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128 results for “carbon biomass”
Impacts of Deer and Moose on Soil Carbon, Soil Respiration, and Root Biomass at Harvard Forest since 2017
Over the past decade, several deer and moose exclosures have been built at Harvard Forest to study the effect of ungulate browsing on tree regeneration, species diversity, and composition. We built on the existing infrastructure to study the impacts of deer and moose browsing on soil carbon stocks (soil C, root biomass) in regenerating forests.
Plant biomass, leaf area, carbon, nitrogen, and phosphorus in wet sedge tundra, 1994, Arctic LTER, Toolik Lake, Alaska.
Plant biomass, leaf area, carbon, nitrogen, and phosphorus were measured in three wet sedge tundra experimental sites. Treatments at each site included factorial NxP and at the Toolik sites greenhouse and shade house. Treatments started in 1985 (Sag site) and in 1988 (Toolik sites).
Plant and root biomass, nitrogen, carbon, and phosphorus concentrations in a mesic acidic tussock tundra experimental site established in 1981(MAT81) and harvested in 2015, Arctic LTER, Toolik Lake, Alaska.
Plant and root biomass, nitrogen, carbon, and phosphorus were measured in 2015 in the Arctic LTER tussock tundra experimental site (MAT81). This site was established in 1981 and has been harvested in previous years (see Shaver and Chapin Ecological Monographs, 61(1), 1991, pp.1-31, https://doi.org/10.2307/1942997). Data tables include the biomass for each harvested quadrat and block summaries for percent carbon, nitrogen, and phosphorus for control and fertilized plots from the original 4-block design. New control plots, established in 2015, are in a separate data table and include biomass, percent carbon, nitrogen, and phosphorus for each quadrat.
Block summaries of biomass, carbon, nitrogen, and phosphorus allocation among tissue types, species, and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment harvests: 2000 and 2015, Toolik Lake Field Station, Alaska.
A complete accounting of biomass, C, N, and P allocation both among tissue types (leaves, stems, rhizomes, roots) and among species and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment’s untreated control plots and plots that were fertilized annually, harvested after 20 and 35 years, near Toolik Lake Field Station, Alaska. Data are gram per meter squared summarized by block.
Quantitative and qualitative aspects of dissolved organic carbon leached from plant biomass in Taylor Slough, Shark River and Florida Bay (FCE) for samples collected in July 2004
Plant biomass was collected from Taylor Slough, Shark River and Florida Bay in Everglades National Park. Samples were taken to the lab and incubated with Milli-Q water in the dark for a period of 36 days. NaN3 was added to half the bottles to test the role of microbial activity on the leaching rates and composition of leachate. Every three days the water was decanted and replaced with fresh Milli-Q water. The decanted samples were filtered and analyzed for DOC concentration, sugar content, and total phenol content.
Microbial biomass and water-extractable carbon on Mt. Kilimanjaro
<p>This dataset presents the value of microbial biomass carbon (MBC) and water-extractable carbon (WOC) at study plots under KiLi project.</p> <p>Microbial biomass carbon (MBC) and water-extractable organic carbon (WOC) – as sensitive and important parameters for soil fertility and C turnover – are strongly affected by land-use changes all over the world. These effects are particularly distinct upon conversion of natural to agricultural ecosystems due to very fast carbon (C) and nutrient cycles and high vulnerability, especially in the tropics. The objective of this study was to use the unique advantage of Mt. Kilimanjaro – altitudinal gradient leading to different tropical ecosystems but developed all on the same soil parent material – to investigate the effects of land-use change and elevation on MBC and WOC contents during a transition phase from dry to wet season. Down to a soil depth of 50 cm, we compared MBC and WOC contents of 2 natural (<em>Ocotea</em> and <em>Podocarpus</em> forest), 3 seminatural (lower montane forest, grassland, savannah), 1 sustainably used (homegarden) and 2 intensively used (maize field, coffee plantation) ecosystems on an elevation gradient from 950 to 2850 m a.s.l.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Peak growing season aboveground biomass 2011-2017. (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-bnz/502/16. The abstract below was extracted from the Level 0 data package and is included for context: This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieve using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. Above ground plant biomass was surveyed non-destructively using a point-intercept method for all vascular and moss species at peak growing season.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Aboveground plant biomass, 2009-2017. (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-bnz/501/17. The abstract below was extracted from the Level 0 data package and is included for context: The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes aboveground plant biomass from winter warming, summer warming, and control treatment plots at CiPEHR.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Peak growing season aboveground biomass 2011-2017. (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/264/3, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-bnz/502/16. The abstract below was extracted from the Level 0 data package and is included for context: This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieve using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. Above ground plant biomass was surveyed non-destructively using a point-intercept method for all vascular and moss species at peak growing season.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Aboveground plant biomass, 2009-2017, 2021
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes aboveground plant biomass from winter warming, summer warming, and control treatment plots at CiPEHR.
VDMBC_vertical_distribution_soil_microbial_biomass_carbon
<p>Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset (VDMBC) is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data were collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data collectd include climate, ecosystem types, and edaphic factors. We developed this dataset by searching the the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data in this dataset met two creteria: 1) there were at least three mineral soil layers along a soil profile, and 2) SMBC was measured using the fumigation extraction method. The data in tables and texts were obtained from literature directly, and the data in figures were extracted by using the GetData Graph digitizer software version 2.25. When climate and soil properties were not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org).</p> <p>The units of all the variables were converted to the standard international units or commonly used ones and the values were converted correspondingly. For example, the value of soil organic matter (SOM) was converted to SOC using the equation (SOC = SOM × 0.58). Soil depth was calculated as the arithmetic mean value of the upper and lower boundaries for a given soil layer.</p> <p>This dataset can be used in predicting global SOC change along soil profiles using the multi-layer soil carbon models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.</p>
Carbon storage in old hedgerows: The importance of below-ground biomass
<p>Dataset to the manuscript: Drexler, S., Thiessen, E., & Don, A. (2023). Carbon storage in old hedgerows: The importance of below-ground biomass. GCB Bioenergy. https://doi.org/10.1111/gcbb.13112</p><ul><li>Drexler_et_al_2023-cn_biomass: contains the data on the biomass C/N measurements</li><li>Drexler_et_al_2023-overallstocks: contains the calculated carbon stocks per subplot for all carbon pools</li><li>Drexler_et_al_2023-soc_cropland: contains the calculated soil organic carbon stocks (0-100cm soil depth) of the reference cropland</li><li>Drexler_et_al_2023-soc_weight_fine_roots: contains the raw data on the dry weight of the fine roots and the raw data on the soil samples (C/N data, dry weight, stone/root fraction) per subplot and sampling depth</li><li>Drexler_et_al_2023-weight_above_ground_biomass: contains the raw data on the dry weight of the harvestable biomass and biomass of the mature trees per subplot</li><li>Drexler_et_al_2023-weight_coarse_roots_litter: contains the raw data on the dry weight of the coarse roots, litter and stumps per subplot</li></ul>
Processing and Data for "Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats"
<p><strong>Description: </strong></p> <p>These files contain processed BGC-Argo float data, figure data, the radiocarbon productivity subset, bootstrapping results, and the associated Python/Matlab code to calculate net primary productivity from daily cycles of optical backscatter and dissolved oxygen.</p> <p>The raw float data used in this study are available from the Argo Global Data Assembly Centers in Brest, France (ftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis) and Monterey, California (ftp://usgodae.org/pub/outgoing/argo/dac/coriolis). The raw MODIS satellite-based productivity data is available from the Oregon State University Ocean Productivity site (<a href="http://orca.science.oregonstate.edu/npp_products.php">http://orca.science.oregonstate.edu/npp_products.php</a>). The raw MODIS satellite-based euphotic depth estimates are available from the NASA L3 browser (<a href="https://oceancolor.gsfc.nasa.gov/l3/">https://oceancolor.gsfc.nasa.gov/l3/</a>). The original ship-based estimates of net primary productivity are available from the Pangaea (<a href="https://doi.pangaea.de/10.1594/PANGAEA.932417">https://doi.pangaea.de/10.1594/PANGAEA.932417</a>) and the British Oceanography Data Centre (<a href="https://www.bco-dmo.org/dataset/814803">https://www.bco-dmo.org/dataset/814803</a>).</p> <p><strong>Please cite as: </strong></p> <p>Stoer, A., and Fennel, K. 2022. Processing and Data for Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats. Zenodo. doi: 10.5281/zenodo.6977161.</p> <p><strong>Python/MATLAB Software Description: </strong></p> <p>dielFit_GOPeqCR.m: This code is from Johnson and Bif (2021). We have added outputs for standard errors for linear and PvE models and sunrise/sunset times. To run this code with the associated Python software a MATLAB engine needs to be installed. Please see: <a href="https://www.mathworks.com/help/matlab/matlab-engine-for-python.html">https://www.mathworks.com/help/matlab/matlab-engine-for-python.html</a></p> <p>argo_so_processing_20220815.py: This code is the first of two pieces of software for estimating net primary productivity from floats in the Southern Ocean. The program below obtains the data from the BGC Argo database (Argo, 2021) and processes it. Simple data quality control, interpolation, biogeochemical calculations, and data binning occur. The processed float data is located in the folder 'Processed Argo Transects'.</p> <p>argo_daily_npp_20220815.py: This code using processed Argo float data that contains oxygen and particle backscatter measurements to infer net primary production. The code combines the float that meet the criteria of sampling at all local hours of the day throughout its lifetime. Then, it constructs diel cycles from this data by finding the median value of each hour and uses the code from Johnson and Bif (2021), which is a modified version from Barone et al. (2019). The algorithm used to convert particle backscatter to particulate organic carbon is from Graff et al. (2015). We assume that dissolved primary productivity accounts for 30% of total primary productivity (Moran et al., 2022).</p> <p>argo_daily_npp_bootstrap_20220815.py: This code using processed Argo float data that contains co-located oxygen and particle backscatter measurements to infer net primary production. This code is very similar to argo_daily_npp_20220815.py but randomly samples a subset of the co-located profiles at different sample sizes before calculating net primary productivity. Productivity is calculated at each sample size 1000 times. The results of this analysis is located in the folder 'Bootstrapped Results'. </p> <p>More details can be found in the code itself. </p> <p><strong>Data Descriptions: </strong></p> Data from 'Processed Argo Transects' Folder | Description for each variable <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>depth</td> <td>Average depth of depth bin</td> <td>m</td> </tr> <tr> <td>mid_depth</td> <td>Center of depth bin</td> <td>m</td> </tr> <tr> <td>pressure</td> <td>Average pressure in depth bin</td> <td>dbar</td> </tr> <tr> <td>profile_index</td> <td>Profile number or index</td> <td> </td> </tr> <tr> <td>profile_longitude</td> <td>Average longitude of profile</td> <td>degE</td> </tr> <tr> <td>profile_latitude</td> <td>Average latitude of profile</td> <td>degN</td> </tr> <tr> <td>profile_time</td> <td>Average UTC time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_time</td> <td>Average local time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_hour</td> <td>The hour of the local timestamp</td> <td> </td> </tr> <tr> <td>salinity</td> <td>Seawater salinity</td> <td>PSU</td> </tr> <tr> <td>temperature </td> <td>Seawater temperature</td> <td>degC</td> </tr> <tr> <td>oxygen</td> <td>Dissolved oxygen concentration</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_saturation</td> <td>Saturated dissolved oxygen concentration calculated from the Garcia and Gordon (1992) equation.</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_anom</td> <td>The difference between observed dissolved oxygen concentration and saturated oxygen </td> <td>umol kg-1</td> </tr> <tr> <td>bbp470</td> <td>Optical backscatter coefficient at 470 nm. Particulate organic carbon is calculated in argo_daily_npp_20220815.py</td> <td>m-1</td> </tr> </tbody> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>wmo</td> <td>WMO number of float</td> <td> </td> </tr> <tr> <td>profile_index</td> <td>Profile index or profile number taken by float</td> <td> </td> </tr> <tr> <td>profile_latitude</td> <td>Average profile latitude</td> <td>degN</td> </tr> <tr> <td>profile_longitude</td> <td>Average profile longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>fod</td> <td>Fraction of day</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Sinusoidal curve fit to oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc</td> <td>Sinusoidal curve fit to particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>oxy_med</td> <td>Hourly median oxygen</td> <td>mol m-3</td> </tr> <tr> <td>oxy_sem</td> <td>Hourly standard error of oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc_med</td> <td>Hourly median particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>poc_sem</td> <td>Hourly standard error of particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N, co-located)</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N) </td> <td> </td> </tr> <tr> <td>depth</td> <td>Depth of profile</td> <td>m</td> </tr> <tr> <td>zeu</td> <td>1% euphotic depth from Lee et al. (2013) algorithm from NASA (2022) L3 satellite products. </td> <td>m</td> </tr> <tr> <td>n_profiles_bpp</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>n_profiles_oxy</td> <td>Number of oxygen profiles</td> <td> </td> </tr> <tr> <td>n_floats_bbp</td> <td>Number of floats with backscatter measurements</td> <td> </td> </tr> <tr> <td>n_floats_oxy</td> <td>Number of floats with oxygen measurements</td> <td> </td> </tr> <tr> <td>gop_do</td> <td>Gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_serr</td> <td>Standard error of gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly oxygen data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly oxygen data</td> <td> </td> </tr> <tr> <td>oxy_sr</td> <td>The calculated sunrise time as a fraction of the day</td> <td> </td> </tr> <tr> <td>oxy_ss</td> <td>The calculated sunset time as a fraction of the day</td> <td> </td> </tr> <tr> <td>gpp_bbp</td> <td>Gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gpp_bbp_serr</td> <td>Standard error of gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_bbp</td> <td>Gross oxygen productivity calculated from gross carbon productivity (gpp_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_bbp_serr</td> <td>Standard error of gross oxygen productivity calculated from gross carbon productivity (gpp_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp</td> <td>Net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp_serr</td> <td>Standard error of net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do</td> <td>Net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do_serr</td> <td>Standard error of net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do_serr)</td> <td>mol m-3 yr-1</td> </tr> </tbody> </table> <table> </table> Data for Fig. S1 | Description for number_of_bbp_profiles_in_each_year.csv and number_of_oxy_profiles_in_each_year.csv <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>year</td> <td>Year</td> <td> </td> </tr> <tr> <td>bbp470</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>oxygen_anom</td> <td>Number of oxygen profiles</td> <td> </td> </tr> </tbody> </table> <table> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>mid_depth</td> <td>Depth of NPP profile</td> <td>m</td> </tr> <tr> <td>mean</td> <td>Mean volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>median</td> <td>Median volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>min</td> <td>Minimum volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>maximum</td> <td>Maximum volumetric 14C-NPP</td> <td>mmol m-3 yr-1</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th><strong>Variable</strong></th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>subset</td> <td>Number of profiles randomly sampled from the co-located dataset</td> <td> </td> </tr> <tr> <td>int_npp_do</td> <td>Euphotic-depth-integrated net primary productivity calculated from oxygen-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>int_npp_bbp</td> <td>Euphotic-depth-integrated net primary productivity calculated from backscatter-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>gop_do_r2</td> <td>R-squared of the sinusoidal curve to the diel cycle of oxygen anomaly</td> <td> </td> </tr> <tr> <td>gpp_bbp_r2</td> <td>R-squared of sinusoidal curve to the diel cycle of particulate organic carbon</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>ROSE</td> <td>Topographic (negative values are below sea level)</td> <td>m</td> </tr> <tr> <td>ETOPO05_Y</td> <td>Latitude</td> <td>degN</td> </tr> <tr> <td>ETOPO05_X</td> <td>Longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>database</td> <td>Database the data was extracted from</td> <td> </td> </tr> <tr> <td>Month</td> <td>Month of NPP measurement</td> <td>month of year</td> </tr> <tr> <td>npp_14c</td> <td>Net primary productivity estimated from the radiocarbon method</td> <td>mmol m-3 y-1</td> </tr> <tr> <td>depth</td> <td>depth of 14C-NPP measurement</td> <td>m</td> </tr> </tbody> </table> <table> </table>
Factors to predict above-ground biomass carbon carrying capacity
<p>The climate data (Mean annual temperature (°C, MAT), mean annual precipitation (mm, MAP), annually accumulated temperature with days ≥ 0°C (°C-days, AAT0), annually accumulated temperature with days ≥ 10°C (°C-days, AAT10), aridity index, and humidity index ), soil properties (soil texture and soil types) and DEM are available from the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn/); The geological elements and hydrological elements data can be found at http://dcc.ngac.org.cn/geologicalData/rest/geologicalData/geologicalDataDetail/402881f75d9bc077015d9bc084160000and https://www.webmap.cn/commres.do?method=result25W; The geomorphology data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/zh-hans/data/63e290d7-7087-462a-acac-50195fba530b/). All data were resampled at 500m resolution.</p>
Estimation of biomass combustion carbon emissions data for 2018 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2018, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2020 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2020, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2019 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2019, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Aboveground plant biomass, 2009-2017. (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/275/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-bnz/501/17. The abstract below was extracted from the Level 0 data package and is included for context: The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes aboveground plant biomass from winter warming, summer warming, and control treatment plots at CiPEHR.
Below ground root biomass, carbon and nitrogen concentrations by depth increments from the Anaktuvuk River Fire site in 2011
Below ground root biomass was measured by depth increments at three sites at and around the Anaktuvuk River Burn: severely burned, moderately burned and unburned. Roots were also analyzed for carbon and nitrogen concentrations.
Summary of below ground root biomass, carbon and nitrogen concentrations from the Anaktuvuk River Fire site in 2011
A summary of below ground root biomass, carbon and nitrogen concentrations, measured at three sites at and around the Anaktuvuk River Burn: severely burned, moderately burned and unburned.
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