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27 results for “Carbon turnover”
Simulations for: The anthropogenic imprint on temperate and boreal forest demography and carbon turnover
<p>LPJ-GUESS model output underlying analysis in:<br> Thomas A. M. Pugh, Rupert Seidl, Daijun Liu, Mats Lindeskog, Louise P. Chini, Cornelius Senf, The anthropogenic imprint on temperate and boreal forest demography and carbon turnover, Global Ecology and Biogeography. 10.1111/geb.13773</p> <p>For a full description of the simulations, please refer to the above paper. If using the data please cite this dataset and the publication above.</p> <p>Files are provided as netcdf4 files. Basic metadata is included in the headers of the individual files.</p> <p># Simulation types<br> _standard_nat_2014 -> Best estimate simulation under natural disturbance. Averaging period 2001-2014<br> _high_nat_2014 -> Upper estimate simulation under natural disturbance. Averaging period 2001-2014<br> _low_nat_2014 -> Lower estimate simulation under natural disturbance. Averaging period 2001-2014<br> _standard_nat_1990 -> Best estimate simulation under natural disturbance. Averaging period 1961-1990<br> _standard_natcc_2014 -> Best estimate simulation based on closed-canopy forest area calculations under natural disturbance. Averaging period 2001-2014<br> _standard_anthro_2014 -> Best estimate simulation under natural and anthropogenic disturbance. Averaging period 2001-2014<br> _high_anthro_2014 -> Upper estimate simulation under natural and anthropogenic disturbance. Averaging period 2001-2014<br> _low_anthro_2014 -> Lower estimate simulation under natural and anthropogenic disturbance. Averaging period 2001-2014<br> _site_recovery_Eurasia_nodist -> Site simulations for 4 Eurasian sites looking at the successional sequence. 800 years long under constant spinup environmental conditions.<br> _site_recovery_America_nodist -> Site simulations for 5 North American sites looking at the successional sequence. 800 years long under constant spinup environmental conditions.</p> <p># Variables (for units see netcdf metadata)<br> Cveg -> Live vegetation carbon<br> Clitter -> Litter carbon<br> Csoil -> Soil carbon<br> LAI -> Leaf area index<br> NPP -> Net primary productivity<br> GPP -> Gross primary productivity<br> distprob -> Natural disturbance probability<br> age -> Stand age structure<br> temprange -> Annual temperature range (based on monthly means)<br> wooddensity -> community mean wood density</p> <p>Note:<br> All nat simulations assume that forest covers the whole grid cell.<br> All anthro simulations assume that forest only covers the primary and secondary fractions of the grid cell, as defined in the LUH2 dataset, however values are given relative to the whole grid cell. I.e. value_on_forest_area * (primary_area_fraction+secondary_area_fraction).</p>
Simulations from the LPJmL3.5 dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the LPJmL3.5 dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the ORCHIDEE dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the ORCHIDEE dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for mortality, leaf phenological turnover and fine root phenological turnover were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the JULES dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the JULES dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 1.875 x 1.25 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the LPJ-GUESS dynamic global vegetation model v3.0 for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the LPJ-GUESS dynamic global vegetation model v3.0 are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for mortality, leaf phenological turnover and fine root phenological turnover were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the SEIB-DGVM dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the SEIB-DGVM dynamic global vegetation model. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Protists regulate microbially-mediated organic carbon turnover in soil aggregates
<p>Soil protists, the major predator of bacteria and fungi, shape the taxonomic and functional structure of soil microbiome via trophic regulation. However, how trophic interactions between protists and their prey influence microbially mediated soil organic carbon turnover remains largely unknown. Here, we investigated the protistan communities and microbial trophic interactions across different aggregates-size fractions in agricultural soil with long-term fertilization regimes. Our results showed that aggregate sizes significantly influenced the protistan community and microbial hierarchical interactions. Bacterivores were the predominant protistan functional group and were more abundant in macroaggregates and silt + clay than in microaggregates, while omnivores showed an opposite distribution pattern. Furthermore, partial least square path modeling revealed positive impacts of omnivores on the C-decomposition genes and soil organic matter (SOM) contents, while bacterivores displayed negative impacts. Microbial trophic interactions were intensive in macroaggregates and silt + clay but were restricted in microaggregates, as indicated by the intensity of protistan-bacterial associations and network complexity and connectivity. Cercozoan taxa were consistently identified as the keystone species in SOM degradation-related ecological clusters in macroaggregates and silt + clay, indicating the critical roles of protists in SOM degradation by regulating bacterial and fungal taxa. Chemical fertilization had a positive effect on soil C sequestration through suppressing SOM degradation-related ecological clusters in macroaggregate and silt + clay. Conversely, the associations between the trophic interactions and SOM contents were decoupled in microaggregates, suggesting limited microbial contributions to SOM turnovers. Our study demonstrates the importance of protists-driven trophic interactions on soil C cycling in agricultural ecosystems.</p>
Archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil
<p>This is the archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil; submitted to the Journal Science of the Total Environment.</p>
Biotic and abiotic factors controlling spatial variation of mean carbon turnover time in forest soil
<p><strong>Data description</strong></p> <p>This dataset is associated with the paper "Biotic and abiotic factors controlling spatial variation of mean carbon turnover time in forest soil". This dataset includes the soil organic carbon turnover time (τ<sub>soc</sub>) based on radiocarbon signals at the global and regional scales.</p> <p><strong>Global synthesis</strong></p> <p>The analysis of global soil radiocarbon data was done using the International Soil Radiocarbon Database (ISRaD v.1.0; Lawrence et al., 2020). ISRaD is an open source data with the records of 8 biomes (<em>i.e.,</em> forest, grassland, cropland, shrubland, savanna, tundra, permafrost, and others). Since we focus on the turnover time of SOC (τ<sub>soc</sub>) based on radiocarbon occurring in the natural forest ecosystem, so we limited our study to data from soil depth within 200 cm in the forest ecosystem. We built a database of radiocarbon-based soil turnover time (τ<sub>soc</sub>) of 1897 soil samples from 245 forest locations worldwide. It covers a wide geographical range (35.65 <sup>o</sup>S ‒ 68.8 <sup>o</sup>N; 159.64 °W – 173.57 °E) and a broad nature climate zone (-5.2 <sup>o</sup>C to 40.0<sup> o</sup>C; 58.66 mm to 6900 mm) over the half a century (1958 – 2017). Where forest age is missing, we derived it from The global forest age dataset (GFAD v 1.0; Poulter et al., 2018). The GFAD database represents the distribution of forest stand age during 2000 – 2010 years.</p> <p><strong>Regional analysis</strong></p> <p><strong> Soil sampling in forests across the Eastern Asian Monsoon region</strong></p> <p>We sampled soils from twelve permanent forest plots in five mountains in the Eastern Asian Monsoon region (Table 1, Figure 1, and S1). Five of the twelve forest plots are members of the Smithsonian Forest Global Earth Observatory network (ForestGEO, https://forestgeo.si.edu/; Anderson-Teixeira et al., 2018; Chu et al., 2019). The other seven forest plots are members of China's National Ecosystem Research Network (CNERN, http://www.cern.ac.cn). In the Eastern Asian Monsoon region, more than half of the total annual rainfall occurs in the summer season (<em>i.e.</em>, June, July, and August) (Tardif et al., 2020; Tian et al., 2003). We estimated the τ<sub>soc</sub> by the radiocarbon dating analysis of up to 100 cm of soil depth in each forest plot. For each plot, we separated the whole soil increment into the surface (0 – 30 cm) and deep (30 – 100 cm) layers to test the radiocarbon signal due to the high financial cost. Details of the location, climate, and vegetation for each sampling site are provided in Table 1 and Supplementary Text 1.</p> <p>Nine soil cores (2.5 cm in diameter) were collected in each forest plot, and a depth of 10 cm separated each soil column from 0 to 100 cm. In total, 108 soil profiles were sampled across the 12 forest plots. The accumulated aboveground litter was collected and measured in an area of 50 cm × 50 cm in each forest plot, with three replicates adjacent to each soil profile. Fine roots (< 2 mm in diameter) were manually picked from soil samples. The litter and root samples were dried at 65 °C for 48 hours using an oven and then weighed for dry mass. The elevation and other geographic information of each forest plot were measured during the soil sampling. </p>
Data for: Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents
<p class="MsoNormal"><strong><span>Aim: </span></strong><span>Theoretically, woody biomass turnover time (τ</span><span>) </span><span>quantified using </span><span>outflux (i.e., tree mortality)</span><span> predicts biomass dynamics better than using influx (i.e., productivity).</span> <span>This study aims at using forest inventory data to empirically test the outflux approach and generate a spatially explicit understanding of woody </span><span>τ</span><span> </span><span>in mature forests. We further compared woody τ</span><span> </span><span>estimates with dynamic global vegetation models (DGVMs) and with a data assimilation product of C stocks and fluxes - CARDAMOM.</span></p> <p class="MsoNormal"><strong><span>Location:</span></strong> <span>Continents</span></p> <p class="MsoNormal"><strong><span>Time period:</span></strong> <span>Historic from </span><span>1951 to 2018</span></p> <p class="MsoNormal"><strong><span>Major taxa studied:</span></strong> <span>Trees and Forests</span></p> <p class="MsoNormal"><strong><span>Methods: </span></strong><span>We compared the approaches of using outflux vs. influx for estimating woody <span>τ</span></span><span> and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody <span>τ</span></span><span> and generated a spatially explicit map of woody <span>τ</span></span><span> at a 0.25-degree resolution across continents using machine learning. We further examined whether six DGVMs and CARDAMOM generally captured the observational pattern of woody <span>τ</span></span><span>. </span></p> <p class="MsoNormal"><strong><span>Results: </span></strong><span>Woody <span>τ</span></span><span> quantified by the </span><span>outflux approach </span><span>better (with R<sup>2</sup> 0.4-0.5) predicted the biomass accumulation rates than the influx approach (with R<sup>2</sup> 0.1-0.4) across continents. We found large spatial variations of woody <span>τ</span></span><span> for mature forests, with highest values </span><span>in temperate forests (</span><span>98.8 ± 2.6 y</span><span>) followed by boreal forests (73</span><span>.9 ± </span><span>3</span><span>.</span><span>6</span><span> y</span><span>) and tropical forests. The map of woody <span>τ</span></span><span> extrapolated from plot data showed higher values in wetter eastern and pacific coast USA, Africa and eastern Amazon. Climate (temperature and aridity index) and vegetation structure (tree density and forest age) were the dominant drivers of woody <span>τ</span></span><span> across continents. The highest woody <span>τ</span></span><span> in temperate forests were not captured by either DGVMs or CARDAMOM. </span></p> <p class="MsoNormal"><strong><span>Main conclusions:</span></strong><span> Our study </span><span>empirically demonstrated the preference of using outflux over influx to estimate woody <span>τ</span></span><span> for predicting biomass accumulation rates. The spatially explicit map of woody </span><span>τ</span><span> and the underlying drivers provide </span><span>valuable information to improve the representation of </span><span>forest demography and </span><span>carbon turnover processes in DGVMs.</span></p>
Species turnover and climates co-dominate the carbon–water relationship in grasslands along an elevational gradient
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Data for: Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents
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Protists regulate microbially-mediated organic carbon turnover in soil aggregates
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Simulations from the CABLE-POP land surface model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the CABLE-POP land surface model are provided for two global simulations. One forced by CRU-NCEP v7 climate data for the period 1901-2015 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Landcover was fixed at 1700 values throughout the simulations. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Observation‐based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models
<p><span>Soil heterotrophic respiration (R<sub>h</sub>) refers to the flux of CO2 released from soil to atmosphere as a result of organic matter decomposition by soil microbes and fauna. As one of the major fluxes in the global carbon cycle, the estimation of global R<sub>h</sub> still exists large uncertainties, which further limited our current understanding of the carbon accumulation in soils. Here, we applied a Random Forest algorithm to create a global dataset of soil R<sub>h</sub>, by linking 761 field observations with both abiotic and biotic predictors. We estimated that the global R<sub>h</sub> was 48.8 ± 0.9 Pg C yr<sup>-1</sup> for 1982–2018, which was 16% less than the ensemble mean (58.6 ± 9.9 Pg C yr<sup>-1</sup>) of 16 terrestrial ecosystem models. By integrating our observational R<sub>h</sub> with independent soil carbon stock datasets, we obtained a global mean soil carbon turnover time of 38.3 ± 11 yr. Using observation-based turnover times as a constraint, we found that terrestrial ecosystem models simulated faster carbon turnovers, leading to a 30% (74 Pg C) underestimation of terrestrial ecosystem carbon accumulation for the past century, which was especially pronounced at high latitudes. This underestimation is equivalent to 45% of the total carbon emissions (164 Pg C) caused by global land use change at the same time. Our analyses highlight the need to constrain ecosystem models using observation-based and locally adapted R<sub>h</sub> values to obtain reliable predictions of the carbon sink capacity of terrestrial ecosystems. </span></p>
Data for: Exogenous carbon turnover within the soil food web strengthens soil carbon sequestration through microbial necromass accumulation
<p>Exogenous carbon turnover within soil food web is important in determining the trade-offs between soil organic carbon (SOC) storage and carbon emission. However, it remains largely unknown how soil food web influences carbon sequestration through mediating the dual roles of microbes as decomposers and contributors, hindering our ability to develop policies for soil carbon management. Here, we conducted a 13C-labeled straw experiment to demonstrate how soil food web regulated the residing microbes to influence the soil carbon transformation and stabilization process after 11 years no-tillage. Our work demonstrated that soil fauna, as a "temporary storage container", indirectly influenced the SOC transformation processes and mediated the SOC sequestration through feeding on soil microbes. Soil biota communities acted as both drivers of and contributors to SOC cycling, with 32.0% of exogenous carbon being stabilizing in the form of microbial necromass as "new" carbon. Additionally, the proportion of mineral-associated organic carbon and particulate organic carbon showed that the "renewal effect" driven by the soil food web promoted the SOC to be more stable. Our study clearly illustrated that soil food web regulated the turnover of exogenous carbon inputs and mediated soil carbon sequestration through microbial necromass accumulation.</p>
Exhaled Carbon Monoxide and Red Blood Cell Turnover
ClinicalTrials.gov study NCT03288233. IPD Sharing: NO. Countries: 1. Publications: 3.
Observation‐based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models
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Data for: Exogenous carbon turnover within the soil food web strengthens soil carbon sequestration through microbial necromass accumulation
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Data from: The decline in plant biodiversity slows down soil carbon turnover under increasing nitrogen deposition in a temperate steppe
1. Nitrogen (N) deposition not only alters the physiological processes of individual plant, but also leads to worldwide biodiversity loss. However, little is known about how the hierarchical responses from individual physiological processes to plant community structure would have cascading effects on soil carbon (C) cycling. 2. Here, we assessed whether changes in plant chemistry and community composition under increasing N input would affect the turnover rate of litter layer and soil C loss via heterotrophic respiration (Rh) in a temperate grassland. 3. We showed that more than a decade's N addition significantly decreased plant species richness, litter layer turnover rate and Rh. The 13C-NMR results showed that, for individual species, N addition either increased the abundance of recalcitrant C groups such as Alkyl and Methoxyl, or decreased labile C groups such as Carbohydrate, resulting in decreases in Carbohydrate C to Methoxyl C ratio (CC/MC) for most species. Our data also showed that with the increase in N deposition, the abundance of relatively high degradable dominant species, such as A. cristatum and A. frigida declined rapidly, and the relatively recalcitrant species such as P. bifurca and L. chinensis become dominate. Changes in individual species' chemistry and plant community composition significantly decreased litter quality at community level, as indicated by the lower community level CC/MC at higher N addition rates. 4. The result of step-AIC model selection further found that plant diversity loss and the decrease in community level CC/MC jointly best explained the decrease in Rh after N addition, and further relative importance partition result showed that these two factors respectively contributed 65.1% and 34.9% of the explained variation. 5. Overall, we demonstrated that changes in plant chemistry and diversity loss due to N addition reduced the quality of plant C input to soil, which further slowed down litter layer turnover rate and inhibited soil heterotrophic respiration. Our study complements the intermediate links of how shifts in plant community structure regulates soil C cycle under global changes.
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