Modular Network Research Articles

Emergence of structure in plant–pollinator networks: low floral resource constrains network specialisation

Specialisation enhances the efficiency of plant–pollinator networks through the exchange of conspecific pollen transfer for floral resources. Floral resources form the currency of plant–pollinator interactions, but the understanding of how floral resources affect the structure of plant–pollinator networks remains modest. Previous theory predicts that optimally foraging animal species will specialise to improve resource acquisition under high resource availability. Although floral resource availability depends on both the plant production and animal consumption of the resources, previous work has assumed that production and availability are equivalent. This potentially may have led to erroneous inferences on the effect of resource availability on specialisation. We develop a mutualistic Lotka–Volterra consumer‐resource model to investigate the influence of floral resource availability on plant–pollinator network structure. The model incorporates animal adaptive foraging behaviour, floral resource dynamics, and density‐dependent dynamics. Specialisation, nestedness and modularity of simulated networks generated from the model under a wide range of parameters were explained using the generalised linear model. We found that the distinction between floral resource dynamics and plant density dynamics was necessary for partial specialisation of plant–pollinator networks. This is because floral resource dynamics constrained animal preference due to its depletion by animal species. Floral resource abundance had a positive effect on network specialisation, but animal density had a negative effect on network specialisation. Floral resource dynamics thus play key roles in the structure of plant–pollinator networks, distinctive from plant species density dynamics.

Community Assembly Processes of Deadwood Mycobiome in a Tropical Forest Revealed by Long-Read Third-Generation Sequencing

Despite the importance of wood-inhabiting fungi on nutrient cycling and ecosystem functions, their ecology, especially related to their community assembly, is still highly unexplored. In this study, we analyzed the wood-inhabiting fungal richness, community composition, and phylogenetics using PacBio sequencing. Opposite to what has been expected that deterministic processes especially environmental filtering through wood-physicochemical properties controls the community assembly of wood-inhabiting fungal communities, here we showed that both deterministic and stochastic processes can highly contribute to the community assembly processes of wood-inhabiting fungi in this tropical forest. We demonstrated that the dynamics of stochastic and deterministic processes varied with wood decomposition stages. The initial stage was mainly governed by a deterministic process (homogenous selection), whereas the early and later decomposition stages were governed by the stochastic processes (ecological drift). Deterministic processes were highly contributed by wood physicochemical properties (especially macronutrients and hemicellulose) rather than soil physicochemical factors. We elucidated that fine-scale fungal-fungal interactions, especially the network topology, modularity, and keystone taxa of wood-inhabiting fungal communities, strongly differed in an initial and decomposing deadwood. This current study contributes to a better understanding of the ecological processes of wood-inhabiting fungi in tropical regions where the knowledge of wood-inhabiting fungi is highly limited.

The ecology of the sewer systems: Microbial composition, function, assembly, and network in different spatial locations

Microbial induced concrete corrosion (MICC) is the primary deterioration affecting global sewers. Disentangling ecological mechanisms in the sewer system is meaningful for implementing policies to protect sewer pipes using trenchless technology. It is necessary to understand microbial compositions, interaction networks, functions, alongside assembly processes in sewer microbial communities. In this study, sewer wastewater samples and microbial samples from the upper part (UP), middle part (MP) and bottom part (BP) of different pipes were collected for 16S rRNA gene amplicon analysis. It was found that BP harbored distinct microbial communities and the largest proportion of unique species (1141) compared to UP and MP. The community in BP tended to be more clustered. Furthermore, significant differences in microbial functions existed in different spatial locations, including the carbon cycle, nitrogen cycle and sulfur cycle. Active microbial sulfur cycling indicated the corrosion risk of MICC. Among the environmental factors, the oxidation‒reduction potential drove changes in BP, while sulfate managed changes in UP and BP. Stochasticity dominated community assembly in the sewer system. Additionally, the sewer microbial community exhibited numerous positive links. BP possessed a more complex, modular network with higher modularity. These deep insights into microbial ecology in the sewer system may guide engineering safety and disaster prevention in sewer infrastructure.

Mediterranean pine forest decline: A matter of root-associated microbiota and climate change

Forest ecosystems worldwide currently face worrying episodes of forest decline, which have boosted weakening and mortality of the trees. In the Mediterranean region, especially in the southeast Iberian Peninsula, Pinus sylvestris forests are severely affected by this phenomenon, and it has been commonly attributed to drought events. Remarkably, the role of root microbiota on pine decline has been overlooked and remains unclear. We therefore used metabarcoding to identify the belowground microbial communities of decline-affected and unaffected pine trees. Taxonomic composition of bacterial and fungal rhizosphere communities, and fungal populations dwelling in root endosphere showed different profiles depending on the health status of the trees. The root endosphere of asymptomatic trees was as strongly dominated by ‘Candidatus Phytoplasma pini’ as the root of decline-affected pines, accounting for >99 % of the total bacterial sequences in some samples. Notwithstanding, the titer of this phytopathogen was four-fold higher in symptomatic trees than in symptomless ones. Furthermore, the microbiota inhabiting the root endosphere of decline-affected trees assembled into a less complex and more modularized network. Thus, the observed changes in the microbial communities could be a cause or a consequence of forest decline phenomenon. Moreover, ‘Ca. Phytoplasma pini’ is positively correlated to Pinus sylvestris decline events, either as the primary cause of pine decline or as an opportunistic pathogen exacerbating the process once the tree has been weaken by other factors.

Non-flooding conditions caused by water table drawdown alter microbial network complexity and decrease multifunctionality in alpine wetland soils

Several soil functions of alpine wetland depend on microbial communities, including carbon storage and nutrient cycling, and soil microbes are highly sensitive to hydrological conditions. Wetland degradation is often accompanied by a decline in water table. With the water table drawdown, the effects of microbial network complexity on various soil functions remain insufficiently understood. In this research, we quantified soil multifunctionality of flooded and non-flooded sites in the Lalu Wetland on the Tibetan Plateau. We employed high-throughput sequencing to investigate the microbial community responses to water table depth changes, as well as the relationships between microbial network properties and soil multifunctionality. Our findings revealed a substantial reduction in soil multifunctionality at both surface and subsurface soil layers (0–20 cm and 20–40 cm) in non-flooded sites compared to flooded sites. The α-diversity of bacteria in the surface soil of non-flooded sites was significantly lower than that in flooded sites. Microbial network properties (including the number of nodes, number of edges, average degree, density, and modularity of co-occurrence networks) exhibited significant correlations with soil multifunctionality. This study underscores the adverse impact of non-flooded conditions resulting from water table drawdown on soil multifunctionality in alpine wetland soils, driven by alterations in microbial community structure. Additionally, we identified soil pH and moisture content as pivotal abiotic factors influencing soil multifunctionality, with microbial network complexity emerging as a valuable predictor of multifunctionality.

Effect of polyethylene microplastics on antibiotic resistance genes: A comparison based on different soil types and plant types

Microplastics (MPs) and antibiotic resistance genes (ARGs) are two types of contaminants that are widely present in the soil environment. MPs can act as carriers of microbes, facilitating the colonization and spread of ARGs and thus posing potential hazards to ecosystem safety and human health. In the present study, we explored the microbial networks and ARG distribution characteristics in different soil types (heavy metal (HM)-contaminated soil and agricultural soil planted with different plants: Bidens pilosa L., Ipomoea aquatica F., and Brassica chinensis L.) after the application of MPs and evaluated environmental factors, potential microbial hosts, and ARGs. The microbial communities in the three rhizosphere soils were closely related to each other, and the modularity of the microbial networks was greater than 0.4. Moreover, the core taxa in the microbial networks, including Actinobacteriota, Proteobacteria, and Myxococcota, were important for resisting environmental stress. The ARG resistance mechanisms were dominated by antibiotic efflux in all three rhizosphere soils. Based on the annotation results, the MP treatments induced changes in the relative abundance of microbes carrying ARGs, and the G1-5 treatment significantly increased the abundance of MuxB in Verrucomicrobia, Elusimicrobia, Actinobacteria, Planctomycetes, and Acidobacteria. Path analysis showed that changes in MP particle size and dosage may indirectly affect soil enzyme activities by changing pH, which affects microbes and ARGs. We suggest that MPs may provide surfaces for ARG accumulation, leading to ARG enrichment in plants. In conclusion, our results demonstrate that MPs, as potentially persistent pollutants, can affect different types of soil environments and that the presence of ARGs may cause substantial environmental risks. Environmental ImplicationMicroplastics (MPs) and antibiotic resistance genes (ARGs) are two emerging pollutants that are widely present in the environment, and both agricultural and heavy metal-contaminated soils are polluted to varying degrees. In this study, the potential impact of MPs on ARGs based on microbial ecological networks and ARG distribution characteristics was explored. Specifically, MPs drive different ecological processes in soil ecosystems, and their presence may lead to ARG enrichment in plants. In addition, the mobility of ARGs in the environment requires more attention than the risk of ARGs themselves.

Mechanistic interactions as the origin of modularity in biological networks.

Biological networks are often modular. Explanations for this peculiarity either assume an adaptive advantage of a modular design such as higher robustness, or attribute it to neutral factors such as constraints underlying network assembly. Interestingly, most insights on the origin of modularity stem from models in which interactions are either determined by highly simplistic mechanisms, or have no mechanistic basis at all. Yet, empirical knowledge suggests that biological interactions are often mediated by complex structural or behavioural traits. Here, we investigate the origins of modularity using a model in which interactions are determined by potentially complex traits. Specifically, we model system elements-such as the species in an ecosystem-as finite-state machines (FSMs), and determine their interactions by means of communication between the corresponding FSMs. Using this model, we show that modularity probably emerges for free. We further find that the more modular an interaction network is, the less complex are the traits that mediate the interactions. Altogether, our results suggest that the conditions for modularity to evolve may be much broader than previously thought.

Quorum sensing bacteria improve microbial networks stability and complexity in wastewater treatment plants

Quorum-sensing bacteria (QSB) are crucial factors for microbial communication, yet their ecological role in wastewater treatment plants (WWTPs) remains unclear. Here, we developed a method to identify QSB by comparing 16S rRNA gene sequences. QSB in 388 activated sludge samples collected from 130 WWTPs across China primarily were identified as rare taxa and conditionally rare taxa. A co-occurrence network shared by all sludge communities revealed that QSB exhibited higher average clustering coefficient (0.46) than non-QSB (0.15). Individual sludge networks demonstrated that quorum sensing microbiomes were positively correlated with network robustness and network complexity, including average clustering coefficient and link density. We confirmed that QSB keystones and QSB nodes have a positive impact on network complexity by influencing network modularity through a structural equation model. Meanwhile, QSB communities directly contributed to maintaining network robustness (r = 0.29, P < 0.05). Hence, QSB play an important role in promoting network complexity and stability. Furthermore, QSB communities were positively associated with the functional composition of activated sludge communities (r = 0.33, P < 0.01), especially the denitrification capacity (r = 0.45, P < 0.001). Overall, we elucidated the ecological significance of QSB and provided support for QS-based regulation of activated sludge microbial communities.

Simultaneous prediction of petrophysical properties and formation layered thickness from acoustic logging data using a modular cascading residual neural network (MCARNN) with physical constraints

Predicting petrophysical properties from logging data using deep neural networks (DNNs) improves the efficiency of subsurface exploration. Traditional methods, however, heavily rely on data pre-processing. Therefore, a novel Modular Cascading Residual Neural Network (MCARNN) is proposed to simultaneously retrieve layered thickness and petrophysical properties of porous media directly from raw acoustic logging data without pre-processing. The MCARNN mainly comprises two cascading modules: the first is tasked with retrieving layered thickness, while the second one further reconstructs petrophysical properties based on the thickness information. To more accurately simulate the uncertainties of the real world, we incorporate some known geophysical knowledge, including the distribution range of actual petrophysical parameters, physical constraints among porous medium parameters, configurations of logging instruments, and stratified geological models. During this process, all Biot's porous parameters are independently randomized to cover a wide range of geological rock types. Furthermore, to enhance MCARNN's generalization ability under different random noise distributions, we independently generate the training and testing datasets using distinct numerical algorithms, thus preventing the “inverse crime” phenomenon. Additionally, varying levels of Gaussian white noises are added to the training dataset, and the transmitter-receiver configuration of the logging instrument is varied to enhance the training dataset. Two numerical experiments are implemented to validate the reliability and superiority of the MCARNN. It is shown that the proposed MCARNN can not only accurately predict porous petrophysical parameters when the background layer configurations are known but also be able to simultaneously reconstruct layered thickness and petrophysical parameters with low model misfit. Notably, MCARNN successfully predicts the porous velocity dispersion curves within a broad frequency band based on limited frequency points—a task typically requiring auxiliary equations in traditional inversion methods, demonstrating the advanced predictive capability of the proposed MCARNN. Through this research, we not only address the deficiencies in existing real-world logging datasets in terms of sample volume and label reliability but also effectively integrate rock physics models with data-driven neural networks, providing a novel and effective pathway for reservoir parameter prediction in the field of geophysical logging.

Motif-based community detection in heterogeneous multilayer networks

Multilayer networks composed of intralayer edges and interlayer edges are an important type of complex networks. Considering the heterogeneity of nodes and edges, it is necessary to design more reasonable and diverse community detection methods for multilayer networks. Existing research on community detection in multilayer networks mainly focuses on multiplexing networks (where the nodes are homogeneous and the edges are heterogeneous), but few studies have focused on heterogeneous multilayer networks where both nodes and edges represent different semantics. In this paper, we studied community detection on heterogeneous multilayer networks and proposed a motif-based detection algorithm. First, the communities and motifs of multilayer networks are defined, especially the interlayer motifs. Then, the modularity of multilayer networks based on these motifs is designed, and the community structure of the multilayer network is detected by maximizing the modularity of multilayer networks. Finally, we verify the effectiveness of the detection algorithm on synthetic networks. In the experiments on synthetic networks, comparing with the classical community detection algorithms (without considering interlayer heterogeneity), the motif-based modularity community detection algorithm can obtain better results under different evaluation indexes, and we found that there exists a certain relationship between motifs and communities. In addition, the proposed algorithm is applied in the empirical network, which shows its practicability in the real world. This study provides a solution for the investigation of heterogeneous information in multilayer networks.

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman’s ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.

Engineering Modular DNA Reaction Networks for Signal Processing.

Diversified molecular information-processing methods have significant implications for nanoscale manipulation and control, monitoring and disease diagnosis of organisms, and direct intervention in biological activities. However, as an effective approach for implementing multifunctional molecular information processing, DNA reaction networks (DRNs) with numerous functionally specialized molecular structures have challenged them on scale and modular design, leading to increased network complexity, further causing problems such as signal leakage, attenuation, and cross-talk in network reactions. Our study developed a strategy for performing various signal-processing tasks through engineering modular DRNs composed of simple molecular structures. This strategy is based on a universal core unit with signal selection capability, and a timeadjustable signal self-resetting module is achieved by combing the core unit and self-resetting unit, which improves the time controllability of modular DRNs. In addition, multi-input and -output signal crosscatalytic and continuously adjustable signal delay modules were realized by combining core and threshold units, providing a flexible, precise method for modular DRNs to process the signal. The strategy simplifies the design of DRNs, helps generate design ideas for largescale integrated DRNs with multiple functions, and provides prospects in biocomputing, gene regulation, and biosensing.

IMCN: Improved modular co-attention networks for visual question answering

IMCN: Improved modular co-attention networks for visual question answering

Lorawan-Based RSSI-Trilateration Model for Node Location: A Simulation Integrating Flora and Omnet++

Abstract This work presents the employing of LoRaWAN (Long Range Wide Area Network) for location applications through a network simulation to determine a mobile node position. We rely on FLoRa (Framework for LoRa) and OMNeT++ (Objective Modular Network Testbed in C++) simulator, which uses Python feature tools, following the calculation of node placement using the trilateration technique. Our method differs from others in that we calculate the FLoRa power loss and determine different simulation settings using the shadowing feature of the log-distance path loss model. We approached RSSI (Received Signal Strength Indicator) to measure the distance between the LoRa gateways and the nodes, establishing a link between these parameters. Our work aims to promote the integration of open-source tools for verifying signal intensity values based on node distance from gateways. We consider it useful for engineers in predicting signal behaviors according to topology and settings variations. During the experimentation, the network underwent different performances according to the transmission parameters considered during the simulation. This was critical when increasing the number of mobile nodes, leading to consuming computer capacity and resources. Through repetition of tests, we confirmed the lower intensity of the received signal as the node moves to farther positions, reaching consistent power indicators and positioning accuracy. Overall, the results show that LoRaWAN integrated with trilateration techniques can be practical in providing adequate performance for node positioning accuracy and long-distance communication with low power consumption.

ATTACK VULNERABILITY OF FRACTAL SCALE-FREE NETWORK

An in-depth analysis of the attack vulnerability of fractal scale-free networks is of great significance for designing robust networks. Previous studies have mainly focused on the impact of fractal property on attack vulnerability of scale-free networks under static node attacks, while we extend the study to the cases of various types of targeted attacks, and explore the relationship between the attack vulnerability of fractal scale-free networks and the fractal dimension. A hierarchical multiplicative growth model is first proposed to generate scale-free networks with the same structural properties except for the fractal dimension. Furthermore, the fractal dimension of the network is calculated using two methods, namely, the box-covering method and the cluster-growing method, to exclude the possibility of differences in conclusions caused by the methods of calculating the fractal dimension for the subsequent relationship analysis. Finally, four attack strategies are used to attack the network, and the network performance is quantitatively measured by three structural indicators. Results on model networks show that compared to non-fractal modular networks, fractal scale-free networks are more robust to both static and dynamic targeted attacks on nodes and links, and the robustness of the network increases as the fractal dimension decreases. However, there is a cost in that as the fractal dimension decreases, the network becomes less efficient and more vulnerable to random failures on links. These findings contribute to a deeper understanding of the impact of fractal property on scale-free network performance and may be useful for designing resilient infrastructures.

Organic 6G networks: ultra-flexibility through extensive stateless functional split

AbstractWith the increase in hardware performance, the 5G mobile network architecture shifted from physical components to software-only micro-services. The very modular network functions can be deployed flexibly on commodity hardware. However, the extensive modularity of these network functions is increasing the number of managed entities, and the core network request latency. Also, it requires extensive procedures to be able to re-select the components for specific devices, a fundamental condition for a potential system scale-down. In this paper, we propose a new organic 6G network architecture that handles these challenges through a new functionality split based on the experience of IT software services. Furthermore, we provide an analysis based on main 5G procedures, showing that the newly proposed architecture is handling the re-selection of functionality significantly better, which is a cornerstone of high-speed scaling (especially scaling-out), as well as migration of functionality and users.

Modular horizontal network within mouse primary visual cortex.

Interactions between feedback connections from higher cortical areas and local horizontal connections within primary visual cortex (V1) were shown to play a role in contextual processing in different behavioral states. Layer 1 (L1) is an important part of the underlying network. This cell-sparse layer is a target of feedback and local inputs, and nexus for contacts onto apical dendrites of projection neurons in the layers below. Importantly, L1 is a site for coupling inputs from the outside world with internal information. To determine whether all of these circuit elements overlap in L1, we labeled the horizontal network within mouse V1 with anterograde and retrograde viral tracers. We found two types of local horizontal connections: short ones that were tangentially limited to the representation of the point image, and long ones which reached beyond the receptive field center, deep into its surround. The long connections were patchy and terminated preferentially in M2 muscarinic acetylcholine receptor-negative (M2-) interpatches. Anterogradely labeled inputs overlapped in M2-interpatches with apical dendrites of retrogradely labeled L2/3 and L5 cells, forming module-selective loops between topographically distant locations. Previous work showed that L1 of M2-interpatches receive inputs from the lateral posterior thalamic nucleus (LP) and from a feedback network from areas of the medial dorsal stream, including the secondary motor cortex. Together, these findings suggest that interactions in M2-interpatches play a role in processing visual inputs produced by object-and self-motion.

Cancer drug sensitivity estimation using modular deep Graph Neural Networks.

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drugs components that are tailored to the transcriptomic profile of a given primary tumor. The SMILES representation of molecules that is used by state-of-the-art drug-sensitivity models is not conducive for neural networks to generalize to new drugs, in part because the distance between atoms does not generally correspond to the distance between their representation in the SMILES strings. Graph-attention networks, on the other hand, are high-capacity models that require large training-data volumes which are not available for drug-sensitivity estimation. We develop a modular drug-sensitivity graph-attentional neural network. The modular architecture allows us to separately pre-train the graph encoder and graph-attentional pooling layer on related tasks for which more data are available. We observe that this model outperforms reference models for the use cases of precision oncology and drug discovery; in particular, it is better able to predict the specific interaction between drug and cell line that is not explained by the general cytotoxicity of the drug and the overall survivability of the cell line. The complete source code is available at https://zenodo.org/doi/10.5281/zenodo.8020945. All experiments are based on the publicly available GDSC data.

Evolution of cooperation on reinforcement-learning driven-adaptive networks.

Complex networks are widespread in real-world environments across diverse domains. Real-world networks tend to form spontaneously through interactions between individual agents. Inspired by this, we design an evolutionary game model in which agents participate in a prisoner's dilemma game (PDG) with their neighboring agents. Agents can autonomously modify their connections with neighbors using reinforcement learning to avoid unfavorable environments. Interestingly, our findings reveal some remarkable results. Exploiting reinforcement learning-based adaptive networks improves cooperation when juxtaposed with existing PDGs performed on homogeneous networks. At the same time, the network's topology evolves from homogeneous to heterogeneous states. This change occurs as players gain experience from past games and become more astute in deciding whether to join PDGs with their current neighbors or disconnect from the least profitable neighbors. Instead, they seek out more favorable environments by establishing connections with second-order neighbors with higher rewards. By calculating the degree distribution and modularity of the adaptive network in a steady state, we confirm that the adaptive network follows a power law and has a clear community structure, indicating that the adaptive network is similar to networks in the real world. Our study reports a new phenomenon in evolutionary game theory on networks. It proposes a new perspective to generate scale-free networks, which is generating scale-free networks by the evolution of homogeneous networks rather than typical ways of network growth and preferential connection. Our results provide new aspects to understanding the network structure, the emergence of cooperation, and the behavior of actors in nature and society.

Enhanced AHL-mediated quorum sensing accelerates the start-up of biofilm reactors by elevating the fitness of fast-growing bacteria in sludge and biofilm communities

Quorum sensing (QS)-based manipulations emerge as a promising solution for biofilm reactors to overcome challenges from inefficient biofilm formation and lengthy start-ups. However, the ecological mechanisms underlying how QS regulates microbial behaviors and community assembly remain elusive. Herein, by introducing different levels of N-acyl-homoserine lactones, we manipulated the strength of QS during the start-up of moving bed biofilm reactors and compared the dynamics of bacterial communities. We found that enhanced QS elevated the fitness of fast-growing bacteria with high ribosomal RNA operon (rrn) copy numbers in their genomes in both the sludge and biofilm communities. This led to notably increased extracellular substance production, as evidenced by strong positive correlations between community-level rrn copy numbers and extracellular proteins and polysaccharides (Pearson's r = 0.529−0.830, P < 0.001). Network analyses demonstrated that enhanced QS significantly promoted the ecological interactions among taxa, particularly cooperative interactions. Bacterial taxa with higher network degrees were more strongly correlated with extracellular substances, suggesting their crucial roles as public goods in regulating bacterial interactions and shaping network structures. However, the assembly of more cooperative communities in QS-enhanced reactors came at the cost of decreased network stability and modularity. Null model and dissimilarity-overlap curve analysis revealed that enhanced QS strengthened stochastic processes in community assembly and rendered the universal population dynamics more convergent. Additionally, these shaping effects were consistent for both the sludge and biofilm communities, underpinning the planktonic-to-biofilm transition. This work highlights that QS manipulations efficiently drive community assembly and confer specialized functional traits to communities by recruiting taxa with specific life strategies and regulating interspecific interactions. These ecological insights deepen our understanding of the rules governing microbial societies and provide guidance for managing engineering ecosystems.