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...

Sponges (phylum Porifera) are early-diverging metazoa renowned for establishing complex microbial symbioses. Here we present a global Porifera microbiome survey, set out to establish the ecological and evolutionary drivers of these host–microbe interactions. We show that sponges are a reservoir of exceptional microbial diversity and major contributors to the total microbial diversity of the world's oceans. Little commonality in species composition or structure is evident across the phylum, although symbiont communities are characterized by specialists and generalists rather than opportunists. Core sponge microbiomes are stable and characterized by generalist symbionts exhibiting amensal and/or commensal interactions. Symbionts that are phylogenetically unique to sponges do not disproportionally contribute to the core microbiome, and host phylogeny impacts complexity rather than composition of the symbiont community. Our findings support a model of independent assembly and evolution in symbiont communities across the entire host phylum, with convergent forces resulting in analogous community organization and interactions.

Abstract. The dichotomy between high microbial abundance (HMA) and low microbial abundance (LMA) sponges has been long recognized. In the present study, 56 sponge species from three geographic regions (greater Caribbean, Mediterranean, Red Sea) were investigated by transmission electron microscopy for the presence of microorganisms in the mesohyl matrix. Additionally, bacterial enumeration by DAPI-counting was performed on a subset of samples. Of the 56 species investigated, 28 were identified as belonging to the HMA and 28 to the LMA category. The sponge orders Agelasida and Verongida consisted exclusively of HMA species, and the Poecilosclerida were composed only of LMA sponges. Other taxa contained both types of microbial associations (e.g., marine Haplosclerida, Homoscleromorpha, Dictyoceratida), and a clear phylogenetic pattern could not be identified. For a few sponge species, an intermediate microbial load was determined, and the microscopy data did not suffice to reliably determine HMA or LMA status. To experimentally determine the HMA or LMA status of a sponge species, we therefore recommend a combination of transmission electron microscopy and 16S rRNA gene sequence data. This study significantly expands previous reports on microbial abundances in sponge tissues and contributes to a better understanding of the HMA-LMA dichotomy in sponge-microbe symbioses.

Marine sponges (phylum Porifera) are a diverse, phylogenetically deep-branching clade known for forming intimate partnerships with complex communities of microorganisms. To date, 16S rRNA gene sequencing studies have largely utilised different extraction and amplification methodologies to target the microbial communities of a limited number of sponge species, severely limiting comparative analyses of sponge microbial diversity and structure. Here, we provide an extensive and standardised dataset that will facilitate sponge microbiome comparisons across large spatial, temporal, and environmental scales. Samples from marine sponges (n = 3569 specimens), seawater (n = 370), marine sediments (n = 65) and other environments (n = 29) were collected from different locations across the globe. This dataset incorporates at least 268 different sponge species, including several yet unidentified taxa. The V4 region of the 16S rRNA gene was amplified and sequenced from extracted DNA using standardised procedures. Raw sequences (total of 1.1 billion sequences) were processed and clustered with (i) a standard protocol using QIIME closed-reference picking resulting in 39 543 operational taxonomic units (OTU) at 97% sequence identity, (ii) a de novo clustering using Mothur resulting in 518 246 OTUs, and (iii) a new high-resolution Deblur protocol resulting in 83 908 unique bacterial sequences. Abundance tables, representative sequences, taxonomic classifications, and metadata are provided. This dataset represents a comprehensive resource of sponge-associated microbial communities based on 16S rRNA gene sequences that can be used to address overarching hypotheses regarding host-associated prokaryotes, including host specificity, convergent evolution, environmental drivers of microbiome structure, and the sponge-associated rare biosphere.

The dichotomy between high microbial abundance (HMA) and low microbial abundance (LMA) sponges has been observed in sponge-microbe symbiosis, although the extent of this pattern remains poorly unknown. We characterized the differences between the microbiomes of HMA (n = 19) and LMA (n = 17) sponges (575 specimens) present in the Sponge Microbiome Project. HMA sponges were associated with richer and more diverse microbiomes than LMA sponges, as indicated by the comparison of alpha diversity metrics. Microbial community structures differed between HMA and LMA sponges considering Operational Taxonomic Units (OTU) abundances and across microbial taxonomic levels, from phylum to species. The largest proportion of microbiome variation was explained by the host identity. Several phyla, classes, and OTUs were found differentially abundant in either group, which were considered “HMA indicators” and “LMA indicators.” Machine learning algorithms (classifiers) were trained to predict the HMA-LMA status of sponges. Among nine different classifiers, higher performances were achieved by Random Forest trained with phylum and class abundances. Random Forest with optimized parameters predicted the HMA-LMA status of additional 135 sponge species (1,232 specimens) without a priori knowledge. These sponges were grouped in four clusters, from which the largest two were composed of species consistently predicted as HMA (n = 44) and LMA (n = 74). In summary, our analyses shown distinct features of the microbial communities associated with HMA and LMA sponges. The prediction of the HMA-LMA status based on the microbiome profiles of sponges demonstrates the application of machine learning to explore patterns of host-associated microbial communities.