Use and abuse of correlation analyses in microbial ecology

Association of food insecurity on gut microbiome and metabolome profiles in a diverse college-based sample

Voluntary caloric restriction (e.g., eating disorders) often results in alterations in the gut microbiota composition and function. However, these findings may not translate to food insecurity, where an individual experiences inconsistent access to healthy food options. In this study we compared the fecal microbiome and metabolome of racially and ethnically diverse first year college students (n = 60) experiencing different levels of food access. Students were dichotomized into food secure (FS) and food insecure (FI) groups using a validated, 2-question screener assessing food security status over the previous 30 days. Fecal samples were collected up to 5 days post survey-completion. Gut microbiome and metabolome were established using 16S rRNA amplicon sequencing, targeted liquid chromatography-tandem mass spectrometry, and gas chromatography-mass spectrometry. FI students experienced significantly greater microbial diversity with increased abundance of Enterobacteriaceae and Eisenbergiella, while FS students had greater abundance of Megasphaera and Holdemanella. Metabolites related to energy transfer and gut–brain-axis communication (picolinic acid, phosphocreatine, 2-pyrrolidinone) were elevated in FI students (q < 0.05). These findings suggest that food insecurity is associated with differential gut microbial and metabolite composition for which the future implications are unknown. Further work is needed to elucidate the longitudinal metabolic effects of food insecurity and how gut microbes influence metabolic outcomes.

Data-driven causal analysis of observational biological time series

Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called ‘model-free’ causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AlV0ttQrjK8).

Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

The relationship between land cover and microbial community composition in European lakes

Microbes are essential for element cycling and ecosystem functioning. However, many questions central to understanding the role of microbes in ecology are still open. Here, we analyze the relationship between lake microbiomes and the lakes' land cover. By applying machine learning methods, we quantify the covariance between land cover categories and the microbial community composition recorded in the largest amplicon sequencing dataset of European lakes available to date. Our results show that the aggregation of environmental features or microbial taxa before analysis can obscure ecologically relevant patterns. We observe a comparatively high covariation of the lakes' microbial community with herbaceous and open spaces surrounding the lake; nevertheless, the microbial covariation with land cover categories is generally lower than the covariation with physico-chemical parameters. Combining land cover and physico-chemical bioindicators identified from the same amplicon sequencing dataset, we develop analytical data structures that facilitate insights into the ecology of the lake microbiome. Among these, a list of the environmental parameters sorted by the number of microbial bioindicators we have identified for them points towards apparent environmental drivers of the lake microbial community composition, such as the altitude, conductivity, and area covered herbaceous vegetation surrounding the lake. Furthermore, the response map, a similarity matrix calculated from the Jaccard similarity of the environmental parameters' lists of bioindicators, allows us to study the ecosystem's structure from the standpoint of the microbiome. More specifically, we identify multiple clusters of highly similar and possibly functionally linked ecological parameters, including one that highlights the importance of the calcium-bicarbonate equilibrium for lake ecology. Taken together, we demonstrate the use of machine learning approaches in studying the interplay between microbial diversity and environmental factors and introduce novel approaches to integrate environmental molecular diversity into monitoring and water quality assessments.

From diversity to complexity: Microbial networks in soils

Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research. Analysing microbial networks in soils opens a window to a better understanding of the complexity of microbial communities. However, this approach is unfortunately often used to draw conclusions which are far beyond the scientific evidence it can provide, which has damaged its reputation for soil microbial analysis. In this Perspectives Paper, we would like to sharpen the view for the real potential of microbial co-occurrence analysis in soils, and at the same time raise awareness regarding its limitations and the many ways how it can be misused or misinterpreted.

Disentangling compartment functions in sessile marine invertebrates

Sessile invertebrates are frequently sampled and processed whole for downstream analyses. However, their apparent structural simplicity is deceptive as these organisms often harbour discrete compartments. These compartments have physicochemical conditions that differ markedly from neighbouring tissues, and that have likely evolved to support specific functions. Here, we argue that such compartments should be specifically targeted when characterising sessile invertebrate biology and we use the coral gastrovascular cavity to support our argument. This complex compartment displays steep and dynamic chemical gradients, harbours distinct microorganisms, and presumably plays a key role in coral biology. Disentangling the functions played by (and amongst) compartments will likely provide transformative insight into the biology of sessile invertebrates and their future under environmental change.

Different spatiotemporal dynamics, ecological drivers and assembly processes of bacterial, archaeal and fungal communities in brackish-saline groundwater

The presence of brackish-saline groundwater (BSG) poses great harms for human health, agricultural and industrial activity. Understanding how the major environmental features in BSG determine microbiota coalescence is crucial for groundwater monitoring optimization. Based on metabarcoding analysis of 242 PCR-amplified samples, we provided the first blueprints about distinct spatiotemporal distributions, ecological drivers and assembly processes of bacterial, archaeal and fungal communities in BSG obtained from new-constructed wells at Xiong'an New Area, China. Our study demonstrated that bacterial and archaeal communities exhibited significant spatial turnovers, while fungal community displayed the most obvious seasonal variation. Environmental filtering drove bacterial compositions more than those of archaea and fungi. Total dissolved solids (TDS), one of the most critical hydrochemical factors for salinization, had a stronger effect on bacterial spatiotemporal turnover than on those of the other two taxonomic groups, while chemical oxygen demand (CODMn) was more significantly associated with prokaryotic community variations. Bacterial and archaeal taxa dominated the metacommunity network and connected closely, and TDS was mostly related to archaeal subnetwork topological features, suggesting a significant influence of TDS on species association patterns within archaea. Specific functional guilds like bacterial nitrite oxidation, anammox, and archaeal methanogenesis were enriched in lower-TDS habitats, while higher TDS favored bacterial communities involved in dark oxidation of sulfur compounds, fumarate respiration, and cellulolysis. Finally, we confirmed that bacterial and archaeal assembly processes were governed by determinism in each season, and that of fungi was more regulated by stochasticity. Higher TDS was speculated to lead bacterial assembly more deterministic and that of fungi more random. Together, these findings provided an integrate theoretical framework about the unique responses of the three life domains to brackish-saline stress, and had important implications for microbial ecological prediction in groundwater.

The dynamic of the potential pathogenic bacteria, antibiotic-resistant bacteria, and antibiotic resistance genes in the water at different growth stages of grass carp pond

Pond aquaculture has become the most important and broadest breeding model in China, and an extremely important source of aquatic products, but the potential hazard factors of potential pathogenic bacteria (PPB), antibiotic resistance bacteria (ARB), and antibiotic resistance genes (ARGs) in aquaculture environment are largely invisible. In the present study, the bacterial communities in the larvae, juvenile, rearing, and harvesting culture stages of great grass carp (Ctenopharyngodon idellus) ponds were investigated and the structure of microbial flora analysis showed that the larvae culture stage has the highest abundance and the most dominant phyla were Proteobacteria (27.8%). A total of 123 significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations and the relative abundance of nine bacterial phenotypes implied that the larvae culture stage had the most abundance of pathogenic potential and mobile elements. The correlation analyses of environmental factors showed that temperature, stocking density, pH, and transparency showed the significant impacts on both the distribution of microbiome and the PPB. More importantly, a total of 40 ARB were identified, and 16 ARGs have the detection rates of 100%, which revealed that they are widely distributed and highly enriched in the aquaculture production. Notably, this is the first robust report to analyze and understand the PPB, ARB, and ARGs characteristics and dynamic changes in the pond aquaculture.

NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. In addition, the GAM transformation increased the predictive power (F1 score) of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

Temporal variations of Microsporidia diversity and discovery of new host-parasite interactions in a lake ecosystem

Microsporidia are a large group of obligate intracellular eukaryotic parasites related to Fungi. Recent studies suggest that their diversity has been greatly underestimated and little is known about their hosts other than metazoans, and thus about their impact on the communities at the base of the food web. In this work, we therefore studied the diversity of Microsporidia over one year and identified potential new hosts in small-sized fractions (<150 μm) in a lake ecosystem using a metabarcoding approach coupled with co-occurrence networks and tyramide signal amplification-fluorescent in situ hybridization. Our analysis shows a great Microsporidia diversity (1 472 OTUs), with an important part of this diversity being unknown. Temporal variations of this diversity have been observed, which might follow temporal variations of their potential hosts such as protists and microzooplankton. New hosts among them were identified as well as associations with phytoplankton. Indeed, repeated infections were observed in Kellicottia (rotifers) with a prevalence of 38% (infected individuals). Microsporidia inside a Stentor (ciliate) were also observed. Finally, potential infections of the diatom Asterionella were identified (prevalence <0.1%). The microsporidian host spectrum could be therefore even more important than previously described, and their role in the functioning of lake ecosystems is undoubtedly largely unknown.

Linking soil fungi to bacterial community assembly in arid ecosystems

Revealing the roles of biotic factors in driving community assembly, which is crucial for the understanding of biodiversity and ecosystem functions, is a fundamental but infrequently investigated subject in microbial ecology. Here, combining a cross-biome observational study with an experimental microcosm study, we provided evidence to reveal the major roles of biotic factors (i.e., soil fungi and cross-kingdom species associations) in determining soil bacterial biogeography and community assembly in complex terrestrial ecosystems of the arid regions of northwest China. The results showed that the soil fungal richness mediates the balance of assembly processes of bacterial communities, and stochastic assembly processes decreased with increasing fungal richness. Our results further suggest that the predicted increase in aridity conditions due to climate change will reduce bacterial α-diversity, particularly in desert soils and subsurface layer, and induce more negative species associations. Together, our study represents a significant advance in linking soil fungi to the mechanisms underlying bacterial biogeographic patterns and community assembly in arid ecosystems under climate aridity and land-use change scenarios.

Biogeographical and seasonal dynamics of the marine Roseobacter community and ecological links to DMSP-producing phytoplankton

Ecological interactions between marine bacteria and phytoplankton play a pivotal role in governing the ocean's major biogeochemical cycles. Among these, members of the marine Roseobacter Group (MRG) can establish mutualistic relationships with phytoplankton that are, in part, maintained by exchanges of the organosulfur compound, dimethylsulfoniopropionate (DMSP). Yet most of what is known about these interactions has been derived from culture-based laboratory studies. To investigate temporal and spatial co-occurrence patterns between members of the MRG and DMSP-producing phytoplankton we analysed 16S and 18S rRNA gene amplicon sequence variants (ASVs) derived from 5 years of monthly samples from seven environmentally distinct Australian oceanographic time-series. The MRG and DMSP-producer communities often displayed contemporaneous seasonality, which was greater in subtropical and temperate environments compared to tropical environments. The relative abundance of both groups varied latitudinally, displaying a poleward increase, peaking (MRG at 33% of total bacteria, DMSP producers at 42% of eukaryotic phototrophs) during recurrent spring-summer phytoplankton blooms in the most temperate site (Maria Island, Tasmania). Network analysis identified 20,140 significant positive correlations between MRG ASVs and DMSP producers and revealed that MRGs exhibit significantly stronger correlations to high DMSP producers relative to other DMSP-degrading bacteria (Pelagibacter, SAR86 and Actinobacteria). By utilising the power of a continental network of oceanographic time-series, this study provides in situ confirmation of interactions found in laboratory studies and demonstrates that the ecological dynamics of an important group of marine bacteria are shaped by the production of an abundant and biogeochemically significant organosulfur compound.

Network structure of resource use and niche overlap within the endophytic microbiome

Endophytes often have dramatic effects on their host plants. Characterizing the relationships among members of these communities has focused on identifying the effects of single microbes on their host, but has generally overlooked interactions among the myriad microbes in natural communities as well as potential higher-order interactions. Network analyses offer a powerful means for characterizing patterns of interaction among microbial members of the phytobiome that may be crucial to mediating its assembly and function. We sampled twelve endophytic communities, comparing patterns of niche overlap between coexisting bacteria and fungi to evaluate the effect of nutrient supplementation on local and global competitive network structure. We found that, despite differences in the degree distribution, there were few significant differences in the global network structure of niche-overlap networks following persistent nutrient amendment. Likewise, we found idiosyncratic and weak evidence for higher-order interactions regardless of nutrient treatment. This work provides a first-time characterization of niche-overlap network structure in endophytic communities and serves as a framework for higher-resolution analyses of microbial interaction networks as a consequence and a cause of ecological variation in microbiome function.

Putting microbial interactions back into community contexts

Microbial interactions are key aspects of the biology of microbiomes. Recently, there has been a shift in the field towards studying interactions in more representative contexts, whether using multispecies model microbial communities or by looking at interactions in situ. Across diverse microbial systems, these studies have begun to identify common interaction mechanisms. These mechanisms include interactions related to toxic molecules, nutrient competition and cross-feeding, access to metals, signaling pathways, pH changes, and interactions within biofilms. Leveraging technological innovations, many of these studies have used an interdisciplinary approach combining genetic, metabolomic, imaging, and/or microfluidic techniques to gain insight into mechanisms of microbial interactions and into the impact of these interactions on microbiomes.

Well-mixed Lotka-Volterra model with random strongly competitive interactions

The random Lotka-Volterra model is widely used to describe the dynamical and thermodynamic features of ecological communities. In this work, we consider random symmetric interactions between species and analyze the strongly competitive interaction case. We investigate different scalings for the distribution of the interactions with the number of species and try to bridge the gap with previous works. Our results show two different behaviors for the mean abundance at zero and finite temperature, respectively, with a continuous crossover between the two. We confirm and extend previous results obtained for weak interactions: at zero temperature, even in the strong competitive interaction limit, the system is in a multiple-equilibria phase, whereas at finite temperature only a unique stable equilibrium can exist. Finally, we establish the qualitative phase diagrams and compare the species abundance distributions in the two cases.

Solutions in microbiome engineering: prioritizing barriers to organism establishment

Microbiome engineering is increasingly being employed as a solution to challenges in health, agriculture, and climate. Often manipulation involves inoculation of new microbes designed to improve function into a preexisting microbial community. Despite, increased efforts in microbiome engineering inoculants frequently fail to establish and/or confer long-lasting modifications on ecosystem function. We posit that one underlying cause of these shortfalls is the failure to consider barriers to organism establishment. This is a key challenge and focus of macroecology research, specifically invasion biology and restoration ecology. We adopt a framework from invasion biology that summarizes establishment barriers in three categories: (1) propagule pressure, (2) environmental filtering, and (3) biotic interactions factors. We suggest that biotic interactions is the most neglected factor in microbiome engineering research, and we recommend a number of actions to accelerate engineering solutions.

Long-term gut microbiome dynamics in Drosophila melanogaster reveal environment-specific associations between bacterial taxa at the family level

The influence of the microbiome on its host is well-documented, but the interplay of its members is not yet well-understood. Even for simple microbiomes, the interaction among members of the microbiome is difficult to study. Longitudinal studies provide a promising approach to studying such interactions through the temporal covariation of different taxonomic units. By contrast to most longitudinal studies, which span only a single host generation, we here present a post hoc analysis of a whole-genome dataset of 81 samples that follows microbiome composition for up to 180 host generations, which cover nearly 10 years. The microbiome diversity remained rather stable in replicated Drosophila melanogaster populations exposed to two different temperature regimes. The composition changed, however, systematically across replicates of the two temperature regimes. Significant associations between families, mostly specific to one temperature regime, indicate functional interdependence of different microbiome components. These associations also involve moderately abundant families, which emphasizes their functional importance, and highlights the importance of looking beyond the common constituents of the Drosophila microbiome.

Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacterium spp. on community butyrate production

Characterizing the metabolic functions of the gut microbiome in health and disease is pivotal for translating alterations in microbial composition into clinical insights. Two major analysis paradigms have been used to explore the metabolic functions of the microbiome but not systematically integrated with each other: statistical screening approaches, such as metabolome-microbiome association studies, and computational approaches, such as constraint-based metabolic modeling. To combine the strengths of the two analysis paradigms, we herein introduce a set of theoretical concepts allowing for the population statistical treatment of constraint-based microbial community models. To demonstrate the utility of the theoretical framework, we applied it to a public metagenomic dataset consisting of 365 colorectal cancer (CRC) cases and 251 healthy controls, shining a light on the metabolic role of Fusobacterium spp. in CRC. We found that (1) glutarate production capability was significantly enriched in CRC microbiomes and mechanistically linked to lysine fermentation in Fusobacterium spp., (2) acetate and butyrate production potentials were lowered in CRC, and (3) Fusobacterium spp. presence had large negative ecological effects on community butyrate production in CRC cases and healthy controls. Validating the model predictions against fecal metabolomics, the in silico frameworks correctly predicted in vivo species metabolite correlations with high accuracy. In conclusion, highlighting the value of combining statistical association studies with in silico modeling, this study provides insights into the metabolic role of Fusobacterium spp. in the gut, while providing a proof of concept for the validity of constraint-based microbial community modeling.

Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

BACKGROUND

Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks.

RESULTS

Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation.

CONCLUSIONS

Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.

The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures

The microbiome emits informative signals of biological organization and environmental pressure that aid ecosystem monitoring and prediction. Are the many signals reducible to a habitat-specific portfolio that characterizes ecosystem health? Does an optimally structured microbiome imply a resilient microbiome? To answer these questions, we applied our novel Eco-Evo Mandala to bacterioplankton data from four habitats within the Great Barrier Reef, to explore how patterns in community structure, function and genetics signal habitat-specific organization and departures from theoretical optimality. The Mandala revealed communities departing from optimality in habitat-specific ways, mostly along structural and functional traits related to bacterioplankton abundance and interaction distributions (reflected by ϵ and λ as power law and exponential distribution parameters), which are not linearly associated with each other. River and reef communities were similar in their relatively low abundance and interaction disorganization (low ϵ and λ) due to their protective structured habitats. On the contrary, lagoon and estuarine inshore reefs appeared the most disorganized due to the ocean temperature and biogeochemical stress. Phylogenetic distances (D) were minimally informative in characterizing bacterioplankton organization. However, dominant populations, such as Proteobacteria, Bacteroidetes, and Cyanobacteria, were largely responsible for community patterns, being generalists with a large functional gene repertoire (high D) that increases resilience. The relative balance of these populations was found to be habitat-specific and likely related to systemic environmental stress. The position on the Mandala along the three fundamental traits, as well as fluctuations in this ecological state, conveys information about the microbiome's health (and likely ecosystem health considering bacteria-based multitrophic dependencies) as divergence from the expected relative optimality. The Eco-Evo Mandala emphasizes how habitat and the microbiome's interaction network topology are first- and second-order factors for ecosystem health evaluation over taxonomic species richness. Unhealthy microbiome communities and unbalanced microbes are identified not by macroecological indicators but by mapping their impact on the collective proportion and distribution of interactions, which regulates the microbiome's ecosystem function.

The soil microbiomics of intact, degraded and partially-restored semi-arid succulent thicket (Albany Subtropical Thicket)

This study examines the soil bacterial diversity in the Portulacaria afra-dominated succulent thicket vegetation of the Albany Subtropical Thicket biome; this biome is endemic to South Africa. The aim of the study was to compare the soil microbiomes between intact and degraded zones in the succulent thicket and identify environmental factors which could explain the community compositions. Bacterial diversity, using 16S amplicon sequencing, and soil physicochemistry were compared across three zones: intact (undisturbed and vegetated), degraded (near complete removal of vegetation due to browsing) and restored (a previously degraded area which was replanted approximately 11 years before sampling). Amplicon Sequence Variant (ASV) richness was similar across the three zones, however, the bacterial community composition and soil physicochemistry differed across the intact and degraded zones. We identified, via correlation, the potential drivers of microbial community composition as soil density, pH and the ratio of Ca to Mg. The restored zone was intermediate between the intact and degraded zones. The differences in the microbial communities appeared to be driven by the presence of plants, with plant-associated taxa more common in the intact zone. The dominant taxa in the degraded zone were cosmopolitan organisms, that have been reported globally in a wide variety of habitats. This study provides baseline information on the changes of the soil bacterial community of a spatially restricted and threatened biome. It also provides a starting point for further studies on community composition and function concerning the restoration of degraded succulent thicket ecosystems.

Long-Term Drought and Warming Alter Soil Bacterial and Fungal Communities in an Upland Heathland

The response of soil microbial communities to a changing climate will impact global biogeochemical cycles, potentially leading to positive and negative feedbacks. However, our understanding of how soil microbial communities respond to climate change and the implications of these changes for future soil function is limited. Here, we assess the response of soil bacterial and fungal communities to long-term experimental climate change in a heathland organo-mineral soil. We analysed microbial communities using Illumina sequencing of the 16S rRNA gene and ITS2 region at two depths, from plots undergoing 4 and 18 years of in situ summer drought or warming. We also assessed the colonisation of Calluna vulgaris roots by ericoid and dark septate endophytic (DSE) fungi using microscopy after 16 years of climate treatment. We found significant changes in both the bacterial and fungal communities in response to drought and warming, likely mediated by changes in soil pH and electrical conductivity. Changes in the microbial communities were more pronounced after a longer period of climate manipulation. Additionally, the subsoil communities of the long-term warmed plots became similar to the topsoil. Ericoid mycorrhizal colonisation decreased with depth while DSEs increased; however, these trends with depth were removed by warming. We largely ascribe the observed changes in microbial communities to shifts in plant cover and subsequent feedback on soil physicochemical properties, especially pH. Our results demonstrate the importance of considering changes in soil microbial responses to climate change across different soil depths and after extended periods of time.

Successional dynamics and alternative stable states in a saline activated sludge microbial community over 9 years

BACKGROUND

Microbial communities in both natural and applied settings reliably carry out myriads of functions, yet how stable these taxonomically diverse assemblages can be and what causes them to transition between states remains poorly understood. We studied monthly activated sludge (AS) samples collected over 9 years from a full-scale wastewater treatment plant to answer how complex AS communities evolve in the long term and how the community functions change when there is a disturbance in operational parameters.

RESULTS

Here, we show that a microbial community in activated sludge (AS) system fluctuated around a stable average for 3 years but was then abruptly pushed into an alternative stable state by a simple transient disturbance (bleaching). While the taxonomic composition rapidly turned into a new state following the disturbance, the metabolic profile of the community and system performance remained remarkably stable. A total of 920 metagenome-assembled genomes (MAGs), representing approximately 70% of the community in the studied AS ecosystem, were recovered from the 97 monthly AS metagenomes. Comparative genomic analysis revealed an increased ability to aggregate in the cohorts of MAGs with correlated dynamics that are dominant after the bleaching event. Fine-scale analysis of dynamics also revealed cohorts that dominated during different periods and showed successional dynamics on seasonal and longer time scales due to temperature fluctuation and gradual changes in mean residence time in the reactor, respectively.

CONCLUSIONS

Our work highlights that communities can assume different stable states under highly similar environmental conditions and that a specific disturbance threshold may lead to a rapid shift in community composition. Video Abstract.

Reconstructing large interaction networks from empirical time series data

Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality is usually high. However, these pose a challenge to existing methods that can quantify only small interaction networks. Here, we proposed a novel approach to reconstruct high-dimensional interaction Jacobian networks using empirical time series without specific model assumptions. This method, named "multiview distance regularised S-map," generalised the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When evaluating this method using time series generated from theoretical models involving hundreds of interacting species, estimated strengths of interaction Jacobians were in good agreement with theoretical expectations. Applying this method to a natural bacterial community helped identify important species from the interaction network and revealed mechanisms governing the dynamical stability of a bacterial community. The proposed method overcame the challenge of high dimensionality in large natural dynamical systems.

Assessing Biotic and Abiotic Interactions of Microorganisms in Amazonia through Co-Occurrence Networks and DNA Metabarcoding

Species may co-occur due to responses to similar environmental conditions, biological associations, or simply because of coincident geographical distributions. Disentangling patterns of co-occurrence and potential biotic and abiotic interactions is crucial to understand ecosystem function. Here, we used DNA metabarcoding data from litter and mineral soils collected from a longitudinal transect in Amazonia to explore patterns of co-occurrence. We compared data from different Amazonian habitat types, each with a characteristic biota and environmental conditions. These included non-flooded rainforests (terra-firme), forests seasonally flooded by fertile white waters (várzeas) or by unfertile black waters (igapós), and open areas associated with white sand soil (campinas). We ran co-occurrence network analyses based on null models and Spearman correlation for all samples and for each habitat separately. We found that one third of all operational taxonomic units (OTUs) were bacteria and two thirds were eukaryotes. The resulting networks were nevertheless mostly composed of bacteria, with fewer fungi, protists, and metazoans. Considering the functional traits of the OTUs, there is a combination of metabolism modes including respiration and fermentation for bacteria, and a high frequency of saprotrophic fungi (those that feed on dead organic matter), indicating a high turnover of organic material. The organic carbon and base saturation indices were important in the co-occurrences in Amazonian networks, whereas several other soil properties were important for the co-exclusion. Different habitats had similar network properties with some variation in terms of modularity, probably associated with flooding pulse. We show that Amazonian microorganism communities form highly interconnected co-occurrence and co-exclusion networks, which highlights the importance of complex biotic and abiotic interactions in explaining the outstanding biodiversity of the region.

BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.

Characterizing the ecological interactions among microbial members is an important step towards understanding the structure and function of diverse microbial communities. Widely used correlation based approaches for inferring interactions from cross-sectional microbiome sequencing data are not able to predict the directionality of interactions, and their results may not be interpretable. We developed an expectation-maximization algorithm (BEEM-Static) that can infer directed interaction networks from cross-sectional data based on an ecological model. Our benchmarking results showed that BEEM-Static inferred presence and directionality of interactions accurately, while correlation based methods had performance slightly better than random guesses. In addition, BEEM-Static was robust to various types of noises using statistical filters to identify and remove data points violating its assumptions. Applying BEEM-Static to a large public dataset of human gut microbiomes, we were able to identify multiple stable equilibria with distinct ecological properties.

Keystone taxa-mediated bacteriome response shapes the resilience of the paddy ecosystem to fungicide triadimefon contamination

The increasing input of fungicides has emerged as a global concern for agroecosystem stability and sustainability. Agroecosystem resilience has been linked to microbiome response, however, is not well understood. Focusing on a widespread triazole-class fungicide triadimefon in the paddy ecosystem, we characterized that the soils and sediments were dominant triadimefon reservoirs with the peak level at 195 μg kg^-1 and 31.3 μg kg^-1, respectively, but essential for the resilience of paddy ecosystem to triadimefon. In paddy simulation models, the half-life of triadimefon in soil-sediment was 8.4-28.9 days, while it was prolonged to 86.6-115.5 days after elimination of resident microbial community. Phospholipid fatty acid profiling and high-throughput sequencing showed that the distinctive bacterial community responses contributed to variable degradation of triadimefon in paddy soils and sediments. Sphingomonas and Xanthomonas were identified as positive responders of the keystone taxa in the responsive bacteriome, whereas Enterobacter were negative responders that declined over time. Synthetic assemblages combined with quantitative polymerase chain reaction further validated that Sphingomonas and Xanthomonas were involved in sustaining soil-sediment resilience to triadimefon contamination. Collectively, our results revealed that the shaping of soil and sediment bacteriomes was responsible for the resilience of the paddy agroecosystem to fungicide contamination.

Seasonal succession of microbes in different size-fractions and their modular structures determined by both macro- and micro-environmental filtering in dynamic coastal waters

Microbes interact with each other in response to various environmental changes in coastal marine ecosystems. To explore how the macroenvironment (environmental filtering) and species-engineered microenvironment (niche construction) affect the ecological network of the marine microbiome in the highly dynamic coastal waters of Korea, we analyzed the modular structures of the microbial community and identified microbial interconnections in different size fractions for a year. Fluctuations in the macroenvironment, such as temperature and nutrient concentrations driven by seasonal changes, are the major factors in determining successive microbial modules. Compared to particle-associated (PA) microbes, free-living (FL) microbes seemed to be more affected by macroenvironmental filtering. Modules related to nutrients were further divided into various modules according to different lifestyles. In addition, a large transient discharge of the Changjiang (Yangtze River) in summer also formed a distinct microbial module, which was related to the high ammonia concentration arising from phytoplankton degradation. Microbes belonging to the SAR11, SAR86, and SAR116 clades, Flavobacteriaceae, and MG IIa-L showed repeated interconnections in temperature-related modules, while the SAR202 clade, Marinimicrobia, DEV007 clade, and Arctic97B-4 and Sva0996 marine groups displayed repeated connections in nutrient-related modules. These 'skeleton'-forming microbes created species-engineered microenvironments, further fine-tuning microbial modular structures. Furthermore, they serve as keystone species for module stability by linking interdependent microbial partners within their own modules through universally beneficial metabolic activities. Therefore, they could reinforce the ecological resilience of microbial communities under abiotic and biotic perturbations in dynamic coastal waters. In conclusion, both macro- and micro-environmental filtering were important for determining the seasonal succession of microbial community structures.

Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models

Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.

Nitrogen and phosphorus fertilization consistently favor pathogenic over mutualistic fungi in grassland soils

Ecosystems across the globe receive elevated inputs of nutrients, but the consequences of this for soil fungal guilds that mediate key ecosystem functions remain unclear. We find that nitrogen and phosphorus addition to 25 grasslands distributed across four continents promotes the relative abundance of fungal pathogens, suppresses mutualists, but does not affect saprotrophs. Structural equation models suggest that responses are often indirect and primarily mediated by nutrient-induced shifts in plant communities. Nutrient addition also reduces co-occurrences within and among fungal guilds, which could have important consequences for belowground interactions. Focusing only on plots that received no nutrient addition, soil properties influence pathogen abundance globally, whereas plant community characteristics influence mutualists, and climate influence saprotrophs. We show consistent, guild-level responses that enhance our ability to predict shifts in soil function related to anthropogenic eutrophication, which can have longer-term consequences for plant communities.

Terrestrial dissolved organic matter inflow drives temporal dynamics of the bacterial community of a subarctic estuary (northern Baltic Sea)

Climate change is projected to cause increased inflow of terrestrial dissolved organic matter to coastal areas in northerly regions. Estuarine bacterial community will thereby receive larger loads of organic matter and inorganic nutrients available for microbial metabolism. The composition of the bacterial community and its ecological functions may thus be affected. We studied the responses of bacterial community to inflow of terrestrial dissolved organic matter in a subarctic estuary in the northern Baltic Sea, using a 16S rRNA gene metabarcoding approach. Betaproteobacteria dominated during the spring river flush, constituting ~ 60% of the bacterial community. Bacterial diversity increased as the runoff decreased during summer, when Verrucomicrobia, Betaproteobacteria, Bacteroidetes, Gammaproteobacteria and Planctomycetes dominated the community. Network analysis revealed that a larger number of associations between bacterial populations occurred during the summer than in spring. Betaproteobacteria and Bacteroidetes populations appeared to display similar correlations to environmental factors. In spring, freshly discharged organic matter favoured specialists, while in summer a mix of autochthonous and terrestrial organic matter promoted the development of generalists. Our study indicates that increased inflows of terrestrial organic matter-loaded freshwater to coastal areas would promote specialist bacteria, which in turn might enhance the transformation of terrestrial organic matter in estuarine environments.

Minerals Determined a Special Ecological Niche and Selectively Enriched Microbial Species from Bulk Water Communities in Hot Springs

Minerals provide physical niches and supply nutrients or serve as electron donors/acceptors for microorganism survival and growth, and thus minerals and microbes co-evolved. Yet, little is known about how sediment minerals impact microbial community assembly in hot springs and to what extent mineralogical composition influences microbial community composition and diversity. Here the influences of minerals on thermophiles in Tengchong hot springs were revealed by network analysis of field samples, as well as in-situ microcosm experiments with minerals. A molecular ecological network was constructed based on high throughput sequencing data of 16S rRNA gene, with a combination of water geochemistry and sedimentary mineralogical compositions. Six modules were identified and this highly modular network structure represents the microbial preference to different abiotic factors, consequently resulting in niche partitioning in sedimentary communities in hot springs. Diverse mineralogical compositions generated special niches for microbial species. Subsequently, the in-situ microcosm experiments with four minerals (aragonite, albite, K-feldspar, and quartz) and spring water were conducted in a silicate-hosted alkaline spring (i.e., Gmq) and a carbonate-hosted neutral hot spring (i.e., Gxs) for 70 days. Different microbial preferences were observed among different mineral types (carbonate versus silicate). Aragonite microcosms in Gmq spring enriched archaeal genera Sulfophobococcus and Aeropyrum within the order Desulfurococcales by comparison with both in-situ water and silicate microcosms. Sulfophobococcus was also accumulated in Gxs aragonite microcosms, but the contribution to overall dissimilarity is much lower than that in Gmq spring. Besides, Caldimicrobium was a bacterial genus enriched in Gxs aragonite microcosms, in contrast to in-situ water and silicate microcosms, whereas Candidatus Kryptobacter and Thermus were more abundant in silicate microcosms. The differences in microbial accumulations among different mineral types in the same spring implied that mineral chemistry may exert extra deterministic selective pressure in drawing certain species from the bulk water communities, in addition to stochastic absorption on mineral surface. Taken together, our results highlight the special niche partitioning determined by mineralogical compositions and further confirm that minerals could be used as “fishing bait” to enrich certain rare microbial species.

Network Properties of Local Fungal Communities Reveal the Anthropogenic Disturbance Consequences of Farming Practices in Vineyard Soils

Agroecosystems are human-managed ecosystems subject to generalized ecological rules. Understanding the ecology behind the assembly and dynamics of soil fungal communities is a fruitful way to improve management practices and plant productivity. Thus, monitoring soil health would benefit from the use of metrics that arise from ecological explanations that can also be informative for agricultural management. Beyond traditional biodiversity descriptors, community-level properties have the potential of informing about particular ecological situations. Here we assess the impact of different farming practices in a survey of 350 vineyard soils from the United States and Spain by estimating network properties based on spatial associations. Our observations using traditional approaches show results concurring with previous literature: the influence of geographic and climatic factors on sample distributions, or different operational taxonomic unit (OTU) compositions depending on agricultural managements. Furthermore, using network properties, we observe that fungal communities ranged from dense arrangements of associations to a sparser structure of associations, indicating differential levels of niche specialization. We detect fungal arrangements capable of thriving in wider or smaller ranges of temperature, revealing that niche specialization may be a critical soil process impacting soil health. Low-intervention practices (organic and biodynamic managements) promoted densely clustered networks, describing an equilibrium state based on mixed collaborative communities. In contrast, conventionally managed vineyards had highly modular sparser communities, supported by a higher coexclusion proportion. Thus, we hypothesize that network properties at the community level may help to understand how the environment and land use can affect community structure and ecological processes in agroecosystems.

IMPORTANCE Soil fungal communities play a key role in agroecosystem sustainability. The complexity of fungal communities, at both taxonomic and functional levels, makes it difficult to find clear patterns connecting community composition with ecosystem function and to understand the impact of biotic (interspecies interactions) and abiotic (e.g., climate or anthropogenic disturbances) factors on it. Here we combine network analysis methods and properties, proposing a novel analytical approach: to infer ecological properties from local networks, which we apply to the study of fungal communities in vineyard soils. We conclude that different levels of farming intensification may lead to different ecological strategies in soil fungal communities settled by particular association arrangements.

Author Video: An author video summary of this article is available.

A Seed Mucilage-Degrading Fungus From the Rhizosphere Strengthens the Plant-Soil-Microbe Continuum and Potentially Regulates Root Nutrients of a Cold Desert Shrub

Seed mucilage plays important roles in the adaptation of desert plants to the stressful environment. Artemisia sphaerocephala is an important pioneer plant in the Central Asian cold desert, and it produces a large quantity of seed mucilage. Seed mucilage of A. sphaerocephala can be degraded by soil microbes, but it is unknown which microorganisms can degrade mucilage or how the mucilage-degrading microorganisms affect rhizosphere microbial communities or root nutrients. Here, mucilage-degrading microorganisms were isolated from the rhizosphere of A. sphaerocephala, were screened by incubation with mucilage stained with Congo red, and were identified by sequencing and phylogenetic analyses. Fungal-bacterial networks based on high-throughput sequencing of rhizosphere microbes were constructed to explore the seasonal dynamic of interactions between a mucilage-degrading microorganism and its closely related microorganisms. The structural equation model was used to analyze effects of the mucilage-degrading microorganism, rhizosphere fungal-bacterial communities, and soil physicochemical properties on root C and N. The fungus Phanerochaete chrysosporium was identified as a mucilage-degrading microorganism. Relative abundance of the mucilage-degrading fungus (MDF) was highest in May. Subnetworks showed that the abundance of fungi and bacteria closely related to the MDF also were highest in May. Interactions between the MDF and related fungi and bacteria were positive, which might enhance mucilage degradation. In addition, the MDF might regulate root C and N by affecting rhizosphere microbial community structure. Our results suggest that MDF from the rhizosphere strengthens the plant-soil-microbe continuum, thereby potentially regulating microbial interactions and root nutrients of A. sphaerocephala.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop

BACKGROUND

Despite their widespread distribution and ecological importance, protists remain one of the least understood components of the soil and rhizosphere microbiome. Knowledge of the roles that protists play in stimulating organic matter decomposition and shaping microbiome dynamics continues to grow, but there remains a need to understand the extent to which biological and environmental factors mediate protist community assembly and dynamics. We hypothesize that protists communities are filtered by the influence of plants on their rhizosphere biological and physicochemical environment, resulting in patterns of protist diversity and composition that mirror previously observed diversity and successional dynamics in rhizosphere bacterial communities.

RESULTS

We analyzed protist communities associated with the rhizosphere and bulk soil of switchgrass (SG) plants (Panicum virgatum) at different phenological stages, grown in two marginal soils as part of a large-scale field experiment. Our results reveal that the diversity of protists is lower in rhizosphere than bulk soils, and that temporal variations depend on soil properties but are less pronounced in rhizosphere soil. Patterns of significantly prevalent protists groups in the rhizosphere suggest that most protists play varied ecological roles across plant growth stages and that some plant pathogenic protists and protists with omnivorous diets reoccur over time in the rhizosphere. We found that protist co-occurrence network dynamics are more complex in the rhizosphere compared to bulk soil. A phylogenetic bin-based null model analysis showed that protists' community assembly in our study sites is mainly controlled by homogenous selection and dispersal limitation, with stronger selection in rhizosphere than bulk soil as SG grew and senesced.

CONCLUSIONS

We demonstrate that environmental filtering is a dominant determinant of overall protist community properties and that at the rhizosphere level, plant control on the physical and biological environment is a critical driver of protist community composition and dynamics. Since protists are key contributors to plant nutrient availability and bacterial community composition and abundance, mapping and understanding their patterns in rhizosphere soil is foundational to understanding the ecology of the root-microbe-soil system. Video Abstract.

Deciphering trophic interactions in a mid-Cambrian assemblage

Exceptionally preserved fossil sites have allowed specimen-based identification of trophic interactions to which network analyses have been applied. However, network analyses of the fossil record suffer from incomplete and indirect data, time averaging that obscures species coexistence, and biases in preservation. Here, we present a high-resolution fossil data set from Raymond Quarry member of the mid-Cambrian Burgess Shale (7,549 specimens, 61 taxa, ∼510 Mya) and formulate a measure of "preservation bias" that aids identification of assemblage subsets to which network analyses can be reliably applied. For these sections, abundance correlation network analyses predicted longitudinally consistent trophic and competitive interactions. Our analyses predicted previously postulated trophic interactions with 83.5% accuracy and demonstrated a shift from specialist interaction-dominated assemblages to ones dominated by generalist and competitive interactions. This approach provides a robust, taphonomically corrected framework to explore and predict in detail the existence and ecological character of putative interactions in fossil data sets.

Fungal-Bacterial Cooccurrence Patterns Differ between Arbuscular Mycorrhizal Fungi and Nonmycorrhizal Fungi across Soil Niches

Soil bacteria and fungi are known to form niche-specific communities that differ between actively growing and decaying roots. Yet almost nothing is known about the cross-kingdom interactions that frame these communities and the environmental filtering that defines these potentially friendly or competing neighbors. We explored the temporal and spatial patterns of soil fungal (mycorrhizal and nonmycorrhizal) and bacterial cooccurrence near roots of wild oat grass, Avena fatua, growing in its naturalized soil in a greenhouse experiment. Amplicon sequences of the fungal internal transcribed spacer (ITS) and bacterial 16S rRNA genes from rhizosphere and bulk soils collected at multiple plant growth stages were used to construct covariation-based networks as a step toward identifying fungal-bacterial associations. Corresponding stable-isotope-enabled metagenome-assembled genomes (MAGs) of bacteria identified in cooccurrence networks were used to inform potential mechanisms underlying the observed links. Bacterial-fungal networks were significantly different in rhizosphere versus bulk soils and between arbuscular mycorrhizal fungi (AMF) and nonmycorrhizal fungi. Over 12 weeks of plant growth, nonmycorrhizal fungi formed increasingly complex networks with bacteria in rhizosphere soils, while AMF more frequently formed networks with bacteria in bulk soils. Analysis of network-associated bacterial MAGs suggests that some of the fungal-bacterial links that we identified are potential indicators of bacterial breakdown and consumption of fungal biomass, while others intimate shared ecological niches.IMPORTANCE Soils near living and decomposing roots form distinct niches that promote microorganisms with distinctive environmental preferences and interactions. Yet few studies have assessed the community-level cooccurrence of bacteria and fungi in these soil niches as plant roots grow and senesce. With plant growth, we observed increasingly complex cooccurrence networks between nonmycorrhizal fungi and bacteria in the rhizosphere, while mycorrhizal fungal (AMF) and bacterial cooccurrence was more pronounced in soil further from roots, in the presence of decaying root litter. This rarely documented phenomenon suggests niche sharing of nonmycorrhizal fungi and bacteria, versus niche partitioning between AMF and bacteria; both patterns are likely driven by C substrate availability and quality. Although the implications of species cooccurrence are fiercely debated, MAGs matching the bacterial nodes in our networks possess the functional potential to interact with the fungi that they are linked to, suggesting an ecological significance of fungal-bacterial cooccurrence patterns.

Linking Bacterial-Fungal Relationships to Microbial Diversity and Soil Nutrient Cycling

Biodiversity is important for supporting ecosystem functioning. To evaluate the factors contributing to the strength of microbial diversity-function relationships in complex terrestrial ecosystems, we conducted a soil survey over different habitats, including an agricultural field, forest, wetland, grassland, and desert. Soil microbial multidiversity was estimated by the combination of bacterial and fungal diversity. Soil ecosystem functions were evaluated using a multinutrient cycling index (MNC) in relation to carbon, nitrate, phosphorus, and potassium cycling. Significant positive relationships between soil multidiversity and multinutrient cycling were observed in all habitats, except the grassland and desert. Specifically, community compositions showed stronger correlations with multinutrient cycling than α-diversity, indicating the crucial role of microbial community composition differences on soil nutrient cycling. Importantly, we revealed that changes in both the neutral processes (Sloan neutral modeling) and the proportion of negative bacterial-fungal associations were linked to the magnitude and direction of the diversity-MNC relationships. The habitats less governed by neutral processes and dominated by negative bacterial-fungal associations exhibited stronger negative microbial α-diversity-MNC relationships. Our findings suggested that the balance between positive and negative bacterial-fungal associations was connected to the link between soil biodiversity and ecosystem function in complex terrestrial ecosystems. This study elucidates the potential factors influencing diversity-function relationships, thereby enabling future studies to forecast the effects of belowground biodiversity on ecosystem function.IMPORTANCE The relationships between soil biodiversity and ecosystem functions are an important yet poorly understood topic in microbial ecology. This study presents an exploratory effort to gain predictive understanding of the factors driving the relationships between microbial diversity and potential soil nutrient cycling in complex terrestrial ecosystems. Our structural equation modeling and random forest analysis revealed that the balance between positive and negative bacterial-fungal associations was clearly linked to the strength of the relationships between soil microbial diversity and multiple nutrients cycling across different habitats. This study revealed the potential factors underpinning diversity-function relationships in terrestrial ecosystems and thus helps us to manage soil microbial communities for better provisioning of key ecosystem services.

Lake Ecosystem Robustness and Resilience Inferred from a Climate-Stressed Protistan Plankton Network

Network analyses of biological communities allow for identifying potential consequences of climate change on the resilience of ecosystems and their robustness to resist stressors. Using DNA metabarcoding datasets from a three-year-sampling (73 samples), we constructed the protistan plankton co-occurrence network of Lake Zurich, a model lake ecosystem subjected to climate change. Despite several documentations of dramatic lake warming in Lake Zurich, our study provides an unprecedented perspective by linking changes in biotic association patterns to climate stress. Water temperature belonged to the strongest environmental parameters splitting the data into two distinct seasonal networks (October–April; May–September). The expected ecological niche of phytoplankton, weakened through nutrient depletion because of permanent thermal stratification and through parasitic fungi, was occupied by the cyanobacterium Planktothrix rubescens and mixotrophic nanoflagellates. Instead of phytoplankton, bacteria and nanoflagellates were the main prey organisms associated with key predators (ciliates), which contrasts traditional views of biological associations in lake plankton. In a species extinction scenario, the warm season network emerged as more vulnerable than the cold season network, indicating a time-lagged effect of warmer winter temperatures on the communities. We conclude that climate stressors compromise lake ecosystem robustness and resilience through species replacement, richness differences, and succession as indicated by key network properties.

Multi-kingdom ecological drivers of microbiota assembly in preterm infants

The gut microbiota of preterm infants develops predictably1-7, with pioneer species colonizing the gut after birth, followed by an ordered succession of microorganisms. The gut microbiota is vital to the health of preterm infants8,9, but the forces that shape these predictable dynamics of microbiome assembly are unknown. The environment, the host and interactions between microorganisms all potentially shape the dynamics of the microbiota, but in such a complex ecosystem, identifying the specific role of any individual factor is challenging10-14. Here we use multi-kingdom absolute abundance quantification, ecological modelling and experimental validation to address this challenge. We quantify the absolute dynamics of bacteria, fungi and archaea in a longitudinal cohort of 178 preterm infants. We uncover microbial blooms and extinctions, and show that there is an inverse correlation between bacterial and fungal loads in the infant gut. We infer computationally and demonstrate experimentally in vitro and in vivo that predictable assembly dynamics may be driven by directed, context-dependent interactions between specific microorganisms. Mirroring the dynamics of macroscopic ecosystems15-17, a late-arriving member of the microbiome, Klebsiella, exploits the pioneer microorganism, Staphylococcus, to gain a foothold within the gut. Notably, we find that interactions between different kingdoms can influence assembly, with a single fungal species-Candida albicans-inhibiting multiple dominant genera of gut bacteria. Our work reveals the centrality of simple microbe-microbe interactions in shaping host-associated microbiota, which is critical both for our understanding of microbiota ecology and for targeted microbiota interventions.

Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combine metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotations, which we provide for experimental testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.

Fine sediment and flow velocity impact bacterial community and functional profile more than nutrient enrichment

Freshwater ecosystems face many simultaneous pressures due to human activities. Consequently, there has been a rapid loss of freshwater biodiversity and an increase in biomonitoring programs. Our study assessed the potential of benthic stream bacterial communities as indicators of multiple-stressor impacts associated with urbanization and agricultural intensification. We conducted a fully crossed four-factor experiment in 64 flow-through mesocosms fed by a pristine montane stream (21 d of colonization, 21 d of manipulations) and investigated the effects of nutrient enrichment, flow-velocity reduction and added fine sediment after 2 and 3 weeks of stressor exposure. We used high-throughput sequencing and metabarcoding techniques (16S rRNA genes), as well as curated biological databases (METAGENassit, MetaCyc), to identify changes in bacterial relative abundances and predicted metabolic functional profile. Sediment addition and flow-velocity reduction were the most pervasive stressors. They both increased α-diversity and had strong taxon-specific effects on community composition and predicted functions. Sediment and flow velocity also interacted frequently, with 88% of all bacterial response variables showing two-way interactions and 33% showing three-way interactions including nutrient enrichment. Changes in relative abundances of common taxa were associated with shifts in dominant predicted functions, which can be extrapolated to underlaying stream-wide mechanisms such as carbon use and bacterial energy production pathways. Observed changes were largely stable over time and occurred after just 2 weeks of exposure, demonstrating that bacterial communities can be well-suited for early detection of multiple stressors. Overall, added sediment and reduced flow velocity impacted both bacterial community structure and predicted function more than nutrient enrichment. In future research and stream management, a holistic approach to studying multiple-stressor impacts should include multiple trophic levels with their functional responses, to enhance our mechanistic understanding of complex stressor effects and promote establishment of more efficient biomonitoring programs.

Aquatic food webs in deep temperate lakes: Key species establish through their autecological versatility

Microbial planktonic communities are the basis of food webs in aquatic ecosystems since they contribute substantially to primary production and nutrient recycling. Network analyses of DNA metabarcoding data sets emerged as a powerful tool to untangle the complex ecological relationships among the key players in food webs. In this study, we evaluated co-occurrence networks constructed from time-series metabarcoding data sets (12 months, biweekly sampling) of protistan plankton communities in surface layers (epilimnion) and bottom waters (hypolimnion) of two temperate deep lakes, Lake Mondsee (Austria) and Lake Zurich (Switzerland). Lake Zurich plankton communities were less tightly connected, more fragmented and had a higher susceptibility to a species extinction scenario compared to Lake Mondsee communities. We interpret these results as a lower robustness of Lake Zurich protistan plankton to environmental stressors, especially stressors resulting from climate change. In all networks, the phylum Ciliophora contributed the highest number of nodes, among them several in key positions of the networks. Associations in ciliate-specific subnetworks resembled autecological species-specific traits that indicate adaptions to specific environmental conditions. We demonstrate the strength of co-occurrence network analyses to deepen our understanding of plankton community dynamics in lakes and indicate biotic relationships, which resulted in new hypotheses that may guide future research in climate-stressed ecosystems.

Ignoring social distancing: advances in understanding multi-species bacterial interactions

Almost every ecosystem on this planet is teeming with microbial communities made of diverse bacterial species. At a reductionist view, many of these bacteria form pairwise interactions, but, as the field of view expands, the neighboring organisms and the abiotic environment can play a crucial role in shaping the interactions between species. Over the years, a strong foundation of knowledge has been built on isolated pairwise interactions between bacteria, but now the field is advancing toward understanding how cohabitating bacteria and natural surroundings affect these interactions. Use of bottom-up approaches, piecing communities together, and top-down approaches that deconstruct communities are providing insight on how different species interact. In this review, we highlight how studies are incorporating more complex communities, mimicking the natural environment, and recurring findings such as the importance of cooperation for stability in harsh environments and the impact of bacteria-induced environmental pH shifts. Additionally, we will discuss how omics are being used as a top-down approach to identify previously unknown interspecies bacterial interactions and the challenges of these types of studies for microbial ecology.