Emergent phases of ecological diversity and dynamics mapped in microcosms

Outbreaks of Ulva prolifera green tides reduce the network complexity and stability of cooccurring planktonic microbial communities

Ulva prolifera green tides are becoming a worldwide environmental problem, especially in the Yellow Sea, China. However, the effects of the occurrence of U. prolifera green tides on the community organization and stability of surrounding microbiomes have still not been determined. Here, the prokaryotic microbial community network stability and assembly characteristics were systematically analyzed and compared between the green tide and non-green tide periods. U. prolifera blooms weaken the community complexity and robustness of surrounding microbiomes, increasing fragmentation and decreasing diversity. Bacteria and archaea exhibited distinct community distributions and assembly patterns under the influence of green tides, and bacterial communities were more sensitive to outbreaks of green tides. The bacterial communities exhibited a greater niche breadth and a lower phylogenetic distance during the occurrence of U. prolifera green tides compared to those during the non-green tide period while archaeal communities remained unchanged, suggesting that the bacterial communities underwent stronger homogeneous selection and more sensitive to green tide blooms than the archaeal communities. Piecewise structural equation model analysis revealed that the different responses of major prokaryotic microbial groups, such as Cyanobacteria, to environmental variables during green tides, were influenced by the variations in pH and nitrate during green tides and correlated with the salinity gradient during the non-green tide period. This study elucidates the response of the adaptability, associations, and stability of surrounding microbiomes to outbreaks of U. prolifera green tides.

Dynamic responses of soil microbial communities to long-term co-contamination with PBAT and cadmium

The co-contamination of plastics and heavy metals poses novel challenges to agricultural soil ecosystems. However, research into the long-term effects of such co-contamination on soil microbial communities remains limited. This study, through a 540-day incubation experiment, investigated the impacts of poly(butylene adipate-co-terephthalate) (PBAT) and cadmium (Cd) co-contamination on the structure and function of soil microbial communities. The findings revealed that co-contamination significantly altered soil nitrate and ammonium nitrogen levels. Furthermore, the co-contamination continuously and significantly reduced bacterial diversity, producing a more pronounced negative impact compared to single pollution. Key microbial groups such as Proteobacteria, acting as core microorganisms, were significantly enriched under co-contamination conditions, with their relative abundance increasing significantly by 40.0 %. This indicates their potential role in plastic degradation and heavy metal resistance. In addition, the co-contamination also drove the shift of bacterial and fungal community assembly from deterministic processes to stochastic processes. These insights not only fill the research gap regarding the effects of long-term co-contamination on soil microorganisms but also provide a scientific foundation for the development of targeted soil management and remediation strategies, especially in regions where plastic and heavy metal pollution coexist.

Extinctions as a vestige of instability: The geometry of stability and feasibility

Species coexistence is a complex, multifaceted problem. At an equilibrium, coexistence requires two conditions: stability under small perturbations; and feasibility, meaning all species abundances are positive. Which of these two conditions is more restrictive has been debated for many years, with many works focusing on statistical arguments for systems with many species. Within the framework of the Lotka-Volterra equations, we examine the geometry of the region of coexistence in the space of interaction strengths, for symmetric competitive interactions and any finite number of species. We consider what happens when starting at a point within the coexistence region, and changing the interaction strengths continuously until one of the two conditions breaks. We find that coexistence generically breaks through the loss of feasibility, as the abundance of one species reaches zero. An exception to this rule - where stability breaks before feasibility - happens only at isolated points, or more generally on a lower dimensional subset of the boundary. The reason behind this is that as a stability boundary is approached, some of the abundances generally diverge towards minus infinity, and so go extinct at some earlier point, breaking the feasibility condition first. These results define a new sense in which feasibility is a more restrictive condition than stability, and show that these two requirements are closely interrelated. We then show how our results affect the changes in the set of coexisting species when interaction strengths are changed: a system of coexisting species loses a species by its abundance continuously going to zero, and this new fixed point is unique. As parameters are further changed, multiple alternative equilibria may be found. Finally, we discuss the extent to which our results apply to asymmetric interactions.

Micron-scale heterogeneity reduction leads to increased interspecies competition in thermophilic digestion microbiome

Microbial spatial heterogeneity is an important determinant of larger-scale community properties, whereas most studies neglect it and therefore only provide average information, potentially obscuring the signal of microbial interactions. Our study takes a step toward addressing this problem by characterizing the spatial heterogeneity of a microbiome with micron-scale resolution. Micron-scale single clusters (40-70 μm) were randomly collected from lab-scale anaerobic digestion (AD) biosystems, and a comparative analysis was performed to evaluate differences between mesophilic and thermophilic systems. Here we reveal a cascading effect from high-temperature selection to global microbial interactions. We observed that thermophilic communities exhibited less spatial heterogeneity than mesophilic communities, which we attribute to the considerable extinction of low-abundant species by high-temperature selection. Then, the low spatial heterogeneity and the high-temperature selection acting in conjunction resulted in a high proportion of competitive interactions in thermophilic communities. Unexpectedly, however, the thermophilic AD, characterized by lower micron-scale spatial heterogeneity, showed more efficient synergistic and syntrophic cooperations involving around Clostridiales, which significantly enhanced hydrolysis performance under thermophilic conditions. In addition, the fact that high temperatures favor slower growers, along with functional redundancy-related competitive advantage, led to the selection of more proficient methanogens in more competitive environments, which are also potentially associated with enhanced methanogenic performance. In summary, our findings underscore the significance of micron-scale resolution for revealing the microbial ecology in spatially structured environments.

Centennial trends in human and climate influences on sediment-associated microorganisms in an oligotrophic lake

Microorganisms in lake ecosystems exhibit sensitive and dynamic changes in response to human activities and climate change. However, studies correlating microbial communities with anthropogenic changes over a century-long timescale are currently lacking. In this study, DNA extracted from sediments and lake sediment environmental proxy analyses were employed to reconstruct a centennial-scale time series of prokaryotic and microeukaryotic community changes, revealing distinct differences in their evolutionary patterns. The results indicated that the heterogeneity of the prokaryotic community was increasing, and the community assembly was consistently influenced by both deterministic and stochastic processes. Microeukaryotes showed significant fluctuations in the relative abundance of the dominant species, a continuous increase in alpha diversity, and stochastic processes as a key mechanism of community assembly. In addition, climate and human activities were identified as key factors influencing microbial communities. It was found that the dynamics of the prokaryotic community were influenced by both biotic and environmental factors, whereas microeukaryotic population dynamics were particularly influenced by external factors. In general, changes in the watershed environment significantly impacted microbial evolutionary patterns, providing new insights into the evolution of lake ecosystems and offering strong support for future lake management and conservation efforts.

Inference of Pairwise Interactions from Strain Frequency Data Across Settings and Context-Dependent Mutual Invasibilities

We propose a method to estimate pairwise strain interactions from population-level frequencies across different endemic settings. We apply the framework of replicator dynamics, derived from a multi-strain SIS model with co-colonization, to extract from 5 datasets the fundamental backbone of strain interactions. In our replicator, each pairwise invasion fitness explicitly arises from local environmental context and trait variations between strains. We adopt the simplest formulation for multi-strain coexistence, where context is encoded in basic reproduction number R 0 and mean global susceptibility to co-colonization k, and trait variations α ij capture pairwise deviations from k. We integrate Streptococcus pneumoniae serotype frequencies and serotype identities collected from 5 environments: epidemiological surveys in Denmark, Nepal, Iran, Brazil and Mozambique, and mechanistically link their distributions. Our results have twofold implications. First, we offer a new proof-of-concept in the inference of multi-species interactions based on cross-sectional data. We also discuss 2 key aspects of the method: the site ordering for sequential fitting, and stability constraints on the dynamics. Secondly, we effectively estimate at high-resolution more than 70% of the 92 × 92 pneumococcus serotype interaction matrix in co-colonization, allowing for further projections and hypotheses testing. We show that, in these bacteria, both within- and between- serotype interaction coefficients' distribution emerge to be unimodal, their difference in mean broadly reflecting stability assumptions on serotype coexistence. This framework enables further model calibration to global data: cross-sectional across sites, or longitudinal in one site over time, - and should allow a more robust and integrated investigation of intervention effects in such biodiverse ecosystems.

Microbial Biotic Associations Dominated Adaptability Differences of Dioecious Poplar Under Salt Stress

How different stress responses by male and female plants are influenced by interactions with rhizosphere microbes remains unclear. In this study, we employed poplar as a dioecious model plant and quantified biotic associations between microorganisms to explore the relationship between microbial associations and plant adaptation. We propose a health index (HI) to comprehensively characterize the physiological characteristics and adaptive capacity of plants under stress. It was found that male poplars demonstrated higher salt stress tolerance than females, and root-secreted citric acid was significantly higher in the rhizospheres of male poplars. Positive biotic association among bacteria increased poplar HI significantly under salt stress, while fungal and cross-domain biotic association (bacteria-fungi) did not. We further identified a keystone bacterial taxon regulating bacterial biotic association, ASV_22706, which was itself regulated by citric acid and significantly positively correlated with host HI. The abundance of keystone fungal taxa was positively correlated with HI of male poplars and negatively correlated with HI of female poplars. Compared with female poplars, male poplars enriched more prebiotics and probiotics under stress. This work primarily reveals the relationship between adaptation differences and microbial interactions in dioecious plants, which suggests a microbial approach to improve plant adaptability to stress conditions.

Automated adjustment of metabolic niches enables the control of natural and engineered microbial co-cultures

Much attention has focused on understanding microbial interactions leading to stable co-cultures. In this work, substrate pulsing was performed to promote better control of the metabolic niches (MNs) corresponding to each species, leading to the continuous co-cultivation of diverse microbial organisms. We used a cell-machine interface, which allows adjustment of the temporal profile of two MNs according to a rhythm, ensuring the successive growth of two species, in our case, a yeast and a bacterium. The resulting approach, called 'automated adjustment of metabolic niches' (AAMN), was effective for stabilizing both cooperative and competitive co-cultures. AAMN can be considered an enabling technology for the deployment of co-cultures in bioprocesses, demonstrated here based on the continuous bioproduction of p-coumaric acid. The data accumulated suggest that AAMN could be used not only for a wider range of biological systems, but also to gain fundamental insights into microbial interaction mechanisms.

Phages-bacteria interactions underlying the dynamics of polyhydroxyalkanoate-producing mixed microbial cultures via meta-omics study

The dynamics of the structure of polyhydroxyalkanoate-producing mixed microbial cultures (PHA-MMCs) during enrichment and maintenance is an unsolved problem. The effect of phages has been proposed as a cause of dynamic changes in community structure, but evidence is lacking. To address this question, five PHA-MMCs were enriched, and biological samples were sampled temporally to study the interactions between phage and bacterial members by combining metagenomics and metatranscriptomics. A total of 963 metagenome-assembled genomes (MAGs) and 4,294 phage operational taxonomic units (pOTUs) were assembled from bulk metagenomic data. The dynamic changes in the structure of phage and bacterial communities were remarkably consistent. Structural equation modeling analysis showed that phages could infect and lyse dominant species to vacate ecological niches for other species, resulting in a community succession state in which dominant species alternated. Seven key auxiliary metabolic genes (AMGs), phaC, fadJ, acs, ackA, phbB, acdAB, and fadD, potentially contributing to PHA synthesis were identified from phage sequences. Importantly, these AMGs were transcribed, indicating that they were in an active expression state. The meta-analysis provides the first catalog of phages in PHA-MMCs and the AMGs they carry, as well as how they affect the dynamic changes in bacterial communities. This study provides a reference for subsequent studies on understanding and regulating the microbial community structure of open microbial systems.IMPORTANCEThe synthesis of biodegradable plastic PHA from organic waste through mixed microbial cultures (PHA-MMCs), at extremely low cost, has the potential for expanded production. However, the dynamics of dominant species in PHA-MMCs are poorly understood. Our results demonstrate for the first time the impact of phages on the structure of bacterial communities in the PHA-MMCs. There are complex interactions between the PHA producers (e.g., Azomonas, Paracoccus, and Thauera) and phages (e.g., Casadabanvirus and unclassified Hendrixvirinae). Phage communities can regulate the activity and structure of bacterial communities. In addition, the AMGs related to PHA synthesis may hitchhike during phage-host infection cycles, enabling their dissemination across bacterial communities, and phages may act as a critical genetic reservoir for bacterial members, facilitating access to PHA synthesis-related functional traits. This study highlights the impact of phages on bacterial community structure, suggesting that phages have the potential to be used as a tool for better controlling the microbial community structure of PHA-MMCs.

Disturbance and stability dynamics in microbial communities for environmental biotechnology applications

Microbial communities are corner stones of environmental biotechnology, driving essential processes such as waste degradation, pollutant removal, and nutrient cycling, all fundamental to industrial bioprocesses and sustainability. The structure and functions of these communities are influenced by environmental disturbances, which can arise from changes in operational conditions. Understanding disturbance-stability dynamics, including the roles of rare taxa and gene potential, is crucial for optimizing processes such as wastewater treatment, bioenergy production, and environmental bioremediation. This review highlights recent theoretical, technical, and experimental advances - including ecological theory, multiscale approaches, and the use of machine learning and artificial intelligence - to predict community responses to disturbances. Together, these insights offer a valuable outlook for developing scalable and robust biotechnology applications.

Connecting the dots: Managing species interaction networks to mitigate the impacts of global change

Global change is causing unprecedented degradation of the Earth's biological systems and thus undermining human prosperity. Past practices have focused either on monitoring biodiversity decline or mitigating ecosystem services degradation. Missing, but critically needed, are management approaches that monitor and restore species interaction networks, thus bridging existing practices. Our overall aim here is to lay the foundations of a framework for developing network management, defined here as the study, monitoring, and management of species interaction networks. We review theory and empirical evidence demonstrating the importance of species interaction networks for the provisioning of ecosystem services, how human impacts on those networks lead to network rewiring that underlies ecosystem service degradation, and then turn to case studies showing how network management has effectively mitigated such effects or aided in network restoration. We also examine how emerging technologies for data acquisition and analysis are providing new opportunities for monitoring species interactions and discuss the opportunities and challenges of developing effective network management. In summary, we propose that network management provides key mechanistic knowledge on ecosystem degradation that links species- to ecosystem-level responses to global change, and that emerging technological tools offer the opportunity to accelerate its widespread adoption.

Microbial populations hardly ever grow logistically and never sublinearly

We investigate the growth dynamics of microbial populations, challenging the conventional logistic model. By analyzing empirical data from various biomes, we demonstrate that microbial growth is better described by a generalized logistic model, the θ-logistic model. This accounts for different growth mechanisms and environmental fluctuations, leading to a generalized gamma distribution of abundance fluctuations. Our findings reveal that microbial growth is never sublinear, so they cannot endorse-at least in the microbial world-the recent proposal of this mechanism as a stability enhancer of highly diverse communities. These results have significant implications for understanding macroecological patterns and the stability of microbial ecosystems.

Revisiting the classical biodiversity-ecosystem functioning and stability relationships in microbial microcosms

The question of how biodiversity influences ecosystem functioning and stability has been a central focus in ecological research. Yet, this question remains unresolved, primarily because of the widely divergent definitions of functioning, stability, and diversity. Consequently, forecasts of ecosystem services will remain speculative until we can establish more precise and comprehensive definitions for these concepts than previously. Here, we investigated how the maximum specific growth rate, productivity, mortality rate, and species interaction in microbial communities vary with a diversity gradient ranging from 1 to 16 species under control conditions, starvation, or saline stress. We found that diversity played a critical role in maintaining community growth and stability under control conditions, with higher diversity associated with increased maximum specific growth rate and decreased mortality rate. However, higher diversity was associated with an increased mortality rate under starvation, while diversity did not affect the mortality rate under saline stress. Diversity stabilized microbial productivity only under control conditions, defying the "diversity begets stability" hypothesis under stress. Beneficial interactions among species were prevalent in most samples, but species interaction increased mortality rates under starvation. Our findings suggest that while biodiversity is crucial for preserving ecosystem functioning and stability, the presence of multiple definitions and contextual dependence on environmental conditions argues against any general relationship between diversity and ecosystem functioning/stability. Furthermore, we provide new insights into the longstanding debate surrounding the "diversity begets stability" hypothesis and the "diversity destabilizes ecosystem" hypothesis in that diversity begets stability under control conditions but destabilizes ecosystems under severe stress.

Migration feedback induces emergent ecotypes and abrupt transitions in evolving populations

We explore the connection between migration patterns and emergent behaviors of evolving populations in spatially heterogeneous environments. Despite extensive studies in systems of ecological and medical importance, a unifying framework that clarifies this connection and makes concrete predictions remains much needed. Using a simple evolutionary model on a network of interconnected habitats with distinct fitness landscapes, we demonstrate a fundamental connection between migration feedback, emergent ecotypes, and a form of discontinuous critical transition. We show how migration feedback-via circulating migration patterns-generates spatially nonlocal niches in which emergent ecotypes specialize. Rugged fitness landscapes lead to a complex, yet understandable, phase diagram in which different sets of ecotypes coexist under different migration patterns. Under certain ecological interactions, a discontinuous transition splits into two continuous transitions-this effect suggests a sensitive experimental probe for the nature and magnitude of the underlying interactions.

Environmental instability reduces shock resistance by enriching specialist taxa with distinct two component regulatory systems

Different microbial communities are impacted disproportionately by environmental disturbances. The degree to which a community can remain unchanged under a disturbance is referred to as resistance1. However, the contributing ecological factors, which infer a community's resistance are unknown. In this study, the impact of historical environmental stability on ecological phenomena and microbial community resistance to shocks was investigated. Three separate methanogenic bioreactor consortia, which were subjected to varying degrees of historical environmental stability, and displayed different levels of resistance to an organic loading rate (OLR) shock were sampled. Their community composition was assessed using high throughput sequencing of 16S rRNA genes and assembly based metagenomics. The effect environmental instability on ecological phenomena such as microbial community assembly, microbial niche breadth and the rare biosphere were assessed in the context of each reactor's demonstrated resistance to an OLR shock. Additionally, metagenome assembled genomes were analysed for functional effects of prolonged stability/instability. The system which was subjected to more environmental instability experienced more temporal variation in community beta diversity and a proliferation of specialists, with more abundant two component regulatory systems. This community was more susceptible to deterministic community assembly and demonstrated a lower degree of resistance, indicating that microbial communities experiencing longer term environmental instability (e.g. variations in pH or temperature) are less able to resist a large disturbance.

Replicating community dynamics reveals how initial composition shapes the functional outcomes of bacterial communities

Bacterial communities play key roles in global biogeochemical cycles, industry, agriculture, human health, and animal husbandry. There is therefore great interest in understanding bacterial community dynamics so that they can be controlled and engineered to optimise ecosystem services. We assess the reproducibility and predictability of bacterial community dynamics by creating a frozen archive of hundreds of naturally-occurring bacterial communities that we repeatedly revive and track in a standardised, complex resource environment. Replicate communities follow reproducible trajectories and the community dynamics closely map to ecosystem functioning. However, even under standardised conditions, the communities exhibit tipping-points, where small differences in initial community composition create divergent compositional and functional outcomes. The predictability of community trajectories therefore requires detailed knowledge of rugged compositional landscapes where ecosystem properties are not the inevitable result of prevailing environmental conditions but can be tilted toward different outcomes depending on the initial community composition. Our results shed light on the relationship between composition and function, opening new avenues to understand the feasibility and limitations of function prediction in complex microbial communities.

Unifying spatial scaling laws of biodiversity and ecosystem stability

While both species richness and ecosystem stability increase with area, how these scaling patterns are linked remains unclear. Our theoretical and empirical analyses of plant and fish communities show that the spatial scaling of ecosystem stability is determined primarily by the scaling of species asynchrony, which is in turn driven by the scaling of species richness. In wetter regions, plant species richness and ecosystem stability both exhibit faster accumulation with area, implying potentially greater declines in biodiversity and stability following habitat loss. The decline in ecosystem stability after habitat loss can be delayed, creating a stability debt mirroring the extinction debt of species. By unifying two foundational scaling laws in ecology, our work underscores that ongoing biodiversity loss may destabilize ecosystems across spatial scales.

Interaction network structures in competitive ecosystems

We present a numerical analysis of local community assembly through weak migration from a regional species pool. At equilibrium, the local community consists of a subset ("clique") of species from the regional community. Our analysis, based on numerical integration of the generalized Lotka-Volterra equations, reveals that the interaction networks of these cliques exhibit nontrivial architectures. Specifically, we demonstrate a pronounced nested structure of the clique interaction matrix in the case of symmetric interactions and a hyperuniform structure seen in asymmetric communities. For a local community to be stable, its composition must meet two requirements: first, it must be feasible on its own, such that internal competition does not lead to species extinction. Second, it must be resistant against invasion by species from the regional community. We show that the nestedness property, although it slightly compromises feasibility, is essential to ensure noninvadability and, thus, characterizes communities with symmetric interactions. In the case of asymmetric interactions, achieving a nested structure is challenging; therefore, the local community at any given moment is hyperuniform, ensuring feasibility but making it invasion-prone. As a result, the dynamics of systems with strong asymmetric interactions is unstable.

Coexistence Theory for Microbial Ecology, and Vice Versa

Classical models from theoretical ecology are seeing increasing uptake in microbial ecology, but there remains rich potential for closer cross-pollination. Here we explore opportunities for stronger integration of ecological theory into microbial research (and vice versa) through the lens of so-called "modern" coexistence theory. Coexistence theory can be used to disentangle the contributions different mechanisms (e.g., resource partitioning, environmental variability) make to species coexistence. We begin with a short primer on the fundamental concepts of coexistence theory, with an emphasis on the relevance to microbial communities. We next present a systematic review, which highlights the paucity of empirical applications of coexistence theory in microbial systems. In light of this gap, we then identify and discuss ways in which: (i) coexistence theory can help to answer fundamental and applied questions in microbial ecology, particularly in spatio-temporally heterogeneous environments, and (ii) experimental microbial systems can be leveraged to validate and advance coexistence theory. Finally, we address several unique but often surmountable empirical challenges posed by microbial systems, as well as some conceptual limitations. Nevertheless, thoughtful integration of coexistence theory into microbial ecology presents a wealth of opportunities for the advancement of both theoretical and microbial ecology.

Collective dynamical regimes predict invasion success and impacts in microbial communities

The outcomes of ecological invasions may depend on either characteristics of the invading species or attributes of the resident community. Here we use a combination of experiments and theory to show that the interplay between dynamics, interaction strength and diversity determine the invasion outcome in microbial communities. We find that the communities with fluctuating species abundances are more invasible and diverse than stable communities, leading to a positive diversity-invasibility relationship among communities assembled in the same environment. As predicted by theory, increasing interspecies interaction strength and species pool size leads to a decrease of invasion probability in our experiment. Our results show a positive correspondence between invasibility and survival fraction of resident species across all conditions. Communities composed of strongly interacting species can exhibit an emergent priority effect in which invader species are less likely to colonize than species in the original pool. However, if an invasion is successful, its ecological effects on the resident community are greater when interspecies interactions are strong. Our findings provide a unified perspective on the diversity-invasibility debate by showing that invasibility and invasion effect are emergent properties of interacting species, which can be predicted by simple community-level features.

The architecture of theory and data in microbiome design: towards an S-matrix for microbiomes

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used a range of mathematical models to understand, predict, and control microbiomes, ranging from mechanistic models, putting microbial populations and their interactions as the focus, to purely statistical approaches, searching for patterns in empirical and experimental data. We review the success and limitations of these modeling approaches when designing novel microbiomes, especially when guided by (inevitably) incomplete experimental data. Although successful at predicting generic patterns of community assembly, mechanistic and phenomenological models tend to fall short of the precision needed to design and implement specific functionality in a microbiome. We argue that to effectively design microbiomes with optimal functions in diverse environments, ecologists should combine data-driven techniques with mechanistic models - a middle, third way for using theory to inform design.

Distribution patterns and community assembly processes of bacterial communities across different sediment habitats of subsidence lakes

Subsidence lakes, formed due to extensive underground coal mining activities, present both ecological challenges and opportunities for alternative land use practices, such as photovoltaic power generation and aquaculture. However, the ecological consequences of these anthropogenic activities on bacterial communities within subsidence lakes remain largely unexplored. To address this knowledge gap, we conducted a comprehensive investigation of bacterial communities in two typical subsidence lake districts located in Huainan, Anhui Province, China. A total of 44 sediment samples were collected across four distinct habitats: photovoltaic zones (PV), aquaculture zones (AC), photovoltaic-aquaculture zones (PV_AC), and unimpacted zones (Natural). Bacterial communities were investigated using 16S rRNA gene sequencing and various statistical methods. Our results revealed that the α-diversity and network complexity of bacterial communities in PV, PV_AC, and AC habitats are significantly higher than habitats of Natural (P < 0.01). Specifically, the α-diversity is the highest in PV habitats, while the network complexity is the highest in AC habitats. The network stability is the highest in PV and PV_AC habitats, while it is the lowest in AC. There were also significant differences in community structure among different habitats, Nitrospirota in PV (31.9%) was higher than that in the other three habitats, the percentage of Firmicute in Natural (21.96%) and PV_AC (13.70%) was higher than other two habitats, Actinobacteriota has the highest proportion in AC (20.93%). Furthermore, stochastic processes dominate community assembly in PV, PV_AC, and AC habitats, it has the highest proportion in PV, particularly on dispersal limitation (DL). However, deterministic processes prevail in Natural habitats, particularly on heterogeneous selection (HeS). Collectively, these findings highlight the significant differences in sediment bacterial communities across various habitats in subsidence lakes. Our study provides valuable insights into understanding the ecological implications of different habitats in subsidence lake ecosystems.

Effect of competition on emergent phases and phase transitions in competitive systems

This paper considers Lotka-Volterra competitive systems characterizing laboratory experiment by Hu et al. (Science, 378:85-89, 2022). Using dynamical systems theory and projection method, we give theoretical analysis and numerical simulation on the model with four species by demonstrating equilibrium stability, periodic oscillation and chaotic fluctuation in the systems. It is shown that varying one competition strength could lead to emergent phases and phase transitions between stable full coexistence, stable partial coexistence, stable persistence of a unique species, persistent periodic oscillation, and persistent chaotic fluctuation in a smooth fashion. Here, the stronger the competition is, the less the number of stable coexisting species, or the higher the amplitude of periodic oscillation, or the more irregular the fluctuation. Our results are consistent with experimental observation and provide new insight. This work is important in understanding effect of competition on emergent phases and phase transitions in competitive systems.

High-throughput ecological interaction mapping of dairy microorganisms

To engineer efficient microbial management strategies in the food industry, a comprehensive understanding of microbial interactions is crucial. Microorganisms live in communities where they influence each other in several ways. Although much attention has been paid to the production of antagonistic metabolites in lactic acid bacteria (LAB), research that accounts for the complexity of their ecological interactions and their dynamics remains limited. This study explores binary interactions within a mock community of 94 strains, including 23 LAB from culture collections and 71 isolated from dairy products. Using a colony-picking robot and image analysis, bidirectional interactions were measured at high throughput on solid media, where one test strain was challenged against other mock community members as the target strains. Assays of 15 test strains (14 LAB and one yeast) yielded 1,142 bidirectionally mapped interactions, classified by ecological type over seven days. The results showed variation in the strength, directionality, and type of interactions depending on the origin of the target strains. Cooperation rates increased for strains isolated from raw milk to pasteurized milk and cheese, while exploitation was more common in raw milk strains. Cooperating strains also exhibited more similar ecological behaviors than competing strains, suggesting the presence of microbial cliques. Interestingly, Lactococcus cremoris ATCC 19257 developed pink-red pigmentation when co-cultured with Pseudomonas aeruginosa. Overall, these findings present an unprecedented exploratory data set that significantly improves our understanding of microbial interactions at the system level, with potential applications in strain selection for food processes such as fermentation, bioprotection, and probiotics.

Global microbial community biodiversity increases with antimicrobial toxin abundance of rare taxa

One of the central questions in microbial ecology is how to explain the high biodiversity of communities. A large number of rare taxa in the community have not been excluded by abundant taxa with competitive advantages, a contradiction known as the biodiversity paradox. Recently, increasing evidence has revealed the central importance of antimicrobial toxins as crucial weapons of antagonism in microbial survival. The powerful effects of antimicrobial toxins result in simple combinations of microorganisms failing to coexist under laboratory conditions, but it is unclear whether they also have a negative impact on the biodiversity of natural communities. Here, we revealed that microbial communities worldwide universally possess functional potential for antimicrobial toxin production. Counterintuitively, the biodiversity of global microbial communities increases, rather than decreases, as the abundance of antimicrobial toxins in rare taxa rises. Rare taxa may encode more antimicrobial toxins than abundant taxa, which is associated with the maintenance of the high biodiversity of microbial communities amid complex interactions. Our findings suggest that the antagonistic interaction caused by antimicrobial toxins may play a positive role in microbial community biodiversity at the global scale.

Influences of fluctuating nutrient loadings on nitrate-reducing microorganisms in rivers

Rivers serve important functions for human society and are significantly impacted by anthropogenic nutrient inputs (e.g. organic and sulfur compounds). Reduced organic and sulfur compounds influence the nitrogen cycle as they are electron donors of microbial nitrate reduction. Water pollution caused by individual nutrients and the mechanisms have been studied, but how the variation in multiple nutrient loadings influences nitrate-reducing microorganisms is less understood. Two sets of microcosms were established and exposed to nitrate, along with either acetate or thiosulfate, at different times. Nutrient concentrations responded to the loading pollutant. The nutrient loading order was more important in shaping microbial community structure and microbial interactions through the exchange of growth-required substances. This indicated that upstream or historical nutrient inflows impacted current nitrate reduction by changing the seeding microbial community, highlighting the importance of river connectivity. Based on metatranscriptome analysis, although the order and type of nutrient loadings were equally important in regulating global transcriptomes, transcripts of enzymes for key metabolisms (nitrate reduction, sulfur oxidation, etc.) more actively responded to the nutrient type. The regulation of a small set of genes was sufficient to make the transition, while most transcripts were not degraded and regenerated. These insights are important for understanding the varying pollution status of rivers and for developing effective solutions, such as remediation.

Symmetry-based approach to species-rich ecological communities

Disordered systems theory provides powerful tools to analyze the generic behaviors of high-dimensional systems, such as species-rich ecological communities or neural networks. By assuming randomness in their interactions, universality ensures that many microscopic details are irrelevant to system-wide dynamics; but the choice of a random ensemble still limits the generality of results. We show here, in the context of ecological dynamics, that these analytical tools do not require a specific choice of ensemble and that solutions can be found based only on a fundamental rotational symmetry in the interactions, encoding the idea that traits can be recombined into new species without altering global features. Dynamical outcomes then depend on the spectrum of the interaction matrix as a free parameter, allowing us to bridge between results found in different models of interactions and extend beyond them to previously unidentified behaviors. The distinctive feature of ecological models is the possibility of species extinctions, which leads to an increased universality of dynamics as the fraction of extinct species increases. We expect these findings can inform new developments in theoretical ecology as well as other families of complex systems.

Assembly Graph as the Rosetta Stone of Ecological Assembly

Ecological assembly-the process of ecological community formation through species introductions-has recently seen exciting theoretical advancements across dynamical, informational, and probabilistic approaches. However, these theories often remain inaccessible to non-theoreticians, and they lack a unifying lens. Here, I introduce the assembly graph as an integrative tool to connect these emerging theories. The assembly graph visually represents assembly dynamics, where nodes symbolise species combinations and edges represent transitions driven by species introductions. Through the lens of assembly graphs, I review how ecological processes reduce uncertainty in random species arrivals (informational approach), identify graphical properties that guarantee species coexistence and examine how the class of dynamical models constrain the topology of assembly graphs (dynamical approach), and quantify transition probabilities with incomplete information (probabilistic approach). To facilitate empirical testing, I also review methods to decompose complex assembly graphs into smaller, measurable components, as well as computational tools for deriving empirical assembly graphs. In sum, this math-light review of theoretical progress aims to catalyse empirical research towards a predictive understanding of ecological assembly.

Synthetic Ecosystems: From the Test Tube to the Biosphere

The study of ecosystems, both natural and artificial, has historically been mediated by population dynamics theories. In this framework, quantifying population numbers and related variables (associated with metabolism or biological-environmental interactions) plays a central role in measuring and predicting system-level properties. As we move toward advanced technological engineering of cells and organisms, the possibility of bioengineering ecosystems (from the gut microbiome to wildlands) opens several questions that will require quantitative models to find answers. Here, we present a comprehensive survey of quantitative modeling approaches for managing three kinds of synthetic ecosystems, sharing the presence of engineered strains. These include test tube examples of ecosystems hosting a relatively low number of interacting species, mesoscale closed ecosystems (or ecospheres), and macro-scale, engineered ecosystems. The potential outcomes of synthetic ecosystem designs and their limits will be relevant to different disciplines, including biomedical engineering, astrobiology, space exploration and fighting climate change impacts on endangered ecosystems. We propose a space of possible ecosystems that captures this broad range of scenarios and a tentative roadmap for open problems and further exploration.

Seasonal dynamics of environmental heterogeneity augment microbial interactions by regulating community structure in different trophic lakes

Understanding how environmental heterogeneity drives microbial communities in lakes is essential for developing effective strategies to manage and restore aquatic ecosystems. However, the mechanisms by which environmental heterogeneity influences microbial community structure, network patterns, and interactions remain largely unexplored. To bridge this gap, we collected 84 water samples from four typical lakes in China (Fuxian, Tianmu, Taihu, and Xingyun) representing a range of trophic levels, across wet and dry seasons. We assessed environmental heterogeneity using 14 water quality parameters, analyzed community structure with Jaccard and Bray-Curtis dissimilarity indices, and developed a comprehensive index to elucidate microbial network complexity. Our study reveals three key findings: (1) Environmental heterogeneity was significantly greater in dry season compared to wet season across all lakes (P < 0.05). (2) Increased environmental heterogeneity led to higher bacterioplankton community dissimilarity, with greater β-diversity observed in dry season (P < 0.05). (3) Shifts in community structure due to increased environmental heterogeneity further enhanced microbial interactions, as evidenced by more complex and interconnected co-occurrence networks in the dry season. In summary, our study demonstrates that environmental heterogeneity significantly impacts bacterioplankton community structure and subsequently enhances microbial interactions. These findings underscore the importance of considering environmental heterogeneity in lake ecosystem management, as it plays a crucial role in regulating microbial community dynamics and interactions.

An ecological and stochastic perspective on persisters resuscitation

Resistance, tolerance, and persistence to antibiotics have mainly been studied at the level of a single microbial isolate. However, in recent years it has become evident that microbial interactions play a role in determining the success of antibiotic treatments, in particular by influencing the occurrence of persistence and tolerance within a population. Additionally, the challenge of resuscitation (the capability of a population to revive after antibiotic exposure) and pathogen clearance are strongly linked to the small size of the surviving population and to the presence of fluctuations in cell counts. Indeed, while large population dynamics can be considered deterministic, small populations are influenced by stochastic processes, making their behaviour less predictable. Our study argues that microbe-microbe interactions within a community affect the mode, tempo, and success of persister resuscitation and that these are further influenced by noise. To this aim, we developed a theoretical model of a three-member microbial community and analysed the role of cell-to-cell interactions on pathogen clearance, using both deterministic and stochastic simulations. Our findings highlight the importance of ecological interactions and population size fluctuations (and hence the underlying cellular mechanisms) in determining the resilience of microbial populations following antibiotic treatment.

Cd indirectly affects the structure and function of plankton ecosystems by affecting trophic interactions at environmental concentrations

The toxic effects of potentially toxic elements have been observed at low concentrations; however, many studies have focused on single-species toxicity testing. Consequently, it is imperative to quantify toxicity at the community level at environmental concentrations. A microcosm approach was employed in conjunction with the Lotka-Volterra model to ascertain the impact of environmentally relevant concentrations of cadmium (Cd) on plankton abundance, community function, and stability. The results demonstrated that Cd led to a reduction in the abundance of Daphnia magna, yet unexpectedly resulted in an increase in the abundance of Brachionus calyciflorus and Paramecium caudatum. Additionally, Cd was observed to impede primary productivity, metabolic capacity and the stability of the planktonic community. Further model analyses revealed that the environmental concentration of Cd directly reduced intrinsic growth rates and intraspecific interactions. In particular, we found that the predation effects of Daphnia magna on Brachionus calyciflorus were significantly weakened. The findings of this study offer quantitative evidence that Cd exposure exerts an indirect influence on the structure and functioning of plankton ecosystems, mediated by alterations in trophic interactions. The findings indicate that the impact of environmental concentrations of potentially toxic elements may be underestimated in single-species experiments.

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary-and largely uncharacterized-genetics of adsorption, injection, cell take-over, and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions among 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. We found that the most effective approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, accurately predicting 86% of interactions while reducing the relative error in the estimated strength of the infection phenotype by 40%. Feature selection revealed key phage λ and Escherchia coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. The method's success in recapitulating strain-level infection outcomes arising during coevolutionary dynamics may also help inform generalized approaches for imputing genetic drivers of interaction phenotypes in complex communities of phage and bacteria.

Quinolone-mediated metabolic cross-feeding develops aluminium tolerance in soil microbial consortia

Aluminium (Al)-tolerant beneficial bacteria confer resistance to Al toxicity to crops in widely distributed acidic soils. However, the mechanism by which microbial consortia maintain Al tolerance under acid and Al toxicity stress remains unknown. Here, we demonstrate that a soil bacterial consortium composed of Rhodococcus erythropolis and Pseudomonas aeruginosa exhibit greater Al tolerance than either bacterium alone. P. aeruginosa releases the quorum sensing molecule 2-heptyl-1H-quinolin-4-one (HHQ), which is efficiently degraded by R. erythropolis. This degradation reduces population density limitations and further enhances the metabolic activity of P. aeruginosa under Al stress. Moreover, R. erythropolis converts HHQ into tryptophan, promoting the synthesis of peptidoglycan, a key component for cell wall stability, thereby improving the Al tolerance of R. erythropolis. This study reveals a metabolic cross-feeding mechanism that maintains microbial Al tolerance, offering insights for designing synthetic microbial consortia to sustain food security and sustainable agriculture in acidic soil regions.

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary - and largely uncharacterized - genetics of adsorption, injection, cell take-over and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. We found that the most effective approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, accurately predicting 86 % of interactions while reducing the relative error in the estimated strength of the infection phenotype by 40 % . Feature selection revealed key phage λ and E. coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. The method's success in recapitulating strain-level infection outcomes arising during coevolutionary dynamics may also help inform generalized approaches for imputing genetic drivers of interaction phenotypes in complex communities of phage and bacteria.

Incorporating Heterogeneous Interactions for Ecological Biodiversity

Understanding the behaviors of ecological systems is challenging given their multifaceted complexity. To proceed, theoretical models such as Lotka-Volterra dynamics with random interactions have been investigated by the dynamical mean-field theory to provide insights into underlying principles such as how biodiversity and stability depend on the randomness in interaction strength. Yet the fully connected structures assumed in these previous studies are not realistic, as revealed by a vast amount of empirical data. We derive a generic formula for the abundance distribution under an arbitrary distribution of degree, the number of interacting neighbors, which leads to degree-dependent abundance patterns of species. Notably, in contrast to the fully interacting systems, the number of surviving species can be reduced as the community becomes cooperative in heterogeneous interaction structures. Our study, therefore, demonstrates that properly taking into account heterogeneity in the interspecific interaction structure is indispensable to understanding the diversity in large ecosystems, and our general theoretical framework can apply to a much wider range of interacting many-body systems.

Glufosinate-ammonium increased nitrogen and phosphorus content in water and shaped microbial community in epiphytic biofilm of Hydrilla verticillata

Glufosinate-ammonium (GLAM) can be released into adjacent water bodies with rainfall runoff and return water from farmland irrigation. However, impacts of GLAM on aquatic organisms remain unclear. In this study, changes in water quality, plant physiological parameters and epiphytic microbial community were investigated in wetlands with Hydrilla verticillata exposed to GLAM for 24 days. We found GLAM addition damaged cell and reduced chlorophyll a content in Hydrilla verticillata leaves, and increased ammonium and phosphorus in water (p < 0.001). The α-diversity increased in bacterial community but decreased in eukaryotic community with GLAM exposure. Neutral community models explained 62.3 % and 55.0 % of the variance in bacterial and eukaryotic communities, respectively. Many GLAM micro-biomarkers were obtained, including some clades from Proteobacteria, Bacteroidete, Actinobacteriota, Phragmoplastophyta, Annelida and Arthropoda. Redundancy analysis revealed that GLAM concentration was positively correlated to Flavobacterium, Gomphonema and Closterium but negatively to Methyloglobulus and Methylocystis. Network analysis revealed that 15 mg/L GLAM disturbed the interactions among phytoplankton, protozoa, metazoan and bacteria and reduced the stability of the microbial communities compared to 8 mg/L GLAM. GLAM shaped the nitrogen and phosphorus cycle related bacterial genes. This study highlights that herbicides are non-neglectable factors affecting the efficiency of aquatic ecological restoration in agricultural areas to control agricultural non-point source pollution.

The shifts in microbial interactions and gene expression caused by temperature and nutrient loading influence Raphidiopsis raciborskii blooms

Climate change and the trophic status of water bodies are important factors in global occurrence of cyanobacterial blooms. The aim of this study was to explore the cyanobacteria‒bacterial interactions that occur during Raphidiopsis raciborskii (R. raciborskii) blooms by conducting microcosm simulation experiments at different temperatures (20 °C and 30 °C) and with different phosphorus concentrations (0.01 mg/L and 1 mg/L) using an ecological model of microbial behavior and by analyzing microbial self-regulatory strategies using weighted gene coexpression network analysis (WGCNA). Three-way ANOVA revealed significant effects of temperature and phosphorus on the growth of R. raciborskii (P < 0.001). The results of a metagenomics-based analysis of bacterioplankton revealed that the synergistic effects of both climate and trophic changes increased the ability of R. raciborskii to compete with other cyanobacteria for dominance in the cyanobacterial community. The antagonistic effects of climate and nutrient changes favored the occurrence of R. raciborskii blooms, especially in eutrophic waters at approximately 20 °C. The species diversity and richness indices differed between the eutrophication treatment group at 20 °C and the other treatment groups. The symbiotic bacterioplankton network revealed the complexity and stability of the symbiotic bacterioplankton network during blooms and identified the roles of key species in the network. The study also revealed a complex pattern of interactions between cyanobacteria and non-cyanobacteria dominated by altruism, as well as the effects of different behavioral patterns on R. raciborskii bloom occurrence. Furthermore, this study revealed self-regulatory strategies that are used by microbes in response to the dual pressures of temperature and nutrient loading. These results provide important insights into the adaptation of microbial communities in freshwater ecosystems to environmental change and provide useful theoretical support for aquatic environmental management and ecological restoration efforts.

Intraspecific predator interference promotes biodiversity in ecosystems

Explaining biodiversity is a fundamental issue in ecology. A long-standing puzzle lies in the paradox of the plankton: many species of plankton feeding on a limited variety of resources coexist, apparently flouting the competitive exclusion principle (CEP), which holds that the number of predator (consumer) species cannot exceed that of the resources at a steady state. Here, we present a mechanistic model and demonstrate that intraspecific interference among the consumers enables a plethora of consumer species to coexist at constant population densities with only one or a handful of resource species. This facilitated biodiversity is resistant to stochasticity, either with the stochastic simulation algorithm or individual-based modeling. Our model naturally explains the classical experiments that invalidate the CEP, quantitatively illustrates the universal S-shaped pattern of the rank-abundance curves across a wide range of ecological communities, and can be broadly used to resolve the mystery of biodiversity in many natural ecosystems.

Deep learning resilience inference for complex networked systems

Resilience, the ability to maintain fundamental functionality amidst failures and errors, is crucial for complex networked systems. Most analytical approaches rely on predefined equations for node activity dynamics and simplifying assumptions on network topology, limiting their applicability to real-world systems. Here, we propose ResInf, a deep learning framework integrating transformers and graph neural networks to infer resilience directly from observational data. ResInf learns representations of node activity dynamics and network topology without simplifying assumptions, enabling accurate resilience inference and low-dimensional visualization. Experimental results show that ResInf significantly outperforms analytical methods, with an F1-score improvement of up to 41.59% over Gao-Barzel-Barabási framework and 14.32% over spectral dimension reduction. It also generalizes to unseen topologies and dynamics and maintains robust performance despite observational disturbances. Our findings suggest that ResInf addresses an important gap in resilience inference for real-world systems, offering a fresh perspective on incorporating data-driven approaches to complex network modeling.

Microbial coadaptation drives the dynamic stability of microecology in mainstream and sidestream anammox systems under exposure of progesterone

Microbial cooperation determines the efficacy of wastewater biological treatment, and the adaptability of microorganisms to environmental stresses varies. Recently, extensive use of hormones results in their inevitable discharge into aquatic environment. Therefore, mainstream and sidestream anammox reactors were constructed in this study to evaluate their removal performance of progesterone and nitrogen simultaneously, the adaptability of anammox consortia to progesterone stress and the corresponding regulation mechanism. Both anammox processes had the resilience to progesterone stress, with the average nitrogen removal efficiency exceeding 90 %. At the same time, progesterone removal efficiency also exceeded 70 %. In contrast, microbial community in the mainstream reactors was more susceptible to progesterone interference. The adaptation of anammox consortia mainly depended on microbial cooperation and molecular regulation. Initially, bacteria secreted more extracellular polymeric substances to detain progesterone. Biodegradation also contributed to mitigating the side effect of progesterone, which was demonstrated by the proliferation of potential degrading bacteria such as Bacillus salacetis, Bacillus wiedmannii and Rhodococcus erythropolis. In addition, the enhancement of microbial interaction intensity drove their cooperation to enhance adaptability and maintain stable performance. Combined with metagenomic and metatranscriptomic analyses, such microbial adaptability was enhanced through molecular regulations, including the energy redistribution for amino acid synthesis and alteration of key metabolic pathways. Related functional gene expressions and microbial interactions were, in turn, regulated by quorum sensing. This work verifies the feasibility of anammox process in hormone-containing wastewater treatment and provides a holistic understanding of molecular mechanism of microbial interaction and coadaptation to stress.

Generalized Lotka-Volterra Systems with Time Correlated Stochastic Interactions

In this Letter, we explore the dynamics of species abundances within ecological communities using the generalized Lotka-Volterra (GLV) model. At variance with previous approaches, we present an analysis of GLV dynamics with temporal stochastic fluctuations in interaction strengths between species. We develop a dynamical mean field theory (DMFT) tailored for scenarios with colored noise interactions, which we term annealed disorder, and simple functional responses. Our DMFT framework enables us to show that annealed disorder acts as an effective environmental noise; i.e., every species experiences a time-dependent environment shaped by the collective presence of all other species. We then derive analytical predictions for the species abundance distribution that well match empirical observations. Our results suggest that annealed disorder in interaction strengths favors species coexistence and leads to a large pool of very rare species in the systems, supporting the insurance theory of biodiversity. This Letter offers new insights not only into the modeling of large ecosystem dynamics but also proposes novel methodologies for examining ecological systems.

Synergistic co-evolution of rhizosphere bacteria in response to acidification amelioration strategies: impacts on the alleviation of tobacco wilt and underlying mechanisms

Soil acidification represents a severe threat to tobacco cultivation regions in South China, exacerbating bacterial wilt caused by Ralstonia solanacearum. The comprehension of the underlying mechanisms that facilitate the restoration of rhizosphere microbial communities in "healthy soils" is imperative for ecologically managing tobacco bacterial wilt. This study focuses on acidified tobacco soils that have been subjected to continuous cultivation for 20 years. The experimental treatments included lime (L), biochar (B), and a combination of lime and biochar (L+B), in addition to a control group (CK). Utilizing rhizosphere biology and niche theory, we assessed disease suppression effects, changes in soil properties, and the co-evolution of the rhizosphere bacterial community. Each treatment significantly reduced tobacco bacterial wilt by 16.67% to 20.14% compared to the control group (CK) (p < 0.05) and increased yield by 7.86% to 27.46% (p < 0.05). The biochar treatment (B) proved to be the most effective, followed by the lime-biochar combination (L+B). The key factors controlling wilt were identified through random forest regression analysis as an increase in soil pH and exchangeable bases, along with a decrease in exchangeable acidity. However, lime treatment alone led to an increase in soil bulk density and a decrease in available nutrients, whereas both biochar and lime-biochar treatments significantly improved these parameters (p < 0.05). No significant correlation was found between the abundance of Ralstonia and wilt incidence. Nonetheless, all treatments significantly expanded the ecological niche breadth and average variation degree (AVD), enhanced positive interactions and cohesion within the community, and intensified negative interactions involving Ralstonia. This study suggests that optimizing community niches and enhancing pathogen antagonism are key mechanisms for mitigating tobacco wilt in acidified soils. It recommends using lime-biochar mixtures as soil amendments due to their potential ecological and economic benefits. This study offers valuable insights for disease control strategies and presents a novel perspective for research on Solanaceous crops.

Hypernetwork modeling and topology of high-order interactions for complex systems

Interactions among the underlying agents of a complex system are not only limited to dyads but can also occur in larger groups. Currently, no generic model has been developed to capture high-order interactions (HOI), which, along with pairwise interactions, portray a detailed landscape of complex systems. Here, we integrate evolutionary game theory and behavioral ecology into a unified statistical mechanics framework, allowing all agents (modeled as nodes) and their bidirectional, signed, and weighted interactions at various orders (modeled as links or hyperlinks) to be coded into hypernetworks. Such hypernetworks can distinguish between how pairwise interactions modulate a third agent (active HOI) and how the altered state of each agent in turn governs interactions between other agents (passive HOI). The simultaneous occurrence of active and passive HOI can drive complex systems to evolve at multiple time and space scales. We apply the model to reconstruct a hypernetwork of hexa-species microbial communities, and by dissecting the topological architecture of the hypernetwork using GLMY homology theory, we find distinct roles of pairwise interactions and HOI in shaping community behavior and dynamics. The statistical relevance of the hypernetwork model is validated using a series of in vitro mono-, co-, and tricultural experiments based on three bacterial species.

Combined influence of the nanoplastics and polycyclic aromatic hydrocarbons exposure on microbial community in seawater environment

Nanoplastics (NPs) and polycyclic aromatic hydrocarbons (PAHs) are recognized as persistent organic pollutant (POPs) with demonstrated physiological toxicity. When present in aquatic environments, the two pollutants could combine with each other, resulting in cumulative toxicity to organisms. However, the combined impact of NPs and PAHs on microorganisms in seawater is not well understood. In this study, we conducted an exposure experiment to investigate the individual and synergistic effects of NPs and PAHs on the composition, biodiversity, co-occurrence networks of microbial communities in seawater. Exposure of individuals to PAHs led to a reduction in microbial community richness, but an increase in the relative abundance of species linked to PAHs degradation. These PAHs-degradation bacteria acting as keystone species, maintained a microbial network complexity similar to that of the control treatment. Exposure to individual NPs resulted in a reduction in the complexity of microbial networks. Furthermore, when PAHs and NPs were simultaneously present, the toxic effect of NPs hindered the presence of keystone species involved in PAHs degradation, subsequently limiting the degradation of PAHs by marine microorganisms, resulting in a decrease in community diversity and symbiotic network complexity. This situation potentially poses a heightened threat to the ecological stability of marine ecosystems. Our work strengthened the understanding of the combined impact of NPs and PAHs on microorganisms in seawater.

Modeling tumors as complex ecosystems

Many cancers resist therapeutic intervention. This is fundamentally related to intratumor heterogeneity: multiple cell populations, each with different phenotypic signatures, coexist within a tumor and its metastases. Like species in an ecosystem, cancer populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity or predict its consequences. Here, we propose that the generalized Lotka-Volterra model (GLV), a standard tool to describe species-rich ecological communities, provides a suitable framework to model the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties provide a new understanding of the disease. We discuss potential extensions of the model and their application to phenotypic plasticity, cancer-immune interactions, and metastatic growth. Our work outlines a set of questions and a road map for further research in cancer ecology.

Characterizing a stable five-species microbial community for use in experimental evolution and ecology

Model microbial communities are regularly used to test ecological and evolutionary theory as they are easy to manipulate and have fast generation times, allowing for large-scale, high-throughput experiments. A key assumption for most model microbial communities is that they stably coexist, but this is rarely tested experimentally. Here we report the (dis)assembly of a five-species microbial community from a metacommunity of soil microbes that can be used for future experiments. Using reciprocal invasion-from-rare experiments we show that all species can coexist and we demonstrate that the community is stable for a long time (~600 generations). Crucially for future work, we show that each species can be identified by their plate morphologies, even after >1 year in co-culture. We characterise pairwise species interactions and produce high-quality reference genomes for each species. This stable five-species community can be used to test key questions in microbial ecology and evolution.

Evolution and stability of complex microbial communities driven by trade-offs

Microbial communities are ubiquitous in nature and play an important role in ecology and human health. Cross-feeding is thought to be core to microbial communities, though it remains unclear precisely why it emerges. Why have multi-species microbial communities evolved in many contexts and what protects microbial consortia from invasion? Here, we review recent insights into the emergence and stability of coexistence in microbial communities. A particular focus is the long-term evolutionary stability of coexistence, as observed for microbial communities that spontaneously evolved in the E. coli long-term evolution experiment (LTEE). We analyze these findings in the context of recent work on trade-offs between competing microbial objectives, which can constitute a mechanistic basis for the emergence of coexistence. Coexisting communities, rather than monocultures of the 'fittest' single strain, can form stable endpoints of evolutionary trajectories. Hence, the emergence of coexistence might be an obligatory outcome in the evolution of microbial communities. This implies that rather than embodying fragile metastable configurations, some microbial communities can constitute formidable ecosystems that are difficult to disrupt.

Beyond the Bloom: Unraveling the Diversity, Overlap, and Stability of Free-Living and Particle-Attached Bacterial Communities in a Cyanobacteria-Dominated Hypereutrophic Lake

In aquatic ecosystems with low nutrient levels, organic aggregates (OAs) act as nutrient hotspots, hosting a diverse range of microbial species compared to those in the water column. Lake eutrophication, marked by intensified and prolonged cyanobacterial blooms, significantly impacts material and energy cycling processes, potentially altering the ecological traits of both free-living (FL) and particle-attached (PA) bacteria. However, the extent to which observed patterns of FL and PA bacterial diversity, community assembly, and stability extend to hypereutrophic lakes remains understudied. To address this gap, we investigated bacterial diversity, composition, assembly processes, and stability within hypereutrophic Lake Xingyun. Our results revealed that FL bacterial communities exhibited higher α-diversity than PA counterparts, coupled with discernible taxonomic compositions. Both bacterial communities showed distinct seasonality, influenced by cyanobacterial bloom intensity. Environmental factors accounted for 71.1% and 54.2% of the variation among FL and PA bacteria, respectively. The assembly of the PA bacterial community was predominantly stochastic, while FL assembly was more deterministic. The FL network demonstrated greater stability, complexity, and negative interactions, indicative of competitive relationships, while the PA network showed a prevalence of positive correlations, suggesting mutualistic interactions. Importantly, these findings differ from observations in oligotrophic, mesotrophic, and eutrophic lakes. Overall, this research provides valuable insights into the interplay among bacterial fractions, enhancing our understanding of nutrient status and cyanobacterial blooms in shaping bacterial communities.

Human gut microbiota interactions shape the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile

Clostridioides difficile can transiently or persistently colonize the human gut, posing a risk factor for infections. This colonization is influenced by complex molecular and ecological interactions with human gut microbiota. By investigating C. difficile dynamics in human gut communities over hundreds of generations, we show patterns of stable coexistence, instability, or competitive exclusion. Lowering carbohydrate concentration shifted a community containing C. difficile and the prevalent human gut symbiont Phocaeicola vulgatus from competitive exclusion to coexistence, facilitated by increased cross-feeding. In this environment, C. difficile adapted via single-point mutations in key metabolic genes, altering its metabolic niche from proline to glucose utilization. These metabolic changes substantially impacted inter-species interactions and reduced disease severity in the mammalian gut. In sum, human gut microbiota interactions are crucial in shaping the long-term growth dynamics and evolutionary adaptations of C. difficile, offering key insights for developing anti-C. difficile strategies.