scholarly journals Toward a Predictive Understanding of the Response of Belowground Microbial Carbon Turnover to Climate Change Drivers in a Boreal Peatland

2018 ◽  
Author(s):  
Joel Kostka ◽  
Jeffrey P. Chanton ◽  
Christopher W. Schadt
Keyword(s):  
Climate Change ◽  
Carbon Turnover ◽  
Microbial Carbon ◽  
Predictive Understanding ◽  
Climate Change Drivers

Spatial variability of topsoil δ13C across Qinghai-Tibet Plateau

2021 ◽  
Author(s):  
Yunsen Lai ◽  
Shaoda Li ◽  
Xiaolu Tang ◽  
Xinrui Luo ◽  
Liang Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Spatial Variability ◽  
Soil Carbon ◽  
Tibet Plateau ◽  
Learning Approach ◽  
Carbon Turnover ◽  
Machine Learning Approach ◽  
Qinghai Tibet Plateau ◽  
Soil Carbon Turnover

<p>Soil carbon isotopes (δ<sup>13</sup>C) provide reliable insights at the long-term scale for the study of soil carbon turnover and topsoil δ<sup>13</sup>C could well reflect organic matter input from the current vegetation. Qinghai-Tibet Plateau (QTP) is called “the third pole of the earth” because of its high elevation, and it is one of the most sensitive and critical regions to global climate change worldwide. Previous studies focused on variability of soil δ<sup>13</sup>C at in-site scale. However, a knowledge gap still exists in the spatial pattern of topsoil δ<sup>13</sup>C in QTP. In this study, we first established a database of topsoil δ<sup>13</sup>C with 396 observations from published literature and applied a Random Forest (RF) algorithm (a machine learning approach) to predict the spatial pattern of topsoil δ<sup>13</sup>C using environmental variables. Results showed that topsoil δ<sup>13</sup>C significantly varied across different ecosystem types (p < 0.05).  Topsoil δ<sup>13</sup>C was -26.3 ± 1.60 ‰ for forest, 24.3 ± 2.00 ‰ for shrubland, -23.9 ± 1.84 ‰ for grassland, -18.9 ± 2.37 ‰ for desert, respectively. RF could well predict the spatial variability of topsoil δ<sup>13</sup>C with a model efficiency (pseudo R<sup>2</sup>) of 0.65 and root mean square error of 1.42. The gridded product of topsoil δ<sup>13</sup>C and topsoil β (indicating the decomposition rate of soil organic carbon, calculated by δ<sup>13</sup>C divided by logarithmically converted SOC) with a spatial resolution of 1000 m were developed. Strong spatial variability of topsoil δ<sup>13</sup>C was observed, which increased gradually from the southeast to the northwest in QTP. Furthermore, a large variation was found in β, ranging from -7.87 to -81.8, with a decreasing trend from southeast to northwest, indicating that carbon turnover rate was faster in northwest QTP compared to that of southeast. This study was the first attempt to develop a fine resolution product of topsoil δ<sup>13</sup>C for QTP using a machine learning approach, which could provide an independent benchmark for biogeochemical models to study soil carbon turnover and terrestrial carbon-climate feedbacks under ongoing climate change.</p>

Subsoil organic carbon turnover is dominantly controlled by soil properties in grasslands across China

2021 ◽  
Author(s):  
Yuehong Shi ◽  
Xiaolu Tang ◽  
Peng Yu ◽  
Li Xu ◽  
Guo Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
Organic Carbon ◽  
Soil Properties ◽  
Soil Carbon ◽  
Carbon Turnover ◽  
Area Index ◽  
Important Indicator ◽  
Environmental Controls ◽  
Biogeochemical Models ◽  
Soc Stock

<p>Soil carbon turnover time (τ, year) is an important indicator of soil carbon stability, and a major factor in determining soil carbon sequestration capacity. Many studies investigated τ in the topsoil or the first meter underground, however, little is known about subsoil τ (0.2 – 1.0 m) and its environmental drivers, while world subsoils below 0.2 m accounts for the majority of total soil organic carbon (SOC) stock and may be as sensitive as that of the topsoil to climate change. We used the observations from the published literatures to estimate subsoil τ (the ratio of SOC stock to net primary productivity) in grasslands across China and employed regression analysis to detect the environmental controls on subsoil τ. Finally, structural equation modelling (SEM) was applied to identify the dominant environmental driver (including climate, vegetation and soil). Results showed that subsoil τ varied greatly from 5.52 to 702.17 years, and the mean (± standard deviation) subsoil τ was 118.5 ± 97.8 years. Subsoil τ varied significantly among different grassland types that it was 164.0 ± 112.0 years for alpine meadow, 107.0 ± 47.9 years for alpine steppe, 177.0 ± 143.0 years for temperate desert steppe, 96.6 ± 88.7 years for temperate meadow steppe, 101.0 ± 75.9 years for temperate typical steppe. Subsoil τ significantly and negatively correlated (p < 0.05) with vegetation index, leaf area index and gross primary production, highlighting the importance of vegetation on τ. Mean annual temperature (MAT) and precipitation (MAP) had a negative impact on subsoil τ, indicating a faster turnover of soil carbon with the increasing of MAT or MAP under ongoing climate change. SEM showed that soil properties, such as soil bulk density, cation exchange capacity and soil silt, were the most important variables driving subsoil τ, challenging our current understanding of climatic drivers (MAT and MAP) controlling on topsoil τ, further providing new evidence that different mechanisms control topsoil and subsoil τ. These conclusions demonstrated that different environmental controls should be considered for reliable prediction of soil carbon dynamics in the top and subsoils in biogeochemical models or earth system models at regional or global scales.</p>

Effects of Climate Change Drivers on Nitrous Oxide Fluxes in an Upland Temperate Grassland

Ecosystems ◽  
2010 ◽  
Vol 14 (2) ◽  
pp. 223-233 ◽  
Author(s):  
Amélie A. M. Cantarel ◽  
Juliette M. G. Bloor ◽  
Nicolas Deltroy ◽  
Jean-François Soussana
Keyword(s):  
Climate Change ◽  
Nitrous Oxide ◽  
Temperate Grassland ◽  
Climate Change Drivers

scholarly journals Elevated CO2 reduces a common soybean leaf endophyte

2021 ◽  
Author(s):  
Natalie Christian ◽  
Baldemar Espino Basurto ◽  
Amber Toussaint ◽  
Xinyan Xu ◽  
Elizabeth A. Ainsworth ◽  
...  
Keyword(s):  
Climate Change ◽  
Plant Physiology ◽  
Global Climate ◽  
Fungal Endophytes ◽  
Operational Taxonomic Unit ◽  
Colony Growth ◽  
Natural Systems ◽  
Soybean Leaf ◽  
Predictive Understanding

Free-air CO2 enrichment (FACE) experiments have elucidated how climate change affects plant physiology and production. However, we lack a predictive understanding of how climate change alters interactions between plants and endophytes, critical microbial mediators of plant physiology and ecology. We leveraged the SoyFACE facility to examine how elevated [CO2] affected soybean (Glycine max) leaf endophyte communities in the field. Endophyte community composition changed under elevated [CO2], including a decrease in the abundance of a common endophyte, Methylobacterium sp. Moreover, Methylobacterium abundance was negatively correlated with co-occurring fungal endophytes. We then assessed how Methylobacterium affected the growth of co-occurring endophytic fungi in vitro. Methylobacterium antagonized most co-occurring fungal endophytes in vitro, particularly when it was more established in culture before fungal introduction. Variation in fungal response to Methylobacterium within a single fungal operational taxonomic unit (OTU) was comparable to inter-OTU variation. Finally, fungi isolated from elevated vs. ambient [CO2] plots differed in colony growth and response to Methylobacterium, suggesting that increasing [CO2] may affect fungal traits and interactions within the microbiome. By combining in situ and in vitro studies, we show that elevated [CO2] decreases the abundance of a common bacterial endophyte that interacts strongly with co-occurring fungal endophytes. We suggest that endophyte responses to global climate change will have important but largely unexplored implications for both agricultural and natural systems.

Synergistic effects of four climate change drivers on terrestrial carbon cycling

2020 ◽  
Vol 13 (12) ◽  
pp. 787-793
Author(s):  
Peter B. Reich ◽  
Sarah E. Hobbie ◽  
Tali D. Lee ◽  
Roy Rich ◽  
Melissa A. Pastore ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Cycling ◽  
Synergistic Effects ◽  
Terrestrial Carbon ◽  
Climate Change Drivers

A spatial emergent constraint on the sensitivity of soil carbon turnover time to global warming

2020 ◽  
Author(s):  
Rebecca Varney ◽  
Peter Cox ◽  
Sarah Chadburn ◽  
Pierre Friedlingstein ◽  
Eleanor Burke ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Soil Carbon ◽  
Turnover Time ◽  
Carbon Turnover ◽  
Climate Change Projections ◽  
Emergent Constraint ◽  
Earth System Models ◽  
Soil Carbon Turnover ◽  
Effective Sensitivity

<p>Carbon cycle feedbacks represent large uncertainties on climate change projections, and the response<br>of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil<br>carbon depend on changes in litter and root inputs from plants, and especially on reductions in the<br>turnover time of soil carbon (τ<sub>s</sub>) with warming. The latter represents the change in soil carbon<br>due to the response of soil turnover time (∆C<sub>s,τ</sub>), and can be diagnosed from projections made with<br>Earth System Models (ESMs). It is found to span a large range even at the Paris Agreement Target<br>of 2<sup>◦</sup>C global warming. We use the spatial variability of τ<sub>s</sub> inferred from observations to obtain a<br>constraint on ∆C<sub>s,τ</sub> . This spatial emergent constraint allows us to greatly reduce the uncertainty in<br>∆C<sub>s,τ</sub> at 2<sup>◦</sup>C global warming. We do likewise for other levels of global warming to derive a best<br>estimate for the effective sensitivity of τ<sub>s</sub> to global warming, and derive a q10 equivalent value for<br>heterotrophic respiration.</p>

Methane in Australian agriculture: current emissions, sources and sinks, and potential mitigation strategies

2015 ◽  
Vol 66 (1) ◽  
pp. 1 ◽  
Author(s):  
Damien Finn ◽  
Ram Dalal ◽  
Athol Klieve
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Plant Biomass ◽  
Methane Flux ◽  
Methane Emissions ◽  
Mitigation Strategies ◽  
Extreme Weather Events ◽  
Future Research ◽  
Sources And Sinks ◽  
Climate Change Drivers

Methane is a potent greenhouse gas with a global warming potential ~28 times that of carbon dioxide. Consequently, sources and sinks that influence the concentration of methane in the atmosphere are of great interest. In Australia, agriculture is the primary source of anthropogenic methane emissions (60.4% of national emissions, or 3 260 kt–1 methane year–1, between 1990 and 2011), and cropping and grazing soils represent Australia’s largest potential terrestrial methane sink. As of 2011, the expansion of agricultural soils, which are ~70% less efficient at consuming methane than undisturbed soils, to 59% of Australia’s land mass (456 Mha) and increasing livestock densities in northern Australia suggest negative implications for national methane flux. Plant biomass burning does not appear to have long-term negative effects on methane flux unless soils are converted for agricultural purposes. Rice cultivation contributes marginally to national methane emissions and this fluctuates depending on water availability. Significant available research into biological, geochemical and agronomic factors has been pertinent for developing effective methane mitigation strategies. We discuss methane-flux feedback mechanisms in relation to climate change drivers such as temperature, atmospheric carbon dioxide and methane concentrations, precipitation and extreme weather events. Future research should focus on quantifying the role of Australian cropping and grazing soils as methane sinks in the national methane budget, linking biodiversity and activity of methane-cycling microbes to environmental factors, and quantifying how a combination of climate change drivers will affect total methane flux in these systems.

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