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Abstract Microbial processes are crucial in producing and oxidizing biological methane (CH₄) in natural wetlands. Therefore, modeling methanogenesis and methanotrophy is advantageous for accurately projecting CH₄cycling. Utilizing the CLM‐Microbe model, which explicitly represents the growth and death of methanogens and methanotrophs, we demonstrate that genome‐enabled model parameterization improves model performance in four natural wetlands. Compared to the default model parameterization against CH₄flux, genomic‐enabled model parameterization added another contain on microbial biomass, notably enhancing the precision of simulated CH₄flux. Specifically, the coefficient of determination (R²) increased from 0.45 to 0.74 for Sanjiang Plain, from 0.78 to 0.89 for Changbai Mountain, and from 0.35 to 0.54 for Sallie's Fen, respectively. A drop inR²was observed for the Dajiuhu nature wetland, primarily caused by scatter data points. Theil's coefficient (U) and model efficiency (ME) confirmed the model performance from default parameterization to genome‐enabled model parameterization. Compared with the model solely calibrated to surface CH₄flux, additional constraints of functional gene data led to better CH₄seasonality; meanwhile, genome‐enabled model parameterization established more robust associations between simulated CH₄production rates and environmental factors. Sensitivity analysis underscored the pivotal role of microbial physiology in governing CH₄flux. This genome‐enabled model parameterization offers a valuable promise to integrate fast‐cumulating genomic data with CH₄models to better understand microbial roles in CH₄in the era of climate change. 

Abstract. Soil microbes play a crucial role in the carbon (C) cycle; however, they have been overlooked in predicting the terrestrial C cycle. We applied a microbial-explicit Earth system model – the Community Land Model-Microbe (CLM-Microbe) – to investigate the dynamics of soil microbes during 1901 to 2016. The CLM-Microbe model was able to reproduce the variations of gross (GPP) and net (NPP) primary productivity, heterotrophic (HR) and soil (SR) respiration, microbial (MBC) biomass C in fungi (FBC) and bacteria (BBC) in the top 30 cm and 1 m, and dissolved (DOC) and soil organic C (SOC) in the top 30 cm and 1 m during 1901–2016. During the study period, simulated C variables increased by approximately 12 PgC yr−1 for HR, 25 PgC yr−1 for SR, 1.0 PgC for FBC and 0.4 PgC for BBC in 0–30 cm, and 1.2 PgC for FBC and 0.7 PgC for BBC in 0–1 m. Increases in microbial C fluxes and pools were widely found, particularly at high latitudes and in equatorial regions, but we also observed their decreases in some grids. Overall, the area-weighted averages of HR, SR, FBC, and BBC in the top 1 m were significantly correlated with those of soil moisture and soil temperature in the top 1 m. These results suggested that microbial C fluxes and pools were jointly governed by vegetation C input and soil temperature and moisture. Our simulations revealed the spatial and temporal patterns of microbial C fluxes and pools in response to environmental change, laying the foundation for an improved understanding of soil microbial roles in the global terrestrial C cycle. 

The positive Arctic–methane (CH₄) feedback forms when more CH₄is released from the Arctic tundra to warm the climate, further stimulating the Arctic to emit CH₄. This study utilized the CLM-Microbe model to project CH₄emissions across five distinct Arctic tundra ecosystems on the Alaska North Slope, considering three Shared Socioeconomic Pathway (SSP) scenarios using climate data from three climate models from 2016 to 2100. Employing a hyper-resolution of 5 m × 5 m within 40,000 m²domains accounted for the Arctic tundra’s high spatial heterogeneity; three sites were near Utqiaġvik (US-Beo, US-Bes, and US-Brw), with one each in Atqasuk (US-Atq) and Ivotuk (US-Ivo). Simulated CH₄emissions substantially increased by a factor of 5.3 to 7.5 under the SSP5–8.5 scenario compared to the SSP1–2.6 and SSP2–4.5 scenarios. The projected CH₄emissions exhibited a stronger response to rising temperature under the SSP5–8.5 scenario than under the SSP1–2.6 and SSP2–4.5 scenarios, primarily due to strong temperature dependence and the enhanced precipitation-induced expansion of anoxic conditions that promoted methanogenesis. The CH₄transport via ebullition and plant-mediated transport is projected to increase under all three SSP scenarios, and ebullition dominated CH₄transport by 2100 across five sites. Projected CH₄emissions varied in temperature sensitivity, with a Q₁₀range of 2.7 to 60.9 under SSP1–2.6, 3.8 to 17.6 under SSP2–4.5, and 5.7 to 17.2 under SSP5–8.5. Compared with the other three sites, US-Atq and US-Ivo were estimated to have greater increases in CH₄emissions due to warmer temperatures and higher precipitation. The fact that warmer sites and warmer climate scenarios had higher CH₄emissions suggests an intensified positive Arctic–CH₄feedback in the 21st century. Microbial physiology and substrate availability dominated the enhanced CH₄production. The simulated intensified positive feedback underscores the urgent need for a more mechanistic understanding of CH₄dynamics and the development of strategies to mitigate CH₄across the Arctic. 

Abstract The theory of microbial stoichiometry can predict the proportional coupling of microbial assimilation of carbon (C), nitrogen (N), and phosphorus (P). The proportional coupling is quantified by the homeostasis value (H). Covariation of H values for C, N, and P indicates that microbial C, N, and P assimilation are coupled. Here, we used a global dataset to investigate the spatiotemporal dynamics of H values of microbial C, N, and P across biomes. We found that land use and management led to the decoupling of P from C and N metabolism over time and across space. Results from structural equation modeling revealed that edaphic factors dominate the microbial homeostasis of P, while soil elemental concentrations dominate the homeostasis of C and N. This result was further confirmed using the contrasting factors on microbial P vs. microbial C and N derived from a machine-learning algorithm. Overall, our study highlights the impacts of management on shifting microbial roles in nutrient cycling. 

We applied a microbial-explicit model – the CLM-Microbe – to investigate the dynamics of C in vegetation, litter, soil, and microbes during 1901-2016. The CLM-Microbe model was able to reproduce global averages and latitudinal trends of gross (GPP) and net (NPP) primary productivity, heterotrophic (HR) and soil (SR) respiration, biomass C in fungi (FBC) and bacteria (BBC) in the top 30 cm and 1 m, dissolved (DOC) and soil organic C (SOC) in the top 30 cm and 1 m. In addition, the CLM-Microbe model captured the grid-level variation in GPP (R2=0.78), NPP (R2=0.63), SR (R2=0.26), HR (R2=0.23), DOC in 0-30 cm (R2=0.2) and 0-1 m (R2=0.22), SOC in 0-30 cm (R2=0.36) and 0-1 m (R2=0.26), FBC (R2=0.22) and BBC (R2=0.32) in 0-30 cm, and MBC in 0-1 m (R2=0.21). From the 1900s to 2007-2016, simulated C variables increased by approximately 30 PgC yr-1 for GPP, 15 PgC yr-1 for NPP, 12 PgC yr-1 for HR, 25 PgC yr-1 for SR, 1.0 PgC for FBC and 0.4 PgC for BBC in 0-30 cm, 1.5 PgC for FBC, 0.8 PgC for BBC, 2.5 PgC for DOC, 40 PgC for SOC, and 5 PgC for litter C in 0-1 m, and 40 PgC for vegetation C. The relative increases in C fluxes and pools varied across the globe. Increases in vegetation C were closely related to warming and increased precipitation, while C accumulation in microbes and soils was jointly governed by vegetation C input and soil temperature and moisture. 

Abstract Bacteria and fungi possess distinct physiological traits. Their macroecology is vital for ecosystem functioning such as carbon cycling. However, bacterial and fungal biogeography and underlying mechanisms remain elusive. In this study, we investigated bacterial versus fungal macroecology by integrating a microbial‐explicit model—CLM‐Microbe—with measured fungal (FBC) and bacterial biomass carbon (BBC) from 34 NEON sites. The distribution of FBC, BBC, and FBC: BBC (F:B) ratio was well simulated across sites, with variations in 99% (P < 0.001), 97% (P < 0.001), and 99% (P < 0.001) being explained by the CLM‐Microbe model, respectively. We found stronger biogeographic patterns of FBC relative to BBC across the United States. Fungal and bacterial turnover rates showed similar trends along latitude. However, latitudinal trends of their component fluxes (carbon assimilation, respiration, and necromass production) were distinct between bacteria and fungi, with those latitudinal trends following inverse unimodal patterns for fungi and showing exponential declining responses for bacteria. Carbon assimilation was dominated by vegetation productivity, and respiration was dominated by mean annual temperature for bacteria and fungi. The dominant factor for their necromass production differs, with edaphic factors controlling fungal and mean annual temperature controlling bacterial processes. The understanding of fungal and bacterial macroecology is an important step toward linking microbial metabolism and soil biogeochemical processes. Distinct fungal and bacterial macroecology contributes to the microbial ecology, particularly on microbial community structure and its association with ecosystem carbon cycling across space. 

Abstract Soil microbes ultimately drive the mineralization of soil organic carbon and thus ecosystem functions. We compiled a dataset of the seasonality of microbial biomass carbon (MBC) and developed a semi-mechanistic model to map monthly MBC across the globe. MBC exhibits an equatorially symmetric seasonality between the Northern and Southern Hemispheres. In the Northern Hemisphere, MBC peaks in autumn and is minimal in spring at low latitudes (<25°N), peaks in the spring and is minimal in autumn at mid-latitudes (25°N to 50°N), while peaks in autumn and is minimal in spring at high latitudes (>50°N). This latitudinal shift of MBC seasonality is attributed to an interaction of soil temperature, soil moisture, and substrate availability. The MBC seasonality is inconsistent with patterns of heterotrophic respiration, indicating that MBC as a proxy for microbial activity is inappropriate at this resolution. This study highlights the need to explicitly represent microbial physiology in microbial models. The interactive controls of environments and substrate on microbial seasonality provide insights for better representing microbial mechanisms in simulating ecosystem functions at the seasonal scale.