Microbial seasonality promotes soil respiratory carbon emission in natural ecosystems: A modeling study

He, Liyuan; Lai, Chun‐Ta; Mayes, Melanie A.; Murayama, Shohei; Xu, Xiaofeng

doi:10.1111/gcb.15627

Cited by 25 publications

(6 citation statements)

References 75 publications

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“…First, the estimates of MRT based on the assumptions of invariant microbial biomass during the steady state of incubation may be biased. Microbial biomass varies at various temporal scales (Díaz‐Raviña et al, 1995; Edwards & Jefferies, 2013; Wardle, 1998), using average biomass might introduce some bias (He, Lai, et al, 2021). Meanwhile, the turnover of microbial residues or other elements (e.g., nitrogen and phosphorus) might be in different ranges given the dynamic microbial C:N:P:S stoichiometry (Xu et al, 2015); therefore, computing MRT based on C is not a perfect calculation.…”

Section: Discussionmentioning

confidence: 99%

“…Third, environmental factors considered in this study are long‐term averages of vegetation, climate, soil microclimate, and edaphic properties. The simplification of environmental variations in situ with long‐term averages may dampen the spatial variations in MRT due to the effects of environmental variation on microbial activities (He, Lai, et al, 2021). Fourth, the microbial community composition is another potential factor affecting MRT, which was not considered as a controlling factor due to lack of data (Rousk & Bååth, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…Third, environmental factors considered in this study are long-term averages of vegetation, climate, soil microclimate, and edaphic properties. The simplification of environmental variations in situ with long-term averages may dampen the spatial variations in MRT due to the effects of environmental variation on microbial activities (He, Lai, et al, 2021).…”

Section: Limitations and Future Workmentioning

confidence: 99%

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Mapping soil microbial residence time at the global scale

2021

Global Change Biology

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Soil microbes are the fundamental engine for carbon (C) cycling. Microbial residence time (MRT) therefore determines the mineralization of soil organic C, releasing C as heterotrophic respiration and contributing substantially to the C efflux in terrestrial ecosystems. We took use of a comprehensive dataset (2627 data points) and calculated the MRT based on the basal respiration and microbial biomass C. Large variations in MRT were found among biomes, with the largest MRT in boreal forests and grasslands and smallest in natural wetlands. Biogeographic patterns of MRT were found along climate variables (temperature and precipitation), vegetation variables (root C density and net primary productivity), and edaphic factors (soil texture, pH, topsoil porosity, soil C, and total nitrogen). Among environmental factors, edaphic properties dominate the MRT variations. We further mapped the MRT at the global scale with an empirical model. The simulated and observed MRT were highly consistent at plot‐ (R2= .86), site‐ (R2 = .88), and biome‐ (R2 = .99) levels. The global average of MRT was estimated to be 38 (±5) days. A clear latitudinal biogeographic pattern was found for MRT with lower values in tropical regions and higher values in the Arctic. The biome‐ and global‐level estimates of MRT serve as valuable data for parameterizing and benchmarking microbial models.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Limitations and Future Workmentioning

confidence: 99%

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Mapping soil microbial residence time at the global scale

2021

Global Change Biology

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“…In our study, we combined the CLM-Microbe model with three footprint algorithms to better scale up to the landscape-scale CH 4 flux. The CLM-Microbe model represents 17 PFTs for vegetation across the globe (Wang et al, 2019;He et al, 2021a;He et al, 2021b); adding Arctic-and boreal-specific PFTs in its vegetation modules makes it more appropriate to capture Arctic vegetation processes.…”

Section: Introductionmentioning

confidence: 99%

Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems

Wang

Yuan

Arndt

et al. 2022

Front. Environ. Sci.

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Spatial heterogeneity in methane (CH4) flux requires a reliable upscaling approach to reach accurate regional CH4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH4 flux in early growing seasons. The simulated monthly CH4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m−2 s−1 and 0.782 using the DF algorithm, but 0.007 μmol m−2 s−1 and 0.758 using HF and 0.007 μmol m−2 s−1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH4 flux for all five tower domains despite relatively weak differences in simulated CH4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH4 flux at both plot and landscape scales at a high temporal resolution, which should be applied to other landscapes. Integrating land surface models with an appropriate algorithm provides a powerful tool for upscaling CH4 flux in terrestrial ecosystems.

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“…He et al, 2021;Mazzetto et al, 2016), and the input of plant organic matter will have a more signi cant impact on fungi(Wang et al, 2018;Delgado-Baquerizo et al, 2018). However, grazing, trampling and excretion of livestock signi cantly affect the characteristics of plant communities.…”

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confidence: 99%

Effects of grazing on the ecosystems multifunctionality of montane meadow-grassland on the northern slope of the Tianshan Mountains, China

Jiang¹,

Zhang²,

Li³

et al. 2022

Preprint

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Ecosystem multifunctionality (EMF) plays an irreplaceable role in maintaining ecological balance and ensuring human survival and development. However, few studies have focused on the effects of different grazing intensities on EMF, and little is known about changes in the function of multiple ecosystems at different grazing intensities. The study investigated EMF of mountain meadow grasslands on the northern slopes of the Tianshan Mountains in China, by way of a plant community survey coupled with high-throughput sequencing technology. The study calculated the EMF using the mean value method and explore the effects of no grazing (CK), light grazing (LG), and heavy grazing (HG) on grassland EMF. Results showed that HG significantly improved moisture regulation (MR) function (p < 0.05), and decreased soil fertility (SF) (p > 0.05), soil carbon storage (SCS) (p > 0.05), nutrient conversion and cycling (NC) (p > 0.05), grassland productivity (GP) function (p < 0.05), and EMF (p < 0.05). The EMF index of the grassland ecosystem under grazing conditions ranged from 0.3328–0.6018. GP, SCS, and NC functions had the highest contribution to EMF under CK, LG, and HG conditions, respectively. Under grazing conditions, EMF showed a cooperative relationship with SF, SCS, and GP, and the correlation coefficient (r) was between 0.62–0.76 (p < 0.05). At the same time, EMF and grassland water MR showed a relationship of trade-offs (r = 0.68, p < 0.05). The results of structural equation models showed that grazing had a significant effect on EMF directly, and also indirectly through soil fungal diversity. Therefore, reasonable reduction of grazing intensity is the most effective management approach for maintaining ecosystem function. At the same time, grazing plays a key role in maintaining EMF by regulating both above- and below-ground ecosystem functions, primarily through soil fungal diversity. This study elucidates the response of mountain grassland EMF to grazing intensity and provides a theoretical basis for restoring degraded grassland and sustainable ecosystem development.

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Microbial seasonality promotes soil respiratory carbon emission in natural ecosystems: A modeling study

Cited by 25 publications

References 75 publications

Mapping soil microbial residence time at the global scale

Mapping soil microbial residence time at the global scale

Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems

Effects of grazing on the ecosystems multifunctionality of montane meadow-grassland on the northern slope of the Tianshan Mountains, China

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