Mutualistic Communities Research Articles

Temporal Shifts in Flower-Visiting Butterfly Communities and Their Floral Resources along a Vegetation Type Altered by Anthropogenic Factors

Habitat disturbance driven by human activities poses a major threat to biodiversity and can disrupt ecological interactions. Butterfly–plant mutualisms represent an ideal model system to study such anthropogenic impacts, as butterflies exhibit intimate dependencies on larval host plants and adult nectar sources, rendering them highly sensitive to habitat changes affecting the availability of these floral resources. This study examined flower-visiting butterfly communities and their associations with flowering plants in a landscape altered by anthropogenic factors in central Mexico. The study area encompassed a mosaic of vegetation types, including native juniper forests, agricultural lands, and introduced eucalyptus plantations, representing different degrees of human-induced habitat modification. Monthly surveys were conducted over a single year, covering both rainy and dry seasons, to analyze butterfly and plant diversity, community composition, and interactions. Results showed the highest diversity in juniper forests, followed by eucalyptus and agricultural sites. Seasonal turnover was the primary driver of community changes, with habitat-based segregation persisting within seasons. Butterfly diversity strongly correlated with flower abundance, while plant richness played a secondary role. SIMPER and indicator species analyses identified key taxa contributing to compositional dissimilarities among habitats and associated with specific vegetation types and seasons. Our research provides insights into temporal dynamics structuring butterfly–plant interactions across this forest disturbance spectrum, highlighting how habitat changes and seasonality shape these mutualistic communities in changing landscapes.

Origin of biogeographically distinct ecotypes during laboratory evolution

Resource partitioning is central to the incredible productivity of microbial communities, including gigatons in annual methane emissions through syntrophic interactions. Previous work revealed how a sulfate reducer (Desulfovibrio vulgaris, Dv) and a methanogen (Methanococcus maripaludis, Mm) underwent evolutionary diversification in a planktonic context, improving stability, cooperativity, and productivity within 300–1000 generations. Here, we show that mutations in just 15 Dv and 7 Mm genes within a minimal assemblage of this evolved community gave rise to co-existing ecotypes that were spatially enriched within a few days of culturing in a fluidized bed reactor. The spatially segregated communities partitioned resources in the simulated subsurface environment, with greater lactate utilization by attached Dv but partial utilization of resulting H2 by low affinity hydrogenases of Mm in the same phase. The unutilized H2 was scavenged by high affinity hydrogenases of planktonic Mm, producing copious amounts of methane. Our findings show how a few mutations can drive resource partitioning amongst niche-differentiated ecotypes, whose interplay synergistically improves productivity of the entire mutualistic community.

Enhanced metabolic entanglement emerges during the evolution of an interkingdom microbial community

While different stages of mutualism can be observed in natural communities, the dynamics and mechanisms underlying the gradual erosion of independence of the initially autonomous organisms are not yet fully understood. In this study, by conducting the laboratory evolution on an engineered microbial community, we reproduce and molecularly track the stepwise progression towards enhanced partner entanglement. We observe that the evolution of the community both strengthens the existing metabolic interactions and leads to the emergence of de novo interdependence between partners for nitrogen metabolism, which is a common feature of natural symbiotic interactions. Selection for enhanced metabolic entanglement during the community evolution repeatedly occurred indirectly, via pleiotropies and trade-offs within cellular regulatory networks, and with no evidence of group selection. The indirect positive selection of metabolic dependencies between microbial community members, which results from the direct selection of other coupled traits in the same regulatory network, may therefore be a common but underappreciated driving force guiding the evolution of natural mutualistic communities.

Unraveling habitat-driven shifts in alpha, beta, and gamma diversity of hummingbirds and their floral resource.

Biodiversity, crucial for understanding ecosystems, encompasses species richness, composition, and distribution. Ecological and environmental factors, such as habitat type, resource availability, and climate conditions, play pivotal roles in shaping species diversity within and among communities, categorized into alpha (within habitat), beta (between habitats), and gamma (total regional) diversity. Hummingbird communities are influenced by habitat, elevation, and seasonality, making them an ideal system for studying these diversities, shedding light on mutualistic community dynamics and conservation strategies. Over a year-long period, monthly surveys were conducted to record hummingbird species and their visited flowering plants across four habitat types (oak forest, juniper forest, pine forest, and xerophytic shrubland) in Tlaxcala, Mexico. Three locations per habitat type were selected based on conservation status and distance from urban areas. True diversity measures were used to assess alpha, beta, and gamma diversity of hummingbirds and their floral resources. Environmental factors such as altitude and bioclimatic variables were explored for their influence on beta diversity. For flowering plants, gamma diversity encompassed 34 species, with oak forests exhibiting the highest richness, while xerophytic shrublands had the highest alpha diversity. In contrast, for hummingbirds, 11 species comprised the gamma diversity, with xerophytic shrublands having the highest richness and alpha diversity. Our data reveal high heterogeneity in species abundance among habitats. Notably, certain floral resources like Loeselia mexicana and Bouvardia ternifolia emerge as key species in multiple habitats, while hummingbirds such as Basilinna leucotis, Selasphorus platycercus, and Calothorax lucifer exhibit varying levels of abundance and habitat preferences. Beta diversity analyses unveil habitat-specific patterns, with species turnover predominantly driving dissimilarity in composition. Moreover, our study explores the relationships between these diversity components and environmental factors such as altitude and climate variables. Climate variables, in particular, emerge as significant contributors to dissimilarity in floral resource and hummingbird communities, highlighting the influence of environmental conditions on species distribution. Our results shed light on the complex dynamics of hummingbird-flower mutualistic communities within diverse habitats and underscore the importance of understanding how habitat-driven shifts impact alpha, beta, and gamma diversity. Such insights are crucial for conservation strategies aimed at preserving the delicate ecological relationships that underpin biodiversity in these communities.

Unraveling emergent network indeterminacy in complex ecosystems: A random matrix approach

Indeterminacy of ecological networks-the unpredictability of ecosystem responses to persistent perturbations-is an emergent property of indirect effects a species has on another through interaction chains. Thus, numerous indirect pathways in large, complex ecological communities could make forecasting the long-term outcomes of environmental changes challenging. However, a comprehensive understanding of ecological structures causing indeterminacy has not yet been reached. Here, using random matrix theory (RMT), we provide mathematical criteria determining whether network indeterminacy emerges across various ecological communities. Our analytical and simulation results show that indeterminacy intricately depends on the characteristics of species interaction. Specifically, contrary to conventional wisdom, network indeterminacy is unlikely to emerge in large competitive and mutualistic communities, while it is a common feature in top-down regulated food webs. Furthermore, we found that predictable and unpredictable perturbations can coexist in the same community and that indeterminate responses to environmental changes arise more frequently in networks where predator-prey relationships predominate than competitive and mutualistic ones. These findings highlight the importance of elucidating direct species relationships and analyzing them with an RMT perspective on two fronts: It aids in 1) determining whether the network's responses to environmental changes are ultimately indeterminate and 2) identifying the types of perturbations causing less predictable outcomes in a complex ecosystem. In addition, our framework should apply to the inverse problem of network identification, i.e., determining whether observed responses to sustained perturbations can reconstruct their proximate causalities, potentially impacting other fields such as microbial and medical sciences.

Structural dynamics of plant-pollinator mutualistic networks.

The discourse surrounding the structural organization of mutualistic interactions mostly revolves around modularity and nestedness. The former is known to enhance the stability of communities, while the latter is related to their feasibility, albeit compromising the stability. However, it has recently been shown that the joint emergence of these structures poses challenges that can eventually lead to limitations in the dynamic properties of mutualistic communities. We hypothesize that considering compound arrangements-modules with internal nested organization-can offer valuable insights in this debate. We analyze the temporal structural dynamics of 20 plant-pollinator interaction networks and observe large structural variability throughout the year. Compound structures are particularly prevalent during the peak of the pollination season, often coexisting with nested and modular arrangements in varying degrees. Motivated by these empirical findings, we synthetically investigate the dynamics of the structural patterns across two control parameters-community size and connectance levels-mimicking the progression of the pollination season. Our analysis reveals contrasting impacts on the stability and feasibility of these mutualistic communities. We characterize the consistent relationship between network structure and stability, which follows a monotonic pattern. But, in terms of feasibility, we observe nonlinear relationships. Compound structures exhibit a favorable balance between stability and feasibility, particularly in mid-sized ecological communities, suggesting they may effectively navigate the simultaneous requirements of stability and feasibility. These findings may indicate that the assembly process of mutualistic communities is driven by a delicate balance among multiple properties, rather than the dominance of a single one.

Beyond the aggregated paradigm: phenology and structure in mutualistic networks

Mutualistic relationships, where species interact to obtain mutual benefits, constitute an essential component of natural ecosystems. The use of ecological networks to represent the species and their ecological interactions allows the study of structural and dynamic patterns common to different ecosystems. However, by neglecting the temporal dimension of mutualistic communities, relevant insights into the organization and functioning of natural ecosystems can be lost. Therefore, it is crucial to incorporate empirical phenology -the cycles of species’ activity within a season- to fully understand the impact of temporal variability on network architecture. In this paper, by using empirical datasets together with a set of synthetic models, we propose a framework to characterize the phenology of plant-pollinator communities and assess how it reshapes their portrayal as a network. Analyses of three empirical cases reveal that non-trivial information is missed when representing the network of interactions as static, which leads to overestimating the value of fundamental structural features. We discuss the implications of our findings for mutualistic relationships and intra-guild competition for common resources. We show that recorded interactions and species’ activity duration are pivotal factors in accurately replicating observed patterns within mutualistic communities. Furthermore, our exploration of synthetic models underscores the system-specific character of the mechanisms driving phenology, increasing our understanding of the complexities of natural ecosystems.

Metabolite cross-feeding enables concomitant catabolism of chlorinated methanes and chlorinated ethenes in synthetic microbial assemblies.

Isolate studies have been a cornerstone for unraveling metabolic pathways and phenotypical (functional) features. Biogeochemical processes in natural and engineered ecosystems are generally performed by more than a single microbe and often rely on mutualistic interactions. We demonstrate the rational bottom-up design of synthetic, interdependent co-cultures to achieve concomitant utilization of chlorinated methanes as electron donors and organohalogens as electron acceptors. Specialized anaerobes conserve energy from the catabolic conversion of chloromethane or dichloromethane to formate, H2, and acetate, compounds that the organohalide-respiring bacterium Dehalogenimonas etheniformans strain GP requires to utilize cis-1,2-dichloroethenene and vinyl chloride as electron acceptors. Organism-specific qPCR enumeration matched the growth of individual dechlorinators to the respective functional (i.e. dechlorination) traits. The metabolite cross-feeding in the synthetic (co-)cultures enables concomitant utilization of chlorinated methanes (i.e. chloromethane and dichloromethane) and chlorinated ethenes (i.e. cis-1,2-dichloroethenene and vinyl chloride) without the addition of an external electron donor (i.e. formate and H2). The findings illustrate that naturally occurring chlorinated C1 compounds can sustain anaerobic food webs, an observation with implications for the development of interdependent, mutualistic communities, the sustenance of microbial life in oligotrophic and energy-deprived environments, and the fate of chloromethane/dichloromethane and chlorinated electron acceptors (e.g. chlorinated ethenes) in pristine environments and commingled contaminant plumes.

When cheating turns into a stabilizing mechanism of plant-pollinator communities.

Mutualistic interactions, such as plant-mycorrhizal or plant-pollinator interactions, are widespread in ecological communities and frequently exploited by cheaters, species that profit from interactions without providing benefits in return. Cheating usually negatively affects the fitness of the individuals that are cheated on, but the effects of cheating at the community level remains poorly understood. Here, we describe 2 different kinds of cheating in mutualistic networks and use a generalized Lotka-Volterra model to show that they have very different consequences for the persistence of the community. Conservative cheating, where a species cheats on its mutualistic partners to escape the cost of mutualistic interactions, negatively affects community persistence. In contrast, innovative cheating occurs with species with whom legitimate interactions are not possible, because of a physiological or morphological barrier. Innovative cheating can enhance community persistence under some conditions: when cheaters have few mutualistic partners, cheat at low or intermediate frequency and the cost associated with mutualism is not too high. Under these conditions, the negative effects of cheating on partner persistence are overcompensated at the community level by the positive feedback loops that arise in diverse mutualistic communities. Using an empirical dataset of plant-bird interactions (hummingbirds and flowerpiercers), we found that observed cheating patterns are highly consistent with theoretical cheating patterns found to increase community persistence. This result suggests that the cheating patterns observed in nature could contribute to promote species coexistence in mutualistic communities, instead of necessarily destabilizing them.

Manipulating network connectance by altering plant attractiveness.

Mutualistic interactions between plants and their pollinating insects are critical to the maintenance of biodiversity. However, we have yet to demonstrate that we are able to manage the structural properties of these networks for the purposes of pollinator conservation and preserving functional outcomes, such as pollination services. Our objective was to explore the extent of our ability to experimentally increase, decrease, and maintain connectance, a structural attribute that reflects patterns of insect visitation and foraging preferences. Patterns of connectance relate to the stability and function of ecological networks. We implemented a 2-year field experiment across eight sites in urban Dublin, Ireland, applying four agrochemical treatments to fixed communities of seven flowering plant species in a randomized block design. We spent ~117 h collecting 1,908 flower-visiting insects of 92 species or morphospecies with standardized sampling methods across the 2 years. We hypothesized that the fertilizer treatment would increase, herbicide decrease, and a combination of both maintain the connectance of the network, relative to a control treatment of just water. Our results showed that we were able to successfully increase network connectance with a fertilizer treatment, and maintain network connectance with a combination of fertilizer and herbicide. However, we were not successful in decreasing network connectance with the herbicide treatment. The increase in connectance in the fertilized treatment was due to an increased species richness of visiting insects, rather than changes to their abundance. We also demonstrated that this change was due to an increase in the realized proportion of insect visitor species rather than increased visitation by common, generalist species of floral visitors. Overall, this work suggests that connectance is an attribute of network structure that can be manipulated, with implications for management goals or conservation efforts in these mutualistic communities.

Habitat restoration and the recovery of metacommunities

Abstract Ecosystem restoration is becoming a widely recognised solution to the biodiversity crisis. However, there is still a gap between restoration science and practice. Specifically, we lack a theoretical framework that would improve our understanding of ecosystems' recovery and allow us to optimise restoration design. Here, we narrow this gap by developing spatially explicit metacommunity models and studying the recovery dynamics of communities during restoration. We show that community response depends on how damaged the landscape is prior to restoration, with highly fragmented landscapes imposing greater challenges to community recovery. In such cases, recovery depends on the type of interaction and the structure of the interaction network. Furthermore, we demonstrate that community recovery can be maximised with careful spatial planning. Specifically, when recovering communities composed of antagonistic interactions, restoration should target areas adjacent to the most species‐rich sites. In the case of mutualistic communities, the same strategy should be adopted in the short term, whereas in the long term, restoration should be extended to sites that improve the overall connectivity of the landscape. Synthesis and applications: Our results highlight the importance of considering interactions between species and spatial planning in restoration projects. Moreover, they provide insights into improving the efficiency of restoration and, thus, can help guide the design of restoration projects.

Rising temperature drives tipping points in mutualistic networks.

The effect of climate warming on species' physiological parameters, including growth rate, mortality rate and handling time, is well established from empirical data. However, with an alarming rise in global temperature more than ever, predicting the interactive influence of these changes on mutualistic communities remains uncertain. Using 139 real plant-pollinator networks sampled across the globe and a modelling approach, we study the impact of species' individual thermal responses on mutualistic communities. We show that at low mutualistic strength plant-pollinator networks are at potential risk of rapid transitions at higher temperatures. Evidently, generalist species play a critical role in guiding tipping points in mutualistic networks. Further, we derive stability criteria for the networks in a range of temperatures using a two-dimensional reduced model. We identify network structures that can ascertain the delay of a community collapse. Until the end of this century, on account of increasing climate warming many real mutualistic networks are likely to be under the threat of sudden collapse, and we frame strategies to mitigate this. Together, our results indicate that knowing individual species' thermal responses and network structure can improve predictions for communities facing rapid transitions.

Predicting cascading extinctions and efficient restoration strategies in plant–pollinator networks via generalized positive feedback loops

The extinction of a species in a plant–pollinator mutualistic community can cause cascading effects and lead to major biodiversity loss. The ecologically important task of predicting the severity of the cascading effects is made challenging by the complex network of interactions among the species. In this work, we analyze an ensemble of models of communities of plant and pollinator species. These models describe the mutualistic inter-species interactions by Boolean threshold functions. We show that identifying generalized positive feedback loops can help pinpoint the species whose extinction leads to catastrophic and substantial damage to the whole community. We compare these results with the damage percentage caused by the loss of species identified as important by previously studied structural measures and show that positive feedback loops and the information gained from them can identify certain crucial species that the other measures fail to find. We also suggest mitigation measures for two specific purposes: (1) prevent the damage to the community by protecting a subset of the species, and (2) restore the community after the damage by restoring a subset of species. Our analyses indicate that the generalized positive feedback loops predict the most efficient strategies to achieve these purposes. The correct identification of species in each category has important implications for conservation efforts and developing community management strategies.

Stochasticity Leads to Coexistence of Generalists and Specialists in Assembling Mutualistic Communities.

AbstractSpecies interaction networks are subject to natural and anthropogenic disturbances that lead to their disassembly, while natural regeneration or restoration efforts facilitate their reassembly. Previous models for assembling ecological networks did not include stochasticity at the level of population dynamics (e.g., demographic noise, environmental noise) and focused mainly on food webs. Here, we present a model for the assembly of mutualistic bipartite networks, such as plant-pollinator networks, and examine the influence of demographic noise on the trajectory of species and strategy diversity, that is, the range of present strategies from specialism to generalism. We find that assembled communities show at intermediate assembly stages a maximum of species diversity and of average generalization. Our model thus provides a mechanism for nonlinear, hump-shaped diversity trajectories at intermediate succession, consistent with the intermediate disturbance hypothesis. Long-term coexistence of specialists and generalists emerges only in the presence of demographic noise and is due to a persistent species turnover. These findings highlight the importance of stochasticity for maintaining long-term diversity.

Metagenome-Assembled Genomes of Komagataeibacter from Kombucha Exposed to Mars-Like Conditions Reveal the Secrets in Tolerating Extraterrestrial Stresses.

Kombucha mutualistic community (KMC) is composed by acetic acid bacteria and yeasts, producing fermented tea with health benefits. As part of the BIOlogy and Mars EXperiment (BIOMEX) project, the effect of Mars-like conditions on the KMC was analyzed. Here, we analyzed metagenome-assembled genomes (MAGs) of the Komagataeibacter, which is a predominant genus in KMC, to understand their roles in the KMC after exposure to Mars-like conditions (outside the International Space Station) based on functional genetic elements. We constructed three MAGs: K. hansenii, K. rhaeticus, and K. oboediens. Our results showed that (i) K. oboediens MAG functionally more complex than K. hansenii, (ii) K. hansenii is a keystone in KMCs with specific functional features to tolerate extreme stress, and (iii) genes related to the PPDK, betaine biosynthesis, polyamines biosynthesis, sulfate-sulfur assimilation pathway as well as type II toxin-antitoxin (TA) system, quorum sensing (QS) system, and cellulose production could play important roles in the resilience of KMC after exposure to Mars-like stress. Our findings show the potential mechanisms through which Komagataeibacter tolerates the extraterrestrial stress and will help to understand minimal microbial composition of KMC for space travelers.

Metagenome Analysis Reveals a Response of the Antibiotic Resistome to Mars-like Extraterrestrial Conditions.

The spread of antibiotic resistance is becoming a serious global health concern. Numerous studies have been done to investigate the dynamics of antibiotic resistance genes (ARGs) in both indoor and outdoor environments. Nonetheless, few studies are available about the dynamics of the antibiotic resistome (total content of ARGs in the microbial cultures or communities) under stress in outer space environments. In this study, we aimed to experimentally investigate the dynamics of ARGs and metal resistance genes (MRGs) in Kombucha Mutualistic Community (KMC) samples exposed to Mars-like conditions simulated during the BIOMEX experiment outside the International Space Station with analysis of the metagenomics data previously produced. Thus, we compared them with those of the respective non-exposed KMC samples. The antibiotic resistome responded to the Mars-like conditions by enriching its diversity with ARGs after exposure, which were not found in non-exposed samples (i.e., tet and van genes against tetracycline and vancomycin, respectively). Furthermore, ARGs and MRGs were correlated; therefore, their co-selection could be assumed as a mechanism for maintaining antibiotic resistance in Mars-like environments. Overall, these results highlight the high plasticity of the antibiotic resistome in response to extraterrestrial conditions and in the absence of anthropogenic stresses.

A patch-dynamic metacommunity perspective on the persistence of mutualistic and antagonistic bipartite networks.

The structure of interactions between species within a community plays a key role in maintaining biodiversity. Previous studies found that the effects of these structures might vary substantially depending on interaction type, for example, a highly connected and nested architecture stabilizes mutualistic communities, while the stability of antagonistic communities is enhanced in modular and weakly connected structures. Here we show that, when network dynamics are modeled using a patch-dynamic metacommunity framework, the qualitative differences between antagonistic and mutualistic systems disappear, with nestedness and modularity interacting to promote metacommunity persistence. However, the interactive effects are significantly weaker in antagonistic metacommunities. Our model also predicts an increase in connectance, nestedness, and modularity over time in both types of interaction, except in antagonistic networks, where nestedness declines. At steady state, we find a strong negative correlation between nestedness and modularity in both mutualistic and antagonistic metacommunities. These predictions are consistent with the structural trends found in a large data set of real-world antagonistic and mutualistic communities.

Relationships among generalized positive feedback loops determine possible community outcomes in plant-pollinator interaction networks.

Attractors in Boolean network models representing complex systems such as ecological communities correspond to long-term outcomes (e.g., stable communities) in such systems. As a result, identifying efficient methods to find and characterize these attractors allows for a better understanding of the diversity of possible outcomes. Here we analyze networks that model mutualistic communities of plant and pollinator species governed by Boolean threshold functions. We propose a novel attractor identification method based on generalized positive feedback loops and their functional relationships in such networks. We show that these relationships determine the mechanisms by which groups of stable positive feedback loops collectively trap the system in specific regions of the state space and lead to attractors. Put into the ecological context, we show how survival units-small groups of species in which species can maintain a specific survival state-and their relationships determine the final community outcomes in plant-pollinator networks. We find a remarkable diversity of community outcomes: up to an average of 43 attractors possible for networks with 100 species. This diversity is due to the multiplicity of survival units (up to 34) and stable subcommunities (up to 14). The timing of species influx or outflux does not affect the number of attractors, but it may influence their basins of attraction.

Adaptive laboratory evolution of microbial co‐cultures for improved metabolite secretion

Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness‐costly metabolites through natural selection. In this strategy, metabolic cross‐feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co‐evolved wild‐type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B‐group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange.

Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.

The metabolic capabilities of the species and the local environment shape the microbial interactions in a community either through the exchange of metabolic products or the competition for the resources. Cells are often arranged in close proximity to each other, creating a crowded environment that unevenly reduce the diffusion of nutrients. Herein, we investigated how the crowding conditions and metabolic variability among cells shape the dynamics of microbial communities. For this, we developed CROMICS, a spatio-temporal framework that combines techniques such as individual-based modeling, scaled particle theory, and thermodynamic flux analysis to explicitly incorporate the cell metabolism and the impact of the presence of macromolecular components on the nutrients diffusion. This framework was used to study two archetypical microbial communities (i) Escherichia coli and Salmonella enterica that cooperate with each other by exchanging metabolites, and (ii) two E. coli with different production level of extracellular polymeric substances (EPS) that compete for the same nutrients. In the mutualistic community, our results demonstrate that crowding enhanced the fitness of cooperative mutants by reducing the leakage of metabolites from the region where they are produced, avoiding the resource competition with non-cooperative cells. Moreover, we also show that E. coli EPS-secreting mutants won the competition against the non-secreting cells by creating less dense structures (i.e. increasing the spacing among the cells) that allow mutants to expand and reach regions closer to the nutrient supply point. A modest enhancement of the relative fitness of EPS-secreting cells over the non-secreting ones were found when the crowding effect was taken into account in the simulations. The emergence of cell-cell interactions and the intracellular conflicts arising from the trade-off between growth and the secretion of metabolites or EPS could provide a local competitive advantage to one species, either by supplying more cross-feeding metabolites or by creating a less dense neighborhood.