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152 results for “Subarctic”
Dataset: Volatile organic compound fluxes in a subarctic peatland and lake
<p>Dataset used in the article "Volatile organic compound fluxes in a subarctic peatland and lake" published in the journal Atmospheric Chemistry and Physics 20: 13399–13416 (2020) <a href="https://doi.org/10.5194/acp-20-13399-2020">https://doi.org/10.5194/acp-20-13399-2020</a> .</p> <p>The tab-delimited file contains direct surface-atmosphere Volatile Organic Compound fluxes, measured by Eddy Covariance with a Proton Transfer Reaction -Time of Flight- Mass Spectrometer (PTR-ToF-MS) at a subarctic fen and a subarctic lake during 2018. It also contains PAR Photosynthetic Active Radiation, air temperature, and vegetation surface temperature.</p>
Spring arctic oscillation as a trigger of summer drought in Siberian subarctic over the past 1494 years
<p>Annually resolved July precipitation and Arctic Oscillation in May reconstructions derived from the d<sup>13</sup>C and d<sup>18</sup>O in larch tree-ring cellulose over the period 516-2009 CE for eastern Taimyr (Siberia). July precipitation reconstruction was obtained under the Russian Science Foundation (RSF) Grant number 21-17-00006 (<a href="https://rscf.ru/en/project/21-17-00006/">https://rscf.ru/en/project/21-17-00006/</a>) granted to Project Investigator Olga V. Churakova.</p>
Wintertime subarctic new particle formation from Kola Peninsula sulphur emissions
<p><strong>Nitrate-ion (NO3-) chemical ionization (CI) </strong><strong>APi-TOF </strong><strong>masspectrometer and ion APi-TOF mass spectrometer data reported in Atmospheric Chemistry and Physics: </strong>Sipilä, M., Sarnela, N., Neitola, K., Laitinen, T., Kemppainen, D., Beck, L., Duplissy, E.-M., Kuittinen, S., Lehmusjärvi, T., Lampilahti, J., Kerminen, V.-M., Lehtipalo, K., Aalto, P. P., Keronen, P., Siivola, E., Rantala, P. A., Worsnop, D. R., Kulmala, M., Jokinen, T., and Petäjä, T.: Wintertime sub-arctic new particle formation from Kola Peninsula sulphur emissions, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-1202, in review, 2021.</p> <p><a href="https://zenodo.org/api/files/0375de20-1ec1-4185-82f6-ec6eda9466a5/H2SO4_vs_time_FIG3-FIG4.dat">H2SO4_vs_time_FIG3-FIG4.dat</a> </p> <p>[Matlab time, measured sulfuric acid concentration] depicted in FIGs 3c and 4c of the above publication.</p> <p><a href="https://zenodo.org/api/files/0375de20-1ec1-4185-82f6-ec6eda9466a5/SA_MSA_IA_vs_time_FIG7.dat">SA_MSA_IA_vs_time_FIG7.dat</a></p> <p>[Matlab time, measured sulfuric acid concentration, measured methane sulphonic acid concentration, measured iodic acid concentration] depicted in FIG7e of the above publication</p> <p><a href="https://zenodo.org/api/files/0375de20-1ec1-4185-82f6-ec6eda9466a5/ion_signals_vs_time_FIG7.dat">ion_signals_vs_time_FIG7.dat</a></p> <p>[Matlab time, ion signal of NO3-, ion signal of HSO4-, ion signal of H2SO4.HSO4-, ion signal of (H2SO4)2.HSO4-, ion signal of NH3.(H2SO4)3.HSO4-, ion signal of NH3.(H2SO4)4.HSO4-] depicted in FIG7f of the above publication</p> <p> </p> <p><a href="https://zenodo.org/api/files/0375de20-1ec1-4185-82f6-ec6eda9466a5/mass_defect_FIG9.dat">mass_defect_FIG9.dat</a></p> <p>[mass (Da), mass defect (Da), signal intensity (AU)] depicted in FIG9 of the above publication</p> <p>Rest of the data reported in the publication can be obtained from https://smear.avaa.csc.fi/</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset for "Fire disturbance promotes biodiversity of plants, lichens and birds in the Siberian subarctic tundra"
<p>Data that support the findings of the study "<strong>Fire disturbance promotes biodiversity of plants, lichens and birds in the Siberian subarctic tundra</strong>".</p>
Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use
<p>This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication<br> <br><strong>Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use</strong></p><p>Julia Schroeder1, Tino Peplau1, Edward Gregorich2, Christoph C. Tebbe3, Christopher Poeplau1</p><p>1 Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany<br>2 Research and Development Centre, Central Experimental Farm, Agriculture and Agri-Food Canada, Ottawa, Canada<br>3 Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany</p><p>DOI: https://doi.org/10.1007/s10533-022-00943-7 </p><p>This study investigated how subarctic soils under different land use will respond to warming and increasing N availability to allow for better predictions of C cycling under global change. The short-term temperature sensitivity as well as N-input effects on microbial CUE, respiration, growth and turnover were assessed in a one-day incubation experiment according to the 18O-CUE approach. The warming and N response of SOM decomposition were assessed in a 50-days incubation experiment via measurement of cumulative respiration. Both experiments were conducted with the following three treatments: incubation at 10 °C, incubation at 20 °C, and incubation at 20 °C plus N-fertiliser addition at an amendment rate of 100 kg N ha-1. The response to warming or N addition were expressed as response ratios RRT = 20°C/10°C and RRN = 20°C+N/20°C for warming and N response, respectively.</p><p>The R code was developed under R v3.6.3 and adapted to work under version R v.4.1.2.</p><p>The repository includes the following files:</p><ul><li>general_soil_parameters_per_sample.csv - general soil data for each field sample (n=27)</li><li>general_soil_parameters_per_plot.csv - general soil data assessed on pooled replicated field samples (n=9)</li><li>respiration_over_50d_incubation.csv - respiration rate and cumulative respiration for each time-point and laboratory sample over the 50-days incubation</li><li>sample_data.csv - data measured for each laboratory sample (n=81)</li></ul><p> </p><ul><li>Warming_and_nitrogen_response_of_CUE_in_subarctic_soils.Rproj - Rproject (load project to work on provided scripts and data)</li><li>load_data_script.R - loads required data</li><li>absolute_values_script.R - summary of absolute ranges of parameters per land-use type and site</li><li>absolute_linear_mixed_effects_model_script.R - run statistical analysis</li><li>correlograms_absolute_soil_params_script.R - correlation analysis to identify what drives absolute values</li><li>plot_correlations_absolute_soil_params_script.R - plot drivers of CUE and cumulative respiration</li><li>RRT_RRN_calculation_script.R - calculates response ratios</li><li>plot_RRT_RRN_script.R - plot response ratios</li><li>RRT_RRN_linear_mixed_effects_models_script.R - run statistical analysis</li><li>correlograms_RRT_RRN_soil_param_script.R - correlation analysis to identify drivers of response ratios</li><li>plot_correlations_RRT_RRN_soil_params_script.R - plot drivers of response ratios</li><li>RRT_RRN_resprate_cumulresp_over_time_50d_incubation_script.R - plot response ratios over time course</li></ul>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Dataset to: Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils
<p>Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils<br> T. Peplau, C. Poeplau, E. Gregorich, J. Schroeder</p> <p>This repository contains a dataset of soil temperature, soil parameters, farm management and additional site informations.</p> <p>Soil_temperature_data_Yukon.zip: Temperature data from different farms across the Yukon.<br> Each .xlsx file contains data from one temperature logger that logged soil temperature every 2 hours. The individual sheets are named in the following scheme:<br> Farm_landuse_depth.xlsx<br> Farm contains two letters corresponding to the identifier in the soil data set<br> landuse contains either F ("Forest"), CM ("Cropland / Market Garden") or G ("Grassland")<br> Depth is either 10 cm or 50 cm</p> <p>teabags.csv contains raw data about the initial weight of the teabags buried, their location and their weight after two years in the soil</p> <p>tea_decomposition contains the mean decomposition (n=3) of the tabags from each plot and corresponding temperature statistics, based on the logger data</p> <p>Soil_I_IV.csv contains soil parameters from soil samples at 0-10 cm and 40-60 cm</p> <p>site_data_R.csv contains geographical information and soil data that has only been measured once per site</p>
DeltaCAN: A new data set of Canadian Arctic and subarctic coastal deltas
<p>Arctic coasts constitute the critical interface between land and sea, and are subject to rapid changes caused by a warming climate. Current trends throughout the Arctic show increasing erosion trends, while other parts of the coast are experiencing prograding trends. Until now, a vast majority of our knowledge of Arctic coastal evolution is confined to site-specific studies with limited geospatial representation. Here, we present DeltaCAN, a novel data set on the locations of Canadian deltas larger than 500 m in width derived by visual interpretation of freely available satellite imagery. DeltaCAN is Canada's first nationwide coastal detection covering 250.000 km of coastline in the Arctic, identifying 2712 deltas. The inventory is based on inspection of remotely-sensed satellite imageries, developed through an expert-based mapping approach where we implemented a quality control mechanism to assess the completeness of the data set. The DeltaCAN data set allows for assessing changes at an unprecedented spatial extent, improving our understanding of delta morphodynamics.</p>
Figure 7 in Fighting an invasive fish parasite in subarctic Norwegian rivers - The end of a long story?
Figure 7. Increase in rotenone concentration in water samples along the riverbank as result of spraying the bank with water of high rotenone concentration.
Figure 6 in Fighting an invasive fish parasite in subarctic Norwegian rivers - The end of a long story?
Figure 6. Temperature at 10 cm depth in substrate at a groundwater influenced riverbank before, during and after flooding the riverbed with rotenone-treated water. The curve shows an instant temperature rise, indicating rotenone treated surface water intruding the groundwater fed substrate.
Figure 5. Crew placing a in Fighting an invasive fish parasite in subarctic Norwegian rivers - The end of a long story?
Figure 5. Crew placing a rotenone disc in a small brook. Brooks of this size were numerous, often remote and typically inhabited with potentially infected arctic char juveniles. The rotenone disc replaced the more bulky 20 litre-can drip stations. Photograph by Dag H. Karlsen.
Figure 4. Spraying a in Fighting an invasive fish parasite in subarctic Norwegian rivers - The end of a long story?
Figure 4. Spraying a groundwater-fed side channel of the Skibotn River with portable backpack mounted pump. Surviving G. salaris infested arctic char was found in this location after the previous treatments in 1988 and 1995. In 2015 and 2016 this and similar locations was treated several times by different teams using both Vectocarb, CatSan hygiene litter saturated with CFT-Legumine and conventional spraying with water of high rotenone concentration. Photograph by Dag H. Karlsen.
Figure 2 in Fighting an invasive fish parasite in subarctic Norwegian rivers - The end of a long story?
Figure 2. Mapping of groundwater influx in the River Signaldalselva. The mapping was done by parallel logging of GPS position and temperatures along the riverbanks at late summer, the time of year with the highest temperature contrasts between surface water and upwelling groundwater. Photograph by Norwegian Veterinary Institute.
Figure 1 in Fighting an invasive fish parasite in subarctic Norwegian rivers - The end of a long story?
Figure 1. The large map shows the rivers (in red) with G. salaris in the Skibotn Region. Orange marks rivers treated without findings of the parasite. All rivers and brooks potentially inhabiting salmonids south of the black line across the Lyngen-fjord were treated. Inserted map shows the location in Norway.
Figure 3 in Fighting an invasive fish parasite in subarctic Norwegian rivers - The end of a long story?
Figure 3. Spraying the riverbank of the River Signaldalselva with water of high rotenone concentration. The iconic mountain Otertind in the background. Photograph by Dag H. Karlsen.
HLWATER V1.0 Optical (water bodies Sentinel-2 TOA reflectance retrievals for 23/08/2019) - Western Nunavik (Subarctic Canada)
<p>This dataset refers to the retrieval of TOA reflectance from the Sentinel-2 L1C 10-m bands for 23/08/2019, having as reference the <a href="https://doi.org/10.5281/zenodo.12196313">Very High Resolution water body delineation dataset</a> computed with the <a href="https://doi.org/10.5281/zenodo.10203553">HLWATER V1.0 model</a> (<a href="https://doi.org/10.1016/j.rse.2024.114047">Freitas et al., 2024</a>) for Western Nunavik (Eastern Hudson Bay), Subarctic Canada. It covers a total area of 41,832 km2 within the latitudes 54° to 58° N and the longitudes 74° to 78° W.</p> <p>The dataset is composed of 167,755 water body reflectance retrievals. Additionally, 1 km2 hexagonal grids are provided with the calculation of the limnodiversity (diversity of water optical groups/colors). The optical groups were automatically defined using K-Means to 11 clusters, according to the highest Pseudo-F Score. Outputs are provided in shapefile and geodatabase formats.</p> <p>The manuscript detailing these outputs has been submitted to GIScience and Remote Sensing.</p>
Moss species and precipitation mediate experimental warming stimulation of growing season N2 fixation in subarctic tundra
<p>Climate change in high latitude regions leads to both higher temperatures and more precipitation but their combined effects on terrestrial ecosystem processes are poorly understood. In nitrogen (N) limited and often moss-dominated tundra and boreal ecosystems, moss-associated N<sub>2</sub> fixation is an important process that provides new N. We tested if high mean annual precipitation enhanced experimental warming effects on growing season N<sub>2</sub> fixation in three common arctic-boreal moss species adapted to different moisture conditions and evaluated their N contribution to the landscape level. We measured <em>in situ</em> N<sub>2</sub> fixation rates in <em>Hylocomium splendens</em>, <em>Pleurozium schreberi</em> and <em>Sphagnum</em> spp. from June to September in subarctic tundra in Sweden. We exposed mosses occurring along a natural precipitation gradient (mean annual precipitation: 571-1155 mm) to eight years of experimental summer warming using open-top chambers before our measurements. We modelled species-specific seasonal N input to the ecosystem at the colony and landscape level. Higher mean annual precipitation increased N<sub>2</sub> fixation, especially during peak growing seasons and in feather mosses. For <em>Sphagnum-</em>associated N<sub>2</sub> fixation,<em> </em>high mean annual<em> </em>precipitation reversed a small negative warming response. By contrast, in the dry-adapted feather moss species higher mean annual precipitation led to negative warming effects<em>.</em> Modelled total growing season N inputs for <em>Sphagnum </em>spp. colonies were 2-3 times that of feather mosses on an area basis. However, at the landscape level where feather mosses were more abundant, they contributed 50% more N than <em>Sphagnum</em>. The discrepancy between modelled estimates of species-specific N input via N<sub>2</sub> fixation at the moss core versus ecosystem scale exemplifies how moss cover is essential for evaluating the impact of altered N<sub>2</sub> fixation. Importantly, combined effects of warming and higher mean annual precipitation may not lead to similar responses across moss species, which could affect moss fitness and their abilities to buffer environmental changes. </p>
Figure 9 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 9. Comparison of frequency distributions between sea surface temperatures (SSTs) and time percent temperatures (TPTs) experienced by satellite-tagged loggerhead turtles <50 cm straight carapace length (SCL) after winter 2011. (A) SSTs collected for locations of eight turtles leaving the Sea of Japan. (B) TPTs collected for locations of eight turtles leaving the Sea of Japan.
Figure 8 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 8. Comparison of frequency distributions between sea surface temperatures (SSTs) and time percent temperatures (TPTs) experienced by satellite-tagged loggerhead turtles <50 cm straight carapace length (SCL) during summer 2011. (A) SSTs collected for locations of eight turtles leaving the Sea of Japan. (B) SSTs collected for locations of seven turtles staying in the Sea of Japan. (C) TPTs collected for locations of eight turtles leaving the Sea of Japan. (D) TPTs collected for locations of three turtles staying in the Sea of Japan.
Figure 7 in Satellite tracking immature loggerhead turtles in temperate and subarctic ocean habitats around the Sea of Japan
Figure 7. Ten-day mean (± SD) sea surface temperatures (SSTs) experienced by satellite-tagged loggerhead turtles <50 cm straight carapace length (SCL). Filled circles: six turtles staying in the Sea of Japan, open circles: three turtles leaving the Sea of Japan.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.