Making the Most of Trait-Based Approaches for Microbial Ecology

"Ectomycorrhizal exploration type" could be a functional trait explaining the spatial distribution of tree symbiotic fungi as a function of forest humus forms

In European forests, most tree species form symbioses with ectomycorrhizal (EM) and arbuscular mycorrhizal (AM) fungi. The EM fungi are classified into different morphological types based on the development and structure of their extraradical mycelium. These structures could be root extensions that help trees to acquire nutrients. However, the relationship between these morphological traits and functions involved in soil nutrient foraging is still under debate.We described the composition of mycorrhizal fungal communities under 23 tree species in a wide range of climates and humus forms in Europe and investigated the exploratory types of EM fungi. We assessed the response of this tree extended phenotype to humus forms, as an indicator of the functioning and quality of forest soils. We found a significant relationship between the relative proportion of the two broad categories of EM exploration types (short- or long-distance) and the humus form, showing a greater proportion of long-distance types in the least dynamic soils. As past land-use and host tree species are significant factors structuring fungal communities, we showed this relationship was modulated by host trait (gymnosperms versus angiosperms), soil depth and past land use (farmland or forest).We propose that this potential functional trait of EM fungi be used in future studies to improve predictive models of forest soil functioning and tree adaptation to environmental nutrient conditions.

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

Niche differentiation in microbial communities with stable genomic traits over time in engineered systems

Microbial communities in full-scale engineered systems undergo dynamic compositional changes. However, mechanisms governing assembly of such microbes and succession of their functioning and genomic traits under various environmental conditions are unclear. In this study, we used the activated sludge and anaerobic treatment systems of four full-scale industrial wastewater treatment plants as models to investigate the niches of microbes in communities and the temporal succession patterns of community compositions. High-quality representative metagenome-assembled genomes revealed that taxonomic, functional, and trait-based compositions were strongly shaped by environmental selection, with replacement processes primarily driving variations in taxonomic and functional compositions. Plant-specific indicators were associated with system environmental conditions and exhibited strong determinism and trajectory directionality over time. The partitioning of microbes in a co-abundance network according to groups of plant-specific indicators, together with significant between-group differences in genomic traits, indicated the occurrence of niche differentiation. The indicators of the treatment plant with rich nutrient input and high substrate removal efficiency exhibited a faster predicted growth rate, lower guanine-cytosine content, smaller genome size, and higher codon usage bias than the indicators of the other plants. In individual plants, taxonomic composition displayed a more rapid temporal succession than functional and trait-based compositions. The succession of taxonomic, functional, and trait-based compositions was correlated with the kinetics of treatment processes in the activated sludge systems. This study provides insights into ecological niches of microbes in engineered systems and succession patterns of their functions and traits, which will aid microbial community management to improve treatment performance.

Data-driven identification of major axes of functional variation in bacteria

The discovery of major axes of correlated functional variation among species and habitats has revealed the fundamental trade-offs structuring both functional and taxonomic diversity in eukaryotes such as plants. Whether such functional axes exist in the bacterial realm and whether they could explain bacterial taxonomic turnover among ecosystems remains unknown. Here, we use a data-driven approach to leverage global genomic and metagenomic datasets to reveal the existence of major axes of functional variation explaining both evolutionary differentiation within Bacteria and their ecological sorting across diverse habitats. We show that metagenomic variation among bacterial communities from various ecosystems is structured along a few axes of correlated functional pathways. Similar clusters of traits explained phylogenetic trait variation among >16,000 bacterial genomes, suggesting that functional turnover among bacterial communities from distinct habitats does not only result from the differential filtering of similar functions among communities, but also from phylogenetic correlations among these functions. Concordantly, functional pathways associated with trait clusters that were most important for defining functional turnover among bacterial communities were also those that had the highest phylogenetic signal in the bacterial genomic phylogeny. This study overall underlines the important role of evolutionary history in shaping contemporary distributions of bacteria across ecosystems.

Species' functional traits and interactions drive nitrate-mediated sulfur-oxidizing community structure and functioning

IMPORTANCE

Understanding the processes and mechanisms governing microbial community assembly and their linkages to ecosystem functioning has long been a core issue in microbial ecology. An in-depth insight still requires combining with analyses of species' functional traits and microbial interactions. Our study showed how species' functional traits and interactions determined microbial community structure and functions by a well-controlled laboratory experiment with nitrate-mediated sulfur oxidation systems using high-throughput sequencing and culture-dependent technologies. The results provided solid evidences that species' functional traits and interactions were the intrinsic factors determining community structure and function. More importantly, our study established quantitative links between community structure and function based on species' functional traits and interactions, which would have important implications for the design and synthesis of microbiomes with expected functions.

Legionella relative abundance in shower hose biofilms is associated with specific microbiome members

Legionella are natural inhabitants of building plumbing biofilms, where interactions with other microorganisms influence their survival, proliferation, and death. Here, we investigated the associations of Legionella with bacterial and eukaryotic microbiomes in biofilm samples extracted from 85 shower hoses of a multiunit residential building. Legionella spp. relative abundance in the biofilms ranged between 0-7.8%, of which only 0-0.46% was L. pneumophila. Our data suggest that some microbiome members were associated with high (e.g. Chthonomonas, Vrihiamoeba) or low (e.g. Aquabacterium, Vannella) Legionella relative abundance. The correlations of the different Legionella variants (30 Zero-Radius OTUs detected) showed distinct patterns, suggesting separate ecological niches occupied by different Legionella species. This study provides insights into the ecology of Legionella with respect to: (i) the colonization of a high number of real shower hoses biofilm samples; (ii) the ecological meaning of associations between Legionella and co-occurring bacterial/eukaryotic organisms; (iii) critical points and future directions of microbial-interaction-based-ecological-investigations.

The predictive power of phylogeny on growth rates in soil bacterial communities

Predicting ecosystem function is critical to assess and mitigate the impacts of climate change. Quantitative predictions of microbially mediated ecosystem processes are typically uninformed by microbial biodiversity. Yet new tools allow the measurement of taxon-specific traits within natural microbial communities. There is mounting evidence of a phylogenetic signal in these traits, which may support prediction and microbiome management frameworks. We investigated phylogeny-based trait prediction using bacterial growth rates from soil communities in Arctic, boreal, temperate, and tropical ecosystems. Here we show that phylogeny predicts growth rates of soil bacteria, explaining an average of 31%, and up to 58%, of the variation within ecosystems. Despite limited overlap in community composition across these ecosystems, shared nodes in the phylogeny enabled ancestral trait reconstruction and cross-ecosystem predictions. Phylogenetic relationships could explain up to 38% (averaging 14%) of the variation in growth rates across the highly disparate ecosystems studied. Our results suggest that shared evolutionary history contributes to similarity in the relative growth rates of related bacteria in the wild, allowing phylogeny-based predictions to explain a substantial amount of the variation in taxon-specific functional traits, within and across ecosystems.

Microbial assemblies with distinct trophic strategies drive changes in soil microbial carbon use efficiency along vegetation primary succession in a glacier retreat area of the southeastern Tibetan Plateau

Soil microbial carbon use efficiency (CUE) is a vital physiological parameter in assessing carbon turnover. Yet, how the microbial assemblies with distinct trophic strategies regulate the soil microbial CUE remains elusive. Based on the oligotrophic-copiotrophic framework, we explored the role of microbial taxa with different trophic strategies in mediating microbial CUE (determined by a ¹³C-labeled approach) along the vegetation primary succession in Hailuogou glacier retreat area of the southeastern Tibetan Plateau. Results showed that soil microbial CUE ranged from 0.54 to 0.72 (averaging 0.62 ± 0.01 across all samples) and increased staggeringly along the vegetation succession. Microbial assemblies with distinct trophic strategies were crucial regulators of soil microbial CUE. Specifically, microbial CUE increased with microbial oligotroph: copiotroph ratios, oligotroph-dominated stage had a higher microbial CUE than copiotroph-dominated ones. The prevalence of oligotrophic members would be the underlying microbial mechanism for the high microbial CUE. Given that oligotrophs predominate in more recalcitrant carbon soils and their higher microbial CUE, we speculate that oligotrophs are likely to potentially enhance carbon sequestration in soils. In addition, the responses of the microbial CUE to fungal oligotroph: copiotroph ratios were higher than bacterial ones. Fungal taxa may play a dominant role in shaping microbial CUE relative to bacterial members. Overall, our results constructed close associations between microbial trophic strategies and CUE and provide direct evidence regarding how microbial trophic strategies regulate microbial CUE. This study is a significant step forward for elucidating the physiological mechanisms regulating microbial CUE and has significant implications for understanding microbial-mediated carbon cycling processes.

Trait-Based Method of Quantitative Assessment of Ecological Functional Groups in the Human Intestinal Microbiome

We propose the trait-based method for quantifying the activity of functional groups in the human gut microbiome based on metatranscriptomic data. It allows one to assess structural changes in the microbial community comprised of the following functional groups: butyrate-producers, acetogens, sulfate-reducers, and mucin-decomposing bacteria. It is another way to perform a functional analysis of metatranscriptomic data by focusing on the ecological level of the community under study. To develop the method, we used published data obtained in a carefully controlled environment and from a synthetic microbial community, where the problem of ambiguity between functionality and taxonomy is absent. The developed method was validated using RNA-seq data and sequencing data of the 16S rRNA amplicon on a simplified community. Consequently, the successful verification provides prospects for the application of this method for analyzing natural communities of the human intestinal microbiota.

Local temperature increases reduce soil microbial residues and carbon stocks

Warming is known to reduce soil carbon (C) stocks by promoting microbial respiration, which is associated with the decomposition of microbial residue carbon (MRC). However, the relative contribution of MRC to soil organic carbon (SOC) across temperature gradients is poorly understood. Here, we investigated the contribution of MRC to SOC along two independent elevation gradients of our model system (i.e., the Tibetan Plateau and Shennongjia Mountain in China). Our results showed that local temperature increases were negatively correlated with MRC and SOC. Further analyses revealed that rising temperature reduced SOC via decreasing MRC, which helps to explain future reductions in SOC under climate warming. Our findings demonstrate that climate warming has the potential to reduce C sequestration by increasing the decomposition of MRC, exacerbating the positive feedback between rising temperature and CO₂ efflux. Our study also considered the influence of multiple environmental factors such as soil pH and moisture, which were more important in controlling SOC than microbial traits such as microbial life-style strategies and metabolic efficiency. Together, our work suggests an important mechanism underlying long-term soil C sequestration, which has important implications for the microbial-mediated C process in the face of global climate change.

Benchmarking Community-Wide Estimates of Growth Potential from Metagenomes Using Codon Usage Statistics

Trait inference from mixed-species assemblages is a central problem in microbial ecology. Frequently, sequencing information from an environment is available, but phenotypic measurements from individual community members are not. With the increasing availability of molecular data for microbial communities, bioinformatic approaches that map metagenome to (meta)phenotype are needed. Recently, we developed a tool, gRodon, that enables the prediction of the maximum growth rate of an organism from genomic data on the basis of codon usage patterns. Our work and that of other groups suggest that such predictors can be applied to mixed-species communities in order to derive estimates of the average community-wide maximum growth rate. Here, we present an improved maximum growth rate predictor designed for metagenomes that corrects a persistent GC bias in the original gRodon model for metagenomic prediction. We benchmark this predictor with simulated metagenomic data sets to show that it has superior performance on mixed-species communities relative to earlier models. We go on to provide guidance on data preprocessing and show that calling genes from assembled contigs rather than directly from reads dramatically improves performance. Finally, we apply our predictor to large-scale metagenomic data sets from marine and human microbiomes to illustrate how community-wide growth prediction can be a powerful approach for hypothesis generation. Altogether, we provide an updated tool with clear guidelines for users about the uses and pitfalls of metagenomic prediction of the average community-wide maximal growth rate. IMPORTANCE Microbes dominate nearly every known habitat, and therefore tools to survey the structure and function of natural microbial communities are much needed. Metagenomics, in which the DNA content of an entire community of organisms is sequenced all at once, allows us to probe the genetic diversity contained in a habitat. Yet, mapping metagenomic information to the actual traits of community members is a difficult and largely unsolved problem. Here, we present and validate a tool that allows users to predict the average maximum growth rate of a microbial community directly from metagenomic data. Maximum growth rate is a fundamental characteristic of microbial species that can give us a great deal of insight into their ecological role, and by applying our community-level predictor to large-scale metagenomic data sets from marine and human-associated microbiomes, we show how community-wide growth prediction can be a powerful approach for hypothesis generation.

Facilitating Reforestation Through the Plant Microbiome: Perspectives from the Phyllosphere

Tree planting and natural regeneration contribute to the ongoing effort to restore Earth's forests. Our review addresses how the plant microbiome can enhance the survival of planted and naturally regenerating seedlings and serve in long-term forest carbon capture and the conservation of biodiversity. We focus on fungal leaf endophytes, ubiquitous defensive symbionts that protect against pathogens. We first show that fungal and oomycetous pathogen richness varies greatly for tree species native to the United States (n = 0-876 known pathogens per US tree species), with nearly half of tree species either without pathogens in these major groups or with unknown pathogens. Endophytes are insurance against the poorly known and changing threat of tree pathogens. Next, we review studies of plant phyllosphere feedback, but knowledge gaps prevent us from evaluating whether adding conspecific leaf litter to planted seedlings promotes defensive symbiosis, analogous to adding soil to promote positive feedback. Finally, we discuss research priorities for integrating the plant microbiome into efforts to expand Earth's forests.

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait, a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Ecological modelling approaches for predicting emergent properties in microbial communities

Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.

Eutrophication dangers the ecological status of coastal wetlands: A quantitative assessment by composite microbial index of biotic integrity

The intertidal wetland ecosystem is vulnerable to environmental and anthropogenic stressors. Understanding how the ecological statuses of intertidal wetlands respond to influencing factors is crucial for the management and protection of intertidal wetland ecosystems. In this study, the community characteristics of bacteria, archaea and microeukaryote from Jiangsu coast areas (JCA), the longest muddy intertidal wetlands in the world, were detected to develop a composite microbial index of biotic integrity (CM-IBI) and to explore the influence mechanisms of stresses on the intertidal wetland ecological status. A total of 12 bacterial, archaea and microeukaryotic metrics were determined by range, responsiveness and redundancy tests for the development of ba-IBI, ar-IBI and eu-IBI. The CM-IBI was further developed via three sub-IBIs with weight coefficients 0.40, 0.33 and 0.27, respectively. The CM-IBI (R² = 0.58) exhibited the highest goodness of fit with the CEI, followed by ba-IBI (R² = 0.36), ar-IBI (R² = 0.25) and eu-IBI (R² = 0.21). Redundancy and random forest analyses revealed inorganic nitrogen (inorgN), total phosphorus (TP) and total organic carbon (TOC) to be key environmental variables influencing community compositions. A conditional reasoning tree model indicated the close associating between the ecological status and eutrophication conditions. The majority of sites with water inorgN<0.67 mg/L exhibited good statuses, while the poor ecological status was observed for inorgN>0.67 mg/L and TP > 0.11 mg/L. Microbial networks demonstrated the interactions of microbial taxonomic units among three kingdoms decreases with the ecological degradation, suggesting a reduced reliability and stability of microbial communities. Multi-level path analysis revealed fishery aquaculture and industrial development as the dominant anthropogenic activities effecting the eutrophication and ecological degradation of the JCA tidal wetlands. This study developed an efficient ecological assessment method of tidal wetlands based on microbial communities, and determined the influence of human activities and eutrophication on ecological status, providing guidance for management standards and coastal development.

Fine-Scale Adaptations to Environmental Variation and Growth Strategies Drive Phyllosphere Methylobacterium Diversity

Methylobacterium is a prevalent bacterial genus of the phyllosphere. Despite its ubiquity, little is known about the extent to which its diversity reflects neutral processes like migration and drift, versus environmental filtering of life history strategies and adaptations. In two temperate forests, we investigated how phylogenetic diversity within Methylobacterium is structured by biogeography, seasonality, and growth strategies. Using deep, culture-independent barcoded marker gene sequencing coupled with culture-based approaches, we uncovered a considerable diversity of Methylobacterium in the phyllosphere. We cultured different subsets of Methylobacterium lineages depending upon the temperature of isolation and growth (20°C or 30°C), suggesting long-term adaptation to temperature. To a lesser extent than temperature adaptation, Methylobacterium diversity was also structured across large (>100 km; between forests) and small (<1.2 km; within forests) geographical scales, among host tree species, and was dynamic over seasons. By measuring the growth of 79 isolates during different temperature treatments, we observed contrasting growth performances, with strong lineage- and season-dependent variations in growth strategies. Finally, we documented a progressive replacement of lineages with a high-yield growth strategy typical of cooperative, structured communities in favor of those characterized by rapid growth, resulting in convergence and homogenization of community structure at the end of the growing season. Together, our results show how Methylobacterium is phylogenetically structured into lineages with distinct growth strategies, which helps explain their differential abundance across regions, host tree species, and time. This work paves the way for further investigation of adaptive strategies and traits within a ubiquitous phyllosphere genus. IMPORTANCE Methylobacterium is a bacterial group tied to plants. Despite the ubiquity of methylobacteria and the importance to their hosts, little is known about the processes driving Methylobacterium community dynamics. By combining traditional culture-dependent and -independent (metabarcoding) approaches, we monitored Methylobacterium diversity in two temperate forests over a growing season. On the surface of tree leaves, we discovered remarkably diverse and dynamic Methylobacterium communities over short temporal (from June to October) and spatial (within 1.2 km) scales. Because we cultured different subsets of Methylobacterium diversity depending on the temperature of incubation, we suspected that these dynamics partly reflected climatic adaptation. By culturing strains under laboratory conditions mimicking seasonal variations, we found that diversity and environmental variations were indeed good predictors of Methylobacterium growth performances. Our findings suggest that Methylobacterium community dynamics at the surface of tree leaves results from the succession of strains with contrasting growth strategies in response to environmental variations.

Cometabolic degradation mechanism and microbial network response of methanotrophic consortia to chlorinated hydrocarbon solvents

The cometabolism mechanism of chlorinated hydrocarbon solvents (CHSs) in mixed consortia remains largely unknown. CHS biodegradation characteristics and microbial networks in methanotrophic consortia were studied for the first time. The results showed that all CHSs can efficiently be degraded via cometabolism with a maximum degradation rate of 4.8 mg/(h·gcell). Chloroalkane and chloroethylene were more easily degraded than chlorobenzenes by methanotrophic consortia, especially nonfully chlorinated aliphatic hydrocarbons, which were converted to Cl- with a production rate of 0.29-0.36 mg/(h·gcell). In addition, the microecological response results indicated that Methylocystaceae (49.0%), Methylomonas (65.3%) and Methylosarcina (41.9%) may be the major functional degraders in methanotrophic consortia. Furthermore, the results of the microbial correlation network suggested that interactive relationships constructed by type I methanotrophs and heterotrophs determined biodegradability. Additionally, PICRUSt analysis showed that CHSs could increase the relative abundance of CHS degradation genes and reduce the relative abundance of methane oxidation genes, which was in good agreement with the experimental results.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

Mechanistic insights into the success of xenobiotic degraders resolved from metagenomes of microbial enrichment cultures

Even though microbial communities can be more effective at degrading xenobiotics than cultured micro-organisms, yet little is known about the microbial strategies that underpin xenobiotic biodegradation by microbial communities. Here, we employ metagenomic community sequencing to explore the mechanisms that drive the development of 49 xenobiotic-degrading microbial communities, which were enriched from 7 contaminated soils or sediments with a range of xenobiotic compounds. We show that multiple microbial strategies likely drive the development of xenobiotic degrading communities, notably (i) presence of genes encoding catabolic enzymes to degrade xenobiotics; (ii) presence of genes encoding efflux pumps; (iii) auxiliary catabolic genes on plasmids; and (iv) positive interactions dominate microbial communities with efficient degradation. Overall, the integrated analyses of microbial ecological strategies advance our understanding of microbial processes driving the biodegradation of xenobiotics and promote the design of bioremediation systems.

Linking Genes to Traits in Fungi

Fungi are mediators of the nitrogen and carbon cycles in terrestrial ecosystems. Examining how nitrogen uptake and organic matter decomposition potential differs in fungi can provide insight into the underlying mechanisms driving fungal ecological processes and ecosystem functioning. In this study, we assessed the frequency of genes encoding for specific enzymes that facilitate nitrogen uptake and organic matter decomposition in 879 fungal genomes with fungal taxa grouped into trait-based categories. Our linked gene-trait data approach revealed that gene frequencies vary across and within trait-based groups and that trait-based categories differ in trait space. We present two examples of how this linked gene-trait approach can be used to address ecological questions. First, we show that this type of approach can help us better understand, and potentially predict, how fungi will respond to environmental stress. Specifically, we found that trait-based categories with high nitrogen uptake gene frequency increased in relative abundance when exposed to high soil nitrogen enrichment. Second, by comparing frequencies of nitrogen uptake and organic matter decomposition genes, we found that most ectomycorrhizal fungi in our dataset have similar gene frequencies to brown rot fungi. This demonstrates that gene-trait data approaches can shed light on potential evolutionary trajectories of life history traits in fungi. We present a framework for exploring nitrogen uptake and organic matter decomposition gene frequencies in fungal trait-based groups and provide two concise examples on how to use our framework to address ecological questions from a mechanistic perspective.

Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Emerging Patterns of Microbial Functional Traits

Functional traits are measurable characteristics that affect an organism's fitness under certain environmental conditions. The use of functional traits in microbial ecology holds great promise for improving our ability to develop biogeochemical models and predict ecosystem responses to global changes. Notably, functional traits could be decoupled from taxonomic relatedness, owing to horizontal gene transfer among microorganisms and adaptive evolution. In recent years, our knowledge about microbial functional traits has been substantially enhanced, thereby revealing the multitude of ecological processes in driving community assembly and dynamics. Here, I summarize the emerging patterns of how microbial functional traits respond to changing environments, which considerably differ from better-studied microbial taxonomy. I use niche and neutral theories to explain microbial functional traits. Finally, I highlight future challenges to analyze, elucidate, and utilize functional traits in microbial ecology.

Life-history strategies of soil microbial communities in an arid ecosystem

The overwhelming taxonomic diversity and metabolic complexity of microorganisms can be simplified by a life-history classification; copiotrophs grow faster and rely on resource availability, whereas oligotrophs efficiently exploit resource at the expense of growth rate. Here, we hypothesize that community-level traits inferred from metagenomic data can distinguish copiotrophic and oligotrophic microbial communities. Moreover, we hypothesize that oligotrophic microbial communities harbor more unannotated genes. To test these hypotheses, we conducted metagenomic analyses of soil samples collected from copiotrophic vegetated areas and from oligotrophic bare ground devoid of vegetation in an arid-hyperarid region of the Sonoran Desert, Arizona, USA. Results supported our hypotheses, as we found that multiple ecologically informed life-history traits including average 16S ribosomal RNA gene copy number, codon usage bias in ribosomal genes and predicted maximum growth rate were higher for microbial communities in vegetated than bare soils, and that oligotrophic microbial communities in bare soils harbored a higher proportion of genes that are unavailable in public reference databases. Collectively, our work demonstrates that life-history traits can distill complex microbial communities into ecologically coherent units and highlights that oligotrophic microbial communities serve as a rich source of novel functions.

Host Genotype and Colonist Arrival Order Jointly Govern Plant Microbiome Composition and Function

The composition of host-associated microbiomes can have important consequences for host health and fitness [1-3]. Yet we still lack understanding of many fundamental processes that determine microbiome composition [4, 5]. There is mounting evidence that historical contingency during microbiome assembly may overshadow more deterministic processes, such as the selective filters imposed by host traits [6-8]. More specifically, species arrival order has been frequently shown to affect microbiome composition [9-12], a phenomenon known as priority effects [13-15]. However, it is less clear whether priority effects during microbiome assembly are consequential for the host [16] or whether intraspecific variation in host traits can alter the trajectory of microbiome assembly under priority effects. In a greenhouse inoculation experiment using the black cottonwood (Populus trichocarpa) foliar microbiome, we manipulated host genotype and the colonization order of common foliar fungi. We quantified microbiome assembly outcomes using fungal marker gene sequencing and measured susceptibility of the colonized host to a leaf rust pathogen, Melampsora × columbiana. We found that the effect of species arrival order on microbiome composition, and subsequent disease susceptibility, depended on the host genotype. Additionally, we found that microbiome assembly history can affect host disease susceptibility independent of microbiome composition at the time of pathogen exposure, suggesting that the interactive effects of species arrival order and host genotype can decouple community composition and function. Overall, these results highlight the importance of a key process underlying stochasticity in microbiome assembly while also revealing which hosts are most likely to experience these effects.

Does Intraspecific Variation in rDNA Copy Number Affect Analysis of Microbial Communities?

Amplicon sequencing of partial regions of the ribosomal RNA loci (rDNA) is widely used to profile microbial communities. However, the rDNA is dynamic and can exhibit substantial interspecific and intraspecific variation in copy number in prokaryotes and, especially, in microbial eukaryotes. As change in rDNA copy number is a common response to environmental change, rDNA copy number is not necessarily a property of a species. Variation in rDNA copy number, especially the capacity for large intraspecific changes driven by external cues, complicates analyses of rDNA amplicon sequence data. We highlight the need to (i) interpret amplicon sequence data in light of possible interspecific and intraspecific variation, and (ii) examine the potential plasticity in rDNA copy number as an important ecological factor to better understand how microbial communities are structured in heterogeneous environments.

Open Science principles for accelerating trait-based science across the Tree of Life

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.