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...

Previous studies of the division of labor in colonies of eusocial Hymenoptera (wasps and bees) have led to two hypotheses regarding the evolution of juvenile hormone (JH) involvement. The novel-or single-function hypothesis proposes that the role of JH has changed from an exclusively reproductive function in primitively eusocial species (those lacking morphologically distinct queen and worker castes), to an exclusively behavioral function in highly eusocial societies (those containing morphologically distinct castes). In contrast, the split-function hypothesis proposes that JH originally functioned in the regulation of both reproduction and behavior in ancestral solitary species. Then, when reproductive and brood-care tasks came to be divided between queens and workers, the effects of JH were divided as well, with JH involved in regulation of reproductive maturation of egg-laying queens, and behavioral maturation, manifested as age-correlated changes in worker tasks, of workers. We report experiments designed to test these hypotheses. After documenting age-correlated changes in worker behavior (age polyethism) in the neotropical primitively eusocial wasp Polistes canadensis, we demonstrate that experimental application of the JH analog methoprene accelerates the onset of guarding behavior, an age-correlated task, and increases the number of foraging females; and we demonstrate that JH titers correlate with both ovarian development of queens and task differentiation in workers, as predicted by the split-function hypothesis. These findings support a view of social insect evolution that sees the contrasting worker and queen phenotypes as derived via decoupling of reproductive and brood-care components of the ancestral solitary reproductive physiology.behavioral development ͉ division of labor ͉ methoprene ͉ worker polyethism

Biogenic amines are widely characterized in pathways evaluating reward and punishment, resulting in appropriate aversive or appetitive responses of vertebrates and invertebrates. We utilized the honey bee model and a newly developed spatial avoidance conditioning assay to probe effects of biogenic amines octopamine (OA) and dopamine (DA) on avoidance learning. In this new protocol non-harnessed bees associate a spatial color cue with mild electric shock punishment. After a number of experiences with color and shock the bees no longer enter the compartment associated with punishment. Intrinsic aspects of avoidance conditioning are associated with natural behavior of bees such as punishment (lack of food, explosive pollination mechanisms, danger of predation, heat, etc.) and their association to floral traits or other spatial cues during foraging. The results show that DA reduces the punishment received whereas octopamine OA increases the punishment received. These effects are dose-dependent and specific to the acquisition phase of training. The effects during acquisition are specific as shown in experiments using the antagonists Pimozide and Mianserin for DA and OA receptors, respectively. This study demonstrates the integrative role of biogenic amines in aversive learning in the honey bee as modeled in a novel non-appetitive avoidance learning assay.

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