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1,449 results for “siberia”
Forest expansion for different warming scenarios simulated for 2010 to 3000 CE with LAVESI for Siberia
<p>Simulations with the spatially explicit and individual-based Siberian forest model LAVESI (Kruse et al., 2016, 2018, 2019) were set-up for transect in four focus regions covering the East Siberian treeline and tundra area (details in Kruse & Herzschuh, submitted). The model was updated to include climate forcing data for 300-800 km long and 20 m wide transects necessary for simulating the forest development between the northern taiga forests and the coast of the Arctic Ocean. Forced with climate forecasts driven by relative concentration pathway (RCP) scenarios 2.6, 4.5 and 8.5 and one with half the warming of RCP 2.6 named 2.6*. These were extended until 3000 AD either following the cooling of the scenarios after peak-warming, or with an arbitrary cooling back to levels of the 20th century.</p> <p>During the simulations, three key variables were extracted in 10-year steps for 2000-3000 AD: single-tree line, treeline, and, forest line, which are defined as the northernmost position of stands with >1 stem (tree > 1.3 m tall) per ha, the northernmost position of a forest cover not falling below 1 stem per ha, and, the northernmost position of a forest cover not falling below 100 stems ha per ha (see for a graphical representation Fig. 2 in Kruse et al., 2019). The determined treeline at year 2000 was used as baseline expansion and subtracted from each following years’ values.</p> <p>Furthermore, the tundra area was estimated for each of the four regions as the area between the treeline and the Arctic Ocean, based on interpolating the treeline position at the four transects over the complete modern treeline (Walker et al., 2005).</p> <ol> <li>Content of Table 1 "Kruse_and_Herzschuh_2022_Forest_expansion_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Region: One of the four regions, from east-to-west Taimyr Peninsula, Buor Khaya Peninsula, Kolyma River Basin, Chukotka</li> <li>Column 3: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 4: Forest line in m</li> <li>Column 5: Treeline in m</li> <li>Column 6: Single-tree line in m</li> </ul> </li> <li>Content of Table 2 "Kruse_and_Herzschuh_2022_Tundra_area_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 3: Tundra area at region Taimyr Peninsula in km²</li> <li>Column 4: Tundra area at region Buor Khaya Peninsula in km²</li> <li>Column 5: Tundra area at region Kolyma River Basin in km²</li> <li>Column 6: Tundra area at region Chukotka in km²</li> </ul> </li> <li>The zip-file "Kruse_and_Herzschuh_2022_Forest_expansion_maps_in_Siberia_2010_to_3000_CE.zip" contains shape files with the tundra area in 10 year steps starting in 2000 until 3000 CE <ul> <li>projection: Albers azimuthal equidistant projection centered at Longitude of 100 °E (PROJ4 string: "+proj=aea +lat_1=50 +lat_2=70 +lat_0=56 +lon_0=100 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")</li> </ul> </li> </ol> <p>This study was supported by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).</p>
A unified dataset of co-located sewage pollution, periphyton, and benthic macroinvertebrate community and food web structure from Lake Baikal (Siberia)
Sewage released from lakeside development can introduce nutrients and micropollutants that can restructure aquatic ecosystems. Lake Baikal, the world's most ancient, biodiverse, and voluminous lake, has been experiencing localized sewage pollution from lakeside settlements. Increasing filamentous algal abundance suggests benthic communities are responding to this localized pollution. We surveyed 40-km of Lake Baikal's southwestern shoreline 19-23 August 2015 for sewage indicators, including pharmaceuticals, personal care products, and microplastics with co-located periphyton, macroinvertebrate, stable isotope, and fatty acid sampling. Unique identifiers corresponding to sampling locations are retained throughout all data files to facilitate interoperability among the dataset's 150+ variables. The data are structured in a tidy format (a tabular arrangement familiar to limnologists) to encourage future reuse. For Lake Baikal studies, these data can support continued monitoring and research efforts. For global studies of lakes, these data can help characterize sewage prevalence and ecological consequences of anthropogenic disturbance across spatial scales.
LAVESI-FIRE simulation output at Lake Satagay, Central Yakutia, Siberia
<p>Simulation output data for long-term fire-vegetation simulations with the individual-based, spatially explicit model LAVESI-FIRE. The individual, numbered simulation folders are described in the included .docx file, as well as in the related research paper. In each folder, following files can be found (_18224XX refers to the climate input used for the simulation):</p> <ul> <li><strong>datatrees_currencies_18224</strong>XX<strong>.csv</strong>: Timeseries data. Simulation area-wide summarized data on tree abundance, climate, environment and fire occurrence. Each row of the table represents an individual annual simulation timestep.</li> <li><strong>databiomassgrid_1_18224</strong>XX<strong>_</strong>XX<strong>00_1_</strong>X<strong>.csv</strong>: Spatial data. Each file includes tree abundance for one species (_1 = <em>Larix gmelinii</em>; _2 = <em>Larix sibirica</em>; _3 = <em>Larix cajanderi</em>; _4 = <em>Picea obovata</em>; _5 = <em>Pinus sylvestris</em>; _6 = <em>Pinus sibirica</em>), summarized in grid cells with x- and y-coordinates.</li> <li><strong>datatrees_Treedensity</strong>XX<strong>00_18224</strong>XX<strong>.csv</strong>: Spatial data. Each file includes tree density, environment, and fire occurrence, summarized in grid cells with x- and y-coordinates.</li> </ul> <p>For more detail, please refer to the linked research paper:</p> <p>Glückler, R., Gloy, J., Dietze, E., Herzschuh, U., & Kruse, S. (2024). Simulating long-term wildfire impacts on boreal forest structure in Central Yakutia, Siberia, since the Last Glacial Maximum. Fire Ecology, 20(1), 1. https://doi.org/10.1186/s42408-023-00238-8</p>
Global River BankFull Discharge (GQBF) - Siberia(SI) & South Pacific/Australia(SP)
<p>The GQBF is the estimated bankfull discharge across ~2.87 million km (length) of global river reaches. The bankfull discharge here is defined as the maximum flow rate contained within a river just before inundation occurs in the surrounding floodplain. We based our river bankfull discharge estimation on a newly developed river network, Global RIver Topology (GRIT), using GRIT’s river reaches as the spatial scale to represent the variation in bankfull discharge. We included all GRIT river reaches that coincided with the Global River Width from Landsat (GRWL) river masks (with overlapping ratio >=0.5). This selects river reaches with satellite-derived width measurements >=30 m, resulting in a total length of ~2.87 million km. Here, the GQBF represents the time-averaged bankfull discharge at <1 km (river length) spatial resolution.</p> <p><strong>Regions</strong></p> <p>Added regions SI, SP Vector files.</p> <ul> <li>SI - Siberia</li> <li>SP - South Pacific/Australia</li> </ul> <p>The subcontinental catchment groups (vector, polygons) can be found at <a href="https://zenodo.org/records/11219313">GRIT domain polygon</a> (GRITv06_domain_GLOBAL.gpkg.zip). They allow for more fine-grained subsetting of data .</p> <p>Vector files are provided in geographic WGS84 coordinates (EPSG:4326).</p> <p><strong>Change log</strong></p> <ul> <li>v0.1 - 2024-09-29<br> <ul> <li>First globally complete dataset published</li> </ul> </li> <li>v0.1 - 2024-11-19 <ul> <li>Add vector files for regions SI, SP</li> </ul> </li> </ul>
Model data from GRL paper: "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone"
<p>This folder includes monthly model data (experiments using SC-WACCM4 and E3SMv1) of temperature (TEMP) and sea level pressure (SLP) that were used in the Geophysical Research Letters paper "<strong>Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone</strong>", # 2020GL088583. See also for additional information/data: <a href="https://zenodo.org/record/3066448">https://zenodo.org/record/3066448</a></p> <p>Labe, Z., Peings, Y., & Magnusdottir, G. (2020). Warm Arctic , cold Siberia pattern : role of full Arctic amplification versus sea ice loss alone. <em>Geophysical Research Letters</em>, 1–26. <a href="https://doi.org/10.1029/2020GL088583">https://doi.org/10.1029/2020GL088583</a></p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL088583">[Paper]</a><a href="https://sites.uci.edu/zlabe/arctic-amplification/">[Plain Language Summary]</a><a href="https://github.com/zmlabe/AA">[GitHub]</a></p>
Bias-corrected monthly precipitation data over South Siberia for 1979-2019
<p>Bias-<strong>C</strong>orrected <strong>P</strong>recipitation data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CPSS 1.2</strong>) contains monthly precipitation data for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2019. CPSS data were combined from monthly total precipitation data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and precipitation data records from ground weather stations (Il’in et al., 2013). The ERA5 data were scaled according to the derived scale coefficient. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CPSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CPSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results. Data provided in NetCDF (Network Common Data Form) format.</p> <p>Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at <a href="https://cds.climate.copernicus.eu/cdsapp#!/home"><em>https://cds.climate.copernicus.eu/cdsapp#!/home</em></a></p> <p>Il’yin, B.M., Bulygina, O.N., Bogdanova, E.G, Veselov, V.M. and Gavrilova, S.Y., 2013. <em>Dataset of monthly precipitation totals, with the elimination of systematic errors of precipitation gauges</em>. Available at <a href="http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov"><em>http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov</em></a></p>
Influence of the tropical Indian Ocean tripole on summertime cold extremes over central Siberia
<p>These experiments are used to study atmospheric circulation responses to SST forcing related to Indian Ocean tripole mode, including the precipitation, zonal and meridional winds.</p>
Supplementary Material no. 2 to the manuscript: Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)
<p>A dataset on the inorganic chemistry of permafrost-related creeks and ice, thermokarst lakes and the Kolyma river and its tributaries in late July 2021.<br> Companion dataset to the manuscript: "Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)".<br> Current abstract of the manuscript (prior to peer review):</p> <p>Permafrost regions are under particular pressure from climate change resulting in widespread landscape changes, which impact also freshwater chemistry. We investigated a snapshot of hydrochemistry in various freshwater environments in the lower Kolyma river basin (North-East Siberia, continuous permafrost zone) to explore the mobility of metals, metalloids and non-metals resulting from permafrost thaw. Particular attention was focused on heavy metals as contaminants potentially released from the secondary source in the permafrozen Yedoma complex. Permafrost creeks represented the Mg-Ca-Na-HCO<sub>3</sub>-Cl-SO<sub>4</sub> ionic water type (with mineralisation in the range 600-800 mg/L), while permafrost ice and thermokarst lake waters were the HCO<sub>3</sub>-Ca-Mg type. Multiple heavy metals (As, Cu, Co, Mn and Ni) showed much higher dissolved phase concentrations in permafrost creeks and ice than in Kolyma and its tributaries, and only in the permafrost samples and one Kolyma tributary have we detected dissolved Ti or Hg. In thermokarst lakes, several metal and metalloid dissolved concentrations increased with water depth (Fe, Mn, Ni and Zn - in both lakes; Al, Cu, K, Sb, Sr and Pb in either lake), reaching 1370 µg/L Cu, 4610 µg/L Mn, and 687 µg/L Zn in the bottom water layers. Permafrost-related waters were also enriched in dissolved phosphorus (up to 512 µg/L in Yedoma-fed creeks). The impact of permafrost thaw on river and lake water chemistry is a complex problem which needs to be considered both in the context of legacy permafrost shrinkage and the interference of the deepening active layer with newly deposited antropogenic contaminants.</p>
Figs 22-23 in Contribution to the knowledge of the genus Quedius S , 1829 of Siberia and Russian Far East (Coleoptera: Staphylinidae: Staphylinini: Quediina)
Figs 22-23: (22) Type locality of Quedius conviva nov.sp.; (23) Type locality of Quedius amurensis nov.sp.
Figure 3 in Description of Orthadenella coulsoni sp. nov. (Acari: Mesostigmata: Melicharidae) from Siberia with a key to the females of Orthadenella
Figure 3. Orthadenella coulsoni sp. nov., female: (A) tritosternum; (B) gnathosoma; (C) sperm access system; (D) epistomes; (E) chelicera; (F) dorsal of palptarsus; (G) ventral of palptarsus.
Data for: Sedimentary ancient DNA and pollen reveal the composition of plant organic matter in Late Quaternary permafrost sediments of the Buor Khaya Peninsula (north-eastern Siberia)
<p>Organic matter deposited in ancient, ice-rich permafrost sediments is vulnerable to climate change and may contribute to the future release of greenhouse gases; it is thus important to get a better characterization of the plant organic matter within such sediments. From a Late Quaternary permafrost sediment core from the Buor Khaya Peninsula, we analysed plant-derived sedimentary ancient DNA (sedaDNA) to identify the taxonomic composition of plant organic matter, and undertook palynological analysis to assess the environmental conditions during deposition. Using sedaDNA, we identified 154 taxa and from pollen and non-pollen palynomorphs we identified 83 taxa. In the deposits dated between 54 and 51 kyr BP, sedaDNA records a diverse low-centred polygon plant community including recurring aquatic pond vegetation while from the pollen record we infer terrestrial open-land vegetation with relatively dry environmental conditions at a regional scale. A fluctuating dominance of either terrestrial or swamp and aquatic taxa in both proxies allowed the local hydrological development of the polygon to be traced. In deposits dated between 11.4 and 9.7 kyr BP (13.4–11.1 cal kyr BP), sedaDNA shows a taxonomic turnover to moist shrub tundra and a lower taxonomic richness compared to the older samples. Pollen also records a shrub tundra community, mostly seen as changes in relative proportions of the most dominant taxa, while a decrease in taxonomic richness was less pronounced compared to sedaDNA. Our results show the advantages of using sedaDNA in combination with palynological analyses when macrofossils are rarely preserved. The high resolution of the sedaDNA record provides a detailed picture of the taxonomic composition of plant-derived organic matter throughout the core, and palynological analyses prove valuable by allowing for inferences of regional environmental conditions.</p>
Temperature of the active layer in the forest-tundra zone in the north of Western Siberia (Pangody) forest-tundra zone in the north of Western Siberia
<p>Temperature of the active layer in the forest-tundra zone in the north of Western Siberia (Pangody) forest-tundra zone in the north of Western Siberia (7 sites). Temperature was measured with a Tr 46908 thermometer (TR di Turoni & c. Snc, Italy) and drilling was carried out using a hand-held motor-drill Stihl BT 360 (Stihl, Germany). Date of field investigations - 16 and 17 August 2020.</p>
Figure 15 in Three new species and new records of Pediculaster (Acari: Pygmephoridae) from Western Siberia, Russia
Figure 15 Pediculaster rarus sp. nov., phoretic female: A – left leg III, dorsal aspect, B – left leg IV, dorsal aspect.
Figure 14 in Three new species and new records of Pediculaster (Acari: Pygmephoridae) from Western Siberia, Russia
Figure 14 Pediculaster rarus sp. nov., phoretic female: A – left leg I, dorsal aspect, B – left leg II, dorsal aspect.
Figure 12 in Three new species and new records of Pediculaster (Acari: Pygmephoridae) from Western Siberia, Russia
Figure 12 Pediculaster bisetus sp. nov., phoretic female: A – right leg III, dorsal aspect, B – right leg IV, dorsal aspect.
Figure 13 in Three new species and new records of Pediculaster (Acari: Pygmephoridae) from Western Siberia, Russia
Figure 13 Pediculaster rarus sp. nov., phoretic female: A – dorsum of the body, B – venter of the body. Legs omitted.
Figure 10 in Three new species and new records of Pediculaster (Acari: Pygmephoridae) from Western Siberia, Russia
Figure 10 Pediculaster bisetus sp. nov., phoretic female: A – dorsum of the body, B – venter of the body. Legs omitted.
Figure 11 in Three new species and new records of Pediculaster (Acari: Pygmephoridae) from Western Siberia, Russia
Figure 11 Pediculaster bisetus sp. nov., phoretic female: A – right leg I, dorsal aspect, B – right leg II, dorsal aspect.
Figure 9 in Three new species and new records of Pediculaster (Acari: Pygmephoridae) from Western Siberia, Russia
Figure 9 DIC micrographs of pharyngeal pumps II and III of phoretic females: A – Pediculaster tjumeniensis sp. nov." B – Pediculaster bisetus sp. nov.
Figure 7 in Three new species and new records of Pediculaster (Acari: Pygmephoridae) from Western Siberia, Russia
Figure 7 Pediculaster tjumeniensis sp. nov., larva: A – dorsum of the body, B – venter of the body. Legs omitted.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.