Theoretical Ecology

Vertical transmission of an infectious disease from parent to offspring is a common transmission route in many systems. Here we investigate the dynamics of a pathogen with both horizontal and vertical transmission within a spatially structured population. We introduce a lattice model with a pair approximation that includes both local and global transmission and reproduction. We find that vertical transmission can determine pathogen invasion and reduce the horizontal transmission rate required for invasion. When the majority of transmission and reproduction is local, vertical transmission can destabilise a host population to cause limit cycles. Given the advantages of a pathogen having both horizontal and vertical transmission routes, we extend the model to investigate the likelihood a mutant strain with both transmission modes will outcompete a resident strain with only horizontal transmission. When there is no trade-off the mutant always invades and when there is a trade-off with horizontal transmission, the mutant emerges when the cost to the horizontal transmission rate is not too large. Depending on how the mutant appears within the host population, it may have an initial advantage over the resident strain even if it cannot outcompete in the long-term. Our work demonstrates the potential importance of vertical transmission within host-pathogen dynamics.

Given the accumulation of evidence that evolution can affect ecological dynamics, especially under global change scenarios, a key question is how such ecoevolutionary dynamics may change the coexistence of species and biodiversity in general. In the present article, we propose a graphical approach allowing to simultaneously discuss ecological coexistence and phenotype evolution. Our graphical approach allows tackling the two aspects in the same parameter space, allowing direct links between ecological and evolutionary perspectives. While evolution is often thought positive for the resilience of ecological systems, we first highlight it does not usually allow for better coexistence for the system as a whole. Even when focusing on the fate of the species that is evolving, evolution often leads to greater vulnerability. The graphical approach we propose is flexible and can be applied to all interaction types and covers variations in trade-off structures. Using this flexibility, we highlight how evolutionary effects can be positive or negative for coexistence, depending on these two components. Finally, we illustrate how the approach can be applied, using empirical examples derived from the literature. We thereby highlight the critical ingredients needed to inform the graphical approach, its potential use for proposing testable scenarios, but also clarify its limits.

Phase synchronized population dynamics of various species constituting a complex ecosystem elevates the risk of their extinction due to both environmental stochasticity and simulateneous low density fluctuations. Therefore, an extremely vital approach to measure the extinction risk of an ecosystem as a whole is to quantify the phase synchrony among the species populations co-habiting and interacting with each other in an ecosystem. Generally, in models describing population dynamics of ecosystems, both trophic and non-trophic inter-species interactions are modelled as interactions between two species. This approach contradicts the fact with such a large number of species living in close proximity, more than two species must partake in the same interaction influencing the population dynamics of each other. To address this, higher-order interactions need to be incorporated in the models describing population dynamics of an ecosystem. Consequently, their effect on phase synchronization of populations also need to be investigated. In this study, we model a species-rich ecosystem as a complex phase oscillator network and examine the phase dynamics of the total population. Each node of this network represents a constituent species, modelled as a Sakugachi-Kuramoto phase oscillator coupled non-linearly to the other nodes through both first-order and higher-order inter-species interactions. These interactions can be both mutualistic (positive) and antagonistic (negative) in nature. Along with the higher-order interactions, we also incorporate inherent asymmetry among the nodes to account for habitat heterogeneity. Further, we investigate the effects of both higher-order coupling and asymmetry on the phase synchronization of the total population. Our findings demonstrate that higher-order interactions above a threshold amplitude enforces a transition from synchronous to asynchronous dynamics of the ecosystem. Further, we find that increase in the size and diversity of the ecosystem leads to an increase in the threshold value of higher order coupling required to reach asynchronous dynamics. We also demonstrate that this higher-order induced asynchrony is further promoted by high asymmetry among the individual nodes. Importantly, negative inter-species interactions, if existing to a high degree also induce asynchrony in the system. Moreover, the size of the network also plays a role in deciding the threshold value of higher order coupling required to induce asynchrony.

Microbes constantly interact with their environment by depleting and transforming food sources. Theoretical studies have mainly focused on Lotka-Volterra models, which do not account for food source dynamics. In contrast, consumer-resource models, which consider food source dynamics, are less explored. In particular, it is still unclear what physical mechanisms control oscillatory dynamics at a single population level, a phenomenon which can only be captured by a consumer-resource model. Here, we present a minimalistic consumer-resource model of a single microbial population with growth and death dynamics, consuming a continuously replenishing substrate. Our model reveals that decaying oscillations can occur around steady state if and only if the timescale of microbial adaptation to food supply changes exceeds the death timescale. This interplay of timescales allows us to rationalize the emergence of oscillatory dynamics when adding various biophysical ingredients to the model. We find that microbial necromass recycling or complementary use of multiple food sources reduces the parameter range for oscillations and increases the decay rate of oscillations. Requiring multiple simultaneous food sources has the opposite effect. Essentially, facilitating growth reduces the likelihood of oscillations around a fixed point. We further demonstrate that such damped oscillatory behavior is correlated with persistent oscillatory behavior in a noisy environment. We hope our work will motivate further investigations of consumer-resource models to improve descriptions of environments where food source distributions vary in space and time.

Successful seagrass restoration will provide habitat for a variety of species. Here, ecological community assembly in a newly planted seagrass meadow has been modelled mathematically using a combination of numerical integration and a permanence-based method, and using real data to parametrise the models. We have studied the transient dynamics of the system: how the ecological communities assemble and change over a 100-year period. Using a trophic structure and a range of species pool sizes, we investigated how much variability there was in community size for a given sized species pool, whether it is possible to use early monitoring to predict the final community size, and to what extent monitoring gives an indication of final vs transient species. For the majority of cases modelled, the community either reached or was headed towards an endpoint community which was uniquely determined by the species pool. However, for 1.4% of cases, no unique endpoint community could be calculated. The simulated communities began to assemble within the first ten years, but 13% had still not reached their endpoint community even after 100 years. In 62% of our models, no consumer species colonised in the first two years, suggesting that monitoring should certainly be continued beyond a two-year period. We counted how many of the species that were present at any observation point in the 100 years would also be present in the endpoint community, and found that this proportion generally decreased with increasing species pool size, to an average of 86% when the species pool had 49-56 consumer species. By monitoring the community over the first ten years, it is not possible to deduce what the final community will be; however a very small number of fauna species present over the first ten years might be used to predict very small endpoint communities.

Emerging infectious diseases are a persistent public health threat that challenge deterministic, mechanistic modeling approaches. Because outbreaks initially start with a low number of infected hosts, their dynamics are highly stochastic, making traditional deterministic methods, e.g. ordinary differential equations, unable to qualitatively or quantitatively capture the transmission dynamics. In place, stochastic models are used, such as Markov chain models, but these models present their own challenges. Often, to infer a quantity of interest with stochastic models, we need to sample the models distribution many times over, introducing an additional source of noise to our analysis. This additional noise makes the inference of our quantity of interest more difficult and computationally expensive. Here, we show how novel tools from the field of uncertainty quantification can be used to efficiently separate these two channels of noise, allowing us to make rigorous statistical inferences about processes important for disease emergence. We illustrate these techniques with a model of yellow fever virus spillover in the Americas, a virus that has seen rapid re-emergence amongst multiple hosts and vectors in South America over the last decade. We show that only a handful of parameters uncertainties greatly affect variation in cumulative disease incidence. In particular, uncertainty in patch connectivity and non-human primate latency has the greatest impact on the variation of cumulative disease incidence of yellow fever virus across patches. Author ContributionsConceptualization: Nate Kornetzke, Helen J. Wearing. Methodology: Nate Kornetzke, Helen J. Wearing. Formal Analysis: Nate Kornetzke. Software: Nate Kornetzke. Visualization: Nate Kornetzke. Writing, Original Draft Preparation: Nate Kornetzke. Writing, Review and Editing: Helen J. Wearing. Supervision: Helen J. Wearing. Author SummaryEmerging infectious diseases are a growing threat to public health. Computational models allow researchers to forecast future disease burdens and to investigate counterfactual scenarios, and often, these models are stochastic, i.e. contain randomness, to capture the qualitative behavior of emergence. A type of statistical analysis known as global sensitivity analysis allows modelers to rigorously analyze how varying the input to a model affects variation in its outputs. This type of analysis helps us infer what mechanisms are important for disease mitigation and control, especially when an infectious disease has a complex ecology consisting of multiple hosts and vectors. Until recently, stochastic models of emerging infectious diseases often proved too computationally expensive to perform a global sensitivity analysis on. Here, we demonstrate how new mathematical tools allow us to streamline this process for complex models of emerging infectious diseases. We analyze the ecology of re-emerging yellow fever in South America, a public health threat occurring in many hosts and vectors across vastly different ecosystems.

In microbial populations, fitness, which is essential to understand and predict evolution, is often defined and measured as the net growth rate of a population in isolation. Applying the same definition to viruses is challenging, both because viral replication involves a host infection process, which is determined by several parameters that are context-dependent, and because viruses compete heavily for resources (susceptible cells). These challenges are particularly exacerbated in spatial range expansions, where multiplicity of infection is often high and resource availability varies in time and space. To assess different fitness definitions and their generalizability, we investigate a model of coupled partial differential equations for phage plaque expansion in one and two dimensions. We find that two commonly used metrics for phage fitness in plaque expansions, i.e., steady state phage densities and front expansion speed in isolation, are unable to reliably predict the winner in one- and two-dimensional direct competitions. More generally, we find that optimal phage traits depend on the dimensionality of the system and the make-up of the phage population, leading to unexpected behaviours, e.g., rock-paper-scissor dynamics and, in high dimensions, enhanced phage density due to the nearby presence of a competitor. We show that the phenomenon stems from the interplay between resource consumption and replication and thus may apply more broadly to any population competing for shared resources.

How and why do species interactions produce unstable dynamics? In the simplest models, the answers are straightforward. In the Rosenzweig-MacArthur predator-prey model, resource self-facilitation due to predation mortality triggers oscillations; in Lotka-Volterra competition, positive feedback from stronger interspecific competition underlies alternative states. However, when unstable dynamics arise with three or more species, how and why answers become more opaque. We propose that dissection of feedback loops, chains of direct species interactions, can answer these questions in meso-scale models. To demonstrate, we disentangle instabilities in epidemics using three variations of a general yet mechanistic resource-host-parasite model. Resources introduce destabilizing self-facilitation but also positive interspecific direct effects on propagule production and transmission rate. Those direct effects then produce instabilities through feedback loops. First, we trace how resource self-facilitation catalyzes oscillations by weakening faster, shorter, lower levels of feedback relative to longer, slower feedback of the whole system. Then, we show how resource-dependent propagule yield introduces positive cascade fueling feedback, creating an Allee threshold inhibiting invasion of parasites. In a third variant, we traced how both resource-dependent components produced those unstable dynamics and more complex behaviors, including a period-doubling route to chaos to which we apply a form of loop tracing. Hence, we show how and why direct, positive effects of resources modulate feedbacks underlying oscillations, Allee effects, and more during epidemics. We propose that loop tracing, a generally applicable method, could empower ecologists to glean much deeper insight into dynamics of species interactions.

A central goal of ecology is understanding how the architecture of food webs, which represent the structural backbone of ecosystems, affects their stability. The analysis of stability in the classical sense of population dynamics (i.e. return to equilibrium) can be successful for a single instance of an empirical food web but ignores the multiplicity of alternative states in which the system could be found as a result of intrinsic variability and fluctuations. Here we propose and test a new methodology to reconstruct, from single empirical observations of a food web, the viable ensemble of alternative realizations respecting the observed resource-consumer linkages and empirical ener-getics. The reconstruction can be handled analytically within a maximum-entropy framework which predicts how empirical food webs access a multitude of alternative states with comparable stability and reactivity. The (measurable) entropy of the reconstructed ensemble directly quantifies this multiplicity and serves as a novel proxy of system resilience, that is the rate of return to equilibrium in response to an external perturbation. We show that the associated ensemble fluctuations provide explicit predictions for the expected response of food webs to external perturbations, such as anthropogenic or climate-induced stresses. We do that by validating the proposed fluctuation-response relation on empirical soil food webs subjected to experimentally controlled perturbations, confirming that intrinsic fluctuations in the unperturbed state predict responses to subsequent stresses. The perturbed states are associated with higher entropy, indicating less likely spontaneous recovery.

Habitat complexity (HC) in part determines the diversity, stability, and behavior of food webs and can influence predation according to a wide variety of functional relationships. Many aquatic species provide habitat complexity and are also consumed by other species (e.g., macrophytes, corals, mussels). However, food web theory does not readily account for these species that act as edible habitat complexity (EHC). Here, we combine existing theory on predator-prey interactions, HC, and prey switching to describe the role of EHC in benthic food web models. We dissect feedback loops in each model to demonstrate how self-regulation of the prey species, mediated by species densities and HC, drives that food webs behavior. HC can stabilize predator-prey interactions by coupling prey self-regulation with HC self-regulation. EHC can further stabilize predator-prey interactions across a wide variety of "HC functions" that relate HC to predation rates. Significance StatementHabitat complexity (HC) plays a critical role in trophic interactions, population dynamics, and food web stability. However, little theory exists to describe edible habitat complexity (EHC), where a species is both consumed and confers habitat complexity for other species. We provide a series of models demonstrating how HC and EHC alter the population dynamics and stability of simple aquatic food webs. HC is strongly stabilizing in food webs by providing safety in rarity for prey. EHC provides safety in rarity for both prey and the EHC species because their predators are omnivorous. Given the prevalence of EHC species in aquatic systems (e.g., macrophytes, corals, mussels), our models demonstrate the importance of maintaining EHC species in aquatic systems for stable food webs.

Haptotaxis is an understudied form of directed cell migration in which movements are biased by gradients of immobilized ligands. For example, fibroblasts and other mesenchymal cells sense and respond to gradients of extracellular matrix (ECM) composition, which is relevant during tissue morphogenesis and repair. As a step towards understanding how haptotactic gradients spatially bias cell adhesion, intracellular signal transduction, and cytoskeletal dynamics, we formulated a phase field model of whole-cell migration, in which the occupancy of potential adhesion sites changes stochastically with time. With careful assignment of parameter values, the model predicts significant haptotactic bias for adhesion-site gradient steepness of a few percent across the cell. We then used the model to predict how the cells removal of surface-bound ECM ligand (as observed in experiment) and/or the presence of a competing, chemotactic gradient influence(s) haptotactic fidelity. An emergent principle is that gains in directional persistence naturally offset losses of directional bias, at the cost of greater cell-to-cell heterogeneity of the response. In the case of orthogonally oriented gradients, this offset manifests as a remarkable robustness of the multi-cue response.

Spatial heterogeneity of abiotic resources is essential for species coexistence. Ecological theory often assumes predefined heterogeneity of resources that constrains community dynamics, but the recent developments of meta-ecosystem ecology and zoogeochemistry highlight nutrient patterns could result from the interactions between the activities and movements of organisms and their abiotic environment. Here we investigate the mechanisms by which biotic-abiotic feedbacks could generate nutrient spatial heterogeneity in a simple plant-herbivore occupancy model where populations forage, recycle, and disperse in a homogenous landscape. By systematically varying organisms ranges of foraging and dispersal, and recycling levels, we found that limited dispersal of plants plays a key role on the emergence of nutrient patchiness by favoring small clusters of vegetation that shape their environment through consumption and recycling. However, herbivores could also create nutrient spatial heterogeneity when large foraging and dispersal ranges, and high recycling, allow them to efficiently track plant hot spots and to increase population persistence. Unexpectedly, strong aggregation of herbivore populations did not necessarily result in nutrient clustering. Rather than via recycling, herbivores mainly affected nutrient distribution indirectly, through their top-down impact on plant distribution. When evenly spread in the landscape, herbivore populations with large foraging ranges created areas of strong herbivory pressure unfavorable to plant colonization where nutrient can accumulate. These results can help understand the dynamical feedback between biota and abiotic resources. In a context where human activities alter both nutrient distribution and species abundances, a better understanding of this biotic-abiotic feedback will be key to anticipate the response of ecosystems to current perturbations.

O_LIStage-dependent interactions, in which different life cycle stages (e.g., juveniles, adults) exert different per-capita competitive effects, are widespread across ecological communities. However, whether explicitly accounting for such ontogenetic variation improves forecasts of stochastic community dynamics remains unclear. We tested how the strength of stage dependence and species life-history strategy influence the predictive accuracy of community models that either include or ignore stage-specific interactions. C_LIO_LIWe constructed stochastic two-species competition models using stage-structured matrix population models spanning five virtual life histories along the fast-slow continuum. Density dependence was imposed separately on juvenile survival, adult survival, progression, retrogression, or fertility, and the strength of stage dependence varied from adult-driven to juvenile-driven competition. We then fitted deterministic projection models with and without stage-dependent interaction terms to simulated time series and quantified predictive performance over 100 time-step forecasts using mean absolute percentage error (MAPE). C_LIO_LIIncreasing stage dependence consistently reduced the predictive accuracy of models that ignored stage structure. However, absolute prediction errors remained small across all scenarios (MAPE < 0.7%), even under strong stage dependence. The influence of life-history strategy depended on which vital rate was density dependent: when juvenile survival was density dependent, faster life histories showed larger errors; when progression, retrogression, or fertility were density dependent, slower life histories exhibited greater errors; and when adult survival was density dependent, no consistent life-history effect emerged. Across simulations, temporal variation in population structure was low (coefficient of variation < 0.036), and prediction error was strongly associated with the magnitude of structural fluctuations rather than life-history pace per se. C_LIO_LISynthesis. Stage-dependent interactions can, in principle, alter stochastic competitive dynamics, but their practical importance for ecological forecasting depends on the extent to which population stage structure fluctuates through time. When environmental stochasticity dominates and stage structure remains near equilibrium, simpler models that ignore stage dependence provide robust approximations of community dynamics. Our results identify conditions under which demographic detail is necessary for forecasting and highlight the central role of structural variability in linking life-history strategy to community-level dynamics. C_LI

Fishing deeply alters marine food webs structure and can drive the evolution of species traits, whether the species are directly targeted or not. Yet, studies rarely account for fisheries-induced evolution, and consequences are generally interpreted at the single-species level. Theory however predicts that eco-evolutionary dynamics within food webs can either promote biodiversity maintenance or accelerate its decline. In this study, we investigate how evolution affects the robustness of trophic networks under fishing pressure. Modifying evolution speed and the allocation of fishing effort across 458 structurally distinct allometric networks enables us to show that evolution most often enhances robustness. Network evolutionary response however becomes more variable (and possibly negative) as evolutionary rates increase and when fishing preferentially targets predators. By contrast, fishing strategies that concentrate effort on lower trophic levels, or distribute it more evenly, promote network persistence through evolutionary rescue while substantially reducing the risk of evolutionary collapse. Moreover, our results appear to be sensitive to the main forces governing ecological dynamics within the network such as competition or predation intensity. Finally, the consequences of network evolution differ across trophic levels. Evolution often drives the collapse of higher trophic levels while simultaneously promoting evolutionary rescue and enhancing diversity at lower levels through increased diversification, thereby generating a trade-off between vertical diversity (number of trophic levels) and total diversity. This highlights the importance of accounting for evolutionary dynamics and food web functioning in fisheries management, and suggests that reducing predator mortality may help prevent network evolutionary collapse.

Parasites play an outsized role in mediating the persistence and stability of host populations. Flour beetles (Tribolium spp.) have long served as classic examples of population dynamics under both disease-free and infected conditions, with elegant combinations of theory and experiments demonstrating, for example, that cannibalism rates can push populations from stability to chaos. As with most organisms in nature, however, flour beetles rarely face just one parasite species, and co-infecting parasites can antagonize or facilitate each other through resources and immunity. To test the prediction that non-neutral interactions would qualitatively alter population stability, we first raised flour beetles (Tribolium castaneum) in infection-free, single-infection, or coinfection microcosms and quantified relative prevalence and parasite intensity. Next, we reworked a classic stage-structured discrete-time model to include single and multiple infections and performed sensitivity and bifurcation analyses to identify the most important (co)infection-associated parameters for population stability. The model predicts that stability is highly sensitive to parasite transmission mode regardless of infection multiplicity, but facilitation among parasites rapidly drives populations into oscillations and chaos under realistic conditions. This study identifies an important mechanism for explaining population variability and highlights the importance of within-host mechanisms for driving dynamics at higher levels of biological organization.

We provide a general theoretical explanation for a longstanding result in spatial ecology: weak dispersal among habitat patches promotes local biodiversity. Using analytical approximations of spatial Lotka-Volterra competition models, we show that species persistence in heterogeneous landscapes can be expressed as a function of regional abundance and local invasion growth rates. We further demonstrate that local multispecies coexistence is governed by the feasibility domain, linking spatial coexistence to a structural property of nonspatial competitive systems. Together, these results explain why weak dispersal increases local species richness and why this effect strengthens with landscape size. We test these predictions using numerical simulations and find that the theory breaks down only when both dispersal and competitive interactions are very strong, in which case dispersal has a unimodal effect on coexistence. In contrast, landscape size retains a positive effect on coexistence whenever an effect is detectable. We then apply the theory to long-term data from a natural Daphnia metacommunity. We detect strong preemptive competition among species and find no detectable effect of dispersal rate on local coexistence, whereas species co-occurrence increases with local landscape size, as predicted by theory. Together, our results identify how dispersal, interaction strength, and landscape size jointly regulate biodiversity in competitive systems.

Complex systems with nonreciprocal interactions are often stratified into layers. Ecosystems are a prime example, where species at one trophic level grow by consuming those at another. Yet the dynamical consequences of such stratified nonreciprocity--where the correlation between growth and consumption differs across trophic levels--remain unexplored. Here, using an ecological model with three trophic levels, we reveal an emergent asymmetry: nonreciprocal interactions between consumers and predators (top and middle level) destabilize ecosystems far more readily than non-reciprocity between consumers and resources (middle and bottom level). We analytically derive the phase diagram for the model and show that its stability boundary is controlled by energy flow across trophic levels. Because energy flows upward--from resources to predators--diversity is progressively lower at higher trophic levels, which we show explains the asymmetry. Lowering energy flow efficiency flips the asymmetry toward resources and remarkably expands the stable region of the phase diagram, suggesting that the famous "10% energy transfer" seen in natural ecosystems might promote stability. More broadly, our findings show that the location of nonreciprocity within a complex network, not merely its magnitude, determines stability.

Fisheries-induced evolution (FIE) affects multiple life-history traits, most notably body size. This evolutionary response is often examined at the single-species level and attributed to direct size-selective harvesting. However, fishing also targets other community members, reshaping trophic interactions and thereby modifying evolutionary constraints due to community changes. Here, we disentangle two forms of fishing-induced selection on body size - direct, arising from size-selective harvesting within species individuals, and indirect, emerging from fishing-induced changes in community structure - and investigate how their interplay shapes evolutionary trajectories. Using an adaptive dynamics framework within a predator-prey model, we show that (i) community destructuring can either amplify or dampen the effects of intraspecific size-selectivity on body-size evolution, (ii) predator evolution is primarily driven by direct selection, whereas prey evolution is mostly constrained indirectly by community structure. We then extend our analysis using a stochastic framework and show that (iii) prey evolution allows the evolutionary rescue of predators, underscoring the importance of community context. Our results demonstrate that eco-evolutionary feedbacks can profoundly alter both community structure and fishery yields, strengthening calls to incorporate evolution into ecosystem-based fisheries management.

A fundamental paradox in ecology is the relationship between species diversity and ecosystem stability: Mays stability condition predicts that species diversity destabilises communities, yet many diverse ecosystems in nature are stable. Here, we show that this paradox can be resolved by explicitly considering resources, which May neglects. Specifically, Mays framework and the competitive exclusion principle jointly predict that resource diversity, which promotes species diversity, should destabilise communities. However, from computer simulations and analytical calculations using the finite-size cavity method, we find the opposite: resource diversity consistently generates stable, species-rich communities. Importantly, this stabilising effect disappears when resource dynamics is neglected (set to steady state). We also show that, contrary to the prevailing belief that interaction heterogeneity is always destabilising, different biological sources of heterogeneity have opposing effects on stability. Our work provides a solution to Mays paradox and demonstrates that resource dynamics are not just negligible background but are central drivers of ecosystem stability.

Many eukaryotic cells produce attractant molecules to which they themselves are also attracted. For example, neutrophils produce leukotriene B4 while swarming. These autoattractants create a secondary signalling layer that can coordinate collective cell behaviour during chemotaxis. Here we use a hybrid agent-based computational model to examine how immune cells migrating along a self-generated gradient may communicate with each other using autoattractants. We find that autoattractant signals strongly enhance cells responses to primary attractant. Efficient removal of autoattractants is also crucial, through depletion by cells, chemical instability, or enzymatic breakdown. Consequently, autoattractants have a lifetime, determined by a balance between production and removal rates. We find that optimal lifetimes exist, and that these are determined by cell speed and attractant diffusion, but are remarkably independent of cell density and primary attractant concentration. We further show that autoattractants whose removal is governed by inherent instability rather than breakdown by cells coordinate migration less efficiently, but work more robustly across different environments. Finally, we find that autoattractant signalling without direct breakdown by the cells involved establishes a characteristic optimal cell-cell distance: too little communication leaves cells uncoordinated, while excessive communication causes cells to aggregate into slow-moving clumps. Strikingly, the conditions that produce optimal chemotaxis lie very close to those that trigger aggregation, suggesting that many autoattractant systems operate near a critical boundary.