Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
205
datasets available to search
ShareScore release 0.9.0
Dataset results
205 results for “Peat”
Fluxes project at North Temperate Lakes LTER: Predicting Peat Depth in a North Temperate Lake District 2008
Peat deposits contain on the order of 1/6 of the Earth's terrestrial fixed carbon (C), but uncertainty in peat depth precludes precise estimates of peat C storage. To assess peat C in the Northern Highlands Lake District (NHLD), a approximately 7000 square km region in northern Wisconsin, United States, with 20 percent peatland by area, we sampled 21 peatlands. In each peatland, peat depth (including basal organic lake sediment, where present) was measured on a grid and interpolated to calculate mean depth. Our study addressed three questions: (1) How spatially variable is peat depth? (2) To what degree can mean peat depth be predicted from other field measurements (water chemistry, water table depth, vegetation cover, slope) and/or remotely sensed spatial data? (3) How much C is stored in NHLD peatlands? Site mean peat depth ranged from 0.1 to 5.1 m. Most of the peatlands had been formed by the in-filling of small lake basins (terrestrialization), and depths up to 15 m were observed. Mean peat depth for small peat basins could be best predicted from basin edge slope at the peatland/upland interface, either measured in the field or calculated from digital elevation (DEM) data (Adj. R2 = 0.70). Upscaling using the DEM-based regression gave a regional mean peat depth of 2.1 plus or minus 0.2 m (including approximately 0.1 to 0.4 m of organic lake sediment) and 144 plus or minus 21 Tg-C in total. As DEM data are widely available, this technique has the potential to improve C storage estimates in regions with peatlands formed primarily by terrestrialization. Number of sites: 21 Sampling Frequency: once for each site
Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Luke) and Geological Survey of Finland (GTK)
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland </strong></p><p><strong>Creators: </strong>Larmola T, Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M </p><p>The dataset consists of peat properties in a subset of 16 undrained peatland sites (32 peat samples) in Geological Survey of Finland (GTK) national peatland inventory. These sites were sampled between 2002 and 2017 and the subset selected from GTK peat sample archives. These 16 sites represented two pine-<i>Sphagnum-</i> dominated site types (IR, KR) and two treeless sedge fen types (VSN, RhSN) all in 4 replicates and sampled in 2 depths 20-40, 40-60cm). </p><p><strong>Peat analyses</strong> The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S).</p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola T, Anttila J, Alm J Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description </strong></p><p>ID - Site identifier</p><p>site - undrained peatland (UDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 20: 0-20 cm, 40: 20-40cm, 60: 40-60cm.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin - UDP site type. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p><strong>References</strong></p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p>
Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland, Natural Resources Institute Finland
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</strong></p><p><strong>Creators: Larmola T, Anttila J, Alm J </strong></p><p>The dataset consists of peat properties in a subsample of 30 drained peatland forests in Finland selected from the permanent sample plots of the 8th National Forest Inventory (systematic sample of plots on drained peatland forests, e.g., Hotanen et al. 2006). The subsample included equally different site types of forestry-drained peatlands of those parts of Finland where drainage for forestry is economically viable (Latitude 60-66 ºN, annual temperature sum > 750 dd). </p><p><strong>The site selection criteria</strong> were average peat layer thickness of over 20 cm, no clear-cut areas, site drained before 1995 and ditching had detectably altered hydrology or vegetation. <strong>Peat analyses</strong> Finnish Forest Research Institute (now Natural Resources Institute Finland) sampled peat cores with a box corer in 2002, samples were analysed for bulk density, archived and remaining samples at depths 20-30, 30-40 cm (total of 58) were analysed in 2021. The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. </p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola, T. Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Version 1) [Dataset]. Zenodo. doi.org/<strong>10.5281/zenodo.10068486</strong></p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description</strong></p><p>ID - Site identifier</p><p>site - Forestry-drained peatland (FDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 30: 20-30 cm, 40: 30-40cm, avg: average of both depths.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin – Origin of the FDP site type at undrained state. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p>n - Number of samples. 2 for averages from both depths, 1 for all other rows.</p><p> </p><p><strong>References</strong></p><p>Hotanen JP, Maltamo M, Reinikainen A (2006) Canopy stratification in peatland forests in Finland. Silva Fennica 40:53–82.</p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p><p> </p>
Summaries of temperature and water table depth prior to peat sampling in Stordalen Mire, 2011-2017
<div> <p>This dataset provides summaries of temperature (T) and water table depth (WTD) conditions prior to the collection of peat samples from Stordalen Mire, Sweden, in July of 2011-2017. These summaries include the following files:</p> <h2><strong>t_wtd_summaries_July2011-2017samplings.csv</strong></h2> </div> <p>This file gives summary statistics over various time intervals for the following environmental measurements:</p> <ul> <li><strong>AirTemperature</strong>: Mean daily air temperature (°C), obtained from automatic sensors at the nearby Abisko Scientific Research Station (ANS) (station ID 188790; the source file [ANS_Daily_Wx_Jul84_Dec17.txt] is not included due to sharing restrictions).</li> <li><strong>WTD</strong>: Water table depths (cm), obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (from Patrick Crill et al.).</li> </ul> <p>The time intervals for these summaries are defined relative to the peat sampling date at each site (see <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>), which varies by site and year. The specific intervals are defined as follows:</p> <ul> <li><strong>7d</strong>: 7 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>14d</strong>: 14 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>21d</strong>: 21 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>28d</strong>: 28 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>growing</strong>: Time from beginning of growing season (defined as June 1) until (and including) the sampling date.</li> <li><strong>all_growing</strong>: Entire growing season (June 1 – Sept. 30).</li> </ul> <p>For clarity, the start and end dates for each time interval (inclusive) are also given under the columns <strong>Start_Date</strong> and <strong>End_Date</strong>, where End_Date=<strong>Sampling_Date</strong> for all intervals except all_growing.</p> <p>Summary statistics for each interval include: measurement count (<strong>n</strong>), median (<strong>median</strong>), mean (<strong>mean</strong>), and standard deviation (<strong>sd</strong>), and are given under the column names beginning with these statistic labels.</p> <p><em>IMPORTANT NOTE: </em>For temperature, these statistics are calculated based on the average temperature measured on each day, meaning that<strong> </strong><em>the standard deviations do NOT account for within-day temperature variation.</em> To provide short-term (1 day) temperature variation context for each sampling date, the within-day mean, minimum, and maximum air temperatures for the sampling date only (taken directly from the corresponding row & columns in the source ANS data file) are provided in the columns <strong>samplingdate_mean_AirTemperature</strong>, <strong>samplingdate_min_AirTemperature</strong>, and <strong>samplingdate_max_AirTemperature</strong>.</p> <div> <div> <h2><strong>wtd_summaries_July2011-2017samples.csv</strong></h2> </div> <p>This file gives the percentage of time that each peat sample's depth midpoint (<strong>DepthAvg__</strong>) was at or below the water table depth (WTD), over each of the longer time intervals (≥21 days) defined above for the temperature & WTD summaries. (Intervals <21 days are not included due to the lower frequency of WTD measurements, which results in low <em>n</em> for shorter intervals.)</p> <p>The first few columns are taken directly from the <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>, for the samples collected in July of 2011-2017 from the MainAutochamber sites. The last set of columns include the following, with the time interval labels (defined as in the above temperature summaries) appended at the end of each column name:</p> <ul> <li><strong>n_WTD_*</strong>: Number of WTD measurements used in the calculation.</li> <li><strong>pct_time_below_WTD_*</strong>: Fraction (relative to 1) of measured WTDs over the given time interval that were at or above the DepthAvg__ for each sample, which equates to the fraction of measurement timepoints during which the given sample was at or below the WTD. This is the same method used for calculating "% Time below water table" in Figure 6 of <a href="https://doi.org/10.1038/s41396-018-0065-5">Singleton et al. (2018)</a>. For palsa sites, this value is automatically set to 0 based on the lack of a water table at all timepoints in the analysis.)</li> </ul> <p>As above, the WTD values used for these calculations were obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (Patrick Crill et al.).</p> <h1>Funding acknowledgments</h1> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>This research was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p> <p>The temperature summary has been made possible by data provided by Abisko Scientific Research Station and the Swedish Infrastructure for Ecosystem Science (SITES).</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> </div>
Peat characteristics, microbial PLFA, and fungal and actinobacterial sequences from Lakkasuo peatland drainage experiment, year 2004
<p>We analysed the response of microbial communities, characterized by phospholipid fatty acids (PLFAs), and fungal and actinobacterial communities, characterized by PCR-DGGE fingerprinting and direct sequencing, to changing hydrological conditions at three different sites in the boreal peatland complex Lakkasuo in southern Finland. Additionally, several peat characteristics were measured. The experimental design involved undrained controls as well as short-term (3 years) and long-term (43 years) water-level drawdown. The sites were, in their undrained state, a herb-rich sedge fen, a sedge fen, and a bog with hummock-lawn-hollow microtopography.</p> <p>Codes are explained in the Notes sheets of the Excel files. The contents of the csv files are identical to the corresponding Excel file data sheets.</p> <p>Please check the decimal separator! Comma is used in Finland, and that may have been carried over. All commas in data columns are decimal separators.</p>
The dataset and model code pertinent to the Everglades Peat Elevation Model (EvPEM): The salinity and inundation mesocosm experiment in freshwater and brackish water sawgrass wetlands in Florida Coastal Everglades (2015-2017).
This is an assembled data and Everglades Peat Elevation Model (EvPEMv1.0) Stella code used to estimate and simulate net ecosystem carbon balance (NECB) and peat elevation change in response to saltwater intrusion and level of inundations. Data from several studies were combined for the estimation of NECB, model parameterization, and calibration (Wilson, 2018; Wilson et al., 2018, 2019; Charles et al., 2019; Servais et al., 2020). The reported data includes aboveground net primary productivity (ANPP), belowground net primary productivity (BNPP), peat elevation change, and decomposition rates that were collected from outdoor laboratory mesocosm experiments conducted at the Florida Bay Interagency Science Center in Key Largo, Florida during 2015-17. The plant-soil monoliths were obtained from a freshwater peat and a brackish water peat marsh located within the Florida Coastal Everglades and transported to the Key Largo facility for the experimental manipulations. In experiments focused on the brackish water marsh, three experiments were carried out reflecting the combined effect of salinity, inundation, and peat exposure to air. The brackish water experiments characterized submerged (SUB), exposed (EXP), and extended depth of exposure of peat surface (EXTEXP) conditions, as we varied water depth relative to the peat surface. Each experiment was subjected to two salinity manipulations: (1) ambient (~10 ppt) porewater salinity (AMB) and (2) elevated (~20 ppt) salinity (SALT). The experimental design included six (2 X 3) treatments: (1) submerged ambient salinity (AMB.SUB), (2) submerged elevated salinity (SALT.SUB.), (3) exposed ambient salinity (AMB.EXP), (4) exposed elevated salinity (SALT.EXP), (5) exposed with extended exposure/dry-down ambient salinity (AMB.EXTEXP), and (6) exposed with extended exposure/dry-down elevated salinity (SALT.EXTEXP). The water level was kept 4 cm above the peat surface for the brackish water SUB treatments. Exposure for the EXP treatment
Indicative distribution map for Ecosystem Functional Group TF1.1 Tropical flooded forests and peat forests
<p>This archive contains indicative distribution maps and profiles for <strong>TF1.1 Tropical flooded forests and peat forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group TF1.6 Boreal, temperate and montane peat bogs
<p>This archive contains indicative distribution maps and profiles for <strong>TF1.6 Boreal, temperate and montane peat bogs</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Effect of Restoration on Physical and Chemical Peat Properties in Previously Drained Boreal Peatlands, latitude 57-63, Sweden, 2021
The major objective behind peatland restoration is to improve ecosystem services, such as increased biodiversity, increased carbon sequestration, increased groundwater storage, and improved surface water quality. However, a century or more of drained conditions has drastically changed the soil properties in relation to natural wetlands and this is likely to profoundly influence the potential for various biogeochemical peat processes. Thus, peatland restoration may result in undesired impacts and potential environmental threats. Two such undesired effects are increased methane production and increased mercury methylation. In this study, we investigated how nine boreal peatlands across a latitudinal gradient in Sweden have been affected by rewetting after up to a century of drained conditions. Each peatland was sampled for three 50 cm deep peat cores that were analyzed for carbon, nitrogen, δ13C, δ15N, bulk density, and organic matter proportion. Adjacent to each restored peatland, we sampled a corresponding pristine (natural) peatland to facilitate a comparison of how the peat properties have been affected by drainage and subsequent rewetting of the peatlands. Groundwater depth was monitored at all peatland locations to confirm restored conditions at the rewetted peatlands. The results indicate that a long period of drained conditions and subsequent rewetting have changed the peat properties, with differences shown in C/N ratio, dry bulk density, and organic matter content. Rewetting will thus not regenerate a pristine environment. Instead, it creates new conditions to which various biogeochemical processes will respond and these do not necessarily represent conditions prior to disturbance. Our study will provide background information to understand the biogeochemical dynamics in peatlands after restoration, especially since the study covers a large span of nutrient conditions and catchment settings. This understanding will be fundamental for the development of strat
Mangrove soil phosphorus addition experiment from June 2013 to August 2013 at the mangrove peat soil mesocosms (FCE), Key Largo, Florida - Nutrients in Porewater, Soil and Roots
Sea levels in South Florida are conservatively predicted to rise by 0.60 m by 2060. The key mechanisms that maintain coastal peatland elevation against increasing sea level are organic matter accumulation via plant production and mineral sedimentation rates (Smoak et al. 2013). Although coastal mangrove soils are regularly inundated with seawater, little is know about the drivers of carbon sequestration (above or below ground) versus atmospheric efflux under different conditions of salinity and elevated phosphorus (P) associated with sea-level rise and storm surge. A recent study using mangrove peat soils found that seawater inundation reduced soil carbon efflux losses and salinity concentration had little effect on carbon retention or loss pathways. The next logical steps are to understand how plant-soil interactions affect above and below ground carbon processes, as well as how increases in P associated with storm surge from the Gulf of Mexico will influence physical, chemical and biological components of mangrove soils that are associated with above and belowground carbon processes. We will manipulate P in inundated peat soil mesocosms with disturbed and undisturbed red mangrove (Rhizophora mangle) seedlings to identify some of the fundamental mechanisms of soil elevation and carbon cycling given expected increases in seawater-based P availability in South Florida coastal mangroves.
Mangrove soil phosphorus addition experiment from July 2013 to August 2013 at the mangrove peat soil mesocosms (FCE), Key Largo, Florida - Nutrients in Surface Water and Aboveground Biomass
Sea levels in South Florida are conservatively predicted to rise by 0.60 m by 2060. The key mechanisms that maintain coastal peatland elevation against increasing sea level are organic matter accumulation via plant production and mineral sedimentation rates (Smoak et al. 2013). Although coastal mangrove soils are regularly inundated with seawater, little is know about the drivers of carbon sequestration (above or below ground) versus atmospheric efflux under different conditions of salinity and elevated phosphorus (P) associated with sea-level rise and storm surge. A recent study using mangrove peat soils found that seawater inundation reduced soil carbon efflux losses and salinity concentration had little effect on carbon retention or loss pathways. The next logical steps are to understand how plant-soil interactions affect above and below ground carbon processes, as well as how increases in P associated with storm surge from the Gulf of Mexico will influence physical, chemical and biological components of mangrove soils that are associated with above and belowground carbon processes. We will manipulate P in inundated peat soil mesocosms with disturbed and undisturbed red mangrove (Rhizophora mangle) seedlings to identify some of the fundamental mechanisms of soil elevation and carbon cycling given expected increases in seawater-based P availability in South Florida coastal mangroves.
Field survey and long-term measurements of biological, geomorphic, and hydrologic properties across ecosystem states in a non-tidal, salinizing peat marsh in Everglades National Park, Florida, USA, June 2018 - ongoing
This dataset package details a field survey and long-term measurements of biological, geomorphic, and hydrologic properties across ecosystem states found within a salinizing, non-tidal peat marsh in the coastal Everglades. There are four datasets included: FCE1250_Lamb_AltStableState_HydroGeo includes point measurements of soil surface elevation, bedrock elevation, soil depth, water depth, and porewater salinity across ecosystem states: emergent marsh (sawgrass dominated), submerged marsh (submerged aquatic vegetation), and unvegetated open water. The survey was conducted between November 2019 to January 2020. FCE1250_Lamb_AltStableState_Bio contains plot-scale (1 m^2) biological measurements from emergent marsh (sawgrass). Biological measurements (total sawgrass, average individual sawgrass biomass, and total sawgrass aboveground biomass), are inherently plot-level but geomorphic and hydrologic measurements were averaged (n = 3) for each plot. Standard deviations are provided. FCE1250_Lamb_AltStableState_Bio_LongTerm contains biological plot-scale (1 m^2) measurements at a bi-monthly timestep from Nov 2019 - July 2022 from a subset of plots (n = 9) included in the FCE_1250_Lamb_AltStableState_Bio dataset. Each bi-monthly measurement is the average value across all 9 nine plots. Standard deviations are also included. FCE_1250_Lamb_AltStableState_SETMH contains long-term measurements of soil surface elevation change and vertical accretion from surface elevation change and marker horizon plots from June 2018 - May 2023. Average and standard errors across all plots are included as well as measurements from each plot. Also included is the cumulative number of dry days based on the average soil surface elevation for all plots. Data collection for all datasets is complete, except for FCE_1250_Lamb_AltStableState_SETMH. SET-MH measurements are still ongoing.
PEATGRIDS: Mapping global peat thickness and carbon stock via digital soil mapping approach, dataset
<p>PEATGRIDS: a dataset containing the first peat thickness and carbon stock maps estimated over peatlands area across the globe at ~1 km x ~1 km resolution. Carbon stock was calculated across all depths of the predicted peat thickness, multiplied by peat bulk density (BD) and carbon content (CC) across five depths: 0-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Mapping effort was performed using quantile random forest regression based on remotely sensed data and environmental covariates, including topography, climate, soil properties, and land cover. The maps cover areas potentially as peatlands according to the UNEP's global peatland map obtained from the <a title="Global Peat Database" href="https://greifswaldmoor.de/global-peatland-database-en.html" target="_blank" rel="noopener">Global Peat Database</a>. We may update this dataset in the future, please consider using the latest version. </p> <p>Note: This version (2.0.1) clarifies the metric units for carbon stock per area in the previous version (2.0). </p>
PEATCLSM_Trop: Integrating peat-specific land surface hydrology of natural and drained tropical peatlands in the GEOS CLSM framework
<p>The datasets archived here include simulation results shown in the peer-reviewed article “Tropical peatland hydrology simulated with a global land surface model“, published in the open access AGU Journal of Advances in Modeling Earth Systems (JAMES; Apers et al., 2022). The output was produced using the Catchment land surface model (CLSM), the land model component of the NASA Goddard Earth Observing System (GEOS) modeling framework, and various versions of peatland-specific adaptations of CLSM, i.e. PEATCLSM. Here, we provide netCDF files (*.nc or *.nc4c) for CLSM, the natural (PEATCLSM<sub>Trop,Nat</sub>), and drained (PEATCLSM<sub>Trop,Drain</sub>) tropical versions of PEATCLSM. The simulations are at a 9-km spatial resolution (EASEv2 grid) for the three major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia, using a peat grid cell distribution that is a combination of the PEATMAP distribution from Xu et al. (2018) and the peat distribution from De Lannoy et al. (2014). Simulations with the northern version of PEATCLSM (PEATCLSM<sub>North,Nat</sub>) are not included in the archived dataset but can be obtained upon request. We provide three types of netCDF files:<br> • daily_images_*.nc4c: daily land states and fluxes for variables discussed in Apers et al., (2022; Table 1), provided as netCDF image-chunked image stack;<br> • daily_mean_*.nc: 20-year mean of the land states and fluxes (Table 1), provided as a single netCDF image;<br> • daily_std_*.nc: 20-year standard deviation of the land states and fluxes (Table 1), provided as a single netCDF image.</p> <p>The file content is described in the file PEATCLSM_Trop-Simulations.pdf.</p> <p>Please contact Sebastian Apers (sebastian.apers@kuleuven.be) or Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.<br> <br> References:<br> Apers, S., De Lannoy, G. J. M., Baird, A. J., Cobb, A. R., Dargie, G. C., del Aguila Pasquel, J., … others (2022). Tropical peatland hydrology simulated with a global land surface model. <em>Journal of Advances in Modeling Earth Systems</em>. https://doi.org/10.1029/2021MS002784<br> Bechtold, M., De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P., Bleuten, W., ... others (2019). PEAT-CLSM: A specific treatment of peatland hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems, 11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574<br> De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P. P., & Liu, Q. (2014). An updated treatment of soil texture and associated hydraulic properties in a global land modeling system. <em>Journal of Advances in Modeling Earth Systems, 6</em>(4), 957– 979. https://doi.org/10.1002/2014MS000330<br> Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018). PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. <em>Catena, 160</em>, 134–140. https://doi.org/10.1016/j.catena.2017.09.010</p>
Data repository for "Spatio-temporal trends of Holocene peat carbon accumulation in China: climatic and human drivers"
<p>Dating results collected from peatlands in China are used to calculate the spatiotemporal trends of the Holocene peat accumulation rate (PAR) and net carbon balance (NCB), including all original dating, calculated intermediate results, and final composite results. This file includes a total of 14 tables (Supplementary Tables S1-S14).</p>
Monitoring long-term peat subsidence with subsidence platens in Zegveld, The Netherlands
<p><span>Peat oxidation in peat meadow areas is causing greenhouse gas emissions as well as land subsidence. Due to yearly fluctuations in soil surface level, long-term monitoring is needed to determine long-term net subsidence rates. In the experimental peat-meadow farm at Zegveld (NL) subsidence platens were installed in 1970 in a field with low ditchwater level, and in 1973 in a field with high ditchwater level. Platens were installed at 7 different depths, allowing to investigate where in the peat profile subsidence occurs. Elevation of platens as well as soil surface has been measured with surveyor’s levelling each year at the end of winter, so that a long timeseries up to 2023 is available. Analysis showed that surface level in the field with high ditchwater level subsided by 23 cm in 50 years (4.6 mm/yr), while in the field with low ditchwater level this was 31 cm in 53 years (5.8 mm/yr). Results also showed that in the field with low ditch water level, most subsidence due to permanent shrinkage and peat oxidation occurred between 40 and 100 cm depth, while for the other field this was between 20 and 40 cm depth. Finally, in 2023 subsidence was still observed under continuously saturated conditions at 140 cm depth. Presumably, in the aerated part of the profile peat oxidation and the associated earthification process is the main cause of subsidence, while the observed subsidence in the saturated soil at 140 cm depth must be due to other processes, such as consolidation and creep.</span></p>
Peat-Hub-Ireland Database Version 1.0
<p>The <strong>Peat Hub Ireland Database</strong> of Research on Irish Peatlands is a freely accessible resource that makes research data on Irish peatlands available to researchers and other stakeholders. The database provides a searchable, updatable, and comprehensive collection of research findings, covering studies from January 2000 to October 2023. This database supported a review that used a comprehensive evidence synthesis approach, combining expert and stakeholder input with peer-reviewed studies and grey literature. The goal of the review was to collate evidence to encourage researchers, civic societies, and policymakers to adopt an unbiased, evidence-based approach toward the future sustainable management and improved planning of Irish peatlands.</p> <p>The PHI database aims to raise awareness amongst researchers and the wider stakeholder community engaging in peatlands research in Ireland and further afield. Alongside the ‘living’ glossary of peatland terminology, the database will also help researchers gain a greater understanding of work from other disciplines to overcome academic silos and facilitate incorporation of insights from a range of disciplines. It will also help researchers avoid duplication of effort, raise awareness of previous research and established methodologies, build upon existing data to identify research gaps and new projects, and encourage collaborative work within the peatland scientific community.</p> <p>The database contains a total of 900 records organised into the following thematic areas: Biodiversity, Soil, Climate Change, Water, Archaeology and Palaeo-environment, Technology and Mapping, Society and Culture, Management, Growing Media/Substrate, Policy and Law. Each record in the database is given a unique ID number and is searchable by Author, Publication year, Theme, Sub-Topic, Title, DOI/URL and the Source of Citation. See Metatdata for details. </p> <p>You can find the replicable Methodology and other outputs of the Peat Hub Ireland project on the website; www.ucd.ie/peat-hub-ireland and in the EPA final report : https://www.epa.ie/publications/</p> <p>Recommended citation: </p> <p>Renou-Wilson, F., D. Wilson, and K. Flood. 2024. Peat-Hub-Ireland Database Version 1.0. DOI:10.5281/zenodo.13911873</p>
Soil biological, chemical and physical parameters and herbage yield in a field experiment with organic and inorganic fertilizers on peat grassland in the Netherlands
<p>To evaluate the performance of organic and inorganic fertilizers for regeneration of ecosystem services in peat grasslands with biodiversity goals, we carried out a field experiment in the western peat district in the Netherlands. The fertilizers tested represent the current practice and potential alternatives for regenerative grassland management on drained peat.</p> <p> </p> <p><strong>Experimental setup</strong></p> <p>The field experiment (2013 – 2015) was conducted on a permanent grassland on peat soil (Terric Histosol; SOM 56 g 100 g<sup>−1</sup> and pH<sub>KCl</sub> of 4.5 in 0-10 cm) at the experimental dairy farm at Zegveld (the Netherlands). In March 2013, a randomized block experiment (six blocks) was laid out with six fertilizer treatments and a control treatment (no fertilizer: “Contr”). The fertilizer used were: conventional dairy cattle slurry manure (“Slurry”), mature compost of kitchen and garden waste (“Comp”), dairy cattle farmyard manure (“FYM”), solid fraction of the cattle slurry manure (“SFrac”, obtained by pressurized filtration), inorganic N fertilizer (“IF”; calcium ammonium nitrate, 27% N) and a combination of inorganic N fertilizer and sawdust (“IF+SD”). Plot size was 4 × 10 m; for the Slurry treatment plots were 5.2 × 10 m. Slurry was applied by slit injection, the other fertilizers were applied by hand. Target application rate was 120 kg total N ha<sup>−1</sup> yr<sup>−1</sup>, divided in two applications per year (February/March and May). This is relatively low for conventional grasslands but usual for grasslands with biodiversity goals (Kleijn et al., 2004). The amount of C<sub>total</sub> applied in Comp was taken for the rate of sawdust to be applied. All plots were fertilized with 200 kg K<sub>2</sub>O ha<sup>−1</sup> yr<sup>−1</sup> (applications in March and May) (Commissie Bemesting Grasland en Voedergewassen, 2019). Fertilizer application quantities and organic matter and nutrient inputs are provided in Fertilizer_intput.csv (dataset).</p> <p>The grassland had an history of conventional management with mainly cutting, winter grazing with sheep and a normal fertilization regime with both slurry manure and inorganic fertilizer. The normal cutting and grazing regime was continued in the first two years of the experiment; during 2015, the monitoring year, the plots were not grazed and only cut for herbage measurements.</p> <p> </p> <p><strong>Measurements</strong></p> <p>From April to October 2015, soil and aboveground measurements were carried out. Most soil parameters were measured in October. Earthworms and insect larvae are an important food source for meadow birds during the pre-breeding period in spring (Galbraith, 1989) and were therefore sampled in April. Soil moisture and penetration resistance were measured both in April and October.</p> <p> </p> <p><em>Soil biological parameters</em></p> <p>Earthworms and insect larvae were sampled in the top soil layer in two soil cubes (20 × 20 × 20 cm) per plot. Earthworms were hand-sorted, counted, weighed and fixed in alcohol prior to identification. Both adults and juveniles were identified to species (Sims and Gerard, 1985; Stöp-Bowitz, 1969) and classified into functional groups (Bouché, 1977). Crane flies (Tipulidae; leatherjackets) or click beetles (Elateridae; wireworms) larvae were counted.</p> <p>Phospholipid fatty acids (PLFA) were measured in October. PLFA were extracted from 4 g of fresh soil (Palojärvi, 2006), and analyzed by gas chromatography (Hewlett-Packard, USA). PLFA i15:0, a15:0, 15:0, i16:0, 16:1ω9, i17:0, a17:0, cy17:0, 18:1ω7 and cy19:0 were chosen to represent bacteria and PLFA 18:2ω6 was used as a marker of saprotrophic fungi (Hedlund, 2002). The neutral lipid fatty acid (NLFA) 16:1ω5 occurs in storage lipids of arbuscular mycorrhizal fungi (AMF) and was used as marker of AMF (Vestberg et al., 2012). PLFA i15:0, a15:0, i16:0, i17:0 and a17:0 were used as a measure of Gram-positive bacteria, and cy17:0 and cy19:0 for Gram-negative bacteria. PLFA 10Me16:0, 10Me17:0 and 10Me18:0 represented actinomycetes.</p> <p> </p> <p><em>Soil chemical parameters</em></p> <p>A soil sample from the 0−10 cm layer (c. 50 randomly taken soil cores) per experimental plot was collected in October (auger diameter 2.3 cm; Eijkelkamp grass plot sampler, Giesbeek, the Netherlands), was sieved (1 cm mesh size) and homogenized. One sub-sample was taken for analysis of hot water extractable carbon (HWC) according to Ghani et al. (2003) and one for chemical analysis. Prior to analysis of soil acidity (pH<sub>KCl</sub>), soil organic matter (SOM), total carbon (C<sub>total</sub>), total nitrogen (N<sub>total</sub>), total phosphorus (P<sub>total</sub>) and ammonium-lactate extractable P (P<sub>AL</sub>) by Eurofins Agro (Wageningen, the Netherlands), the sub sample was dried at 40°C. Soil pH<sub>KCl</sub> was measured according to NEN-ISO 10390 2005. SOM was determined by loss-on-ignition (NEN 5754 2005). C<sub>total</sub> was measured by incineration at 1150°C, and determination of the CO<sub>2</sub> produced by an infrared detector (LECO Corporation, St. Joseph, Mich., USA). For N<sub>total</sub>, evolved gasses after incineration were reduced to N<sub>2</sub> and measured with a thermal-conductivity detector (LECO Corporation, St. Joseph, Mich., USA). P<sub>total</sub> was analysed with Fleishmann acid (Houba et al., 1997). P<sub>AL</sub> is used to assess the P supply capacity of grassland soils (Reijneveld et al., 2014) and was determined according to Egnér et al. (1960) (NEN 5793).</p> <p> </p> <p><em>Soil physical parameters</em></p> <p>Soil moisture was determined in April and October in a homogenized 0−10 cm soil sample after drying at 105°C for 24 hrs. Moisture content was expressed as percentage of fresh soil weight.</p> <p>Penetration resistance was measured (April and October) with a penetrologger (Eijkelkamp, Giesbeek, the Netherlands; cone of 2.0 cm<sup>2</sup> penetration surface and 60° apex angle. Penetration resistance was expressed as an average of 7 penetrations per plot and per soil layer of 0−10, 10−20, and 20−30 cm.</p> <p>Soil structure and rooting density were assessed in October in the 0−10 cm and 10−25 cm layers. The percentage of crumbs, sub-angular blocky elements and angular blocky elements was estimated by one experienced person as described by Peerlkamp (1959) and Shepherd (2000), Root density was estimated by scoring visible roots (score 1–10; 1 for no roots and 10 for above average).</p> <p>Water infiltration rate was measured in October at three spots per experimental plot in 5 of the 6 blocks (35 plots). A PVC pipe (15 cm high, 15 cm diameter) was pushed into the soil to a depth of 10 cm. 500 ml water was poured into each pipe and the infiltration time was recorded. If the infiltration time exceeded 15 min, the remaining water volume was estimated to calculate the infiltration rate (mm min<sup>−1</sup>).</p> <p> </p> <p><em>Grass yield and botanical composition</em></p> <p>Grass dry matter (DM) and N yield were determined during 2015 with a Haldrup plot harvester (J. Haldrup a/s, Løgstør, Denmark). The four harvest dates were May 15, June 29, August 19 and September 30. Fresh biomass, DM content (70°C for 24 hrs) and total N content (Kjeldahl) were determined for each harvest. Herbage DM yield (Mg DM ha<sup>−1</sup>) and herbage N yield (kg N ha<sup>−1</sup>) were calculated. Apparent N recovery (ANR; kg N.kg N<sup>−1</sup>) was calculated as (N yield<sub>(fertilized)</sub> – N yield<sub>(non-fertilized)</sub>)/(N fertilization rate) (Vellinga and André, 1999).</p> <p>In June 2015, botanical composition was measured by visually estimating the relative soil cover of the sward and the proportion of each species therein (Sikkema, 1997).</p> <p> </p> <p><strong>Data files</strong></p> <ul> </ul> <p> </p> <p><em><strong>Data_soil_grass.csv</strong></em></p> <p><em>Content:</em></p> <p>Dataset with soil biological (earthworms, microbial PLFA), soil chemical, soil physical parameters, herbage dry matter and N yields, and botanical parameters.</p> <p><em>Column names and units:</em></p> <ul> <li>plot: Experimental plot number (1-42)</li> <li>treatment: Treatment code (see text)</li> <li>block: Block number (1-6)</li> <li>EW_species_number: Earthworm - number of species</li> <li>EW_totalnumber: Earthworm - total number per m2</li> <li>EW_epigeic: Earthworm - number of epigeic adults and juveniles per m2</li> <li>EW_endogeic: Earthworm - number of endogeic adults and juveniles per m2</li> <li>EW_adults: Earthworm - number of adults per m2</li> <li>EW_juveniles: Earthworm - number of juveniles per m2</li> <li>EW_adult_epigeic: Earthworm - number of epigeic adults per m2</li> <li>EW_adult_endogeic: Earthworm - number of endogeic adults per m2</li> <li>EW_juven_epigeic: Earthworm - number of epigeic juveniles per m2</li> <li>EW_juven_endogeic: Earthworm - number of endogeic juveniles per m2</li> <li>EW_L_rubellus: Earthworm - number of L. rubellus adults and juveniles per m2</li> <li>EW_A_chlorotica: Earthworm - number of A. chlorotica adults and juveniles per m2</li> <li>EW_A_caliginosa: Earthworm - number of A. caliginosa adults and juveniles per m2</li> <li>EW_O_lacteum: Earthworm - number of O. lacteum adults and juveniles per m2</li> <li>EW_A_rosea: Earthworm - number of A. rosea adults and juveniles per m2</li> <li>EW_O_cyaenum: Earthworm - number of O. cyaneum adults and juveniles per m2</li> <li>EW_L_castaneus: Earthworm - number of L. castaneus adults and juveniles per m2</li> <li>EW_D_rubida: Earthworm - number of D. rubida adults and juveniles per m2</li> <li>EW_adult_L_rubellus: Earthworm - number of L. rubellus adults per m2</li> <li>EW_adult_A_chlorotica: Earthworm - number of A. chlorotica adults per m2</li> <li>EW_adult_A_caliginosa: Earthworm - number of A. caliginosa adults per m2</li> <li>EW_adult_O_lacteum: Earthworm - number of O. lacteum adults per m2</li> <li>EW_adult_A_rosea: Earthworm - number of A. rosea adults per m2</li> <li>EW_adult_O_cyaenum: Earthworm - number of O. cyaneum adults per m2</li> <li>EW_adult_L_castaneus: Earthworm - number of L. castaneus adults per m2</li> <li>EW_adult_D_rubida: Earthworm - number of D. rubida adults per m2</li> <li>EW_juven_L_rubellus: Earthworm - number of L. rubellus juveniles per m2</li> <li>EW_juven_A_chlorotica: Earthworm - number of A. chlorotica juveniles per m2</li> <li>EW_juven_A_caliginosa: Earthworm - number of A. caliginosa juveniles per m2</li> <li>EW_non_determined: Earthworm - number of non determined individuals per m2</li> <li>EW_total_biomass: Earthworm - total fresh biomass per m2</li> <li>Leatherjackets: number of leatherjackets per m2</li> <li>Wireworms: number of wireworms per m2</li> <li>TOTmicrPLFA: total microbial PLFA in nmol.g-1 dry soil</li> <li>bactPLFA: bacterial PLFA in nmol.g-1 dry soil</li> <li>saprofungPLFA: saprotrophic fungal PLFA in nmol.g-1 dry soil</li> <li>Fung_bactPLAF_ratio: ratio of fungal to bacterial PLFA</li> <li>GramPLUSplfa: gram positive PLFA in nmol.g-1 dry soil</li> <li>GramMINplfa: gram negative PLFA in nmol.g-1 dry soil</li> <li>ratioGram_PLUS_MIN: ratio of gram positive to gram negative PLFA</li> <li>AMFsporNLFA: AMF spores NLFA in nmol.g-1 dry soil</li> <li>ActinomPLFA: Actinomycetes PLFA in nmol.g-1 dry soil</li> <li>ShannonPLFA: PLFA shannon diversity index</li> <li>SOM: soil organic matter in g.100 g-1 dry soil</li> <li>Ctotal: total C in g.100 g-1 dry soil</li> <li>HWC: hot water extractable C in μg.100 g-1 dry soil</li> <li>Ntotal: total N in g.100 g-1 dry soil</li> <li>Ptotal: total P2O5 in mg.100 g-1 dry soil</li> <li>P_AL: total P-AL in mg.100 g-1 dry soil</li> <li>pH_KCl: pH-KCl</li> <li>CN_ratio: C:N ratio</li> <li>C_SOM: C:SOM ratio</li> <li>Soilmoisture_April: soil moisture content in April in g.100g-1 fresh soil</li> <li>Penetrationresistance_April_cm010: penetration resistance in April in 10-20 cm in Newton</li> <li>Penetrationresistance_April_cm1020: penetration resistance in April in 20-30 cm in Newton</li> <li>Penetrationresistance_April_cm2030: penetration resistance in April in 0-10 cm in Newton</li> <li>Soilmoisture_October: soil moisture content in October in g.100g-1 fresh soil</li> <li>Penetrationresistance_October_cm010: penetration resistance in October in 10-20 cm in Newton</li> <li>Penetrationresistance_October_cm1020: penetration resistance in October in 20-30 cm in Newton</li> <li>Penetrationresistance_October_cm2030: penetration resistance in October in 0-10 cm in Newton</li> <li>crumb_struct_cm010: percentage of crumb elements in 0-10 cm</li> <li>round_struct_cm011: percentage of sub-angular elements in 0-10 cm</li> <li>rootdensity_cm010: score (1-10) of root density in 0-10 cm</li> <li>crumb_struct_cm1025: percentage of crumb elements in 10-25 cm</li> <li>round_struct_cm1025: percentage of sub-angular elements in 10-25 cm</li> <li>sharp_struct_cm1025: percentage of angular elements in 10-25 cm</li> <li>rootdensity_cm1025: score (1-10) of root density in 10-25 cm</li> <li>water_infiltration: water infiltration rate in mm per minute</li> <li>DM_yield_year: total herbage dry matter yield in kg.ha-1 per year</li> <li>DM_yield_H1: herbage dry matter yield of harvest 1 in kg.ha-1</li> <li>DM_yield_H2: herbage dry matter yield of harvest 2 in kg.ha-1</li> <li>DM_yield_H3: herbage dry matter yield of harvest 3 in kg.ha-1</li> <li>DM_yield_H4: herbage dry matter yield of harvest 4 in kg.ha-1</li> <li>N_yield_year: total herbage N yield in kg.ha-1 per year</li> <li>N_yield_H1: herbage N yield of harvest 1 in kg.ha-1</li> <li>N_yield_H2: herbage N yield of harvest 2 in kg.ha-1</li> <li>N_yield_H3: herbage N yield of harvest 3 in kg.ha-1</li> <li>N_yield_H4: herbage N yield of harvest 4 in kg.ha-1</li> <li>DMperc_yield_year: herbage dry matter content (per year; weighed average over the 4 harvests) in g.100g-1 fresh weight</li> <li>DMperc_yield_H1: herbage dry matter content of harvest 1 in g.100g-1 fresh weight</li> <li>DMperc_yield_H2: herbage dry matter content of harvest 2 in g.100g-1 fresh weight</li> <li>DMperc_yield_H3: herbage dry matter content of harvest 3 in g.100g-1 fresh weight</li> <li>DMperc_yield_H4: herbage dry matter content of harvest 4 in g.100g-1 fresh weight</li> <li>Ncontent_yield_year: herbage N content (per year; weighed average over the 4 harvests) in g.kg-1 dry matter</li> <li>Ncontent_yield_H1: herbage N content of harvest 1 in g.kg-1 dry matter</li> <li>Ncontent_yield_H2: herbage N content of harvest 2 in g.kg-1 dry matter</li> <li>Ncontent_yield_H3: herbage N content of harvest 3 in g.kg-1 dry matter</li> <li>Ncontent_yield_H4: herbage N content of harvest 4 in g.kg-1 dry matter</li> <li>fresh_yield_H1: herbvage fresh yield of harvest 1 in Mg.ha-1</li> <li>ANR: apparent N recovery in kg N.kg N-1</li> <li>productive_grasses: cover percentage of L. perenne and P trivialis</li> <li>monocotyledons: cover percentage of monocotyledons</li> <li>dicotyledons: cover percentage of dicotyledons</li> <li>plant_species: number of plant species</li> <li>monocot_species: number of monocotyledon species</li> <li>dicot_species: number of dicotyledon species</li> <li>Lolium_perenne: plant cover %</li> <li>Poa_trivialis: plant cover %</li> <li>Phleum_pratense: plant cover %</li> <li>Elytrigia_repens: plant cover %</li> <li>Poa_annua: plant cover %</li> <li>Agrostis_stolonifera: plant cover %</li> <li>Holcus_lanatus: plant cover %</li> <li>Alopecurus_pratensis: plant cover %</li> <li>Alopecurus_geniculatus: plant cover %</li> <li>Trifolium_repens: plant cover %</li> <li>Taraxacum_officinale: plant cover %</li> <li>Ranunculus_arvensis: plant cover %</li> <li>Rumex_obtusifolius: plant cover %</li> <li>Rumex_crispus: plant cover %</li> <li>Ranunculus_acris: plant cover %</li> <li>Stellaria_media: plant cover %</li> <li>Cardamine_pratensis: plant cover %</li> <li>Bellis_perennis: plant cover %</li> <li>Rumex_acetosa: plant cover %</li> <li>Ranunculus_sceleratus: plant cover %</li> <li>Polygonum_aviculare: plant cover %</li> <li>Capsella_bursa-pastoris: plant cover %</li> <li>Glechoma_hederacea: plant cover %</li> <li>Geranium_molle: plant cover %</li> </ul> <p> </p> <p><em><strong>Fertilizer_input.csv</strong></em></p> <p><em>Content:</em></p> <p>Application quantities of fertilizers and ash, organic matter, C and mineral inputs, and fertilizer C:N ratio. Total N input is the sum of mineral N (Nmin) and organic N (Norg). Average values per hectare and per year over the years 2013−2015.</p> <p><em>Column names and units:</em></p> <ul> <li>Treatment: Treatment code (see text)</li> <li>Fertilizer_fresh: Applied fertilizer in Mg.ha<sup>-1</sup> per year (fresh weight)</li> <li>Fertilizer_DM: Applied fertilizer in Mg.ha<sup>-1</sup> per year (dry matter weight); for IF+SD this is the sum of 2.72 Mg sawdust + 0.45 Mg N fertilizer</li> <li>Ash: Mineral fraction in kg.ha<sup>-1</sup> per year</li> <li>OM: Organic matter in kg.ha<sup>-1</sup> per year</li> <li>C: Total C in kg.ha<sup>-1</sup> per year</li> <li>Nmin: Mineral N in kg.ha<sup>-1</sup> per year</li> <li>Norg: Organic N in kg.ha<sup>-1</sup> per year</li> <li>P2O5: kg.ha<sup>-1</sup> per year</li> <li>C_N_ratio: C:N ratio</li> </ul>
A map of global peatland extent created using machine learning (Peat-ML)
<p>Map of global peatland extent estimated by machine learning. The download includes both a netcdf file version and a GeoTIFF (as a zip archive)</p> <p>Abstract from associated paper:</p> <p>Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and<br> hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed<br> by Earth System Models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here,<br> we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning<br> techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of<br> peatland formation and include spatially distributed climate, geomorphological and soil data, along with remotely-sensed<br> vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with<br> mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualititative comparisons to other maps<br> in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out<br> cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r<sup>2</sup><br> of 0.73 with a root mean squared error and mean bias error of 9.11% and -0.36%, respectively. Our second error estimate<br> was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited<br> Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping<br> products generated through more traditional approaches, at least for boreal peatlands.</p>
Spatial predictions of suitable environments for palsas and peat plateaus in the Northern Hemisphere for recent and future periods
<p>Here we provide raster files of suitable environments for palsas and peat plateaus in the Northern Hemisphere. These files are results of a scientific study by Könönen et al. (2022, preprint). Files are provided in TIFF-format, and they describe the occurrence probability of the suitable environments for palsas and peat plateaus.</p> <p> </p> <p>Könönen, O. H., Karjalainen, O., Aalto, J., Luoto, M., and Hjort, J.: Environmental spaces for palsas and peat plateaus are disappearing at a circumpolar scale, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-135, in review, 2022.</p>
ScienceDex guides
Understand access before you commit
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.