13,160 papers in this slice of arXiv.
Quentin Thommen
Environmental sharing changes the value of diversification even when the marginal statistics experienced by each lineage remain unchanged. A minimal model of cell division couples this effect to the inheritance of a conserved protective reserve. Each mother partitions its reserve between two daughters, and each fixed partition policy generates a random demographic operator whose top Lyapunov exponent determines long-term growth. Weak environmental sharing favors symmetric inheritance, whereas sufficiently shared innovation selects a separated asymmetric branch near α≃ 0.2. The transition therefore occurs by a finite branch crossing rather than by a continuous departure from equal partition. The asymmetric phase persists when the protection law or reserve turnover is changed, although its boundary depends on protection nonlinearity and reserve memory. Because shared and private environmental innovations have identical marginal statistics, the mean reproductive operator is independent of the shared-innovation fraction. A dominant-mode second-order approximation then separates the asymmetric advantage into a mean-operator growth cost of specialization and a reduction of sensitivity to collective fluctuations. This approximation predicts the finite asymmetric branch selected by the full random-operator dynamics, while the full operator product determines the numerical crossing. Environmental sharing can therefore drive symmetry breaking in the inheritance of a conserved protective resource when the loss of diversification between lineages increases the value of diversification generated at division.
Hammed O. Fatoyinbo, Indranil Ghosh, Parul Tiwari +3
Highly pathogenic avian influenza (HPAI) outbreaks require rapid control during active transmission and evidence-based decisions on the safe restocking of depopulated farms. We developed a stochastic spatial SEIR-based metapopulation model for a synthetic HPAI outbreak on the fictional Jolly Island. Farms were classified as `Broiler-2', `organic duck', or `Other' production systems. The model incorporated local, environmental, movement-mediated, and distance-dependent transmission, together with reactive and preventive culling, production-specific confinement, and capacity-based restocking. The simulated epidemic was geographically concentrated and differed substantially among production classes. Preventive culling reduced mean cumulative burden from 16,362.7 to 13,631.9 infectious-farm-days, with an overall reduction of 16.7%. Earlier confinement substantially reduced epidemic magnitude, while stronger environmental transmission increased the epidemic peak. Restocking risk declined as the epidemic approached resolution. Under the model assumptions, 24 May 2026 was the first candidate date satisfying the predefined rebound-probability threshold of 0.20. For restocking on 15 March 2026, none of the tested restocking fractions met this criterion. Capacity-based restocking reduced cumulative burden by 8.45% and rebound probability from 0.780 to 0.533, compared with restocking relative to the baseline population. These findings demonstrate the value of integrating epidemic control and post-outbreak recovery within a single modelling framework. Timely confinement, targeted preventive culling, and phased capacity-based restocking may reduce both epidemic burden and resurgence risk, although operational decisions should also incorporate surveillance, biosecurity, economic considerations, and regulatory requirements.
Mauro Mobilia
Microbial populations generally evolve in fluctuating environments under time-varying conditions. These are often described by binary switching models, sometimes seen as coarse-grained feast-famine cycles, in which resource availability switches abruptly between abundant and scarce conditions. However, experimental studies suggest that feast-famine environments actually exhibit more complex temporal dynamics. Here, we study how two strains, one growing slightly slower than the other, compete for the same resources in fluctuating environments comprising a finite number of intermediate states, each having its own carrying capacity. Environmental switching between these states and their carrying capacities represents gradual changes in nutrient availability. This class of multi-state stochastic switching models can be interpreted as a coarse-grained description of feast--famine cycles and allows us to investigate strain competition under the gradual recovery and depletion of resources. By computational and analytical means, we characterise the population dynamics in these multi-state fluctuating environments. In particular, we study how the switching rates and distribution of carrying capacities affect the population-size statistics, fixation probability, and mean fixation time. By comparing these results with their counterparts in binary environments, we clarify how the frequency and amplitude of environmental fluctuations influence population dynamics in coarse-grained feast-famine cycles.
Christian Grilletta, Tommaso Anfodillo, Gaia Pasqualotto +4
Scaling relations linking species size, abundance, and resource availability are among the most robust empirical regularities in ecology. However, a mechanistic explanation for how these community-level laws emerge from ecological processes remains elusive. Here, we address this gap by developing a minimal spatially explicit dynamical framework for forest communities that incorporates seed dispersal, growth limited by local light availability, local competition, and global resource constraints grounded in metabolic scaling principles. By deriving an analytical solution for the tree-size distribution, we show that its stationary state exhibits two distinct power-law regimes whose exponents are controlled by the relative strength of resource and spatial competition. The crossover between these regimes is set by the interplay between seed injection and local resource availability, establishing an explicit link between the scaling exponent of the size distribution and forest condition. Finally, we show that boundary disturbances can break the ecological balance between competing species and induce effects that propagate deeply into the forest bulk, far beyond the single-plant dispersal range. Together, these results provide a unifying dynamical perspective on forest scaling laws with potential applications to a broad range of biological communities.
Ouerdia Arezki, Ali Zemouche
A subcritical age-structured branching population dies out almost surely. Conditioned on survival, it converges to its Yaglom limit, a quasi-stationary equilibrium that we take as the operating point for control. We model preventive removal (culling) as an age-dependent actuator that raises the mortality rate and leaves the offspring law untouched, and we show that its authority over this equilibrium is bounded for structural reasons. Two facts drive the result. First, the input is matched to the killing rate but unmatched with respect to the Foster--Lyapunov drift, so the transmission barrier set by reproduction alone is invariant under such actuation. Second, and this does not follow from invariance alone, the supremum of the reachable decay rates is +ν^⋆, where ν⋆≤0 is the Malthusian parameter of the lineage conditioned never to die childless; the gap ∣ν⋆∣ is given in closed form and vanishes exactly when no individual has two or more offspring. Consequently no removal law of this class reaches the barrier, and along the admissibility boundary the achievable decay rate is governed by the shape of the actuator rather than by its size. We illustrate these results on a model calibrated to the 2001 Cumbrian foot-and-mouth outbreak.
Hannah M. Moots, Dilek Koptekin, Matthew P. Williams +4
Here, we present an overview of how archaeogenomic data can be used to investigate past mobilities and how it can be integrated with archaeological research to reconstruct models of human mobility at different scales. We first seek to explain the importance of a clear terminological and theoretical approach to mobility, discussing terms such as migration, admixture, ancestry, and replacement. We then describe the theoretical principles and state-of-the-art tools for inferring past mobility from ancient DNA data, including exploratory approaches and formal modeling, and the relevance of using diverse lines of evidence including allele-frequency patterns, haplotype-sharing, genetic relatedness, and uniparental markers. We further discuss the power and limitations of these methods for inferring different modes of mobility, such as large-scale and long-distance mobility, which we refer to as migration, and also small-scale and short-distance movements, which we argue constituted the bulk of past human mobility and may have had the major share in shaping cultural landscapes. We also describe inference of sex-biased mobility events. Finally, we present examples of joint analyses of genomic data with archaeological, bioarchaeological and historical evidence, demonstrating the potential of interdisciplinary analyses in building comprehensive models of past mobilities. We finish by calling for further integration across fields.
Michal Pecho, Jakub Svoboda, Lenka Kopfová +2
Evolutionary dynamics in finite structured populations are commonly modeled by the Moran Birth-death process. A key quantity is the fixation probability of a single invader attempting to take over a population of residents. A recent work introduced a new neighborhood-aware phenotype called a replacer. A replacer never wastes their reproductive turn by always replacing an individual of the other type (if available). In this work, we study the evolutionary stability of resident replacers who are invaded by mutant replacers. We find that residents are strongly protected against such invasions, and we quantify the strength of this effect by showing three types of results. First, we show that on well-mixed populations of size N, the invader fixation probability is exponentially small in N, even when the invader has a fixed relative reproductive rate r>1, and the same holds for all high-degree graphs. Second, we study bounded-degree graphs. We prove that on cycles, the fixation probability of an advantageous invader decreases only by a constant factor. However, we also present graphs with maximum degree 4, where the invader fixation probability is exponentially small in N whenever r≤1.9. Thus, high degrees are sufficient for evolutionary stability, whereas with low degrees the evolutionary stability depends on specific features of the underlying spatial structure. Third, we prove general bounds for arbitrary graphs. Namely, we show that for any graph G the invader fixation probability drops below the natural baseline given by the standard Moran process with oblivious individuals on a well-mixed population, both for r≈1 and for r≥2. Together, our results establish that the evolutionary dynamics of replacers is better characterized by the phrase ``survival of the first'' rather than the classic ``survival of the fittest''.
Artem Kaznatcheev
Many local search methods for problems in artificial intelligence can be viewed as an uphill climb on a corresponding discrete fitness landscapes. Finding even local peaks in these fitness landscapes is computationally intractable in theory, but often works in practice. So what features of fitness landscapes allow for efficient local search? Since any fitness landscapes can be represented by a (hyper)graph of valued constraints, I re-frame this as a question of parameterized complexity: what structural parameter of a valued constraint graph guarantees that a strict local search will find a local peak in the corresponding fitness landscape efficiently? Given a valued constrain graph of vertex cover number k, I prove that greedy local search will find the a local fitness peak in at most 22k⋅(n−k+1) steps and random uphill local search will find a local fitness peak in an expected number of at most 2k⋅n(n−k) steps. I also show that these results are asymptotically good for strict local search because there are valued constraint graphs of vertex cover number k where every ascent from some initial assignment has a length of 1289⋅2k⋅(n−k) or greater. This suggests vertex cover number as a good structural parameter for the complexity of local search.
Martin A Nowak
Direct reciprocity is a mechanism for evolution of cooperation based on repeated interactions between the same individuals. Direct reciprocity can help natural selection to favor cooperators over defectors, but whether or not cooperation prevails depends on the details of the evolutionary dynamics. We describe simple processes of evolution that have the astonishing ability of driving direct reciprocity to maximum payoff in all social dilemmas which we study. The basic process is based on mutation and pairwise comparison. Mutation samples strategies near the boundary of the strategy space. Pairwise comparison includes a parameter for intensity of selection. For large population sizes, intermediate to high mutation rates and intermediate to strong intensities of selection, we find that the process leads to communities of strategies that reach maximum payoff in Prisoner's Dilemma, Snowdrift, Stag Hunt and Harmony games. Maximum payoff in all four games is consistently achieved if players have access to memory-2 strategies. Memory-1 strategies have the capacity to resolve all four social dilemmas, but they are usually defeated by a ``Hold-trap'' in Snowdrift games.
Philip A. White, Henry A. Frye, Jasper A. Slingsby +3
Generalized dissimilarity models (GDMs) have emerged as a valuable tool for formal statistical analysis of biodiversity. In particular, beta diversity, measured using dissimilarity measures, e.g., the Bray-Curtis dissimilarity in our case, provides a statistical summary of the difference in species composition between sites. It also provides novel data for spatial and spatio-temporal modeling as it resides over the product space of one space-time pair and a second space-time pair. In earlier work we developed the spatial generalized dissimilarity mixed model (spGDMM) to remedy some of the stochastic issues concerned with the foundational GDM in the literature. Here, we extend that work to include dynamics. We find much richer modeling opportunities as we consider beta diversity with regard to change in time as well as space. We illustrate with a dataset from the Cape Floristic Region (CFR) in South Africa.
Eduard Campillo-Funollet, James Van Yperen
Parameter identification from observations of dynamical systems is a fundamental problem in population biology. Mechanistic models of ecological systems rely on optimization methods that require accurate initial guesses to guarantee convergence. In ecological applications, datasets contain observation noise and are collected at sparse time points. This sparsity creates irregular likelihoods that cause standard optimization methods to struggle, while the ordinary differential equation solvers can become stiff or unstable in certain regions of the parameter space. These instabilities cause long running times or runtime errors. Here we present a computational framework for parameter identification that addresses these numerical instabilities by employing Natural Gradient Ascent, and we apply it to the classical Lotka-Volterra predator-prey model. We exploit the non-dimensionalization of the ordinary differential equations to treat scaling factors as nuisance parameters, reducing the dimensionality of the optimization problem. To prevent the solver step from becoming small, we implement an adaptive solver that switches between two independent second-order equations derived from the two components of the model. This approach allows Natural Gradient Ascent to converge in fewer iterations and with more stability than standard gradient ascent or BFGS methods. This framework provides a reliable method for parameter estimation in ecology when data is limited. The method can be generalized to other dynamical systems as long as the different components of the system do not become numerically problematic at the same time.
Rossella Della Marca, Gabriele Grifò, Annalisa Iuorio +1
We investigate the emergence of traveling front solutions in a spatial SIRS epidemic model with diffusion acting on the infected population. The model exhibits a natural slow-fast structure due to the presence of a small parameter governing the loss of immunity, which induces a separation of scales in the dynamics. Using a traveling wave reduction, the PDE system is transformed into a singularly perturbed system of ODEs, which we analyze within the framework of Geometric Singular Perturbation Theory. In the singular limits, we study the fast excursions governed by the layer problem, and the slow evolution close to the critical manifold. In particular, we identify an entry-exit mechanism tracking the transitions between slow and fast regimes, and derive a quantitative characterization of the entry-exit dynamics. Numerical simulations of the full system confirm the validity of the proposed geometric picture. The traveling front is shown to consist of a concatenation of local, fast, and slow segments, in agreement with the theoretical analysis.
Joseph Cummings, Maize Curiel, Bryan Currie +3
Several statistical methods of phylogenetic network inference and testing for non-tree-like relationships are based on assessing genomic data through quartet Concordance Factors, the frequencies of 4-taxon topological relationships on gene trees. While such an approach obviates making several undesirable modeling assumptions, it also results in non-identifiability issues for network roots and for small cycles. In this work, an algorithm and accompanying Macaulay2 implementation are provided for computing n-tet Concordance Factors on any phylogenetic network. We employ this algorithm on quintet Concordance Factors, summarizing 5-taxon gene trees, to explore identifiability of level-1 networks under the Network Multispecies Coalescent model. We show some additional network features become identifiable that are not through quartets. As identifiability is a necessary prerequisite to inference by any method, this lays a foundation for future inference work.
Sara L. Loo, Danya Rose, Michael Weight +2
The question of why males invest more into competition than offspring care is an age-old problem in evolutionary biology. On one hand, paternal care could increase the fraction of offspring surviving to maturity. On the other hand, competition could increase the likelihood of more paternities and thus the relative number of offspring produced. While drivers of these behaviours are often intertwined with a wide range of other constraints, here we present a simple dynamic model to investigate the benefits of these two alternative fitness-enhancing pathways. Using this framework we evaluate the sensitivity of equilibrium dynamics to changes in payoffs for male allocation to mating versus parenting. Even with strong effects of care on offspring survivorship, small competitive benefits can outweigh benefits from care. We consider an application of the model that includes men's competition for hunting reputations where big game supplies a benefit to all, and find a frequency-dependent parameter region within which, depending on initial population proportions, either strategy may outperform the other. Results demonstrate that allocation to competition gives males greater fitness than offspring care for a range of circumstances that are dependent on life-history parameters and, for the large-game hunting application, frequency dependent. The greater the collective benefit, the more individuals can be selected to supply it.
Stein Andreas Bethuelsen, Erik Broman, Samuel Modée
We study a variant of the stochastic SIR model on graphs that has previously been introduced in the physics literature for modelling zombie outbreaks and here referred to as the Zombie Infection Model (ZIM). In this model, initially each node of a graph is either susceptible, infected or removed. As in the SIR model, a susceptible node becomes infected at rate λ times the number of its infected neighbours. Moreover, in the ZIM, an infected node is removed at rate 1 times the number of its susceptible neighbours. This process exhibits rich and sometimes counterintuitive behaviour. By combining various coupling techniques, we provide a rigorous mathematical analysis of the model, focusing on monotonicity properties and the probability of the infection spreading indefinitely. One of our main results is that this probability is monotone with respect to an increase of λ for the process on trees, but that there are graphs of bounded degree for which it is continuous and yet not monotone. We also establish bounds on this probability for the process on general graphs, and derive more precise results for complete graphs, regular trees, and the d-dimensional integer lattice.
Ouerdia Arezki, Paul-Marie Grollemund, Ali Zemouche
In this paper, we construct a Fleming-Viot particle system for a class of subcritical Bellman-Harris processes. We prove that it selects the Yaglom limit at a polynomial rate in the number of particles. Since lifetimes are non-exponential, the population size is not Markov, and the analysis must therefore be carried out on the space of age configurations. In this setting, the Lyapunov functions used for Galton-Watson processes are no longer norm-like. Nevertheless, we establish a Yaglom theorem that strengthens the classical result: the conditional laws converge in total variation at an exponential rate, with decay rate given by the Malthusian parameter. We also prove that the drift condition, which links the hazard rate to the offspring law, is necessary within a natural class of Lyapunov functions, showing that it is a feature of the measure-valued lift rather than a defect of the estimates. Finally, we illustrate the estimator through an application to livestock epidemic surveillance, where the Yaglom limit is the null distribution of a change-detection test.
Robert C. Froemke
What is a natural behavior? I argue that the study of natural behaviors is often the study of the spontaneous behaviors of animals placed in quantifiably different environments. For behavioral generalists such as rodents, humans, and many other species, there may be no such definable construct as a native habitat or natural behavior, due to their successful abilities and needs to rapidly adapt to a wide range of different ecosystems. Instead of prioritizing naturalness, it may be more essential to determine objective outcome measures related to specific behaviors; i.e., which sequences of behaviors and adaptive mechanisms allow animals to survive and reproduce, across a range of dynamic or hazardous physical and social environments.
Federico Pigozzi, Michael Levin
Agents that exert causal power in the world are thought to be the product of selection among diverse replicators; what is the causal structure of a medium before replicators appear, and evolution takes hold? We studied information-theoretic dynamics of the popular GARD model that captures several dynamics thought to be important at life's origin and found that causal emergence predicted the initial appearance of self-replication. Moreover, interventions that drove causal emergence up increased the longevity of self-replicators, while interventions that drove causal emergence down decreased the abundance of these self-replicators, suggesting causal emergence as a functional control knob. Thus, progressive increases in integrated causality are detectable in active media before evolutionary dynamics begin to operate, which may have implications for the origin of life across highly diverse scenarios and for understanding the forces driving the rise in causal power observed in the biosphere on Earth.
Ilya Horiguchi, Hiroki Sayama
Hash Chemistry is a family of minimalistic evolutionary models in which a deterministic hash function assigns a scalar score to entities of arbitrary size, opening a combinatorially vast possibility space (a ``cardinality leap''). Since its introduction, the idea has been realized in several settings, from the original spatial formulation to a fast non-spatial variant and then to structural cellular models. Here we review the Hash Chemistry family as a coherent modeling framework and use it to explore how minimal systems can demonstrate the mechanisms behind multiscale open-ended evolutionary dynamics. The most recent model, Structural Cellular Hash Chemistry (SCHC), successfully demonstrated multiscale ecological interaction/adaptation and complexity growth of replicators in a computationally efficient manner. In this study, we first extend SCHC to incorporate spatial locality and dyadicity of competitive interactions among replicating structures. We show this extension substantially enhances SCHC's evolutionary dynamics. Furthermore, we explore SCHC in a significantly larger spatial domain using a GPU-accelerated implementation. We show that the size of the space acts as a control parameter for a stochastic, nucleation-like transition between a compact-replicator regime and a runaway size-dominance regime, and we separate the responsible mechanism into a non-spatial, size-biased sampling feedback and a finite-size spatial effect. Altogether, these results illustrate the rich potential of Hash Chemistry as a minimal, mechanistically transparent testbed for studying open-ended evolution across scales.
Wenxuan Li, Xu Chen, Yu Gao +1
The soybean pod borer (Leguminivora glycinivorella) poses a severe threat to global soybean production.In this study, we developed a stage-structured host-parasitoid dynamic model that explicitly couples the holometabolous life cycle of the pest with the obligate egg-parasitism mechanism of Trichogramma wasps. Utilizing field monitoring data from Changchun, Jilin Province, key biological parameters were rigorously estimated via the Markov Chain Monte Carlo (MCMC) method.This calibration facilitated the establishment of a precise Economic Injury Level (QEIL) of 0.0389 individuals/m2, based solely on the destructive larval stage. Through theoretical and numerical analyses of different intervention scenarios, we identified an optimal continuous release rate (C∗=2.645) that efficiently suppresses the outbreak without causing wasteful parasitoid accumulation. Furthermore, simulations demonstrate that a 5-day impulsive release interval provides the optimal balance between strict pest suppression and field operational costs. This study bridges the gap between theoretical population dynamics and applied agricultural management, providing a directly applicable mathematical decision-making tool for the precise biological control of crop pests.