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bioRxiv2023

Phylogeny structures species' interactions in experimental ecological communities

The advent of molecular phylogenetics provided a new perspective on the structure and function of ecological communities. In particular, the hypothesis that traits responsible for species interactions are largely determined by shared evolutionary history has suggested the possibility of connecting the phylogeny of ecological communities to their functioning. However, statistical tests of this link have yielded mixed results. Here we propose a novel framework to test whether phylogeny influences the patterns of coexistence and abundance of species assemblages, and apply it to analyze data from large biodiversity-ecosystem functioning experiments. In our approach, phylogenetic trees are used to parameterize species interactions, which in turn determine the abundance of species in a specified assemblage. We use a maximum likelihood-based approach to score models parameterized with a given phylogenetic tree. To test whether evolutionary history structures interactions, we fit and score ensembles of randomized trees, allowing us to determine if phylogenetic information helps to predict species abundances. Moreover, we can determine the contribution of each branch of the tree to the likelihood, revealing particular clades in which interaction strengths are closely tied to phylogeny. We find strong evidence of phylogenetic signal across a range of published experiments and a variety of models. The flexibility of our framework permits incorporation of ecological information beyond phylogeny, such as functional groups or traits, and provides a principled way to test hypotheses about which factors shape the structure and function of ecological communities.

ecology↗

bioRxiv2023

Individual tree crown maps for the National Ecological Observatory Network

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network. Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of six species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling.

ecology↗

bioRxiv2023

A single changing hypernetwork to represent (social-)ecological dynamics

AO_SCPLOWBSTRACTC_SCPLOWTo understand and manage (social-)ecological systems, we need an intuitive and rigorous way to represent them. Recent ecological studies propose to represent interaction networks into modular graphs, multiplexes and higher-order interactions. Along these lines, we argue here that non-dyadic (non-pairwise) interactions are common in ecology and environmental sciences, necessitating fresh concepts and tools for handling them. In addition, such interaction networks often change sharply, due to appearing and disappearing species and components. We illustrate in a simple example that any ecosystem can be represented by a single hypergraph, here called the ecosystem hypernetwork. Moreover, we highlight that any ecosystem hypernetwork exhibits a changing topology summarizing its long term dynamics (e.g., species extinction/invasion, pollutant or human arrival/migration). Qualitative and discrete-event models developed in computer science appear suitable for modeling hypergraph (topological) dynamics. Hypernetworks thus also provide a conceptual foundation for theoretical as well as more applied studies in ecology (at large), as they form the qualitative backbone of ever-changing ecosystems.

ecology↗

bioRxiv2023

Co-habiting ants and silverfish display a converging feeding ecology

O_LISpecies from a great number of animal taxa have specialized to living with social hosts. Depending on their level of specialization, these symbiotic animals are characterized by distinct behavioural, chemical, and morphological traits that enable close heterospecific interactions. Despite its functional importance, our understanding of the feeding ecology of specialized animals living with social hosts remains limited. C_LIO_LIIn this study, we examined how host specialization of silverfish co-habiting with ants affects several components of their feeding ecology. We combined stable isotope profiling, feeding assays, phylogenetic reconstruction, and microbial community characterization of the Neoasterolepisma silverfish genus and a wider nicoletiid and lepismatid silverfish panel where divergent myrmecophilous lifestyles are observed. C_LIO_LIStable isotope profiling ({delta}13C and {delta}15N) showed that the isotopic niches (proxy for trophic niches) of granivorous Messor ants and Messor-specialized Neoasterolepisma exhibit a remarkable overlap within an ant nest. Trophic experiments and gut dissections further supported that these specialized Neoasterolepisma silverfish transitioned to a diet that includes plant seeds. In contrast, the isotopic niches of generalist Neoasterolepisma silverfish and generalist nicoletiid silverfish were clearly different from their ant hosts within the shared nest environment. C_LIO_LIThe impact of the myrmecophilous lifestyle on feeding ecology was also evident in the internal silverfish microbiome. Compared to generalists, Messor-specialists exhibited a higher bacterial density and a higher proportion of heterofermentative lactic acid bacteria. Moreover, the nest environment explained the infection profile (or the 16S rRNA genotypes) of Weissella bacteria in Messor-specialized silverfish and the ant hosts. C_LIO_LITogether, we show that social hosts are important determinants for the feeding ecology of symbiotic animals, and can induce diet convergence. C_LI

ecology↗

bioRxiv2023

Probabilistic ecological risk assessment for deep-sea mining: a Bayesian Network for Chatham Rise, SW Pacific Ocean

Increasing interest in seabed resource use in the ocean is introducing new pressures on deep-sea environments, the ecological impacts of which need to be evaluated carefully. The complexity of these ecosystems and the dearth of comprehensive data pose significant challenges to predicting potential impacts. In this study, we demonstrate the use of Bayesian Networks (BNs) as a modelling framework to address these challenges and enhance the development of robust quantitative predictions concerning the effects of human activities on deep-seafloor ecosystems. The approach consists of iterative model building with experts, and quantitative probability estimates of the relative decrease in abundance of different functional groups of benthos following seabed mining. The model is then used to evaluate two alternative seabed mining scenarios to identify the major sources of uncertainty associated with the mining impacts. By establishing causal connections between the pressures associated with potential mining activities and various components of the benthic ecosystem, our model offers an improved comprehension of potential impacts on the seafloor environment. We illustrate this approach using the example of potential phosphorite nodule mining on the Chatham Rise, offshore Aotearoa/New Zealand, SW Pacific Ocean, and examine ways to incorporate knowledge from both empirical data and expert assessments into quantitative risk assessments. We further discuss how ecological risk assessments can be constructed to better inform decision-making, using metrics relevant to both ecology and policy. The findings from this study highlight the valuable insights that BNs can provide in evaluating the potential impacts of human activities. However, continued research and data collection are crucial for refining and ground truthing these models and improving our understanding of the long-term consequences of deep-sea mining and other anthropogenic activities on marine ecosystems. By leveraging such tools, policymakers, researchers, and stakeholders can work together towards human activities in the deep sea that minimise ecological harm and ensure the conservation of these environments.

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bioRxiv2024

Advancing ecological networks: moving beyond binary classification to probabilistic interactions.

Leveraging trophic interactions to deduce macro-ecological patterns has become a prevalent method, taking advantage of the extensive databases on binary trophic interactions (i.e., prey-predator relationships). However, this binary approach oversimplifies complex ecological dynamics and fails to capture the nuanced structure of food webs. The challenge lies in the scarcity and limited availability of data on non-binary interactions, which are crucial for a more comprehensive understanding of ecological networks. This study explores the use of binary classifiers, particularly the XGBOOST algorithm to address the limitations of traditional binary approaches to prey-predator relationships. By predicting predation probabilities among nine mammalian predators using species traits, my findings demonstrate the classifiers robust predictive capabilities to binary predictions but also a good correlation between probabilistic predation derived from binary classifiers and observed prey preferences. It also highlighted the importance of selecting informative species traits for predicting interaction, with performance contrastingly superior to null models. Despite a small sample size, this work provides insightful results and sets a foundation for future research to expand these models to broader ecological networks, emphasizing the need for comprehensive prey preference data.

ecology↗

bioRxiv2024

Inferring stability and persistence in the vaginal microbiome: A stochastic model of ecological dynamics

The vaginal microbiome is dynamic, yet the ecological and stochastic forces shaping its stability and persistence remain poorly understood. We developed a multi-species stochastic population model to analyze time-series data from 135 individuals sampled daily over 70 days, integrating ecological theory with microbial dynamics. Our framework explicitly incorporates stochasticity, ecological feedback, and sampling error to quantify community stability. We show that intra- and inter-species interactions and environmental fluctuations critically shape microbial population trajectories. This approach enabled the estimation of species competition coefficients and the identification of distinct stability regimes across individuals. We also introduce a Risk Prediction Monitoring (RPM) tool to track persistence probabilities of key taxa, particularly Lactobacillus spp., mirroring extinction risk models in conservation biology. Our findings challenge static "healthy" vs. "dysbiotic" categorizations and offer a quantitative framework for assessing microbiome resilience. This has direct implications for microbiome-targeted therapies aimed at promoting ecological stability in vaginal bacterial communities.

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bioRxiv2024

Multi-objective management of naturally regenerating beech forests: An ecological-economic optimization approach

How can we meet economic objectives of timber harvesting while maintaining the functioning of diverse forest ecosystems? Existing forest models that address this type of question are often complex, data-intensive, challenging to couple with economic optimization models, or can not easily be generalised for uneven-aged mixed-species forests. Here, we develop an ecological-economic optimization model, which integrates a state-of-the-art demographic forest model with a continuous cover forestry harvesting model to optimise efficient and sustainable timber harvesting. As a proof-of-concept, we apply the model to a beech-dominated forest in the Hainich-Dun region in Thuringia, Germany, with the goal of optimising multiple objectives such as timber yield and the biodiversity value of the forest. The ecological module is the Perfect Plasticity Approximation (PPA) demographic forest model that simulates forest dynamics based on individual tree growth and survival rates in the canopy and understory layers, respectively, as well as recruitment rates. We used repeated forest inventory data from a 28-ha forest plot to quantify these demographic rates and validated the predictions of the ecological module against the structure of old-growth beech forests in Europe. The economic module includes the optimization of net revenues (market revenues net of harvesting cost) from harvesting timber. As an indicator of the biodiversity value of the forest, we use the number of retained habitat trees (>70 cm diameter). The forest model delivered reasonable predictions of structural attributes of unmanaged old-growth beech forests. When net revenues from timber harvest were maximised, trees were logged when they reached 55 cm in diameter. This is similar to current management practices in beech forests. We found a linear trade-off between timber net revenues and biodiversity value with about 2.5% of the maximum benefit of timber harvest being lost with each additionally retained habitat tree. We established a generic ecological-economic modelling framework that reliably represents forest dynamics as well as optimising forest management. To our knowledge, this is the first forest model for central European forests capable of identifying optimal harvesting over the full set of feasible strategies, rather than merely comparing predefined management scenarios. The framework can be extended to mixed-species forests and support forest management for diverse ecosystem services.

ecology↗

bioRxiv2024

Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge

Being able to infer the interactions between a set of species from observations of the system is of paramount importance to obtain explaining and predictive models in ecology. We tackled this challenge by employing qualitative modeling frameworks and logic methods for the synthesis of mathematical models that can integrate both observations and expert knowledge on the system. After devising a formal link between ecological networks and the causal structure of Boolean networks, we applied a generic model synthesis engine to infer Boolean models that are able to reproduce the observed dynamics of a protist community. Our inference method supports optimization criteria to derive most parsimonious and most precise models. It is also able to integrate prior knowledge on the ecological network, adding constraints on impossible interactions, which is necessary to obtain realistic predictions. Such constraints may however prove to be too strict, in which case our method is able to conclude on the absence of a model compatible with both the observations and the input hypotheses. We demonstrated our methodology on experimental data of a protist system, and showed its ability to recover essential and sufficient ecological interactions to explain the observed dynamics.

ecology↗

bioRxiv2024

LONG-TERM ECOLOGICAL MONITORING IN INDIA: A SYNTHESIS

Long-term ecological monitoring (LTEM) is crucial for understanding ecological processes and responses to environmental change, informing management of natural resources, and biodiversity conservation. Systematic LTEM efforts began in India in the mid-1900s, but there is a lack of comprehensive synthesis of LTEM efforts in the country. Here, we use a wide-ranging questionnaire survey of ecologists coupled with a survey of published literature on LTEM efforts in India to synthesise their thematic and geographical spread, and types of data being collected, and identify key challenges to the establishment and maintenance of LTEM projects in the country. Studies monitor 77 unique subjects across 272 LTEM efforts in India. LTEM efforts are more often located in the Western Ghats and Eastern Himalaya, focused on forest vegetation, and monitoring factors such as abundance, distribution, species richness, and biomass. Regions such as North-Eastern, Eastern, Central, and North-Western India, ecosystems such as grasslands, deserts and wetlands, organisms such as macrofungi, amphibians, and reptiles (other than turtles) are underrepresented. Short turnover times of funding and permits were most frequently reported as hurdles in sustaining LTEM efforts. Data from LTEM efforts have been largely used to produce academic outputs such as journal articles, but have also found use for on-the-ground conservation efforts. Overall, this synthesis can help draw attention to the need for systematic long-term ecological monitoring, help efficient utilisation of existing long-term ecological data, identify regions, species and ecosystem components that are underrepresented in Indian LTEM efforts, foster collaborations, and serve as a starting point to address challenges in sustaining LTEM efforts in India.

ecology↗

bioRxiv2024

Eltonian niche modelling: applying joint hierarchical niche models to ecological networks

There is currently a dichotomy in the modelling of Grinnellian and Eltonian niches. Despite similar underlying data, Grinnellian niches are modelled with species-distribution models (SDMs), whereas Eltonian niches are modelled with ecological-network analysis, mainly because the sparsity of species-interaction data prevents the application of SDMs to Eltonian-niche modelling. Here, we propose to adapt recently developed joint species distribution models (JSDMs) to data on ecological networks, functional traits, and phylogenies to model species Eltonian niches. JSDMs overcome sparsity and improve predictions for individual species by considering non-independent relationships among co-occurring species; this unique ability makes them particularly suited for sparse datasets such as ecological networks. Our Eltonian JSDMs reveal strong relationships between species Eltonian niches and their functional traits and phylogeny. Moreover, we demonstrate that JSDMs can accurately predict the interactions of species for which no empirical interaction data are available, based solely on their functional traits. This facilitates prediction of new interactions in communities with altered composition, e.g. following climate-change induced local extinctions or species introductions. The high interpretability of Eltonian JSDMs will provide unique insights into mechanisms underlying species interactions and the potential impacts of environmental changes and invasive species on species interactions in ecological communities.

ecology↗

bioRxiv2024

BiMultiNetPlot: An R package for visualizing ecological bipartite multilayer networks

O_LIWith the increasing study of ecological multilayer bipartite networks, the visualization of these networks has become more important. However, tools for visualizing multilayer networks are still lacking. C_LIO_LII present BiMultiNetPlot, an R package designed for visualizing ecological bipartite multilayer networks. C_LIO_LII demonstrate how to use BiMultiNetPlot through a series of examples that represent the most common types of ecological multilayer networks. C_LIO_LIBiMultiNetPlot is an open-source, flexible package within the ggplot2 environment, helping ecologists better understand the multilayer nature of ecological networks. C_LI

ecology↗

bioRxiv2024

Enhanced Prediction of Seafloor Ecological State Using 16S Nanopore Sequencing

Anthropogenic stress on benthic habitats, particularly from aquaculture, calls for accurate and efficient monitoring of the macrofauna ecological state. Recent advancements in Oxford Nanopore Technology (ONT) together with environmental DNA offers cost-effective and rapid, on-site monitoring of such ecosystems. Previous studies have demonstrated that Nanopore sequencing provides sufficient precision for predicting ecological state, despite reported challenges with sequencing accuracy. In this study, we aim to predict the seafloor ecological state with both Illumina and Nanopore 16S rRNA gene sequencing data and using a combination of machine learning and feature selection. We analyzed 88 seafloor samples from aquaculture sites located on a north-south gradient along the Norwegian coast. Both sequencing methods were evaluated in combination with various bioinformatic approaches in the context of predicting the normalized EQR index (nEQR, standard ecological index based on macroinvertebrate counting) as a metric of seafloor ecosystem status. Our results show that the predictive performance of Illumina and Nanopore sequencing platforms are comparable, establishing Nanopore as a feasible alternative to illumina sequencing. By employing a stabilized LASSO regression, the feature set (potential taxa) was efficiently optimized from thousands to 40-60 OTUs. The feature selection reduced prediction errors to less than half of what was obtained through full feature modeling. This feature set demonstrated strong predictive accuracy across both sequencing technologies, with a high correlation between observed and predicted nEQR values. The Pearson correlation coefficient of 0.98 for Illumina and 0.95 (mean prediction error: {+/-}0.04) for Nanopore data (mean prediction error: {+/-}0.06). This study demonstrates that continual improvements in Nanopore sequencing accuracy, in combination with optimized feature selection on a broader set of samples, provides a precise and cost-effective monitoring method for marine benthic environments.

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bioRxiv2024

Ecological networks across interaction types are modular and highly driven by sampling intensity at biogeographical scales

Understanding how the structure of ecological communities varies across biotic and abiotic dimensions is a fundamental goal in ecology. This challenge is now approachable due to the increasing availability of data on community structure across the globe. Ecological communities are often defined with respect to the guilds considered and the interactions they engage in, but it is unclear whether interactions of different types respond similarly to large-scale environmental gradients. Therefore, we lack a deeper understanding of how the emergent structure of interaction networks varies across biogeographical gradients, and how this effect may change depending on their constituent interaction types. Here, using a unique dataset of 952 networks across the globe, we provide a first comparison of network structural metrics and their large-scale variability for five overarching interaction types (feeding, frugivory, herbivory, parasitism and pollination). We show that degree distribution, but not connectance alone, helps us understand the observed network structures, and this pattern is maintained across interaction types (with the partial exception of food webs). Moreover, degree distribution descriptors are generally explained by differences across studies, which represent a proxy for variability in sampling and network construction methods. Environ-mental factors show weaker but robust effects on network degree distribution, and food webs are generally more sensitive to changes in environmental factors than networks of other interaction types. By analysing common descriptors of the degree distributions of ecological networks, this study underscores for the first time generalities and differences across networks of different interaction types and their response to environmental and anthropogenic factors.

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bioRxiv2025

Recovering ecological interactions by mining non-target data from whole genome re-sequencing projects

The study of parasitic species can shed light on aspects of their hosts ecology. Such interactions are however often unknown or understudied due to the difficulty to detect and/or quantify many infections. Whole-genome sequencing and re-sequencing data have been generated at an increasing rate and reduced costs over the last two decades. Projects based on whole organisms, like whole insect specimens, provide genomic material for the target taxon, but may also include sequencing reads from associated microbes and other parasites. Here, we screened for the presence of non-host reads in a collection of whole-genome (re-) sequence projects from the Glanville fritillary butterfly, Melitaea cinxia, a model organism in research on the ecology and evolution of species in spatially structured and fragmented landscapes. We identified infections with different bacteria and eukaryotic parasites, which are shared between populations and underly both previously known and new biotic interactions for this butterfly species. The bacterial symbiont Wolbachia, usually common in insects, was found at relatively low prevalence, while Spiroplasma was ubiquitous across samples from several European populations of the butterfly. Additionally, we confirmed expected rates of larval parasitism by at least two parasitoid wasps. Such results provide proof of principle that key ecological interactions can be uncovered efficiently from existing data, an important first step to characterising the role host-associated organisms play in shaping the ecology and evolutionary history of their host species.

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bioRxiv2025

Finding space for rewilding: participatory scenarios reveal ecological opportunities based on plural values of nature

O_LIRewilding aims to restore degraded ecosystems in human-dominated areas by promoting self-sustained ecosystems and reducing human control over ecological processes. However, co-benefits and trade-offs between rewilding actions and the interests of local societies must be thoroughly considered. IPBES proposed the Nature Futures framework, a scenario tool to engage society in conservation planning. However, its potential for co-designing ecological restoration remains to be explored. C_LIO_LIThis paper presents a novel methodology that utilises the Nature Futures Framework to address opportunities, co-benefits and trade-offs using rewilding. The study aims to investigate the Nature futures Framework effectiveness in rewilding by involving stakeholders in participatory scenario development. To accomplish this, we conducted stakeholder interviews to co-design three distinct scenarios that reflect diverse values of nature and rewilding strategies for the German Oder Delta. C_LIO_LIThe scenario development process identified spatial priorities for three scenarios: Nature for Nature, Nature for Society (NfS) and Nature as Culture. (NaC). The first, focused on rewilding and increasing forest and river connectivity across 65.8% of the study area. NfS narratives covering 14.4% of the area emphasised de-intensification of forestry and reflooding of peatlands, while NaC prioritised transformation towards extensive agriculture and reflooding for paludiculture across 8.6%. C_LIO_LIThe study identified regions with single and multiple values of nature. Further, there were regions of overlap between scenarios, highlighting the interconnectedness of different rewilding actions and priorities. By pinpointing corresponding values, the research highlighted actions that could result in co-benefits. The findings emphasise the importance of considering pluralistic values when designing rewilding actions, which encourages stakeholders to highlight areas of less resistance to rewilding and co-adapt to the changes in the landscape. C_LIO_LIThis study presents a methodological framework for participatory scenario development for rewilding initiatives. By combining participatory processes with the Nature Futures Framework, our structured approach helps identify priority areas and navigate socio-ecological challenges in landscape restoration. It employs participatory mapping to aid the visualization of land-use changes and aligning rewilding actions with ecological and societal needs. This approach enhances governance frameworks and promotes inclusive rewilding efforts, creating a replicable model for landscape restoration that connects rewilding with sustainability and societal well-being goals. C_LI

ecology↗

bioRxiv2025

Awake and hungry: artificial light at night disrupts behaviour and reproductive ecology in a wild migratory bird

Artificial light at night (ALAN), a growing consequence of global urbanisation, disrupts natural light cues that regulate biological rhythms across taxa. However, the behavioural pathways linking ALAN exposure to broader ecological impacts remain poorly understood. Here, we experimentally introduced ALAN into nestboxes of a long-distance migratory bird, the collared flycatcher (Ficedula albicollis), breeding in Gotland, Sweden, to assess ALAN effects on development and reproductive ecology. Nestlings were exposed to ALAN from two days post-hatching until fledging, and we video-recorded parental and nestling activity over 24 hours on day 8 post-hatching. From a high-resolution behavioural dataset (32,100 nestling and 3,709 parental events), we found that ALAN-exposed nestlings begged more frequently and for longer at night compared to dark controls, revealing disrupted nocturnal behaviour. These effects cascaded to parental care: both females and males in ALAN nests extended their daily activity, initiating feeding earlier and terminating later, but traded off this extension by reducing their hourly feeding counts compared to parents in dark conditions. Consequently, nestlings under ALAN fledged at an older age and had a lower fledging rate after day eight post-hatching, despite no difference in total fledging numbers compared to controls. Our findings provide the first comprehensive experimental evidence that ALAN alters the behaviour of both parents alongside their offspring and reduces reproductive success in a long-distance migrant, a species group that increasingly encounters light pollution during nocturnal migration. ALAN, though directly affecting mainly nestlings and females in nestboxes, triggered socially-mediated responses that altered the circadian behaviour of entire bird families. This study underscores the need to consider behavioural disruption as a critical mechanism underlying the ecological impacts of ALAN in natural populations. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/658843v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1808fborg.highwire.dtl.DTLVardef@1f5bfd1org.highwire.dtl.DTLVardef@104607borg.highwire.dtl.DTLVardef@5a3604_HPS_FORMAT_FIGEXP M_FIG Summary figure: Cascading effects of artificial light at night, an aspect of human-induced light pollution, on the behaviour and reproductive ecology of collared flycatchers C_FIG

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bioRxiv2025

Metabolic overflow prevents ecological collapse in microbial populations

Metabolic overflow is defined by the excretion of partially oxidized byproducts, such as acetate, ethanol, and lactate, which could otherwise be used to produce energy. This behavior, widespread across diverse biological systems, is paradoxical because it suggests a seemingly wasteful use of resources that could instead be employed to maximize energy production. In this work, we investigate the ecological implications of metabolic overflow, using bacterial acetate metabolism as a model. We formulate a second-order generalization of the logistic growth equation, incorporating resource availability and acceleration dynamics to provide an explicit mechanistic link between metabolic overflow and ecological collapse. We demonstrate that acetate-mediated growth deceleration reduces the risk of overshoot and collapse, especially under conditions of resource oversupply. By inhibiting excessive proliferation, acetate overflow enhances the survival and stability of bacterial communities in fluctuating environments. In this context, acetate acts as a quorum-sensing signal, coordinating population-wide responses and enabling the active regulation of growth rates before resource exhaustion occurs. Our findings suggest that overflow metabolism is an adaptive strategy evolved to mitigate the risk of ecological collapse and stabilize microbial populations. These insights highlight the dual role of metabolic byproducts as both inhibitors of growth and regulators of population dynamics, with significant implications for microbial ecology and biotechnological applications.

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