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.
307
datasets available to search
ShareScore release 0.7.1
Dataset results
307 results for “Peatland”
Hydrometeorological dataset of West Siberian boreal peatland: a 10-year records from the Mukhrino field station.
<p>Northern peatlands represent one of the largest carbon pools in the biosphere the carbon they store are increasingly vulnerable to perturbations from climate and land-use change. Meteorological observations directly at peatland areas in Siberia are unique and rare, while peatlands characterized by a specific local climate. This paper presents a hydrological and meteorological dataset collected at the Mukhrino peatland, Khanty–Mansi Autonomous Okrug – Yugra, Russia over the period of 08 May 2010 to 31 December 2019. Hydrometeorological data collected from stations located at the small pine-shrub-Sphagnum ridge and Scheuchzeria-Sphagnum hollow at the ridge–hollow complexes of ombrotrophic peatland. Monitored meteorological variables include air temperature, air humidity, atmospheric pressure, wind speed and direction, incoming and reflected photosynthetically active radiation, net radiation, soil heat flux, precipitation (rain), and snow depth. The gap-filling procedure based on the gaussian process regression model with exponential kernel was developed to obtain a continuous time series. For the record from 2010 to 2019, the average mean annual air temperature site was −1.0 ◦C, with a mean monthly temperature of the warmest month (July) recorded as 17.4 ◦C and for the coldest month (January) −21.5 ◦C. The average net radiation was about 35.0 W m<sup>-2</sup>, the soil heat flux was 2.4 and 1.2 W m<sup>-2</sup> for the hollow and the ridge sites, respectively.</p> <p>DATASETS:</p> <p><strong>meteo_MFC_raw.dat</strong> - Raw data collected from the automated weather station at the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). Note: The time step differs during measured period 01.01.2010 – 15.07.2012: 15 minutes; 15.07.2012 – 20.06.2014: 1 hour; 20.06.2014 – 31.12.2020: 30 minutes.</p> <p><strong>meteo_MFC_qq_1h.dat</strong> – Quality controlled data collected from the automated weather station at the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). The time step is 60 minutes. The missing data denoted by “NA”.</p> <p><strong>meteo_MFC_gapfilled_1h.dat</strong> – Quality controlled and gap-filled hydrometeorological data for the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). The time step is 60 minutes. The missing data denoted by “NA”.</p> <p><strong>meteo_MFC_raw.dat parameters:</strong></p> <p>1. date - Date and time ( DD/MM/YYYY hh:mm:ss ).</p> <p>2. ta_H - Air temperature at 2 m, hollow ( oC ).</p> <p>3. ta_R - Air temperature at 2 m, ridge ( oC ).</p> <p>4. rh_H - Relative air humidity at 2 m, hollow ( % ).</p> <p>5. rh_R - Relative air humidity at 2 m, ridge ( % ).</p> <p>6. vp_H - Water vapor pressure at 2 m, hollow ( kPa ).</p> <p>7. vp_R - Water vapor pressure at 2 m, ridge ( kPa ).</p> <p>8. ws_10m - Wind speed at 10 m ( m s-1 ).</p> <p>9. wd_10m - Wind direction at 10 m ( deg ).</p> <p>10. ws_2m - Wind speed at 2 m ( m s-1 ).</p> <p>11. wd_2m - Wind direction at 2 m ( deg ).</p> <p>12. stdwd_10m - Standard deviation of wind direction at 10 m for the period of measurement ( m s-1 ).</p> <p>13. stdwd_2m - Standard deviation of wind direction at 2 m for the period of measurement ( m s-1 ).</p> <p>14. ipar_H - Incoming PAR, hollow ( µmol m-2 s-1 ).</p> <p>15. ipar_R - Incoming PAR, ridge ( µmol m-2 s-1 ).</p> <p>16. rpar_H - Reflected PAR, hollow ( µmol m-2 s-1 ).</p> <p>17. rpar_R - Reflected PAR, ridge ( µmol m-2 s-1 ).</p> <p>18. nr_H - Net radiation balance, hollow ( Uncalibrated ).</p> <p>19. nr_R - Net radiation balance, ridge ( Uncalibrated ).</p> <p>20. shf_H - Soil heat flux, hollow ( Uncalibrated ).</p> <p>21. shf_R1 - Soil heat flux, ridge, site 1 ( Uncalibrated ).</p> <p>22. shf_R2 - Soil heat flux, ridge, site 2 ( Uncalibrated ).</p> <p>23. ts_2cm_R1 - Soil temperature at 2 cm, ridge, site 1 ( oC ).</p> <p>24. ts_5cm_R1 - Soil temperature at 5 cm, ridge, site 1 ( oC ).</p> <p>25. ts_10cm_R1 - Soil temperature at 10 cm, ridge, site 1 ( oC ).</p> <p>26. ts_20cm_R1 - Soil temperature at 20 cm, ridge, site 1 ( oC ).</p> <p>27. ts_50cm_R1 - Soil temperature at 50 cm, ridge, site 1 ( oC ).</p> <p>28. ts_2cm_R2 - Soil temperature at 2 cm, ridge, site 2 ( oC ).</p> <p>29. ts_5cm_R2 - Soil temperature at 5 cm, ridge, site 2 ( oC ).</p> <p>30. ts_10cm_R2 - Soil temperature at 20 cm, ridge, site 2 ( oC ).</p> <p>31. ts_20cm_R2 - Soil temperature at 20 cm, ridge, site 2 ( oC ).</p> <p>32. ts_50cm_R2 - Soil temperature at 50 cm, ridge, site 2 ( oC ).</p> <p>33. ts_2cm_H1 - Soil temperature at 2 cm, hollow, site 1 ( oC ).</p> <p>34. ts_5cm_H1 - Soil temperature at 5 cm, hollow, site 1 ( oC ).</p> <p>35. ts_10cm_H1 - Soil temperature at 10 cm, hollow, site 1 ( oC ).</p> <p>36. ts_20cm_H1 - Soil temperature at 20 cm, hollow, site 1 ( oC ).</p> <p>37. ts_50cm_H1 - Soil temperature at 50 cm, hollow, site 1 ( oC ).</p> <p>38. ts_2cm_H2 - Soil temperature at 2 cm, hollow, site 2 ( oC ).</p> <p>39. ts_5cm_H2 - Soil temperature at 5 cm, hollow, site 2 ( oC ).</p> <p>40. ts_10cm_H2 - Soil temperature at 20 cm, hollow, site 2 ( oC ).</p> <p>41. ts_20cm_H2 - Soil temperature at 20 cm, hollow, site 2 ( oC ).</p> <p>42. ts_50cm_H2 - Soil temperature at 50 cm, hollow, site 2 ( oC ).</p> <p>43. T_cont - Temperature at data logger ( oC ).</p> <p>44. batt_1 - Battery output voltage at data logger 1 ( V ).</p> <p>45. batt_2 - Battery output voltage at data logger 2 ( V ).</p> <p>46. batt_2 - Battery output voltage at data logger 3 ( V ).</p> <p><strong>meteo_MFC_qq_1h.dat</strong> <strong>and meteo_MFC_gapfilled_1h.dat</strong> <strong>parameters:</strong></p> <p>1. date - Date and time ( DD/MM/YYYY hh:mm:ss ).</p> <p>2. ta_H - Air temperature at 2 m, hollow ( oC ).</p> <p>3. ta_R - Air temperature at 2 m, ridge ( oC ).</p> <p>4. vp_H - Water vapor pressure at 2 m, hollow ( kPa ).</p> <p>5. vp_R - Water vapor pressure at 2 m, ridge ( kPa ).</p> <p>6. ipar_H - Incoming PAR, hollow ( µmol m-2 s-1 ).</p> <p>7. ipar_R - Incoming PAR, ridge ( µmol m-2 s-1 ).</p> <p>8. rpar_H - Reflected PAR, hollow ( µmol m-2 s-1 ).</p> <p>9. rpar_R - Reflected PAR, ridge ( µmol m-2 s-1 ).</p> <p>10. alb_H - Albedo PAR, hollow ( [] ).</p> <p>11. alb_R - Albego PAR, ridge ( [] ).</p> <p>12. nr_H - Net radiation balance, hollow ( W m-2 ).</p> <p>13. nr_R - Net radiation balance, ridge ( W m-2 ).</p> <p>14. shf_H - Soil heat flux, hollow ( W m-2 ).</p> <p>15. shf_R1 - Soil heat flux, ridge, site 1 ( W m-2 ).</p> <p>16. shf_R2 - Soil heat flux, ridge, site 2 ( W m-2 ).</p> <p>17. ws_10m - Wind speed at 10 m ( m s-1 ).</p> <p>18. wd_10m - Wind direction at 10 m ( deg ).</p> <p>19. ws_2m - Wind speed at 2 m ( m s-1 ).</p> <p>20. wd_2m - Wind direction at 2 m ( deg ).</p> <p>21. wU_10m - U component of wind at 10 m ( m s-1 ).</p> <p>22. wV_10m - V component of wind at 10 m ( m s-1 ).</p> <p>23. wU_2m - U component of wind at 2 m ( m s-1 ).</p> <p>24. wV_2m - V component of wind at 2 m ( m s-1 ).</p> <p>25. prs - Atmospheric pressure ( hPa ).</p> <p>26. sdp - Snow depth ( cm ).</p> <p>27. prc - Liquid precipitations ( mm ).</p>
Data from: Predicting peatland carbon fluxes from non-destructive plant traits
1. Determining the plant traits that best predict carbon (C) storage is increasingly important as global change drivers will affect plant species composition and ecosystem C cycling. Despite the critical role of peatlands in the global C cycle, trait-flux relationships in peatlands are relatively unknown. 2. We assessed the ability of four non-destructive plant traits to predict carbon dioxide (CO2) and methane (CH4) fluxes over two growing seasons in a temperate peatland in Ontario, Canada. We examined relationships between C-fluxes and leaf area, leaf persistence (deciduous, evergreen), growth form (woody, herbaceous), and aerenchyma tissue. To explore potential inconsistencies between different scales of data aggregation, traits were analysed at the level of plots, species and microsites. 3. CO2 fluxes showed a positive relationship with leaf area and leaf persistence, and a negative relationship with proportion of woody species. CH4 fluxes showed a positive relationship with aerenchyma and leaf area. The significance of trait-flux relationships differed based on whether data were averaged at the level of plot, species or microsite. 4. We recommend applying leaf area as a non-destructive trait to other systems where it is not ideal to measure traits destructively. A better understanding of the relationships between above and belowground traits is likely needed to further explain variation in ecosystem respiration and CH4 fluxes from plant traits.
Data from: Nurse species and indirect facilitation through grazing drive plant community functional traits in tropical alpine peatlands
Facilitation among plants mediated by grazers occurs when an unpalatable plant extends its protection against grazing to another plant. This type of indirect facilitation impacts species coexistence and ecosystem functioning in a large array of ecosystems worldwide. It has nonetheless generally been understudied so far in comparison with the role played by direct facilitation among plants. We aimed at providing original data on indirect facilitation at the community scale to determine the extent to which indirect facilitation mediated by grazers can shape plant communities. Such experimental data are expected to contribute to refining the conceptual framework on plant–plant–herbivore interactions in stressful environments. We set up a 2-year grazing exclusion experiment in tropical alpine peatlands in Bolivia. Those ecosystems depend entirely on a few, structuring cushion-forming plants (hereafter referred to as "nurse" species), in which associated plant communities develop. Fences have been set over two nurse species with different strategies to cope with grazing (direct vs. indirect defenses), which are expected to lead to different intensities of indirect facilitation for the associated communities. We collected functional traits which are known to vary according to grazing pressure (LDMC, leaf thickness, and maximum height), on both the nurse and their associated plant communities in grazed (and therefore indirect facilitation as well) and ungrazed conditions. We found that the effect of indirectly facilitated on the associated plant communities depended on the functional trait considered. Indirect facilitation decreased the effects of grazing on species relative abundance, mean LDMC, and the convergence of the maximum height distribution of the associated communities, but did not affect mean height or cover. The identity of the nurse species and grazing jointly affected the structure of the associated plant community through indirect facilitation. Our results together with the existing literature suggest that the "grazer–nurse–beneficiary" interaction module can be more complex than expected when evaluated in the field.
Data from: Nutrient limitation or home field advantage: does microbial community adaptation overcome nutrient limitation of litter decomposition in a tropical peatland?
Litter decomposition is an important control on carbon accumulation in tropical peatlands. Stoichiometric theory suggests that decomposition is regulated by elemental ratios in litter while the home field advantage (HFA) hypothesis predicts that decomposer communities are adapted to local conditions. To date, the relative importance of these contrasting theories for litter decomposition and therefore the carbon balance of tropical peatlands remain poorly understood. We conducted two in situ litter decomposition experiments in a lowland tropical peatland. The first experiment tested the importance of the stoichiometric theory using a factorial nutrient addition experiment at two sites with contrasting vegetation (Raphia taedigera and Campnosperma panamensis) to assess how nutrient addition affected microbial enzyme activity and litter mass loss at the peat surface and at 50 cm depth. The second experiment tested the importance of HFA by reciprocal translocation of leaf litter from R. taedigera and C. panamensis forests, which differed in both litter chemistry and soil nutrient availability, to separate the influence of litter chemistry and soil/site properties on litter mass loss. The activities of hydrolytic enzymes involved in the decomposition of large plant polymers were stimulated by nitrogen addition only where nitrogen availability was low relative to phosphorus, and were stimulated by phosphorus addition where phosphorus availability was low. The addition of nitrogen, but not phosphorus, increased leaf litter decomposition under waterlogged conditions at 50 cm depth, but not at the peat surface. Decomposition was greatest for autochthonous litter irrespective of site nutrient status, indicating that adaptation of the microbial community to low nutrients can partly overcome nutrient limitation, and suggesting that HFA can influence litter decomposition rates. Synthesis. Our study shows that leaf litter decomposition and the activity of microbial enzymes in tropical peatlands are constrained in part by nutrient availability. However, such nutrient limitation of litter decomposition can be overcome by adaptation of the microbial community.
Data from: Creating fen initiation conditions: a new approach for peatland reclamation in the oil sands region of Alberta
Reclaiming peatland ecosystems is challenging our understanding of how to rebuild functioning landscapes. Assisted succession may provide a practical approach to guide the reestablishment of peatlands in denuded landscapes. In Alberta, the majority of peatlands began as fens during the paludification process. This research focuses on creating fen initiation conditions to establish fen moss species on mineral sediment as an approach for peatland reclamation in the oil sands region. In a field mesocosm experiment, we evaluated the establishment of five common fen mosses (Drepanocladus aduncus, Ptychostomum (Bryum) pseudotriquetrum, Campylium stellatum, Tomentypnum nitens, and Aulacomnium palustre) introduced in equal proportions to clay loam. To determine the optimal hydrologic conditions for the establishment of each species, we tested four water levels (0, -10, -20, and -30 cm). We created vegetation types similar to those identified at the peat–mineral interface in peat profiles to determine the effect of herbaceous plant, low shrub, and wood-strand mulch cover treatments on moss establishment. Three seasons after introduction, total moss cover averaged 40%, and was greatest under all cover treatments and at the 0 cm water level. Total moss biomass averaged 95.5 g m−2 in moss introduction mesocosms and was greatest under low shrubs and herbaceous plants and at the 0 cm and -30 cm water levels. Fen moss species distribution was significantly influenced by water table depth. Drepanocladus aduncus and Ptychostomum pseudotriquetrum were most common at 0 cm and Aulacomnium palustre and Tomentypnum nitens at -30 cm. In this approach, we created vegetation types similar to those found on mineral sediments at the base of Alberta peat cores and successfully established distinct fen moss communities along a water table gradient and under shade cover. Introducing a suite of fen moss species that inhabit a range of hydrologic niches under low shrubs or herbaceous plants improves moss establishment. Synthesis and applications. Our research shows that it is possible to create fen initiation conditions on clay loam sediment by introducing foundation moss and vascular plant species at optimal water levels. Restoring the community structure and biomass accumulation that occurs in the initial stages of fen development appears to be a suitable target for peatland reclamation. These methods introduce a practical strategy to reclaim peatlands in the heavily impacted oil sands region of Alberta.
Data from: Trophic interactions regulate peatland carbon cycling
<p>Peatlands are the most efficient natural ecosystems for long-term storage of atmospheric carbon. Our understanding of peatland carbon cycling is based entirely on bottom-up controls regulated by low nutrient availability. Recent studies have shown that top-down controls through predator-prey dynamics can influence ecosystem function, yet this has not been evaluated in peatlands to date. Here, we used a combination of nutrient enrichment and trophic-level manipulation to test the hypothesis that interactions between nutrient availability (bottom-up) and predation (top-down) influence peatland carbon fluxes. Elevated nutrients stimulated bacterial biomass and organic matter decomposition. In the absence of top-down regulation, carbon dioxide (CO<sub>2</sub>) respiration driven by greater decomposition was offset by elevated algal productivity. Herbivores accelerated CO<sub>2</sub> emissions by removing algal biomass, while predators indirectly reduced CO<sub>2</sub> emissions by muting herbivory in a trophic cascade. This study demonstrates that trophic interactions can mitigate CO<sub>2</sub> emissions associated with elevated nutrient levels in northern peatlands.</p>
Data for: Andic soil properties and tephra layers hamper C turnover in Icelandic peatlands - selective dissolution of Al, Fe, Si and decomposition proxies
<p>Due to frequent volcanic activity and erosion of dryland soils, peatlands in Iceland receive regular additions of mineral aeolian deposits (tephra and eroded material). Therefore, their soils may develop not only histic, but also andic characteristics. Here we present data sets of a study that elucidates interactions between carbon characteristics and andic soil properties in Histosols of three Icelandic peatlands. The data sets contain information about the soil carbon structure derived by <sup>13</sup>C NMR spectroscopy, andic soil properties based on selective dissolution of Al, Fe and Si, decomposition proxies C/N, δ<sup>13</sup>C and δ<sup>15</sup>N, information on total carbon and nitrogen content, dry bulk density, soil organic matter content and pH measured in deionised water and NaF solution.</p>
Bringing Back the Manchester Argus Coenonympha tullia ssp. davus (Fabricius 1777): Quantifying the habitat resource requirements to inform the successful reintroduction of a specialist peatland butterfly
<p>2021-30 has been designated the UN decade of ecosystem restoration. A landscape scale peatland restoration project is being undertaken on Chat Moss, Greater Manchester, UK, with conservation translocations an important component of this work. The Manchester Argus Coenonympha tullia ssp. davus, a specialist butterfly of lowland raised bogs in the northwest of England, UK is under threat due to severe habitat loss and degradation. A species reintroduction was planned for spring 2020. </p> <p>This study aimed to quantify the resource thresholds for C. tullia, in order to assess potential risks for the project. Thirteen peatland habitat patches with either recent historic or current C. tullia populations were surveyed for biotic and abiotic factors based on previous qualitative research on the species' requirements. </p> <p>Percentage cover of two habitat resources were found to be the strongest predictors in models of C. tullia presence: cross-leaved heath Erica tetralix and hair's-tail cotton-sedge Eriophorum vaginatum. </p> <p>Critical inflection points on logistic regression curves were used to make quantitative estimates of the minimum requirement of each resource for population survival and the near-optimum abundance of each resource. </p> <p>The results of this study improve our understanding of C. tullia's ecology and the restoration of peatlands for its reintroduction. Additionally, the method has wider utility for the quantitative assessment of habitat readiness before attempting species reintroductions.</p>
Carbon loss pathways in degraded peatlands: Repository dataset
<p>The data on the following sheets is associated with the paper:</p> <p> </p> <p>Evans, M.G., Alderson, D.M., Evans, C.D., Stimson, A., Goulsbra, C., Allott,T.E.H., Worrall, F., Crouch, T., Walker, J., Garnett, M.H., Rowson, J. (2022). Carbon Loss Pathways in Degraded Peatlands: New Insights from Radiocarbon Measurements of Peatland Waters. </p> <p> </p> <p>The data are organised according to the figures within the paper, with the data for each figure corresponding to an individual datasheet. The methods for data collection can be found in the associated manuscript.</p> <p> </p> <p>For further information please contact Martin Evans (martin.g.evans@manchester.ac.uk) or Danielle Alderson (danielle.alderson@manchester.ac.uk).</p> <p> </p> <p>Figures 1, 7 and 11 either do not contain data or are visual model outputs and are therefore not included. </p> <p> </p>
Distribution of PAHs in hummocky tundra peatlands: Fibric Floatic Histosols.
<p><span>Table 1. Distribution of PAHs in hummocky tundra peatlands: Fibric Floatic Histosols. The ecotone zone at the boundary between the southern tundra and forest tundra (the Usa river basin, Vorkuta district of the Komi Republic).</span></p> <p><span>Table 2. Distribution of PAHs in hummocky tundra peatlands: Fibric Floatic Histosols. The ecotone of the northern and southern tundra (watershed of the Padimey-Ty-Vis and Korotaikha rivers, the Nenets Autonomous Okrug.</span></p>
Distribution of PAHs in hummocky tundra peatlands: Hemic Folic Cryic Histosols and Hemic Folic Cryic Histosols (Turbic).
<p><span>Table 1. Distribution of PAHs in hummocky tundra peatlands: Hemic Folic Cryic Histosol (s1-1). The ecotone zone at the boundary between the southern tundra and forest tundra (site 1, the Usa river basin, Vorkuta district of the Komi Republic). </span></p> <p><span>Table 2. Distribution of PAHs in hummocky tundra peatlands: Hemic Folic Cryic Histosols (Turbic) (s1-2). The ecotone zone at the boundary between the southern tundra and forest tundra (site 1, the Usa river basin, Vorkuta district of the Komi Republic).</span></p> <p><span>Table 3. Distribution of PAHs in hummocky tundra peatlands: Hemic Folic Cryic Histosol (s2-1). The ecotone of the northern and southern tundra (site 2, watershed of the Padimey-Ty-Vis and Korotaikha rivers, the Nenets Autonomous Okrug.</span></p> <p><span>Table 4. Distribution of PAHs in hummocky tundra peatlands: Hemic Folic Cryic Histosol (Turbic) (s2-2). The ecotone of the northern and southern tundra (site 2, watershed of the Padimey-Ty-Vis and Korotaikha rivers, the Nenets Autonomous Okrug.</span></p>
Data: The effects of glucose addition and water table manipulation on peat quality of drained peatland forests with different management practices
<p>The file contain data on peat chemical quality and peat decomposition. We studied how glucose addition, water table and forest harvesting affect the chemical composition of peat and decomposition rate (carbon dioxide fluxes) and soil water quality. Experiments and results are presented in:</p> <p>Aaltonen H., Zhu X., Khatun R., Laurén A., Palviainen M., Könönen M., Peltomaa E., Berninger F., Köster K., Ojala A., Pumpanen J. 2022. The effects of glucose addition and water table manipulation on peat quality of drained peatland forests with different management practices. Soil Science Society of America Journal 86:1625–1638. <a href="https://doi.org/10.1002/saj2.20419">https://doi.org/10.1002/saj2.20419</a></p> <p> </p>
Data for: Enhanced Hydrologic Connectivity and Solute Dynamics Following Wildfire and Drought in a Contaminated Temperate Peatland Catchment
<p>Data underlying the publication: "Enhanced Hydrologic Connectivity and Solute Dynamics Following Wildfire and Drought in a Contaminated Temperate Peatland Catchment".</p>
Continental scale α- and β-diversity patterns of terrestrial eukaryotic microbes: effect of climate and microhabitat on testate amoeba assemblages in Eurasian peatlands
Open the record for dataset details and reuse information.
Plant macrofossil, peat geochemical and chronological data from sub-Arctic European peatlands
<p>This dataset consists of raw data from peat records analysed for plant macrofossils, peat geochemical properties supplemented by chronological control data from high-latitude Sweden, Finland and European Russia. Altogether, 33 peat cores were analysed from 16 peatlands. Peat cores were collected from seasonally thawed active peat layer with a box corer or a so-called Russian corer. Peat records cover both currently intermediate (n= 25) and dry (n= 8) surfaces. Majority of the sites (n= 12) are permafrost peatlands either with sporadic or discontinuous permafrost. Changes in peatland vegetation and peat and carbon accumulation were studied to resolve how high-latitude peatlands react to past and recent changes in climate. Peat properties were examined for bulk density, carbon (C), nitrogen (N) and C/N ratio. C accumulation was calculated for the past two millennia. To establish chronological control, peat layers were dated with 210Pb and radiocarbon 14. To create age-depth models we used Plum and the ages retrieved form the models are found in this dataset. The data have been analysed between 2016 and 2020. More information about the methods can be found from Piilo et al. “Consistent centennial-scale change in European sub-Arctic peatland vegetation towards <em>Sphagnum</em> dominance – implications for carbon sink capacity”. Global Change Biology.</p>
FIGURE 2. Bulbine capitata. A in Bulbine decastroi (Asphodelaceae subfam. Asphodeloideae), a new peatland species with grass tuft-like rosettes from Mpumalanga, South Africa
FIGURE 2. Bulbine capitata. A. Growing in a recently burned grassland. Note the arid substrate. B. The leaves are most often much broader than those of B. decastroi. C. Close-up of a raceme and flowers. Note the long pedicels. All photographs: Neil R. Crouch.
FIGURE 1. Bulbine decastroi. A in Bulbine decastroi (Asphodelaceae subfam. Asphodeloideae), a new peatland species with grass tuft-like rosettes from Mpumalanga, South Africa
FIGURE 1. Bulbine decastroi. A. In its natural peatland habitat. The peatland burned a few months before the photograph was taken. Note the high water table in the bottom right of the image. B. This section of the peatland did not burn. The vegetation is very dense. C. Contractile roots. D. A dense cluster of 40 rosettes. Note the glaucous sheen to the leaves. E. Close-up of racemes and flowers. F. Tony de Castro (1970–) after whom B. decastroi is named. Bulbine decastroi grows in the foreground. All photographs: Gideon F. Smith.
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output, Jan 2010 through Sep 2021)
<p>The dataset archived here includes an extended version of the analysis output shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide a single NetCDF file of the analysis output (9-km resolution EASEv2 grid, period Jan 2010 through Sep 2021, and between 45°N and 70°N, NE Asia excluded):<br> • daily_images.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20230830.pdf</p> <p>Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.</p> <p>Data usage statement:<br> This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/<br> If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks!</p> <p>References:</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM</p> <p>Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. <em>Remote Sensing of Environment</em>. https://doi.org/10.1016/j.rse.2020.111805</p> <p>Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(10), 3106–3130. https://doi.org/10.1029/2019MS001729</p>
Phenological time lapse images from ground camera MC129 in Lompolojänkkä Peatland
<p>This record contains phenological time lapse images from camera Lompolojänkkä Peatland. Camera was mounted at ground view level at location 67.99725;24.2091(N;E, WGS84).</p> <p>First set of images were taken between 02.04.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact mika.aurela@fmi.fi</p>
Permafrost thaw in boreal peatlands is rapidly altering forest community composition
Open the record for dataset details and reuse information.
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.