A primary aim of microbial ecology is to determine patterns and drivers of community distribution, interaction, and assembly amidst complexity and uncertainty. Microbial community composition has been shown to change across gradients of environment, geographic distance, salinity, temperature, oxygen, nutrients, pH, day length, and biotic factors 1-6 . These patterns have been identified mostly by focusing on one sample type and region at a time, with insights extra polated across environments and geography to produce generalized principles. To assess how microbes are distributed across environments globally-or whether microbial community dynamics follow funda mental ecological 'laws' at a planetary scale-requires either a massive monolithic cross environment survey or a practical methodology for coordinating many independent surveys. New studies of microbial environments are rapidly accumulating; however, our ability to extract meaningful information from across datasets is outstripped by the rate of data generation. Previous meta analyses have suggested robust gen eral trends in community composition, including the importance of salinity 1 and animal association 2 . These findings, although derived from relatively small and uncontrolled sample sets, support the util ity of meta analysis to reveal basic patterns of microbial diversity and suggest that a scalable and accessible analytical framework is needed.The Earth Microbiome Project (EMP, http://www.earthmicrobiome. org) was founded in 2010 to sample the Earth's microbial communities at an unprecedented scale in order to advance our understanding of the organizing biogeographic principles that govern microbial commu nity structure 7,8 . We recognized that open and collaborative science, including scientific crowdsourcing and standardized methods 8 , would help to reduce technical variation among individual studies, which can overwhelm biological variation and make general trends difficult to detect 9 . Comprising around 100 studies, over half of which have yielded peer reviewed publications (Supplementary Table 1), the EMP has now dwarfed by 100 fold the sampling and sequencing depth of earlier meta analysis efforts 1,2 ; concurrently, powerful analysis tools have been developed, opening a new and larger window into the distri bution of microbial diversity on Earth. In establishing a scalable frame work to catalogue microbiota globally, we provide both a resource for the exploration of myriad questions and a starting point for the guided acquisition of new data to answer them. As an example of using this Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of r...

The biological effects and expected fate of the vast amount of oil in the Gulf of Mexico from the Deepwater Horizon blowout are unknown owing to the depth and magnitude of this event. Here, we report that the dispersed hydrocarbon plume stimulated deep-sea indigenous γ-Proteobacteria that are closely related to known petroleum degraders. Hydrocarbon-degrading genes coincided with the concentration of various oil contaminants. Changes in hydrocarbon composition with distance from the source and incubation experiments with environmental isolates demonstrated faster-than-expected hydrocarbon biodegradation rates at 5°C. Based on these results, the potential exists for intrinsic bioremediation of the oil plume in the deep-water column without substantial oxygen drawdown.Assessing the environmental and public health impacts of the Deepwater Horizon blowout is difficult owing to the extreme depth of the blowout and the large volumes of oil released. Moreover, the effectiveness of the primary initial mitigation strategy (e.g., injecting the oil dispersant Corexit 9500 directly at the wellhead in a water depth of 1544 m) is difficult to assess despite initial analysis of its potential toxicity (1). An optional strategy for remediation of the deep underwater plume is to use the intrinsic bioremediation potential of deep-sea microorganisms to degrade the oil. This strategy depends on a number of environmental factors, including a favorable response of indigenous microorganisms to an increased concentration of hydrocarbons and/or dispersant.To determine the impact of the deep hydrocarbon plume on the marine microbes residing in the plume and the rates of hydrocarbon biodegradation, we collected deep-water samples from two ships between 25

The Deepwater Horizon oil spill in the Gulf of Mexico resulted in a deep-sea hydrocarbon plume that caused a shift in the indigenous microbial community composition with unknown ecological consequences. Early in the spill history, a bloom of uncultured, thus uncharacterized, members of the Oceanospirillales was previously detected, but their role in oil disposition was unknown. Here our aim was to determine the functional role of the Oceanospirillales and other active members of the indigenous microbial community using deep sequencing of community DNA and RNA, as well as single-cell genomics. Shotgun metagenomic and metatranscriptomic sequencing revealed that genes for motility, chemotaxis and aliphatic hydrocarbon degradation were significantly enriched and expressed in the hydrocarbon plume samples compared with uncontaminated seawater collected from plume depth. In contrast, although genes coding for degradation of more recalcitrant compounds, such as benzene, toluene, ethylbenzene, total xylenes and polycyclic aromatic hydrocarbons, were identified in the metagenomes, they were expressed at low levels, or not at all based on analysis of the metatranscriptomes. Isolation and sequencing of two Oceanospirillales single cells revealed that both cells possessed genes coding for n-alkane and cycloalkane degradation. Specifically, the near-complete pathway for cyclohexane oxidation in the Oceanospirillales single cells was elucidated and supported by both metagenome and metatranscriptome data. The draft genome also included genes for chemotaxis, motility and nutrient acquisition strategies that were also identified in the metagenomes and metatranscriptomes. These data point towards a rapid response of members of the Oceanospirillales to aliphatic hydrocarbons in the deep sea.

The Deepwater Horizon oil spill in the Gulf of Mexico is the deepest and largest offshore spill in the United State history and its impacts on marine ecosystems are largely unknown. Here, we showed that the microbial community functional composition and structure were dramatically altered in a deep-sea oil plume resulting from the spill. A variety of metabolic genes involved in both aerobic and anaerobic hydrocarbon degradation were highly enriched in the plume compared with outside the plume, indicating a great potential for intrinsic bioremediation or natural attenuation in the deep sea. Various other microbial functional genes that are relevant to carbon, nitrogen, phosphorus, sulfur and iron cycling, metal resistance and bacteriophage replication were also enriched in the plume. Together, these results suggest that the indigenous marine microbial communities could have a significant role in biodegradation of oil spills in deep-sea environments.

The Deepwater Horizon oil spill produced large subsurface plumes of dispersed oil and gas in the Gulf of Mexico that stimulated growth of psychrophilic, hydrocarbon degrading bacteria. We tracked succession of plume bacteria before, during and after the 83-day spill to determine the microbial response and biodegradation potential throughout the incident. Dominant bacteria shifted substantially over time and were dependent on relative quantities of different hydrocarbon fractions. Unmitigated flow from the wellhead early in the spill resulted in the highest proportions of n-alkanes and cycloalkanes at depth and corresponded with dominance by Oceanospirillaceae and Pseudomonas. Once partial capture of oil and gas began 43 days into the spill, petroleum hydrocarbons decreased, the fraction of aromatic hydrocarbons increased, and Colwellia, Cycloclasticus, and Pseudoalteromonas increased in dominance. Enrichment of Methylomonas coincided with positive shifts in the δ(13)C values of methane in the plume and indicated significant methane oxidation occurred earlier than previously reported. Anomalous oxygen depressions persisted at plume depths for over six weeks after well shut-in and were likely caused by common marine heterotrophs associated with degradation of high-molecular-weight organic matter, including Methylophaga. Multiple hydrocarbon-degrading bacteria operated simultaneously throughout the spill, but their relative importance was controlled by changes in hydrocarbon supply.

Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive.

Microbial communities can potentially mediate feedbacks between global change and ecosystem function, owing to their sensitivity to environmental change and their control over critical biogeochemical processes. Numerous ecosystem models have been developed to predict global change effects, but most do not consider microbial mechanisms in detail. In this idea paper, we examine the extent to which incorporation of microbial ecology into ecosystem models improves predictions of carbon (C) dynamics under warming, changes in precipitation regime, and anthropogenic nitrogen (N) enrichment. We focus on three cases in which this approach might be especially valuable: temporal dynamics in microbial responses to environmental change, variation in ecological function within microbial communities, and N effects on microbial activity. Four microbially-based models have addressed these scenarios. In each case, predictions of the microbial-based models differ-sometimes substantially-from comparable conventional models. However, validation and parameterization of model performance is challenging. We recommend that the development of microbial-based models must occur in conjunction with the development of theoretical frameworks that predict the temporal responses of microbial communities, the phylogenetic distribution of microbial functions, and the response of microbes to N enrichment.

The affinity of iron oxides and hydroxides for phosphorus is thought to contribute to phosphorus limitation to net primary productivity in humid tropical forests on acidic, highly weathered soils. Perennially warm, humid conditions and high biological activity in these soils can result in fluctuating redox potential that in turn leads to considerable iron reduction in the presence of labile carbon and humic substances. We investigated the effects of reducing conditions in combination with the addition of labile carbon substrates (glucose and acetate) and an electron shuttle compound on iron reduction and phosphorus release in a humid tropical forest soil. Glucose or acetate was added to soils as a single dose at the beginning of the experiment, and as pulsed inputs over time, which more closely mimics patterns in labile carbon availability. Iron reduction and phosphorus mobilization were weakly stimulated by a single low level addition of carbon, and the addition of the electron shuttle compound with or without added carbon. Pulsed labile carbon additions produced a significant increase in soil pH, soluble iron, and phosphorus concentrations. Pulsed labile carbon inputs also promoted the precipitation of ferrous hydroxide complexes which could increase the capacity for P sorption, although our results suggest that rates of P solubilization exceeded re-adsorption. Plant and microbial P demand are also likely to serve as an important sinks for released P, limiting the role of P re-adsorption. Our results suggest that reducing conditions coupled with periodic carbon inputs can stimulate iron reduction and a corresponding increase in soil phosphorus mobilization, which may provide a source of phosphorus to plants and microorganisms previously undocumented in these ecosystems.