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2,078 results for “soil data”
Soil chemical and physical property data for Green Lakes Valley, 1986.
Laboratory analyses were performed on soil samples from the Green Lakes Valley in the City of Boulder Watershed in order to characterize the chemical characteristics of the soil resource. The samples were collected during the 1986 excavations that were required during installation of zero-tension soil solution samplers.
Soil interstitial water chemistry data for Green Lakes Valley, 1987 - 1992.
Soil interstitial water samples were collected from tension and zero-tension samplers at various locations within the Green Lakes Valley in the City of Boulder Watershed. Chemical analyses were performed on the samples in order to characterize soil solution concentrations and estimate fluxes of important chemical constituents. Samples were collected at several depths from within several soil types and topographic situations in this alpine/subalpine environment.
Soil and microbial N data for Saddle and South of saddle, 1992.
Net nitrogen mineralization, nitrification, and microbial nitrogen were measured in dry and moist meadow plots on Niwot Ridge from early June to mid-October 1992. Dry meadow plots in the City of Boulder Watershed had been fertilized with N during the two years prior to measurements, and both dry and moist meadow plots in the Saddle were being watered and fertilized concurrent to measurements.
Soil and microbial N data for Saddle, South of saddle, North of saddle, 1993.
Gross N mineralization, respiration, and microbial biomass N were measured using soil from dry, moist, and wet meadow sites on Niwot Ridge at 3 times during the year: immediately following snowmelt, at approximately peak plant biomass, and following aboveground senescence. Microbial biomass N was measured on replicate soil cores from plots of each community, using the chloroform fumigation-extraction technique. Those cores were subsequently composited and subsamples were incubated at 15 degrees Celsius in the laboratory for measurement of CO2 evolution and 15N pool dilution-based estimations of gross mineralization.
Soil water and microbial N data for Saddle, 1993.
The effects of water, carbon (glucose), or nitrogen ((NH4)2SO4) additions on N transformations and the chloroform-labile microbial N pool were assessed in laboratory incubations of moist meadow soil collected from the T1M trace gas study plot on Niwot Ridge. These incubations were conducted at monthly intervals during the snow-free season of 1993, to detect any seasonal changes in the microbial response to different amendments. Soil respiration was measured concurrently.
Snakeweed (Gutierrezia sarothrae) Habitat Soils Data from the Sevilleta National Wildlife Refuge, New Mexico (1984)
In 1984, a research project was initiated on a relatively small disturbance patch just south of Deep Well. This disturbance was thought to be the result of an old praire dog town, probably dating back to when a nearby ranch was active, and a lot of old mammal mounds remained in the disturbed area. One of the things that made the disturbance patch particularily noticeable was the lush growth of snakeweed (Gutierrezia sarothrae) within the patch. This prompted the designation of the disturbance patch as the "snakeweed patch" or "Gutierrezia patch." In addition, there was an obvious increase in bare ground and a shift in vegetation composition across the patch boundary. The dominant vegetation was not consistent around the boundary, with a marked dominance of black grama on the west side of the plot and a blue/black grama mix on the other three sides. To obtain information on the cause and/or effect of this disturbance, a survey of the soil and vegetation was performed.
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Soil Temperature Data from the Sevilleta National Wildlife Refuge, New Mexico (1/2006 - 7/2009)
This data set provides soil temperature data in each plot of the warming experiment (see SEV176). Data are collected with automated soil temperature probes at 15-minute intervals at two soil depths under grass and bare patches in each of the 40 plots.
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Soil Moisture Data from the Sevilleta National Wildlife Refuge, New Mexico (1/2006-8/2009)
This data set provides soil moisture data in each plot of the warming experiment (see SEV176). Data are collected with automated soil moisture probes at 30-minute intervals at two soil depths under grass and bare patches in each of the 40 plots.
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Soil Temperature, Moisture, and Carbon Dioxide Data from the Sevilleta National Wildlife Refuge, New Mexico
Humans are creating significant global environmental change, including shifts in climate, increased nitrogen (N) deposition, and the facilitation of species invasions. A multi-factorial field experiment is being performed in an arid grassland within the Sevilleta National Wildlife Refuge (NWR) to simulate increased nighttime temperature, higher N deposition, and heightened El Nino frequency (which increases winter precipitation by an average of 50%). The purpose of the experiment is to better understand the potential effects of environmental drivers on grassland community composition, aboveground net primary production and soil respiration. The focus is on the response of two dominant grasses (Bouteloua gracilis and B eriopoda), in an ecotone near their range margins and thus these species may be particularly susceptible to global environmental change. It is hypothesized that warmer summer temperatures and increased evaporation will favor growth of black grama (Bouteloua eriopoda), a desert grass, but that increased winter precipitation and/or available nitrogen will favor the growth of blue grama (Bouteloua gracilis), a shortgrass prairie species. Treatment effects on limiting resources (soil moisture, nitrogen availability, species abundance, and net primary production (NPP) are all being measured to determine the interactive effects of key global change drivers on arid grassland plant community dynamics and ecosystem processes. This dataset shows values of soil moisture, soil temperature, and the CO2 flux of the amount of CO2 that has moved from soil to air. On 4 August 2009 lightning ignited a ~3300 ha wildfire that burned through the experiment and its surroundings. Because desert grassland fires are patchy, not all of the replicate plots burned in the wildfire. Therefore, seven days after the wildfire was extinguished, the Sevilleta NWR Fire Crew thoroughly burned the remaining plots allowing us to assess experimentally the effects of interactions among multip
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Soil Nitrogen Data from the Sevilleta National Wildlife Refuge, New Mexico (2006 - 2020)
Humans are creating significant global environmental change, including shifts in climate, increased nitrogen (N) deposition, and the facilitation of species invasions. A multi-factorial field experiment is being performed in an arid grassland within the Sevilleta National Wildlife Refuge (NWR) to simulate increased nighttime temperature, higher N deposition, and heightened El Niño frequency (which increases winter precipitation by an average of 50%). The purpose of the experiment is to better understand the potential effects of environmental drivers on grassland community composition, aboveground net primary production and soil respiration. The focus is on the response of two dominant grasses (Bouteloua gracilis and B eriopoda), in an ecotone near their range margins and thus these species may be particularly susceptible to global environmental change. It is hypothesized that warmer summer temperatures and increased evaporation will favor growth of black grama (Bouteloua eriopoda), a desert grass, but that increased winter precipitation and/or available nitrogen will favor the growth of blue grama (Bouteloua gracilis), a shortgrass prairie species. Treatment effects on limiting resources (soil moisture, nitrogen availability, species abundance, and net primary production (NPP) are all being measured to determine the interactive effects of key global change drivers on arid grassland plant community dynamics and ecosystem processes. On 4 August 2009 lightning ignited a ~3300 ha wildfire that burned through the experiment and its surroundings. Because desert grassland fires are patchy, not all of the replicate plots burned in the wildfire. Therefore, seven days after the wildfire was extinguished, the Sevilleta NWR Fire Crew thoroughly burned the remaining plots allowing us to assess experimentally the effects of interactions among multiple global change presses and a pulse disturbance on post-fire grassland dynamics. This data set provides soil N availability in each
Monsoon Rainfall Manipulation Experiment (MRME): Soil Nitrogen Data from the Sevilleta National Wildlife Refuge, New Mexico (2007 - 2020)
The Monsoon Rainfall Manipulation Experiment (MRME) is designed to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, by altering rainfall pulses, and thus soil moisture, that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and frequency will alter grassland productivity, ecosystem processes, and plant community dynamics. Treatments include (1) a monthly addition of 20 mm of rain in addition to ambient, and a weekly addition of 5 mm of rain in addition to ambient during the months of July, August and September. We predict that soil N availability with interact with rainfall event size to alter net primary productivity during the summer monsoon. Specifically, productivity will be higher on fertilized relative to control plots, and productivity will be highest on N addition plots in treatments with a small number of large events because these events infiltrate deeper and soil moisture is available longer following large compared to small events.
Fungal responses to plant invasion are greater under soil warming than simulated nitrogen deposition data
<p>This file includes the data associated with analyzing scripts for the following github entry: <a href="https://gitlab.ethz.ch/manthony/swan-x-invasion">https://gitlab.ethz.ch/manthony/swan-x-invasion</a>. </p> <p>This project studied the combined effects of nitrogen deposition, soil warming, and Alliaria petiolata (garlic mustard) invasion on soil fungal communities, functional genes, and soil physical and chemical properties.</p>
Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>.</p> <p>The R code was written for R version 3.6.3.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p>
Biological soil covers: data on lichen, bryophyte and algae coverage in soils gathered by SoilSkin citizen science program using eBryoSoil app for smartphones
<p>Biological soil covers (BSC) are small-sized topsoil communities composed mainly by lichens, bryophytes and algae that cover the terrestrial surface and play an essential role in maintaining the quality of the soil. However, little is known about their distribution, conservation, and ecosystem functions. The SoilSkin citizen science project aims to expand the scientific knowledge about the distribution of biological soil covers as an important step to evaluate the vulnerability of soil ecosystems of the Iberian Peninsula in the face of global change.</p> <p>The project has a dedicated free of charge app for smartphones (eBryoSoil, available at Google Play <a href="https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US">https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US</a>) that is designed to obtain information about the coverage of the BSC communities. To use this app, users must select a sampling location and capture the three soil pictures required to complete a transect. These photographs are taken at a 27 cm distance from the soil, in a straight line with 15 meters of distance between each picture. After the acquisition of each image, users can quantify the coverage percentage of biological soil covers and select the type of habitat where the transect took place. The transect is complete when all three pictures and their respective information are uploaded.</p> <p>The data presented here contains the records from SoilSkin participants, which mainly include a characterization of the cover patterns of biological soil covers, the type of habitat and the coordinates where each record was taken. The data set is composed by 279 unique records taken by 37 unique users from 28/11/2019 to 12/12/2020, across the Iberian Peninsula. These records specifically detail the percentage of cover occupied by three types of lichen growth forms (crustose, foliose and fruticose); liverworts; two types of moss growth forms (acrocarpous and pleurocarpous); algae; and soil. Moreover, each record also contains a description of the main type of habitat where the transect took place, that was selected from a list contained in the app with the following habitats:</p> <ul> <li>Dense forest - Habitat characterized by trees of more than 2 meters tall and canopy over 60%.</li> <li>Open forest – Habitat characterized by trees with more than 2 meters tall and a canopy below 60%.</li> <li>Shrubland – Habitat characterized by woody vegetation with less than 2 meters tall.</li> <li>Grassland – Habitat characterized by herbaceous plants.</li> <li>Agricultural land – Habitat characterized by temporary or woody crops.</li> <li>Coastal habitat – Habitat characterized by a landscape where land is in contact with the sea, creating a visibly different landscape from inner terrestrial one’s.</li> <li>Urban green spaces – Habitat characterized by a landscape in which man-made structures are present.</li> </ul> <p>The database was revised to correct any possible mistakes (e.g., miscalculation of total percentages; habitat missing in some registers; removal of invalid registers).</p> <p>The data file contains the following columns:</p> <ul> <li>Date: numerical variable indicating the “day”/”month”/”year” when the register was generated.</li> <li>User_ID: categorical variable with the identification number of the user who gathered the record.</li> <li>Transect: categorical variable with the identification of the number of the transect.</li> <li>Photo_number: numeric variable that takes values of 1, 2 or 3 and corresponds with the identification of the photographs within each transect.</li> <li>Photo_label: character string with the identification of the photograph from each record.</li> <li>Register_localization: categorical variable with the identification of the geographic area where the record was done.</li> <li>Latitude: integer, variable indicating the latitude of the sampling location in decimal degrees.</li> <li>Longitude: integer, variable indicating the longitude of the sampling location in decimal degrees.</li> <li>Accuracy: integer, variable indicating the accuracy of the coordinates given by the GPS.</li> <li>Habitat_type: categorical variable with the description of the main type of habitat of the sampling location.</li> <li>Lichen_Crustose: integer, variable indicating the percentage of crustose lichen cover quantified in the record.</li> <li>Lichen_Foliose: integer, variable indicating the percentage of foliose lichen cover quantified in the record.</li> <li>Lichen_Fruticose: integer, variable indicating the percentage of fruticose lichen cover quantified in the record.</li> <li>Total_lichen: integer, variable indicating the sum of all lichen coverage quantified in the record.</li> <li>Liverwort: integer, variable indicating the percentage of liverwort cover quantified in the record.</li> <li>Moss_Acrocarpous: integer, variable indicating the percentage of acrocarpous moss cover quantified in the record.</li> <li>Moss_Pleurocarpous: integer, variable indicating the percentage of pleurocarpous moss cover quantified in the record.</li> <li>Total_ moss: integer, variable indicating the sum of all moss coverage quantified in the record.</li> <li>Algae: integer, variable indicating the percentage of algae cover quantified in the record.</li> <li>Soil: integer, variable indicating the percentage of soil visible in the record.</li> </ul> <p> </p>
Data from: Soil organic carbon stability in forests: distinct effects of tree species identity and traits
Rising atmospheric CO2 concentrations have increased interest in the potential for forest ecosystems and soils to act as carbon (C) sinks. While soil organic C contents often vary with tree species identity, little is known about if, and how, tree species influence the stability of C in soil. Using a 40‐year‐old common garden experiment with replicated plots of eleven temperate tree species, we investigated relationships between soil organic matter (SOM) stability in mineral soils and 17 ecological factors (including tree tissue chemistry, magnitude of organic matter inputs and their turnover, microbial community descriptors, and soil physico‐chemical properties). We measured five SOM stability indices, including heterotrophic respiration, C in aggregate‐occluded particulate organic matter (POM) and mineral‐associated SOM, and bulk SOM δ15N and ∆14C. The stability of SOM varied substantially among tree species and this variability was independent of the amount of organic C in soils. Thus, when considering forest soils as C sinks, the stability of C stocks must be considered in addition to their size. Further, our results suggest tree species regulate soil C stability via the composition of their tissues, especially roots. Stability of SOM appeared to be greater (as indicated by higher δ15N and reduced respiration) beneath species with higher concentrations of nitrogen and lower amounts of acid‐insoluble compounds in their roots, while SOM stability appeared to be lower (as indicated by higher respiration and lower proportions of C in aggregate‐occluded POM) beneath species with higher tissue calcium contents. The proportion of C in mineral‐associated SOM and bulk soil ∆14C, though, were negligibly dependent on tree species traits, likely reflecting an insensitivity of some SOM pools to decadal‐scale shifts in ecological factors. Strategies aiming to increase soil C stocks may thus focus on particulate C pools, which can more easily be manipulated and are most sensitive to climate change.
Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest
Throughout most of the northern hemisphere, snow cover decreased in almost every winter month from 1967 to 2012. Because snow is an effective insulator, snow cover loss has likely enhanced soil freezing and the frequency of soil freeze–thaw cycles, which can disrupt soil nitrogen dynamics including the production of nitrous oxide (N2O). We used replicated automated gas flux chambers deployed in an annual cropping system in the upper Midwest US for three winters (December–March, 2011–2013) to examine the effects of snow removal and additions on N2O fluxes. Diminished snow cover resulted in increased N2O emissions each year; over the entire experiment, cumulative emissions in plots with snow removed were 69% higher than in ambient snow control plots and 95% higher than in plots that received additional snow (P < 0.001). Higher emissions coincided with a greater number of freeze–thaw cycles that broke up soil macroaggregates (250–8000 µm) and significantly increased soil inorganic nitrogen pools. We conclude that winters with less snow cover can be expected to accelerate N2O fluxes from agricultural soils subject to wintertime freezing.
Raw data for the submitted manuscript: Effects of microplastics on enchytraeid multigeneration reproduction and soil physicochemical properties
<p>Survival and reproduction data from multigenerational single species tests involving four types of plastic materials.</p> <p> </p>
Soil respiration data measured with LI-7810 CH4/CO2/H2O Trace Gas Analyzer in Tangermuende/Germany in August 2022
<p>The dataset is associated with the publication Koschorreck, M., Kamjunke, N., Koedel, U., Rode, M., Schuetze, C., and Bussmann, I.: Diurnal versus spatial variability of greenhouse gas emissions from an anthropogenic modified German lowland river, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-176, in review, 2023. </p> <p>It shows CO2 and CH4 initial concentrations and flux data measured with the LICOR 7810 instrument and calculated with the SoilFluxPro software V5.3.</p>
Raw sequencing data for studying the colonization of soil communities after glacier retreat
<p>Glaciers show a pattern of retreat at the global scale. Deglaciated areas are exposed and colonized by multiple organisms, but lack of global studies hampers a complete understanding of the future of these ecosystems. Until now, the complete reconstruction of soil communities was hampered by the complex identification of organisms, thus analyses at broad geographical and taxonomic scale have been so far impossible. The dataset used for this study represents the assemblages of Bacteria, Mycota, Eukaryota, Collembola (springtails), Oligochaeta (Earth worms), Insecta, Arthropoda and Vascular Plants obtained using environmental DNA (eDNA) metabarcoding. eDNA was extracted from soil samples collected from multiple glacier forelands representative of some of the main mountain chains of Europe, Asia, the Americas and Oceania. We investigated chronosequences of glacier retreat (i.e., the chronological sequence of specific geomorphological features along deglaciated areas for which the date of glacier retreat is known) ranging from recent years to the Little Ice Age (~1850). We used this newly assembled global DNA metabarcoding dataset to obtain a complete reconstruction of community changes in novel ecosystems after glacier retreat. Information on assemblages can be then combined with analyses of soil, landscape and climate to identify the drivers of community changes.</p>
NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH
<p>This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected around the SPLASH campaign areas near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from between June 1st, 2022 and September 18th, 2023, under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office under award NA21OAR4590363.</p><p>Two file formats are provided: one version is text csv format and the second version is in NetCDF.</p><p><strong>Volumetric water content calculations: </strong></p><p>Data were calibrated and adjusted, with a soil-specific sample set, to improve accuracy and compensate for the meter's default "standard" soil type used in the sampling. VWC data was correlated by measuring the weight of a known volume of soil from a range of saturation values. Samples were measured and weighed, dried at 105 degrees C for 48 hours, then weighed again. Calculations of VWC (VWC<strong> </strong>= 100*(Mwet - Mdry)/(w*Vtot) )were plotted against TDR readings. Where: </p><p>Mwet, Mdry = mass (g) of wet and dry soil respectively </p><p>Vtot = total soil volume (ml) </p><p>w = density of water (1g/ml) </p><p>A regression analysis to correlate TDR readings to the samples is below and was applied to the dataset.</p><p>vwc_calculated = vwc_probe * slope + intercept</p><p>slope = 1.20665, intercept = 0.0837017 m3/m3, slope_std_error = 0.09229, intercept_std_error = 0.0217403 m3/m3</p><p><strong>Definitions:</strong></p><p>TDR (Time Domain Reflectometry): A technique for measuring soil moisture content that uses the fact that water has a much higher dielectric permittivity than air, soil minerals, and organic matter. </p><p>VWC (Volumetric Water Content): The ratio of the volume of water in a given volume of soil to the total soil volume expressed as a decimal or a percentage. The percent of the soil volume that is filled with water. At saturation, the VWC will equal the soil porosity (Saturation is typically around 50%).</p><p>EC (Electrical Conductivity): A measure of how well the soil solution conducts electricity. The EC is influenced by the amount of salt and water in the soil. </p><p>The VWC measured by TDR is an average over the length of the waveguide. </p><p><strong>Soil Characteristics:</strong></p><p>Soil at both Kettle Ponds (KEP1 and KPA) locations and Avery Picnic (AYP) were lab tested for composition as follows:</p><p><strong>Sample ID Depth(in.) Sand(%) Silt(%) Clay(%) Soil Texture</strong></p><p>------------------------------------------------------------------------------------------------------ </p><p>KEP1 2 43 35 22 Loam</p><p>AYP 2 40 35 25 Loam</p><p>KPA 2 35 42 22 Loam</p><p>------------------------------------------------------------------------------------------------------</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.