The community-function landscape of microbial consortia

Macroecological patterns in experimental microbial communities

Ecology has historically benefited from the characterization of statistical patterns of biodiversity within and across communities, an approach known as macroecology. Within microbial ecology, macroecological approaches have identified universal patterns of diversity and abundance that can be captured by effective models. Experimentation has simultaneously played a crucial role, as the advent of high-replication community time-series has allowed researchers to investigate underlying ecological forces. However, there remains a gap between experiments performed in the laboratory and macroecological patterns documented in natural systems, as we do not know whether these patterns can be recapitulated in the lab and whether experimental manipulations produce macroecological effects. This work aims at bridging the gap between experimental ecology and macroecology. Using high-replication time-series, we demonstrate that microbial macroecological patterns observed in nature exist in a laboratory setting, despite controlled conditions, and can be unified under the Stochastic Logistic Model of growth (SLM). We found that demographic manipulations (e.g., migration) impact observed macroecological patterns. By modifying the SLM to incorporate said manipulations alongside experimental details (e.g., sampling), we obtain predictions that are consistent with macroecological outcomes. By combining high-replication experiments with ecological models, microbial macroecology can be viewed as a predictive discipline.

Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales

All biological systems are subject to perturbations arising from thermal fluctuations, external environments, or mutations. Yet, while biological systems consist of thousands of interacting components, recent high-throughput experiments have shown that their response to perturbations is surprisingly low dimensional: confined to only a few stereotyped changes out of the many possible. In this review, we explore a unifying dynamical systems framework-soft modes-to explain and analyze low dimensionality in biology, from molecules to ecosystems. We argue that this soft mode framework makes nontrivial predictions that generalize classic ideas from developmental biology to disparate systems, namely phenocopying, dual buffering, and global epistasis. While some of these predictions have been borne out in experiments, we discuss how soft modes allow for a surprisingly far-reaching and unifying framework in which to analyze data from protein biophysics to microbial ecology.

Positive Linkage in Bacterial Microbiota at the Plant-Insect Interface Benefits an Invasive Bark Beetle

Symbiotic microbes facilitate rapid adaptation of invasive insects on novel plants via multifaceted function provisions, but little was known on the importance of cross linkages in symbiotic microbiota to insect invasiveness. Novel host pine Pinus tabuliformis is inherently unsuitable for invasive red turpentine beetle (RTB) in China; however, Novosphingobium and Erwinia/Serratia in gallery microbiota (at the interface between RTB larvae and pine phloem) have been discovered to help beetles via biodegrading pine detrimental compounds naringenin and pinitol, respectively. Here, we further revealed significant positive linkage of the two functions, with higher activity level conferring more growth benefit to RTB larvae. Abundance of Erwinia/Serratia was remarkably increased in response to pinitol, while naringenin-biodegrading Novosphingobium was unable to utilize this main phloem carbohydrate directly. High-activity bacterial microbiota produced nutritive metabolites (sucrose and hexadecanoic acid) from pinitol consumption that facilitated growth of both Novosphingobium and beetle larvae. Functional proteins of several bacterial taxa were enriched in high-activity microbiota that appeared to form a metabolic network collectively to regulate the nutrient production. Our results indicate that positive interaction between Erwinia/Serratia and Novosphingobium is critical for RTB invasion success, while Bacilli bacteria might restrict this linkage, providing new insights into symbiotic microbial interactions for insect herbivores.

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.

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.

Hindguts of Kyphosus sydneyanus harbor phylogenetically and genomically distinct Alistipes capable of degrading algal polysaccharides and diazotrophy

The genus Alistipes (Bacteroidota) is most often associated with human clinical samples and livestock. However, Alistipes are also prevalent in the hindgut of the marine herbivorous fish Kyphosus sydneyanus (Silver Drummer), and analysis of their carbohydrate-active enzyme (CAZyme) encoding gene repertoires suggests Alistipes degrade macroalgal biomass to support fish nutrition. To further explore host-associated traits unique to K. sydneyanus-derived Alistipes, we compared 445 high-quality genomes of Alistipes available in public databases (e.g., human and ruminant associated) with 99 metagenome-assembled genomes (MAGs) from the K. sydneyanus gut. Analyses showed that Alistipes from K. sydneyanus are phylogenetically distinct from other hosts and comprise 26 species based on genomic average nucleotide identity (ANI) analyses. Ruminant- and fish-derived Alistipes had significantly smaller genomes than human-derived strains, and lower GC contents, possibly reflecting a symbiotic relationship with their hosts. The fish-derived Alistipes were further delineated by their genetic capacity to fix nitrogen, biosynthesize cobalamin (vitamin B12), and utilize marine polysaccharides (e.g., alginate and carrageenan). The distribution of CAZymes encoded by Alistipes from K. sydneyanus was not phylogenetically conserved. Distinct CAZyme gene compositions were observed between closely related species. Conversely, CAZyme gene clusters (operons) targeting the same substrates were found across diverse species. Nonetheless, transcriptional data suggest that closely related Alistipes target specific groups of substrates within the fish hindgut. Results highlight host-specific adaptations among Alistipes in the fish hindgut that likely contribute to K. sydneyanus digesting their seaweed diet, and diverse and redundant carbohydrate-degrading capabilities across these Alistipes species.IMPORTANCEDespite numerous reports of the Alistipes genus in humans and ruminants, its diversity and function remain understudied, and there is no clear consensus on whether it positively or negatively impacts host health. Given the symbiotic role of gut communities in the Kyphosus sydneyanus hindgut, where Alistipes are prevalent, and the diversity of carbohydrate-active enzymes (CAZymes) encoded that likely contribute to the breakdown of important substrates in the host diet, it is likely that this genus provides essential services to the fish host. Therefore, considering its metabolism in various contexts and hosts is crucial for understanding the ecology of the genus. Our study highlights the distinct genetic traits of Alistipes based on host association, and the potential of fish-associated Alistipes to transform macroalgae biomass into nutraceuticals (alginate oligosaccharides, β-glucans, sulfated galactans, and sulfated fucans).

Characterizing A21: Natural Cyanobacteria-Based Consortium with Potential for Steroid Bioremediation in Wastewater Treatment

Microalga-bacteria consortia are increasingly recognized for their effectiveness in wastewater treatment, leveraging the metabolic synergy between microalgae and bacteria to enhance nutrient removal and overall treatment efficiency. These systems offer a sustainable approach to addressing pollutants such as nitrogen and phosphorus. However, their potential in removing specific contaminants like steroid hormones is less explored. In this study, a natural microbial consortium, A21, has been characterized and isolated from primary sewage treatment in Madrid and its potential for bioremediation of steroid hormone effluents has been evaluated. The A21 consortium includes Alphaproteobacteria genera Sphingopyxis and Pseudorhizobium and the Cyanobacterium Cyanobium. Sphingopyxis (31.78%) is known for biodegradation, while Pseudorhizobium (15.68%) exhibits detoxification abilities. Cyanobium (14.2%) may contribute to nutrient uptake and oxygen production. The effects of pH, nitrogen sources, and Sodium chloride concentrations on growth were evaluated. The optimal growth conditions were determined to be a pH range of 7 to 9, a salt concentration below 0.1 M, and the presence of a nitrogen source. The consortium also demonstrated effective growth across various types of wastewaters (primary, secondary, and tertiary treatment effluents). Additionally, A21 exhibited the ability to grow in the presence of steroids and transform them into other compounds, such as converting androstenedione (AD) into androsta-1,4-diene-3,17-dione (ADD) and β-estradiol into estrone.

Environmental modulators of algae-bacteria interactions at scale

Interactions between photosynthetic and heterotrophic microbes play a key role in global primary production. Understanding phototroph-heterotroph interactions remains challenging because these microbes reside in chemically complex environments. Here, we leverage a massively parallel droplet microfluidic platform that enables us to interrogate interactions between photosynthetic algae and heterotrophic bacteria in >100,000 communities across ∼525 environmental conditions with varying pH, carbon availability, and phosphorus availability. By developing a statistical framework to dissect interactions in this complex dataset, we reveal that the dependence of algae-bacteria interactions on nutrient availability is strongly modulated by pH and buffering capacity. Furthermore, we show that the chemical identity of the available organic carbon source controls how pH, buffering capacity, and nutrient availability modulate algae-bacteria interactions. Our study reveals the previously underappreciated role of pH in modulating phototroph-heterotroph interactions and provides a framework for thinking about interactions between phototrophs and heterotrophs in more natural contexts.

Artificial selection improves pollutant degradation by bacterial communities

Artificial selection is a promising way to improve microbial community functions, but previous experiments have only shown moderate success. Here, we experimentally evaluate a new method that was inspired by genetic algorithms to artificially select small bacterial communities of known species composition based on their degradation of an industrial pollutant. Starting from 29 randomly generated four-species communities, we repeatedly grew communities for four days, selected the 10 best-degrading communities, and rearranged them into 29 new communities composed of four species of equal ratios whose species compositions resembled those of the most successful communities from the previous round. The best community after 18 such rounds of selection degraded the pollutant better than the best community in the first round. It featured member species that degrade well, species that degrade badly alone but improve community degradation, and free-rider species that did not contribute to community degradation. Most species in the evolved communities did not differ significantly from their ancestors in their phenotype, suggesting that genetic evolution plays a small role at this time scale. These experiments show that artificial selection on microbial communities can work in principle, and inform on how to improve future experiments.

The optimization of microbial functions through rational environmental manipulations

Microorganisms play a central role in biotechnology and it is key that we develop strategies to engineer and optimize their functionality. To this end, most efforts have focused on introducing genetic manipulations in microorganisms which are then grown either in monoculture or in mixed-species consortia. An alternative strategy to optimize microbial processes is to rationally engineer the environment in which microbes grow. The microbial environment is multidimensional, including factors such as temperature, pH, salinity, nutrient composition, etc. These environmental factors all influence the growth and phenotypes of microorganisms and they generally "interact" with one another, combining their effects in complex, non-additive ways. In this piece, we overview the origins and consequences of these "interactions" between environmental factors and discuss how they have been built into statistical, bottom-up predictive models of microbial function to identify optimal environmental conditions for monocultures and microbial consortia. We also overview alternative "top-down" approaches, such as genetic algorithms, to finding optimal combinations of environmental factors. By providing a brief summary of the state of this field, we hope to stimulate further work on the rational manipulation and optimization of the microbial environment.

Paternal and induced gut microbiota seeding complement mother-to-infant transmission

Microbial colonization of the neonatal gut involves maternal seeding, which is partially disrupted in cesarean-born infants and after intrapartum antibiotic prophylaxis. However, other physically close individuals could complement such seeding. To assess the role of both parents and of induced seeding, we analyzed two longitudinal metagenomic datasets (health and early life microbiota [HELMi]: N = 74 infants, 398 samples, and SECFLOR: N = 7 infants, 35 samples) with cesarean-born infants who received maternal fecal microbiota transplantation (FMT). We found that the father constitutes a stable source of strains for the infant independently of the delivery mode, with the cumulative contribution becoming comparable to that of the mother after 1 year. Maternal FMT increased mother-infant strain sharing in cesarean-born infants, raising the average bacterial empirical growth rate while reducing pathogen colonization. Overall, our results indicate that maternal seeding is partly complemented by that of the father and support the potential of induced seeding to restore potential deviations in this process.

Global epistasis and the emergence of function in microbial consortia

The many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.

Relative dispersion ratios following fecal microbiota transplant elucidate principles governing microbial migration dynamics

Microorganisms frequently migrate from one ecosystem to another. Yet, despite the potential importance of this process in modulating the environment and the microbial ecosystem, our understanding of the fundamental forces that govern microbial dispersion is still lacking. Moreover, while theoretical models and in-vitro experiments have highlighted the contribution of species interactions to community assembly, identifying such interactions in vivo, specifically in communities as complex as the human gut, remains challenging. To address this gap, here we introduce a robust and rigorous computational framework, termed Relative Dispersion Ratio (RDR) analysis, and leverage data from well-characterized fecal microbiota transplant trials, to rigorously pinpoint dependencies between taxa during the colonization of human gastrointestinal tract. Our analysis identifies numerous pairwise dependencies between co-colonizing microbes during migration between gastrointestinal environments. We further demonstrate that identified dependencies agree with previously reported findings from in-vitro experiments and population-wide distribution patterns. Finally, we explore metabolic dependencies between these taxa and characterize the functional properties that facilitate effective dispersion. Collectively, our findings provide insights into the principles and determinants of community dynamics following ecological translocation, informing potential opportunities for precise community design.

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

Tapping the biosynthetic potential of marine Bacillus licheniformis LHG166, a prolific sulphated exopolysaccharide producer: structural insights, bio-prospecting its antioxidant, antifungal, antibacterial and anti-biofilm potency as a novel anti-infective lead

The escalating global threat of antimicrobial resistance necessitates prospecting uncharted microbial biodiversity for novel therapeutic leads. This study mines the promising chemical richness of Bacillus licheniformis LHG166, a prolific exopolysaccharide (EPSR2-7.22 g/L). It comprised 5 different monosaccharides with 48.11% uronic acid, 17.40% sulfate groups, and 6.09% N-acetyl glucosamine residues. EPSR2 displayed potent antioxidant activity in DPPH and ABTS+, TAC and FRAP assays. Of all the fungi tested, the yeast Candida albicans displayed the highest susceptibility and antibiofilm inhibition. The fungi Aspergillus niger and Penicillium glabrum showed moderate EPSR2 susceptibility. In contrast, the fungi Mucor circinelloides and Trichoderma harzianum were resistant. Among G+ve tested bacteria, Enterococcus faecalis was the most susceptible, while Salmonella typhi was the most sensitive to G-ve pathogens. Encouragingly, EPSR2 predominantly demonstrated bactericidal effects against both bacterial classes based on MBC/MIC of either 1 or 2 superior Gentamicin. At 75% of MBC, EPSR2 displayed the highest anti-biofilm activity of 88.30% against B. subtilis, while for G-ve antibiofilm inhibition, At 75% of MBC, EPSR2 displayed the highest anti-biofilm activity of 96.63% against Escherichia coli, Even at the lowest dose of 25% MBC, EPSR2 reduced biofilm formation by 84.13% in E. coli, 61.46% in B. subtilis. The microbial metabolite EPSR2 from Bacillus licheniformis LHG166 shows promise as an eco-friendly natural antibiotic alternative for treating infections and oxidative stress.

Challenges in quantifying functional redundancy and selection in microbial communities

Microbiomes can exhibit large variations in species abundances but high reproducibility in abundances of functional units, an observation often considered evidence for functional redundancy. Based on such reduction in functional variability, selection is hypothesized to act on functional units in these ecosystems. However, the link between functional redundancy and selection remains unclear. Here, we show that reduction in functional variability does not always imply selection on functional profiles. We propose empirical null models to account for the confounding effects of statistical averaging and bias toward environment-independent beneficial functions. We apply our models to existing data sets, and find that the abundances of metabolic groups within microbial communities from bromeliad foliage do not exhibit any evidence of the previously hypothesized functional selection. By contrast, communities of soil bacteria or human gut commensals grown in vitro are selected for metabolic capabilities. By separating the effects of averaging and functional bias on functional variability, we find that the appearance of functional selection in gut microbiome samples from the Human Microbiome Project is artifactual, and that there is no evidence of selection for any molecular function represented by KEGG orthology. These concepts articulate a basic framework for quantifying functional redundancy and selection, advancing our understanding of the mapping between microbiome taxonomy and function.

Microbial consortia-mediated arsenic bioremediation in agricultural soils: Current status, challenges, and solutions

Arsenic poisoning in agricultural soil is caused by both natural and man-made processes, and it poses a major risk to crop production and human health. Soil quality, agricultural production, runoff, ingestion, leaching, and absorption by plants are all influenced by these processes. Microbial consortia have become a feasible bioremediation technique in response to the urgent need for appropriate remediation solutions. These diverse microbial populations collaborate to combat arsenic poisoning in soil by facilitating mechanisms including oxidation-reduction, methylation-demethylation, volatilization, immobilization, and arsenic mobilization. The current state, problems, and remedies for employing microbial consortia in arsenic bioremediation in agricultural soils are examined in this review. Among the elements affecting their success include diversity, activity, community organization, and environmental conditions. Also, we emphasize the sensitivity and accuracy limits of existing assessment techniques. While earlier reviews have addressed a variety of arsenic remediation options, this study stands out by concentrating on microbial consortia as a viable strategy for arsenic removal and presents performance evaluation and technical problems. This work gives vital insights for tackling the major issue of arsenic pollution in agricultural soils by explaining the potential methods and components involved in microbial consortium-mediated arsenic bioremediation.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

How collectively integrated are ecological communities?

Beyond abiotic conditions, do population dynamics mostly depend on a species' direct predators, preys and conspecifics? Or can indirect feedback that ripples across the whole community be equally important? Determining where ecological communities sit on the spectrum between these two characterizations requires a metric able to capture the difference between them. Here we show that the spectral radius of a community's interaction matrix provides such a metric, thus a measure of ecological collectivity, which is accessible from imperfect knowledge of biotic interactions and related to observable signatures. This measure of collectivity integrates existing approaches to complexity, interaction structure and indirect interactions. Our work thus provides an original perspective on the question of to what degree communities are more than loose collections of species or simple interaction motifs and explains when pragmatic reductionist approaches ought to suffice or fail when applied to ecological communities.

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes

Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

Statistically learning the functional landscape of microbial communities

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes-analogues to fitness landscapes-that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically inferred landscapes quantitatively predict community functions from knowledge of species presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes appear to be not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show that this observation holds across a wide class of ecological models, suggesting community-function landscapes can be efficiently inferred across a broad range of ecological regimes. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.

Metabolic similarity and the predictability of microbial community assembly

When microbial communities form, their composition is shaped by selective pressures imposed by the environment. Can we predict which communities will assemble under different environmental conditions? Here, we hypothesize that quantitative similarities in metabolic traits across metabolically similar environments lead to predictable similarities in community composition. To that end, we measured the growth rate and by-product profile of a library of proteobacterial strains in a large number of single nutrient environments. We found that growth rates and secretion profiles were positively correlated across environments when the supplied substrate was metabolically similar. By analyzing hundreds of in-vitro communities experimentally assembled in an array of different synthetic environments, we then show that metabolically similar substrates select for taxonomically similar communities. These findings lead us to propose and then validate a comparative approach for quantitatively predicting the effects of novel substrates on the composition of complex microbial consortia.

Deconstructing taxa x taxa xenvironment interactions in the microbiota: A theoretical examination

A major objective of microbial ecology is to identify how the composition of microbial taxa shapes host phenotypes. However, most studies focus on pairwise interactions and ignore the potentially significant effects of higher-order microbial interactions.Here, we quantify the effects of higher-order interactions among taxa on host infection risk. We apply our approach to an in silico dataset that is built to resemble a population of insect hosts with gut-associated microbial communities at risk of infection from an intestinal parasite across a breadth of nutrient environmental contexts.We find that the effect of higher-order interactions is considerable and can change appreciably across environmental contexts. Furthermore, we show that higher-order interactions can stabilize community structure thereby reducing host susceptibility to parasite invasion.Our approach illustrates how incorporating the effects of higher-order interactions among gut microbiota across environments can be essential for understanding their effects on host phenotypes.

Geometry of ecological coexistence and niche differentiation

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze consumer resource models using geometric arguments. This has led to broadly applicable principles such as Tilman's R^{*} and species coexistence cones. Here, we extend these arguments by constructing a geometric framework for understanding species coexistence based on convex polytopes in the space of consumer preferences. We show how the geometry of consumer preferences can be used to predict species which may coexist and enumerate ecologically stable steady states and transitions between them. Collectively, these results provide a framework for understanding the role of species traits within niche theory.

Microbial interactions in theory and practice: when are measurements compatible with models?

Most predictive models of ecosystem dynamics are based on interactions between organisms: their influence on each other's growth and death. We review here how theoretical approaches are used to extract interaction measurements from experimental data in microbiology, particularly focusing on the generalised Lotka-Volterra (gLV) framework. Though widely used, we argue that the gLV model should be avoided for estimating interactions in batch culture - the most common, simplest and cheapest in vitro approach to culturing microbes. Fortunately, alternative approaches offer a way out of this conundrum. Firstly, on the experimental side, alternatives such as the serial-transfer and chemostat systems more closely match the theoretical assumptions of the gLV model. Secondly, on the theoretical side, explicit organism-environment interaction models can be used to study the dynamics of batch-culture systems. We hope that our recommendations will increase the tractability of microbial model systems for experimentalists and theoreticians alike.

Evolutionary repeatability of emergent properties of ecological communities

Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.