Theoretical Ecology

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.

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.

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.

Interactions among co-circulating parasite species influence infection risk and disease progression. Such interactions can occur within hosts, for example altering susceptibility, or indirectly through host demography or movement, potentially affecting landscape-scale transmission. Despite their ubiquity, the spatial implications of these interactions have received limited attention. We combine spatially-explicit modelling with laboratory experiments to investigate how different parasite-parasite interactions influence disease spread. We model within-host, demographic, and dispersal-related interactions across a linear landscape, showing that within-host interactions modifying host susceptibility have the strongest effects on parasite prevalence, spatial heterogeneity, and rate of spread. Furthermore, these effects are amplified when parasites invade sequentially, generating pronounced patch-level spatial priority effects. We tested these predictions experimentally using a protist host (Paramecium caudatum) and two bacterial parasites (Holospora undulata and H. obtusa). Consistent with model predictions, we found that H. obtusa reduces prevalence and spatial spread of H. undulata through reductions in host susceptibility, and found evidence for spatial priority effects, observing reduced H. undulata prevalence when introduced after H. obtusa. Our theoretical and experimental results highlight that parasite-parasite interactions can have important implications for parasite spatial epidemiology, but the magnitude of those effects depend on the interaction type and the timing of invasion.

The Allee effect is a phenomenon where individual fitness is reduced in small populations, for example because of mate-finding difficulties or increased predation. Allee effects matter in conservation biology because they can drive small populations to extinction. The severity of Allee effects can depend on traits such as mate-search rate and defense against predators. Many natural populations exhibit considerable intraspecific trait variation (ITV) in such traits, but most studies so far assume these traits to be constant. Thus the impact of ITV on populations with Allee effect is largely unknown. Here we create two individual-based stochastic models that simulate a small population experiencing either a mate-finding Allee effect or a predator-driven Allee effect. We analyze how ITV, trait inheritance, and mutation affect the proportion of surviving populations. Under the mate-finding Allee effect, higher ITV hindered population survival and increased Allee thresholds. This can be explained by Jensens inequality and the negative curvature of the mate-finding function. Under the predator-driven Allee effect, ITV effects were weak, but higher mutation standard deviations were beneficial, likely because they provided more substrate for selection to act on. We thus recommend to take into account ITV when dealing with threatened populations with an Allee effect.

Cross-scale integration remains a persistent challenge in ecology. Mechanistic network models have advanced this integration by linking individual behavior to community dynamics. Their complexity, however, often limits exploration to numerical simulations, which tend to be insufficient for fully unveiling the fundamental rules governing system behavior. Extracting these rules requires moving beyond numerical observation to establish exact, analytical constraints. Here, a complete mathematical analysis of a mechanistically detailed plant-pollinator model is presented. This cross-scale analysis decouples transient and equilibrium dynamics, proving that pollination strictly gates plant persistence while recruitment competition caps equilibrium abundance. The precise behavioral mechanisms scaling up to determine network stability are determined: nestedness stabilizes communities by generating floral reward gradients that guide adaptive foraging, whereas connectance destabilizes by eroding these rescue pathways. Additionally, native community persistence and biological invasions are conceptually unified; a single, multi-scale reward threshold (R*) is shown to govern both native survival and alien establishment. These analytical derivations are distilled into conceptual frameworks and visual summaries accessible for empiricists interested in theory and conceptual unification. By translating numerical observations into rigorous, trait-grounded proofs, this analysis demonstrates that complex, cross-scale networks are tractable, revealing the precise conditions under which communities assemble, persist, and collapse.

Vector-borne pathogens cause 17% of all human infectious diseases, and rising global temperatures are shifting the distribution and abundance of mosquito vectors. Because mosquitoes are ectotherms, temperature strongly governs biological rates and physiology; however, mosquitoes also experience other environmental factors that may interact with temperature to shape the thermal performance of traits driving population dynamics. Here, we use a factorial life-table experiment spanning five relative humidities (30-90%) and seven temperatures (16-38{whitebullet}C) to show that humidity modifies the thermal performance of key fitness traits in adult Anopheles stephensi, an invasive urban malaria vector. When integrated into a demographic model, humidity markedly reshapes projections of population fitness relative to temperatureonly models, suppressing growth and contracting year-round suitability in hot, arid regions while enhancing fitness in more humid or high-elevation climates characteristic of South Asia and Africa. Together, these results highlight the need to integrate multiple environmental drivers into projections of climatic suitability, as temperature-only approaches may mischaracterize both the magnitude and spatial structure of mosquito population fitness. More broadly, our findings demonstrate how moisture availability reshapes thermal niches, population fitness, and climate-driven projections of vector distributions.

Phenotypic diversity is often thought to arise from the evolutionary modification of developmental processes. However, developmental processes are tightly coupled in space and time, with each process beginning from conditions set by the one before it. While we know from dynamical systems theory that initial conditions can significantly affect a systems out-come, their importance as a source of phenotypic evolvability has been largely overlooked. Here we show for the first time, that phenotypic evolution can proceed through changes in developmental initial conditions while the underlying developmental process remains conserved. Somitogenesis is the process by which vertebral precursors, known as somites, are periodically patterned in the pre-somitic mesoderm (PSM). Somitic count (total number of somites) is thought to diversify through the evolution of components of somitogenesis such as the tempo of the segmentation clock or the mechanisms driving axial morphogenesis. Using two closely related species of Lake Malawi cichlid fishes that differ in vertebral counts, we show that somite count evolution has happened without changes to somitogenesis itself, but instead, by altering the size of the PSM at the onset of this process. This work will expand what we consider developmental drivers of phenotypic evolution and highlight the importance of comparative studies to understand the diversification of phenotypes.

Multiple frameworks have been developed to investigate the evolution of species interactions on fitness landscapes, each with unique strengths and weaknesses. These include adaptive dynamics, which uses linear stability analyses to predict eco-evolutionary outcomes resulting from the invasion of rare mutants into a resident population, and population genetics, which mechanistically models finite populations and stochastic processes in finite time. Though there are some known correspondences between these frameworks, it is not clear that they will always result in the same eco-evolutionary outcomes. Moreover, while adaptive dynamics is very powerful for predicting outcomes, it is not always straightforward to relate these predictions to the data generated in experimental evolution studies. Here, we use a data-driven model of microbial species interactions to compare and contrast the predictions of population genetics and adaptive dynamics. We derive expected outcomes for one-species and two-species evolutionary trajectories by using the invasion fitness landscape concept from adaptive dynamics, and then use analytical theory and forward-in-time simulations to set these predictions within the context of population genetic models. In the context of our one-species models, we show that the timescale of evolution depends on mutation supply and effect sizes, when populations are initialized both along and off a trade-off function. For two-species competition models, we show that mutation supply, effect sizes, and asymmetries between competing species result in discrepancies between adaptive dynamics and population genetics, especially in cases where adaptive dynamics predicts stable coexistence. Our study provides insight into the role of finite timescales, mutation supplies and population sizes in the evolution of species interactions, and facilitates further research that leverages the invasion fitness landscape concept within the realm of population genetics.

Theory suggests that different components of environmental fluctuations, from daily and seasonal cycles to multidecadal trends, can have distinct and even opposing effects on species abundances and community dynamics, depending on their specific adaptations. But empirical research that deconstructs the influence of these different cycles on communities is lacking. Here, we used long-term biological monitoring data together with flow records of rivers across New Zealand to (i) investigate the role of fast, slow, and seasonal river-flow fluctuations in structuring macroinvertebrate communities; and (ii) to assess whether life-history and mobility traits mediate the response. Using joint species distribution models, we found striking differences in taxon and community responses to the different components of river flow variation. Responses to slow fluctuations were generally stronger and better predicted by traits, while responses to seasonal fluctuations were highly heterogeneous. Fast increases in flow, typical of flooding events, had pervasive negative effects on species abundances, but the severity of impact partly depended on mobility traits. Our results suggest that different ecological mechanisms underpin the response to distinct environmental fluctuations, highlighting the value of jointly considering multiple temporal scales of variation and species functional traits to understand and predict how communities reorganise under fluctuating environmental regimes.

Resource availability is a central driver of ecological and evolutionary processes, yet its effects on infectious disease and virulence are not fully understood. A key limitation is that many studies consider only a narrow range of resource conditions or a limited subset of host and pathogen traits, potentially obscuring non-linear relationships. Here, we quantify how a gradient of six food levels simultaneously shapes host fitness and pathogen performance in the Daphnia magna- Ordospora colligata system. Across two laboratory experiments, we measured infection rates, pathogen burden, host fecundity, survival, and filtration rates. Increased food availability enhanced pathogen fitness, with both infection rates and spore burden increasing with provisioning. In contrast, host responses were trait-specific: while fecundity increased with food availability, pathogen-induced reductions in fecundity (i.e., virulence) peaked at intermediate resource levels, despite continued increases in pathogen load. This pattern indicates that resource availability alters host tolerance as well as pathogen growth, generating non-linear disease outcomes. Host survival was unaffected by either food provisioning or infection, further demonstrating that resource availability can simultaneously influence host and pathogen traits in different directions. Our results highlight the importance of integrating multiple fitness components across provisioning levels to understand disease dynamics and suggest that ongoing anthropogenic changes in resource availability may alter host-pathogen interactions.

Fuel availability and fuel dryness are consistently shown to be the primary drivers of wildfire intensity and burnt area. Here we hypothesise that differences in the timing of fuel build up and drying determine the optimal time for wildfire occurrence. We use gross primary production (GPP) as a measure of biomass production and hence fuel availability, and vapour pressure deficit (VPD) as a measure of fuel drying. We use the phase difference in the seasonal time course and magnitude of GPP and VPD to cluster regions that should therefore have distinct wildfire behaviour. We then show that each of the resultant clusters is distinctive in terms of one or more fire properties, specifically number of ignitions, burnt area, size, speed, duration, intensity, and length of the wildfire season. The emergence of distinct regimes as a function of two biophysical drivers reflects the fact that both vegetation and wildfire properties are a consequence of eco-evolutionary adaptions to environmental conditions. We then examine the degree to which human activities or vegetation properties modify these fire regimes within each of these clusters. Variability in GPP and VPD largely explains the within-cluster variation in fire properties. The type of vegetation cover has an influence on burnt area and carbon emissions in particular, while human activities are more important for fire properties such as size, rate of spread and duration largely through their influence of landscape fragmentation. Although both human activities and vegetation properties modify wildfire regimes, the ability to distinguish wildfire regimes using GPP and VPD alone emphasizes that land management, fire use and fire suppression are constrained by environmental conditions. This eco-evolutionary optimality approach to characterising wildfire regimes provides a basis for designing a simple fire model for Earth System modelling.

O_LISpecies diversity potentially has a dual effect on communities: a generally positive effect on overall community biomass, reflecting the expression of species response and interaction traits, and a poorly characterised effect on mass-specific species contribution to ecosystem functions, reflecting the expression of their effect traits. Disentangling the effects of biodiversity on total biomass from those on effect trait expression would help settle a long-standing debate by clarifying how biodiversity relates to both facets of species effects on ecosystem functioning. C_LIO_LIFollowing the classical BEF approach, we calculate expected ecosystem function based on observed functioning in monoculture. We then derive a net biodiversity effect (NBE) and decompose it into four components: the classical complementarity and selection effects on total community biomass, and complementarity and selection effects on effect trait expression. The latter two reflect, respectively, a complementarity or facilitation in how effect traits influence the function, and how species with the highest potential for increasing the function become dominant in the community. C_LIO_LIWe illustrate this NBE decomposition with three ecosystem functions (nitrogen retention capacity, soil hydraulic conductivity improvement, and forage digestibility) measured in assembled communities under controlled experimental conditions of perennial grassland plants. Regarding nitrogen retention, we find a positive complementary effect via total biomass, but a negative biodiversity effect via effect trait expression. For hydraulic conductivity improvement, biodiversity effects are mostly mediated by total biomass. As for forage digestibility, we found a positive complementarity effect on trait expression, outweighed however by a negative selection effect. This analysis reveals how biodiversity may have contrasting effects on ecosystem functions via its impact on biomass and effect trait expression. C_LI SynthesisSeparating between the effect of biodiversity on plant community biomass and on effect trait expression at the community level is one important step towards understanding the pathways by which diverse plant communities drive ecosystem functioning.

Predicting the stability of ecological communities in changing environments is challenging. Classical theory posits that community stability cannot be understood without considering interspecific interactions. A contrasting view is that species environmental responses and their variation (response diversity) influence stability to the extent that effects of interspecific interactions can be ignored. Surprisingly, few studies have evaluated the relative importance of interactions versus species responses. Moreover, trait-based measures of response diversity often show limited predictability. Here, we introduce community performance curves, the aggregate of species performance curves, as a powerful mechanistic link between community composition and stability. This approach reveals that species responses predict most of the variation in community stability in simulated communities, even when the strength of interspecific interactions varies. An experiment with ciliate communities corroborates these findings, while a literature review reveals how rarely both mechanisms are assessed jointly. By moving from summary traits to community performance curves, we reconcile the two perspectives: while species interactions undeniably shape community dynamics, community performance curves are sufficient to predict stability. This provides the opportunity to predict community stability, even when information about the multitude and diversity of interspecific interactions is unavailable.

Evolving populations, especially in the strong-selection-weak-mutation limit, can be modelled as adaptive walks on fitness landscapes, moving in fitness-increasing mutational steps until reaching a fitness peak--a local optimum. Simulations of such adaptive walks--on a multi-peaked empirical landscape of the folA gene and on landscapes generated by the Rough Mount Fuji (RMF) model-- have shown that some landscapes are highly navigable, meaning that the highest x% of peaks are reached by >> x% of adaptive walks. This prompts the question of how adaptive walks can be so successful despite the local, myopic rules behind each adaptive step. Here, we investigate this question using simulations and mathematical approximations of random adaptive walks on a simplified RMF landscape. The landscape has a low-to-intermediate fitness region, whose size reconciles a low peak density with a high peak number. Despite the high number of peaks, walkers are likely to exit this region without terminating at a peak because the probability of a peak transition at each step is low and a fitness gradient guides walkers to the high-fitness region in few steps. Thus, three features are sufficient to explain why adaptive walks in the simplified RMF landscape are likely to reach a small fraction of top-ranking peaks: a low-to-intermediate fitness region with a high number of peaks, a low peak-transition probability, and which is crossed in few steps. We find that these three features are also present in the empirical folA landscape, suggesting that similar principles may apply.

Biotic interactions can drive evolutionary diversification, but the underlying mechanisms differ depending on the type of interaction. For instance, Ehrlich and Ravens escape-and-radiate coevolution provides a pathway of diversification in antagonistic interactions, whereas in mutualistic networks, coevolution is hypothesized to result in trait convergence rather than diversification. The combined effect of mutualism and antagonism on diversification remains unclear, even though organisms naturally engage in multiple types of interactions simultaneously. Using an eco-evolutionary simulation model, we investigate diversification in tripartite ecological networks such as plant-pollinator-herbivore networks. We find that diversification patterns vary according to the way mutualism and antagonism are connected on the trait level. If the two interactions are governed by uncorrelated plant traits, we observe little diversification in the mutualistic and substantial diversification in the antagonistic subnetwork. By contrast, if the same plant trait mediates both mutualism and antagonism (an example of ecological pleiotropy), diversification rates in all guilds become interdependent. In this case, even the mutualistic guild diversifies considerably when antagonism is strong, while strong mutualism restricts diversification also in the antagonistic guild. Our study underlines that the inclusion of multiple interaction types is necessary to advance our understanding of evolutionary dynamics in ecological networks.

O_LISeasonal patterns of species appearances constitute a major component of diversity variation. Theory attributes this phenological structuring of communities to the alignment of life cycles to suitable moments and to constraints of seasonality on development, yet the specific mechanisms operating across taxa remain largely unresolved. In insects, body size and colour are key functional traits that contribute to driving spatial community assembly through their link to thermoregulatory performance and development. C_LIO_LIHere we analyse variation in mean body size and colour lightness of 483 butterfly assemblages across Great Britain and throughout the season to test whether trait alignment with seasonal environment and developmental constraints may shape the phenological structuring of communities. C_LIO_LIBoth body size and body colour varied more along season than across space, emphasizing the importance of phenology on diversity variation. Body size was larger early and late in the season, i.e. under conditions of low temperature and solar radiation. This pattern contrasted with the spatial trends found and was driven by species overwintering as adults, which we interpret as being likely due to energetic constraints. Body colour, conversely, was darker early and late in the season, mirroring the spatial pattern found, and suggesting a thermoregulatory alignment with seasonal conditions. Furthermore, covariation between body size and colour suggests a thermoregulatory interaction between both traits. C_LIO_LIOur findings suggest that life-cycle constraints and seasonal thermoregulatory alignment contribute to shaping the phenological structure of insect communities. C_LI

The hockey-stick (HS) stock recruitment relationship (SRR) has been widely used as an empirical alternative to conventional SRRs such as the Beverton-Holt (BH) and Ricker (RI) models. However, the management performance and risks associated with estimating maximum-sustainable-yield (MSY) reference points (RPs) based on HS remain insufficiently understood. This study first defines deterministic and stochastic MSY RPs under the HS model and provides an overview of their properties. We then conduct simulation experiments to investigate the bias and management consequences that arise when MSY RPs are estimated from the HS model (HS-derived MSY RPs) rather than from the true SRR (e.g., BH) across a range of biological and stochastic parameters, with particular focus on scenarios with insufficient data contrast. Our results show that HS-derived MSY RPs tend to exhibit higher bias but lower variance than MSY RPs derived from the true SRR. Management strategy evaluation simulations further reveal that management procedures combining HS-derived MSY RPs with adaptive model learning and some precautionary measures gradually reduce this bias and achieve average spawning biomass and yield that are comparable to those obtained under management based on the true BH SRR. We also show that the management effectiveness of the precautionary measures depends on life-history traits and recruitment variability. These findings indicate that although HS-derived MSY RPs may be biased and require cautious use, combining them with appropriate precautionary measures allows management to remain robust while limiting variability and yield losses. This broadens the range of management options that are available for supporting sustainable fisheries management.

Asymmetric self-organization is a hallmark of cell polarity, yet the diversity of observed polarization patterns is frequently attributed to specialized, complex biochemical mechanisms motifs beyond simple positive feedback. Here, we demonstrate that spatial heterogeneity alone fundamentally reshapes polarization dynamics within minimal stochastic reaction-diffusion processes. We show that weak differences in reaction rates between distinct spatial domains strongly bias polarization timing and determine which region ultimately polarizes. In systems containing two distant favored regions, a "stochastic winner-takes-all" mechanism--driven by long-range competition mediated by a shared cytoplasmic pool--induces stochastic switching that manifests as pole-to-pole oscillations. By relaxing the assumption of a perfectly mixed cytoplasm and incorporating finite cytoplasmic diffusion, we reveal a qualitative shift in this competitive dynamic. Specifically, as the total particle abundance increases, the system transitions from monopolar to bipolar activation, capturing the essence of the New-End Take-Off (NETO) phenomenon during cell growth and provides a physical basis for pole coexistence. These results demonstrate that spatial heterogeneity alone can strongly influence polarization dynamics in minimal models, highlighting the potential importance of quenched spatial variability in biological reaction-diffusion systems. Author summaryCells often need to choose a specific site for growth, division, or shape change. This process, known as cell polarization, is a fundamental organizing principle in biology. The wide variety of polarization patterns seen in living cells is often explained by proposing complex biochemical mechanisms beyond basic positive feedback among signaling molecules. In this work, we asked whether some of this diversity could instead arise from a simpler source: fixed spatial differences within the cell. Using minimal stochastic reaction-diffusion models, we found that even small local differences can strongly influence where polarization appears and how quickly it develops. When two favored sites are present, they can compete for a shared pool of molecules in cytoplasm, so that one site dominates at a time and the polarized state can switch stochastically between them. We also found that this competition changes when the shared molecular pool does not mix instantly: under these conditions, two polarized sites can start to coexist. This behavior offers a simple physical explanation for phenomena such as the appearance of a new growth site during cell development. Our results show that spatial heterogeneity alone can generate behaviors that might otherwise seem to require much more complicated biochemical mechanisms.

Neuronal activity alters extracellular ion concentrations and electric potentials. Ephaptic effects refer to the feedback influence that these extracellular changes can have on neuronal activity. While electric ephaptic effects occur on a fast timescale due to extracellular potential perturbations, ionic ephaptic effects are driven by slower, accumulative changes in ion concentrations. Among the previous computational studies of ephaptic effects, the vast majority have focused exclusively on electric effects, while ionic ephaptic effects have largely been neglected. In this work, we present an electrodiffusive computational framework consisting of two-compartment neurons that interact via a shared extracellular space. By accounting for both electric potentials and ion-concentration dynamics in a self-consistent manner, our framework enables us to explore the relative roles of electric and ionic ephaptic effects. Through numerical experiments, we demonstrate that ionic and electric ephaptic interactions play very different roles. While ionic ephaptic interactions increase population firing rates, electric ephaptic interactions primarily drive subtle shifts in spike timing. Furthermore, we show that these spike shifts cause the phase difference (the distance in spike times between a small collection of neurons) to converge to a stable, unique phase difference, which we coin the ephaptic intrinsic phase preference. Author summaryNeurons predominantly communicate through synapses: specialized contact points where a brief electrical signal, known as a spike or action potential, in one neuron influences another. Neurons generate these spikes by exchanging ions with the surrounding extracellular space. This way, spiking neurons alter extracellular ion concentrations and electric potentials. Since neurons are sensitive to such changes in their environment, they can also influence one another indirectly through the shared extracellular medium. This form of non-synaptic interaction is known as ephaptic coupling. Most computational models of neuronal activity neglect ephaptic interactions, and those that include them typically consider only electric effects while ignoring ionic contributions. As a result, the relative roles of electric and ionic ephaptic effects remain poorly understood. Here, we introduce a computational framework that accounts for both mechanisms in a self-consistent way. Our results show a functional distinction: ionic ephaptic effects act slowly, regulating population firing rates, whereas electric ephaptic effects act on millisecond timescales and subtly shift spike timing. These shifts cause spike-time differences between neurons to converge to a stable value, a phenomenon we call ephaptic intrinsic phase preference.