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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: 2023-03-16
    Description: Here, we provide the raw pollen data archived in three Siberian lake sediment cores spanning the mid-Holocene to the present (7.6-0 cal ka BP), from northern typical tundra to southern open larch forest in the Omoloy region. There are three cores: 1. 14-OM-20B, Lat. / °: 70.53, Lon. / °: 132.91, Ele. / m a.s.l.: 52, Modern vegetation: open larch forest, Lake area / km2: 0.26, Maximal depth / m: 3.4 2. 14-OM-02B, Lat. / °: 70.72, Lon. / °: 132.67, Ele. / m a.s.l.: 58, Modern vegetation: forest tundra, Lake area / km2: 0.08, Maximal depth / m: 3.5 3. 14-OM-12A, Lat. / °: 70.96, Lon. / °: 132.57, Ele. / m a.s.l.: 60, Modern vegetation: tundra, Lake area / km2: 0.09, Maximal depth / m: 4.5 Three lake sediment cores, 14OM12A (33 cm long), 14OM02B (49.5 cm long) and 14OM20B (86 cm long), were recovered from three sites using a UWITEC gravity corer (6 cm internal diameter) equipped with a hammer tool in July 2014. From the three cores, 16 bulk organic carbon samples were selected because of the lack of macrofossil remains and radiocarbon dated using accelerator mass spectrometry (AMS) at Poznań radiocarbon laboratory of Adam Mickiewicz University, Poland. In addition, 30 freeze-dried samples per core at 0.25 or 0.5 cm intervals between 0 and 15 cm were analysed for 210Pb/137Cs at the Liverpool University Environmental Radioactivity Laboratory. In this project, we analyse pollen and sedaDNA (Liu et al., 2020; doi:10.5061/dryad.69p8cz900) from three lake sediment cores from the Omoloy region in north-eastern Siberia (northern Yakutia), which are currently surrounded by different vegetation types ranging from typical tundra to open larch forest. First, our aim is to compare sedaDNA with the pollen data to see whether both methods track the same pattern with respect to compositional changes and diversity changes across the northern Russian treeline zone or are complementary to each other. Second, we reconstruct the mid- to late-Holocene changes of vegetation composition along a north–south transect. Third, we use the sedaDNA data to reconstruct variations in species richness and relate this to vegetation and climate change.
    Keywords: AWI_Envi; dating; Lake Omoloy; mid-holocene; north-eastern Siberia; Polar Terrestrial Environmental Systems @ AWI; Pollen
    Type: Dataset
    Format: application/zip, 5 datasets
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  • 3
    Publication Date: 2023-06-27
    Keywords: 14-OM-02B; 14-OM-12A; 14-OM-20B; Age, dated; Age, dated standard error; AWI_Envi; AWI Arctic Land Expedition; Calendar age; Comment; Core; dating; DEPTH, sediment/rock; Event label; GCUWI; Gravity corer, UWITEC; Laboratory code/label; Lake Omoloy; mid-holocene; north-eastern Siberia; Omoloy2014; Omoloy region, north-eastern Siberia; Polar Terrestrial Environmental Systems @ AWI; RU-Land_2014_Omoloy
    Type: Dataset
    Format: text/tab-separated-values, 84 data points
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  • 4
    Publication Date: 2023-06-27
    Keywords: 14-OM-02B; 14-OM-12A; 14-OM-20B; Accumulation rate per year; Age; Age, error; Age, ²¹⁰Pbₓₛ/¹³⁷Cs Lead-Caesium; AWI_Envi; AWI Arctic Land Expedition; Calculated; Comment; Cumulative mass; dating; DEPTH, sediment/rock; Error, relative; Event label; GCUWI; Gravity corer, UWITEC; Lake Omoloy; mid-holocene; north-eastern Siberia; Omoloy2014; Omoloy region, north-eastern Siberia; Polar Terrestrial Environmental Systems @ AWI; RU-Land_2014_Omoloy; Sedimentation rate per year
    Type: Dataset
    Format: text/tab-separated-values, 207 data points
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  • 5
    Publication Date: 2023-11-01
    Keywords: 14-OM-12A; AGE; Alnus fruticosa-type; Amaranthaceae; Antennaria villifera; Artemisia; Asteraceae; Astragalus; AWI_Envi; AWI Arctic Land Expedition; Betula nana; Betula pubescens; Bistorta; Brassicaceae; Campanula; Caryophyllaceae undifferentiated; Comarum palustre; Counting, palynology; Cyperaceae; dating; Dianthus; Diapensia obovata; Dry mass; Ephedra; Ericaceae undifferentiated; Fabaceae; GCUWI; Gentiana; Gravity corer, UWITEC; Ilex; Labiatae; Lake Omoloy; Larix; Ledum palustre; Lycopodium (counted); Lycopodium spores added; mid-holocene; Minuartia arctica; north-eastern Siberia; Omoloy2014; Omoloy region, north-eastern Siberia; Pachypleurum; Pedicularis; Picea; Pinus diploxylon-type; Pinus subgen. Strobus-type; Plantago; Poaceae; Polar Terrestrial Environmental Systems @ AWI; Pollen; Pollen, total; Pollen indeterminata; Polygonaceae; Polygonum; Populus; Potentilla-type; Ranunculaceae; Ranunculus lapponicus; Rhodiola rosea; Rhododendron; Rosaceae; Rubiaceae; Rubus; RU-Land_2014_Omoloy; Rumex; Salix; Sample ID; Saxifraga; Senecio congestus; Silene; Taraxacum-type; Thalictrum; Unknown; Vaccinium vitis-idaea; Valeriana
    Type: Dataset
    Format: text/tab-separated-values, 532 data points
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  • 6
    Publication Date: 2023-11-01
    Keywords: 14-OM-02B; Abies; AGE; Alnus fruticosa-type; Amaranthaceae; Antennaria villifera; Artemisia; Asteraceae; Astragalus; AWI_Envi; AWI Arctic Land Expedition; Betula nana; Betula pubescens; Bistorta; Brassicaceae; Campanula; Caryophyllaceae undifferentiated; Chamaenerion latifolium; Comarum palustre; Counting, palynology; Cyperaceae; dating; Dianthus; Draba hirta; Dry mass; Ericaceae undifferentiated; Fabaceae; GCUWI; Gravity corer, UWITEC; Labiatae; Lake Omoloy; Larix; Ledum palustre; Lycopodium (counted); Lycopodium spores added; mid-holocene; Minuartia arctica; north-eastern Siberia; Omoloy2014; Omoloy region, north-eastern Siberia; Orthilia obtusata; Pachypleurum; Pedicularis; Pinus diploxylon-type; Pinus subgen. Strobus-type; Plantago; Poaceae; Polar Terrestrial Environmental Systems @ AWI; Pollen; Pollen, total; Pollen indeterminata; Polygonaceae; Polygonum; Potentilla-type; Ranunculaceae; Ranunculus; Rhodiola rosea; Rhododendron; Rosaceae; Rubus; RU-Land_2014_Omoloy; Rumex; Salix; Sample ID; Saxifraga; Scrophulariaceae; Senecio congestus; Silene; Taraxacum-type; Thalictrum; Vaccinium vitis-idaea; Valeriana
    Type: Dataset
    Format: text/tab-separated-values, 526 data points
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  • 7
    Publication Date: 2023-11-01
    Keywords: 14-OM-20B; AGE; Alnus fruticosa-type; Amaranthaceae; Antennaria villifera; Artemisia; Asteraceae; Astragalus; AWI_Envi; AWI Arctic Land Expedition; Betula nana; Betula pubescens; Brassicaceae; Chamaenerion latifolium; Comarum palustre; Counting, palynology; Cyperaceae; dating; Dianthus; Draba hirta; Dry mass; Ericaceae undifferentiated; Fabaceae; GCUWI; Gentiana; Gravity corer, UWITEC; Labiatae; Lake Omoloy; Larix; Ledum palustre; Lycopodium (counted); Lycopodium spores added; mid-holocene; Minuartia arctica; north-eastern Siberia; Omoloy2014; Omoloy region, north-eastern Siberia; Oxyria; Pedicularis; Picea; Pinus diploxylon-type; Pinus subgen. Strobus-type; Plantago; Poaceae; Polar Terrestrial Environmental Systems @ AWI; Pollen; Pollen, total; Pollen indeterminata; Polygonaceae; Polygonum; Potentilla-type; Ranunculaceae; Ranunculus lapponicus; Rhododendron; Rosaceae; RU-Land_2014_Omoloy; Rumex; Salix; Sample ID; Saussurea; Saxifraga; Scrophulariaceae; Senecio congestus; Silene; Taraxacum-type; Thalictrum; Urtica; Vaccinium vitis-idaea; Valeriana
    Type: Dataset
    Format: text/tab-separated-values, 492 data points
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  • 8
    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
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  • 9
    Publication Date: 2024-02-06
    Description: In this dataset, we provide the age-depth model of a lake sediment core covering the last 28 thousand years from the Siberian forest-tundra ecotone. The age model provides temporal information for a project that applies sedimentary ancient DNA metabarcoding using the plant-specific g and h primers of the trnL gene to track the compositional and diversity changes of terrestrial plants. Lake Ilirney is bounded by the Anadyr Mountains (up to 1790 m a.s.l.) to the north. According to the meteorological station at Ilirney, mean annual temperature is -13.5°C, and mean January and July temperatures are -33.4 and 12.1°C, respectively. Core “16-KP-01-L02 Long 3” was obtained from Lake Ilirney (67.34148, 168.30443) in summer 2016 as part of a joint Russian-German Expedition. The coring was accomplished using a UWITEC gravity corer equipped with a hammer action (Vyse et al. 2020; doi:10.1016/j.quascirev.2020.106607). The core has a total length of 235 cm. The age-depth model is based on Accelerator Mass Spectrometry (AMS) radiocarbon dating of seven bulk total organic carbon samples from this core (Andreev et al. 2020 in review) and correlation to a nearby 1040 cm sediment core with 25 dates (Vyse et al. 2020). 14C ages were calibrated using the IntCal13 calibration curve and modelled according to Andreev et al. (in review).
    Keywords: 16-KP-01-L02_Long_3; Age, 14C calibrated, IntCal13 (Reimer et al., 2013); Age, dated; Arctic Russia; AWI_Envi; AWI Arctic Land Expedition; Chukotka; DEPTH, sediment/rock; GCUWI; Glacial; Gravity corer, UWITEC; Holocene; Keperveem_2016; Polar Terrestrial Environmental Systems @ AWI; RU-Land_2016_Keperveem; Siberia; Treeline; Tschukotka, Sibiria, Russia; Tundra
    Type: Dataset
    Format: text/tab-separated-values, 58 data points
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  • 10
    Publication Date: 2021-06-30
    Description: Relationships between climate, species composition, and species richness are of particular importance for understanding how boreal ecosystems will respond to ongoing climate change. This study aims to reconstruct changes in terrestrial vegetation composition and taxa richness during the glacial Late Pleistocene and the interglacial Holocene in the sparsely studied southeastern Yakutia (Siberia) by using pollen and sedimentary ancient DNA (sedaDNA) records. Pollen and sedaDNA metabarcoding data using the trnL g and h markers were obtained from a sediment core from Lake Bolshoe Toko. Both proxies were used to reconstruct the vegetation composition, while metabarcoding data were also used to investigate changes in plant taxa richness. The combination of pollen and sedaDNA approaches allows a robust estimation of regional and local past terrestrial vegetation composition around Bolshoe Toko during the last ∼35,000 years. Both proxies suggest that during the Late Pleistocene, southeastern Siberia was covered by open steppe-tundra dominated by graminoids and forbs with patches of shrubs, confirming that steppe-tundra extended far south in Siberia. Both proxies show disturbance at the transition between the Late Pleistocene and the Holocene suggesting a period with scarce vegetation, changes in the hydrochemical conditions in the lake, and in sedimentation rates. Both proxies document drastic changes in vegetation composition in the early Holocene with an increased number of trees and shrubs and the appearance of new tree taxa in the lake’s vicinity. The sedaDNA method suggests that the Late Pleistocene steppe-tundra vegetation supported a higher number of terrestrial plant taxa than the forested Holocene. This could be explained, for example, by the “keystone herbivore” hypothesis, which suggests that Late Pleistocene megaherbivores were able to maintain a high plant diversity. This is discussed in the light of the data with the broadly accepted species-area hypothesis as steppe-tundra covered such an extensive area during the Late Pleistocene.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev , info:eu-repo/semantics/article
    Format: application/pdf
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