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Soil microbial and physicochemical data from watersheds impacted by different management practices or wildfire in the Southern Appalachian Mountains, 2023
Four forested watersheds in Western North Carolina with different management practices or disturbance were sampled in the summer of 2023 to compare soil physicochemical, microbial, and functional differences. These data include mineral soil physicochemical properties (location, elevation, aspect, gravimetric moisture content, pH, total carbon and nitrogen, total organic carbon, dissolved organic carbon and nitrogen, total dissolved nitrogen, dissolved inorganic nitrogen (NO3 and NH4), and microbial biomass carbon and nitrogen), soil microbial properties (16S ASV community sequences, ITS ASV community sequences, extracellular enzyme activity, carbon mineralization rates, and ammonium mineralization rates), and organic soil properties (total organic carbon, total carbon and nitrogen, 16S ASV sequences, pH, and moisture). Together, this dataset provides context to understanding the impacts of different management practices and relevant disturbances, such as severe wildfire, on soil in the Southern Appalachian region.
Dynamics of Old-Growth Forests on Wachusett Mountain in Princeton MA 1996-1997
One of the largest old-growth forests in southern New England was recently "discovered" on the exposed upper slopes of Wachusett Mountain, one of the most heavily used recreational areas in Massachusetts, less than 50 miles from urban Boston. Presettlement and early post-settlement data suggest that most of the area's forests were comprised of a mixture of Quercus rubra and northern hardwood species. Individual species abundances and recruitment dynamics in the four stands exhibit highly variable spatial and temporal patterns across sites that differ in aspect and exposure. Three uneven-aged hardwood stands contain Quercus rubra in the largest size classes, various amounts of Fagus grandifolia, Acer and Betula species in the middle size classes and dense thickets of Acer pennsylvanicum, Acer spicatum, and Hamamelis virginiana in the small size classes. Several individuals of Q. rubra, B. lenta, and B. alleghaniensis are at or very near the maximum longevity known for these species. Of particular significance is the presence of Q. rubra exceeding 250 - 300 years of age. A Tsuga canadensis stand contains unimodal size and age distributions, with trees less than 60-cm dbh and 100 to 300 years old. Widespread Quercus rubra recruitment occurred on all sites from the 1600s through the early 1800s, when it dropped precipitously except for scattered individuals on more open talus sites, and was replaced by either Tsuga or Acer and Betula species. The historical change in recruitment from Quercus to more shade-tolerant species evidently was driven by a change in disturbance regime from an early period when fire was a major factor controlling forest dynamics to a period over the last two centuries when hurricanes (1815 and 1938), frequent wind, ice, and snow damage, but no fire, are documented. The asynchronous nature of tree-ring releases and suppression and the relatively low amount of coarse woody debris corroborate this interpretation. Chronic canopy damage through time p
Ants of the Adirondack Mountains High Peaks 2016
The ants of the Adirondack Mountains are rarely sampled, and it was of interest to inventory their diversity as part of a general biodiversity survey of the Adirondack Park in New York State. A first set of collections were done in 2016 in the “High Peaks” region (mountains over 1200 meters (4000 feet) high). Ants were collected haphazardly from nests encountered by Adirondack Mountain trail stewards during June and July 2016 when they were doing regular botanical inventories and trail maintenance. Eleven species were collected, with the cold-climate species Formica neorufibarbis and Camponotus herculeanus accounting for 75% of the nests encountered. The species composition of ants in the Adirondack High Peaks is similar to that of other alpine and subalpine habitats in adjacent New England.
Isotopic analysis of extracted water from a larch (Larix decidua) stand in a high mountain watershed (Vallon de Nant - Switzerland)
<p>A total of 185 samples of soil and trees were taken from a stand of larch (Larix decidua) spanning from 1500 to 1600 m.a.s.l. in the Vallon de Nant in the Swiss canton of Vaud. Twenty individual trees and soil were sampled along two transects perpendicular to the main river channel of the Avançon de Nant at approximately midday on seven days in the foliage season between July 2017 and June 2018.</p><p>The data consists of two data (csv) and one document (pdf) files. The sample data file includes the date and estimated time of sampling, the type of sample (vegetation or soil), the transect and tree ID for look-up in the tree metadata file, the soil depth in centimeters, and the determined mean and standard deviation of deuterium, oxygen-18, and oxygen-17. The tree metadata file includes the transect (north or south), the tree ID number, the latitude, longitude, elevation (meters above sea level), and tree height and diameter at breast height in centimeters. Finally, a document describing the methods in more detail is included. </p>
Long-term record of lake and stream biogeochemistry from the Loch Vale Watershed, Rocky Mountain National Park, Colorado, USA: 1981-2024
The Loch Vale Watershed (LVWS) Project is a long-term research and monitoring program that addresses watershed-scale ecosystem processes, particularly as they respond to atmospheric deposition and climate variability. The LVWS is a 7-km2 high-altitude basin located within Rocky Mountain National Park in the Colorado Front Range (Colorado, United States of America). This dataset includes year-round measurements of physical water parameters, nutrients, major ions, trace metals, silica, and chlorophyll collected from lakes and streams within the LVWS basin. Related data entities: Scanned field notebooks from the Loch Vale Watershed Project from 1981-2023 are available via this published data release: https://www.sciencebase.gov/catalog/item/6723cba2d34e4f57573e8e45. Quality assurance reports from the Loch Vale Watershed Project are available for specific time periods and can be found at the following locations: 1983-1987: included in this data release under "Other Entities", file name LWVS_QAreport_1983to1987_Denning 1988: included in this data release under "Other Entities", file name LWVS_QAreport_1988_Denning 1989-1990: included in this data release under "Other Entities", file name LWVS_QAreport_1989to1990_Edwards 1995-1998: https://doi.org/10.3133/ofr99111 1999-2002: https://doi.org/10.3133/ofr20041306 2003-2009: https://doi.org/10.3133/ofr20111137 2010-2019: https://doi.org/10.3133/tm1D9 The most recent methods manual is included in full in this data release under "Other Entities", file name "LVWS Methods Manual". Please refer to this manual for the detailed methods.
Northern red oak regeneration in burned and unburned stands in the White Mountain National Forest, New Hampshire, USA, 2023-2024
This project aimed to determine whether prescribed burning of managed forest stands improves the regeneration of Quercus rubra near its northern range limit in New Hampshire. We measured oak seedling density and growth rates in three pairs of managed stands in which one had received a prescribed burn since 2017. We also measured the density of competing seedlings and shrubs, leaf area index above seedling height, soil nutrients, mycorrhizal colonization, foliar carbon/nitrogen ratio, and stable isotopes of nitrogen and carbon. We found greater oak seedling density and faster oak seedling growth rates in burned stands relative to unburned stands. A subset of these measurements were also collected in additional burned and unburned study stands in the region. A companion mesocosm experiment showed faster growth in oak seedlings grown in soil from burned vs. unburned stands. Together these studies show that the benefits of fire to oak regeneration are mediated both via greater light availability as well as effects mediated via soil.
Filtered chlorophyll a time series for Beaverdam Reservoir, Carvins Cove Reservoir, Claytor Lake, Falling Creek Reservoir, Gatewood Reservoir, Smith Mountain Lake, Spring Hollow Reservoir in southwestern Virginia, and Lake Sunapee in Sunapee, New Hampshire, USA during 2014-2025
Water column chlorophyll a was analyzed from 2014 to 2025 in seven freshwater reservoirs in southwestern Virginia (VA), USA, and one freshwater lake in central New Hampshire (NH), USA. These waterbodies are: Beaverdam Reservoir (Vinton, VA), Carvins Cove Reservoir (Roanoke, VA), Claytor Lake (Pulaski, VA), Falling Creek Reservoir (Vinton, VA), Gatewood Reservoir (Pulaski, VA), Smith Mountain Lake (Bedford, VA), Spring Hollow Reservoir (Salem, VA), and Lake Sunapee (Sunapee, NH). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia; Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia; and Smith Mountain Lake is jointly treated by the Bedford Regional Water Authority and the Western Virginia Water Authority as a drinking water source for Franklin County, Virginia. Claytor Lake is managed for hydroelectric power generation by the Appalachian Power Company. Lake Sunapee is a glacially-formed lake known for its oligotrophic water quality. The dataset consists of depth profiles of chlorophyll a samples generally measured at the deepest site of each reservoir adjacent to the dam or at the buoy site of Lake Sunapee. The water column samples were collected approximately fortnightly from March-April and weekly from May-October from 2014 - present at Falling Creek Reservoir and Beaverdam Reservoir, approximately fortnightly from May-August in most years at Carvins Cove Reservoir, approximately fortnightly from May-August in Gatewood and Spring Hollow Reservoirs from 2014-2016, approximately fortnightly from May-August of 2014 in Smith Mountain Lake, sporadically from May-August of 2014 in Claytor Lake, and sporadically from June-August of 2021-2022 and 2024-2025 in Lake Sunapee. From 2018-2025, samples were collected primarily at a single depth in each reservoir, with sample collection at two depths in F
Identification of an altitudinal migration pattern of Abies pinsapo in the Baetic Mountains through the presence of its life stages
<p>This data set is used to explore the altitudinal shift of <em>Abies pinsapo</em> Boiss. in the Baetic System. We analysed the potential distribution of the realised and reproductive niches of <em>A. pinsapo</em> populations in the Ronda Mountains (Southern Spain) by using species distribution models (SDMs) for two life stages within the current populations. The realised and reproductive niches of <em>A. pinsapo</em> are different to one another, which may indicate a displacement in its altitudinal distribution.</p>
Ground temperature time series in European mountain permafrost
<p>RELATED PUBLICATION</p> <p>This dataset is related to the following publication:</p> <p><strong>Noetzli J., Isaksen, K., Barnett, J., Chrisitiansen, H.H., Delaloye, R., Etzelmueller, B., Farinotti, D., Gallemann, T., Guglielmin, M., Hauck, C., Hilbich, C., Hoelzle, M., Lambiel, C., Magnin, F., Oliva, M., Paro, L, Pogliotti, P., Riedl, C., Schoeneich, P., M., Valt, M., Vieli A., Philliips, M. (2024). Enhanced permafrost warming in Euro­pean mountains in the 21st century. Nature Communications, 15, 10508, <a href="https://doi.org/10.1038/s41467-024-54831-9">https://doi.org/10.1038/s41467-024-54831-9</a>.</strong></p> <p><strong>==> </strong></p> <p><strong>For information on the measurements, selection criteria, processing information and data providers please refer to the methods, data availability and acknowledgements sections of the related publication ! </strong></p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>CONTENT</p> <p>The dataset includes monthly and annual time series of ground temperatures measured in 64 boreholes in European mountain permafrost areas and corresponding metadata.</p> <p>Temporal coverage: at least 10 years until 2022</p> <p>Spatial coverage: European mountain regions (Svalbard, Scandinavia, Iceland, European Alps, Sierra Nevada)</p> <p>Depth of measurements: at least 10 m; for all boreholes data of the sensors closest to 5, 10 and 20 m depth are included</p> <p>Monthly means are calculated from daily values and annual values are derived from monthly mean values.</p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>DATA COMPILATION</p> <p>The data were compiled to derive 10-year and 20-year warming rates in European mountain permafrost in the study by Noetzli et al. (in review, see above). Data were collected from national permafrost observation networks as well as from individual institutions (e.g, universities, environmental agencies).</p> <p>The aquisition of long time series over decades requires long-term committment from the responsible institutions to maintain instruments and to collect and curate the data. Details on the data source for each time series can be found in the metadata file as well as in the related publication. The main data sources by country are given in the list below.</p> <table> <tbody> <tr> <td><strong>Country</strong></td> <td><strong>Data source (institution or national network)</strong></td> </tr> <tr> <td>Austria</td> <td>GeoSphere Austria</td> </tr> <tr> <td>France</td> <td>Réseau français d'observation du permafrost (PermaFrance, <a href="https://wslch365-my.sharepoint.com/personal/jeannette_noetzli_slf_ch/Documents/PermafrostEurope/permafrance.osug.fr">permafrance.osug.fr</a>)</td> </tr> <tr> <td>Germany</td> <td>Bavarian Environment Agency</td> </tr> <tr> <td>Iceland</td> <td>University of Oslo</td> </tr> <tr> <td>Italy</td> <td>ARPA Piemonte, ARPA Valle d'Aosta, ARPA Veneto, University of Insubria</td> </tr> <tr> <td>Norway</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Spain</td> <td>Universitat de Barcelona</td> </tr> <tr> <td>Svalbard</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Sweden</td> <td>University of Stockholm</td> </tr> <tr> <td>Switzerland</td> <td>Swiss Permafrost Monitoring Network PERMOS (<a href="http://www.permos.ch">http://www.permos.ch</a>)</td> </tr> </tbody> </table> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>FILES AND FORMAT</p> <p>This data set includes three csv-files: <br>1) metadata with information on the measurement location and data provider<br>2) monthly ground temperature time series and <br>3) annual ground temperature time series. </p> <p>The variables in the three files are described below. Data files are in long data format.</p> <p><strong>File 1 – borehole_overview.csv<br></strong>Key information on the boreholes, responsible institutions and contact persons.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Name</td> <td>Name of the borehole (as used in the related study)</td> </tr> <tr> <td>Country</td> <td>Alpha-2 code</td> </tr> <tr> <td>Region</td> <td>Larger region</td> </tr> <tr> <td>First_year</td> <td>First year of data</td> </tr> <tr> <td>Elevation [m asl.]</td> <td>Elevation of the borehole</td> </tr> <tr> <td>Lat [° N]</td> <td>Latitude</td> </tr> <tr> <td>Lon [° E]</td> <td>Longitude</td> </tr> <tr> <td>Depth [m]</td> <td>Total depth of the borehole</td> </tr> <tr> <td>DZAA [m]</td> <td>Depth of the Zero Annual Amplitude (uppermost sensor with annual amplitude ≤0.1)</td> </tr> <tr> <td>Phase lag</td> <td>Phase lag at 10 m depth compared to surface in months</td> </tr> <tr> <td>Morphology</td> <td>Main morphology of the site</td> </tr> <tr> <td>Surface_cover</td> <td>Main surface cover at the site</td> </tr> <tr> <td>Lithology</td> <td>Main lithology of the site</td> </tr> <tr> <td>Ice_content</td> <td>Basic classification by ground ice content at the site (no ice, ice-poor, ice-bearing, ice-rich), see publication for details</td> </tr> <tr> <td>Institution</td> <td>Responsible institution (in the year 2024)</td> </tr> <tr> <td>Contact_person</td> <td>Contact person (in the year 2024)</td> </tr> <tr> <td>Special_remarks</td> <td>Remarks on location, e.g. horizontal borehole</td> </tr> </tbody> </table> <p> </p> <p><strong>File 2 – permafrost_temperatures_european_mountains_monthly_2022.csv<br></strong>Time series of monthly mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY-MM-DD]</td> <td>Date</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Monthly mean ground temperature (aggregated from daily values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum daily ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum daily ground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of daily values available to calculate monthly mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table> <p> </p> <p><strong>File 3 – permafrost_temperatures_european_mountains_annual_2022.csv<br></strong>Time series of annual mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY]</td> <td>Year</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Annual mean ground temperature (aggregated from monthly values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum monthly ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum monthlyground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of monthly values available to calculate annual mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>CONTACT</p> <p>For question related to this dataset please contact the corresponding author: jeannette.noetzli@slf.ch. <br>For questions related to a specific time series, see metadata for contact information.</p>
Barn owl diet and prey fluctuations in the Jura mountains, eastern France
<p>Based on pellet collection, the diet of the Barn Owl (<em>Tyto alba</em>) was studied over a 8-year period in the Jura mountains, eastern France, during two population surges of its main prey (common vole, <em>Microtus arvalis </em>and montane water vole, <em>Arvicola amphibius</em>); Small mammals were sampled by trapping and index methods. Results have been published in the Canadian Journal of Zoology (<a href="http://doi.org/10.1139/z10-011">Bernard et al. 2010</a>). Dominique Michelat collected Barn Owl pellets and identified prey items, Pierre Delattre, Jean-Pierre Quéré Jean-Pierre Damange and Patrick Giraudoux sampled small mammals. Patrick Giraudoux managed the data.</p> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a>is the file of the raw trapping results for small mammals (instant abundance index i<sub>t</sub> in the article)</p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a> is a file with:</p> <ul> <li>the small mammal density computed by season (d<sub>t</sub> in the article, rough estimate of densities in number of individuals per ha for <em>Apodemus spp.</em>, <em>Myodes glareolus</em>, <em>Microtus arvalis</em>, or weighted interpolated i<sub>t</sub> for the other species)</li> <li>the ratios of each category of prey items on the total number of items collected in the church tower of three sites, <a href="https://www.openstreetmap.org/#map=16/46.9536/6.1189">Levier</a>, <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9321%2C6.1666#map=16/46.9321/6.1666">Chapelle d'Huin</a> and <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9382%2C6.1976#map=16/46.9382/6.1976">Le Souillot</a>.</li> </ul> <p>For details see the material and methods of the article.</p> <p><strong>FILE DESCRIPTION</strong></p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a></p> <ul> <li>date, year and season: year on two digits, then P, E, A, H respectively for<em> Printemps</em> (Spring), <em>Été</em> (Summer), <em>Automne</em> (Autumn), <em>Hiver</em> (Winter)</li> <li>at_t, abundance index of <em>Arvicola amphibius</em> (ex A.<em> terrestris</em>)</li> <li>ap_t, rough density estimate of <em>Apodemus sp.</em></li> <li>cg_t, rough density estimate of <em>Myodes glareolus</em></li> <li>ma_t, rough density estimate of <em>Microtus arvalis</em></li> <li>sa_t, relative abundance of <em>Sorex spp.</em></li> <li>n_l, number of prey items at Levier</li> <li>ma_p_l, ratio of <em>M. arvalis prey</em> items at Levier</li> <li>at_p_l, ratio of <em>A. amphibius</em> prey items at Levier</li> <li>apcg_p_l, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Levier</li> <li>sa_p_l, ratio of <em>Sorex spp.</em> prey items at Levier</li> <li>au_p_l, ratio of other prey items at Levier</li> <li>n_ch, number of prey items at Chapelle d'Huin</li> <li>map_ch, ratio of <em>M. arvalis prey</em> items at Chapelle d'Huin</li> <li>at_p_ch, ratio of <em>A. amphibius</em> prey items at Chapelle d'Huin</li> <li>apcg_p_ch, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Chapelle d'Huin</li> <li>sa_p_ch, ratio of <em>Sorex spp. </em>prey items at Chapelle d'Huin</li> <li>au_p_ch, ratio of other prey items at Chapelle d'Huin</li> <li>n_ls, number of prey items at Le Souillot</li> <li>ma_p_ls, ratio of <em>M. arvalis prey</em> items at Le Souillot</li> <li>at_p_ls, ratio of <em>A. amphibius</em> prey items at Le souillot</li> <li>apcg_p_ls, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Le Souillot</li> <li>sa_p_ls, ratio of <em>Sorex spp.</em> prey items at Le Souillot</li> <li>au_p_ls, ratio of other prey items at Le Souillot</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a></p> <ul> <li>date, trapping period: digit 1-2, year; digit 3-4, month. Example: 8704 = April 1987.</li> <li>n_traplines_f, number of traplines in forest</li> <li>ap_f, average number of <em>Apodemus spp</em>. captured in forest</li> <li>cg_f, average number of <em>Myodes glareolus</em> captured in forest</li> <li>sa_f, average number of <em>Sorex spp</em>. captured in forest</li> <li>n_traplines_hfb, number of traplines in hedges and forest borders</li> <li>ap_hfb, average number of <em>Apodemus spp</em>. captured in hedges and forest borders</li> <li>cg_hfb, average number of <em>Myodes glareolus</em> captured in hedges and forest borders</li> <li>sa_hfb, average number of <em>Sorex spp.</em> captured in hedges and forest borders</li> <li>n_traplines_g, number of traplines in grassland</li> <li>ma_g, average number of Microtus arvalis captured in grassland</li> <li>sa_g, average number of <em>Sorex spp.</em> captured in grassland</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/SmallMammalSamplingArea.kml?download=1">SmallMammalSamplingArea.kml</a> kml file locating the small mammal sampling area<br> </p>
From the collective to the individual: transformation processes at the transition from the 4th to the 3rd millennium BC in the German low mountain zone
<p>Data collected by Clara Drummer, Kiel 2022.</p> <p>Clara Drummer, Vom Kollektiv zum Individuum: Transformationsprozesse am Übergang vom 4. zum 3. Jahrtausend v. Chr. in der Deutschen Mittelgebirgszone. Scales of transformation Bd. 13 (Leiden 2022).https://d-nb.info/1241580332</p> <p>CRC 1266: "Scales of Transformation - Human-Environmental Interaction in Prehistoric and Archaic Societies."<br> "Regional and Local Patterns of 3rd Millennium Transformations of Social and Economic Practic-es in the Central German Mountain Range (D2)" Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 128675135 https://gepris.dfg.de/gepris/projekt/316739879</p> <p>Data for the analyses of the decisive transformation in the Hessian-Westphalian area from the Wartberg society to the Corded Ware groups. The work discusses above all the social aspects of the change. This includes, on the one hand, a more detailed analysis of burial rituals and, on the other hand, the integration of, for example, available aDNA results into the overall analysis.</p>
Survey of Alarka Laurel and Rich Mountain Red Spruce (Picea rubens) Overstory, Saplings, and Seedlings in western North Carolina in 2007, 2022, and 2023
In the southern Appalachians, disjunct red spruce (Picea rubens) populations persist at low latitudes at elevations above 1,370 m. However, research on the condition of these disjunct red spruce populations is limited. This study compared baseline health, recruitment, and stand dynamics of two of the southern-most red spruce populations in eastern North America, the Rich Mountain and Alarka Laurel spruce bog basins in Nantahala National Forest, North Carolina. We collected data on overstory (DBH>10 cm), saplings (DBH< 10 cm, and height >2 m), and seedlings (height<10 cm) from Alarka Laurel in 2007 and 2022. Data from Rich Mountain were collected in 2023. We used 10-m wide belt transects noted the species and diameter at breast height (DBH) of overstory species, counted and noted the DBH of red spruce saplings, and counted and noted the height of red spruce seedlings. In 2022 and 2023, we gave a health score from 0-3 for all three categories of trees (overstory, saplings, and seedlings), with 0 being dead and 3 being healthy with little to no signs of disease or stress. Overall, both stands did not yet appear affected by climatic warming, despite the southern latitude and relatively low elevation. Our findings reveal that red spruce is the dominant overstory species, comprising an average of 25.6% of all measured overstory trees, with seedlings and saplings making up 72.8% of the red spruce population, indicating sustainable recruitment. Red spruce basal area declined by 13.9% from 2007 to 2022 in Alarka Laurel, with a concomitant increase in some hardwood species. However, both Alarka Laurel and Rich Mountain showed high levels of sapling and seedling recruitment. Overall, red spruce trees are healthy, particularly seedlings, representing the healthiest age category. Our results suggest the stands are relatively stable and provide essential baseline data for monitoring of forest conditions in the context of intensifying climate change. This research contributes to b
Plant and carbon data, snowmelt manipulation experiment, Rocky Mountain Biological Laboratory (RMBL), 2023
These data are from a 2023 snowmelt manipulation experiment in Vera Meadow at the Rocky Mountain Biological Laboratory. We experimentally advanced the snowmelt date in a montane meadow by approximately 12 days using black shade cloths and assessed the effect on plant and carbon dynamics. We measured net ecosystem exchange, gross primary productivity, and soil respiration using a Li-COR 7500 five times biweekly from June to August, plant community composition using the pin-drop method five times biweekly from June to August, and root biomass nine times using bulk soil cores. Using drone imagery, we measured the Normalized Difference Vegetation Index (NDVI). This data package is completed.
Temperature, floral density, and Osmia pollen usage data from seven study sites around the Rocky Mountain Biological Laboratory, Colorado: 2013-2023
Data were collected as part of a study of population dynamics of solitary, cavity-nesting Hymenoptera. Nesting structures ("trap-nests") were established at five study sites along an elevational gradient around the Rocky Mountain Biological Laboratory in 2013. Two additional study sites were added in 2014, and one of the original study sites was dropped at the end of 2015. At each site, a HOBO data-logger placed under a centrally located trap-nest records air temperatures hourly. Floral densities are recorded at each site, typically 1-2 times per week, throughout the growing season, for specific plant taxa known to be used as pollen sources by cavity-nesting bees. In addition, pollen samples are taken from the nests of cavity-nesting bees and the constituent plant taxa identified by microscopic comparison with a reference pollen collection from the study area.
Bird Abundances at the Hubbard Brook Experimental Forest (1969-present) and on three replicate plots (1986-2000) in the White Mountain National Forest
Bird abundances have been determined from timed censuses, territory maps and nest locations at the Hubbard Brook Experimental Forest from 1969 to the present. This data set includes counts of the number of adult birds (males and females) per 10 ha at HBEF (1969 - present) and on three additional plots within the White Mountain National Forest (1986 - 2000). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
High-mountain Asia glacier elevation change trend (dh/dt) map for the period spanning 2000 to 2018
<p>See manuscript for methodology and dataset description:</p> <p>Shean DE, Bhushan S, Montesano P, Rounce DR, Arendt A and Osmanoglu B (2020) A Systematic, Regional Assessment of High-Mountain Asia Glacier Mass Balance. Front. Earth Sci. 7:363. DOI: 10.3389/feart.2019.00363</p> <p>https://www.frontiersin.org/articles/10.3389/feart.2019.00363/full</p> <p>GeoTiff header contains relevant metadata and georeferencing information (30 m pixel size, Albers Equal Area projection). Proj string is '+proj=aea +lat_1=25 +lat_2=47 +lat_0=36 +lon_0=85 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'</p> <p>External overview file (.ovr) contains pyramidal overviews for improved visualization performance at different zoom levels.</p>
DataSet of "No renal dysfunction or salt and water retention in acute mountain sickness at 4,559 m among young resting males after passive ascent"
<p><strong>Abstract</strong></p> <p><strong>Purpose</strong>: This study examined the role and function of the kidney at high altitude in relation to fluid balance and the development of acute mountain sickness (AMS), avoiding confounders that have contributed to conflicting results in previous studies.</p> <p><strong>Methods</strong>: We examined 18 healthy male volunteers (18 - 40 years) not acclimatized to high altitude while on a controlled diet and resting recumbently for 24 h at Lausanne (altitude: 560 m) followed by a period of 44 hours after reaching the Regina Margherita hut (4,559 m) by helicopter.</p> <p><strong>Results</strong>: AMS scores peaked after 20 h at 4,559 m. AMS was defined as functional Lake Louise score <span class="math-tex">\({\ge}\)</span> 2.There were no significant differences between 10 subjects with and 8 subjects without AMS for urinary flow, fluid balance and weight change. Sodium excretion rate was lower in those with AMS after 24 h at altitude. Microalbuminuria increased at altitude but not differently between the groups. Creatinine clearance was not affected by altitude or AMS, while sinistrin and PAH clearances decreased slightly, more markedly in those without AMS. Plasma concentrations of epinephrine, norepinephrine, atrial natriuretic factor and vasopressin increased while renin activity, angiotensin and aldosterone decreased at altitude. Hormones levels did not differ between those with and without AMS.</p> <p><strong>Conclusions</strong>: 1) Renal function is not affected by hypoxia at 4,559 m in resting subjects except for minor microalbuminuria, 2) high altitude diuresis does not occur and 3) AMS is not associated with salt and water retention or renal dysfunction.</p>
An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)
<p>This is a multiannual ground temperature dataset covering sixteen high elevation sites (3493-4377 m a.s.l.) in the Bale Mountains, southern Ethiopian Highlands</p> <p>The dataset is described in detail in the corresponding data paper by Groos et al. 2021 (https://doi.org/10.5194/essd-2021-268)</p> <p>The repository contains a readme file ("readme.txt"), a GeoPackage ("Data_Logger_Location.gpkg"), a thermal infrared time-lapse video ("thermal_infrared_time-lapse_video.mp4"), a metadata file for the video ("video_metadata.txt"), and two sub-folders: "raw_data" and "processed_data"</p> <p>The GeoPackage provides information on the location and environmental setting of each logger and can be easily opened and displayed in a Geographic Information System. The coordinate reference system is WGS84 / Geographic (EPSG code: 4326).</p> <p>The thermal infrared time-lapse video (<a href="https://vimeo.com/676294827">https://vimeo.com/676294827</a>) visualises the phenomenon of nocturnal cold air drainage and ponding in the Bale Mountains (for more information see the metadata file and Appendix C in the corresponding data paper).</p> <p>The folder "raw_data" contains the original logfiles of all GT and TM data loggers (see Table 1) in a tab-delimited text format with the logger ID and download date encoded in the file name. The date format of the GT data loggers is YYYY.MM.DD hh:mm:ss East Africa Time (EAT). The date format of the TM data loggers is DD.MM.YYYY hh:mm:ss EAT.</p> <p>The folder "processed_data" contains the followings two files:</p> <p>"Information_Sheet_Data_Gap-Filling.ods": An overview table with relevant information regarding the filling of (longer) data gaps in the ground temperature time series. The gap-filling procedure based on simple linear regression models is described individually for each logger.</p> <p>"Hourly_Ground_Temperatures.csv": Compilation of hourly ground temperature data from all GT and TM data loggers installed in the Bale Mountains (see Table 1 in the data paper). The dataset covers the period from 1 January 2017 to 31 January 2020, but individual time series may be shorter or contain data gaps (see Fig. 3 in the data paper). We use the international date format (ISO 8601): YYYY-MM-DD hh:mm:ss EAT. The following numerical indices (or a combination of them) in the columns starting with "Flag_*" are used to provide additional information on the post-processing of each hourly measurement of each time series:</p> <p>0 no data available<br> 1 original data (no post-processing)<br> 2 data interpolated to full hour<br> 3 erroneous data corrected<br> 4 erroneous data removed<br> 5 data gap-filled</p> <p>The meteorological data from the ten automatic weather stations in the Bale Mountains, which are operated since 2017, are currently post-processed and analysed in the framework of the DFG Research Unit 2358 "The Mountain Exile Hypothesis". The data will be made publicly available at some point in the future. However, individual access to the weather station data may be granted before on request to the coordination board of the research unit (bale@staff.uni-marburg.de).</p>
Modern aridity in the Altai-Sayan Mountain Range derived from multiple millennial proxies
<p><em>1500-year stable carbon and oxygen isotopes in larch tree-ring cellulose from the Altai-Sayan Mountain Range region </em>(49-51N, 87-89 E)</p> <p><em>Regional summer (June-July-August) precipitation reconstruction for the Altai-Sayan Mountain Range region based on d<sup>13</sup>C in tree-ring cellulose (d<sup>13</sup>C<sub>cell </sub>) combined with Co/Inc and Rb/Sr from Teletskoe Lake core sediments (TLs).</em></p> <p><em>Regional summer air temperature (June-July-August) reconstruction based on d<sup>18</sup>O<sub> </sub>in tree-ring cellulose (d<sup>18</sup>O<sub>cell</sub>), tree-ring width (TRW), latewood density (MXD) and elemental concentrations (Ca, Ti, Br/Sr) in the Teletskoe Lake core sediments (TLs).</em></p>
Data on ecosystem services in mountain pastures
<p><strong>Datasets used for analyses in Ecosystem services in mountain pastures: a complex network of site conditions, climate and management:</strong></p> <p><strong>(1) Plot information data</strong></p> <table> <tbody> <tr> <td>Plot-ID</td> <td>Plot-ID</td> </tr> <tr> <td>Farm-ID</td> <td>Farm-ID</td> </tr> <tr> <td>Region-ID</td> <td>Region-ID</td> </tr> <tr> <td>X_WGS84</td> <td>X coordinate</td> </tr> <tr> <td>Y_WGS84</td> <td>Y cooordinate</td> </tr> <tr> <td>Plant-biomass-g_m2</td> <td>Plant biomass (g DM m-2)</td> </tr> <tr> <td>Digestibility_Perc</td> <td>Digestibility (%)</td> </tr> <tr> <td>SOC-kg_m2</td> <td>Soil C content (kg m-2)</td> </tr> <tr> <td>Color-abundance-Perc</td> <td>Share of coloured species (%)</td> </tr> <tr> <td>Pollinator-ress_Ind</td> <td>Floral reward indicator</td> </tr> <tr> <td>Plant-species-richness</td> <td>No. of plant species</td> </tr> <tr> <td>P-conten</td> <td>Soil P (mg/kg)</td> </tr> <tr> <td>pH</td> <td>Soil pH</td> </tr> <tr> <td>Slope_Perc</td> <td>Terrain slope (%)</td> </tr> <tr> <td>SummerTemp</td> <td>Mean Temperature May-Sept (°C)</td> </tr> <tr> <td>SummerPrecip</td> <td>Mean Precipitation May-Sept (mm)</td> </tr> <tr> <td>logLU_total</td> <td>Cattle presence (LU ha-1 a-1)</td> </tr> <tr> <td>Distance</td> <td>Distance to farm centre (m)</td> </tr> </tbody> </table> <p> </p> <p><strong>(2) Vegetation data</strong></p> <p>Presence-absence cross-table</p> <p>columns: Plots</p> <p>rows: Plant species</p> <p>Taxonomic names according to Lauber et al. (2001) Flora Helvetica</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.