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128 results for “carbon biomass”
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Off Plot Soil Incubation By Depth I - Soil Properties and Final Microbial Biomass 2013-2014
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. We investigated C and nitrogen (N) mineralization within the soil profile by incubating soil cores collected adjacent to (but not within) the CiPEHR site. These soil cores spanned the entire active layer and approximately 30 cm of permafrost. Soil cores were separated into 10 cm depth intervals and incubated for 241 days at 15 degC and field moisture was maintained with water additions. This dataset contains data on the bulk soil %C, bulk soil %N, soil bulk density, initial gravimetric water content measured prior to the incubation. Microbial biomass was measured at the end of the incubation and is presented here as well.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): leaf SLA, C, N, P, Ca, delta-13C, delta-15N at peak biomass, 2017
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 carbon (C) and nitrogen (N) elemental and isotope content in leaves collected from winter warming, summer warming, and control treatment plots at CiPEHR.
A Chronosequence of Biomass and Carbon and Nitrogen Stocks Across Boreal Deciduous, Mixed, and Black Spruce Forests in Interior Alaska
This dataset contains forest structure data and estimates of above- and belowground carbon and nitrogen pools for a chronosequence of sites that vary in time after fire. Sites are spread throughout interior Alaska and were selected to represent the range of forest composition, from black spruce (Picea mariana) dominance, to deciduous tree dominance (Betula neoalaskana and Populus tremuloides). Multiple transects were measured in each site
Plant aboveground biomass carbon and nitrogen: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Mangrove diversity enhances plant biomass production and carbon storage in Hainan Island, China
<p>Mangrove forests, one of the highest carbon density ecosystems, are very different from other forests as they occupy saline and tidal habitats. Although previous studies in forests, shrublands, and grasslands have shown a positive effect of biodiversity on plant biomass and carbon storage, it remains unclear whether this relation to biodiversity also exists in mangrove forests. Here, we evaluate the possible effects of mangrove species diversity, structural characteristics, and environmental factors on mangrove biomass production and carbon storage, using survey data from 234 field plots of 30 transects in the mangrove forests along the coastlines of Hainan Island, China, during 2017 and 2018.We found that mangrove species diversity had a positive effect, not only on mangrove biomass production, but also on soil carbon storage. This positive effect was more strongly evident in the forest communities than in either the shrub communities or forest-shrub mixed communities, with the forests type having the biggest mangrove biodiversity and carbon storage. In addition, the diversity effect was affected by structural characteristics, namely, mangrove biomass increased exponentially with tree stem diameter and decreased with tree density. Furthermore, we observed a resource-dependent mediation of the mangrove ecosystem when linking diversity to biomass. The areas with high soil Nitrogen content and Mean annul precipitation (MAP) showed higher mangrove biomass and carbon storage. This suggests that the spatial pattern of mangrove carbon storage and diversity was driven by both climate factors (MAP) and soil fertility (soil N).Our findings suggest that mangrove forests with greater diversity also have higher carbon storage capacities and conservation potential. Thus, biodiversity conservation is crucial for mangrove to mitigate the greenhouse effect. Our findings strengthen the understanding of the diversity effects on mangrove ecosystem services and have important implications for mangrove restoration and conservation.</p>
Measurement report: quantifying source contribution of fossil fuels and biomass-burning black carbon aerosol in the southeastern margin of the Tibetan Plateau
<p>Anthropogenic emissions of Black carbon (BC) aerosol are transported from Southeast Asia to the southwestern Tibetan Plateau (TP) during the pre-monsoon; however, the quantities of BC from different anthropogenic sources and the transport mechanisms are still not well constrained because there have been no high-time-resolution BC source apportionments. Intensive measurements were taken in a transport channel for pollutants from Southeast Asia to the southeastern margin of TP during the pre-monsoon to investigate the influences of fossil fuels and biomass burning on BC. A receptor model coupled multi-wavelength absorption with aerosol species concentrations was used to retrieve site-specific Ångström exponents (AAE) and mass absorption cross-sections (MAC) for BC. An ‘aethalometer model’ that used those values showed that biomass burning had a larger contribution to BC mass than fossil fuels (BCbiomass = 57% versus BCfossil = 43%). The potential source contribution function indicated that BCbiomass was transported to the site from northeastern India and northern Burma, The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) model indicated that 40% of BCbiomass originated from Southeast Asia, while the high BCfossil was transported from the southwest of sampling site. A radiative transfer model indicated that the average atmospheric direct radiative effects (DRE) of BC was +4.6 ± 2.4 W m<sup>-2</sup> with +2.5 ± 1.8 W m<sup>-2</sup> from BCbiomass and +2.1 ± 0.9 W m<sup>-2</sup> from BCfossil. The DRE of BCbiomass and BCfossil produced heating rates of 0.07 ± 0.05 and 0.06 ± 0.02 K day<sup>-1</sup>, respectively. This study provides insights into sources of BC over a transport channel to the southeastern TP and the influence of the cross-border transportation of biomass burning emissions from Southeast Asia during the pre-monsoon.</p>
Dataset on soil and soil microbial biomass carbon, nitrogen, and phosphorus stoichiometry
<p>Dataset on soil and soil microbial biomass carbon, nitrogen, and phosphorus stoichiometry. This dataset is compiled for for the scientific paper entitled "Interpreting stoichiometric homeostasis and flexibility of soil microbial biomass carbon, nitrogen, and phosphorus" (doi: 10.1016/j.ecolmodel.2022.110018).</p>
Data for the journal article "Brown Carbon from Biomass Burning Imposes Strong Circum-Arctic Warming"
<p>The data for the 3 figures in the journal article "Brown Carbon from Biomass Burning Imposes Strong Circum-Arctic Warming"</p>
Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale
<p>R code, raw datasets, and predicted global maps of soil microbial biomass C, N, and P and their stoichiometric ratios at 0-30 cm depth.</p> <p>When using any of these layers, please cite: Gao et al., Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale (2022). Global Change Biology. DOI: 10.1111/gcb.16374</p>
Data from: The distribution of tree biomass carbon within the pacific coastal temperate rainforest, a disproportionally carbon dense forest
<p>Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha<sup>-1</sup> or 218 (SD = 169) Mg C ha<sup>-1</sup>. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% - 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history.</p>
Synthetic Pelagic Biomass Size Spectra of the Tropical and Subtropical Atlantic - biovolume and carbon biomass data
<p>Synthetic Pelagic Biomass Size Spectra of the Tropical and Subtropical Atlantic</p> <p>Normalized size spectra data are presented for (a) biovolume and (b) for carbon contents for the following ecosystem components: Phytoplankton, zooplankton and micronekton</p> <p>Dataset Biovolume_NBSS contains the following variables, three heading lines</p> <table> <tbody> <tr> <td> <p>COLUMN</p> </td> <td> <p>HEADER</p> </td> <td> <p>DESCRIPTION</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>ConsecutiveNumber</p> </td> <td> <p>Control number</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>Cruise</p> </td> <td> <p>Cruise name</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>Reference</p> </td> <td> <p>Cruise/data record reference</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>CruiseID</p> </td> <td> <p>ID</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>StationID</p> </td> <td> <p>ID</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>Net_index</p> </td> <td> <p>ID, opt.</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>Date</p> </td> <td> <p>YYYY-MM-DD</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>Longitude</p> </td> <td> <p>Position, decimal degrees</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>Latitude</p> </td> <td> <p>Position, decimal degrees</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>Target organisms</p> </td> <td> <p>Phytoplankton</p> <p>Zooplankton</p> <p>Detritus + zooplankton (only for UVP)</p> <p>Mesopelagic fishes</p> <p>invMicronekton – invertebrate micronekton only</p> <p>totMicronekton – mesopelagic fishes + invertebrate Micronekton</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>Gear</p> </td> <td> <p>Gear applied</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>Operation mode</p> </td> <td> <p>Gear operation mode</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>Catching Depth max [m]</p> </td> <td> <p>Catching depth maximum</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>Catching Depth min [m]</p> </td> <td> <p>Catching depth minimum</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>Biomass determination</p> </td> <td> <p>Biomass Determination method</p> </td> </tr> <tr> <td> <p>16</p> </td> <td> <p>Contact Person</p> </td> <td> <p>Contact person</p> </td> </tr> <tr> <td> <p>17</p> </td> <td> <p>Day/night</p> </td> <td> <p>Sampling time</p> </td> </tr> <tr> <td> <p>18</p> </td> <td> <p>Region</p> </td> <td> <p>Region affiliation</p> </td> </tr> <tr> <td> <p>19-74</p> </td> <td>mm3 m-3 mm-3</td> <td> <p>Biovolume data normalized, additionally with reference to "Size class interval (mm-mm]" and "log mm3/individual"</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>Dataset Carbon_NBSS contains the following variables, three heading lines</p> <table> <tbody> <tr> <td> <p>COLUMN</p> </td> <td> <p>HEADER</p> </td> <td> <p>DESCRIPTION</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>ConsecutiveNumber</p> </td> <td> <p>Control number</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>Cruise</p> </td> <td> <p>Cruise name</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>Reference</p> </td> <td> <p>Cruise/data record reference</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>CruiseID</p> </td> <td> <p>ID</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>StationID</p> </td> <td> <p>ID</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>Net_index</p> </td> <td> <p>ID, opt.</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>Date</p> </td> <td> <p>YYYY-MM-DD</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>Longitude</p> </td> <td> <p>Position, decimal degrees</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>Latitude</p> </td> <td> <p>Position, decimal degrees</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>target organisms</p> </td> <td> <p>Phytoplankton</p> <p>Zooplankton</p> <p>Mesopelagic fishes</p> <p>invMicronekton – invertebrate micronekton only</p> <p>totMicronekton – mesopelagic fishes + invertebrate Micronekton</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>Gear</p> </td> <td> <p>Gear applied</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>Operation mode</p> </td> <td> <p>Gear operation mode</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>Catching Depth max [m]</p> </td> <td> <p>Catching depth maximum</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>Catching Depth min [m]</p> </td> <td> <p>Catching depth minimum</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>Biomass determination</p> </td> <td> <p>Biomass Determination method</p> </td> </tr> <tr> <td> <p>16</p> </td> <td> <p>Contact Person</p> </td> <td> <p>Contact person</p> </td> </tr> <tr> <td> <p>17</p> </td> <td> <p>filtered volume [m3]</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>18</p> </td> <td> <p>Day/night</p> </td> <td> <p>Sampling time</p> </td> </tr> <tr> <td> <p>19</p> </td> <td> <p>SST (C)</p> </td> <td> <p>In situ SST</p> </td> </tr> <tr> <td> <p>20</p> </td> <td> <p>Temperature (C)</p> </td> <td> <p>In situ temperature</p> </td> </tr> <tr> <td> <p>21</p> </td> <td> <p>Salinity</p> </td> <td> <p>In situ salinity (PSU)</p> </td> </tr> <tr> <td> <p>22</p> </td> <td> <p>Oxygen (umol/kg)</p> </td> <td> <p>In situ oxygen (µmol/kg)</p> </td> </tr> <tr> <td> <p>23</p> </td> <td> <p>Region</p> </td> <td> <p>Region affiliation</p> </td> </tr> <tr> <td> <p>24-79</p> </td> <td>gC m-3 g-1C</td> <td> <p>Carbon biomass data normalized, additionally with reference to " Size class number" and "</p> <p>Exponent"</p> </td> </tr> </tbody> </table> <p> </p>
Inverted microscopy image dataset -- Carbon biomass of microplankton assemblages in southern Patagonian fjords and channels
<p>Images of main microplanktonic items (folders) obtained under inverted (mostly) and electronic microscope used to estimate biovolume and carbon biomass. Scale bar is shown on each picture and label of each image indicate the station ID (St.) and sampling depth (m). A table is provided with biovolume and equivalent spherical diameter calculations for each planktonic item. </p>
Abiotic and biotic drivers of tree trait effects on soil microbial biomass and soil carbon concentration
<p>Forests are critical ecosystems to understand the global carbon budget, due to their carbon sequestration potential in both above- and belowground compartments, especially in species-rich forests. Soil carbon sequestration is strongly linked to soil microbial communities, and this link is mediated by the tree community, likely due to modifications of micro-environmental conditions (i.e., biotic conditions, soil properties, and microclimate). We studied soil carbon concentration and the soil microbial biomass of 180 local neighborhoods along a gradient of tree species richness ranging from 1 to 16 tree species per plot in a Chinese subtropical forest experiment (BEF-China). Tree productivity and different tree functional traits were measured at the neighborhood level. We tested the effects of tree productivity, functional trait identity and dissimilarity on soil carbon concentrations, and their mediation by the soil microbial biomass and micro-environmental conditions. Our analyses showed a strong positive correlation between soil microbial biomass and soil carbon concentrations. Besides, soil carbon concentration increased with tree productivity and tree root diameter while it decreased with litterfall C:N content. Moreover, tree productivity and tree functional traits (e.g. root fungal association and litterfall C:N ratio) modulated micro-environmental conditions with substantial consequences for soil microbial biomass. We also showed that soil history and topography should be considered in future experiments and tree plantations, as soil carbon concentrations were higher where historical (i.e., at the beginning of the experiment) carbon concentrations were high, themselves being strongly affected by the topography. Altogether, these results imply that the quantification of the different soil carbon pools is critical for understanding microbial community–soil carbon stock relationships and their dependence on tree diversity and micro-environmental conditions.</p>
Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.1
<p>This data is based on the paper: Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE 1.1 (unpublished).</p>
Tree biomass does not correlate with soil carbon stocks in forest-tundra ecotones along a 1100 km latitudinal gradient in Norway
Due to climate warming, forests are expanding to higher elevations and latitudes at the expense of tundra vegetation. While the subsequent increase in aboveground biomass is well-documented, there is much speculation regarding the effects on soil organic carbon (SOC) stocks. To provide insight into the consequences of tree encroachment into treeless tundra, we sampled SOC stocks across 36 forest-tundra ecotones along a 1100 km latitudinal gradient in Norway. Our results show that SOC stocks vary greatly within, as well as among treeline ecotones, and that SOC stocks do not correlate with tree biomass and tree species. SOC stocks do increase with temperature, and vary with slope steepness, slope aspect, and soil parent material. Applying a 'space-for-time substitution' perspective, our findings suggest that tree encroachment into tundra is unlikely to have immediate consequences for SOC stocks.
Long-term warming of a forest soil reduces microbial biomass and its carbon and nitrogen use efficiencies
<p>Global warming impacts biogeochemical cycles in terrestrial ecosystems, but it is still unclear how the simultaneous cycling of carbon (C) and nitrogen (N) in soils could be affected in the longer-term. Here, we evaluated how 14 years of soil warming (+4°C) affected the soil C and N cycle across different soil depths and seasons in a temperate mountain forest. We used H<sub>2</sub><sup>18</sup>O incorporation into DNA and <sup>15</sup>N isotope pool dilution techniques to determine gross rates of C and N transformation processes. Our data showed different warming effects on soil C and N cycling, and these were consistent across soil depths and seasons. Warming decreased microbial biomass C (−22%), but at the same time increased microbial biomass-specific growth (+25%) and respiration (+39%), the potential activity of β-glucosidase (+31%), and microbial turnover (+14%). Warming reduced gross rates of protein depolymerization (−19%), but stimulated gross N mineralization (+63%) and the potential activities of N-acetylglucosaminidase (+106%) and leucine-aminopeptidase (+46%), and had no impact on gross nitrification (+1%). Microbial C and N use efficiencies were both lower in the warming treatment (−15% and −17%, respectively). Overall, our results suggest that long-term warming drives soil microbes to incorporate less C and N into their biomass (and necromass), and to release more inorganic C and N to the environment, causing lower soil C and N storage in this forest, as indicated by lower soil C and total N contents. The decreases in microbial CUE and NUE were likely triggered by increasing microbial P constraints in warmed soils, limiting anabolic processes and microbial growth and promoting pervasive losses of C and N from the soil.</p>
Data of cell size, cell carbon content, and biomass of dinoflagellates and diatoms in the oceanic ecosystem of the Southern Gulf of Mexico
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Forest composition drives bryophyte biomass, carbon and nitrogen storage in the boreal-temperate ecotone
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Data from: The distribution of tree biomass carbon within the pacific coastal temperate rainforest, a disproportionally carbon dense forest
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Soil physical, biological, chemical, and carbon data and cover crop biomass data from Sac Valley almond orchard comparing multiple cover crop compositions with resident vegetation for effects on soil health and nematodes
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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.