH8X; Charles Darwin; CHAT_1k; CLIVAMPcruises; Cocos Ridge; COMPCORE; Composite Core; CONDOR-Ia; Coral Sea; Core; CORE; D184; DED87-07; DEDALE87; Denmark Strait; DGKS9603; Discovery (1962); DRILL; Drilling/drill rig; E26-1; E27-23; E27-30; E45-027; E45-078; E45-102; E45-29; E48-003; E48-022; E48-023; E48-027; E48-028; E48-035; E48-11A; E49-023; E49-18; E49-21; E55-6; East Atlantic; East Brazil Basin; Eastern Basin; Eastern Rio Grande Rise; eastern Romanche Fracture Zone; Eastern slope of Kurile Basin; East Pacific; Emperor Seamounts; EN6610; Equatorial Atlantic; Equatorial East Pacific; ETNA80; ETNA82; Event label; FAEGAS_IV; Faeroes Bank; FGGE-Equator 79 - First GARP Global Experiment; FR1/94-GC3; FR10/95; FR10/95-11; FR10/95-14; FR10/95-17; FR10/95-20; FR10/95-29; FR10/95-GC05; FR10/95 GC-29; FR2/96; FR2/96-10; FR2/96-17; FR2/96-27; Fram Strait; Franklin; French Guiana; GC; Genesis III, RR9702A; GeoB10029-4; GeoB10038-4; GeoB10042-1; GeoB10043-3; GeoB10053-7; GeoB10069-3; GeoB1008-3; GeoB1016-3; GeoB1028-5; GeoB1031-4; GeoB1032-3; GeoB1034-3; GeoB1041-3; GeoB1101-5; GeoB1105-4; GeoB1112-4; GeoB1115-4; GeoB1117-2; GeoB1214-1; GeoB1220-1; GeoB12615-4; GeoB1306-1; GeoB1309-2; GeoB1312-2; GeoB1413-4; GeoB1417-1; GeoB1419-2; GeoB1501-4; GeoB1503-1; GeoB1505-1; GeoB1508-4; GeoB1515-1; GeoB1523-1; GeoB16224-1; GeoB1701-4; GeoB1706-2; GeoB1711; GeoB1711-4; GeoB1722-1; GeoB18530-1; GeoB1903-3; GeoB1905-3; GeoB2004-2; GeoB2016-1; GeoB2019-1; GeoB2021-5; GeoB20616-1; GeoB2104-3; GeoB2109-1; GeoB2116-4; GeoB2117-1; GeoB2125-1; GeoB2202-4; GeoB2204-2; GeoB2215-10; GeoB2819-1; GeoB3104-1; GeoB3117-1; GeoB3175-1; GeoB3176-1; GeoB3302-1; GeoB3603-2; GeoB3722-2; GeoB3801-6; GeoB3808-6; GeoB3813-3; GeoB5112-5; GeoB5115-2; GeoB5121-2; GeoB5133-3; GeoB5140-3; GeoB7010-2; GEOFAR; GEOTROPEX 83, NOAMP I; Giant box corer; Giant piston corer; GIK12309-2; GIK12310-4; GIK12328-5; GIK12329-6; GIK12337-5; GIK12345-5; GIK12347-2; GIK12379-1; GIK12392-1; GIK13289-3; GIK13291-1; GIK13519-1; GIK13521-1; GIK15612-2; GIK15627-3; GIK15637-1; GIK15669-1; GIK16017-2; GIK16396-1; GIK16397-2; GIK16415-2; GIK16457-2; GIK16458-1; GIK16458-2; GIK16772-2; GIK16776-1; GIK16867-2; GIK17045-3; GIK17049-6; GIK17050-1; GIK17051-3; GIK17055-1; GIK17724-2; GIK17725-1; GIK17730-4; GIK17938-2; GIK17940-2; GIK17954-1; GIK21533-3 PS11/412; GIK21730-2 PS13/224; GIK23056-2; GIK23065-2; GIK23071-3; GIK23074-1; GIK23230-1 PS05/416; GIK23262-2; GIK23294-4; GIK23351-1; GIK23354-6; GIK23419-8; GIK23519-5; GKG; GL1090; GL-1090; GL-77; Glomar Challenger; GPC; Gravity corer; Gravity corer (Kiel type); Greenland Sea; Greenland Slope; Guinea Basin; Gulf of Cádiz, Atlantic Ocean; Hikurangi margin; HU87-033-008; HU90-13-013; HU91-045-090; HUD90/13; Hudson; Hunter Channel; Identification; IMAGES I; IMAGES III - IPHIS; IMAGES IV-IPHIS III; IMAGES V; IMAGES VIII - MONA; IMAGES VII - WEPAMA; IMAGES XV - Pachiderme; Indian Ocean; Jean Charcot; Joides Resolution; JOPSII-6; K12; K708-004; K708-006; K708-007; K708-008; K714-3; KAL; KALMAR II; Kasten corer; KET80-03; KET80-19; KET80-22; KET80-39; KET82-16; KF09; KF13; KF16; KH90-3-P2; KH92-1-3a; KH92-1-5a; KL; KL96; KN708-1; KOL; KOMEX II; KS310; LATITUDE; LC01; LC04_LGM; LC07; LC21, LC-21; Leg108; Leg111; Leg117; Leg122; Leg134; Leg138; Leg145; Leg160; Leg161; Leg165; Leg181; Leg202; Leg70; Leg90; Le Noroit; Le Suroît; LGM; LONGITUDE; LV29-114-3; LV29-2; M11/1; M12/1; M123; M123_175-1; M12392-1; M13/2; M15/2; M16/1; M16/2; M17/2; M2/2; M20/2; M23/1; M23/2; M23/3; M25; M29/2; M34/1; M34/2; M34/3; M35/1; M35003-4; M35027-1; M39; M41/3; M49/4; M51; M53; M53_169; M57; M6/5; M6/6; M60; M65; M7/2; M7/3; M7/5; M75/2; M75/2_103-4; M9/4; Marge Ibérique; Maria S. Merian; Marion Dufresne (1972); Marion Dufresne (1995); MARUM; MATACORE; MD012390; MD01-2390; MD012394; MD01-2394; MD012398; MD01-2398; MD012409; MD01-2409; MD012416; MD01-2416; MD022489; MD02-2489; MD022520; MD02-2520; MD022523; MD02-2523; MD022529; MD02-2529; MD032607; MD03-2607; MD03-2705; MD04-2797CQ; MD04-2805CQ; MD04-2805Q; MD04-2845; MD062986; MD06-2986; MD07-3082; MD07-3088; MD07-3100; MD10; MD101; MD104; MD106; MD111; MD114; MD122; MD126; MD13; MD131; MD134; MD140; MD141; MD152; MD159; MD76-131; MD77-169; MD77-171; MD77-179; MD77-180; MD77-191; MD77-194; MD77-203; MD81; MD81-LC07; MD81-LC21; MD84-627; MD84-629; MD84-632; MD84-641; MD88-770; MD90-901; MD90-917; MD952011; MD95-2011; MD952012; MD95-2012; MD952039; MD95-2039; MD952040; MD95-2040; MD952041; MD95-2041; MD952042; MD95-2042; MD952043; MD95-2043; MD96-2048; MD972121; MD97-2121; MD972138; MD97-2138; MD972142; MD97-2142; MD972148; MD97-2148; MD972151; MD97-2151; MD982195; MD98-2195; MD982196; MD98-2196; MD99-2281; MD99-2285; MD99-2331; MD99-2339; MD99-2344; MD99-2346; ME0005A-3JC; Mediterranean Sea; Meteor (1964); Meteor (1986); Mid Atlantic Ridge; MONITOR MONSUN; MSM20/3; MSM39; MUC; MultiCorer; N.Faeroes; NA87-22; Namibia continental slope; NE-Brazilian continental margin; Neofan; Niger Sediment Fan; NOE; North Atlantic; Northeast Atlantic; North East Atlantic; Northern Cape Basin; North Levantine Basin; North Pacific/MOUND; North Pacific Ocean; Northwest Atlantic; Norwegian-Greenland Sea; Norwegian Sea; NS07-25; Number of samples; OD-041-04; Oden; ODEN-96; off Gabun; off Iceland; off Liberia; off NW Africa; off West Africa; OSIRIS5; OSIRIS II; OSIRIS III; P69; PABESIA; Pacific/off Hawaii; PALAEOFLUX; PALEOCINAT; PALEOCINAT II; PC; PC17; PEGASE; PICABIA; Piston corer; Piston corer (BGR type); Piston corer (Kiel type); Planktonic foraminifera; PO158/B; Polarstern; Porto Seamount; POS158/2; POS210/2; Poseidon; PRIVILEGE; PROMETEI; PROMETEII; PS05; PS08; PS11; PS1230-1; PS13 GRÖKORT; PS1533-3; PS17; PS17/242; PS17/245; PS17/251; PS17/290; PS1730-2; PS19/100; PS19/112; PS1919-2; PS1922-1; PS1927-2; PS1951-1; PS19 EPOS II; PS2129-1; PS2138-1; PS2446-4; PS2613-6; PS2644-5; PS27; PS27/020; PS2837-5; PS2876-1; PS2887-1; PS31; PS31/113; PS31/160-5; PS44; PS44/065; PS45; PS45/029; PS45/058; Q200; Q585; R657; RC08; RC08-145; RC08-148; RC08-39; RC08-78; RC09; RC09-124; RC09-126; RC09-150; RC09-161; RC09-162; RC09-178; RC09-191; RC09-225; RC09-49; RC10; RC10-11_LGM; RC10-131; RC10-16_LGM; RC10-50; RC10-62; RC10-97; RC11; RC1112; RC11-120; RC11-121; RC11-126; RC11-134; RC11-145; RC11-147; RC11-21; RC11-210; RC11-213; RC11-22; RC11-23; RC11-26; RC11-86; RC12; RC12-10; RC12-109; RC12-113; RC12-234; RC12-241; RC12-267; RC12-291; RC12-294; RC12-328; RC12-339; RC12-340; RC12-341; RC12-343; RC12-344; RC12-36; RC13; RC13-11; RC13-110; RC13-115; RC13-151; RC13-152; RC13-153; RC13-
    Type: Dataset
    Format: text/tab-separated-values, 4529 data points
    Location Call Number Limitation Availability
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  • 3
    Publication Date: 2024-05-21
    Description: Estimates of Last Glacial Maximum annual mean seawater temperatures at 50 m depth. The temperature estimates are derived using the Modern Analogue Technique using the ForCenS synthesis (https://doi.org/10.1594/PANGAEA.873570; https://doi.org/10.1038/sdata.2017.109) and World Ocean Atlas 1998 temperature (http://www.nodc.noaa.gov/oc5/woa98.html) for calibration. Dissimilarity to the core top data was calculated using the square-chord distance and the temperatures are the weighted averages of the 10 closest analogues. Estimates were averaged for sites where more than a single sample is available.
    Keywords: Center for Marine Environmental Sciences; LGM; MARUM; Planktonic foraminifera; Seawater temperature; transfer function
    Type: Dataset
    Format: application/zip, 2 datasets
    Location Call Number Limitation Availability
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  • 4
    Publication Date: 2024-02-07
    Description: Planktonic Foraminifera are unique paleo-environmental indicators through their excellent fossil record in ocean sediments. Their distribution and diversity are affected by different environmental factors including anthropogenically forced ocean and climate change. Until now, historical changes in their distribution have not been fully assessed at the global scale. Here we present the FORCIS (Foraminifera Response to Climatic Stress) database on foraminiferal species diversity and distribution in the global ocean from 1910 until 2018 including published and unpublished data. The FORCIS database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump, and contains similar to 22,000, similar to 157,000, similar to 9,000, similar to 400 subsamples, respectively (one single plankton aliquot collected within a depth range, time interval, size fraction range, at a single location) from each category. Our database provides a perspective of the distribution patterns of planktonic Foraminifera in the global ocean on large spatial (regional to basin scale, and at the vertical scale), and temporal (seasonal to interdecadal) scales over the past century.
    Type: Article , PeerReviewed
    Format: text
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  • 5
    Publication Date: 2024-05-17
    Description: The cold Last Glacial Maximum, around 20,000 years ago, provides a useful test case for evaluating whether climate models can simulate climate states distinct from the present. However, because of the indirect and uncertain nature of reconstructions of past environmental variables such as sea surface temperature, such evaluation remains ambiguous. Instead, here we evaluate simulations of Last Glacial Maximum climate by relying on the fundamental macroecological principle of decreasing community similarity with increasing thermal distance. Our analysis of planktonic foraminifera species assemblages from 647 sites reveals that the similarity-decay pattern that we obtain when the simulated ice age seawater temperatures are confronted with species assemblages from that time differs from the modern. This inconsistency between the modern temperature dependence of plankton species turnover and the simulations arises because the simulations show globally rather uniform cooling for the Last Glacial Maximum, whereas the species assemblages indicate stronger cooling in the subpolar North Atlantic. The implied steeper thermal gradient in the North Atlantic is more consistent with climate model simulations with a reduced Atlantic meridional overturning circulation. Our approach demonstrates that macroecology can be used to robustly diagnose simulations of past climate and highlights the challenge of correctly resolving the spatial imprint of global change in climate models.
    Type: Article , PeerReviewed
    Format: text
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  • 6
    Publication Date: 2023-09-27
    Description: 〈jats:p〉Anthropogenic climate change is altering global biogeographical patterns. However, it remains difficult to quantify how bioregions are changing because pre‐industrial records of species distributions are rare. Marine microfossils, such as planktonic foraminifera, are preserved in seafloor sediments and allow the quantification of bioregions in the past. Using a recently compiled data set of pre‐industrial species composition of planktonic foraminifera in 3802 worldwide seafloor sediments, we employed multivariate and statistical model‐based approaches to study spatial turnover in order to 1) quantify planktonic foraminifera bioregions and 2) understand the environmental drivers of species turnover. Four latitudinally banded bioregions emerge from the global assemblage data. The polar and temperate bioregions are bi‐hemispheric, supporting the idea that planktonic foraminifera species are not limited by dispersal. The equatorial bioregion shows complex longitudinal patterns and overlaps in sea surface temperature (SST) range with the tropical bioregion. Compositional‐turnover models (Bayesian bootstrap generalised dissimilarity models) identify SST as the strongest driver of species turnover. The turnover rate is constant across most of the SST gradient, showing no SST threshold values with rapid shifts in species composition, but decelerates above 25°C, suggesting SST is less predictive of species composition in warmer waters. Other environmental predictors affect species turnover non‐linearly, and their importance differs across regions. In the Pacific ocean, net primary productivity below 500 mgC m〈jats:sup〉−2〈/jats:sup〉 day〈jats:sup〉−1〈/jats:sup〉 drives fast compositional change. Water depth values below 3000 m (which affect calcareous microfossil preservation) increasingly drive changes in species composition among death assemblages in the Pacific and Indian oceans. Together, our results suggest that the dynamics of planktonic foraminifera bioregions are expected to be highly responsive to climate change; however, at lower latitudes, environmental drivers other than SST may affect these dynamics.〈/jats:p〉
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
    Format: application/pdf
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  • 7
    Publication Date: 2023-09-22
    Description: Thresholds and tipping points are frequently used concepts to address the risks of global change pressures and their mitigation. It is tempting to also consider them to understand biodiversity change and design measures to ensure biotic integrity. Here, we argue that thresholds and tipping points do not work well in the context of biodiversity change for conceptual, ethical, and empirical reasons. Defining a threshold for biodiversity change (a maximum tolerable degree of turnover or loss) neglects that ecosystem multifunctionality often relies on the complete entangled web of species interactions and invokes the ethical issue of declaring some biodiversity dispensable. Alternatively defining a threshold for pressures on biodiversity might seem more straightforward as it addresses the causes of biodiversity change. However, most biodiversity change appears to be gradual and accumulating over time rather than reflecting a disproportionate change when transgressing a pressure threshold. Moreover, biodiversity change is not in synchrony with environmental change, but massively delayed through inertia inflicted by population dynamics and demography. In consequence, formulating environmental management targets as preventing the transgression of thresholds is less useful in the context of biodiversity change, as such thresholds neither capture how biodiversity responds to anthropogenic pressures nor how it links to ecosystem functioning. Instead, addressing biodiversity change requires reflecting the spatiotemporal complexity of altered local community dynamics and temporal turnover in composition leading to shifts in distributional ranges and species interactions.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
    Format: application/pdf
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  • 8
    Publication Date: 2024-03-13
    Description: The cold Last Glacial Maximum, around 20,000 years ago, provides a useful test case for evaluating whether climate models can simulate climate states distinct from the present. However, because of the indirect and uncertain nature of reconstructions of past environmental variables such as sea surface temperature, such evaluation remains ambiguous. Instead, here we evaluate simulations of Last Glacial Maximum climate by relying on the fundamental macroecological principle of decreasing community similarity with increasing thermal distance. Our analysis of planktonic foraminifera species assemblages from 647 sites reveals that the similarity-decay pattern that we obtain when the simulated ice age seawater temperatures are confronted with species assemblages from that time differs from the modern. This inconsistency between the modern temperature dependence of plankton species turnover and the simulations arises because the simulations show globally rather uniform cooling for the Last Glacial Maximum, whereas the species assemblages indicate stronger cooling in the subpolar North Atlantic. The implied steeper thermal gradient in the North Atlantic is more consistent with climate model simulations with a reduced Atlantic meridional overturning circulation. Our approach demonstrates that macroecology can be used to robustly diagnose simulations of past climate and highlights the challenge of correctly resolving the spatial imprint of global change in climate models.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
    Location Call Number Limitation Availability
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