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Abstract <bold>Background</bold>Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation. <bold>Results</bold>Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability. <bold>Conclusions</bold>Our empirical study shows that the proposed cross-validation method is useful for hyper-parameter selection (training) and comparing the quality of inferred networks between different algorithms (testing). This advancement represents a significant step forward in microbiome network analysis, providing researchers with a reliable tool for understanding complex microbial interactions. The method’s applicability extends beyond microbiome studies to other fields where network inference from high-dimensional compositional data is crucial, such as gene regulatory networks and ecological food webs. Our framework establishes a new standard for validation in network inference, potentially accelerating discoveries in microbial ecology and human health. 

ABSTRACT Microbial nitrogen fixation (diazotrophy) is a critical ecological process. We curated DiazoTIME (Diazotroph Taxonomic Identity and MEtabolism), a comprehensive database of diazotroph genomes including taxonomic annotation and metabolic prediction. DiazoTIME is unique among databases for classifying diazotrophs because it resolves both taxonomy and metabolic functionality. 

Measuring the growth rate of a microorganism is a simple yet profound way to quantify its effect on the world. The absolute growth rate of a microbial population reflects rates of resource assimilation, biomass production and element transformation—some of the many ways in which organisms affect Earth’s ecosystems and climate. Microbial fitness in the environment depends on the ability to reproduce quickly when conditions are favourable and adopt a survival physiology when conditions worsen, which cells coordinate by adjusting their relative growth rate. At the population level, relative growth rate is a sensitive metric of fitness, linking survival and reproduction to the ecology and evolution of populations. Techniques combining omics and stable isotope probing enable sensitive measurements of the growth rates of microbial assemblages and individual taxa in soil. Microbial ecologists can explore how the growth rates of taxa with known traits and evolutionary histories respond to changes in resource availability, environmental conditions and interactions with other organisms. We anticipate that quantitative and scalable data on the growth rates of soil microorganisms, coupled with measurements of biogeochemical fluxes, will allow scientists to test and refine ecological theory and advance process-based models of carbon flux, nutrient uptake and ecosystem productivity. Measurements of in situ microbial growth rates provide insights into the ecology of populations and can be used to quantitatively link microbial diversity to soil biogeochemistry. 

The growth rate of a microorganism is a simple yet profound way to quantify its impact on the world. Microbial fitness in the environment depends on the ability to reproduce quickly when conditions are favorable and adopt a survival physiology when conditions worsen, which cells coordinate by adjusting their growth rate. At the population level, per capita growth rate is a sensitive metric of fitness, linking survival and reproduction to the ecology and evolution of populations. The absolute growth rate of a microbial population reflects rates of resource assimilation, biomass production, and element transformation, some of the many ways that organisms affect Earth’s ecosystems and climate. For soil microorganisms, most of our understanding of growth is based on observations made in culture. This is a crucial limitation given that many soil microbes are not readily cultured and in vitro conditions are unlikely to reflect conditions in the wild. New approaches in ‘omics and stable isotope probing make it possible to sensitively measure growth rates of microbial assemblages and individual taxa in nature, and to couple these measurements to biogeochemical fluxes. Microbial ecologists can now explore how the growth rates of taxa with known traits and evolutionary histories respond to changes in resource availability, environmental conditions, and interactions with other organisms. We anticipate that quantitative and scalable data on the growth rates of soil microorganisms will allow scientists to test and refine ecological theory and advance processbased models of carbon flux, nutrient uptake, and ecosystem productivity. Measurements of in situ microbial growth rates provide insights into the ecology of populations and can be used to quantitatively link microbial diversity to soil biogeochemistry.  

Abstract When leaves fall in rivers, microbial decomposition commences within hours. Microbial assemblages comprising hundreds of species of fungi and bacteria can vary with stream conditions, leaf litter species, and decomposition stage. In terrestrial ecosystems, fungi and bacteria that enter soils with dead leaves often play prominent roles in decomposition, but their role in aquatic decomposition is less known. Here, we test whether fungi and bacteria that enter streams on senesced leaves are growing during decomposition and compare their abundances and growth to bacteria and fungi that colonize leaves in the water. We employ quantitative stable isotope probing to identify growing microbes across four leaf litter species and two decomposition times. We find that most of the growing fungal species on decomposing leaves enter the water with the leaf, whereas most growing bacteria colonize from the water column. Results indicate that the majority of bacteria found on litter are growing, whereas the majority of fungi are dormant. Both bacterial and fungal assemblages differed with leaf type on the dried leaves and throughout decomposition. This research demonstrates the importance of fungal species that enter with the leaf on aquatic decomposition and the prominence of bacteria that colonize decomposing leaves in the water. 

As terrestrial leaf litter decomposes in rivers, its constituent elements follow multiple pathways. Carbon leached as dissolved organic matter can be quickly taken up by microbes, then respired before it can be transferred to the macroscopic food web. Alternatively, this detrital carbon can be ingested and assimilated by aquatic invertebrates, so it is retained longer in the stream and transferred to higher trophic levels. Microbial growth on litter can affect invertebrates through three pathways, which are not mutually exclusive. First, microbes can facilitate invertebrate feeding, improving food quality by conditioning leaves and making them more palatable for invertebrates. Second, microbes can be prey for invertebrates. Third, microbes can compete with invertebrates for resources bound within litter and may produce compounds that retard carbon and nitrogen fluxes to invertebrates. As litter is broken down into smaller particles, there are many opportunities for its elements to reenter the stream food web. Here, I describe a conceptual framework for evaluating how traits of leaf litter will affect its fate in food webs and ecosystems that is useful for predicting how global change will alter carbon fluxes into and out of streams. 

Abstract Study of life history strategies may help predict the performance of microorganisms in nature by organizing the complexity of microbial communities into groups of organisms with similar strategies. Here, we tested the extent that one common application of life history theory, the copiotroph-oligotroph framework, could predict the relative population growth rate of bacterial taxa in soils from four different ecosystems. We measured the change of in situ relative growth rate to added glucose and ammonium using both 18O–H2O and 13C quantitative stable isotope probing to test whether bacterial taxa sorted into copiotrophic and oligotrophic groups. We saw considerable overlap in nutrient responses across most bacteria regardless of phyla, with many taxa growing slowly and few taxa that grew quickly. To define plausible life history boundaries based on in situ relative growth rates, we applied Gaussian mixture models to organisms’ joint 18O–13C signatures and found that across experimental replicates, few taxa could consistently be assigned as copiotrophs, despite their potential for fast growth. When life history classifications were assigned based on average relative growth rate at varying taxonomic levels, finer resolutions (e.g., genus level) were significantly more effective in capturing changes in nutrient response than broad taxonomic resolution (e.g., phylum level). Our results demonstrate the difficulty in generalizing bacterial life history strategies to broad lineages, and even to single organisms across a range of soils and experimental conditions. We conclude that there is a continued need for the direct measurement of microbial communities in soil to advance ecologically realistic frameworks.