q-bio.PE — Pith

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math.PR 2026-04-30

Degree and distance contact rules slow epidemic growth

by Zylan Benjert, Júlia Komjáthy +3 more

Degree-dependent and distance-dependent contact rates interpolate between explosive, exponential and polynomial epidemic growth

Even mild dependencies shift spread from explosive to polynomial rates on networks with geometry.

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It is a fundamental question in epidemiology to estimate, model and predict the growth rate of a pandemic. Analogously, analysing the diffusion of innovation, (fake) news, memes, and rumours is of key importance in the social sciences. The resulting epidemic growth curves can be classified according to their growth rates. These have been found to range from exponential to both faster super-exponential curves and slower subexponential or polynomial curves. Previous research has lacked a unified explanatory framework capable of accommodating super-exponential, (stretched) exponential, and polynomial growth patterns within the same contact network. In this paper we propose a simple agent-based network model that can capture all these phases. We provide such a framework by modelling how transmission rates depend on spatial distance and on individuals' numbers of contacts. By comparing the growth rate of spreading processes with or without degree-dependent and/or distance-dependent contact rates through data-driven and synthetic simulations on real and modelled networks with underlying geometry, we find evidence that even a 'sublinear presence' of these causes may cause a significant slow down of the growth rate on the same underlying network. We find that the growth rate is governed by a combination of three factors: geometry, the prevalence of weak ties, and superspreaders. We confirm our results with rigorous proofs in a theoretical model, using a spatial multiscale-argument in long-range heterogeneous first passage percolation. Our results give a plausible explanation of why the consecutive waves of a single pandemic can differ in their growth even if their spreading mechanisms are similar.

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q-bio.PE 2026-07-02

Factor-two peak approximation holds under Erlang scaling in multistage SIR

by Denis Tverskoi, Andrew Gothard +1 more

Approximating Peak Prevalence in Multistage SIR Epidemics

The delay limit expresses prevalence and weighted stages as moving averages of incidence, showing when the approximation is accurate and how

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Estimating peak prevalence is a central problem in epidemic modeling because it determines the period of greatest infectious burden and is closely linked to health-care demand. In multistage SIR models, however, peak prevalence is generally less tractable than in the classical model with exponentially distributed infectious periods. Motivated by the use of weighted infectious-stage aggregates as surrogates for prevalence, we investigate the relationship between the prevalence peak and the maximum of a weighted stage functional in deterministic SI$(k)$R epidemic models. We show that this relationship depends critically on how the stage-progression rate is scaled as the number of infectious stages increases. Under naive scaling, in which the progression rate remains fixed, the weighted peak is asymptotically equivalent to the prevalence peak and the commonly used factor-two approximation fails. Under Erlang scaling, which preserves the mean infectious period, the multistage model converges to a delay formulation in which prevalence and the weighted stage functional become unweighted and triangularly weighted moving averages of incidence. This limiting representation provides a theoretical basis for the factor-two approximation and identifies the regimes in which it is accurate. It also explains why this approximation deteriorates as epidemic waves become more sharply peaked. We derive analytical error bounds and develop curvature-based and parameter-based corrections that substantially improve accuracy. Numerical studies confirm these improvements across a broad range of epidemiological parameters. Overall, the results show when weighted-stage peaks can be used reliably as proxies for peak prevalence and how the resulting estimates can be refined when the standard approximation loses accuracy.

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q-bio.PE 2026-07-02

Immune history stabilizes recurrent variant epidemics

by Ryuichi Kumata, Yuma Fujimoto +2 more

Immune history shapes recurrent epidemics of antigenically related variants

Recurrence map shows equal-sized waves are stable but size peaks at moderate transmission due to cross-immunity

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Population immunity carried over from past epidemics of an antigenically variable pathogen influences the epidemic of new variants based on their antigenic similarity to the previous ones. We develop a recurrent SIR model where a population faces sequential, antigenically related variants. The model yields a recurrence map for the population susceptibility to successive variants under the assumption of status-based population immunity. The model reveals that stable, equal-sized recurrent epidemics occur across broad parameter ranges, but can be destabilized when transmission is strong and antigenic escape is limited, leading to period-2 or more, or even more complex epidemic dynamics. Epidemic size is maximized at an intermediate basic reproduction number: higher transmissibility boosts immediate infection but also enhances cross-immunity, reducing future susceptibility of the population. Our results clarify how immune history shapes recurrent epidemics and why success in one wave does not ensure larger future epidemics.

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q-bio.PE 2026-07-02

Birth-regulated species fixate more often despite neutral mean growth

by Yunbei Pan, Tom Chou

Effective population sizes for asymmetrically regulated birth-death processes

How regulation splits between birth suppression and death elevation biases stochastic outcomes even when deterministic rates match.

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In multispecies birth-death processes, how population regulation -- through suppressed replication, elevated mortality, or both -- affects macroscopic stochastic dynamics has escaped detailed analysis. Here, we show that the distribution of regulation mechanisms can be invisible in deterministic or mean-field dynamics but play a significant role in the diffusive evolution of population frequencies. By introducing a tunable regulation partitioning parameter $\alpha_i$ and projecting a $d$-species birth-death process onto a $(d{-}1)$-dimensional Moran process, we find a regulation-mechanism-dependent diffusion tensor. For the simple two-species case, we derive exact fixation times and probabilities to show how different regulation mechanisms stochastically favors a more birth-regulated species, even under complete deterministic neutrality. Our model also allows us to define an $\alpha$-dependent effective population size $N_{\rm e}(\alpha)$ among neutral species, generalizing its classical interpretation. For near-neutral populations or populations that are heterogeneous in their regulation mechanism, we used perturbation theory to calculate the spectral gap, identifying it with a diversity loss timescale which can also be interpreted as setting an effective population size. Our results are particularly applicable to interacting subpopulations of T cells ("clones") which are near-neutral, are regulated through proliferation and apoptosis, and lose diversity with time.

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q-bio.PE 2026-07-02

Pontryagin principle yields optimal control for heterogeneous SI epidemics

by Elisa Paparelli (SU)

Optimal control on a heterogeneous SI epidemic model

Minimizes final infection size under total drug-supply limit using the reduced macroscopic dynamics

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This work addresses an optimal control problem for a SI epidemic model incorporating heterogeneities in resistance and viral load at the population level. Building upon the heterogeneous SI framework developed in [1], a minimization problem constrained to the macroscopic counterpart of the SI dynamics derived therein is proposed. Unlike traditional optimal control problems in homogeneous epidemic models, the present approach focuses on an optimal control problem that accounts for population heterogeneity, offering insights from a microscale perspective. The contribution aims to minimize the final size of the infection within a finite time horizon by developing a pharmaceutical strategy, under a supply constraint that translates into an integral equality constraint in the control function. By applying the Pontryagin Minimum Principle, a characterization of an optimal control is provided.

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q-bio.PE 2026-07-02

Land cover beats single metrics as bird diversity predictor

by Dilusha Chandrasiri, Maneesha Herath +7 more

How Environment and Urbanization Shape Bird Diversity in Sri Lanka

Sri Lanka analysis of thinned grids shows ALAN favors generalists while cutting overall richness at multiple scales.

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This study presents a comprehensive analysis of bird diversity across Sri Lanka by integrating spatial, temporal, and environmental data. Bird observation records were combined with environmental variables, including weather conditions, air pollution, the Normalized Difference Vegetation Index (NDVI), land cover, elevation, and Artificial Light At Night (ALAN), and rigorously preprocessed to ensure data quality. Spatial analyses were conducted on multiple grid scales (2 km, 5 km, 10 km) to evaluate patterns in species richness while minimizing sampling bias through spatial thinning. Temporal trends were assessed using effort-corrected metrics including rarefied richness and occupancy rates to account for variations in observation effort over time. Environmental drivers of bird diversity were examined using multivariate statistical models, including Poisson Generalized Linear Models (GLMs) and correlation analyses, to identify key associations between ecological factors and species richness. Additionally, community structure, dominance patterns, and beta diversity were analyzed to understand variations in species composition across regions and time. The study found that land-cover type is a stronger predictor of bird diversity than individual continuous variables such as NDVI or temperature alone. Urbanization, measured by ALAN, exhibits nuanced scale-dependent effects, supporting high abundances of a few generalist species while reducing overall richness. The findings provide actionable insights into the patterns and drivers of avian diversity in Sri Lanka, offering a scalable and reproducible framework for biodiversity research and conservation planning.

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cs.CV 2026-07-02

One image yields pairwise fitness flows in expanding colonies

by Faruk Alpay, Baris Basaran

Radial Interaction Tomography: Recognizing Non-Transitive Evolutionary Games from One Range-Expansion Image

Boundary curves in log-polar view recover interaction histories and flag non-transitive games.

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Colored sectors in a microbial range expansion encode more than lineage survival counts. We formulate a computer-vision inverse problem: from one endpoint image of an accretive multi-type expansion, recover the radius-indexed pairwise boundary-flow field and test whether the visual pattern is compatible with a transitive scalar fitness hierarchy. The observable is a geometric signal extracted from sector-boundary curves in log-polar coordinates. We prove endpoint observability and stability for frozen fronts, weighted transitive/cyclic decomposition, contact-complete circular design, physical-clock and mechanism non-identifiability, exact Gaussian cyclicity testing, and Bonferroni-valid interval scanning. The benchmark is deterministic: analytic endpoint images, blurred/noisy pixel round trips, scalar-null stress tests, public-image tracing, multi-resolution mechanistic endpoints, and a non-learning frozen-front simulator. The implementation recovers pairwise edge-flow histories from endpoint images, detects cyclic residuals in a mechanistic four-type expansion, and uses those residuals as forcing signals for a dimensionless active design-control layer covering reaction-diffusion control, phenotype-frontier optimization, protocol synthesis, Monte Carlo robustness, and a downstream population-state bridge.

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q-bio.PE 2026-07-01

Senescence mortality matches multi-level selection patterns

by Ananda Shikhara Bhat, Hanna Kokko

Demographic senescence as multi-level selection in miniature

A two-level Moran process models both group competition and damage buildup, producing equivalent age-specific death rates through selective

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Multi-level selection and senescence do not at first sight have much in common. Here, we demonstrate that the emergent mortality patterns generated by demographic senescence can be understood as the product of multi-level selection. We formulate a two-level Moran type process and use its scaling limits to illustrate that a simple mathematical framework that models multi-level selection in group-structured populations also models damage accumulation patterns and resultant mortality curves in ageing organisms. To verbally make the connection, observe that defectors spread within a group consisting of cooperators and defectors; when groups compete against each other, defector-rich groups suffer, and between-group selection causes such groups to be systematically under-represented. Exactly analogously, senescing individuals accumulate damage to physiological sub-systems, and `damage begets damage'; individuals who are more damaged are more likely to die, hence damage-rich individuals are systematically under-represented in later age classes. Thus, emergent senescence patterns in complex, integrated organisms are formally equivalent to the patterns generated by a within-generation multi-level selection process in which intra-organismal sub-systems play the role of particles, organisms play the role of collectives, and selective disappearance plays the role of group selection.

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q-bio.PE 2026-07-01

Size-dependent dispersal creates four invasion growth regimes

by Ulysse Marquis

Invasion with size-dependent dispersion range

Main colony shifts from linear to blow-up expansion as range scales with size, but symmetry breaks in explicit models

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The coalescing colony model provides a minimal framework for biological invasions with long-range dispersion. In its standard formulation, the dispersion range is assumed independent of the size of the invading population. Here, we relax this assumption and consider size-dependent dispersal: a main colony of linear size $r$ emits secondary colonies at distance $r^\mu$, with $0 \leq \mu \leq 1$. We derive the generalized dynamical equations for this extended model and map out the growth phase diagram for the leading order contribution. Depending on $\mu$, the main colony exhibits distinct regimes: linear expansion, power-law growth, exponential regime and finite-time blow-up. We confront these theoretical predictions with a spatially explicit physical model. While the coalescing colony approach correctly captures the scaling of the perimeter, it fails to predict the scaling of the volume. We trace this discrepancy to an effective breakdown of circular symmetry in the morphology of the main colony. Finally, we quantify temporal evolution of the population fraction residing outside of the main colony. The coalescing colony model predicts its decay to~$0$ like a power-law when~$\mu<1$, and a macroscopic amount of the population remains in the secondary colonies at~$\mu=1$. Simulations of the physical model reveal a persistent satellite population not captured by the theory at~$\mu>\mu^*\approx 0.7$. Broadly, our findings highlight how coupling dispersal range to population size fundamentally alters invasion dynamics, with implications for biological invasions, metastatic growth, and urban expansion.

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q-bio.PE 2026-07-01

Full ITN use by one host can raise disease in another

by Shravani Shetgaonkar, Anupama Sharma

Nonlinear Feedbacks Between Host Behavior and Vector Adaptation in a Multi-Host Vector-Borne Disease Model

Vectors redirect bites when nets protect the primary host, increasing transmission to the secondary host in the model.

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Insecticide-treated nets (ITN) are an effective and low-cost intervention for controlling vector-borne disease (VBD), however, their use depends on individual decisions based on perceived cost and risk of infection. This study investigates a nonlinear multi-host model for the transmission of VBD with endogenous strategic control. We assume that hosts' adoption of ITN emerges from the payoff-based decision-making, creating a nonlinear coupling with disease prevalence. We model vector preference as a function of ITN coverage to probe the complex interplay among individual choices, disease prevalence, and its control in a multi-host setting. The qualitative behavior of the system is characterized by the thresholds $R_0$ and $R_c$, which determine the existence and local stability of the disease-free and endemic equilibria. The system exhibits rich dynamical behavior; hence, we provide a bifurcation analysis identifying the conditions for saddle-node and Hopf bifurcations. Our results demonstrate that the interaction between the perceived cost of ITN and the infection risk can induce critical transitions, including regime shift from stable endemic states to sustained periodic oscillations. Furthermore, we identify a counterintuitive effect whereby complete ITN adoption by the primary host can increase the overall prevalence in the secondary host due to adaptive shifts of vector feeding behavior.

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cs.GT 2026-07-01

Extrinsic dynamics cap cooperation

by Harry Foster, Vince Knight +1 more

The Cooperation Ceiling: Extrinsic Population Dynamics and the Intrinsic Escape

In the public goods game with varying contributions, outward payoff comparison hits a limit that inward evaluation can exceed.

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Evolutionary game theory provides a framework by which to study the emergence of cooperation in a population of self-interested actors. In such a framework, players' decisions on whether or not to cooperate evolve according to decision rules called population dynamics. However, often games are studied under the assumption that all individuals play under the same conditions, and many common choices of update rule are not well suited for a heterogeneous population. In this paper, we categorise and compare four different population dynamics in such a population as ``extrinsic'', where players learn by looking outward at the payoffs of other players, and ``intrinsic'', where players look inwardly at their own attributes or potential payoffs. We show that extrinsic population dynamics admit a ceiling on the rate of cooperation which can be exceeded by intrinsic population dynamics, and demonstrate this using the public goods game with heterogeneous contributions.

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q-bio.PE 2026-07-01

Transposable elements link development to ageing

by Alessandro Fontana

A conceptual model for the evo-devo role of transposable elements and its implications for the ageing phenomenon

Early epigenetic repression of their regulatory activity is released later, contributing to transcriptional randomness and decline.

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The Evolvable Soma Theory of Ageing is a recently proposed model that frames development as a continuous process of change accompanying organisms throughout the lifespan. This process is driven by developmental genes which encode epigenetic changes on target cells, whereas ageing reflects the expression of late-acting modifications, that are subject to ongoing evolutionary optimisation and function as somatic "experiments" to explore phenotypic novelty. In this work we examine the role of transposable elements in the model. Our proposal acknowledges that these elements facilitate the expansion and diversification of gene regulatory networks by providing transcription factor binding sites. To minimise disruption, their regulatory activity is tightly repressed by epigenetic mechanisms during early development, which may be progressively released by genetically driven, age-associated epigenetic changes in later life, thereby contributing to transcriptional pseudo-randomness and ageing-associated phenotypes. Within this framework, transposable elements are integrated into a unified view of evolution, development and ageing, providing a conceptual basis for their dual role in regulatory innovation and age-related decline.

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q-bio.PE 2026-07-01

Mutation induces effective mortality threshold for population persistence

by Phil. Pollett

Persistence, Thresholds, and Trait Composition in a Regulated Mutation-Selection Model

In two-trait regulated models this sets survival conditions, with initial composition mattering when inheritance dominates mutation.

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We study a population model in which individuals carry one of two traits and evolve under mutation, selection, and density-dependent regulation. A deterministic large-population limit yields a nonlinear system coupling logistic growth with mutation-selection dynamics. We identify threshold conditions governing extinction, persistence, and long-term trait composition. In particular, mutation induces an effective mortality rate that determines whether the population can be sustained. When inheritance dominates mutation, a second threshold emerges: population establishment depends on initial trait composition as well as overall growth rates. Although extinction ultimately occurs, the system typically exhibits long-lived quasi-equilibrium behaviour. A diffusion approximation provides a tractable description of this, and reveals a transition in the sign of trait correlations. The model thus illustrates how mutation, selection, and resource limitation jointly shape both ecological persistence and evolutionary outcomes.

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q-bio.PE 2026-06-29

Generative models couple protein sequences to evolutionary dynamics

by Matteo Bisardi, Leonardo di Bari +4 more

Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics

Probabilistic landscapes from sequence families are linked to population-genetic rules to simulate change on lab and tree timescales.

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Protein sequences carry a record of evolutionary history shaped by mutation, selection, drift, and epistasis. Recent generative models trained on homologous sequence families offer a new way to read this record: they define probabilistic landscapes that score sequences, generate viable variants, and capture constraints that are difficult to measure experimentally. In this review, we discuss how such landscapes can be used not only for protein design or mutation-effect prediction, but also for modeling evolutionary dynamics. We focus particularly on Direct Coupling Analysis as an interpretable and experimentally validated framework, while placing it in the broader context of generative sequence modeling. We first describe how generative sequence landscapes are inferred and assessed, then review how they can be coupled to population-genetic or substitution-model dynamics to simulate protein evolution across experimental and phylogenetic timescales. Applications include viral evolution, laboratory drift experiments, historical contingency, entrenchment, epistatic drift over time, and long-term sequence-space exploration. We conclude by discussing open challenges, including score-fitness calibration, phylogenetic structure, codon-level mutation biases, indels, and the integration of experimental data.

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math.DS 2026-06-29

Alzheimer's model reaches one equilibrium when new plaque formation stops

by Ruoyun Lang, Hui Zhou

Global stability analysis of a mathematical model from Alzheimer's disease

System of differential equations converges globally to a unique positive steady state from any starting concentrations.

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This study focuses on a mathematical model of Alzheimer's disease involving $\beta$-amyloid, cellular prion protein and their complex. The global asymptotic stability of the model indicates that the complex continues to induce neuronal damage regardless of the initial states. To investigate the dynamics of this system, we have rigorously proved that when the formation rate of new plaques is zero, the system is unconditional globally asymptotically stable without any limitation proposed in previous work. Numerical simulations further validate the theoretical analysis, regardless of the random initial state, demonstrating that the system consistently converges to a unique positive equilibrium. From a therapeutic perspective, we propose targeted therapeutic strategies and verify their effectiveness through numerical simulations. These results provide a universal theoretical basis for understanding dynamic mechanisms of Alzheimer's disease and offer critical guidance for developing targeted therapeutics.

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cs.GT 2026-06-29

One simulation run drives evolutionary updates in queues

by Vincent Knight, Geraint I. Palmer-Liyu +1 more

Discrete Event Population Updates: finding game theoretic emergent behaviour in queueing systems with simulation

DEPU feeds payoffs from a long discrete-event run straight into replicator or Moran rules, removing the closed-form barrier and speeding ana

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Strategic behaviour in queueing systems has been studied extensively in the behavioural queueing literature, but almost exclusively for systems that admit closed-form expressions for the cost or utility experienced by a strategic user. Evolutionary game theory offers a mature framework for analysing populations whose individual payoffs depend on the composition of the population itself, and would in principle apply to a much wider class of queueing systems; its application has, however, been constrained by the same closed-form requirement. We introduce Discrete Event Population Updates (DEPU), a general algorithmic framework that couples a single long run of a discrete event simulation (DES) directly to an evolutionary population update rule, removing that constraint. We present two implementations: Discrete Event Replicator Dynamics (DERD), which follows an Euler discretisation of the replicator dynamics equation, and Discrete Event Moran Replacement (DEMR), which maintains a finite population updated via Moran-style copying events. Both are applied to a multi-server jockeying model for which no closed-form fitness expressions are available. On the jockeying model considered, DEPU reaches comparable precision tens of times faster than the standard practice of nesting short simulations inside an outer evolutionary loop, and because each operating point then costs only a single simulation run it also makes systematic parameter sweeps tractable. This brings the toolkit of evolutionary dynamics within reach of any system a modeller can build in a discrete event simulator.

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q-bio.PE 2026-06-29

Maximum-likelihood paths often atypical of real evolutionary histories

by Roberto Netti, Martin Weigt

Reconstructability of evolutionary intermediates in generative epistatic landscapes

Generative protein landscapes show conditional sampling recovers plausible ensembles better than point estimates, with topology setting the

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Evolutionary intermediates connect observed proteins, but the sequence of steps that produced them is rarely recoverable from extant data alone. Here we ask what can, and cannot, be inferred about such intermediates from the endpoints. Using generative sequence landscapes as controlled models of protein-family evolution, we benchmark data-driven reconstruction against ground-truth simulated trajectories. We find that the best point prediction is not necessarily the most faithful evolutionary reconstruction: maximum-likelihood intermediates can be residue-wise accurate yet statistically atypical, whereas conditional sampling better captures the ensemble of plausible histories. Predictability is limited by the topology of the landscape. Constrained, low-mutability regions preserve information about the path, while permissive high-mutability regions open many alternative routes and erase path-specific memory. We also show that sequence divergence alone is an insufficient measure of elapsed evolutionary time; incorporating endpoint mutability provides a more reliable way to place intermediates in the landscape. These results recast intermediate reconstruction as a calibrated probabilistic problem. Rather than seeking a single "true" sequence, data-driven models should identify when endpoints contain evolutionary information, and return realistic ensembles.

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q-bio.BM 2026-06-29

Method adds coevolution to ancestral protein reconstruction

by Alya Zeinaty, Leonardo di Bari +4 more

Towards coevolution-aware ancestral sequence reconstruction

DCA-integrated phylogeny produces ensembles that match both trees and natural sequence statistics under epistasis

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Ancestral sequence reconstruction (ASR) is a powerful approach for studying molecular evolution and the emergence of protein function. Yet most ASR methods assume that sites evolve independently, neglecting the epistatic constraints that shape protein structure, stability, and function. This simplification affects both ancestral inference and its evaluation: maximum-a-posteriori reconstructions may over-concentrate probability into a single over-idealized sequence, whereas independent posterior sampling can generate implausible or poorly functional ancestors. Here, we introduce a coevolution-aware ASR framework that combines standard phylogenetic inference with Direct Coupling Analysis (DCA), thereby preserving site-wise ancestral uncertainty while enforcing residue-residue constraints learned from extant protein families. To benchmark the method, we develop a controlled forward-evolution framework based on a DCA evolutionary sampler, allowing reconstructed ancestors to be compared with known ground-truth sequences generated under realistic epistatic constraints. Applied to beta-lactamases and DNA-binding domains, the approach improves reconstruction when ancestral states are epistatically constrained, and yields ensembles of candidate ancestors that are both phylogenetically consistent and statistically compatible with natural protein families. This framework bridges the gap between single-sequence MAP reconstruction and unconstrained posterior sampling, providing a practical route toward ancestral reconstructions that better reflect the coupled nature of protein evolution.

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cs.GT 2026-06-29

Reactive Nash equilibria map one-to-one to action subsets

by Franziska Lesigang, Christian Hilbe +1 more

Characterisation of reactive Nash equilibria in repeated additive games

Linear conditions on strategy parameters fully characterize symmetric cases and recover equalizers when all actions are supported.

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In this paper, we study reactive strategies in repeated additive games between two players with finitely many actions. Reactive strategies condition only on the opponent's previous action, making them one of the simplest ways players can respond to past interactions. Additive games include important models of cooperation, such as the donation game and games with a punishment option. We show that, for this class of games and strategies, the conditions for symmetric Nash equilibria reduce to a system of linear equalities and inequalities in the strategy parameters, allowing us to characterise all such equilibria. We establish a one-to-one correspondence between non-empty subsets S of the action set and equilibrium classes, which we call S-supporting equilibria. These are equilibria that use exactly the actions in S when playing against themselves. As a special case, we recover the well-known equalizer strategies as the equilibria supported on the entire action set. To assess which equilibrium classes are most evolutionarily relevant, we complement our analytical characterisation with simulations of social learning dynamics. We find that their prevalence is determined by two factors: how likely they are to be generated and how robust they are against invasion.

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q-bio.PE 2026-06-26

Phylogenetic likelihoods gain up to 10x on multi-core CPUs and GPUs

by Karthik Gangavarapu, Xiang Ji +5 more

BEAGLE 4.1: A high-performance library for computation on phylogenetic trees across diverse parallel architectures

New gradient algorithms and hardware support in the library scale with taxa and site patterns for both nucleotide and codon models.

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Efficient evaluation of sequence data likelihoods and their high-dimensional gradients on phylogenetic trees improves inference under both maximum-likelihood and Bayesian frameworks. Here, we present BEAGLE 4.1, a high-performance library for statistical phylogenetics that incorporates new algorithms to evaluate these gradients on phylogenetic trees. We also provide new hardware implementations for both likelihoods and gradients supporting ARM NEON intrinsics and optimized matrix multiplication units -- called tensor cores -- on NVIDIA graphics processing units (GPUs). We benchmark the performance scaling of the library across a number of patterns and taxa on multi-core CPUs and GPUs, and compare the speedup afforded by NVIDIA and AMD GPUs as well as performance scaling with an increasing number of GPUs. We show that multi-core CPU implementations provide up to a fourfold speedup over single-threaded CPU implementations and up to an tenfold speedup for nucleotide and codon models, respectively, with performance generally improving as the number of taxa and site patterns increases. GPUs outperform multi-threaded CPU implementations for a realistic number of patterns, even for nucleotide models with a small state-space size of 4, while for codon models they provide substantially higher performance gains even for a single pattern or four taxa. Tensor cores on GPUs provide up to 2-fold speedup relative to standard CUDA cores for codon models. Using NEON instructions on ARM CPUs affords up to a $\sim 1.3$-fold speedup over non-SIMD implementation with the speedup going down to 1.1-fold at 8 CPU threads. We provide these new algorithms to evaluate the gradient and efficient hardware implementations for both likelihood and gradient calculations through BEAGLE 4.1, such that they can be readily integrated into phylogenetic software packages.

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q-bio.PE 2026-06-26

Library scales phylogenetic shape inference to 850-node trees

by Gefan Yang, Marcus Teller +3 more

Hyperiax and Phylogenetic Inference from Shape Data

Hyperiax applies BFFG to 118-landmark butterfly wings and 79-landmark bird beaks on trees far larger than earlier studies allowed.

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Phylogenetic inference on high-dimensional morphological traits requires algorithms that account for both the nonlinear geometry of the shape data and the phylogenetic tree structure. The Backward Filtering Forward Guiding (BFFG) framework provides smoothing for nonlinear stochastic processes on trees and enables inference of parameters and ancestral states. As practical adoption has been limited by a lack of efficient implementations, we present Hyperiax, an open-source library for tree traversal algorithms and message passing using JAX, designed particularly to support operations needed for BFFG. Hyperiax enables efficient execution of operations on trees with large numbers of nodes and, coupled with the BFFG-specific operations, this allows efficient inference in both discrete-time and stochastic differential equation models. Concretely, we demonstrate that Hyperiax enables parameter inference and ancestral reconstruction for butterfly wing shapes represented by landmarks in two dimensions, and analyses of avian beaks from landmarks in three dimensions. Both cases demonstrate application of BFFG on two substantially larger phylogenetic trees with 850 and 696 nodes with higher resolution shape data (118 two-dimensional landmarks and 79 three-dimensional landmarks, specifically) than previously possible.

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math.OC 2026-06-26

Constant-factor approximation for parental parsimony score

by Martin Frohn

The parental parsimony problem on binary, tree-child phylogenetic networks

First guaranteed-quality algorithm for the PPS on binary semi-simplex tree-child networks, which remain NP-hard.

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Phylogenetic reconstruction is one of the major challenges in computational biology. Among existing reconstruction methods for phylogenetic networks, an important subtask emerges in extending a leaf-labelling on a phylogenetic network to determine a most parsimonious tree inside the network. There exist different variants of this subtask depending on the biological model assumptions for which distinct evolutionary phenomena are captured by the network. In this article we assume that next to hybridization or recombination events, also allopolyploidy or incomplete lineage sorting are present. Then, finding the most parsimonious tree inside the network is called the parental parsimony score problem (PPS), a NP-hard combinatorial optimization problem. We provide the first constant-factor approximation for the PPS on arbitrary but fixed leaf labels and a class of networks on which the PPS remains NP-hard, namely binary, semi-simplex, tree-child phylogenetic networks. Furthermore, we introduce a novel exact solution algorithm for the PPS on binary, tree-child phylogenetic networks and analyze its performance on simulated data.

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q-bio.PE 2026-06-26

Fixed milking order for cow cohorts limits H5N1 spread

by Oliver Eales, Scott Ison +4 more

On-farm management strategies for reducing H5N1 transmission in dairy cattle

Models show the grouping works across uncertain transmission routes and performs best when set up before any infection arrives.

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Introductions of H5N1 clade 2.3.4.4b into dairy cattle have resulted in outbreaks on dairy farms across the United States since early-2024. Outbreaks have significant consequences for animal health, result in economic losses for the dairy industry, and pose a threat to human health. Though the relative contributions of different on-farm transmission pathways remain a key uncertainty, a major route is considered to be through repeated contamination of milking stalls (i.e. the equipment and area where an individual cow is milked) due to the milking of infected animals. Here we develop mathematical models of H5N1 transmission dynamics on dairy farms, considering multiple possible transmission pathways, and identify factors that contribute to outbreak risk and on-farm interventions for mitigating risk. In particular, we demonstrate that dividing cattle into 'milking cohorts', with cohorts kept in separate pens or paddocks and milked in the same order every day, would be highly effective at mitigating outbreaks irrespective of the dominant transmission pathway. Cohorting cattle is most effective when implemented pre-emptively (i.e. before an outbreak) and when newly introduced cattle are kept in the final milking cohort. Additionally, we demonstrate that frequent bulk milk sample testing (e.g. weekly) would enable the rapid detection of outbreaks and implementation of reactive interventions (or scaling up of existing interventions). Our findings can support the development of management guidelines for effectively responding to H5N1 outbreaks in dairy cattle.

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q-bio.PE 2026-06-26

Network triangles evade identification in Jukes-Cantor models

by Bryan Currie, Aviva K. Englander +8 more

Semialgebraic Conditions for Identifying Triangles in Phylogenetic Networks

Three models share full-dimensional probability regions, so edge orientations in triangles cannot be recovered from data.

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An important consideration for a model-based method of phylogenetic network inference is the identifiability of the network parameter of the model. A recurring theme in previous works exploring this issue is that it is often difficult to identify the orientation of edges in a triangle of the network. In fact, it has been shown that for some models it is impossible to determine the orientation of triangle edges utilizing the standard algebraic technique of phylogenetic invariants. In this work, we consider one such model with a Jukes-Cantor site-substitution process and no coalescence. We give a complete semialgebraic description of three, 3-leaf Jukes-Cantor phylogenetic network models with embedded triangles. By describing these base cases, we resolve several questions about the identifiability of networks with embedded triangles. We show that for any pair of models, the intersection and set differences of the models are full-dimensional regions of the space of site-pattern probability distributions. Thus, despite being algebraically indistinguishable, these network models are not identical, nor are they identifiable (or generically identifiable). Our results also yield a straightforward biological interpretation--that the signal from a hybridization event may be immediately detectable but decays over time until it is impossible to identify the orientation of edges in the triangle of a network.

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q-bio.PE 2026-06-25

Spline clock recovers true time-varying rates with tighter intervals

by Pratyusa Datta, Philippe Lemey +1 more

Smoothly Time-Varying Continuous Time Markov Chains in Phylogenetics

Cubic B-splines inside inhomogeneous CTMCs let phylogenies track smooth changes in substitution speed, outperforming standard clocks in simu

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The dependence of evolutionary rate estimates on the timeframe of sampling poses a fundamental challenge for reconstructing evolutionary histories from molecular sequence data, which is central to evolutionary biology and infectious disease research. We present a novel and flexible approach to accommodate time-varying evolutionary rates by modeling the sequence substitution process using inhomogeneous continuous-time Markov chains (ICTMCs) acting along the branches of the phylogeny, and parameterizing the log transformed rate as a smooth function of time using a cubic B-spline basis expansion. Following the parlance of phylogenetics that refers to rates of molecular substitutions as molecular clocks, we call this a spline clock model. Integrals of the rate function over all branches, required for likelihood evaluation, are approximated efficiently using Gauss-Legendre quadrature, and smoothness is enforced by assigning a Gaussian Markov random field prior to the spline coefficients. Through a simulation study, we demonstrate that the spline clock model recovers the true time-varying rates more accurately and with tighter credible intervals than competing clock models. We apply the spline clock model to examine the evolutionary rate of foamy virus and the rate of spatial diffusion of SARS-CoV-2 across Europe, recovering strong time-varying signal in both settings.

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q-bio.PE 2026-06-25

Additional food raises network deficiency in predator-prey models

by Urvashi Verma, Kanishka Goyal +3 more

An Investigation of Additional Food Models with Generalised Functional Response

Generalized models show global stability of coexistence under stated conditions, yet Holling type IV cases admit codimension-3 bifurcations

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Additional food sources are often used to improve the effectiveness of predators in controlling pest populations. However, the non-symmetric structure of additional food predator-prey models can cause certain aspects of their dynamics challenging to analyze. In this work, we study a general class of additional food models and establish conditions under which the coexistence equilibrium is globally stable. We then focus on a Holling type IV functional response with AF and show the existence of a Bogdanov-Takens bifurcation of codimension 3. We also study these models through the lens of deterministic chemical reaction network theory. Our analysis shows that the introduction of additional food increases the deficiency of the underlying reaction network and suggests a possible link between higher deficiency and complex bifurcations.

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q-bio.PE 2026-06-25

Closed-form sensitivities derived for evolutionary entropy in Lefkovitch matrices

by Henrqiue M. Oliveira

Sensitivity of evolutionary entropy in Lefkovitch matrices

Explicit expressions cover stationary distributions, generation times and partial derivatives with respect to fertility, transitions and ret

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Evolutionary entropy, introduced by Demetrius, is a demographic invariant that quantifies the temporal organization of structured populations. Explicit sensitivity expressions for this quantity were derived by Demetrius, Gundlach and Ziehe for age-structured Leslie matrices, establishing the foundations of entropy-based perturbation theory. In this paper we develop a complete sensitivity theory for evolutionary entropy in irreducible Lefkovitch matrices. Using the Perron--Frobenius representation of the associated Markov chain, we derive explicit closed-form expressions for the stationary distribution, generation time, evolutionary entropy and its partial derivatives with respect to fertility, transition and retention parameters. The resulting identities are expressed directly in terms of demographic coefficients, Perron eigenvectors, the dominant eigenvalue and the reproductive potential. The entropy representation obtained here gives a natural decomposition into transition and retention components and clarifies the distinct mechanisms through which demographic uncertainty is generated in stage-structured populations. We further show that the theory specializes immediately to open-group Leslie matrices, a class that has been shown to comprise a large fraction of empirical demographic models. The results extend the entropy sensitivity theory of Demetrius--Gundlach--Ziehe from age-structured to general stage-structured populations and provide practical tools for comparative demographic analysis, perturbation studies, demographic robustness, and the investigation of life-history strategies. Several biological examples are presented, illustrating how entropy decomposition and sensitivity analysis reveal complementary aspects of population organization.

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q-bio.PE 2026-06-25

Binary absent-word matrix yields ML trees with branch support

by Papri Saha, Sudipta Kumar Das +2 more

ML-MAWS: Alignment-Free Maximum Likelihood Phylogeny Estimation Using Minimal Absent Words

Method recovers near-correct splits on bacterial, mitochondrial and viral benchmarks while adding probabilistic confidence values absent fro

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Alignment-free methods in phylogenetic tree construction have major benefits in computational efficiency over alignment-based methods, but most sacrifice sequence information to pairwise distances, losing the statistical power of maximum likelihood (ML) inference. We describe ML-MAWS, an algorithm that fills this gap by encoding Minimal Absent Words (MAWs) as a binary presence/absence character matrix and estimating using an ML tree under the Lewis Mkv model using ascertainment bias correction. MAWs are obtained in linear time through the traversal of a suffix automaton. Three new elements contribute to the phylogenetic signal: strand-aware filtering combines forward and reverse complement MAW sets to eliminate compositional artifacts; entropy-based multi-length selection uses Shannon entropy maximization to select the most informative lengths of MAWs; and parsimony-informative character capping only retains the most discriminative columns. We tested ML-MAWS on 14 benchmark datasets of bacterial, mitochondrial, viral, and simulated genomes with normalized Robinson Foulds distances and matching split distances, against published reference trees. The results show that the coarse binary encoding of MAWs can lead to higher topological errors than continuous-valued distance baselines, while ML-MAWS can successfully recover near-correct splits and can uniquely provide per-branch statistical confidence as well as a rigorous probabilistic framework that is lacking in these methods.

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math.CO 2026-06-24

Parameterized H_alpha indices extend B2 balance to phylogenetic networks

by François Bienvenu, Jean-Jil Duchamps +1 more

A parameterized family of balance indices for phylogenetic networks

Grafting property decomposes each index across biconnected components, yielding minima, maxima and distributions under Yule and PDA models.

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We introduce a new family of balance indices for phylogenetic networks: the $H_\alpha$ indices, where $\alpha$ is a positive real number. This family includes the $B_2$ index as a special case ($\alpha = 1$) and provides a natural extension of the Sackin index to phylogenetic networks. We show that the $H_\alpha$ indices share many structural properties with the $B_2$ index, most notably a "grafting property" that makes it possible to express the $H_\alpha$ index of a network in terms of the $H_\alpha$ indices of its biconnected components. These properties allow us to identify networks that minimize / maximize $H_\alpha$ for various classes of phylogenetic networks, and to study its distribution for several models of random trees and networks (in particular, Galton-Watson trees and binary Markov branching trees, with a focus on the Yule and PDA models). Finally, we show how local limits can be used to analyze the asymptotic behavior of $H_\alpha$ for large trees and networks, and we obtain general results for the moments of $H_\alpha$ for a broad class of random phylogenetic networks known as blowups of Galton-Watson trees.

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cs.DM 2026-06-24

Phylogenetic networks from triples built in polynomial time

by Patricia A. Ebert, Anna Lindeberg +1 more

Novel Triple-Based Problems for the Construction of Phylogenetic Networks via Least Common Ancestors

LCA-constraint reduction solves consistency for rooted and anchored triples and constructs the network efficiently

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Evolutionary histories are often represented by rooted phylogenetic networks, whose leaves correspond to extant taxa and whose internal vertices represent ancestral lineages. Since such histories must usually be inferred from incomplete data, in particular from genomic sequences of present-day taxa, one often obtains only local information about relative evolutionary proximity. For instance, sequence data may suggest that two taxa $x$ and $y$ are more closely related to each other than either is to a third taxon $z$. This information is classically encoded by a rooted triple $xy|z$. In this paper, we study rooted triples in phylogenetic networks under an ancestor-based interpretation: $xy|z$ is displayed if the unique least common ancestor (LCA) of $x$ and $y$ lies strictly below the unique LCA of $x$ and $z$, respectively of $y$ and $z$, and the latter two LCAs coincide. We also introduce anchored triples $\underline{x}y|z$, which retain only the asymmetric comparison that the LCA of $x$ and $y$ lies below the LCA of $x$ and $z$. This relaxation is natural in networks, where different pairwise ancestral relationships need not behave as they do in trees. We consider several variants of consistency problems for ordinary and anchored triples, both with and without forbidden triples. Somewhat surprisingly, these ancestor-based consistency questions for triples in phylogenetic networks do not appear to have been addressed before despite their direct biological interpretation and the fact that such constraints can be inferred naturally from genomic sequence data. By translating these questions into realization problems for required and forbidden LCA-constraints, we show that all resulting problems can be solved in polynomial time. Moreover, whenever a solution exists, a suitable realizing DAG and phylogenetic network can be constructed within the same time bound.

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math.CO 2026-06-24

Orchard networks enumerated by product of matching polynomials

by Josep Batle

Exact Enumeration of Phylogenetic Networks: The Tree-Child, Reticulation-Visible and Orchard Hierarchy

Column generating functions are rational with denominator the product from j=2 to ℓ of the matching polynomial of K_j, resolving counts for

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We develop a unified framework for the exact enumeration and asymptotic analysis of the three most studied classes of phylogenetic networks: tree-child (TC), reticulation-visible (RV) and orchard networks, whose cardinalities satisfy the strict ordering $|\mathrm{TC}_{\ell,k}|<|\mathrm{RV}_{\ell,k}|<|\mathrm{Orch}_{\ell,k}|$ for reticulation number $k\geq2$ (with $\mathrm{TC}\subsetneq\mathrm{RV}$ and $\mathrm{TC}\subsetneq\mathrm{Orch}$, while $\mathrm{RV}$ and $\mathrm{Orch}$ are incomparable as sets). Using the Chang--Fuchs structural theorem, we derive a two-level master functional equation for the RV bivariate generating function and obtain exact closed-form identities for the differences $\Delta_k(\ell):=|RV_{\ell,k}|-|TC_{\ell,k}|$ for $k=2,3$, with the asymptotic universality $\Delta_k(\ell)/|TC_{\ell,k}|\sim k!/\ell$. For orchard networks, we prove a \emph{universal hypergeometric law} that resolves the exact enumeration problem for all $\ell$: the column generating function $F_\ell(v)$ is rational with denominator $D_\ell(v)=\prod_{j=2}^\ell X_j(v)$, where \[ X_\ell(v) = \sum_{k=0}^{\lfloor\ell/2\rfloor}(-1)^k\, \frac{\ell!}{(\ell-2k)!\,k!}\,v^k \] is the matching polynomial of the complete graph $K_\ell$ and a rescaled Jacobi polynomial. This immediately resolves the intractable $\ell=9$ case: $D_9$ has degree 20, dominant growth rate $\approx40.73$, and all spectral roots are positive real. A complete enumeration table is provided extending the published data of Cardona, Ribas and Pons.

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q-bio.PE 2026-06-23

Rare migration reduces deme evolution to low-dimensional chain

by Yi Fu, Natalia L. Komarova

Mutant Fixation for a Stochastic Evolutionary Model in Fragmented Populations

Fixation probabilities and absorption times of the full process are asymptotically given by a coarse-grained Markov chain on all-mutant and

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Population fragmentation is a common feature of many biological systems. Understanding mutant fixation in such systems is challenging because the underlying stochastic dynamics are high-dimensional. In this work, we develop a general mathematical framework for analyzing stochastic evolution in fragmented populations connected by rare migration. The framework is sufficiently general to accommodate heterogeneous deme sizes, deme-dependent birth and death processes, and migration on arbitrary strongly connected directed networks with asymmetric migration rates. We show that, in the limit where migration occurs on a much slower timescale than within-deme dynamics, the full stochastic process can be reduced to a lower-dimensional Markov chain whose states correspond to configurations of fully mutant and fully wild-type demes. The reduction theorem establishes that fixation probabilities and absorption times of the original process are asymptotically determined by the corresponding quantities of a reduced chain. As an application, we derive explicit formulas for mutant fixation probabilities and fixation times in fragmented populations initiated by the introduction of a single mutant. The results provide a general and tractable approach for studying evolutionary dynamics in complex fragmented populations.

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q-bio.PE 2026-06-23

Lab bee survival data scaled to landscape model yields negligible population effects

by Florian Schunck, Agnieszka Bednarska +4 more

From Lab to Landscape: Assessing the Impact of Pesticides on Pollinator Populations Based on Laboratory Data by Combining ALMaSS and BufferGUTS

BufferGUTS-ALMaSS coupling produces daily survival probabilities; extreme sulfoxaflor rates still show little impact unless larval stages ar

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Pesticides are designed to eradicate pests from crops, fulfilling an important role in the current agricultural system. However, nature conservation requires that pesticide applications are protective for non-target organisms, which provide ecosystem services on the other hand. Environmental risk assessment (ERA) is supposed to strike this balance, but the current use of laboratory derived toxicity thresholds in the landscape context, without consideration of population and landscape dynamics might be too coarse to achieve this task. Here, we propose to overcome this limitation by coupling the Animal, Landscape, and Man Simulation System with the BufferGUTS model for non-target arthropods. We conducted a case study of the solitary bee Osmia bicornis exposed to the pesticide formulation Closer (a.i. sulfoxaflor) to assess the integration. Laboratory survival data of topical and oral exposure to Closer were used to calibrate BufferGUTS models. The resulting parameters were used to parametrise model organisms in ALMaSS simulations to extrapolate the effects of sulfoxaflor at different exposure levels on population dynamics. The integration of BufferGUTS into ALMaSS landscape simulation was achieved with high numerical precision, allowing for the calculation of daily survival probabilities for model organisms in the ALMaSS framework. We found that even extreme application rates only led to negligible population effects in ALMaSS simulations, but an exploratory analysis of pesticide-driven larval mortality showed that effects might be more severe when all life stages are considered. The work demonstrates how mechanistic modelling embedded into individual based modelling frameworks can support ERA by combining exposure and effect in systems-based ERA tools, bridging the gap between controlled laboratory experiments and realistic landscape-scale risk assessments for next generation ERA.

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q-bio.PE 2026-06-23

Movement restrictions alone leave Salmonella Dublin endemic on Öland

by Stefan Widgren, Ivana Ewerlöf +3 more

Bayesian modelling of herd-level infection dynamics in cattle: Local spread as the primary driver of Salmonella Dublin persistence on \"Oland

Bayesian model attributes half the force of infection to local spread, so cattle-movement controls cannot push average Rt below 1

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Salmonella Dublin (S. Dublin), a zoonotic serotype adapted to cattle, causes animal welfare issues and economic losses. The disease has proven particularly challenging to control in \"Oland, Sweden. This study uses Bayesian simulation-based inference of bulk tank milk sample results to analyse the S. Dublin infection dynamics in \"Oland cattle. The infection process was formulated as a dynamic state-space model and particle Markov-chain Monte Carlo methods were applied to infer the underlying infection dynamics and estimate the basic reproduction number ($R_0$) as well as the effective reproduction number ($R_t$). These metrics provide insight into transmission dynamics, enabling assessment of the effectiveness of the current S. Dublin control in Swedish cattle and identification of interventions that may reduce the prevalence. The results show that most holdings on \"Oland have $R_0 < 1$, indicating that infection is expected to die out after introduction. However, in a subset of holdings $R_0 > 1$, and there the risk for spread of S. Dublin is higher. Furthermore, the analysis reveals that on average, $R_t \approx 1$, suggesting a stable endemic presence unless effective interventions are implemented. In addition, the results show that it is insufficient to restrict the movements of infected cattle on \"Oland to bring $R_t < 1$, as local spread and within-herd transmission contribute equally to the force of infection (approximately 50% each). These findings demonstrate how Bayesian data-driven analysis can support evidence-based decision making for the control and eradication of S. Dublin in cattle.

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q-bio.PE 2026-06-23

Abundance correlations reveal no niche overlap in standard models

by Akiva Goldberg, Nadav M. Shnerb

When do correlations reflect biological similarity in ecological dynamics?

Stochastic Lotka-Volterra systems cannot embed biologically grounded noise without breaking the link between forcing similarity and observed

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The structure of competitive ecological communities is shaped by the strength of interactions between species, which in turn reflects their biological similarity. At the same time, the stochastic forcing that drives abundance fluctuations is itself biologically grounded: species that are more similar may be expected to respond more similarly to environmental variation. This motivates the increasingly common use of correlations in abundance time series, particularly in microbial communities, as proxies for biological similarity or niche overlap. Here we analyze the relation between biological similarity and abundance correlations in stochastic community models. We require that the stochastic forcing acting on different species be correlated in proportion to their biological similarity, and ask how such forcing is reflected in abundance correlations. We show that this requirement cannot, in general, be satisfied within the widely used stochastic Lotka-Volterra framework, and that even when it is, abundance correlations carry no information about niche overlap. In contrast, consumer-resource models provide a natural framework for biologically grounded stochasticity. In this setting, however, the interpretation of abundance correlations depends strongly on the pathway through which noise enters the system: direct forcing of consumers and resource-mediated fluctuations encode different biological quantities. These results have implications both for the modeling of stochastic ecological communities and for understanding what can, and cannot, be inferred from correlations in community time series.

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q-bio.PE 2026-06-22

Optimal network degree maximizes upstream reciprocity

by Vikash Kumar Dubey, Sagar Chakraborty

Upstream reciprocity versus downstream reciprocity: Catalyzing cooperation

The peak holds across update mechanisms while downstream reciprocity supports overall cooperation in structured groups.

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Why would anyone help a stranger, knowing they may never meet again? Indirect reciprocity offers one of the most compelling evolutionary answers, yet its two canonical forms -- upstream reciprocity (experience-based), and downstream reciprocity (reputation-based) -- have been studied mostly in isolation. Their joint dynamics in finite and structured populations remain largely unexplored. Here, we fill this gap using agent-based simulations in which an agent is behaviourally either defector, upstream reciprocator, or downstream reciprocator, and the agents' population state is temporally updated using different evolutionary update mechanisms. We show that update mechanism plays a surprisingly decisive role in shaping the fate of downstream and especially upstream reciprocators. Whether agents' experiences and reputations are updated globally or locally can shift outcomes from rich behavioural coexistence to the dominance of downstream reciprocators alone. Intriguingly, we uncover a robust structural feature that persists across all the explored update rules and population sizes: an optimal network degree at which upstream reciprocity is maximized, reflecting a fundamental tug-of-war between cooperative clustering and exposure to defectors. Our results highlight that while downstream reciprocity can either foster or inhibit upstream reciprocity depending on the update mechanism, its net effect on cooperation remains largely positive.

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cs.NE 2026-06-22

Three LLM agents create evolving culture in decaying store

by Simon Jones, Sabine Hauert

Emergent Culture in Minimal LLM Systems

Minimal collectives develop storage strategies and long-range coherence beyond message decay, without top-down design.

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What happens when LLM agents operate with no context outside a turn, minimal prompting, and simple tools? Inspired by swarm engineering, we give collectives of three agents the ability to send messages and manipulate a shared actively decaying text store, introducing evolutionary pressure. The agents spontaneously cooperate, develop storage management strategies, and generate complex evolving cultural artifacts, with no top-down engineering. Using tools from dynamical systems analysis, we show that these behaviours exhibit structured long-range coherence beyond the entropy horizon of the decaying store, consistent with emergent culture in the Sperberian sense.

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q-bio.PE 2026-06-22

R package detects introgression from gene tree discordance

by Ethan A. Baldwin, James H. Leebens-Mack

quaint: An R Package for detecting introgression across a phylogeny using discordant gene tree topologies

quaint applies the ABBA-BABA test to whole gene trees, extending detection beyond the reach of site-based methods.

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Premise: Hybrid speciation and introgressive hybridization are increasingly recognized as important evolutionary phenomena across the tree of life. One widely used class of methods to detect introgression includes D statistics and related methods which employ the ABBA-BABA test using nucleotide site patterns. Recent studies have applied this theoretical framework to phylogenomic datasets using gene tree topologies instead, but no software packages using this method have been developed. Methods and Results: An R package was developed to facilitate the inference of introgression given a set of gene trees and a species tree. Using an ABBA-BABA framework, this package summarizes patterns of gene tree discordance to infer introgression across large phylogenies. Conclusions: Using gene tree topologies, quaint overcomes the limitations of site-based methods, enabling the detection of introgression across broad phylogenomic contexts. This R package provides an accessible and reproducible tool for researchers investigating reticulate evolution.

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q-bio.PE 2026-06-22

Neural nets predict flu clade fitness from genomes at R² above 0.95

by Ursula Senam Nkonu, Richard Annan +2 more

Inferring and Predicting Clade-Level Relative Transmission Fitness in Seasonal Influenza A Using Differential Population Growth Rate and Deep Learning

Growth rates inferred from GISAID sequences match surveillance patterns and let models forecast which H3N2 and H1N1 clades will dominate.

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Seasonal influenza A evolves rapidly, allowing newly emerged clades to replace previously dominant lineages and complicate surveillance and vaccine evaluation. Here, we applied the Differential Population Growth Rate (DPGR) framework to GISAID-derived H3N2 and H1N1 surveillance data collected from 1 January 2014 to 12 February 2026, including the 2025-2026 influenza season, to estimate clade-level relative transmission fitness across continents and within the United States. We identified windows of co-circulation with sliding-window regression, reconstructed relative-fitness relationships among clades, and compared inferred growth advantages with independent WHO and CDC surveillance patterns. We further trained subtype-specific convolutional neural networks on complete viral genomes to predict DPGR from sequence, quantified predictive uncertainty with conformal prediction, and used SHAP to localize genomic contributors to fitness. DPGR recovered recurrent lineage turnover in both subtypes and consistently identified the emerging H3N2 subclade K as fitter than the 2025-2026 vaccine-lineage background across multiple regions. Genome-based models predicted DPGR accurately for H3N2 ($R^2 = 0.9577$) and H1N1 ($R^2 = 0.9871$), while interpretation highlighted known haemagglutinin antigenic sites together with contributions from internal genes. These results support DPGR as an interpretable surveillance signal and show that influenza fitness can be linked to genomic prediction and biological interpretation in a unified framework.

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q-bio.PE 2026-06-22

Entropy predicts reproductive windows across 130 animal species

by Jorge Buescu, Saber Elaydi +1 more

Evolutionary Entropy Shapes Reproductive Lifespan in Age-Structured Populations

Normalized post-maturity distributions fix entropy and generation time under rescaling in Leslie demographic models

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Evolutionary entropy measures the temporal organization of reproductive contributions along the life cycle of an age-structured population. We develop a mathematical and empirical framework showing that, in iteroparous animal populations represented by Leslie-type demographic matrices, reproductive windows are frequently organized near the age classes selected by entropy maximization. Evolutionary entropy complements the classical net reproductive number and asymptotic growth rate: whereas these measure lifetime replacement and growth, entropy measures the temporal dispersion of the growth-adjusted reproductive distribution. Our central result is a reduction principle: under Euler--Lotka normalization, evolutionary entropy and generation time are invariant under multiplicative rescaling of survivorship and fertility on the reproductive interval. The relevant entropy is determined not by absolute survivorship, fertility, or juvenile mortality, but by the normalized post-maturity reproductive distribution. We derive explicit entropy functionals for finite and open-group Leslie models, including geometric reproductive tails. For the geometric regime, governed by we prove a sharp critical threshold separating populations with a unique finite entropy-maximizing endpoint from those whose entropy increases toward an asymptotic value in terms solely of the age at first reproduction. The theory is tested on 130 animal species. Entropy-derived predictions, computed from the demographic matrices alone, are compared with independent life-history variables. Predicted and observed reproductive medians coincide exactly for a majority of species, over 90% are predicted within three reproductive classes, and associations remain strong after phylogenetic correction. These results identify a quantitative regularity across taxa, with geometric reproductive distributions playing a central role.

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cond-mat.stat-mech 2026-06-22

Gamma mixing of stopping rates produces heavy-tailed subdiffusive kernels

by Luis F. Gordillo, Priscilla E. Greenwood

Heavy-Tailed Dispersal Kernels from Stopped Subdiffusive Fractional Brownian Motion

Heterogeneous exponential stopping times on fractional Brownian motion yield both clumping and long-distance dispersal in one model.

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Subdiffusive fractional Brownian motions produce localized aggregation when particles are stopped at exponentially distributed times. In applications where clumping and long-distance dispersal events are observed simultaneously, such as in some instances of seed dispersal, this model fails to describe the tails of the data. The resulting redistribution kernel has only an exponentially decaying tail, whereas a heavier tail is needed for modeling the long-distance dispersal observed. Here we propose a model in which subdiffusive particles stop at exponentially distributed times, but with a rate parameter that is Gamma distributed. This heterogeneity in stopping rates causes the density of final radial positions to have a heavy-tailed distribution. Our model retains the strong localized clumping characteristic of subdiffusive fractional Brownian motion while simultaneously generating the heavy tails required for realistic long-distance dispersal.

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q-bio.PE 2026-06-22

Convergent mutations map fitness tradeoffs across 10,000 antibody clonotypes

by Daniel PGH Wong, Aleksandra M. Walczak +1 more

Surveying the adaptive landscapes of 10,000 antibodies

A parameter-free method extracts clonotype-specific selection signals and matches patterns seen in virus-specific antibodies.

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Affinity maturation is the Darwinian process by which antibodies improve antigen binding through somatic hypermutation and selection. The adaptive landscape, which defines the set of antibody-specific mutations that improve functional characteristics like antigen binding, has been explored in only a handful of antibodies. Identifying the sites of adaptive mutations in a given antibody sequence, and how these sites vary across the antibody repertoire, can inform the design of therapeutic antibodies. We develop a parameter-free population genetic framework that leverages the statistics of convergent affinity maturation in B cell lineages sharing similar naive sequences, called public clonotypes, to identify beneficial mutations. Applying this framework to more than 10,000 public clonotypes represented by multiple lineages across 20 healthy individuals, we identify widespread signatures of clonotype-dependent selection of individual mutations. We estimate the prevalence and typical fitness effects of mutations across the V gene at the single-site level, uncovering a general tradeoff between prevalence and fitness effect. These inferred landscapes broadly reproduce the statistics of convergent mutation in antibodies specific to SARS-CoV-2 and influenza. Finally, we use our framework to benchmark predictions from existing antibody language models, and show that while these models are dominated by non-selective signatures, a simple renormalization procedure can expose signatures of clonotype-dependent positive selection consistent with our predictions.

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q-bio.PE 2026-06-19

Quantum Game of Life fits West Nile virus spread in Italy

by Andrea Fontana, Simone Tambascia +4 more

West Nile virus outbreak in Italy modelled with the quantum Game of Life

Matches cumulative infections at local and regional scales by optimizing only mosquito birth and removal rates.

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In the last years, an anomalously high spreading of West Nile virus (WNV) has been observed in Italy, with particularly high peaks of infections in southern Lazio, Campania and Veneto regions. The main disease vector for WNV is represented by Culex pipiens mosquitoes, which spread human infections through their bites. Here, we investigate WNV fever epidemic diffusion during summer season 2025 in Italy through a computational approach based on a quantum version of the Game of Life (GOL) cellular automaton model. Specifically, human dynamics evolves according to the GOL rules, while stochastic dynamics of disease vectors, i.e., mosquitoes, as well as their interaction with humans, simultaneously occur. We show that this model fits the curves of cumulative infected individuals with high accuracy, either at local and average-regional level, with only optimization of mosquito birth and removal rates parameters. Furthermore, leveraging model flexibility, we show that changes in model parameters values elucidate system response to environmental variations. For instance, we quantify, e.g., the impact of mosquito spreading containment measures or sudden mosquito increasing abundance due to climatic and ecological changes. Overall, we provide a general, quantitative description of WNV infection spreading in Italy which could represent a supportive tool to test different environmental scenarios and could be useful to devise strategies for decision makers to monitor disease vector dynamics and to control consequent virus diffusion.

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q-bio.QM 2026-06-18

ODEs give likelihood for SIR on contact trees from tracing data

by Augustine Okolie, Johannes Müller +2 more

Multi-type branching inference on contact trees with application to COVID-19

Closed-form equations for unobserved clades and sampled tips recover transmission parameters and contact heterogeneity from COVID-19 data wi

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Inferring epidemiological parameters from transmission trees is essential for understanding infectious disease dynamics. Existing tree-based likelihood methods, including the multi-type birth-death models originally applied in phylodynamic settings, provide powerful tools, but most assume homogeneous mixing and rarely capture how transmission potential changes as an individual infects more of their contacts. In this work, we develop a likelihood framework that operates directly on transmission trees, in which nodes are individuals and edges are reported transmission events, with no sequence data involved. We derive a likelihood for a stochastic SIR process on a rooted contact tree in which each infected individual is characterised by the total number of effective contacts, and the number of already infected downstream contacts. We obtain closed-form ordinary differential equations for the probability that a clade goes entirely unobserved and for the probability density that it produces an observed (sampled) tip in a given state. The resulting likelihood can be evaluated for a rooted contact tree with known tip states, and we extend it to partially resolved trees by treating internal branching times as latent variables. Validation on simulated outbreaks confirms accurate parameter recovery and well calibrated uncertainty. Application to empirical COVID-19 contact-tracing data from Karnataka, India, demonstrates the framework's utility for real epidemiological settings. By incorporating contact-degree heterogeneity in a multi-type branching likelihood, our work provides a principled baseline for inferring both transmission dynamics and contact structure from fully or partially resolved transmission trees, complementing rather than relying on sequence-based phylodynamic inference

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q-bio.PE 2026-06-18

Annealed noise expands cooperation and extinction regions

by Janguk Kim, Seung-Woo Son +1 more

Effects of spatial environmental noise on evolution of cooperation

Temporal fluctuations shift both phase boundaries upward in spatial games while fixed heterogeneity leaves them unchanged.

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We investigate the effects of environmental noise on cooperation in a spatial evolutionary game model with variable population size. Building on a one-dimensional lattice model in which vacancies promote cooperation through spatial selection, we add random noise to the environmental quality parameter and consider two distinct types: annealed noise, where the environmental quality fluctu ates independently at each site and each time step, and quenched noise, where each site is assigned a permanently fixed random value. For annealed noise, we develop a mean-field theory by replacing the noise-dependent death probabilities with their distribution averages, and find that increasing the noise intensity shifts both the cooperator-defector phase boundary and the absorbing boundary upward in the parameter space, simultaneously expanding the cooperative regime and the extinc tion region. These predictions are confirmed by numerical simulations. In contrast, quenched noise leaves the phase boundary nearly unchanged across all noise levels, exerting only a weak effect on cooperator frequency. Together, these results demonstrate that temporal fluctuations, rather than static spatial heterogeneity, are the primary driver of noise-induced shifts in the cooperative phase structure.

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q-bio.PE 2026-06-17

Aggregation shortens extinction time in fear-Allee models

by Kwadwo Antwi-Fordjour, Eric M. Takyi

Aggregation as a Double-Edged Sword: Fear, Allee Effects, and Finite-Time Collapse

Stronger grouping enlarges the collapse region and tightens the upper bound on time to total ecosystem extinction in a disease-structured pr

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Prey aggregation is widely regarded as a defense against predation, yet we show that in disease-structured populations subject to predator-induced fear and demographic Allee thresholds, aggregation can paradoxically accelerate ecosystem collapse. We develop and analyze a susceptible-infectious-predator model incorporating dual fear responses -- together with a sublinear aggregation-based predation term and an Allee effect. Critically, we derive an explicit upper bound on the extinction time that decreases as predator pressure increases or aggregation strengthens, quantifying for the first time how behavioral and demographic parameters jointly determine the speed of ecological collapse. This finite-time extinction subsequently triggers a cascade collapse of the infected prey and predator populations, driving the entire ecological community to extinction. Bifurcation analysis reveals transcritical, saddle-node, and Hopf bifurcations as fear intensity, aggregation strength, and Allee threshold vary. Two-parameter continuation further identifies the precise regions of the fear--Allee parameter plane in which stable coexistence, oscillatory coexistence, predator exclusion, and finite-time extinction occur, demonstrating that stronger aggregation monotonically enlarges the finite-time extinction region while weaker aggregation supports a richer landscape of coexistence dynamics. These results demonstrate that behavioral defenses operating at the population level can generate abrupt ecological tipping points when they interact with disease dynamics and demographic vulnerability.

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q-bio.PE 2026-06-16

Hybrid SIT strategy cuts sterile males for Anopheles elimination by 5%

by Abba Gumel, C. Alex Safsten

Tipping the Balance: Allee Thresholds, Saddle-Node Bifurcations, and Optimal Sterile-Male Release Strategies for Anopheles Mosquitoes

A mix of constant and responsive releases exploits the Allee threshold more efficiently than either alone to drive populations to extinction

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We formulate and analyze a sex- and stage-structured model for Anopheles dynamics under the sterile insect technique (SIT), motivated by the need for tools robust to insecticide resistance and outdoor transmission. The model tracks aquatic stages, adult males, unmated females, and females mated with wild or sterile males; includes egg-laying capacity and larval competition; and uses a refractory period followed by density-dependent mate search. The resulting Holling type-II mating term generates a mate-finding Allee effect. After establishing well-posedness, we prove that this Allee effect makes the mosquito-free equilibrium locally stable for all admissible parameters and globally asymptotically stable when a quick-mate-search reproduction number $R_0^q$ is below one. When $R_0^q>1$, habitat capacity is large, and larval competition is weak, two positive equilibria arise through a saddle-node bifurcation: a stable natural equilibrium and an unstable Allee equilibrium separating persistence from extinction. For a reduced model, a Goh-Volterra Lyapunov functional estimates the persistence basin. We then show how constant and population-responsive sterile-male releases reshape this bistability. Sufficiently large releases annihilate the positive equilibria in a second saddle-node bifurcation, while a sufficiently large constant release drives local elimination from every admissible initial state. Thus SIT need only push the population across the Allee separatrix, after which mate-finding failure can complete extinction. In a free-horizon optimization framework with an Allee-threshold stopping rule, a hybrid release strategy reduces the sterile-male requirement by about $5\%$ relative to the best constant-only strategy and $39\%$ relative to the best population-responsive-only strategy. These results recast the Allee effect as a control lever for vector suppression.

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physics.soc-ph 2026-06-12

Local observation plus global reputation stabilizes cooperation

by Mari Kawakatsu, Yohsuke Murase +2 more

A model of local and global reciprocity

Conditional cooperators resist both unconditional cooperators and defectors when friends are tracked directly and strangers judged by reputa

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We often decide how to treat friends based on observations of their past behavior, whereas actions toward strangers are typically guided by their public reputations. These two kinds of information underlie two classical mechanisms for the evolution of cooperation$\unicode{x2014}$direct and indirect reciprocity$\unicode{x2014}$which have largely been studied in isolation. They are not interchangeable: we can recall the past actions of only a small circle of close contacts, whereas for the far larger pool of strangers we must rely on public reputations. Here we develop a mathematical framework built on this distinction. Each individual engages in direct reciprocity in local games within a finite neighborhood of friends, whose actions they observe directly, and in indirect reciprocity in global games with a large population of strangers, known only by reputation. Separating local and global interactions allows us to address two questions. First, can cooperation persist under a cognitively simple norm of judgment? We show that combining direct and indirect reciprocity resolves the scoring dilemma: conditional cooperators resist invasion by both unconditional cooperators and unconditional defectors, where indirect reciprocity alone would fail. Second, how should one treat a friend whose past behavior conflicts with their public reputation? We find that the strategies that maximize cooperation are forgiving$\unicode{x2014}$overlooking whichever piece of information is unfavorable$\unicode{x2014}$and that these forgiving strategies can often remain robust to invasion. By distinguishing between local and global scales of interaction and integrating information across them, our framework offers a more cognitively realistic account of how reciprocity sustains cooperation.

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q-bio.QM 2026-06-12

Framework recovers both growth laws and noise from data

by Rebecca M. Crossley, Ruth E. Baker

A likelihood-based framework for simultaneously learning both noise and growth dynamics using biologically-informed neural networks

Joint optimization of dynamics and a learnable noise model improves predictions over methods that assume constant variability.

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In recent years, neural ordinary differential equation frameworks such as Biologically-Informed Neural Networks (BINNs) have shown promise for learning mechanistic laws from sparse data. However, most existing approaches implicitly assume homoscedastic Gaussian noise, and therefore do not account for potentially meaningful structure in biological variability. Here, we present an extension to the existing BINNs framework that includes a learnable noise model, allowing discovery of the noise model directly from data. Using population growth as an example, we demonstrate that the framework accurately recovers the underlying noise structure and improves predictions of the underlying growth laws compared to existing approaches. As such, this work establishes a general likelihood-based framework for jointly learning dynamics and heteroscedastic noise within mechanistic neural network approaches.

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q-bio.QM 2026-06-11

EvAMs predict intervention effects via do-operator on parameters

by Ramon Diaz-Uriarte, Íñigo Ríos-Arroyo +1 more

A structural causal framework for interventions on evolutionary accumulation models

The approach supplies concrete parameter changes that replace simple conditioning and distinguishes killing from inactivating actions on clo

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Evolutionary accumulation models (EvAMs), also known as cancer progression models (CPMs), infer dependencies in the order of accumulation of mutations during tumor progression from cross-sectional data. It has been suggested that EvAMs could be used to identify therapeutic targets, but there is no procedure in the literature for how to extract predictions under intervention from these models. A simple approach of conditioning on the absence of a mutation gives incorrect predictions. We address this gap by formalizing what "intervene" means for all currently available EvAM methods (OT, OncoBN, CBN, H-ESBCN, MHN, HyperHMM, HyperTraPS), using Pearl's do operator and conditional interventions. For each model, we show how to implement the intervention (in most cases as specific parameter modifications), identify equivalent implementation procedures, and analyze whether the modularity assumption -- required for the intervention to be well-defined -- is justified. Drawing on individual-level causal DAGs that make fitness an explicit variable, we distinguish two types of intervention (killing and inactivating) that are conflated in standard EvAM representations. Since the goal is to prioritize intervention candidates, we recast the problem as one of ranking: we define three intervention objectives and provide a protocol for evaluating how well EvAMs rank targets. Our framework is not specific to cancer or EvAMs; it applies wherever fitted computational models can be interpreted as structural causal models. Code available from https://github.com/rdiaz02/scm-interv-evams.

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physics.soc-ph 2026-06-11

School model shows teen substance use can lock into high or low states

by Tamantha Pizarro, Jinni Su +2 more

SCAR dynamics of adolescent substance use: peer influence, dropout, and bifurcation structure in a school-based model

Bistability between substance-free and high-use equilibria means outcomes hinge on initial conditions and the rate of return after dropout.

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We develop a four-compartment susceptible--casual--addicted--resistant (SCAR) model for adolescent substance use in a high-school setting. The model divides students into susceptible non-users, casual or experimental users, students with sustained or substance-use-disorder (SUD)-level involvement, and resistant students in protective anti-use environments. It includes peer-driven initiation, escalation from casual to problematic use, protective peer influence, school disengagement, and partial re-entry after rehabilitation. Qualitative analysis and bifurcation diagrams show three main results. First, the return parameter $\phi$ separates two regimes: when $\phi=1$, the total population is conserved and interior equilibria may exist; when $\phi<1$, problematic use causes net school-population loss, so positive scaled equilibria may not represent true endemic equilibria. Second, initiation and escalation are governed by distinct thresholds, meaning first use and progression to problematic use are dynamically different. Third, the model can exhibit multistability, including bistability between a substance-free state and a stable high-use state, so long-term outcomes may depend on initial conditions. These findings suggest that effective school policy should combine universal prevention, early intervention for casual users, targeted support for students at risk of problematic use, recovery-supportive environments, and strong school re-engagement pathways.

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q-bio.QM 2026-06-10

Embeddings turn discrete biome maps into continuous predictors

by Maxwell B. Joseph, Flávia De Souza Mendes +3 more

Continuous biome representations from Earth observation embeddings

Softmax probabilities on satellite embeddings lift species occurrence AUC from 0.57 to 0.62 across Brazilian forest plots.

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Biotic communities vary continuously across space, yet biome maps impose categorical boundaries that compress this variation, particularly at ecotones where transitional communities are ecologically distinct. Could Earth observation (EO) foundation models, which encode spectral, spatial, and temporal information with dense embeddings, convert discrete biome maps into continuous representations that better capture ecological variation? Here, we fit a linear classifier on Clay v1.5 satellite image embeddings to predict biome labels from a categorical map. The softmax output yields a continuous probability vector whose dimensions correspond to named biome classes. We evaluate this approach using six Brazilian biomes, 1.3 million embeddings, and 10,015 withheld forest inventory plots spanning 4,672 plant species. The continuous biome representation outperforms discrete biome labels for predicting species occurrence (mean per-species AUC 0.618 vs. 0.570 across 10 spatial cross-validation folds). Decomposing this gain shows that continuity in the graded probability output, rather than label reassignment, accounts for the improvement; the pattern holds across all distances from biome boundaries. The raw 1024-dimensional embedding remains the strongest predictor we tested (mean AUC 0.646 vs. 0.618), but the continuous representation recovers most of the embedding's gain over discrete labels. This simple approach provides a probabilistic replacement for categorical map labels, preserving their meaning while encoding graded variation that discrete maps suppress.

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q-bio.PE 2026-06-10

Omnivory stabilizes marine food webs while longer chains increase chaos

by Ilaria Cunico, Guido Occhipinti +2 more

Chaos and stability in the marine trophic network: the importance of interactions over complexity

Simulations of networks with microbial recycling find interaction signs, not species count, decide steady states versus chaotic behavior.

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Understanding the dynamics of real world complex networks is crucial for assessing their predictability, resilience, and improving ecosystem management, especially in the context of climate change. The relationship between stability and complexity in ecological networks is still debated in the literature. In this modeling study, we investigate whether a complex marine trophic network, characterized by multiple trophic interactions and environmental constraints, exhibits predominantly stable, periodic or chaotic dynamics. We incorporate the microbial loop into a trophic network model, which includes one to three primary producers, one or two consumers, and up to three trophic levels of predators. The microbial loop is a key process in which bacteria recycle detritus from higher trophic levels into nutrients available for the growth of primary producers, ensuring mass conservation within the system. We perform numerical simulations to investigate the dynamic behavior of the network, exploring several configurations by turning off predator prey links between species and varying the high dimensional parameter space. Our results show that (i) longer trophic chains and (ii) a higher number of consumers increase system chaoticity, whereas (iii) omnivorous interactions promote stability. Notably, many of the configurations exhibit high percentages of chaotic behavior. Feedback loop analysis suggests that the balance between negative and positive interactions plays a key role in the convergence of the system toward a steady state. This study shows that interactions and feedback, rather than complexity, are key drivers of stability, pointing to the absence of a clear stability complexity relationship and instead highlighting a stability interaction dependence. Chaotic dynamics may also play an important role, with potential implications for predictability and ecosystem management.

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physics.soc-ph 2026-06-10

COVID models gained data but rarely modeled behavior responses

by Elena D'Agnese, Alessia Melegaro +5 more

A systematic review of COVID-19 epidemic models with endogenous human behaviour. What's next?

Review of SARS-CoV-2 models shows shortfalls in behavioural data use and structural novelty that affect future preparedness.

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Human behaviour and epidemic dynamics are intertwined, yet accounting for this feedback remains one of the key challenges of epidemiological modelling. The COVID-19 pandemic was an opportunity to overcome the traditional limitations of the field, raising expectations that data-informed endogenous approaches to behaviour modelling would advance substantially. To quantify the progresses made, we conducted a systematic review of SARS-CoV-2 transmission models endogenously including human behaviour in response to epidemic dynamics. The COVID-19 pandemic saw great strides in terms of the expanded use of empirical data in epi-behavioural modelling. However, it also showed shortcomings with respect to limited use of behavioural empirical data, lack of innovation in model structure, and limited engagement with other disciplines and decision-makers. Overall, our results suggest that identifying priorities in model design and behavioural data, building an adequate data collection infrastructure, leveraging on AI advancements, and fostering interdisciplinarity are strategies of utmost importance for pandemic preparedness.

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q-bio.PE 2026-06-10

Lower trap dispersal cuts needed trap area to 5 percent

by Matthew H Holden

Modeling pest dynamics in trap cropping to improve yield: the effects of attraction, retention, and land allocation

Model shows retention, not just attraction, determines whether trap cropping is practical at commercial scale

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Trap crops reduce damage to a cash (main) crop by attracting pests away from it. Yet this protection is weakened when pests disperse back into the cash crop. In this paper, we focus on the importance of preventing this backflow, showing that effective trap cropping depends jointly on how strongly pests are attracted to trap plants and how rarely they leave them. Together with the proportion of the field devoted to trap plants, these processes determine both the efficacy and feasibility of trap cropping at commercial scales. We formalise this relationship using a simple yield-maximisation framework, in which growers weigh pest suppression benefits against the land sacrificed to trap plants. The model shows that when dispersal from trap plants equals that from the cash crop, optimal trap coverage can exceed 20 to 30 percent of the landscape, levels rarely acceptable to growers. However, reducing pest dispersal off trap plants to just one-quarter of cash crop dispersal lowers the optimal required trap area to approximately 5 percent, transforming trap cropping from impractical to feasible. Understanding these relationships can guide trap-cropping design, from plant choice to targeted interventions that reduce pest movement, to minimise damage, maximise yield, and make trap cropping a reliable component of sustainable pest management.

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nlin.AO 2026-06-10

Uninformed agents delay polarization onset via direction-free dissipation

by Leonardo Colombo, Mar{i}a Emma Eyrea Irazu +2 more

Stabilizing Role of Uninformed Participants in Collective Decision Making

They push observable group splits to higher conflicts without moving the structural threshold

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For groups without strict hierarchy, collective decisions often emerge through compromise. We develop a second-order network model of collective decision-making using a dissipative Hamiltonian formulation, in which informed agents introduce preferred directions while uninformed participants contribute only direction-free dissipation. We show that under low conflict, the model admits a locally unique, exponentially stable compromise state. Using a structured modular network we further show that as conflict increases the local compromise branch terminates through a saddle-node fold rather than through a smooth mean-field symmetry-breaking transition. Modular polarized states persist on branches that are locally separated from the compromise branch. Direction-free dissipation does not shift the static structural threshold, but it delays escape from the saddle-node ghost and pushes the observable onset of polarization to larger conflicts. Our work identifies a dissipation-mediated mechanism, complementary to connectivity-based accounts, through which uninformed participants stabilize collective behavior in biological and engineered swarms.

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q-bio.PE 2026-06-09

Reachable equilibria control percolation in ecological networks

by Dario Sergo, Cédric Koller +2 more

Percolation and clustering in ecological communities: A dynamical theory

A discrete Lotka-Volterra model on random graphs shows how dynamics select which clusters of surviving sites form and percolate.

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Ecological communities with structured interactions exhibit collective phenomena such as percolation and clustering of occupied sites. While these effects have been documented in experiments and simulations, systematic analytical understanding has remained limited. In this paper, we develop a dynamical theory of these phenomena for competitive ecological systems defined on random interaction graphs. We introduce a discrete version of the generalized Lotka-Volterra model that preserves key macroscopic features of continuous ecological dynamics while enabling analytical treatment. Within this framework, we characterize the emergence of percolating clusters and describe the spatial organization of surviving sites. Our analysis uncovers which equilibria can be reached by the dynamics and shows how this dynamical accessibility governs the onset of clustering and percolation. In doing so, our framework complements classical Lotka-Volterra theory by providing a dynamical perspective on the collective organization of structured communities.

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q-bio.PE 2026-06-09

Mean fitness relaxes inversely with time after catastrophe

by Jesse Young Lin, Omer Granek +4 more

Natural Selection in the Wake of Catastrophe

E. coli antibiotic recovery data show the scaling depends on trait number and adaptation follows Levenberg-Marquardt optimization rather tha

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Living organisms, from bacteria to humans, are more likely to survive if their traits enhance fitness. In populations well adapted to their environmental niches, natural selection proceeds via rarely beneficial mutations. But when a catastrophe wipes out niche diversity, sudden adaptation often follows. Here, we present a data-validated theory of natural selection in the wake of catastrophe and unveil a simple law that emerges during recovery: the mean fitness relaxes inversely with time, with a prefactor proportional to the number of traits coupled to the post-catastrophe environment. We put our approach to test using experimental fitness landscapes measured following antibiotic administration to E. coli. The resulting mean trait adaptation is not described by gradient ascent on a fitness landscape, instead it follows an algorithm known as Levenberg-Marquardt optimization. Near fitness peaks, evolutionary trajectories are biased against greediness - from an optimization perspective, post-catastrophic selection is optimistic.

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stat.ME 2026-06-08

Triangular matrices enable distance metrics on phylogenetic networks

by Jiayang Wang, Julia A. Palacios +1 more

Matrix representations and distance metrics for unlabeled ranked phylogenetic networks

Standard matrix norms now compare ranked networks that differ in hybrid events and sampling times.

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Phylogenetic networks are graphs inferred from molecular sequence data that represent ancestral histories shaped by reticulate processes such as recombination, hybridization, and horizontal gene transfer. We introduce a family of distance metrics for rooted, ranked, unlabeled phylogenetic networks, extending a previously developed distance for ranked trees. Our approach relies on a bijective triangular matrix representation of phylogenetic networks that captures the temporal order of internal events, speciations, and hybridizations. Our metrics, defined as standard matrix norms, allow efficient quantitative comparisons of network topologies, timed networks and networks with differing numbers of hybridizations. Our distance can be used for both isochronous networks where all tips are sampled at one time point, and heterochronous networks where tips are allowed to be sampled at different time points. We show that our metrics capture biologically meaningful differences among evolutionary histories in both simulations and empirical posterior distributions of viral phylogenetic networks. These tools fill a methodological gap, enabling principled comparisons of ranked, unlabeled phylogenetic networks, including ancestral recombination graphs.

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q-bio.PE 2026-06-08

Two latent infections at boarding best fit Andes virus cruise outbreak

by Raj Kumar Subedi, Hamed Karami +3 more

Cruise Ship-Associated Andes Virus Cluster aboard MV Hondius, 2026: A Stochastic Scenario Analysis

Stochastic analysis of MV Hondius 2026 cluster ranks scenario with two pre-infected passengers highest for the 13 cases

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In April 2026, the MV Hondius expedition cruise ship became the site of the first documented cruise ship-associated Andes hantavirus (ANDV) cluster, with 13 confirmed and probable cases and 3 deaths among 149 passengers and crew. We applied a stochastic epidemic model to evaluate four embarkation scenarios under reproductive numbers anchored to published ANDV estimates. Scenario D, involving two latent infected persons at embarkation, was most consistent with the observed outbreak, yielding P(final size >= 13) = 11.6% and P(takeoff) = 58.5% at R0 = 2.12. Approximate Bayesian computation provided complementary support for multiple latent infections at embarkation, especially E1(0)=1 and E3(0)=2, but R0 remained weakly identifiable. A day-35 transmission reduction changed takeoff probability little in this counterfactual model. Findings support exposure-history assessment, early onboard surveillance, rapid isolation of symptomatic cases, and postdisembarkation monitoring for travelers from ANDV-endemic regions.

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physics.soc-ph 2026-06-08

GNN vaccine ranking cuts epidemic peaks below centrality heuristics

by Mordecai Opoku Ohemeng, Bernard Asamoah Afful

Network-Based Multi-Layer Model Using Machine Learning for Optimal Vaccine Prioritization in Heterogeneous Populations

On a real email contact network, learned node selection lowers peak infections, final size, and time to peak compared with degree or between

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This work advances epidemic control beyond traditional mass vaccination models by integrating population heterogeneity, network structure, and machine-learning-based decision policies. Using the Email-Eu-core contact network, we compare classical centrality-driven vaccination strategies with graph neural network (GNN) and reinforcement learning (RL) approaches. Across 30 stochastic simulations, classical heuristics, including degree, betweenness, and layer-based vaccination, exhibit similar performance, reflecting the network's dense connectivity and modest community structure. In contrast, the GNN-based strategy substantially reduces peak infection, final epidemic size, and time to peak, demonstrating its ability to identify structurally critical nodes that classical metrics overlook. These results show that learning-based vaccination policies can significantly outperform traditional heuristics by exploiting higher-order relational patterns in real-world networks, offering a powerful framework for targeted epidemic intervention.

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q-bio.PE 2026-06-08

Seagrass meadow collapses to 0.2 ha after 80-year record

by Takehisa Yamakita, Yoji Igarashi +3 more

Feasibility to detect rapid change and disappearance of seagrass: Lessons from nearly 80 years of vegetation change in the Ako, Seto Inland Sea, Japan

Long-term imagery shows the 2025 drop stayed low through winter, unlike prior fluctuations, with implications for monitoring standards.

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This study analyses the Ako tidal flat in the Seto Inland Sea, Japan, where nearly all Zostera marina disappeared within a single year in 2025. Using aerial photographs from the 1940s onward, high-resolution satellite imagery, GRUS images (2.5-5 m), and monthly Sentinel-2 composites (10 m), we reconstructed approximately 80 years of seagrass distribution. YOLO-based segmentation using deep learning achieved high accuracy (overall accuracy >= 0.9) across these datasets; although species could not be discriminated, the models captured the major temporal dynamics in vegetation area. The long-term mean seagrass area was 6.8 ha, but values fluctuated widely, from 3.5 ha in 1974 to 41.3 ha in 1989 except 0.2 ha in 2025. Sentinel-2 composites from 2019 to 2026 revealed clear seasonality, with vegetation increasing in early summer and declining from autumn. In 2025, however, the area decreased sharply after summer and remained anomalously low throughout the winter of 2025-2026. Our results, indicating that the 2025 event was not a normal fluctuation but a rapid ecosystem shift involving the loss of the dominant canopy-forming species, most plausibly driven by regionally elevated summer water temperatures. The findings also have implications for seagrass Essential Ocean Variables (EOVs) and the State of Nature (SoN) metrics used in TNFD-aligned nature-related disclosures. Unlike forests, seagrass meadows require finer temporal resolution because both pronounced seasonality and abrupt collapse strongly influence area-based indicators. Therefore, in addition to previously noted issues such as species-level classification accuracy, we recommend that (1) baselines be defined over the longest available record and justified ecologically, (2) seasonal standardization be applied before inter-annual comparisons, and (3) years with extreme area anomalies be flagged rather than used as reference points.

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stat.ME 2026-06-08

Constrained projections define potential biodiversity benchmarks

by Shinto Eguchi

An information-geometric framework for mapping maximum potential biodiversity

The framework separates how diverse a community is from how far it is from its locally admissible ecological capacity on the species simplex

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Biodiversity measures are often used descriptively: one computes a diversity index from an observed or estimated community composition and maps the resulting values across space. Conservation planning, however, also requires a site-specific benchmark against which the observed community can be compared. This chapter develops an information-geometric framework for such \emph{potential diversity} and the associated \emph{diversity gap}. The central object is a pair of probability vectors on the species simplex: an observed or realized composition $p^{\mathrm{obs}}$, and a potential composition $p^{\mathrm{pot}}$ obtained by a constrained variational principle. The gap is then defined by comparing a diversity functional at these two compositions. The framework is developed for both Hill-type diversity, which measures abundance and evenness, and Rao's quadratic entropy, which incorporates trait, phylogenetic, or ecological dissimilarities among species. A spatial point-process interpretation clarifies how local ecological capacities can be defined before passing to the simplex. Escort constraints, capacity constraints, and divergence projections then provide a unified way to define nontrivial benchmarks beyond the uniform distribution. The resulting formulation separates two distinct questions: how diverse a community is, and how far it is from a locally admissible potential benchmark. It also connects the ecological idea of dark diversity with a continuous, abundance-weighted comparison on the probability simplex. We also outline a dynamic extension in which capacities, species migration, and climate-driven shifts vary over time. Empirical implementation with large-scale citizen-science biodiversity data and trait databases is left for future work.

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physics.soc-ph 2026-06-08

Truck routes connect swine farms 99 percent denser than pig shipments

by Jason A. Galvis, Nicolas C. Cardenas +1 more

Multi-network comparison of between-farm contacts for infectious disease surveillance in swine production

Vehicle networks flag largely different high-risk farms than animal movements or distance links, so surveillance needs multiple pathways.

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Understanding how swine farms are interconnected, directly and indirectly, is essential to characterizing infectious disease transmission. This study aimed to describe the connectivity of swine farms across 11 network types, including vehicle movements (i.e., trucks and trailers), animal movements, and distance-based farm-to-farm contacts, to identify links among production types and farms likely to be consistently characterized as super-spreaders. Truck and trailer movement networks were the most densely connected, particularly for feed transport, showing connectivity levels between 98.7% and 99.7% higher than those of pig movement and distance-based networks. These networks also exhibited the highest degree and frequency of connections between farms, while the aggregated truck network, which included all truck types, showed the greatest potential to act as a bridge connecting farms. Finisher farms were highly interconnected with other farm types across all networks. Sow farms were frequently reached by other farm types, especially through feed truck movements, representing up to 8.7% of these links. We demonstrated that in vehicle movements and proximity networks, finisher farms played a major role as super-spreaders. When comparing the top 50 farms ranked by super-spreader score in each network, vehicle-based networks showed the highest similarity, with up to 89% of top-ranked farms shared between vehicle networks. In contrast, pig movement and distance-based networks identified largely distinct sets of top-ranked farms, sharing at most 4% and 8%, respectively, with other contact networks. Overall, each network exhibited a distinct connectivity structure, resulting in different sets of high-risk farms, particularly regarding potential transmission to breeding farms. These findings support the integration of multiple transmission pathways into disease surveillance.

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math.OC 2026-06-08

Nine-compartment model fits Italian COVID data at R^2 0.966-0.999

by Lokman Rachid Melhani, Antonino Sferlazza +7 more

A Nine-Compartment Nonlinear Epidemic Model with Spline-Based Identification of Time-Varying Transmission and Vaccination Dynamics: Application to the COVID-19 Third Wave in Italy

Spline parameterization of time-varying rates proves epidemic decay when effective reproduction number stays below one

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We develop a nine-compartment nonlinear epidemic model incorporating two co-circulating viral strains (ancestral I1 and the Alpha variant B.1.1.7 I2, which is 43-90% more transmissible, c2=1.5), a super-spreader subpopulation, partial vaccine-induced immunity with waning, and explicit hospitalization dynamics with differentiated mortality. Transmission and vaccination rates are treated as time-varying control inputs and identified from Italian COVID-19 data (January-May 2021) via a Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) control-node parameterization, reducing calibration to a fourteen-variable Sequential Quadratic Programming (SQP) problem with monotonicity and box constraints. A parametric bootstrap (n=1000) quantifies parameter uncertainty. The calibrated model achieves R^2=0.966 for active hospitalizations, R^2=0.987 for cumulative fatalities, and R^2=0.999 for cumulative vaccinations. Well-posedness, the basic reproduction number in closed form, and local and global stability of the disease-free equilibrium are established analytically. An L-infinity approximation error bound shows that the PCHIP control-node parameterization converges to the true time-varying parameters at rate O(h^2) as the node spacing vanishes. Local identifiability and a noise stability bound are established via the Fisher information matrix. A sufficient threshold condition proves epidemic decay under time-varying suppression whenever the effective reproduction number remains persistently below one. Sensitivity analyses consistently rank hospital throughput parameters above the transmission rate, providing a mathematical basis for the observation that reactive containment measures cannot prevent a hospitalization peak already driven by the pre-existing latent viral load.

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q-bio.PE 2026-06-08

Heteroclinic orbits expose subnullclines in Hawk-Dove eco-evolution

by Krzysztof Argasinski, Manjyot Singh Bedi +1 more

Nullclines, Subnullclines and the Asymptotic and Transient Attractors in Eco-Evolutionary Dynamics

Seasonal mortality turns these manifolds into barriers that limit how far perturbations spread before the system returns to cycling.

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In the demographic framework, mortality payoff function describes the cost of an interaction and fertility payoff function describes its reward. So while mortality cost depends on opponent's strategy, fertility reward can be affected by the density-dependent juvenile recruitment survival. This motivates an analysis of the eco-evolutionary dynamics of the classical Hawk-Dove game. It is shown that the stable and unstable equilibria (determined by the intersections of frequency and density nullclines) are connected by heteroclinic orbits, which attract nearby trajectories. The resulting bundle of trajectories leads to the discovery of the so-called subnullcines (manifolds placed between frequency and density nullcline) before they converge to the stable rest point. The initial isolated system is then extended by adding environmental seasonality (periodic background mortality), which acts as an external factor. This leads to complex cycling behavior and the subnullclines act as barriers to the propagation of the perturbation (resilience/resistance threshold). Thus, in a way, this paper completes, yet extends, previous works on the eco-evolutionary dynamics of games with demographic payoffs.

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nlin.CD 2026-06-04

Generalized Allee map has exact tricritical extinction point

by Marcelo A. Pires, José S. Andrade Jr. +1 more

Tricriticality and chaos in a generalized Allee-logistic map

Intermediate Allee strength produces a point separating continuous and jump-like extinction, located by closed-form expression.

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We present a novel nonlinear dynamical model, the generalized Allee-logistic (GAL) map given by $x_{t+1} = r x_t (1 - x_t) G(x_t)$ where $G(x_t) = m (x_t - h) + 1 - m$ incorporates the Allee effect with magnitude $m$ and threshold $h$. The case $m = 0$ yields the logistic map with a continuous transition to extinction. Conversely, $m = 1$ recovers a previously studied model that undergoes only a discontinuous extinction-to-active transition. Between these extremes, the GAL map exhibits nontrivial phenomena, including tricriticality with a closed-form expression for the tricritical point and a universal crossover function. Under a small external input, we verify Widom-like relations. We also note that the Allee effect disfavors the onset of chaos. Our work establishes additional bridges between analytically tractable chaotic maps, nonequilibrium tricriticality, and Allee effects.

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q-bio.PE 2026-06-04

Recursive algorithm computes tree likelihoods under QBD trait model

by Habtu Kiros Nigus, Barbara R. Holland +1 more

Quasi-birth-and-death processes evolving within trees: Applications to comparative phylogenetics

Discretized continuous traits evolve on phylogenies via processes that duplicate at speciation events.

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We consider a quasi-birth-and-process (QBD) that duplicates itself at some fixed times within a tree that contains information about duplication times and potentially partially observed states. We analyse a continuous trait by discretising it to obtain the QBD level variable. Then, the phase variable is used to model the dynamics of the underlying environment. Here, we extend the framework of Soewongsono et al. to enable a more general analysis. We develop an efficient recursive algorithm for computing the likelihood of an observed tree under this model and construct several numerical examples to illustrate its application potential. Through our synthetic data examples, we show a range of potential behaviours that could be modelled with this approach. Further, we apply the framework to two empirical examples from comparative phylogenetics (the evolution of range area and body size traits across a phylogeny of 49 mammals) to gain different insights into the evolution of these continuous traits. In this setting duplication of the QBD represents speciation and continuous trait evolution is modelled in a discretised state space. In our empirical examples, we explore the impact of different parameter choices on the corresponding likelihood of observing a given phylogenetic tree and the observed levels at its tips.

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q-bio.PE 2026-06-03

Mean fitness splits into selection plus average mutation causal effects

by Jacopo Iacovacci

Evolution as a Process of Causal Inference

The unnormalised quasispecies equation recovers Fisher's theorem while adding a term for the fitness-weighted average causal effect of all m

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Recently, the mapping of the replicator equation onto Bayes' theorem has been recognised, leading to an analogy between evolutionary dynamics and Bayesian learning. However, this analogy holds only for pure selection in infinite populations and breaks down when mutations -- a central mechanism of evolution -- are introduced. Here I propose that evolution by natural selection, at least for populations of haploid replicators in static environments, is best understood not as a learning process but as a process of causal inference. Each mutation event constitutes a natural experiment in which the parent serves as the control and the mutant offspring as the treated unit. Natural selection screens the causal effect of the mutation on fitness, retaining mutations with non-negative effects. I formalise this view within the Neyman-Rubin potential-outcomes framework. I first develop the general theory using a generic fitness outcome and show how the core identification assumptions in causal inference (Stable Unit Treatment Value Assumption, Consistency, Unconfoundedness, Positivity) map onto evolutionary biology. Using the unnormalised quasispecies equation, I prove that the intergenerational change in mean fitness decomposes exactly into a selection term -- recovering Fisher's Fundamental Theorem -- plus a mutation term that corresponds to a fitness-weighted average of the cumulated effect of all mutations over all parental genotypes. I show that this decomposition extends, under suitable assumptions, to the generalised replicator-mutator equation and that the frequencies of populations of matched parents-offspring update in proportion to the average causal effect of mutations on fitness.

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q-bio.PE 2026-06-03

Group selection maintains cooperation in spatial Prisoner's Dilemma

by Yaroslav Ispolatov, Michael Doebeli

Evolution of cooperation in two-level Prisoner's Dilemma

Between-group fission and extinction events counteract within-group defection, but only in local spatial settings.

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We consider continuous Prisoner's Dilemma played in spatial setting by group-structured populations. The population dynamics consists of individual-level birth and death and group-level fission and extinction events. Each individual plays games with all other individuals within their group, while groups play games against their nearest neighbours. Payoffs from individual-level games affect birth rates of individuals, and payoffs from group-level games affect group extinction and fission probabilities. We show that a certain level of cooperation is maintained due to specific between-group dynamics even though the within-group evolution by itself always results in a complete loss of cooperation. The spatial nature of games and resulting fissioning and extinction events is essential for the evolution of cooperation: without it cooperation is never maintained. Analyzing various scenarios of between-group fission and extinction events, we find that higher levels of cooperation evolve when the selection affecting fission and extinction events is local rather than global.

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stat.AP 2026-06-03

Method computes final epidemic sizes in multi-type Galton-Watson models

by Yuta Okada, Hiroshi Nishiura

Computing the final epidemic size distributions of a multi-type Galton--Watson process

Enables inference of transmission parameters from cluster size observations in heterogeneous epidemics.

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The Galton--Watson process (GWP) is a discrete-time branching process model that provides a powerful tool for analyzing epidemic data and estimating key epidemiological parameters such as the basic reproduction number. When used with surveillance-based cluster size data, the GWP can also elicit information about the extent of transmission heterogeneity, even when each transmission process is not directly observable. When cluster size distribution data are available, the parameters that govern the transmission can be statistically inferred by using the probability mass function that corresponds to the observed cluster size data. For multi-type GWPs, however, real-world applications remain limited, possibly because of the absence of conceptually and practically straightforward approaches for deriving the closed-form solution of the final size distribution. In the present study, we propose a framework for computing the final size distribution of multi-type GWPs, using a method for the choice of the Cauchy integral contour. We provide examples of how our framework can be applied to both simulated data and real-world data of Middle East respiratory syndrome, and discuss potential pitfalls surrounding the identifiability of parameters for statistical inference when using likelihoods that are not conditioned on extinction.

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cs.MA 2026-06-02

LLM agents cut epidemic peaks by matching human quarantine rates

by Petra Ferencz, Ava Keeling +5 more

The Epi-LLM Framework: probing LLM behavioral priors through epidemiological agent-based models

Perceived severity drives their decisions at rates close to human trials, creating a risk-free testbed for outbreak response strategies.

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Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging. Here we introduce the Epi-LLM framework: a novel integration of agent-based modelling, real-life epigames, and large language models (LLMs) in which a synthetic society of agents reasons and adapts dynamically over an outbreak contact network. Comparing synthetic agent behaviour against a no-intervention SEIR baseline and human participant data from the AUIB epigame study, we find that LLM agents across four different architectures reduced peak active infections, with quarantine compliance peaking at 58-65% on day six of the 15-day simulation. A binomial generalised linear model showed that perceived health severity was the strongest predictor of quarantine behaviour ($\beta = 0.33, p = 0.002$), yielding a pseudo-$R^2$ of 0.055, comparable to the 0.072 observed in the human trial. LLM architecture is a key determinant of epidemic dynamics: low-variance architectures offer greater internal validity for testing behavioural rules, while high-variance models may better represent real-world decision-making. Geographic labels alone do not induce culturally differentiated behaviour; explicit attitudinal parameterisation is required. This proof-of-principle work lays the groundwork for deploying the Epi-LLM framework as a scalable, risk-free simulation environment for pandemic preparedness research.

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q-bio.PE 2026-06-02

Evolved agents call to regulate their own escape behavior

by Joshua Nunley

Self-Regulation through Communication in Evolved Neural Agents

In predator avoidance runs, 20 percent of perfect-fitness agents depend on hearing their own vocalizations to sustain flight, unlike those s

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Communication is typically understood as indication: signals that transfer information from sender to receiver. We present a minimal predator avoidance task in which pairs of evolved CTRNN agents use communication for robust survival, and in which agents hear their own vocalizations, as in natural systems. Across 112 perfect-fitness agents from over 2,000 evolutionary runs, three dominant strategies emerge (accounting for 81% of agents): safety calling (39%), where agents signal from safe cover; alarm indication (22%), where agents vocalize when a threat is present without relying on self-hearing; and self-regulatory calling (20%), where agents depend on hearing their own call to sustain escape behavior. Self-hearing dependency is common among agents that call during an active threat (47%), but rare among agents that call only after reaching safe cover (10%; p < 10^-4). This pattern is consistent with a difference in causal order: safety callers act then communicate, while self-regulatory callers communicate in order to act. Removing self-hearing selectively impairs self-regulatory callers (fitness 0.40) while safety callers remain functional (0.90; p < 10^-9). These results show that communication can evolve to serve the caller's own behavioral regulation, not just information transfer to others.

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q-bio.PE 2026-06-01

Cooperation evolves by layer-specific σ-rules in multiplex networks

by Zijie Chen, Xingru Chen +1 more

Evolution of cooperation in the multiplex

Multi-phenotype homophily lets each network layer set its own condition for selection to favor cooperation, independent of fitness integrati

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Across biological and social systems, cooperation often depends on phenotypic cues rather than random encounters. To account for real-world interactions unfolding across multiple, simultaneous dimensions, here we develop a general framework for the evolution of cooperation in multiplex networks governed by multi-phenotype homophily. We derive analytical conditions for natural selection to favor cooperation across phenotypic traits that are independent or exhibit epistasis and under different modes of mutation coupling. Despite the integration of fitness across layers, the conditions for cooperation resolve into layer-specific $\sigma$-rules, depending only on the local payoff structure, the effective number of phenotypes, and the mutation rates. We show that phenotypic diversity fosters cooperation by partitioning populations into assortative niches. Furthermore, in finite populations, intensifying the prisoner's dilemma shifts the dependence of cooperation on strategy mutation from monotonically decreasing, through U-shaped, to monotonically increasing. Our work provides a unified account of how multi-phenotype homophily underpins the evolutionary dynamics of cooperation in heterogeneous populations.

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q-bio.PE 2026-06-01

Ten mechanisms govern how pandemics unfold

by Seba Contreras, Philipp Dönges +25 more

Mechanics of Pandemics

Principles from natural laws and biology shape disease spread and societal responses across outbreaks.

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COVID-19 and previous pandemics have shown how diseases can disrupt, threaten, and transform daily life. Since pathogens and societies are continuously evolving, every pandemic is different. However, certain fundamental principles of disease transmission appear to hold true across different outbreaks. These ``mechanisms'' are grounded in natural laws or the very structure of our biology and societies. This paper compiles ten fundamental mechanisms, curated by a multidisciplinary team with backgrounds spanning public health, medicine, epidemiology, political science, mathematics, physics, and psychology. These mechanisms, although perhaps underappreciated, substantially shape how pandemics unfold and are controlled. The better we succeed in understanding these mechanisms and establishing this knowledge in our societies, the better we will be able to prepare for future pandemics and respond appropriately when they occur.

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q-bio.PE 2026-06-01

Consensus virus rates differ from mutational rates

by David J Pascall

Consensus-level substitution rates are distinct from the virion-level rate

The two quantities are both real but cannot stand in for each other when estimating evolutionary dynamics from sequence data.

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Estimating viral substitution rates is central to evolutionary epidemiology, and recent interest in within-host evolution has sharpened the question of what such rates measure. I distinguish two classes of evolutionary rate estimand that are rarely separated in phylogenetic analysis: the virion-level substitution rate (VLSR), a molecular quantity counting mutational events along lineages, and consensus-level substitution rates (CLSRs), population-summary quantities counting changes in the consensus sequences. CLSRs are indexed by the consensus-generation rule. The VLSR and CLSRs are both biologically meaningful, but not interchangeable. Because the consensus-generation rule defines a given CLSR, it should be a routine reporting requirement. This reflection should help analysts make more informed methodological choices when working with sets of virus sequences.

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math.AP 2026-06-01

Memory term in disease model yields local weak solutions

by Hassan El Bouz, Karim Faraj +2 more

Derivation, Analysis and Simulation of a Spatio-Temporal Epidemiology Model with Memory

Integro-differential reaction-diffusion system models incubation periods for epidemic simulation in space and time

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In this paper, we propose an integro-differential model for the spatio-temporal evolution of infectious diseases with asymptomatic transmission. The model consists of a reaction-diffusion system with an integral memory term accounting for the distribution of the incubation period. We first analyze the asymptotic behavior and the properties of the integro-differential model. Then, we prove the local existence of a weak solution of the system by means of the Faedo-Galerkin method and a compactness argument. The model is applied to simulate the geographical evolution of a disease in Lebanon.

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cs.DS 2026-06-01

Tree Containment solved in 4^{k + k log k} n time

by Leo van Iersel, Mark Jones +1 more

Tree Containment Parameterized by Scanwidth

Matching ETH lower bound shows no 2^{o(c log c)} algorithm exists even on binary networks.

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TREE CONTAINMENT is a central decision problem in mathematical phylogenetics, asking whether a given rooted phylogenetic tree is embeddable in ("displayed by") a given rooted phylogenetic network. While the problem is NP-complete for general networks, many algorithmic advances have relied on structural parameters that capture how "tree-like" a network is. In this paper we investigate TREE CONTAINMENT under the structural parameter scanwidth, a directed width measure generalizing popular parameters measuring tree-likeness of phylogenetic networks. We first present a parameterized algorithm that solves the problem in $O(4^{k + k\log{k}} n + nm^2)$ time, where $n$ and $m$ are the numbers of nodes and arcs in the network and $k$ is the width of a given tree-extension. Complementing this upper bound, we prove a matching lower bound under the Exponential-Time Hypothesis (ETH), showing that there is no algorithm for TREE CONTAINMENT that runs in $2^{o(c\log{c})} n^{O(1)}$ time, even on binary inputs, where $c$ is the directed cutwidth of the input network, which upper-bounds the scanwidth $k$.

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q-bio.PE 2026-06-01

Intermediate human activity sparks multiple vector population shifts

by Orville Wright Happi-Tchakounte, Ivric Valaire Yatat-Djeumen +2 more

Analysis of a two patch model for disease vector-animal dynamics with non-linear anthropization-driven migration

A two-patch model finds the coexistence-to-extinction transition occurs via concurrent bifurcations at medium anthropization levels.

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Landscape dynamics are key drivers of the movement and distribution of sylvatic hematophagous disease vectors and their (wild) animal hosts. Their habitats are undergoing increasing change, particularly fragmentation, through anthropogenic activity. In this article, we present and analyse a novel mathematical model that explicitly combines anthropization-induced landscape dynamics with the population dynamics of hematophagous vectors and (wild) animals dynamics. We develop a phenomenological and analytically tractable two-patch model in which the migration terms between the patches nonlinearly depend on the anthropization level of the patches. Our model analysis comprising analytical stability analysis and numerical bifurcation analysis provides information on how changes in model parameters, especially anthropization levels, shape the long-term dynamics in the model. Precisely, we find that low anthropogenic activity allows for a vector-animal coexistence state, while high anthropization leads to a vector extinction state. However, we establish that for intermediate anthropization levels, the transition between the two states is not necessarily monotonic, but may instead occur via a sequence of concurrent bifurcations along the anthropization axis.

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q-bio.PE 2026-06-01

Geometric habitat metrics overestimate population persistence

by E. H. Colombo, L. Menon +2 more

Morphological routes to extinction: A mechanistic assessment of habitat loss

Mechanistic models based on growth near the extinction threshold show accelerating loss, unlike the moderate slowdown geometry predicts.

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Habitat loss driven by climate and anthropogenic pressures alters patch morphology, with critical consequences for population persistence. Geometric and mechanistic metrics are commonly used to quantify degradation, yet their respective limitations remain poorly understood. Here, we address this gap using a reaction-diffusion framework for population growth and dispersal in a viable patch embedded in a hostile environment. We compare geometric descriptors of patch shape with a mechanistic metric derived from population growth near the extinction threshold. Along degradation trajectories, we find that geometric metrics systematically overestimate persistence, suggesting moderate and decelerating impacts, whereas mechanistic indicators reveal rapid, accelerating approaches to extinction. These results highlight fundamental limitations of geometric approaches and underscore the need for mechanistic assessments when evaluating biodiversity loss in complex landscapes.

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