Hundreds of millions of litres of petroleum enter the environment from both natural and anthropogenic sources every year. The input from natural marine oil seeps alone would be enough to cover all of the world's oceans in a layer of oil 20 molecules thick. That the globe is not swamped with oil is testament to the efficiency and versatility of the networks of microorganisms that degrade hydrocarbons, some of which have recently begun to reveal the secrets of when and how they exploit hydrocarbons as a source of carbon and energy.

Degradation of oil on beaches is, in general, limited by the supply of inorganic nutrients. In order to obtain a more systematic understanding of the effects of nutrient addition on oil spill bioremediation, beach sediment microcosms contaminated with oil were treated with different levels of inorganic nutrients. Oil biodegradation was assessed respirometrically and on the basis of changes in oil composition. Bacterial communities were compared by numerical analysis of denaturing gradient gel electrophoresis (DGGE) profiles of PCR-amplified 16S rRNA genes and cloning and sequencing of PCR-amplified 16S rRNA genes. Nutrient amendment over a wide range of concentrations significantly improved oil degradation, confirming that N and P limited degradation over the concentration range tested. However, the extent and rate of oil degradation were similar for all microcosms, indicating that, in this experiment, it was the addition of inorganic nutrients rather than the precise amount that was most important operationally. Very different microbial communities were selected in all of the microcosms. Similarities between DGGE profiles of replicate samples from a single microcosm were high (95% ؎ 5%), but similarities between DGGE profiles from replicate microcosms receiving the same level of inorganic nutrients (68% ؎ 5%) were not significantly higher than those between microcosms subjected to different nutrient amendments (63% ؎ 7%). Therefore, it is apparent that the different communities selected cannot be attributed to the level of inorganic nutrients present in different microcosms. Bioremediation treatments dramatically reduced the diversity of the bacterial community. The decrease in diversity could be accounted for by a strong selection for bacteria belonging to the alkane-degrading Alcanivorax/Fundibacter group. On the basis of Shannon-Weaver indices, rapid recovery of the bacterial community diversity to preoiling levels of diversity occurred. However, although the overall diversity was similar, there were considerable qualitative differences in the community structure before and after the bioremediation treatments.

Knowledge about the relationship between microbial community structure and hydrogeochemistry (e.g., pollution, redox and degradation processes) in landfill leachate-polluted aquifers is required to develop tools for predicting and monitoring natural attenuation. In this study analyses of pollutant and redox chemistry were conducted in parallel with culture-independent profiling of microbial communities present in a well-defined aquifer (Banisveld, The Netherlands). Degradation of organic contaminants occurred under iron-reducing conditions in the plume of pollution, while upstream of the landfill and above the plume denitrification was the dominant redox process. Beneath the plume iron reduction occurred. Numerical comparison of 16S ribosomal DNA (rDNA)-based denaturing gradient gel electrophoresis (DGGE) profiles of Bacteria and Archaea in 29 groundwater samples revealed a clear difference between the microbial community structures inside and outside the contaminant plume. A similar relationship was not evident in sediment samples. DGGE data were supported by sequencing cloned 16S rDNA. Upstream of the landfill members of the ␤ subclass of the class Proteobacteria (␤-proteobacteria) dominated. This group was not encountered beneath the landfill, where gram-positive bacteria dominated. Further downstream the contribution of gram-positive bacteria to the clone library decreased, while the contribution of ␦-proteobacteria strongly increased and ␤-proteobacteria reappeared. The ␤-proteobacteria (Acidovorax, Rhodoferax) differed considerably from those found upstream (Gallionella, Azoarcus). Direct comparisons of cloned 16S rDNA with bands in DGGE profiles revealed that the data from each analysis were comparable. A relationship was observed between the dominant redox processes and the bacteria identified. In the iron-reducing plume members of the family Geobacteraceae made a strong contribution to the microbial communities. Because the only known aromatic hydrocarbon-degrading, iron-reducing bacteria are Geobacter spp., their occurrence in landfill leachate-contaminated aquifers deserves more detailed consideration.Contamination of groundwater is a serious environmental problem throughout the world as it affects drinking water resources and has an impact on oligotrophic environments. In The Netherlands, an important source of contamination is landfill leachate. In the past, landfilling was performed without the presence of appropriate liners to prevent percolation of leachate into underlying aquifers. Although many old landfills are closed now, the cessation of landfill operations does not stop chemical release into the environment. Organic compounds originating from household and industrial waste are found in most municipal landfills. Dramatic changes in aquifer geochemistry and microbiology downstream of landfills occur as a result of the high organic load of leachate (11). A sequence of redox zones develops in time and space, as the organic matter is microbiologically degraded and electron acceptors are depleted...

Summary The impact of various agricultural practices on soil biodiversity and, in particular, on arbuscular mycorrhizal fungi (AMF), is still poorly understood, although AMF can provide benefit to plants and ecosystems. Here, we tested whether organic farming enhances AMF diversity and whether AMF communities from organically managed fields are more similar to those of species‐rich grasslands or conventionally managed fields. To address this issue, the AMF community composition was assessed in 26 arable fields (13 pairs of organically and conventionally managed fields) and five semi‐natural grasslands, all on sandy soil. Terminal restriction fragment length polymorphism community fingerprinting was used to characterize AMF community composition. The average number of AMF taxa was highest in grasslands (8.8), intermediate in organically managed fields (6.4) and significantly lower in conventionally managed fields (3.9). Moreover, AMF richness increased significantly with the time since conversion to organic agriculture. AMF communities of organically managed fields were also more similar to those of natural grasslands when compared with those under conventional management, and were less uniform than their conventional counterparts, as expressed by higher β‐diversity (between‐site diversity). We suggest that organic management in agro‐ecosystems contributes to the restoration and maintenance of these important below‐ground mutualists.

A central focus in studies of microbial communities is the elucidation of the relationships between genotype, phenotype, and dynamic community structure. Here, we present a new computational method called community flux balance analysis (cFBA) to study the metabolic behavior of microbial communities. cFBA integrates the comprehensive metabolic capacities of individual microorganisms in terms of (genome-scale) stoichiometric models of metabolism, and the metabolic interactions between species in the community and abiotic processes. In addition, cFBA considers constraints deriving from reaction stoichiometry, reaction thermodynamics, and the ecosystem. cFBA predicts for communities at balanced growth the maximal community growth rate, the required rates of metabolic reactions within and between microbes and the relative species abundances. In order to predict species abundances and metabolic activities at the optimal community growth rate, a nonlinear optimization problem needs to be solved. We outline the methodology of cFBA and illustrate the approach with two examples of microbial communities. These examples illustrate two useful applications of cFBA. Firstly, cFBA can be used to study how specific biochemical limitations in reaction capacities cause different types of metabolic limitations that microbial consortia can encounter. In silico variations of those maximal capacities allow for a global view of the consortium responses to various metabolic and environmental constraints. Secondly, cFBA is very useful for comparing the performance of different metabolic cross-feeding strategies to either find one that agrees with experimental data or one that is most efficient for the community of microorganisms.