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Abstract BackgroundAnthropogenic activities have increased the inputs of atmospheric reactive nitrogen (N) into terrestrial ecosystems, affecting soil carbon stability and microbial communities. Previous studies have primarily examined the effects of nitrogen deposition on microbial taxonomy, enzymatic activities, and functional processes. Here, we examined various functional traits of soil microbial communities and how these traits are interrelated in a Mediterranean-type grassland administrated with 14 years of 7 g m⁻²year⁻¹of N amendment, based on estimated atmospheric N deposition in areas within California, USA, by the end of the twenty-first century. ResultsSoil microbial communities were significantly altered by N deposition. Consistent with higher aboveground plant biomass and litter, fast-growing bacteria, assessed by abundance-weighted average rRNA operon copy number, were favored in N deposited soils. The relative abundances of genes associated with labile carbon (C) degradation (e.g.,amyAandcda) were also increased. In contrast, the relative abundances of functional genes associated with the degradation of more recalcitrant C (e.g.,mannanaseandchitinase) were either unchanged or decreased. Compared with the ambient control, N deposition significantly reduced network complexity, such as average degree and connectedness. The network for N deposited samples contained only genes associated with C degradation, suggesting that C degradation genes became more intensely connected under N deposition. ConclusionsWe propose a conceptual model to summarize the mechanisms of how changes in above- and belowground ecosystems by long-term N deposition collectively lead to more soil C accumulation. 

ABSTRACT Nitrous oxide (N₂O) reductase, the sole natural microbial sink for N₂O, exists in two microbial clades:nosZI andnosZII. Although previous studies have explored inter‐clade ecological differentiation, the intra‐clade variations and their implications for N₂O dynamics remain understudied. This study investigated both inter‐ and intra‐clade ecological differentiation among N₂O reducers, the drivers influencing these patterns, and their effects on N₂O emissions across continental‐scale river systems. The results showed that bothnosZI andnosZII community turnovers were associated with similar key environmental factors, particularly total phosphorus (TP), but these variables explained a larger proportion of variation in thenosZI community. The influence of mean annual temperature (MAT) on community composition increased for more widespread N₂O‐reducing taxa. We identified distinct ecological clusters within each clade of N₂O reducers and observed identical ecological clustering patterns across both clades. These clusters were primarily characterized by distinct MAT regimes, coarse sediment texture as well as low TP levels, and high abundance of N₂O producers, with MAT‐related clusters constituting predominant proportions. Intra‐clade ecological differentiation was a crucial predictor of N₂O flux and reduction efficiency. Although different ecological clusters showed varying or even contrasting associations with N₂O dynamics, the shared ecological clusters across clades exhibited similar trends. Low‐MAT clusters in both thenosZI andnosZII communities were negatively correlated with denitrification‐normalized N₂O flux and the N₂O:(N₂O + N₂) ratio, whereas high‐MAT clusters showed positive correlations. This contrasting pattern likely stems from low‐MAT clusters being better adapted to eutrophic conditions and their more frequent co‐occurrence with N₂O‐producing genes. These findings advance our understanding of the distribution and ecological functions of N₂O reducers in natural ecosystems, suggesting that warming rivers may have decreased N₂O reduction efficiency and thereby amplify temperature‐driven emissions. 

Whether and how CO 2 and nitrogen (N) availability interact to influence carbon (C) cycling processes such as soil respiration remains a question of considerable uncertainty in projecting future C–climate feedbacks, which are strongly influenced by multiple global change drivers, including elevated atmospheric CO 2 concentrations (eCO 2 ) and increased N deposition. However, because decades of research on the responses of ecosystems to eCO 2 and N enrichment have been done largely independently, their interactive effects on soil respiratory CO 2 efflux remain unresolved. Here, we show that in a multifactor free-air CO 2 enrichment experiment, BioCON (Biodiversity, CO 2 , and N deposition) in Minnesota, the positive response of soil respiration to eCO 2 gradually strengthened at ambient (low) N supply but not enriched (high) N supply for the 12-y experimental period from 1998 to 2009. In contrast to earlier years, eCO 2 stimulated soil respiration twice as much at low than at high N supply from 2006 to 2009. In parallel, microbial C degradation genes were significantly boosted by eCO 2 at low but not high N supply. Incorporating those functional genes into a coupled C–N ecosystem model reduced model parameter uncertainty and improved the projections of the effects of different CO 2 and N levels on soil respiration. If our observed results generalize to other ecosystems, they imply widely positive effects of eCO 2 on soil respiration even in infertile systems. 

Abstract Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer–resource model with a neutral model in stochastic differential equations. Using time‐series data from anaerobic bioreactors that target microbial 16S rRNA genes, we tested the applicability of three ecological models: the consumer–resource model, the neutral model, and the combined model. Our results revealed that model performances varied substantially as a function of population abundance and/or process conditions. The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated. In contrast, the neutral model showed the best performance for rare taxa. Our analysis further indicated that immigration rates decreased with taxa abundance and competitions between taxa were strongly correlated with phylogeny, but within a certain phylogenetic distance only. The determinism underlying taxa and community dynamics were quantitatively assessed, showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning. Given its mechanistic basis, the framework developed here is expected to be potentially applicable beyond microbial ecology. 

Abstract Soil carbon (C) and nitrogen (N) cycles and their complex responses to environmental changes have received increasing attention. However, large uncertainties in model predictions remain, partially due to the lack of explicit representation and parameterization of microbial processes. One great challenge is to effectively integrate rich microbial functional traits into ecosystem modeling for better predictions. Here, using soil enzymes as indicators of soil function, we developed a competitive dynamic enzyme allocation scheme and detailed enzyme‐mediated soil inorganic N processes in the Microbial‐ENzyme Decomposition (MEND) model. We conducted a rigorous calibration and validation of MEND with diverse soil C‐N fluxes, microbial C:N ratios, and functional gene abundances from a 12‐year CO₂ × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO₂fluxes and multiple N variables, the model correctly predicted microbial C:N ratios and their negative response to enriched N supply. Model validation further showed that, compared to the changes in simulated enzyme concentrations and decomposition rates, the changes in simulated activities of eight C‐N‐associated enzymes were better explained by the measured gene abundances in responses to elevated atmospheric CO₂concentration. Our results demonstrated that using enzymes as indicators of soil function and validating model predictions with functional gene abundances in ecosystem modeling can provide a basis for testing hypotheses about microbially mediated biogeochemical processes in response to environmental changes. Further development and applications of the modeling framework presented here will enable microbial ecologists to address ecosystem‐level questions beyond empirical observations, toward more predictive understanding, an ultimate goal of microbial ecology.