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Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community assembly processes, defining their relative importance is very challenging. Here, we present a framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). iCAMP shows high accuracy (0.93–0.99), precision (0.80–0.94), sensitivity (0.82–0.94), and specificity (0.95–0.98) on simulated communities, which are 10–160% higher than those from the entire community-based approach. Application of iCAMP to grassland microbial communities in response to experimental warming reveals dominant roles of homogeneous selection (38%) and ‘drift’ (59%). Interestingly, warming decreases ‘drift’ over time, and enhances homogeneous selection which is primarily imposed on Bacillales. In addition, homogeneous selection has higher correlations with drought and plant productivity under warming than control. iCAMP provides an effective and robust tool to quantify microbial assembly processes, and should also be useful for plant and animal ecology. Studies of microbial community assembly mechanisms typically use metrics for turnover within the whole community. Here, the authors develop an alternative approach based on turnover within lineages and dissect mechanistic change in grassland bacterial assembly under experimental warming.

Nutrient scarcity is pervasive for natural microbial communities, affecting species reproduction and co-existence. However, it remains unclear whether there are general rules of how microbial species abundances are shaped by biotic and abiotic factors. Here we show that the ribosomal RNA gene operon ( rrn ) copy number, a genomic trait related to bacterial growth rate and nutrient demand, decreases from the abundant to the rare biosphere in the nutrient-rich coastal sediment but exhibits the opposite pattern in the nutrient-scarce pelagic zone of the global ocean. Both patterns are underlain by positive correlations between community-level rrn copy number and nutrients. Furthermore, inter-species co-exclusion inferred by negative network associations is observed more in coastal sediment than in ocean water samples. Nutrient manipulation experiments yield effects of nutrient availability on rrn copy numbers and network associations that are consistent with our field observations. Based on these results, we propose a “hunger games” hypothesis to define microbial species abundance rules using the rrn copy number, ecological interaction, and nutrient availability. Environmental and biotic factors control ecological communities. Here, the authors study community ribosomal rRNA gene copy number in coastal sediment and ocean bacterial communities, and in microcosm nutrient addition experiments, to propose a conceptual framework of how nutrient supply and ecological interactions shape the community.

Metal-organic frameworks that feature hybrid fluorescence and phosphorescence offer unique advantages in white-emitting communities based on their multiple emission centers and high exciton utilization. However, it poses a substantial challenge to realize superior white-light emission in single-component metal-organic frameworks without encapsulating varying chromophores or integrating multiple phosphor subunits. Here, we achieve a high-performance white-light emission with photoluminescence quantum yield of 81.3% via boosting triplet excitons distribution through pressure treatment in single-component Zn-IPA metal-organic frameworks. A novel metal-ligand asymmetrical chelate coordination is successfully integrated into the Zn-IPA after a high-pressure treatment over ~20.0 GPa. This modification unexpectedly endows the targeted sample with a new emergent electronic state to narrow the singlet-triplet energy gap, which effectively accelerates the spin-flipping process for boosted triplet excitons population. Time delay phosphor-converted light-emitting diodes are fabricated with long emission time up to ~7 s after switching off, providing significant advancements for white-light and time-delay lighting applications. Pressure-treatment can alter the optical properties of metal-organic frameworks. Here the authors induce amorphization in Zn-IPA that remains after decompression and enables efficient white-light emission through narrowing the singlet-triplet gap.

Increasing soil organic carbon (SOC) in croplands by switching from conventional to conservation management may be hampered by stimulated microbial decomposition under warming. Here, we test the interactive effects of agricultural management and warming on SOC persistence and underlying microbial mechanisms in a decade-long controlled experiment on a wheat-maize cropping system. Warming increased SOC content and accelerated fungal community temporal turnover under conservation agriculture (no tillage, chopped crop residue), but not under conventional agriculture (annual tillage, crop residue removed). Microbial carbon use efficiency (CUE) and growth increased linearly over time, with stronger positive warming effects after 5 years under conservation agriculture. According to structural equation models, these increases arose from greater carbon inputs from the crops, which indirectly controlled microbial CUE via changes in fungal communities. As a result, fungal necromass increased from 28 to 53%, emerging as the strongest predictor of SOC content. Collectively, our results demonstrate how management and climatic factors can interact to alter microbial community composition, physiology and functions and, in turn, SOC formation and accrual in croplands. Agricultural soil C dynamics under climate change are difficult to predict. Here, the authors report that experimental warming increases soil organic C stocks in conservation agriculture but not in conventional agriculture, which appears driven by soil microbial responses to no tillage and C inputs from the crops.

As the second-largest terrestrial carbon (C) flux, soil respiration ( R S ) has been stimulated by climate warming. However, the magnitude and dynamics of such stimulations of soil respiration are highly uncertain at the global scale, undermining our confidence in future climate projections. Here, we present an analysis of global R S observations from 1987–2016. R S increased ( P  < 0.001) at a rate of 27.66 g C m −2  yr −2 (equivalent to 0.161 Pg C yr −2 ) in 1987–1999 globally but became unchanged in 2000–2016, which were related to complex temporal variations of temperature anomalies and soil C stocks. However, global heterotrophic respiration ( R h ) derived from microbial decomposition of soil C increased in 1987–2016 ( P  < 0.001), suggesting accumulated soil C losses. Given the warmest years on records after 2015, our modeling analysis shows a possible resuscitation of global R S rise. This study of naturally occurring shifts in R S over recent decades has provided invaluable insights for designing more effective policies addressing future climate challenges. Soils hold massive amounts of carbon that hangs in the balance of microbial respiration and climate warming. Here the authors analyze a global dataset starting in 1987 and find through modeling that though soil respiration change had flatlined, recently it has resumed increasing owing to global warming.

The trait-based strategies of microorganisms appear to be phylogenetically conserved, but acclimation to climate change may complicate the scenario. To study the roles of phylogeny and environment on bacterial responses to sudden moisture increases, we determine bacterial population-specific growth rates by 18 O-DNA quantitative stable isotope probing ( 18 O-qSIP) in soils subjected to a free-air CO 2 enrichment (FACE) combined with warming. We find that three growth strategies of bacterial taxa – rapid, intermediate and slow responders, defined by the timing of the peak growth rates – are phylogenetically conserved, even at the sub-phylum level. For example, members of class Bacilli and Sphingobacteriia are mainly rapid responders. Climate regimes, however, modify the growth strategies of over 90% of species, partly confounding the initial phylogenetic pattern. The growth of rapid bacterial responders is more influenced by phylogeny, whereas the variance for slow responders is primarily explained by environmental conditions. Overall, these results highlight the role of phylogenetic and environmental constraints in understanding and predicting the growth strategies of soil microorganisms under global change scenarios. Microbial ecological strategies are expected to be phylogenetically conserved, but plasticity and acclimation to environmental change may complicate the picture. Here, the authors show that shifts in soil bacterial ecological strategies deviate from phylogenetic-based predictions after acclimation to long-term warming and CO 2 enrichment.

Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO 2 , is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO 2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO 2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management. IMPORTANCE The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO 2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research. The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO 2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.

Background Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Results Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now ( http://ieg2.ou.edu/MENA ). Conclusions The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

City parks can cool the surrounding environment and mitigate the urban heat island (UHI) effect, considerable improving the city’s adaptability to climate. In this study, 20 city parks in Nanjing, China, were considered, and four indexes for quantifying the cooling benefits from a cumulative impact perspective were proposed. These indexes are park cooling area (PCA), park cooling efficiency (PCE), park cooling intensity (PCI), and park cooling gradient (PCG). The results reveal the following: first, city parks have a positive impact on the surrounding thermal environment. The factors park area (PA), park perimeter (PP), landscape shape index (LSI), and normalized difference vegetation index (NDVI) determine cooling benefits. Second, PA and PP are significantly positively correlated with PCA but are significantly negatively correlated with PCE. LSI is negatively correlated with PCE, while NDVI is positively correlated with PCI and PCG. No significant correlation exists between the four cooling indexes and modified normalized difference water index(MNDWI). Finally, different parks exhibit variations in their ability to provide cooling benefits. Special or community parks are more appropriately situated in areas with constrained urban land resources. In designing comprehensive parks, the intricate boundary features and vegetation conditions need to be considered to optimize their cooling effects. Moreover, a larger number of residents are allowed to enjoy cooling services. The findings of this project will aid in the construction and optimization of city parks in future to combat the UHI effect.

Understanding the structure, functions, activities and dynamics of microbial communities in natural environments is one of the grand challenges of 21st century science. To address this challenge, over the past decade, numerous technologies have been developed for interrogating microbial communities, of which some are amenable to exploratory work (e.g., high-throughput sequencing and phenotypic screening) and others depend on reference genes or genomes (e.g., phylogenetic and functional gene arrays). Here, we provide a critical review and synthesis of the most commonly applied “open-format” and “closed-format” detection technologies. We discuss their characteristics, advantages, and disadvantages within the context of environmental applications and focus on analysis of complex microbial systems, such as those in soils, in which diversity is high and reference genomes are few. In addition, we discuss crucial issues and considerations associated with applying complementary high-throughput molecular technologies to address important ecological questions.