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Mortality rates during the COVID-19 pandemic have varied by orders of magnitude across communities in the United States1. Individual, socioeconomic, and environmental factors have been linked to health outcomes of COVID-192,3,4,5. It is now widely appreciated that the environmental microbiome, composed of microbial communities associated with soil, water, atmosphere, and the built environment, impacts immune system development and susceptibility to immune-mediated disease6,7,8. The human microbiome has been linked to individual COVID-19 disease outcomes9, but there are limited data on the influence of the environmental microbiome on geographic variation in COVID-19 across populations10. To fill this knowledge gap, we used taxonomic profiles of fungal communities associated with 1,135 homes in 494 counties from across the United States in a machine learning analysis to predict COVID-19 Infection Fatality Ratios (the number of deaths caused by COVID-19 per 1000 SARS-CoV-2 infections1; ‘IFR’). Here we show that exposure to increased fungal diversity, and in particular indoor exposure to outdoor fungi, is associated with reduced SARS-CoV-2 IFR. Further, we identify seven fungal genera that are the predominant drivers of this protective signal and may play a role in suppressing COVID-19 mortality. This relationship is strongest in counties where human populations have remained stable over at least the previous decade, consistent with the importance of early-life microbial exposures11. We also assessed the explanatory power of 754 other environmental and socioeconomic factors, and found that indoor-outdoor fungal beta-diversity is amongst the strongest predictors of county-level IFR, on par with the most important known COVID-19 risk factors, including age12. We anticipate that our study will be a starting point for further integration of environmental mycobiome data with population health information, providing an important missing link in our capacity to identify vulnerable populations. Ultimately, our identification of specific genera predicted to be protective against COVID-19 mortality may point toward novel, proactive therapeutic approaches to infectious disease.

BackgroundDuring a pandemic, estimates of geographic variability in disease burden are important but limited by the availability and quality of data.MethodsWe propose a framework for estimating geographic variability in testing effort, total number of infections, and infection fatality ratio (IFR). Because symptomatic people are more likely to seek testing, we use a noncentral hypergeometric model that accounts for differential probability of positive tests. We apply this framework to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs from March 1, 2020 to October 31, 2020. Using data on population size, number of observed cases, number of reported deaths in each U.S. county and state, and number of tests in each U.S. state, we develop a series of estimators to identify the number of SARS-CoV-2 infections and IFRs at the county level. We then perform a simulation and compare the estimated values to simulated values to demonstrate the validity of our approach.FindingsApplying the county-level estimators to the real, unsimulated COVID-19 data spanning March 1, 2020 to October 31, 2020 from across the U.S., we found that IFRs varied from 0 to 0.0273, with an interquartile range of 0.0022 and a median of 0.0018. The estimators for IFRs, number of infections, and number of tests showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.88, 0.95, and 0.74, respectively.InterpretationWe propose an estimation framework that can be used to identify area-level variation in IFRs and performs well to estimate county-level IFRs in the U.S. COVID-19 pandemic.

Microbial communities play a major role in disease, biogeochemical cycling, agriculture, and bioremediation. However, identifying the ecological processes that govern microbial community assembly and disentangling the relative impacts of those processes has proven challenging. Here, we propose that this discord is due to microbial systems being studied at different spatial, temporal, and phylogenetic scales. We argue that different processes dominate at different scales, and that through a more explicit consideration of spatial, temporal, and phylogenetic grains and extents (the two components of scale) a more accurate, clear, and useful understanding of microbial community assembly can be developed. We demonstrate the value of applying ecological concepts of scale to microbiology, specifically examining their application to nestedness, legacy effects, and taxa-area relationships of microbial systems. These proposed considerations of scale will help resolve long-standing debates in microbial ecology regarding the processes determining the assembly of microbial communities, and provide organizing principles around which hypotheses and theories can be developed.

Author(s): Vangay, Pajau; Burgin, Josephine; Johnston, Anjanette; Beck, Kristen L; Berrios, Daniel C; Blumberg, Kai; Canon, Shane; Chain, Patrick; Chandonia, John-Marc; Christianson, Danielle; Costes, Sylvain V; Damerow, Joan; Duncan, William D; Dundore-Arias, Jose Pablo; Fagnan, Kjiersten; Galazka, Jonathan M; Gibbons, Sean M; Hays, David; Hervey, Judson; Hu, Bin; Hurwitz, Bonnie L; Jaiswal, Pankaj; Joachimiak, Marcin P; Kinkel, Linda; Ladau, Joshua; Martin, Stanton L; McCue, Lee Ann; Miller, Kayd; Mouncey, Nigel; Mungall, Chris; Pafilis, Evangelos; Reddy, TBK; Richardson, Lorna; Roux, Simon; Schriml, Lynn M; Shaffer, Justin P; Sundaramurthi, Jagadish Chandrabose; Thompson, Luke R; Timme, Ruth E; Zheng, Jie; Wood-Charlson, Elisha M; Eloe-Fadrosh, Emiley A | Abstract: Volume 6, no. 1, e01194-20, 2021, https://doi.org/10.1128/mSystems.01194-20. The article byline and affiliation line should read as given in this correction.

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

SAR86 is an abundant and ubiquitous heterotroph in the surface ocean that plays a central role in the function of marine ecosystems. We hypothesized that despite its ubiquity, different SAR86 subgroups may be endemic to specific ocean regions and functionally specialized for unique marine environments. However, the global biogeographical distributions of SAR86 genes, and the manner in which these distributions correlate with marine environments, have not been investigated. We quantified SAR86 gene content across globally-distributed metagenomic samples and modeled these gene distributions as a function of 51 environmental variables. We identified five distinct clusters of genes within the SAR86 pangenome, each with a unique geographic distribution associated with specific environmental characteristics. Gene clusters are characterized by strong taxonomic enrichment of distinct SAR86 genomes and partial assemblies, as well as differential enrichment of certain functional groups, suggesting differing functional and ecological roles of SAR86 ecotypes. We then leveraged our models and high-resolution, remote sensing-derived environmental data to predict the distributions of SAR86 gene clusters across the world’s oceans, creating global maps of SAR86 ecotype distributions. Our results reveal that SAR86 exhibits previously unknown, complex biogeography, and provide a framework for exploring geographic distributions of genetic diversity from other microbial clades.

© 2016 The Authors Urban green space provides health benefits for city dwellers, and new evidence suggests that microorganisms associated with soil and vegetation could play a role. While airborne microorganisms are ubiquitous in urban areas, the influence of nearby vegetation on airborne microbial communities remains poorly understood. We examined airborne microbial communities in parks and parking lots in Eugene, Oregon, using high-throughput sequencing of the bacterial 16S rRNA gene on the Illumina MiSeq platform to identify bacterial taxa, and GIS to measure vegetation cover in buffer zones of different diameters. Our goal was to explore variation among highly vegetated (parks) versus non-vegetated (parking lots) urban environments. A secondary objective was to evaluate passive versus active collection methods for outdoor airborne microbial sampling. Airborne bacterial communities from five parks were different from those of five parking lots (p = 0.023), although alpha diversity was similar. Direct gradient analysis showed that the proportion of vegetated area within a 50 m radius of the sampling station explained 15% of the variation in bacterial community composition. A number of key taxa, including several Acidobacteriaceae were substantially more abundant in parks, while parking lots had higher relative abundance of Acetobacteraceae. Parks had greater beta diversity than parking lots, i.e. individual parks were characterized by unique bacterial signatures, whereas parking lot communities tended to be similar to each other. Although parks and parking lots were selected to form pairs of nearby sites, spatial proximity did not appear to affect compositional similarity. Our results also showed that passive and active collection methods gave comparable results, indicating the “settling dish” method is effective for outdoor airborne sampling. This work sets a foundation for understanding how urban vegetation may impact microbial communities, with potential implications for designing neighborhoods and open space systems that foster better human health.

In the version of this article initially published, four people were missing from the alphabetical list of IMG/M Data Consortium members: Lauren V. Alteio of the Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria; Jeffrey L. Blanchard of the Biology Department, University of Massachusetts Amherst, Amherst, MA, USA; Kristen M. DeAngelis of the Department of Microbiology, University of Massachusetts Amherst, Amherst, MA, USA; and William Rodriguez-Reillo of the Research Computing Division, Harvard Medical School, Boston, MA, USA. The error has been corrected in the PDF and HTML versions of the article.

BackgroundRecent studies using batch-fermentation suggest that the red macroalgaeAsparagopsis taxiformismight reduce methane (CH4) emission from beef cattle by up to ~99% when added to rhodes grass hay, a common feed in the Australian beef industry. These experiments have shown significant reductions in methane without compromising other fermentation parameters (i.e. volatile fatty acid production) withA. taxiformisorganic matter (OM) inclusion rates of up to 5%. In the study presented here,A. taxiformiswas evaluated for its ability to reduce methane production from dairy cattle fed a mixed ration widely utilized in California; the largest milk producer in the US.ResultsFermentation in a semi-continuousin-vitrorumen system suggests thatA. taxiformiscan reduce methane production from enteric fermentation in dairy cattle by 95% when added at a 5% OM inclusion rate without any obvious negative impacts on volatile fatty acid production. High-throughput 16S ribosomal RNA (rRNA) gene amplicon sequencing showed that seaweed amendment effects rumen microbiome communities consistent with the Anna Karenina hypothesis, with increased beta-diversity, over time scales of approximately three days. The relative abundance of methanogens in the fermentation vessels amended withA. taxiformisdecreased significantly compared to control vessels, but this reduction in methanogen abundance was only significant when averaged over the course of the experiment. Alternatively, significant reductions of methane in theA. taxiformisamended vessels was measured in the early stages of the experiment. This suggests thatA. taxiformishas an immediate effect on the metabolic functionality of rumen methanogens whereas its impact on microbiome assemblage, specifically methanogen abundance, is delayed.ConclusionsThe methane reducing effect ofA. taxiformisduring rumen fermentation makes this macroalgae a promising candidate as a biotic methane mitigation strategy in the largest milk producing state in the US. But its effectin-vivo(i.e. in dairy cattle) remains to be investigated in animal trials. Furthermore, to obtain a holistic understanding of the biochemistry responsible for the significant reduction of methane, gene expression profiles of the rumen microbiome and the host animal are warranted.

There have been many studies highlighting how plant and animal communities lag behind climate change, causing extinction and diversity debts that will slowly be paid as communities equilibrate. By virtue of their short generation times and dispersal abilities, soil bacteria might be expected to respond to climate change quickly and to be effectively in equilibrium with current climatic conditions. We found strong evidence to the contrary in Tibet and North America. These findings could significantly improve understanding of climate impacts on soil microbial communities., Soil bacteria are key to ecosystem function and maintenance of soil fertility. Leveraging associations of current geographic distributions of bacteria with historic climate, we predict that soil bacterial diversity will increase across the majority (∼75%) of the Tibetan Plateau and northern North America if bacterial communities equilibrate with existing climatic conditions. This prediction is possible because the current distributions of soil bacteria have stronger correlations with climate from ∼50 years ago than with current climate. This lag is likely associated with the time it takes for soil properties to adjust to changes in climate. The predicted changes are location specific and differ across bacterial taxa, including some bacteria that are predicted to have reductions in their distributions. These findings illuminate the widespread potential of climate change to influence belowground diversity and the importance of considering bacterial communities when assessing climate impacts on terrestrial ecosystems. IMPORTANCE There have been many studies highlighting how plant and animal communities lag behind climate change, causing extinction and diversity debts that will slowly be paid as communities equilibrate. By virtue of their short generation times and dispersal abilities, soil bacteria might be expected to respond to climate change quickly and to be effectively in equilibrium with current climatic conditions. We found strong evidence to the contrary in Tibet and North America. These findings could significantly improve understanding of climate impacts on soil microbial communities.

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the USA, UK, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes as compared to environmental samples, demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations, uncover new molecules and kinds of molecular communities in the human stool metabolome, and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness, and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education.ImportanceWe show that a citizen-science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n=1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet which we confirm with untargeted metabolomics on hundreds of samples.

It has been known for centuries that microorganisms are ubiquitous in the atmosphere, where they are capable of long-distance dispersal. Likewise, it is well-established that these airborne bacteria and fungi can have myriad effects on human health, as well as the health of plants and livestock. However, we have a limited understanding of how these airborne communities vary across different geographic regions or the factors that structure the geographic patterns of near-surface microbes across large spatial scales. We collected dust samples from the external surfaces of ∼1,200 households located across the United States to understand the continental-scale distributions of bacteria and fungi in the near-surface atmosphere. The microbial communities were highly variable in composition across the United States, but the geographic patterns could be explained by climatic and soil variables, with coastal regions of the United States sharing similar airborne microbial communities. Although people living in more urbanized areas were not found to be exposed to distinct outdoor air microbial communities compared with those living in more rural areas, our results do suggest that urbanization leads to homogenization of the airborne microbiota, with more urban communities exhibiting less continental-scale geographic variability than more rural areas. These results provide our first insight into the continental-scale distributions of airborne microbes, which is information that could be used to identify likely associations between microbial exposures in outdoor air and incidences of disease in crops, livestock, and humans.

Understanding beta-diversity has strong implications for evaluating the extent of biodiversity and formulating effective conservation policy. Here, we show that the distance-decay relationship, an important measure of beta-diversity, follows a universal form which we call the piecewise quadratic model. To derive the piecewise quadratic model, we develop a new conceptual framework which is based on geometric probability and several key insights about the roles of study design (e.g., plot dimensions and spatial distributions). We fit the piecewise quadratic model to six empirical distance-decay relationships, spanning a range of taxa and spatial scales, including surveys of tropical vegetation, mammals, and amphibians. We find that the model predicts the functional form of the relationships extremely well, with coefficients of determination in excess of 0.95. Moreover, the model predicts a phase transition at distance scales where sample plots are overlapping, which we confirm empirically. Our framework and model provide a fundamental, quantitative link between distance-decay relationships and the shapes of ranges of taxa.

Many questions in ecology are best addressed using observational data because they concern spatial or temporal scales where experimentation is impractical. Null models, which make predictions in the absence of a particular ecological mechanism, are instrumental for making inferences in these situations, but which null models to use or how to best test them is often unclear; this ambiguity is problematic because different null models and tests can yield different results, suggesting contradictory ecological mechanisms. To address these challenges, this paper presents an overar ching framework for the development and testing of null models, in which desirable models and tests are obtained as solutions to mathematical optimization problems. As an example of how the framework can be applied, this paper shows how it can be used to design null model tests to check for effects of interspecific interactions on species co-occurrence patterns. A minimal sufficient statistic (metric) for effects of interspecific interactions is derived, which achieves the maximal level of data compression without losing information present in the data about interspecific in teractions. Existing, intuitive statistics are shown to lack this property. The paper then derives a statistical hypothesis test that has the greatest possible power (sen sitivity) for detecting effects of competition and facilitation given a controlled false positive rate. This test is shown numerically to improve greatly over existing tests. The optimization paradigm allows the most accurate inferences possible, and should be applicable throughout ecology where null models are used to make inferences.

A central aim of ecology is understanding the mechanisms of community assembly. To address this problem, community assembly is often modeled as a sampling process, in which species are selected from a pool of available species, possibly with effects of interspecific interactions, habitat filtering, and other ecological mechanisms. However, the fundamental stochastic sampling process by which species are selected from the pool remains unexplored. Here we demonstrate the distinctness of four canonical sampling processes, the Bernoulli, Plackett-Luce, multinomial, and fractional multinomial processes. Each process can be affected by ecological mechanisms or it can occur in their absence. Although all four of the processes are a priori plausible and the first two are widely used in ecological models, we show that the multinomial and fractional multinomial processes broadly underlie community assembly.

Soil bacteria are key to ecosystem function and maintenance of soil fertility. Leveraging associations of current geographic distributions of bacteria with historic climate, we predict that soil bacterial diversity will increase across the majority (~75%) of the Tibetan Plateau and northern North America if bacterial communities equilibrate with existing climatic conditions. This prediction is possible because the current distributions of soil bacteria have stronger correlations with climate from ~50 years ago than with current climate. This lag is likely associated with the time it takes for soil properties to adjust to changes in climate. The predicted changes are location specific and differ across bacterial taxa, including some bacteria that are predicted to have reductions in their distributions. These findings demonstrate the widespread influence that climate change will have on belowground diversity and highlight the importance of considering bacterial communities when assessing climate impacts on terrestrial ecosystems.IMPORTANCEThere have been many studies highlighting how plant and animal communities lag behind climate change, causing extinction and diversity debts that will slowly be paid as communities equilibrate. By virtue of their short generation times and dispersal abilities, soil bacteria might be expected to respond to climate change quickly and to be effectively in equilibrium with current climatic conditions. We found strong evidence to the contrary in Tibet and North America. These findings could significantly improve understanding of climate impacts on soil microbial communities.

We examined hypotheses of spatial association arising from direct or indirect competitive interactions, using thirteen years of gridded mammal census data from Kruger National Park, South Africa. As interactions occur at different scales, we explored the data at 1, 5, 10, and 15 km2. We proposed four hypotheses structuring the mammal community: H1. direct competition between carnivores and herbivores; H2-4: indirect competition produced by each of three types of herbivore diet specialization: H2. ruminants and non-ruminants, H3. grazers and browsers, and H4. a four-way division of small and large grazers and browsers. We used the software CoOccur to implement a robus test for evidence of our hypotheses against null models of community assemblage. At 5, 10, and 15km2 scales, the results supported a competition mechanism in the majority of years for hypotheses H1, H3, and H4, and facilitation in H2. At the finest spatial scale (1km2), we saw evidence for a mixture of competitive, neutral and facilitative process. These results suggest strong, large-scale effects of interspecific interactions on distributions of African megafauna, which may not operate at a more local (1km2) scale, underscoring the importance of scale and mechanism in the guild structure of communities.