SUMMARY Freshwater bacteria are at the hub of biogeochemical cycles and control water quality in lakes. Despite this, little is known about the identity and ecology of functionally significant lake bacteria. Molecular studies have identified many abundant lake bacteria, but there is a large variation in the taxonomic or phylogenetic breadths among the methods used for this exploration. Because of this, an inconsistent and overlapping naming structure has developed for freshwater bacteria, creating a significant obstacle to identifying coherent ecological traits among these groups. A discourse that unites the field is sorely needed. Here we present a new freshwater lake phylogeny constructed from all published 16S rRNA gene sequences from lake epilimnia and propose a unifying vocabulary to discuss freshwater taxa. With this new vocabulary in place, we review the current information on the ecology, ecophysiology, and distribution of lake bacteria and highlight newly identified phylotypes. In the second part of our review, we conduct meta-analyses on the compiled data, identifying distribution patterns for bacterial phylotypes among biomes and across environmental gradients in lakes. We conclude by emphasizing the role that this review can play in providing a coherent framework for future studies.

Members of the marine Roseobacter lineage have been characterized as ecological generalists, suggesting that there will be challenges in assigning well-delineated ecological roles and biogeochemical functions to the taxon. To address this issue, genome sequences of 32 Roseobacter isolates were analyzed for patterns in genome characteristics, gene inventory, and individual gene/ pathway distribution using three predictive frameworks: phylogenetic relatedness, lifestyle strategy and environmental origin of the isolate. For the first framework, a phylogeny containing five deeply branching clades was obtained from a concatenation of 70 conserved single-copy genes. Somewhat surprisingly, phylogenetic tree topology was not the best model for organizing genome characteristics or distribution patterns of individual genes/pathways, although it provided some predictive power. The lifestyle framework, established by grouping isolates according to evidence for heterotrophy, photoheterotrophy or autotrophy, explained more of the gene repertoire in this lineage. The environment framework had a weak predictive power for the overall genome content of each strain, but explained the distribution of several individual genes/pathways, including those related to phosphorus acquisition, chemotaxis and aromatic compound degradation. Unassembled sequences in the Global Ocean Sampling metagenomic data independently verified this global-scale geographical signal in some Roseobacter genes. The primary findings emerging from this comparative genome analysis are that members of the lineage cannot be easily collapsed into just a few ecologically differentiated clusters (that is, there are almost as many clusters as isolates); the strongest framework for predicting genome content is trophic strategy, but no single framework gives robust predictions; and previously unknown homologs to genes for H 2 oxidation, proteorhodopsin-based phototrophy, xanthorhodpsin-based phototrophy, and CO 2 fixation by Form IC ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) expand the possible mechanisms for energy and carbon acquisition in this remarkably versatile bacterial lineage.

Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

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The fecal microbiome of cattle plays a critical role not only in animal health and productivity but also in food safety, pathogen shedding, and the performance of fecal pollution detection methods. Unfortunately, most published molecular surveys fail to provide adequate detail about variability in the community structures of fecal bacteria within and across cattle populations. Using massively parallel pyrosequencing of a hypervariable region of the rRNA coding region, we profiled the fecal microbial communities of cattle from six different feeding operations where cattle were subjected to consistent management practices for a minimum of 90 days. We obtained a total of 633,877 high-quality sequences from the fecal samples of 30 adult beef cattle (5 individuals per operation). Sequence-based clustering and taxonomic analyses indicate less variability within a population than between populations. Overall, bacterial community composition correlated significantly with fecal starch concentrations, largely reflected in changes in the Bacteroidetes, Proteobacteria, and Firmicutes populations. In addition, network analysis demonstrated that annotated sequences clustered by management practice and fecal starch concentration, suggesting that the structures of bovine fecal bacterial communities can be dramatically different in different animal feeding operations, even at the phylum and family taxonomic levels, and that the feeding operation is a more important determinant of the cattle microbiome than is the geographic location of the feedlot.The enteric microbiota of cattle affects animal health and food safety and is used as an indicator of fecal pollution, which can affect the types and concentrations of indicator organisms in recreational surface waters. The presence of pathogenic bacteria such as Escherichia coli O157:H7 in the bovine gastrointestinal tract has been linked to disease outbreaks due to the consumption of contaminated beef, milk, and drinking water (3). The average feedlot steer produces 1.62 kg of feces (dry matter) per day (2), resulting in more than 18 million metric tons of feces (dry matter) per year in the United States alone. When bovine fecal waste is moved from feedlot operations for land application as fertilizer or is accidentally discharged into the environment due to severe storms, hazardous events, or failure of onsite waste management practices, pathogenic members of this microbial community, such as E. coli O157:H7, Campylobacter jejuni, Salmonella spp., Leptospira interrogans, and Cryptosporidium parvum (5,14,22,41,44), can pose a serious public health risk.Because of the enormous influence the fecal bacterial community of cattle has on the beef and dairy industry, the economy, and public health, a great deal of research has been conducted to characterize the effects of animal age, disease state, feeding practices, and antibiotic treatments on cattle fecal microorganisms. Many of the most comprehensive studies use DNA-based methodologies, such as sequencing of the full-length 16S rRNA gen...