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  • 1
    Publication Date: 2022-09-22
    Description: Arctic and alpine aquatic ecosystems are changing rapidly under recent global warming, threatening water resources by diminishing trophic status and changing biotic composition. Macrophytes play a key role in the ecology of freshwaters and we need to improve our understanding of long‐term macrophytes diversity and environmental change so far limited by the sporadic presence of macrofossils in sediments. In our study, we applied metabarcoding using the trnL P6 loop marker to retrieve macrophyte richness and composition from 179 surface‐sediment samples from arctic Siberian and alpine Chinese lakes and three representative lake cores. The surface‐sediment dataset suggests that macrophyte richness and composition are mostly affected by temperature and conductivity, with highest richness when mean July temperatures are higher than 12°C and conductivity ranges between 40 and 400 μS cm−1. Compositional turnover during the Late Pleistocene/Holocene is minor in Siberian cores and characterized by a less rich, but stable emergent macrophyte community. Richness decreases during the Last Glacial Maximum and rises during wetter and warmer climate in the Late‐glacial and Mid‐Holocene. In contrast, we detect a pronounced change from emergent to submerged taxa at 14 ka in the Tibetan alpine core, which can be explained by increasing temperature and conductivity due to glacial runoff and evaporation. Our study provides evidence for the suitability of the trnL marker to recover modern and past macrophyte diversity and its applicability for the response of macrophyte diversity to lake‐hydrochemical and climate variability predicting contrasting macrophyte changes in arctic and alpine lakes under intensified warming and human impact.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809
    Description: Second Tibetan Plateau Scientific Expedition and Research Program
    Description: https://doi.pangaea.de/10.1594/PANGAEA.920866
    Description: https://doi.org/10.5061/dryad.k6djh9w4r
    Keywords: ddc:577.63
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2024-02-09
    Description: Compilation of environmental data for the 262 investigated localities, which include additional intra-lake localities taken within three large lakes namely: 16-KP-01-L02 (9 samples), 16-KP-03-L10 (5 samples), 16-KP-04-L19 (4 samples). The table includes information about the geographic coordinates, elevation, type of sample material, geographic region, water depth (at which samples were taken), pH, water conductivity, mean annual precipitation (MAP), mean annual temperature (MAP), July and January mean temperature. Annual mean temperature, mean temperature in July and January, and mean annual precipitation were downloaded from WorldClim 2 (www.worldclim.org), and are based on the average climate data for the years 1970–2000 at a spatial resolution of 30 seconds (ca. 1 km^2^). The site-specific climate data was interpolated to the location area by using the R packages raster Hijmans 2020. Hijmans RJ (2020) raster: Geographic Data Analysis and Modeling. R package version 3.1-5. URL: https://CRAN.R-project.org/package=raster. Plant diversity in the Arctic and at high altitudes strongly depends on and rebounds to climatic and environmental variability and is nowadays tremendously impacted by recent climate warming. Therefore, past changes in plant diversity in the high Arctic and high-altitude regions are used to infer climatic and environmental changes through time and allow future predictions. Sedimentary DNA is an established proxy for the detection of local plant diversity in lake sediments, but still relationships between environmental conditions and preservation of the plant sedDNA proxy are far from being fully understood. Studying modern relationships between environmental conditions and plant sedDNA will improve our understanding under which conditions sedDNA is well-preserved helping to a.) evaluate suitable localities for sedDNA approaches, b.) provide analogues for preservation conditions and c.) conduct reconstruction of plant diversity and climate change. This study investigates modern plant diversity applying a plant-specific metabarcoding approach on sedimentary DNA (sedDNA) of surface sediment samples from 262 lake localities covering a large geographical, climatic and ecological gradient. Latitude ranges between 25°N and 73°N and longitude between 81°E and 161°E, including lowland lakes and elevated lakes up to 5168 m a.s.l. Further, our sampling localities cover a climatic gradient ranging in mean annual temperature between -15°C to +18°C and in mean annual precipitation between 36–935 mm. The localities in Siberia span over a large vegetational gradient including tundra, open woodland and boreal forest. Lake localities in China include alpine meadow, shrub, forest and steppe and also cultivated areas. The assessment of plant diversity in the underlying data set was conducted by a specific plant metabarcoding approach.
    Keywords: 05-LC-02; 05-LC-03; 05-LC-04; 05-LC-05; 05-LC-06; 05-LC-07; 05-LC-08; 05-LC-10; 05-LC-11; 05-Yak-03; 05-Yak-05; 05-Yak-06; 05-Yak-08; 05-Yak-10; 05-Yak-11; 05-Yak-14; 05-Yak-16; 05-Yak-17; 05-Yak-18; 05-Yak-19; 05-Yak-21; 05-Yak-23; 05-Yak-24; 05-Yak-25; 05-Yak-26; 05-Yak-27; 05-Yak-28; 07-SA-07; 07-SA-21; 07-SA-23; 07-SA-26; 07-SA-31; 07-SA-33; 07-SA-34; 08-KO-01; 08-KO-03; 08-KO-04; 08-KO-06; 08-KO-07; 08-KO-09; 08-KO-11; 08-KO-12; 08-KO-13; 08-KO-14; 08-KO-15; 08-KO-16; 08-KO-17; 08-KO-18; 08-KO-19; 08-KO-20; 08-KO-21; 08-KO-22; 08-KO-23; 08-KO-24; 08-KO-26; 08-KO-28; 08-KO-29; 08-KO-30; 08-KO-31; 08-KO-32; 08-KO-33; 09-TIK-03; 09-TIK-04; 09-TIK-05; 09-TIK-08; 09-TIK-09; 09-TIK-13; 09-TIK-14; 11-CH-02; 11-CH-05; 11-CH-06; 11-CH-09; 11-CH-10; 11-CH-11; 11-CH-12; 11-CH-13; 11-CH-14; 11-CH-15; 11-CH-17; 11-CH-18; 11-CH-19; 11-CH-20; 13-TY-01; 13-TY-02; 13-TY-03; 13-TY-04; 13-TY-05; 13-TY-06; 13-TY-07; 13-TY-08; 13-TY-09; 13-TY-10; 13-TY-11; 13-TY-12; 13-TY-13; 13-TY-14; 13-TY-15; 13-TY-16; 13-TY-17; 13-TY-18; 13-TY-19; 13-TY-20; 13-TY-21; 13-TY-22; 13-TY-23; 13-TY-24; 13-TY-25; 13-TY-26; 13-TY-27; 13-TY-28; 13-TY-29; 13-TY-30; 13-TY-31; 13-TY-32; 14-OM-01; 14-OM-02; 14-OM-03; 14-OM-04; 14-OM-05; 14-OM-06; 14-OM-07; 16-KP-01-L01; 16-KP-01-L02; 16-KP-01-L02_2; 16-KP-01-L02_3; 16-KP-01-L02_4; 16-KP-01-L02_5; 16-KP-01-L02_6; 16-KP-01-L02_7; 16-KP-01-L02_8; 16-KP-01-L02_9; 16-KP-01-L03; 16-KP-01-L04; 16-KP-02-L05; 16-KP-02-L06; 16-KP-02-L07; 16-KP-02-L08; 16-KP-02-L09; 16-KP-03-L10; 16-KP-03-L10_2; 16-KP-03-L10_3; 16-KP-03-L10_4; 16-KP-03-L10_5; 16-KP-03-L11; 16-KP-03-L13; 16-KP-03-L14; 16-KP-03-L15; 16-KP-03-L16; 16-KP-03-L17; 16-KP-03-L18; 16-KP-04-L19; 16-KP-04-L19_1; 16-KP-04-L19_2; 16-KP-04-L19_4; 16-KP-04-L20; 16-KP-04-L21; 16-KP-04-L22; Ailike; Angrenjin_Co; Arctic; Argaa-Bere; AWI_Envi; AWI Arctic Land Expedition; B-6; Balybyrbym; Bamuco; based on WORLDCLIM data; Beringia/Kolyma_2008; Bosten; Byluyng Kjuel; Central and eastern Siberia; Chagan_nuur; Chatanga2011; Chloroplast DNA; Choktokhoi; Cona; Conductivity, electrical; Daihai; Dajiaco; Dali; Dazeco; DEPTH, water; Donggi-Cona; E_ling; EG; Ekman grab; Elevation of event; Elgene Kjuel; Event label; Gahai_DLH; Gahai_QHH; Hala; HAND; Jiang_Co; Jinzihai; Keluke; Keperveem_2016; Kh; Kocha; Kol_01; Kol_03; Kol_04; Kol_06; Kol_07; Kol_09; Kol_11; Kol_12; Kol_13; Kol_14; Kol_15; Kol_16; Kol_17; Kol_18; Kol_19; Kol_20; Kol_21; Kol_22; Kol_24; Kol_27; Kol_28; Kol_29; Kol_30; Kol_31; Kolyma River; Kubalakh; Kuhai; Kyunde; lakes; Lang_Co; Latitude of event; Location; Longitude of event; Metabarcoding; MULT; Multiple investigations; Northern China and Xingjiang; Ochugui-Kengerime; Oibon; Omoloy2014; Pengco; pH; Pipa; Plant diversity; Polar Terrestrial Environmental Systems @ AWI; Precipitation, annual mean; Qinghai-Lake; RU-Land_2005_Verkhoyansk; RU-Land_2007_Saskylakh; RU-Land_2008_Kolyma; RU-Land_2009_Lena-transect; RU-Land_2011_Khatanga; RU-Land_2013_Taymyr; RU-Land_2014_Omoloy; RU-Land_2016_Keperveem; S-02; S-03; S-06; S-07; S-08; S-09; S-10; S101; S105; S-12; S123; S133; S-14; S143; S-15; S16; S-16; S169; S-17; S200; S213; S-22; S229; S237; S244; S-25; S259; S-26; S263; S310; S344; S350; S358; S374; S379; S414; S417; S420; S434; S439; S440; S62; Sailimu; Sample ID; Sample material; Sampling by hand; Saskylakh2007; Saskylakh region (Sakha), Russia; Sedimentary DNA; Selinco; SET-01; SET-02; SET-03; SET-04; SET-05; SET-06; SET-07; SET-08; SET-09; SET-11; SET-12; SET-13; SET-14; SET-15; SET-16; SET-18; SET-19; SET-20; SET-21; SET-22; SET-23; SET-24; SET-25; SET-26; SET-27; SET-28; SET-29; SET-30; SET-31; SET-32; Shengco; Species dominance; Species present; Sugan; Taymyr2013; Temperature, air, January; Temperature, air, July; Temperature, annual mean; Tibet; Tibet Plateau; Tiksi2009; Toiogoi; Toson; trnL P6 loop; Tschukotka, Sibiria, Russia; Vegetation; Vegetation type; Water sampler, UWITEC; WSUWI; Wulungu; Xingxinghai; Yakutia2005; Zhaling; Zharinanmu_Co
    Type: Dataset
    Format: text/tab-separated-values, 4325 data points
    Location Call Number Limitation Availability
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  • 3
    Publication Date: 2024-05-01
    Description: Radiocarbon dating performed on the total organic carbon content of 40 bulk sediments, measured at the MICADAS (Mini Carbon Dating System) Laboratory of AWI in Bremerhaven.
    Keywords: Accelerator mass spectrometry, Ionplus, Mini Carbon Dating System (MiCaDaS AWI); Age, 14C calibrated; Age, dated; Age, dated material; Age, dated standard deviation; AWI Arctic Land Expedition; Calendar age, maximum/old; Calendar age, minimum/young; DEPTH, sediment/rock; EN21103; Laboratory code/label; Lake sediment core; Lake Ulu; Oymyakon; Piston coring system, UWITEC Niederreiter, HYBRID 90 mm UHP 30450; radiocarbon age; RU-Land_2021_Yakutia; Siberia; Yakutsk
    Type: Dataset
    Format: text/tab-separated-values, 240 data points
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  • 4
    Publication Date: 2024-05-01
    Description: Element composition including total carbon (TC), total organic carbon (TOC), total inorganic carbon (TIC), and total nitrogen (TN), measured with a soli TOC cube (Elementar) and a rapid MAX N exceed (Elementar) at the Carbon and Nitrogen Laboratory of AWI in Potsdam. The minimum detectable concentration for natural sediment samples is 0.1% for both machines.
    Keywords: AGE; AWI Arctic Land Expedition; Carbon, inorganic, total; Carbon, organic, total; Carbon, total; Carbon Analyzer, Elementar, soli TOC cube; DEPTH, sediment/rock; EN21103; Geochemical proxies; Lake sediment core; Lake Ulu; Nitrogen, total; Nitrogen Analyzer, Elementar, rapid MAX N exceed; Oymyakon; Piston coring system, UWITEC Niederreiter, HYBRID 90 mm UHP 30450; radiocarbon age; RU-Land_2021_Yakutia; Siberia; TC; TIC; TN; TOC; Yakutsk
    Type: Dataset
    Format: text/tab-separated-values, 288 data points
    Location Call Number Limitation Availability
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  • 5
    Publication Date: 2024-05-01
    Description: This model was established based on the Bayesian method with a hiatus at 138 cm and calibrated using the IntCal20 dataset via the Bacon function in the R-package rbacon. A reservoir effect of approximately 351 ± 24 radiocarbon years has been determined by the dated surface sample (0−0.5 cm). We assumed a constant reservoir effect over time and therefore subtracted a value of 351 years from all radiocarbon ages.
    Keywords: Age model, Bacon (Blaauw & Christen, 2011); AWI Arctic Land Expedition; Calendar age; DEPTH, sediment/rock; EN21103; Geochemical proxies; Lake sediment core; Lake Ulu; Oymyakon; Piston coring system, UWITEC Niederreiter, HYBRID 90 mm UHP 30450; radiocarbon age; RU-Land_2021_Yakutia; Siberia; Yakutsk
    Type: Dataset
    Format: text/tab-separated-values, 2184 data points
    Location Call Number Limitation Availability
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  • 6
    Publication Date: 2024-05-01
    Description: Stable carbon isotopes of organic matter (d13C), measured with a Delta V Advantage Mass Spectrometer at the ISOLAB Facility of AWI in Potsdam. All isotopic values are reported in standard δ-notation in per mil (‰) relative to VPDB.
    Keywords: AGE; AWI Arctic Land Expedition; d13C; DEPTH, sediment/rock; EN21103; Geochemical proxies; Lake sediment core; Lake Ulu; Mass spectrometer Thermo Delta V Advantage; Oymyakon; Piston coring system, UWITEC Niederreiter, HYBRID 90 mm UHP 30450; RU-Land_2021_Yakutia; Siberia; TOC; Yakutsk; δ13C, organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 53 data points
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  • 7
    Publication Date: 2024-05-01
    Description: X-ray fluorescence (XRF) scanning data, scanned with an AvaaTech XRF core scanner at AWI in Potsdam. All elements were measured at X-ray voltages of 10 kV and 30 kV and currents of 0.45 mA and 0.15 mA, respectively, with a step size of 0.5 cm and an exposure time of 10 s. The data are semiquantitative and provide relative fluctuations in the chemical element compositions as counts per second (cps).
    Keywords: AGE; Aluminium; Argon; AWI Arctic Land Expedition; Bromine; Calcium; Chlorine; Copper; DEPTH, sediment/rock; EN21103; Geochemical proxies; Iron; Lake sediment core; Lake Ulu; Magnesium; Manganese; Molybdenum; Nickel; Niobium; Oymyakon; Phosphorus; Piston coring system, UWITEC Niederreiter, HYBRID 90 mm UHP 30450; Potassium; Rhodium, coherent scattering intensity; Rhodium, incoherent scattering intensity; Rubidium; RU-Land_2021_Yakutia; Siberia; Silicon; Strontium; Sulfur; Titanium; Vanadium; X-ray fluorescence core scanner (XRF), Avaatech; XRF; Yakutsk; Yttrium; Zinc; Zirconium
    Type: Dataset
    Format: text/tab-separated-values, 57078 data points
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  • 8
    Publication Date: 2024-05-03
    Description: Here are the bundled geochemical data of an 11-m-long sediment core EN21103 collected from Lake Ulu (63.34°N, 141.05°E) in the Oymyakon region (eastern Siberia) during the 'Yakutia 2021' expedition, which consists of five datasets. (1) Dataset 1. Radiocarbon dating performed on the total organic carbon content of 40 bulk sediments, measured at the MICADAS (Mini Carbon Dating System) Laboratory of AWI in Bremerhaven. (2) Dataset 2. Bacon age-depth model. This model was established based on the Bayesian method with a hiatus at 138 cm and calibrated using the IntCal20 dataset via the Bacon function in the R-package rbacon. A reservoir effect of approximately 351 ± 24 radiocarbon years has been determined by the dated surface sample (0−0.5 cm). We assumed a constant reservoir effect over time and therefore subtracted a value of 351 years from all radiocarbon ages. (3) Dataset 3. Element composition including total carbon (TC), total organic carbon (TOC), total inorganic carbon (TIC), and total nitrogen (TN), measured with a soli TOC cube (Elementar) and a rapid MAX N exceed (Elementar) at the Carbon and Nitrogen Laboratory of AWI in Potsdam. The minimum detectable concentration for natural sediment samples is 0.1% for both machines. (4) Dataset 4. Stable carbon isotopes of organic matter (d13C), measured with a Delta V Advantage Mass Spectrometer at the ISOLAB Facility of AWI in Potsdam. All isotopic values are reported in standard δ-notation in per mil (‰) relative to VPDB. (5) Dataset 5. X-ray fluorescence (XRF) scanning data, scanned with an AvaaTech XRF core scanner at AWI in Potsdam. All elements were measured at X-ray voltages of 10 kV and 30 kV and currents of 0.45 mA and 0.15 mA, respectively, with a step size of 0.5 cm and an exposure time of 10 s. The data are semiquantitative and provide relative fluctuations in the chemical element compositions as counts per second (cps).
    Keywords: d13C; Geochemical proxies; Lake sediment core; Lake Ulu; Oymyakon; radiocarbon age; Siberia; TC; TIC; TN; TOC; XRF
    Type: Dataset
    Format: application/zip, 5 datasets
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  • 9
    Publication Date: 2021-10-12
    Description: Plant diversity in the Arctic and at high altitudes strongly depends on and rebounds to climatic and environmental variability and is nowadays tremendously impacted by recent climate warming. Therefore, past changes in plant diversity in the high Arctic and high-altitude regions are used to infer climatic and environmental changes through time and allow future predictions. Sedimentary DNA (sedDNA) is an established proxy for the detection of local plant diversity in lake sediments, but still relationships between environmental conditions and preservation of the plant sedDNA proxy are far from being fully understood. Studying modern relationships between environmental conditions and plant sedDNA will improve our understanding under which conditions sedDNA is well-preserved helping to a.) evaluate suitable localities for sedDNA approaches, b.) provide analogues for preservation conditions and c.) conduct reconstruction of plant diversity and climate change. This study investigates modern plant diversity applying a plant-specific metabarcoding approach on sedimentary DNA of surface sediment samples from 262 lake localities covering a large geographical, climatic and ecological gradient. Latitude ranges between 25°N and 73°N and longitude between 81°E and 161°E, including lowland lakes and elevated lakes up to 5168 m a.s.l. Further, our sampling localities cover a climatic gradient ranging in mean annual temperature between -15°C and +18°C and in mean annual precipitation between 36­ and 935 mm. The localities in Siberia span over a large vegetational gradient including tundra, open woodland and boreal forest. Lake localities in China include alpine meadow, shrub, forest and steppe and also cultivated areas. The assessment of plant diversity in the underlying dataset was conducted by a specific plant metabarcoding approach.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 10
    Publication Date: 2022-11-02
    Description: Alpine ecosystems on the Tibetan Plateau are being threatened by ongoing climate warming and intensified human activities. Ecological time-series obtained from sedimentary ancient DNA (sedaDNA) are essential for understanding past ecosystem and biodiversity dynamics on the Tibetan Plateau and their responses to climate change at a high taxonomic resolution. Hitherto only few but promising studies have been published on this topic. The potential and limitations of using sedaDNA on the Tibetan Plateau are not fully understood. Here, we (i) provide updated knowledge of and a brief introduction to the suitable archives, region-specific taphonomy, state-of-the-art methodologies, and research questions of sedaDNA on the Tibetan Plateau; (ii) review published and ongoing sedaDNA studies from the Tibetan Plateau; and (iii) give some recommendations for future sedaDNA study designs. Based on the current knowledge of taphonomy, we infer that deep glacial lakes with freshwater and high clay sediment input, such as those from the southern and southeastern Tibetan Plateau, may have a high potential for sedaDNA studies. Metabarcoding (for microorganisms and plants), metagenomics (for ecosystems), and hybridization capture (for prehistoric humans) are three primary sedaDNA approaches which have been successfully applied on the Tibetan Plateau, but their power is still limited by several technical issues, such as PCR bias and incompleteness of taxonomic reference databases. Setting up high-quality and open-access regional taxonomic reference databases for the Tibetan Plateau should be given priority in the future. To conclude, the archival, taphonomic, and methodological conditions of the Tibetan Plateau are favorable for performing sedaDNA studies. More research should be encouraged to address questions about long-term ecological dynamics at ecosystem scale and to bring the paleoecology of the Tibetan Plateau into a new era.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
    Format: application/pdf
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