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...

b16S rRNA sequencing, commonly used to survey microbial communities, begins by grouping individual reads into operational taxonomic units (OTUs). There are two major challenges in calling OTUs: identifying bacterial population boundaries and differentiating true diversity from sequencing errors. Current approaches to identifying taxonomic groups or eliminating sequencing errors rely on sequence data alone, but both of these activities could be informed by the distribution of sequences across samples. Here, we show that using the distribution of sequences across samples can help identify population boundaries even in noisy sequence data. The logic underlying our approach is that bacteria in different populations will often be highly correlated in their abundance across different samples. Conversely, 16S rRNA sequences derived from the same population, whether slightly different copies in the same organism, variation of the 16S rRNA gene within a population, or sequences generated randomly in error, will have the same underlying distribution across sampled environments. We present a simple OTU-calling algorithm (distribution-based clustering) that uses both genetic distance and the distribution of sequences across samples and demonstrate that it is more accurate than other methods at grouping reads into OTUs in a mock community. Distribution-based clustering also performs well on environmental samples: it is sensitive enough to differentiate between OTUs that differ by a single base pair yet predicts fewer overall OTUs than most other methods. The program can decrease the total number of OTUs with redundant information and improve the power of many downstream analyses to describe biologically relevant trends.

Summary 1.It has been recently showed that one bacterial strain isolated from the uropygial gland of a nestling hoopoe Upupa epops produced antimicrobial peptides active against a broad spectrum of pathogenic bacteria. These bacteria might thus mediate antimicrobial properties of the uropygial secretions as a consequence of the symbiotic association with hoopoes. 2. We study antimicrobial properties of white (from males and non-breeding females) and brown (from nestlings and breeding females) uropygial gland secretions of hoopoes Upupa epops , as well as the association with the presence of bacteria living inside their uropygial gland. 3. We found that brown, but not white secretions contained bacteria and showed antimicrobial activity against the feather degrading bacterium Bacillus licheniformis . The antagonistic activity of bacterial colonies was mediated by antimicrobial peptides because protease inhibited antimicrobial properties. 4. All except one identified bacterium in aerobic cultures were of the genus Enterococcus , and the microscopic study of uropygial secretions and glands confirmed a high density of bacteria within the gland. 5. Furthermore, we studied potential benefits of antimicrobial peptides produced by symbiotic bacteria of hoopoes by adding protease to incubating nests. 6. The experiment increased bacterial growth and hatching failures in hoopoes but not in spotless starlings Sturnus unicolor , a species that does not harbour bacteria in its uropygial gland. 7. Thus, microbiological, anatomical and ecological results suggest a tight symbiotic interaction between bacteria that produce antibiotic substances and the hoopoes.

Because microbial plankton in the ocean comprise diverse bacteria, algae, and protists that are subject to environmental forcing on multiple spatial and temporal scales, a fundamental open question is to what extent these organisms form ecologically cohesive communities. Here we show that although all taxa undergo large, near daily fluctuations in abundance, microbial plankton are organized into clearly defined communities whose turnover is rapid and sharp. We analyze a time series of 93 consecutive days of coastal plankton using a technique that allows inference of communities as modular units of interacting taxa by determining positive and negative correlations at different temporal frequencies. This approach shows both coordinated population expansions that demarcate community boundaries and high frequency of positive and negative associations among populations within communities. Our analysis thus highlights that the environmental variability of the coastal ocean is mirrored in sharp transitions of defined but ephemeral communities of organisms.