Drying-rewetting cycles are ubiquitous across natural and managed ecosystems. These cycles are known to mobilize carbon (C) in soils producing dramatic pulses in microbial respiration. While many factors contribute to these pulses, the drying-rewetting history of soils affecting carbon emissions remains unclear, especially in irrigated soils where soil moisture fluctuations are more repetitive and/or frequent than natural, seasonally influenced soils. To understand the controls of repeated wet-up and dry down effects on agricultural soils, we used a systems approach to examine the cross section of a furrow irrigated orchard to delineate soil C dynamics. Specifically, we compared two contrasting water regimes, (1) soils temporarily but repeatedly inundated during water delivery (i.e., furrows) and (2) soils at the base of trees (i.e., berms) that only receive water during precipitation events in a semi-arid Mediterranean climate. Overall, our findings show that the heterogeneous landscape of a furrow irrigated field results in two separate systems within the field scale in gaseous release of C as CO2, microbial selectivity of substrates, and mechanisms for C stabilization. By monitoring soil moisture as a function of depth for over two years, our results reveal that furrow soils undergo dramatic wet-dry cycles, while moisture within the berm is relatively constant. We were able to capture the distinct heterogeneity of soil moisture changes within the furrow and berm soils by continuously monitoring CO2 flux throughout water input events in both the wet and dry season. Soil CO2 efflux is suppressed upon irrigation within furrows, while carbon oxidation in berm soils exhibits pore-connectivity limitations that result in lower fluxes when dry. Solid phase soil C speciation determined by C 1s NEXAFS demonstrated C of higher aromaticity remained in furrow soil compared to berm soils. Microbial community analysis shows significantly different communities reside within berm and furrow soils, where furrow soils support more anaerobic metabolisms and spore-formers while berm soils have relatively higher abundance of aerobic microbes capable of degrading larger, more complex C compounds. Our findings show that water regime (periodic inundation vs episodic rainfall) controlling rewetting history can greatly differentiate C respiration within managed soils.

Drying-rewetting cycles are ubiquitous across natural and managed ecosystems. These cycles are known to mobilize carbon (C) in soils producing dramatic pulses in microbial respiration. While many factors contribute to these pulses, how the drying-rewetting history of soils affects carbon emissions remains unclear, especially in irrigated soils where soil moisture fluctuations are more repetitive and/or frequent than natural, seasonally influenced soils. To understand the controls of repeated wet-up and dry down effects on agricultural soils, we used a systems-level approach to examine the cross section of a furrow irrigated orchard to delineate soil C dynamics. Specifically, we compared two contrasting water regimes: soils are temporarily but repeatedly inundated during water delivery (i.e., furrows) and soils at the base of trees (i.e., berms) that only receive water during precipitation events in a semi-arid Mediterranean climate. Overall, our findings show that the heterogeneous landscape of a furrow irrigated field results in two separate systems within the field scale in gaseous release of C as CO2, microbial selectivity of substrates, and mechanisms for C stabilization. By monitoring soil moisture as a function of depth for over two years, our results reveal that furrow soils undergo dramatic wet-dry cycles, while moisture within the berm is relatively constant. We were able to capture the contrasting response to soil moisture changes within the furrow and berm soils by continuously monitoring CO2 flux throughout water input events in both the wet and dry season. Soil CO2 efflux is suppressed upon irrigation within furrows, while carbon oxidation in berm soils exhibit pore-connectivity limitations that result in lower fluxes when dry. Solid phase soil C speciation determined by C 1s NEXAFS demonstrated C of higher aromaticity remained in furrow soil compared to berm soils. Microbial community analysis shows significantly different communities reside within berm and furrow soils, where furrow soils support more anaerobic metabolisms and spore-formers while berm soils have relatively higher abundance of aerobic microbes capable of degrading larger, more complex C compounds. Our findings show that water regime (periodic inundation vs episodic rainfall) controlling rewetting history can greatly differentiate C respiration within managed soils.

Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water.

The Deepwater Horizon (DWH) accident released an estimated 4.1 million barrels of oil and 1010 mol of natural gas into the Gulf of Mexico, forming deep-sea plumes of dispersed oil droplets and dissolved gases that were largely degraded by bacteria. During the course of this 3-mo disaster a series of different bacterial taxa were enriched in succession within deep plumes, but the metabolic capabilities of the different populations that controlled degradation rates of crude oil components are poorly understood. We experimentally reproduced dispersed plumes of fine oil droplets in Gulf of Mexico seawater and successfully replicated the enrichment and succession of the principal oil-degrading bacteria observed during the DWH event. We recovered near-complete genomes, whose phylogeny matched those of the principal biodegrading taxa observed in the field, including the DWH Oceanospirillales (now identified as a Bermanella species), multiple species of Colwellia, Cycloclasticus, and other members of Gammaproteobacteria, Flavobacteria, and Rhodobacteria. Metabolic pathway analysis, combined with hydrocarbon compositional analysis and species abundance data, revealed substrate specialization that explained the successional pattern of oil-degrading bacteria. The fastest-growing bacteria used short-chain alkanes. The analyses also uncovered potential cooperative and competitive relationships, even among close relatives. We conclude that patterns of microbial succession following deep ocean hydrocarbon blowouts are predictable and primarily driven by the availability of liquid petroleum hydrocarbons rather than natural gases.

A laboratory simulation revealed the succession patterns of oil degradation and microbial community changes during the Deepwater Horizon oil spill, one of the largest environmental catastrophes in human history (1). Hu et al. successfully recovered genomes of several oil-degrading bacteria, including one of “ Candidatus Bermanella macondoprimitus ,” with high identity to the dominant Oceanospirillales 16S rRNA gene sequences recovered from the deep-ocean hydrocarbon plumes (1, 2). Delmont and Eren (3) reanalyzed metagenomic data generated from these plumes (4) to argue that this Bermanella was not present. We find that Delmont and Eren (3) draw their conclusions from methods that are flawed. Importantly, the authors (3) use samples that underwent an emulsion PCR (4) [according to Mason et al. (4) overloaded by ∼20 molecules per droplet (5)], which likely resulted in a heavy distortion of the sequenced community and observed abundance patterns (6). Delmont and Eren (3) failed to use any metagenome assembler that attempts to account … [↵][1]1To whom correspondence should be addressed. Email: glandersen{at}lbl.gov. [1]: #xref-corresp-1-1

Recycling human waste for beneficial use has been practiced for millennia. Aerobic (thermophilic) composting of sewage sludge has been shown to reduce populations of opportunistically pathogenic bacteria and to inactivate both Ascaris eggs and culturable Escherichia coli in raw waste, but there is still a question about the fate of most fecal bacteria when raw material is composted directly. This study undertook a comprehensive microbial community analysis of composting material at various stages collected over 6 months at two composting facilities in Haiti. The fecal microbiota signal was monitored using a high-density DNA microarray (PhyloChip). Thermophilic composting altered the bacterial community structure of the starting material. Typical fecal bacteria classified in the following groups were present in at least half the starting material samples, yet were reduced below detection in finished compost: Prevotella and Erysipelotrichaceae (100% reduction of initial presence), Ruminococcaceae (98-99%), Lachnospiraceae (83-94%, primarily unclassified taxa remained), Escherichia and Shigella (100%). Opportunistic pathogens were reduced below the level of detection in the final product with the exception of Clostridium tetani, which could have survived in a spore state or been reintroduced late in the outdoor maturation process. Conversely, thermotolerant or thermophilic Actinomycetes and Firmicutes (e.g., Thermobifida, Bacillus, Geobacillus) typically found in compost increased substantially during the thermophilic stage. This community DNA-based assessment of the fate of human fecal microbiota during thermophilic composting will help optimize this process as a sanitation solution in areas where infrastructure and resources are limited.

Because of the extreme conditions of the Deepwater Horizon (DWH) release (turbulent flow at 1500m depth and 5°C water temperature) and the sub-surface application of dispersant, small but neutrally buoyant oil droplets <70μm were formed, remained in the water column and were subjected to in-situ biodegradation processes. In order to investigate the biodegradation of Macondo oil components during the release, we designed and performed an experiment to evaluate the interactions of the indigenous microbial communities present in the deep waters of the Gulf of Mexico (GOM) with oil droplets of two representative sizes (10μm and 30μm median volume diameter) created with Macondo source oil in the presence of Corexit 9500 using natural seawater collected at the depth of 1100-1300m in the vicinity of the DWH wellhead. The evolution of the oil was followed in the dark and at 5°C for 64days by collecting sacrificial water samples at fixed intervals and analyzing them for a wide range of chemical and biological parameters including volatile components, saturated and aromatic hydrocarbons, dispersant markers, dissolved oxygen, nutrients, microbial cell counts and microbial population dynamics. A one phase exponential decay from a plateau model was used to calculate degradation rates and lag times for more than 150 individual oil components. Calculations were normalized to a conserved petroleum biomarker (30αβ-hopane). Half-lives ranged from about 3days for easily degradable compounds to about 60days for higher molecular weight aromatics. Rapid degradation was observed for BTEX, 2-3 ring PAHs, and n-alkanes below n-C23. The results in this experimental study showed good agreement with the n-alkane (n-C13 to n-C26) half-lives (0.6-9.5days) previously reported for the Deepwater Horizon plume samples and other laboratory studies with chemically dispersed Macondo oil conducted at low temperatures (<8°C). The responses of the microbial populations also were consistent with what was reported during the actual oil release, e.g. Colwellia, Cycloclasticus and Oceanospirillales (including the specific DWH Oceanospirillales) were present and increased in numbers indicating that they were degrading components of the oil. The consistency of the field and laboratory data indicate that these results could be used, in combination with other field and model data to characterize the dissipation of Macondo oil in the deepwater environment as part of the risk assessment estimations.

Sources of fecal indicator bacteria are difficult to identify in watersheds that are impacted by a variety of non-point sources. We developed a molecular source tracking test using the PhyloChip microarray that detects and distinguishes fecal bacteria from humans, birds, ruminants, horses, pigs and dogs with a single test. The multiplexed assay targets 9001 different 25-mer fragments of 16S rRNA genes that are common to the bacterial community of each source type. Both random forests and SourceTracker were tested as discrimination tools, with SourceTracker classification producing superior specificity and sensitivity for all source types. Validation with 12 different mammalian sources in mixtures found 100% correct identification of the dominant source and 84-100% specificity. The test was applied to identify sources of fecal indicator bacteria in the Russian River watershed in California. We found widespread contamination by human sources during the wet season proximal to settlements with antiquated septic infrastructure and during the dry season at beaches during intense recreational activity. The test was more sensitive than common fecal indicator tests that failed to identify potential risks at these sites. Conversely, upstream beaches and numerous creeks with less reliance on onsite wastewater treatment contained no fecal signal from humans or other animals; however these waters did contain high counts of fecal indicator bacteria after rain. Microbial community analysis revealed that increased E. coli and enterococci at these locations did not co-occur with common fecal bacteria, but rather co-varied with copiotrophic bacteria that are common in freshwaters with high nutrient and carbon loading, suggesting runoff likely promoted the growth of environmental strains of E. coli and enterococci. These results indicate that machine-learning classification of PhyloChip microarray data can outperform conventional single marker tests that are used to assess health risks, and is an effective tool for distinguishing numerous fecal and environmental sources of pathogen indicators. (C) 2016 Elsevier Ltd. All rights reserved.

Wheat and maize are increasingly used as alternative crops to sunflower monocultures that dominate the Hetao Irrigation District in China. Shifts from sunflower monocultures to alternate cropping systems may have significant effects on below-ground microbial communities which control nutrient cycling and influence plant productivity. In this research, rhizosphere bacterial communities were compared among sunflower, wheat and maize cropping systems by 454 pyrosequencing. These cropping systems included 2 years wheat (cultivar Yongliang 4) and maize (cultivar Sidan 19) monoculture, more than 20 years sunflower (cultivar 5009) monoculture, and wheat-sunflower and maize-sunflower rotation. In addition, we investigated rhizosphere bacterial communities of healthy and diseased plants at maturity to determine the relationship between plant health and rotation effect. The results revealed taxonomic information about the overall bacterial community. And significant differences in bacterial community structure were detected among these cropping systems. Eight of the most abundant groups including Proteobacteria, Bacteroidetes, Acidobacteria, Gemmatimonadetes, Chloroflexi, Actinobacteria, Planctomycetes and Firmicutes accounted for more than 85% of the sequences in each treatment. The wheat-wheat rhizosphere had the highest proportion of Acidobacteria, Bacteroidetes and the lowest proportion of unclassified bacteria. Wheat-sunflower cropping system showed more abundant Acidobacteria than maize-sunflower and sunflower monoculture, exhibiting some influences of wheat on the succeeding crop. Maize-maize rhizosphere had the highest proportion of γ-Proteobacteria, Pseudomonadales and the lowest proportion of Acidobacteria. Sunflower rotation with wheat and maize could increase the relative abundance of the Acidobacteria while decrease the relative abundance of the unclassified phyla, as was similar with the health plants. This suggests some positive impacts of rotation with wheat and maize on the bacterial communities within a single field. These results demonstrate that different crop rotation systems can have significant effects on rhizosphere microbiomes that potentially alter plant productivities in agricultural systems.

ABSTRACT 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. IMPORTANCE Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial communities can be used as environmental sensors that respond to and capture perturbations caused by human impacts.

Deciphering microbial communities and their role in Earth's biosphere is crucial for addressing challenges in human health, agriculture, bioremediation and other natural processes. While next-generation sequencing platforms are still under development to improve accuracy, read length and sequencing depth, microarray-based methods have become an attractive alternative for 16S rRNA gene microbial community comparisons. The hybridization method is well-established in the laboratory. Thus, main areas of improvement lie with the development of enhanced bioinformatics and statistical procedures for microarray data, rather than with improvements to the platform itself. In this communication we applied recently-developed bioinformatics tools to re-analyse G3 PhyloChip (TM) DNA microarray data acquired from deep ocean samples collected during the 2010 Deepwater Horizon oil spill in the Gulf of Mexico. We show that data collected with the G3 PhyloChip assay can be analysed at various stages of resolution, from individual probes to pairs of probes to quartets of probes and finally at the commonly used probe-set level where each probe-set is associated with one operational taxonomic unit (OTU). Our analysis methods comprised topological data analysis (TDA) to facilitate the detection of outlier bio-specimens and the reconstruction of empirical OTUs (eOTUs) in an unsupervised manner, without the need for pre-defined reference OTUs (rOTUs). We observed that the quartet level provided sufficient resolution for identifying a subtle outlier sample with TDA, while the eOTU reconstruction was useful for annotation of the taxa associated with significant population changes in the elevated hydrocarbon waters. The presented methods will improve the deduction of important biological processes from G3 PhyloChip experiments.

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 δ13C 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.

Two forested study sites, one well and one poorly drained, were used for investigation of the effects of variation in drainage, microclimate, and addition of inorganic nitrogen (N) on the whole soil microbial community and its methane cycling capacity. Both sites were capable of consuming and releasing large quantities of methane. The composition of the soil microbial community was investigated using the 3rd generation PhyloChip, a bacterial and archaeal 16S rRNA gene microarray. The PhyloChip was also used to target the composition of methane- and some N-cycling microorganisms. Relative abundance of functional genes involved in methane production and consumption was evaluated with qPCR.Soil drainage condition determined the microbial community structure within and between sites. Greater community structure variation, richness of methanotrophs, and higher abundances of both methanotrophs and methanogens were all found in the poorly drained site, as was higher soil moisture and C content and methane release. In the poorly drained site, high N (67 kg NH4NO3 ha(-1) yr(-1)) increased methanotroph and methanogen abundance, overall taxonomic richness of Bacteria and Archaea, and richness of nitrifiers and methanotrophs. In the well drained site, high N decreased taxonomic richness. Results may indicate that high N concentrations stimulated oxidative reactions, including ammonia and methane oxidation and nitrification in the short term. The resultant increase in release of methane from the high N plots of the poorly-drained site may have been due to indirect inhibition of methane oxidation by the increase in other oxidative reactions. Alternatively, both methanogens and methanotrophs may have been stimulated by high N. Well-drained site high N decreased the taxonomic richness of the soil, but did not impact methane-cycling microbes. These findings begin to bridge the gap between microbial-scale community dynamics and ecosystem-scale ecological functions. (C) 2013 Elsevier Ltd. All rights reserved.

Molecular microbial community analyses provide information on thousands of microorganisms simultaneously, and integrate biotic and abiotic perturbations caused by fecal contamination entering water bodies. A few studies have explored community methods as emerging approaches for microbial source tracking (MST), however, an evaluation of the current state of this approach is lacking. Here, we utilized three types of community-based methods with 64 blind, single- or dual-source, challenge samples generated from 12 sources, including: humans (feces), sewage, septage, dogs, pigs, deer, horses, cows, chickens, gulls, pigeons, and geese. Each source was a composite from multiple donors from four representative geographical regions in California. Methods evaluated included terminal restriction fragment polymorphism (TRFLP), phylogenetic microarray (PhyloChip), and next generation (Illumina) sequencing. These methods correctly identified dominant (or sole) sources in over 90% of the challenge samples, and exhibited excellent specificity regardless of source, rarely detecting a source that was not present in the challenge sample. Sensitivity, however, varied with source and community analysis method. All three methods distinguished septage from human feces and sewage, and identified deer and horse with 100% sensitivity and 100% specificity. Method performance improved if the composition of blind dual-source reference samples were defined by DNA contribution of each single source within the mixture, instead of by Enterococcus colony forming units. Data analysis approach also influenced method performance, indicating the need to standardize data interpretation. Overall, results of this study indicate that community analysis methods hold great promise as they may be used to identify any source, and they are particularly useful for sources that currently do not have, and may never have, a source-specific single marker gene.

Microbial source tracking (MST) methods were evaluated in the Source Identification Protocol Project (SIPP), in which 27 laboratories compared methods to identify host sources of fecal pollution from blinded water samples containing either one or two different fecal types collected from California. This paper details lessons learned from the SIPP study and makes recommendations to further advance the field of MST. Overall, results from the SIPP study demonstrated that methods are available that can correctly identify whether particular host sources including humans, cows and birds have contributed to contamination in a body of water. However, differences between laboratory protocols and data processing affected results and complicated interpretation of MST method performance in some cases. This was an issue particularly for samples that tested positive (non-zero Ct values) but below the limits of quantification or detection of a PCR assay. Although false positives were observed, such samples in the SIPP study often contained the fecal pollution source that was being targeted, i.e., the samples were true positives. Given these results, and the fact that MST often requires detection of targets present in low concentrations, we propose that such samples be reported and identified in a unique category to facilitate data analysis and method comparisons. Important data can be lost when such samples are simply reported as positive or negative. Actionable thresholds were not derived in the SIPP study due to limitations that included geographic scope, age of samples, and difficulties interpreting low concentrations of target in environmental samples. Nevertheless, the results of the study support the use of MST for water management, especially to prioritize impaired waters in need of remediation. Future integration of MST data into quantitative microbial risk assessments and other models could allow managers to more efficiently protect public health based on site conditions.