Theoretical Ecology

Environmental variability can strongly alter coexistence among competing species and their extinction risk, particularly when population dynamics are shaped by behavioral interactions, such as fear. In this work, we develop a novel stochastic differential equation competition model that incorporates both non-consumptive fear effects and environmental variability to investigate how behavioral interactions influence species coexistence under random fluctuations. Our result reveals that environmental stochasticity can drive species to extinction even when the corresponding deterministic system admits coexistence. In particular, under an explicit stability condition on the fear and competition parameters and sufficiently strong averaged noise intensities, we prove that both competing species become extinct exponentially almost surely. Conversely, we derive a stochastic persistence criterion in terms of fear, competition, and noise-induced suppression parameters for the fearful species. We further demonstrate that environmental noise may reverse classical competition-exclusion outcomes, leading to qualitatively different long-term dynamics from those predicted deterministically. These results provide rigorous thresholds separating stochastic extinction from persistence and highlight the critical role of environmental variability in fear-mediated competitive ecosystems. From an applied perspective, these results provide insight into how behavioral interactions and environmental variability influence species survival, with potential applications in ecological management and conservation.

Under complex perturbation regimes, biodiversity dynamics show temporal variability in species and community abundance around long-term population trends. Many species indeed show long-term declines while other species increase, putting natural communities far from stationary regimes, while variability is often studied near equilibrium. We contribute to bridging this gap by investigating population and community variability during long-term trends caused by press perturbations in stochastic models of population dynamics. By estimating the deterministic changes in mean and variance during the transient regime, we show that population variability deviates from stationary expectations. Moreover, the deviation strongly depends on the sign of the population trends: increases generate excesses of variability while declines generate deficits. Scaling up to community variability, we propose a decomposition of community variability deviation, allowing to highlight that community variability in the transient regime depends on how the press perturbation is distributed within species relative abundances and growth rates. These results challenge the equilibrium assumption and open new perspectives for the study of the variability of ecological systems under multiple perturbation types.

Microbes exhibit complex dynamic behavior as the result of a large number of biochemical processes, spatial and temporal interactions, environmental variations, and evolutionary pressure. Although significant progress has been achieved in understanding microbial ecological dynamics, multiple open questions remain, including the microscopic mechanisms of growth and the roles of nutrients and stochasticity. In this work, we present a minimal theoretical approach to clarify the link between consumption of resources by microbes and their growth. A stochastic model that accounts for a single microbial species consuming a single type of resource while growing via cell division is studied analytically and via Monte Carlo computer simulations. We identify three distinct dynamical regimes of microbial growth determined by the relative magnitudes of resource uptake and division rates and initial conditions. We also show that stochasticity influences the dynamic behavior when the amounts of microbes or resources are low. The model recovers Monod growth kinetics and provides a mechanistic interpretation of the Monod constant and maximal growth rate. The theoretical framework presented captures a wide spectrum of dynamic behaviors in microbial systems, providing a clearer microscopic picture to explain their underlying complex mechanisms.

The impacts of a changing abiotic environment on fitness and performance arise not only from low tolerance to new environmental conditions, but also from changes in the abundance and interaction intensity with other species. The strength of the interaction may itself depend on how well each species performs across environments, but there is a dearth of studies investigating how intrinsic fitness and interaction intensity covary across an abiotic environmental gradient. We addressed this question in a hypersaline consumer-resource system: the microalga Dunaliella spp. grazed by the brine shrimp Artemia franciscana. We exposed four Dunaliella strains to a range of salinities above seawater, with or without brine shrimps, and tracked their population sizes over time and the survival of their predators, to estimate basic parameters of a Lotka-Volterra model. We found that the intrinsic growth rate of algae, the survival rate of predators, and the per-capita predation rate, all varied with salinity and algal strain. Significant interactions between strain and salinity further revealed that these ecological responses to salinity are evolvable. Together with correlations between demographic parameters across salinity, this suggests that predation may influence the evolution of salinity tolerance curves, blurring the line between the fundamental and realized niches.

Agroecosystems are dynamic ecosystems, constituted by patches of vegetation and agricultural use, where biodiversity is shaped by spatial and temporal variability. While most studies have focused on spatial composition and configuration, the role of temporal variability remains poorly understood. Yet, temporal dynamics can strongly modify species composition, abundance, and persistence in ecological communities. Temporal variability is particularly relevant in agroecosystems with rainfed agriculture where environmental conditions shift dramatically between rainy and dry seasons. In this paper, we assess the role of temporal variability on biodiversity maintenance in an agricultural matrix using a metacommunity model that simulates an agricultural landscape under rainfed conditions, that is, with abrupt seasonal changes in the agricultural patches. This model couples a local community network dynamic with a migration dynamic and is based on empirically documented features of rainfed agricultural matrices. Our results show that temporal variability provides new opportunities for species to recover from low densities. However, the effect of temporal variability is not straightforward. It depends on the initial and final conditions, the migration and mortality rates and the intensity of temporal variability. Overall, our findings highlight the need to further investigate temporal variability to better understand its role in shaping biodiversity in agricultural landscapes.

Cell shape and mechanics are intricately connected and tightly regulated by mechanochemical events including biochemical signaling, cytoskeletal remodeling, and plasma membrane mechanics. While experimental advances in microscopy have shed light on the intricate coordination involved in cell shape change in response to different cues, the ability to conduct three-dimensional simulations in realistic geometries remains an open computational challenge. In this work, we develop a finite-element framework that incorporates advection-diffusion-reaction equations coupled with equations governing the kinematics of a deformable interface representing the cell membrane. We applied this framework to three distinct coupled mechanochemical systems, each governed by geometric partial differential equations, resulting in large deformations of the interface. In all three examples, our simulations revealed the emergence of feedback between cellular signaling, cytoskeletal organization, and cell shape. In our first two sets of simulations, we observed that cell migration and neutrophil protrusion were regulated by membrane tension-mediated feedback. In our final application, we predicted shape changes of a dendritic spine starting from a realistic geometry, and found that the complex shape of the spine gives rise to localized regimes of actin cytoskeleton remodeling not previously observed with idealized geometries. Thus, our finite-element framework allows us to generate new mechanistic insights for biophysical problems.

In order to feed a growing global population without silencing nature, conceiving agricultural management strategies reconciling yield and conservation goals is key. Using numerical simulations of a metacommunity model, we explore the possibilities for compromise offered by spatial management strategies of farmed areas. Each strategy is characterized by its farming intensity, the proportion of farmed lands and their spatial aggregation. We show that achieving equitable yield-biodiversity compromise is difficult. While conciliatory strategies offering top yield and biodiversity are typically not possible, accepting slightly lower yields (ie, "Pretty Good Yield strategies") allows to recover substantial biodiversity. Such reconciliation possibilities are limited for species with small dispersal. Yield increases mainly through farmland expansion, whereas farming intensity strongly influences biodiversity, increasing it at low intensity before decreasing with further intensification. Finally, we demonstrate that reconciliation is easier if agricultural production relies on biodiversity through ecosystem services.

An outbreak of New World screwworm has recently been spreading across Mexico, after more than 30 years of absence. The sterile insect technique, which consists of the massive release of sterilized males, has proven to be one of the most efficient methods for controlling the screwworm pest. However, given the limited number of sterile males available, improving the release strategy is critical. We propose a mathematical model of population dynamics adapted to the biology of Cochliomyia hominivorax and derive a feedback control function to determine the number of sterile males to release. We further construct a Luenberger observer to estimate wild fly populations from infected animal counts--the variable monitored by Mexican sanitary authorities--enabling field implementation of the control function. We show that eradication is achievable within approximately 60-100 weeks and that eradication time is governed primarily by the intrinsic biology of the system rather than by infestation magnitude. We then extend the model to a spatially explicit framework and show that when sterile male releases are applied at the outbreak focus and within a 120 km radius, eradication of the pest is attainable.

The impacts of increasingly frequent and intense heatwaves on parasitism are an important frontier for understanding disease risk under climate change. These impacts are complex because parasitism arises from multiple interacting host and parasite traits that can vary in thermal sensitivity and among populations adapted to different temperature regimes. Here, we used a lab microcosm experiment to investigate the effects of heatwaves occurring during two different phases of a winter-adapted mosquito host - ciliate parasite interaction, for six pairs of sympatric host and parasite populations sourced from two geographic regions with differing histories of winter heat. We found that because heatwaves allowed mosquito larvae to evade infection, they reduced parasitism and increased survival. An early heatwave during initial parasite attack had stronger effects than a later heatwave occurring after infections had established. We did not find evidence of local adaptation to heatwaves: impacts were consistent regardless of population, and were mechanistically predictable from previously measured thermal performance curves that described lower infection and stronger host defenses at warm constant temperatures. The results suggest that increasingly frequent heatwaves may accelerate geographic shifts in parasitism, and demonstrate how fundamental host - parasite thermal biology links to the impacts of extreme temperature events.

AO_SCPLOWBSTRACTC_SCPLOWO_LICommunity assembly graphs (CAGs) summarize which species combinations can coexist and how single-species invasions drive transitions between them, encoding the pathways, alternative endpoints, and cycles that make up a communitys assembly history. Constructing CAGs from dynamical models requires methods that are both computationally tractable and faithful to the underlying ecological dynamics. However, existing methods rely on restrictive assumptions, such as global stability, that exclude alternative stable states and non-equilibrium dynamics known to occur in empirical systems. C_LIO_LIWe develop a computational pipeline that constructs CAGs from any generalized Lotka-Volterra model. Building on the invasion graph framework and its connection to permanence, the pipeline verifies that community dynamics are bounded, identifies which subsets of species coexist in the sense of permanence, determines which single-species invasions are dynamically realized, and assigns each community a topographic height equal to the length of the longest assembly path leading to it. We also provide a numerical algorithm to simulate the dynamics of community assembly. C_LIO_LIWe prove several general properties of the resulting graphs, including that a successful invader is never subsequently excluded and that, in the absence of assembly cycles, permanent communities can be reassembled by introducing their species one at a time in the right order. We prove that the CAG faithfully reproduces the compositional shifts seen in the numerically simulated dynamics of assembly. Applying the pipeline to three empirically based models (a New Zealand grassland, a European pasture, and a Puerto Rican ant community), we show how competition strength and mutualistic feedbacks reshape the assembly landscape and how intransitive competition generates assembly cycles. C_LIO_LIOur approach accommodates alternative stable states and non-equilibrium dynamics without requiring global stability, and it turns the long-standing landscape metaphor into a quantitative, mechanistically grounded object by resolving what "height" means. More broadly, it makes the topography of the assembly pathways measurable, providing a way to compare the historical contingency and predictability of the assembly in ecological systems. C_LI

Arctic and Boreal raptor communities will continue to be affected by borealization and other climate change related processes, providing a challenge for ecologists predicting future sates. However, by using community assembly theory and species traits, future communities may be predictable. In this study, we analyzed variation in reproduction traits as a consequence of diet specialization for 29 raptors, 2 skuas and 3 corvids. We assessed and implemented foraging traits for specialists and generalists into predator-prey models from which successful invasion conditions were derived. Specialist raptors produced larger clutch sizes, had a higher proportion of fledged per clutch and also expressed more variation compared to generalist raptors. These results suggest a relationship between diet specialization and reproductive traits which was also observed within phylogenetic orders. Specialist owls (Strigiformes) produced higher clutch sizes with a larger clutch range compared to generalist owls. The same pattern was observed for falcons (Falconiformes). No clear difference in reproduction was observed for specialist and generalist hawks, kites and eagles (Accipitriformes). Corvids expressed clutch sizes similar to that of specialist raptors while having the lowest proportion of fledged per clutch. Differences in foraging traits between specialists and generalists could be distinguished using functional response curves. A predator-prey model parameterized with foraging trait data showed that a generalist can coexist with a resident specialist if it has access to prey unavailable to the resident specialist. Otherwise, the native specialist outcompetes the invading generalist due to foraging efficiency. The combined empirical and theoretical findings in this study show how diet specialization affects both reproduction and the potential invasion success of raptors.

The cytoplasm is a crowded and dynamic fluid within which cellular building blocks such as mRNA, proteins, or organelles undergo transport and mixing. Although small things like proteins can eventually mix through diffusion, the high viscosity of cytoplasm means that it should be difficult to obtain significant mixing for structures in the size range of mRNA, multi-protein complexes or organelles. In large amoeboid cells, the cytoplasm undergoes active streaming coupled to cell motility, but this streaming is laminar flow which should not be effective for mixing. In this work we used a combination of live cell tracking of injected beads and computational analysis of motion and mixing in giant amoeba Chaos carolinensis with the initial goal of testing the possibility that large-scale cellular deformations during pseudopod formation might implement chaotic mixing by a Baker-transform like process. Instead, we found that Chaos carolinensis accelerates cytoplasmic mixing using a novel cytoplasmic gel state capture and release strategy. While it was previously thought that the amoeba sol to gel state transitions only occur at the trailing and leading edge of the cell body, our work indicates that these transitions occur frequently throughout the mid-cell region, driving the cytoplasmic mixing of beads and organelles. These results indicate that amoeba achieves nearly complete mixing between 1 and 2 cytoplasmic stream/flow cycle, effectively approximating the Bernoulli mixing regime and thus representing one of the theoretically fastest possible mixers.

The photosynthesis-transpiration-stomatal conductance (An-E-gs) model framework is widely used for estimating photosynthesis, transpiration, and stomatal conductance in plants. The model equations are solved by numerical iteration, and the converged model values are deemed the solution. However, there has been no general guarantee that the iterative procedure converges to a solution or that the procedure leads to convergence. Building on the recent proof of the existence of a unique set of solutions, we herewith propose a numerical algorithm that is guaranteed to converge to the solution for the An-E-gs model framework. We first analytically prove that the proposed algorithm necessarily converges to a solution. We then demonstrate the convergence across contrasting combinations of leaf temperature, relative humidity, light, atmospheric CO2, and wind speed. We further demonstrate rapid convergence with the algorithm: no more than ca. 10 iterations for approximately 10-3 mol CO2 m-2 s-1 precision in net photosynthesis and no more than ca. 20 iterations for 10-7 mol CO2 m-2 s-1 precision. By guaranteeing convergence to the solution, this algorithm eliminates concerns about nonconvergence in leaf gas-exchange calculations and is expected to serve as a robust foundation for a range of studies from leaf-level gas exchange to global-scale carbon and water cycle dynamics.

Cetaceans reduce the net cost of sustained swimming through intermittent locomotion, alternating active fluking with unpowered gliding. The energy balance of this strategy is central to understanding survival rates, population sustainability, and the effects of anthropogenic and environmental pressures. While active-phase energetics have been characterized extensively, the glide phase remains largely unexplored. Here we derive the optimal glide duration (Topt) and the maximum glide duration beyond which energy savings vanish (Tzero) for three odontocetes spanning a 20-fold range in body mass, using high-fidelity CAD models and wall-modeled large eddy simulations. We show analytically that speed retention at Topt and mass-specific peak energy savings are both fully determined by the active-to-passive drag ratio, propulsive efficiency, and swimming speed, independently of body morphometry and drag coefficient, and are therefore invariant across species at any given speed. These passive-phase optima extend the known size-independent active-phase invariants to the glide phase, towards a scale-independent energetic framework for burst-and-glide locomotion in small cetaceans.

Climate-driven disease forecasts typically assess whether environmental conditions favor pathogen growth, yet epidemic spread depends critically on how physiological processes within infected hosts shape transmission over time. This distinction is particularly consequential for vector-borne plant diseases, where vectors acquire infection from hosts whose pathogen load, symptom severity, and recovery are themselves temperature-dependent. Here, we develop a mechanistic epidemic framework that couples temperature-driven within-host pathogen dynamics to vector-mediated transmission. Infected hosts progress through ordered infection stages with stage-specific infectiousness, while transitions among stages-both progression and regression-are governed by thermal effects on pathogen accumulation and decay. We parameterize the model using experimental data for Pierce's disease of grapevine, caused by Xylella fastidiosa, and analyze epidemic invasion under constant, seasonal, stochastic, and empirical temperature regimes. We show that temperature affects invasion not only by altering pathogen growth rates but also by reshaping the time hosts spend in transmissible infection stages. This generates a slow-growth paradox: temperatures that maximize within-host pathogen growth need not maximize epidemic spread, because rapid progression shortens the effective transmission window, whereas mildly suboptimal temperatures can prolong infectiousness and sustain larger epidemics. Conversely, cold conditions can suppress invasion by either halting progression or inducing regression and recovery. Analytical expressions for the basic reproduction number under constant and seasonal forcing capture these mechanisms and predict final epidemic size across diverse climatic regimes. Short-term temperature variability has its strongest effects near thermal thresholds, and empirical temperature series from invaded regions generate markedly different epidemic trajectories despite similar invasion suitability. These results show that ignoring the coupling between within-host physiology and transmission can qualitatively mislead predictions of plant disease dynamics under climate change, misidentifying the thermal regimes that pose the greatest epidemic risk.

Despite the ecological importance of ungulate migrations, we lack a complete understanding of why some ungulates migrate and others do not. Though progress has been made towards understanding differences across species and between populations, migratory behavior varies even within populations: in many populations, some individuals remain behind as residents (partial migration). Theoretical population-level work has suggested that these different migratory tactics can coexist, but such approaches stop short of providing insights into how individuals make the decision to stay or go each year. Using long-term data from three ungulate populations, we find that individuals probabilities of migrating are highly variable across years, which points to a non-trivial context-dependent decision-making process, whose underlying mechanisms must be probed via individual-level modeling. Drawing on existing knowledge, we propose a decision-making model of ungulate migration onset wherein individuals probabilistically decide to start migrating based on the local intensity of environmental and/or social cues. Residents arise as a robust collective organization phenomenon in our model. At sufficiently large population sizes, the number of residents is invariant with total population size, consistent with empirical patterns. Instead, resident numbers are influenced by the severity of the bad season, by relevant character differences among individuals, and by how individuals contribute and respond to environmental and/or social cues; for instance, when social cues contribute to decision-making in addition to environmental ones, fewer residents result, and migration is more likely to be complete. Overall, our model provides a potential mechanistic explanation for how residents might emerge within migratory ungulate populations.

Cells adjust their internal circuits in response to changes in their environment. Hence, exposing cells to changing conditions provides a way to probe the intrinsic dynamics of cellular internal circuits. Metabolic networks are examples of such circuits since metabolic fluxes dynamically adjust when environmental conditions are transiently altered. Most existing theoretical frameworks focus on cellular metabolic steady states and do not consider the dynamics of changes in metabolic fluxes. In this work, we applied transfer function analysis from control theory to analyze the changes of NADH oxidative fluxes in the mitochondria and cytoplasm in mouse oocytes in response to dynamical perturbations of oxygen depletion and recovery. We observed an overshoot of NADH oxidative flux in the cytoplasm upon oxygen recovery which is absent in the mitochondrial NADH oxidative flux. Metabolic perturbation experiments and transfer function analysis indicate that this cytoplasmic NADH overshoot results from the coupling of the mitochondrial and cytoplasmic NADH cycles. The degree of overshoot is determined by competing timescales associated with the exchange rates of lactate and pyruvate with the media and their interconversion rates catalyzed by lactate dehydrogenase. Applying control theory to the data enables the inference of the exchange and conversion rates of pyruvate and lactate, allowing predictions of the contribution of lactate to mitochondrial respiration. Our work indicates that the oocytes maintain a homeostatic respiration rate across nutrient conditions by modulating the contribution of lactate to mitochondrial respiration.

One of the most intuitive ideas in ecology is that diversity at lower trophic levels in food webs provides niches to support diversity at higher trophic levels. This accumulation of diversity can be limited by survival of species in the landscape, but revealing these limits has been challenging. We analyze spatial turnover in a hyperdiverse parasitoid-caterpillar-plant food web across 75,000 km2 of continuous lowland rainforest in Papua New Guinea. Species turnover across sites is higher in parasitoids than in their caterpillar hosts. Furthermore, turnover of interactions is also higher in parasitoid-caterpillar than caterpillar-plant networks. Spatial turnover thus amplifies upwards across trophic levels, forcing parasitoids to live closer to spatial persistence limits. Consequently, progressing rainforest fragmentation can especially endanger parasitoids.

Neutrophils move through narrow pores, convoluted channels, and tight spaces in tissue to find infection sites. Their ability to sense weak chemical gradients, undergo directed motion, and solve such path-finding problems rests on internal GTPase signaling circuits that control the front protrusion and rear retraction of the cell. Here we explore several variants of known core polarity circuits, with local and long-ranged negative feedback, including inhibitor downstream of Rac, Rac-Rho antagonism, and effects of membrane tension. The resulting reaction-diffusion (RD) equations for Rac and Rho are then used to simulate protrusion-retractions along the edge of a simulated motile cell. We visualize how cells navigate through narrow tracks with sharp corners and weak chemical gradients in 2D. Our metrics for cell performance include polarity initiation, wall-collision intensity, and track completion. In this way, we expose how Rac and Rho, together with their immediate down and upstream components can fine-tune neutrophil motility through complex environments. Author SummaryWhite blood cells, attracted to sites of infection, migrate through complex tissues to find their target. Such movement requires a balance between robust polarity in one direction versus flexibility in response to spatial cues such as obstacles and sharp turns. Here we use mathematical modeling to explore known intracellular circuits that regulate front protrusion and rear retraction in directed cell migration. We test several such circuits in simulations of cells moving along zigzag tracks with sharp turns. We demonstrate that a basic cell polarity circuit, on its own, has limited success, since cells tend to get trapped in sharp corners. Known modulators of this core, which add local negative feedback, mutual front-back antagonism, and long-range feedback from membrane tension, improve cell performance. A cell with the full front-back-membrane tension regulatory circuit avoids delays due to traps and obstacle collisions, and moves swiftly through a convoluted passage to its target site.

Evolutionary game theory and ecological coexistence theory both seek to predict the outcome of competition between biological entities, be they strategies or species, but the two fields have relied on largely separate approaches. Replicator equations provide a foundation for analyzing strategy competition, yet they do not explicitly separate the mechanisms that stabilize competition from those that equalize fitness differences between strategies. Here, we extend modern coexistence theory from community ecology to develop strategic coexistence theory (SCT), a framework for quantifying strategic niche and fitness differences between competing strategies. SCT recovers the classic classification of two-strategy games, distinguishing competitive exclusion, coexistence, and priority effects within a shared niche-fitness difference space. Applying SCT to five mechanisms for the evolution of cooperation further reveals that these mechanisms promote cooperation through distinct dynamical routes: kin selection, network reciprocity, and group selection primarily reduce fitness differences, whereas direct and indirect reciprocity destabilize competition and generate priority effects. Finally, applying SCT to microbial public-goods game shows that nonlinear microbial growth can both stabilize and equalize competition between cooperators and defectors, allowing coexistence. Together, these results show that SCT provides a complementary framework for comparing evolutionary games and teasing apart the coexistence mechanisms underlying strategy competition.