Search

The sources and sinks of nitrous oxide, as control emissions to the atmosphere, are generally poorly constrained for most environmental systems. Initial depth-resolved analysis of nitrous oxide flux from observation wells and the proximal surface within a nitrate contaminated aquifer system revealed high subsurface production but little escape from the surface. To better understand the environmental controls of production and emission at this site, we used a combination of isotopic, geochemical, and molecular analyses to show that chemodenitrification and bacterial denitrification are major sources of nitrous oxide in this subsurface, where low DO, low pH, and high nitrate are correlated with significant nitrous oxide production. Depth-resolved metagenomes showed that consumption of nitrous oxide near the surface was correlated with an enrichment of Clade II nitrous oxide reducers, consistent with a growing appreciation of their importance in controlling release of nitrous oxide to the atmosphere. Our work also provides evidence for the reduction of nitrous oxide at a pH of 4, well below the generally accepted limit of pH 5.

Incorporating microbial processes into soil biogeochemical models has received growing interest. However, determining the parameters that govern microbially driven biogeochemical processes typically requires case‐specific model calibration in various soil and ecosystem types. Here each case refers to an independent and individual experimental unit subjected to repeated measurements. Using the Microbial‐ENzyme Decomposition model, this study aimed to test whether a common set of microbially‐relevant parameters (i.e., generalized parameters) could be obtained across multiple cases based on a two‐year incubation experiment in which soil samples of four distinct soil series (i.e., Coland, Kesswick, Westmoreland, and Etowah) collected from forest and grassland were subjected to cellulose or no cellulose amendment. Results showed that a common set of parameters controlling microbial growth and maintenance as well as extracellular enzyme production and turnover could be generalized at the soil series level but not land cover type. This indicates that microbial model developments need to prioritize soil series type over plant functional types when implemented across various sites. This study also suggests that, in addition to heterotrophic respiration and microbial biomass data, extracellular enzyme data sets are needed to achieve reliable microbial‐relevant parameters for large‐scale soil model projections. Incorporating soil microbial processes can improve soil model projections, and achieving a common set of microbial parameters across sites remains less studied. Based on a two‐year soil incubation data set, this study showed that key microbial parameters could be generalized at the soil series level (four distinct soil series) but not land cover type (forest vs. grassland). The common set of parameters includes those processes controlling microbial growth and maintenance as well as extracellular enzyme production and turnover. This study informs that future microbial model developments prioritize soil series type over plant functional types when implemented across various sites. Besides the heterotrophic respiration and microbial biomass data, soil extracellular enzyme data sets are particularly needed to achieve reliable microbial‐relevant parameters for large‐scale soil model projections. Generalizing microbial relevant parameters in soil biogeochemical models were achieved at soil series level but not plant typeThe common set of parameters includes processes of microbial growth and maintenance as well as extracellular enzyme production and turnoverSoil heterotrophic respiration, microbial biomass data, and extracellular enzyme data sets are needed for large‐scale soil model projections Generalizing microbial relevant parameters in soil biogeochemical models were achieved at soil series level but not plant type The common set of parameters includes processes of microbial growth and maintenance as well as extracellular enzyme production and turnover Soil heterotrophic respiration, microbial biomass data, and extracellular enzyme data sets are needed for large‐scale soil model projections

Small genes (<150 nucleotides) have been systematically overlooked in phage genomes. We employ a large-scale comparative genomics approach to predict >40,000 small-gene families in ∼2.3 million phage genome contigs. We find that small genes in phage genomes are approximately 3-fold more prevalent than in host prokaryotic genomes. Our approach enriches for small genes that are translated in microbiomes, suggesting the small genes identified are coding. More than 9,000 families encode potentially secreted or transmembrane proteins, more than 5,000 families encode predicted anti-CRISPR proteins, and more than 500 families encode predicted antimicrobial proteins. By combining homology and genomic-neighborhood analyses, we reveal substantial novelty and diversity within phage biology, including small phage genes found in multiple host phyla, small genes encoding proteins that play essential roles in host infection, and small genes that share genomic neighborhoods and whose encoded proteins may share related functions.

Bioremediation of uranium was investigated in microcosm experiments containing contaminated sediments from Oak Ridge, Tennessee to explore the importance of electron donor selection for uranium reduction rate and extent. In these experiments, all of the electron donors, including ethanol, glucose, methanol, and methanol with added humic acids, stimulated the reduction and immobilization of aqueous uranium by the indigenous microbial community. Uranium loss from solution began after the completion of nitrate reduction but essentially concurrent with sulfate reduction. When electron donor concentrations were normalized for their equivalent electron donor potential yield, the rates of uranium reduction were nearly equivalent for all treatments (0.55–0.95 μmol L−1d−1). Uranium reduction with methanol proceeded after a 15‐d longer lag time relative to that of ethanol or glucose. Significant differences were not found with the inclusion of humic acids. The extent of U reduction in sediment slurries measured by XANES at various time periods after the start of the experiment increased in the order of ethanol (5–7% reduced at 77 and 153 d), glucose (49% reduced at 53 d), and methanol (93% reduced at 90 d). The microbial diversity of ethanol‐ and methanol‐amended microcosms in their late stage of U reduction was analyzed with 16S rRNA gene amplification. Members of the Geobacteraceae were found in all microcosms as well as other potential uranium‐reducing organisms, such as Clostridiumand Desulfosporosinus.The effectiveness of methanol relative to ethanol at reducing aqueous and sediment‐hosted uranium suggests that bioremediation strategies that encourage fermentative poising of the subsurface to a lower redox potential may be more effective for long‐term uranium immobilization as compared with selecting an electron donor that is efficiently metabolized by known uranium‐reducing microorganisms.

Environmental changes are anticipated to generate substantial impacts on carbon cycling in peatlands, affecting terrestrial‐climate feedbacks. Understanding how peatland methane (CH4) fluxes respond to these changing environments is critical for predicting the magnitude of feedbacks from peatlands to global climate change. To improve predictions of CH4fluxes in response to changes such as elevated atmospheric CO2concentrations and warming, it is essential for Earth system models to include increased realism to simulate CH4processes in a more mechanistic way. To address this need, we incorporated a new microbial‐functional group‐based CH4module into the Energy Exascale Earth System land model (ELM) and tested it with multiple observational data sets at an ombrotrophic peatland bog in northern Minnesota. The model is able to simulate observed land surface CH4fluxes and fundamental mechanisms contributing to these throughout the soil profile. The model reproduced the observed vertical distributions of dissolved organic carbon and acetate concentrations. The seasonality of acetoclastic and hydrogenotrophic methanogenesis—two key processes for CH4production—and CH4concentration along the soil profile were accurately simulated. Meanwhile, the model estimated that plant‐mediated transport, diffusion, and ebullition contributed to ∼23.5%, 15.0%, and 61.5% of CH4transport, respectively. A parameter sensitivity analysis showed that CH4substrate and CH4production were the most critical mechanisms regulating temporal patterns of surface CH4fluxes both under ambient conditions and warming treatments. This knowledge will be used to improve Earth system model predictions of these high‐carbon ecosystems from plot to regional scales. A new CH4module was integrated into an Earth system model to predict CH4fluxes in a northern Minnesota peatlandThe model accurately predicts the seasonal cycle of methane production and net fluxesCH4substrate and production were the most critical mechanisms regulating temporal patterns of CH4fluxes A new CH4module was integrated into an Earth system model to predict CH4fluxes in a northern Minnesota peatland The model accurately predicts the seasonal cycle of methane production and net fluxes CH4substrate and production were the most critical mechanisms regulating temporal patterns of CH4fluxes

Earth system models have predicted that there will be more frequent and severe precipitation and drought events in terrestrial ecosystems. Microbially mediated decomposition of soil organic carbon (SOC) tends to increase as soils wet and decrease as soils dry. However, the long‐term SOC change under intensified moisture extremes remains poorly known as it depends on the frequency and intensity of soil drying and wetting. In this study, we explored long‐term SOC dynamics under scenarios of alternating drying‐wetting cycles using the Microbial‐ENzyme Decomposition model, a mechanistic microbial model. The model was parameterized with 11 years of observations from a temperate deciduous broadleaf forest site, showing satisfactory model performance in both model calibration (R2= 0.67) and validation (R2= 0.69) against heterotrophic respiration. We then used the model to simulate the long‐term SOC dynamics under five scenarios of alternating drying‐wetting cycles with different frequencies and severities over a period of 100 years. Results showed that the changes in active microbial biomass C and the corresponding turnover rates of SOC pools were more sensitive to soil drying than soil wetting. As a result, the cumulative soil carbon emission from microbial respiration decreased by 433.7 g C m−2after the 100‐year simulation in the highest frequency and intensity moisture scenario, but was not significantly affected by the lowest frequency and intensity scenario. This study emphasizes the nonlinear response of SOC decomposition to soil moisture changes, which causes decreased decomposition by microbes under drying that is, not compensated by increased decomposition under wetting conditions. Soils store a large amount of carbon and thus play a critical role in regulating global carbon cycling and climate change. Soil organic carbon (SOC) decomposition is dependent upon microbes which are sensitive to moisture change. In this study, we explore microbially mediated SOC decomposition in response to intensified dry and wet conditions using a mechanistic model. The model is calibrated at a temperate forest and used to predict future scenarios. Results suggest that SOC decomposition responds nonlinearly to moisture changes, showing a greater sensitivity to drying than wetting. The long‐term CO2emissions from SOC decomposition decrease under scenarios with intensified moisture extremes. We conducted a mechanistic modeling analysis to study the response of soil organic carbon (SOC) decomposition to intensified moisture extremesActive microbial biomass C and the corresponding turnover rates of SOC pools were more sensitive to extreme soil drying than soil wettingSOC decomposition decreased under intensified moisture extremes We conducted a mechanistic modeling analysis to study the response of soil organic carbon (SOC) decomposition to intensified moisture extremes Active microbial biomass C and the corresponding turnover rates of SOC pools were more sensitive to extreme soil drying than soil wetting SOC decomposition decreased under intensified moisture extremes