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167 results for “Stoichiometry”
Effect of Prey Availability on Sarracenia Purpurea Stoichiometry at Belvidere Bog, Vermont 2002
The carnivorous pitcher plant Sarracenia purpurea receives nutrients from both captured prey and atmospheric deposition, making it a good subject for the study of ecological stoichiometry and nutrient limitation. We added prey in a manipulative field experiment and measured nutrient accumulation in pitcher-plant tissue and pitcher liquid, as well as changes in plant morphology, growth, and photosynthetic rate. Prey addition had no effect on traditional measures of nutrient limitation (leaf morphology, growth, or photosynthetic rate). However, stoichiometric measures of nutrient limitation were affected, as the concentration of both N and P in the leaf tissue increased with the addition of prey. Pitcher fluid pH and nitrate concentration did not vary among treatments, although dissolved oxygen levels decreased and ammonia levels increased with prey addition. Ratios of N:P, N:K, and K:P in pitcher-plant tissues suggest that prey additions shifted these carnivorous plants from P limitation under ambient conditions to N limitation with the addition of prey.
Stoichiometry of Bogs and Bog Plants in Massachusetts and Vermont 2002
Geographic trends in surface water chemistry and leaf tissue nutrients may reflect gradients of nutrient limitation and broad-scale anthropogenic inputs. In 24 bogs and poor fens in Massachusetts and Vermont, we measured nutrient and metal concentrations in pore-water and in leaf tissues of three common bog plants – leather leaf (Chamaedaphne calyculata), northern pitcher plant (Sarracenia purpurea), and peat moss (Sphagnum spp.). The concentrations of N, P, and K were low in leaf tissues of all three plant genera, as were the concentrations of many trace heavy metals, including Cr, Cu, Co, Cd, Mo, and Pb. Stoichiometric ratios of macronutrients (N:P, P:K, and N:K) in plant leaves suggested that plant growth in the sampled bogs was limited by P, or was co-limited by all three macronutrients. N:P and N:K nutrient ratios of Sarracenia purpurea and Sphagnum spp. increased toward the northwest and with elevation, but stoichiometric ratios of Chamaedaphne calyculata did not show any clear geographic trends. A principal components analysis revealed additional distinct differences among the three plant genera in their nutrient and metal concentrations. Furthermore, dissolved organic carbon (DOC), dissolved organic nitrogen (DON), Cu, Mg, NO3, Al, and K in porewater increased from the northwest (northwestern Vermont) to the southeast (Cape Cod and eastern Massachusetts near Boston), a gradient of increasing human population density and urbanization. In contrast, pore-water concentrations of SO4 and Al were highest in the western sites, and SO4 concentrations increased with elevations. These patterns may reflect atmospheric inputs from the Ohio River Valley leading to increased acidic deposition, causing Al to be leached from soils. Because bogs are naturally low in nutrients and do not receive substantial inputs from surrounding groundwater, the chemical signatures and nutrient stoichiometry of specific bog plant species or genera may provide useful indicators for assessing
LAGOS - Lake nitrogen, phosphorus, stoichiometry, and geospatial data for a 17-state region of the U.S.
This dataset includes information about total nitrogen (TN) concentrations, total phosphorus (TP) concentrations, TN:TP stoichiometry, and 12 driver variables that might predict nutrient concentrations and ratios. All observed values came from LAGOSLIMNO v. 1.054.1 and LAGOSGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes greater than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, we compiled chemistry data from lakes with concurrent observations of TN and TP from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOSLIMNO v. 1.054.1 (2002-2011). We report the median TN, TP and molar TN:TP values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics that might be important controls on lake nutrients, including: land use (agricultural, pasture, row crop, urban, forest), nitrogen deposition, temperature, precipitation, hydrology (baseflow), maximum depth, and the ratio of lake area to watershed area, which is used to approximate residence time. These data were used to identify drivers of lake nutrient stoichiometry at sub-continental and regional scales (Collins et al, submitted). This research was supported by the NSF Macrosystems Biology program (awards EF-1065786 and EF-1065818) and by the NSF Postdoctoral Research Fellowship in Biology (DBI-1401954).
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>
Raw data for High Temperature Photochromism of Fe-Doped SrTiO3 Caused by UV Induced Bulk Stoichiometry Changes
<p>In the following the raw data lying the foundation of the paper High Temperature Photochromism of Fe-Doped SrTiO3 Caused by UV Induced Bulk Stoichiometry Changes (Viernstein et al.) published in Advanced Functional Materials Vo. 29 Issue 23 (WILEY-VCH Verlag GmbH & Co. KGaA, Germany) in 2019 are described. They were obtained under the funding provided by Austrian Science Fund (FWF) (project F4509-N16, FOXSI) and the European Union`s Horizon 2020 research and innovation program under the grant agreement No. 824072 and consist of UV/VIS spectra, van der Pauw measurements, electrochemical impedance spectra, and laser ablation ICP-MS data.</p> <p>The UV/VIS measurements were carried out in air, at 440 °C using a deuterium and a tungsten lamp (Edmund Optics Inc., Germany) as light source and an Ocean Optics QE6500 (Halma plc, England) as spectrometer. The data include background, I<sub>0</sub> and I absorption measurements of Fe doped SrTiO<sub>3</sub> (STO) single crystals before, during, and after illumination with UV light (365 nm). The data files are labeled for example as “UVvis_FeSTO_background_1” or ”UVvis_FeSTO_I_440C_UVon_90s”, to state the type auf measurement, temperature, and status of the experiment. In each of them the average of 30 spectra is given and each exhibits two columns, namely wavelength, and intensity.</p> <p>The van der Pauw measurements were performed on two Keithley 20 multimeter and a 2410 1100 V source meter (Keithley Instruments, USA). They are labeled in the following way: “date_applied voltage_atmosphere_sample identification_temperature cycle_status of the measurement”. Each file consists out of a header giving time, cycle number, temperature (real and set) and six columns, t[s], (applied) U[V], (measured) I[A], R [Ohm], and two unnamed columns ((applied) U[V] and (measured) U [V]).</p> <p>The electrochemical impedance spectra were obtained before, during, and after UV exposure, using an Electrochemical Test Station POT/GAL 30 V/2 A or a Novocontrol Alpha‐A high‐performance frequency analyzer, respectively (both Novocontrol Technologies GmbH & Co. KG, Germany). Each spectrum is named after the following description: “date_sample name_UVonoff_real temperature_atmosphere_spectra number”. A header with date, time, cycle number, and temperature followed by four columns, namely Freq [Hz], Re (real part of the impedance spectra), Im (imaginary part), Amp (amplitude), and Pha (phase) are given.</p> <p>Laser ablation ICP-MS measurements were performed on a NWR213 laser ablation system (ESI; USA) and an iCAP Q ICP-MS (Thermo Fisher Scientific, Germany). The obtained data file is labeled as Iaser_ablation_ICP_MS_FeSTO and exhibits sample names, names of the measured masses (isotopes) and the obtained counts.</p>
Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
<p>Dataset of the results published in the <a href="https://zenodo.org/record/4742379#.YMzExOgzYmJ">J. Mater. Chem. A, 2021, 9, 10466</a>. The files represent: i) the measured compositional and optoelectronic data of each solar cell, as well as the data generated from the Raman spectra analysis; ii) Raman spectra of the representative cells; iii) Machine Learning discriminants.</p> <p>The elemental composition of the different cells of the combinatorial sample was determined by X-ray fluorescence (XRF) using a Fischerscope XDV system with a 1 mm spot diameter, a 50 kV acceleration voltage, a Ni10 lter and a 45 s acquisition time. Raman analysis with blue (442 nm) and green (532 nm) excitation wavelengths were performed on the bare absorber, while measurements with NIR (785 nm) were performed in complete devices using Horiba Jobin Yvon FHR640 and iHR320 monochromators coupled with CCD detectors. The first monochromator is optimized for the UV and visible spectral ranges and was used with 442 nm (He–Cd gas laser) and 532 nm (solid state laser) excitation wavelengths. The second monochromator is optimized for the NIR range and was used with a 785 nm (solid state laser) excitation wavelength. The power density of the lasers was kept below 150 W cm<sup>2</sup> and the spot size was ~70 <span class="math-tex">\(\mu\)</span>m. The measurements were performed in a backscattering configuration through a specific probe designed at IREC. The J–V characteristics of the devices were obtained under simulated AM1.5 illumination (1000 W m2 intensity at room temperature) using a pre-calibrated Class AAA solar simulator (Abet Technologies Sun 3000).</p>
Biomass, stoichiometry, and isotopic signatures of stream microbial mats, McMurdo Dry Valleys, Antarctica (2012-2013)
We conducted a field survey to quantify the biomass (chlorophyll-a and ash-free dry mass), nutrient ratios (molar C:N:P), and isotopic signatures (δ13C and δ15N) of four microbial mat types (green, orange, black, and red) in the glacial meltwater streams of the McMurdo Dry Valleys, Antarctica. All samples were taken from late December to late January during the 2011-2012 and 2012-2013 austral summers, and included sites from Taylor, Miers, Garwood, and Wright valleys. Most collection sites were located at the lake outlet of streams, but for a subset (e.g. Delta, Von Guerard, Onyx, Miers, and Canada) more than one sample site is included per stream system.
Raw data used in Kumar et al. 2020: Barley shoot biomass responds strongly to N:P stoichiometry and intraspecific competition, whereas roots only alter their foraging
<p>Raw data used in Kumar et al. 2020: Barley shoot biomass responds strongly to N:P stoichiometry and intraspecific competition, whereas roots only alter their foraging</p>
Foliar stoichiometry of woody plants worldwide
<p>This data includes foliar N, P, K % in DW of mature leaves in woody plants worldwide. It also contains the georeferenced information and specie. It gathers data from 230 published articles, TRY database (<a href="http://www.try-db.org/TryWeb/dp.php),">http://www.try-db.org/TryWeb/dp.php),</a> ICP forest database (<a href="http://icp-forests.net/page/data-requests),">http://icp-forests.net/page/data-requests),</a> Tundra Trait Team and the Catalan Forest Inventory (Gracia et al., 2004).</p>
Flexible foliar stoichiometry with CTSM5.1
This data explores the importance of flexible foliar stoichiometry in mediating terrestrial carbon cycle and hydrologic responses to climate change using CTSM5.1. We find a strong reduction in terrestrial productivity and the land C sink over the 21st century when we allow foliar stoichiometry to increase with rising concentrations of CO2 in the atmosphere.
Predicting global patterns in the biomolecular composition of marine phytoplankton and their stoichiometry
<p>Data to predict the biomolecular composition of marine phytoplankton and their stoichiometry using key environmental properties (temp, nutrients, light, etc.)</p>
Supplementary Material of "Fuel Starvation in Automotive PEMFC Stacks: Hydrogen Stoichiometry and Electric Cell-to-Cell Interaction"
<p>This video contains the discussed experimental data of the following journal publication, which explains experimental setup, test cycle and the shown data in detail.</p> <p><strong>Nissen, J., Boye, J. P., Schwämmlein, J. N., & Hölzle, M. (2024). Fuel starvation in automotive PEMFC stacks: hydrogen stoichiometry and electric cell-to-cell interaction. <em>Journal of Physics: Energy</em>. <br><a title="https://doi.org/10.1088/2515-7655/ad5f54" href="https://doi.org/10.1088/2515-7655/ad5f54">https://doi.org/10.1088/2515-7655/ad5f54</a></strong></p> <p>Version 01: Video as .MKV file. Quite large and not supported for in-browser visualization by zenodo.</p> <p>Version 02: Changed video format from .MKV to .MP4 to reduce file size and allow in-browser visualization by zenodo. Identical content as Version 01.</p>
The Emiliania huxleyi stoichiometry database
<p>The <em>Emiliania huxleyi</em> stoichiometry database contains data for the growth rate, cellular elemental content (particulate inorganic carbon - PIC, organic carbon - C, nitrogen - N and phosphorous - P) and C:N:P stoichiometry (PIC:C, C:N, N:P, C:P) compiled through a meta-analysis of literature reporting the results of laboratory experiments (cultures) on the coccolithophore species <em>Emiliania huxleyi</em>, an important calcifying marine phytoplankton.</p> <p>The database also reports selected additional parameters including cell size and/or volume, and chlorophyll<em> a </em>as well as additional meta-data associated with the original data source including strain details and culture experimental conditions. A description of the parameters contained in the database can be found in the file "The Emiliania huxleyi stoichiometry database data description".</p> <p>Please cite this dataset as:</p> <p>Sheward et al. (2023) The <em>Emiliania huxleyi</em> stoichiometry database. doi:10.5281/zenodo.7594880</p>
Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis
<p><strong><em>Data description</em></strong></p> <p>This dataset includes detailed information about five different types of land use change reported in “The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis (Agriculture, Ecosystems and Environment; <a href="https://doi.org/10.1016/j.agee.2023.108402)">https://doi.org/10.1016/j.agee.2023.108402)</a>”. </p> <p> </p> <p>Lists of five different types of land use change</p> <p>1) conversion of primary forest to cropland</p> <p>2) conversion of primary forest to grassland</p> <p>3) conversion of cropland to forest</p> <p>4) conversion of grassland to forest</p> <p>5) conversion of grassland to cropland</p> <p> </p> <p>Lists of detailed information</p> <ul> <li>Land use change (pre-LUC, post-LUC)</li> <li>Country, Location, Geographic position (Longitude, Latitude) </li> <li>Altitude (m)</li> <li>Climate zone</li> <li>Weather [rainfall (mm yr<sup>-1</sup>) and temperature (°C)]</li> <li>Reported time of change (years)</li> <li>Vegetation type (pre-LUC, post-LUC)</li> <li>Fertilizer (pre-LUC, post-LUC: type, application; change)</li> <li>Soil sampling depth (cm)</li> <li>Soil type [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil pH, bulk density, CEC [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil organic carbon [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total nitrogen [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total phosphorus [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:N [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil N:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Reference</li> </ul> <p> </p> <p><em><strong>Data collection method</strong></em></p> <p>We analyzed five different types of LUC: 1) conversion of primary forest to cropland, 2) conversion of primary forest to grassland, 3) conversion of cropland to forest, 4) conversion of grassland to forest, and 5) conversion of grassland to cropland.</p> <p>We classified primary forest as forest that had not previously been cleared and used for other land uses. The conversion of cropland or grassland to forest includes naturally generated and intentionally planted forest. Cropland is land used for growing agricultural crops and may include short pasture phases, and grassland is land used continuously for grazing purposes, but may include occasional and repeated pasture-renewal phases.</p> <p>While we tried to make categorical distinctions between these land-use types, land uses are often more fluid in practice, which may not always have been stated in the publications underlying our data compilation.</p> <p>When a paper reported both contents and stocks, we used the stock-based measure. We used reported stocks if the original work had already been corrected to equivalent soil mass (Ellert and Bettany, 1995) or if corrected stocks had been reported in previous reviews or meta-analyses (Don et al., 2011; Poeplau et al., 2011; Guo and Gifford, 2002). Where bulk-density correction had not been applied, we tried to make those corrections to estimate changes to equivalent soil mass if studies provided sufficient information on soil bulk density and depth, using the method of Zhang et al. (2004). If that was not possible, we used the reported SOC, TN, or TP contents.</p> <p> </p> <p><em><strong>Acknowledgements</strong></em></p> <p>We thank scientists who measured, analyzed, and published the data compiled for this study. We are especially grateful to Drs. Axel Don, Christopher Poeplau, Lex Bouwman, and Gaihe Yang, who provided their global meta-data through personal communication. D.-G.K. acknowledges support from the IAEA CRP D15020. M.U.F.K and L.L.L. were supported by the Strategic Science Investment Fund (SSIF) of New Zealand’s Ministry of Business, Innovation and Employment.</p>
Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. Please refer to the READMEs in the directories, which are summarized below.</p> <p>The record contains the following files:<br> <br> `clusters.tsv`: <strong> </strong>contains the cluster id, name and colour of clusters in the paper</p> <p><strong>scATAC.zip</strong></p> <p>Analysis products for the single-cell ATAC-seq data. Contains:</p> <p>- `cells.tsv`: list of barcodes that pass QC. Columns include:<br> - `barcode`<br> - `sample`: (time point)<br> - `umap1`<br> - `umap2`<br> - `cluster`<br> - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root<br> - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root<br> - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `features.tsv`: 50 dimensional representation of each cell <br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed` </p> <p><strong>scATAC_clusters.zip</strong></p> <p>Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data. </p> <p>- `clusters.tsv`: contains the cluster id, name and colour used in the paper<br> - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format.<br> - `fragments`: contains per cluster fragment files </p> <p><strong>scATAC_scRNA_integration.zip</strong></p> <p>Analysis products from the integration of scATAC with scRNA. Contains:</p> <p>- `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45.<br> - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type.<br> - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode.</p> <p><strong>scRNA.zip</strong></p> <p>Analysis products for the single-cell RNA-seq data. Contains:</p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC)<br> - `genes.txt`: list of all genes<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC) <br> - `sample`: sample name (D0, D2, .., D14, iPSC)<br> - `umap1`<br> - `umap2`<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `cluster`<br> - `percent.mt`: percent of mitochondrial transcripts in cell<br> - `percent.oskm`: percent of OSKM transcripts in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` <br> - `pca.tsv`: first 50 PC of each cell<br> - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv`</p> <p><strong>multiome.zip</strong></p> <p><em>multiome/snATAC:</em></p> <p>These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2). </p> <p>- `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:<br> - `barcode`<br> - `umap1`: These are the coordinates used for the figures involving multiome in the paper.<br> - `umap2`: ^^^ <br> - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq<br> - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels. <br> - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`.<br> - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`.<br> - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`.</p> <p><em>multiome/snRNA:</em></p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency.<br> - `genes.txt`: list of all genes (this is different from the list in scRNA analysis)<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively) <br> - `sample`: sample name (D1M, D2M)<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `percent.oskm`: percent of OSKM genes in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` </p>
Data from: Eco–enzymatic stoichiometry unveils resource limitations in soil microorganisms during subtropical vegetation restoration
Open the record for dataset details and reuse information.
Silicon concentrations and stoichiometry in two agricultural watersheds: implications for management and downstream water quality
Agriculture alters the biogeochemical cycling of nutrients such as nitrogen (N), phosphorus (P), and silicon (Si) which contributes to the stoichiometric imbalance among these nutrients in aquatic systems. Limitation of Si relative to N and P can facilitate the growth of non-siliceous, potentially harmful, algal taxa which has severe environmental and economic impacts. Planting winter cover crops can retain N and P on the landscape, yet their effect on Si concentrations and stoichiometry is unknown. We analyzed three years of biweekly concentrations and loads of dissolved N, P, and Si from subsurface tile drains and stream water in two agricultural watersheds in northern Indiana. Intra-annual patterns in Si concentrations and stoichiometry showed that cover crop vegetation growth did not reduce in-stream Si concentrations as expected, although, compared to fallow conditions, winter cover crops increased Si:N ratios to conditions more favorable for diatom growth. To assess the risk of non-siliceous algal growth, we calculated a stoichiometric index to quantify biomass growth facilitated by excess N and P relative to Si. Index values showed a divergence between predicted algal growth and what we observed in the streams, indicating other factors influence algal community composition. The stoichiometric imbalance was more pronounced at high flows, suggesting increased risk of harmful blooms as climate change increases the frequency and intensity of precipitation in the midwestern U.S. Our data include some of the first published measurements of Si within small agricultural watersheds and provide the groundwork for understanding the role of agriculture on Si export and stoichiometry.
Impacts of nutrient addition on soil carbon and nitrogen stoichiometry and stability in globally-distributed grasslands
Global changes will modify future nutrient availability with implications for grassland biogeochemistry. Soil organic matter (SOM) is central to grasslands for both provision of nutrients and climate mitigation through carbon (C) storage. While we know that C and nitrogen (N) in SOM can be influenced by greater nutrient availability, we lack understanding of nutrient effects on C and N coupling and stability in soil. Different SOM fractions have different functional relevance and mean residence times, i.e., mineral-associated organic matter (MAOM) has a higher mean residence time than particulate organic matter (POM). By separating effects of nutrient supply on the different SOM fractions, we can better evaluate changes in soil C and N coupling and stability and associated mechanisms. To this end, we studied responses of C and N ratios and distributions across POM and MAOM to 6-10 years of N, phosphorus (P), potassium and micronutrients (K+µ), and combined NPK+µ additions at 11 grassland sites spanning 3 continents and globally relevant environmental gradients in climate, plant growth, soil texture, and nutrient availability. Data associated with this study are provided here.
McMurdo Dry Valleys LTER: Stoichiometry Experiment in Taylor Valley, Antarctica from 2007 to 2016
Soil communities in the McMurdo Dry Valleys are subject to many limitations, including resource limitations. However, the nutrients that are predominantly limiting to growth and diversity of soil biota are not known. Additionally, landscape history (N deposition, P weathering) and native N and P content (glacial till provenance) may influence the ability of soil communities to respond to nutrient additions or changes in nutrient availability associated with environmental change. Long-term experiments in the Bonney and Fryxell basins have been established with yearly application of aqueous nutrient additions: C, N, P, CN, and CP. Multiple nutrients additions were made at the Redfield Ratio of 106:16:1 (C:N:P). Responses of soil chemistry, CO2 flux, and biota are reported.
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