PlasmidHunter: accurate and fast prediction of plasmid sequences using gene content profile and machine learning.

Plasmids are extrachromosomal DNA found in microorganisms. They often carry beneficial genes that help bacteria adapt to harsh conditions. Plasmids are also important tools in genetic engineering, gene therapy, and drug production. However, it can be difficult to identify plasmid sequences from chromosomal sequences in genomic and metagenomic data. Here, we have developed a new tool called PlasmidHunter, which uses machine learning to predict plasmid sequences based on gene content profile. PlasmidHunter can achieve high accuracies (up to 97.6%) and high speeds in benchmark tests including both simulated contigs and real metagenomic plasmidome data, outperforming other existing tools.

Disentangling direct from indirect relationships in association networks.

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal (P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

Multiple spatial scales of bacterial and fungal structural and functional traits affect carbon mineralization.

Studying the functional heterogeneity of soil microorganisms at different spatial scales and linking it to soil carbon mineralization is crucial for predicting the response of soil carbon stability to environmental changes and human disturbance. Here, a total of 429 soil samples were collected from typical paddy fields in China, and the bacterial and fungal communities as well as functional genes related to carbon mineralization in the soil were analysed using MiSeq sequencing and GeoChip gene microarray technology. We postulate that CO2 emissions resulting from bacterial and fungal carbon mineralization are contingent upon their respective carbon consumption strategies, which rely on the regulation of interactions between biodiversity and functional genes. Our results showed that the spatial turnover of the fungal community was 2-4 times that of the bacterial community from hundreds of meters to thousands of kilometres. The effect of spatial scale exerted a greater impact on the composition rather than the functional characteristics of the microbial community. Furthermore, based on the establishment of functional networks at different spatial scales, we observed that both bacteria and fungi within the top 10 taxa associated with carbon mineralization exhibited a prevalence of generalist species at the regional scale. This study emphasizes the significance of spatial scaling patterns in soil bacterial and fungal carbon degradation functions, deepening our understanding of how the relationship between microbial decomposers and soil heterogeneity impacts carbon mineralization and subsequent greenhouse gas emissions.

Intrinsic microbial temperature sensitivity and soil organic carbon decomposition in response to climate change.

Soil microbes are essential for regulating carbon stocks under climate change. However, the uncertainty surrounding how microbial temperature responses control carbon losses under warming conditions highlights a significant gap in our climate change models. To address this issue, we conducted a fine-scale analysis of soil organic carbon composition under different temperature gradients and characterized the corresponding microbial growth and physiology across various paddy soils spanning 4000 km in China. Our results showed that warming altered the composition of organic matter, resulting in a reduction in carbohydrates of approximately 0.026% to 0.030% from humid subtropical regions to humid continental regions. These changes were attributed to a decrease in the proportion of cold-preferring bacteria, leading to significant soil carbon losses. Our findings suggest that intrinsic microbial temperature sensitivity plays a crucial role in determining the rate of soil organic carbon decomposition, providing insights into the temperature limitations faced by microbial activities and their impact on soil carbon-climate feedback.

Community assembly of organisms regulates soil microbial functional potential through dual mechanisms.

Unraveling the influence of community assembly processes on soil ecosystem functioning presents a major challenge in the field of theoretical ecology, as it has received limited attention. Here, we used a series of long-term experiments spanning over 25 years to explore the assembly processes of bacterial, fungal, protist, and nematode communities using high-throughput sequencing. We characterized the soil microbial functional potential by the abundance of microbial genes associated with carbon, nitrogen, phosphorus, and sulfur cycling using GeoChip-based functional gene profiling, and determined how the assembly processes of organism groups regulate soil microbial functional potential through community diversity and network stability. Our results indicated that balanced fertilization (NPK) treatment improved the stochastic assembly of bacterial, fungal, and protist communities compared to phosphorus-deficient fertilization (NK) treatment. However, there was a nonsignificant increase in the normalized stochasticity ratio of the nematode community in response to fertilization across sites. Our findings emphasized that soil environmental factors influenced the assembly processes of the biotic community, which regulated soil microbial functional potential through dual mechanisms. One mechanism indicated that the high phosphorus levels and low soil nutrient stoichiometry may increase the stochasticity of bacterial, fungal, and protist communities and the determinism of the nematode community under NPK treatment, ultimately enhancing soil microbial functional potential by reinforcing the network stability of the biotic community. The other mechanism indicated that the low phosphorus levels and high soil nutrient stoichiometry may increase the stochastic process of the bacterial community and the determinism of the fungal, protist, and nematode communities under NK treatment, thereby enhancing soil microbial functional potential by improving the ß-diversity of the biotic community. Taken together, these results provide valuable insights into the mechanisms underlying the assembly processes of the biotic community that regulate ecosystem functioning.

Contribution of Microorganisms with the Clade II Nitrous Oxide Reductase to Suppression of Surface Emissions of Nitrous Oxide.

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.

Coupling temperature-dependent spatial turnover of microbes and plants using the metabolic theory of ecology.

There is an urgent need to understand the coupled relationship between belowground microbes and aboveground plants in response to temperature under climate change. The metabolic theory of ecology (MTE) provides a way to predict the metabolic rate and species diversity, but the spatial scale dependence and connections between plants and microorganisms are still unclear. Here, we used two independent datasets to address this question. One is from comprehensive sampling of paddy fields targeting bacteria and microbial functional genes, and the other is a global metadata of spatial turnover for microorganisms (bacteria, fungi and archaea, n = 139) and plants (n = 206). Results showed that spatial turnover of bacterial communities and microbial functional genes increased with temperature and fitted MTE. Through meta-analysis, the temperature-dependent spatial scale pattern was further extended to the global scale, with the spatial turnover of microorganisms and plants being consistent with MTE. Belowground microorganisms and aboveground plants were closely linked with each other even when controlling for temperature, suggesting that factors other than shared relationships with temperature also contribute to their linkages. These results implied a broad application of MTE in biology and have important implications for predicting the ecological consequences of future climate warming.

Home-based microbial solution to boost crop growth in low-fertility soil.

Soil microbial inoculants are expected to boost crop productivity under climate change and soil degradation. However, the efficiency of native vs commercialized microbial inoculants in soils with different fertility and impacts on resident microbial communities remain unclear. We investigated the differential plant growth responses to native synthetic microbial community (SynCom) and commercial plant growth-promoting rhizobacteria (PGPR). We quantified the microbial colonization and dynamic of niche structure to emphasize the home-field advantages for native microbial inoculants. A native SynCom of 21 bacterial strains, originating from three typical agricultural soils, conferred a special advantage in promoting maize growth under low-fertility conditions. The root : shoot ratio of fresh weight increased by 78-121% with SynCom but only 23-86% with PGPRs. This phenotype correlated with the potential robust colonization of SynCom and positive interactions with the resident community. Niche breadth analysis revealed that SynCom inoculation induced a neutral disturbance to the niche structure. However, even PGPRs failed to colonize the natural soil, they decreased niche breadth and increased niche overlap by 59.2-62.4%, exacerbating competition. These results suggest that the home-field advantage of native microbes may serve as a basis for engineering crop microbiomes to support food production in widely distributed poor soils.

Microbial autotrophy explains large-scale soil CO2 fixation.

Microbial communities play critical roles in fixing carbon from the atmosphere and fixing it in the soils. However, the large-scale variations and drivers of these microbial communities remain poorly understood. Here, we conducted a large-scale survey across China and found that soil autotrophic organisms are critical for explaining CO2 fluxes from the atmosphere to soils. In particular, we showed that large-scale variations in CO2 fixation rates are highly correlated to those in autotrophic bacteria and phototrophic protists. Paddy soils, supporting a larger proportion of obligate bacterial and protist autotrophs, display four-fold of CO2 fixation rates over upland and forest soils. Precipitation and pH, together with key ecological clusters of autotrophic microbes, also played important roles in controlling CO2 fixation. Our work provides a novel quantification on the contribution of terrestrial autotrophic microbes to soil CO2 fixation processes at a large scale, with implications for global carbon regulation under climate change.

Long-term elevated precipitation induces grassland soil carbon loss via microbe-plant-soil interplay.

Global climate models predict that the frequency and intensity of precipitation events will increase in many regions across the world. However, the biosphere-climate feedback to elevated precipitation (eP) remains elusive. Here, we report a study on one of the longest field experiments assessing the effects of eP, alone or in combination with other climate change drivers such as elevated CO2 (eCO2 ), warming and nitrogen deposition. Soil total carbon (C) decreased after a decade of eP treatment, while plant root production decreased after 2 years. To explain this asynchrony, we found that the relative abundances of fungal genes associated with chitin and protein degradation increased and were positively correlated with bacteriophage genes, suggesting a potential viral shunt in C degradation. In addition, eP increased the relative abundances of microbial stress tolerance genes, which are essential for coping with environmental stressors. Microbial responses to eP were phylogenetically conserved. The effects of eP on soil total C, root production, and microbes were interactively affected by eCO2 . Collectively, we demonstrate that long-term eP induces soil C loss, owing to changes in microbial community composition, functional traits, root production, and soil moisture. Our study unveils an important, previously unknown biosphere-climate feedback in Mediterranean-type water-limited ecosystems, namely how eP induces soil C loss via microbe-plant-soil interplay.

Polyethylene Degradation by a Rhodococcous Strain Isolated from Naturally Weathered Plastic Waste Enrichment.

Polyethylene (PE) is the most widely produced synthetic polymer and the most abundant plastic waste worldwide due to its recalcitrance to biodegradation and low recycle rate. Microbial degradation of PE has been reported, but the underlying mechanisms are poorly understood. Here, we isolated a Rhodococcus strain A34 from 609 day enriched cultures derived from naturally weathered plastic waste and identified the potential key PE degradation enzymes. After 30 days incubation with A34, 1% weight loss was achieved. Decreased PE molecular weight, appearance of C-O and C═O on PE, palmitic acid in the culture supernatant, and pits on the PE surface were observed. Proteomics analysis identified multiple key PE oxidation and depolymerization enzymes including one multicopper oxidase, one lipase, six esterase, and a few lipid transporters. Network analysis of proteomics data demonstrated the close relationships between PE degradation and metabolisms of phenylacetate, amino acids, secondary metabolites, and tricarboxylic acid cycles. The metabolic roadmap generated here provides critical insights for optimization of plastic degradation condition and assembly of artificial microbial communities for efficient plastic degradation.

Stimulation of soil respiration by elevated CO2 is enhanced under nitrogen limitation in a decade-long grassland study.

Whether and how CO2 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 CO2 concentrations (eCO2) and increased N deposition. However, because decades of research on the responses of ecosystems to eCO2 and N enrichment have been done largely independently, their interactive effects on soil respiratory CO2 efflux remain unresolved. Here, we show that in a multifactor free-air CO2 enrichment experiment, BioCON (Biodiversity, CO2, and N deposition) in Minnesota, the positive response of soil respiration to eCO2 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, eCO2 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 eCO2 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 CO2 and N levels on soil respiration. If our observed results generalize to other ecosystems, they imply widely positive effects of eCO2 on soil respiration even in infertile systems.

Electrostimulation triggers an increase in cross-niche microbial associations toward enhancing organic nitrogen wastewater treatment.

As an efficient wastewater pretreatment biotechnology, electrostimulated hydrolysis acidification (eHA) has been used to accelerate the removal of refractory pollutants, which is closely related to the effects of electrostimulation on microbial interspecies associations. However, the ecological processes underpinning such linkages remain unresolved, especially for the microbial communities derived from different niches, such as the electrode surface and plankton. Herein, the principles of cross-niche microbial associations and community assembly were investigated using molecular ecological network and phylogenetic bin-based null model analysis (iCAMP) based on 16S rRNA gene sequences. The electrostimulated planktonic sludge and electrode biofilm displayed significantly (P < 0.05) 1.67 and 1.53 times higher organic nitrogen pollutant (azo dye Alizarin Yellow R) degradation efficiency than non-electrostimulation group, and the corresponding microbial community composition and structure were significantly (P < 0.05) changed. Electroactive bacteria and functional degraders were enriched in the electrode biofilm and planktonic sludge, respectively. Notably, electrostimulation strengthened the synergistic microbial associations (1.8 times more links) between sludge and biofilm members. Additionally, both electrostimulation and cross-niche microbial associations induced greater importance of deterministic assembly. Overall, this study highlights the specificity of cross-electrode surface microbial associations and ecological processes with electrostimulation and advances our understanding of the manipulation of sludge microbiomes in engineered wastewater treatment systems.

Ecophysiological and genomic analyses of a representative isolate of highly abundant Bacillus cereus strains in contaminated subsurface sediments.

Bacillus cereus strain CPT56D-587-MTF (CPTF) was isolated from the highly contaminated Oak Ridge Reservation (ORR) subsurface. This site is contaminated with high levels of nitric acid and multiple heavy metals. Amplicon sequencing of the 16S rRNA genes (V4 region) in sediment from this area revealed an amplicon sequence variant (ASV) with 100% identity to the CPTF 16S rRNA sequence. Notably, this CPTF-matching ASV had the highest relative abundance in this community survey, with a median relative abundance of 3.77% and comprised 20%-40% of reads in some samples. Pangenomic analysis revealed that strain CPTF has expanded genomic content compared to other B. cereus species-largely due to plasmid acquisition and expansion of transposable elements. This suggests that these features are important for rapid adaptation to native environmental stressors. We connected genotype to phenotype in the context of the unique geochemistry of the site. These analyses revealed that certain genes (e.g. nitrate reductase, heavy metal efflux pumps) that allow this strain to successfully occupy the geochemically heterogenous microniches of its native site are characteristic of the B. cereus species while others such as acid tolerance are mobile genetic element associated and are generally unique to strain CPTF.

Development of a Markerless Deletion Mutagenesis System in Nitrate-Reducing Bacterium Rhodanobacter denitrificans.

Rhodanobacter has been found as the dominant genus in aquifers contaminated with high concentrations of nitrate and uranium in Oak Ridge, TN, USA. The in situ stimulation of denitrification has been proposed as a potential method to remediate nitrate and uranium contamination. Among the Rhodanobacter species, Rhodanobacter denitrificans strains have been reported to be capable of denitrification and contain abundant metal resistance genes. However, due to the lack of a mutagenesis system in these strains, our understanding of the mechanisms underlying low-pH resistance and the ability to dominate in the contaminated environment remains limited. Here, we developed an in-frame markerless deletion system in two R. denitrificans strains. First, we optimized the growth conditions, tested antibiotic resistance, and determined appropriate transformation parameters in 10 Rhodanobacter strains. We then deleted the upp gene, which encodes uracil phosphoribosyltransferase, in R. denitrificans strains FW104-R3 and FW104-R5. The resulting strains were designated R3_Δupp and R5_Δupp and used as host strains for mutagenesis with 5-fluorouracil (5-FU) resistance as the counterselection marker to generate markerless deletion mutants. To test the developed protocol, the narG gene encoding nitrate reductase was knocked out in the R3_Δupp and R5_Δupp host strains. As expected, the narG mutants could not grow in anoxic medium with nitrate as the electron acceptor. Overall, these results show that the in-frame markerless deletion system is effective in two R. denitrificans strains, which will allow for future functional genomic studies in these strains furthering our understanding of the metabolic and resistance mechanisms present in Rhodanobacter species. IMPORTANCE Rhodanobacter denitrificans is capable of denitrification and is also resistant to toxic heavy metals and low pH. Accordingly, the presence of Rhodanobacter species at a particular environmental site is considered an indicator of nitrate and uranium contamination. These characteristics suggest its future potential application in bioremediation of nitrate or concurrent nitrate and uranium contamination in groundwater ecosystems. Due to the lack of genetic tools in this organism, the mechanisms of low-pH and heavy metal resistance in R. denitrificans strains remain elusive, which impedes its use in bioremediation strategies. Here, we developed a genome editing method in two R. denitrificans strains. This work marks a crucial step in developing Rhodanobacter as a model for studying the diverse mechanisms of low-pH and heavy metal resistance associated with denitrification.

Embracing mountain microbiome and ecosystem functions under global change.

Mountains are pivotal to maintaining habitat heterogeneity, global biodiversity, ecosystem functions and services to humans. They have provided classic model natural systems for plant and animal diversity gradient studies for over 250 years. In the recent decade, the exploration of microorganisms on mountainsides has also achieved substantial progress. Here, we review the literature on microbial diversity across taxonomic groups and ecosystem types on global mountains. Microbial community shows climatic zonation with orderly successions along elevational gradients, which are largely consistent with traditional climatic hypotheses. However, elevational patterns are complicated for species richness without general rules in terrestrial and aquatic environments and are driven mainly by deterministic processes caused by abiotic and biotic factors. We see a major shift from documenting patterns of biodiversity towards identifying the mechanisms that shape microbial biogeographical patterns and how these patterns vary under global change by the inclusion of novel ecological theories, frameworks and approaches. We thus propose key questions and cutting-edge perspectives to advance future research in mountain microbial biogeography by focusing on biodiversity hypotheses, incorporating meta-ecosystem framework and novel key drivers, adapting recently developed approaches in trait-based ecology and manipulative field experiments, disentangling biodiversity-ecosystem functioning relationships and finally modelling and predicting their global change responses.

Permafrost thaw with warming reduces microbial metabolic capacities in subsurface soils.

Microorganisms are major constituents of the total biomass in permafrost regions, whose underlain soils are frozen for at least two consecutive years. To understand potential microbial responses to climate change, here we examined microbial community compositions and functional capacities across four soil depths in an Alaska tundra site. We showed that a 5-year warming treatment increased soil thaw depth by 25.7% (p = .011) within the deep organic layer (15-25 cm). Concurrently, warming reduced 37% of bacterial abundance and 64% of fungal abundances in the deep organic layer, while it did not affect microbial abundance in other soil layers (i.e., 0-5, 5-15, and 45-55 cm). Warming treatment altered fungal community composition and microbial functional structure (p < .050), but not bacterial community composition. Using a functional gene array, we found that the relative abundances of a variety of carbon (C)-decomposing, iron-reducing, and sulphate-reducing genes in the deep organic layer were decreased, which was not observed by the shotgun sequencing-based metagenomics analysis of those samples. To explain the reduced metabolic capacities, we found that warming treatment elicited higher deterministic environmental filtering, which could be linked to water-saturated time, soil moisture, and soil thaw duration. In contrast, plant factors showed little influence on microbial communities in subsurface soils below 15 cm, despite a 25.2% higher (p < .05) aboveground plant biomass by warming treatment. Collectively, we demonstrate that microbial metabolic capacities in subsurface soils are reduced, probably arising from enhanced thaw by warming.

Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling.

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 CO2 fluxes 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 CO2 concentration. 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.

Microbially enhanced methane uptake under warming enlarges ecosystem carbon sink in a Tibetan alpine grassland.

The alpine grasslands of the Tibetan Plateau store 23.2 Pg soil organic carbon, which becomes susceptible to microbial degradation with climate warming. However, accurate prediction of how the soil carbon stock changes under future climate warming is hampered by our limited understanding of belowground complex microbial communities. Here, we show that 4 years of warming strongly stimulated methane (CH4 ) uptake by 93.8% and aerobic respiration (CO2 ) by 11.3% in the soils of alpine grassland ecosystem. Due to no significant effects of warming on net ecosystem CO2 exchange (NEE), the warming-stimulated CH4 uptake enlarged the carbon sink capacity of whole ecosystem. Furthermore, precipitation alternation did not alter such warming effects, despite the significant effects of precipitation on NEE and soil CH4 fluxes were observed. Metagenomic sequencing revealed that warming led to significant shifts in the overall microbial community structure and the abundances of functional genes, which contrasted to no detectable changes after 2 years of warming. Carbohydrate utilization genes were significantly increased by warming, corresponding with significant increases in soil aerobic respiration. Increased methanotrophic genes and decreased methanogenic genes were observed under warming, which significantly (R2  = .59, p < .001) correlated with warming-enhanced CH4 uptakes. Furthermore, 212 metagenome-assembled genomes were recovered, including many populations involved in the degradation of various organic matter and a highly abundant methylotrophic population of the Methyloceanibacter genus. Collectively, our results provide compelling evidence that specific microbial functional traits for CH4 and CO2 cycling processes respond to climate warming with differential effects on soil greenhouse gas emissions. Alpine grasslands may play huge roles in mitigating climate warming through such microbially enhanced CH4 uptake.

A general framework for quantitatively assessing ecological stochasticity.

Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. Here we propose a general mathematical framework to quantify ecological stochasticity under different situations in which deterministic factors drive the communities more similar or dissimilar than null expectation. An index, normalized stochasticity ratio (NST), was developed with 50% as the boundary point between more deterministic (<50%) and more stochastic (>50%) assembly. NST was tested with simulated communities by considering abiotic filtering, competition, environmental noise, and spatial scales. All tested approaches showed limited performance at large spatial scales or under very high environmental noise. However, in all of the other simulated scenarios, NST showed high accuracy (0.90 to 1.00) and precision (0.91 to 0.99), with averages of 0.37 higher accuracy (0.1 to 0.7) and 0.33 higher precision (0.0 to 1.8) than previous approaches. NST was also applied to estimate stochasticity in the succession of a groundwater microbial community in response to organic carbon (vegetable oil) injection. Our results showed that community assembly was shifted from more deterministic (NST = 21%) to more stochastic (NST = 70%) right after organic carbon input. As the vegetable oil was consumed, the community gradually returned to be more deterministic (NST = 27%). In addition, our results demonstrated that null model algorithms and community similarity metrics had strong effects on quantifying ecological stochasticity.