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...

There are national and regional efforts aimed at increasing fertilizer use in sub-Saharan Africa, where nitrogen (N) inputs must be increased by an order of magnitude or more to reach recommended rates. Fertilizer inputs increase N availability and cycling rates and subsequently emissions of nitrous oxide (N 2 O), a powerful greenhouse gas and the primary catalyst of stratospheric ozone depletion. We established experimental maize (Zea mays L.) plots in western Kenya to quantify the relationship between N inputs and N 2 O emissions. Mean N 2 O emissions were marginally, but not significantly, better described by an exponential model relating emissions to N input rate in 2011; in 2012, an exponential relationship provided the best fit compared to linear and other nonlinear models. Most N 2 O fluxes occurred during the 30 days following the second fertilizer application. Estimates of fertilizer N lost as N 2 O annually were well below the 1% Intergovernmental Panel on Climate Change default emission factor, ranging from 0.07% to 0.11% in 2011 and from 0.01% to 0.09% in 2012. In both years, the largest impact on annual N 2 O emissions occurred when inputs increased from 100 to 150 kg N ha

The nitrogen-fixing legume kudzu (Pueraria montana) is a widespread invasive plant in the southeastern United States with physiological traits that may lead to important impacts on ecosystems and the atmosphere. Its spread has the potential to raise ozone levels in the region by increasing nitric oxide (NO) emissions from soils as a consequence of increasing nitrogen (N) inputs and cycling in soils. We studied the effects of kudzu invasions on soils and trace N gas emissions at three sites in Madison County, Georgia in 2007 and used the results to model the effects of kudzu invasion on regional air quality. We found that rates of net N mineralization increased by up to 1,000%, and net nitrification increased by up to 500% in invaded soils in Georgia. Nitric oxide emissions from invaded soils were more than 100% higher (2.81 vs. 1.24 ng NO-N cm). We used the GEOS-Chem chemical transport model to evaluate the potential impact of kudzu invasion on regional atmospheric chemistry and air quality. In an extreme scenario, extensive kudzu invasion leads directly to an increase in the number of high ozone events (above 70 ppb) of up to 7 days each summer in some areas, up from 10 to 20 days in a control scenario with no kudzu invasion. These results establish a quantitative link between a biological invasion and ozone formation and suggest that in this extreme scenario, kudzu invasion can overcome some of the air quality benefits of legislative control.invasive species | nitrogen fixation | nitrous oxide | tropospheric ozone | biogeochemical processes

Many nations responded to the COVID-19 pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO 2 , other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. Twelve models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate. Plain Language Summary Many nations responded to the COVID-19 pandemic by restricting travel and other activities during 2020. This caused a temporary reduction in emissions of CO 2 and other pollutants. We compare results from twelve Earth system models to see if the emissions reductions affected climate. These twelve models performed over 300 experiments using multiple initial-conditions. We find a consensus that aerosol amounts were reduced, especially over southern and eastern Asia, during 2020-2024. This led to increases in solar radiation reaching the surface in this region. However, we could not detect any associated impact on temperature or rainfall. We recommend more analyses on regional scales. We also suggest that analysis of extreme weather and air quality would be useful to test the impact on climate of emission reductions due to COVID-19.