Journal of The Royal Society Interface

Dynamic soaring harvests energy from a spatiotemporal wind gradient, allowing albatrosses to glide over vast distances. However, its use is challenging to demonstrate empirically, and has yet to be confirmed in other seabirds. Here we investigate how flap-gliding Manx Shearwaters optimise their flight for dynamic soaring. We do so by deriving a new metric, the horizontal wind effectiveness, that quantifies how effectively flight harvests energy from a shear layer. We evaluate this metric empirically for fine-scale trajectories reconstructed from bird-borne video data using a simplified flight dynamics model. We find that the birds undulations are phased with their horizontal turning to optimise energy harvesting. We also assess the opportunity for energy harvesting in long-range, GPS-logged foraging trajectories, and find that Manx Shearwaters optimise their flight to increase the opportunity for dynamic soaring during favourable wind conditions. Our results show how small-scale dynamic soaring impacts large-scale Manx Shearwater distribution at sea. TeaserFlap-gliding shearwaters harvest wind energy by fine-scale trajectory optimization and this impacts their large-scale distribution at sea.

Centimeter-scale fliers that combine wings with springy elements must contend with the high power requirements and mechanical constraints of flapping wing flight. Insects utilize elastic energy exchange to reduce the inertial costs of flapping wing flight and potentially match wingbeat frequencies to a mechanical resonance. Flying at resonance may be energetically favorable under steady conditions, but it is difficult to modulate the frequency of a resonant system. Evidence suggests that insects utilize frequency modulation over long time scales to adjust aerodynamic forces, but it remains an open question the extent to which insects can modulate frequency on the wingstroke-to-wingstroke timescale. If wingbeat frequencies deviate from resonance, the musculature must work against the elastic flight system, thereby potentially increasing energetic costs. To assess how insects address the simultaneous needs for power and control, we tested the capacity for wingstroke-to-wingstroke wingbeat frequency modulation by perturbing free hovering Manduca sexta with vortex rings while recording high-speed video at 2000 fps. Because hawkmoth flight muscles are synchronous, there is at least the potential for the nervous system to modulate frequency on each wingstroke. We observed {+/-} 16% wingbeat frequency modulation in just a few wing strokes. Via instantaneous phase analysis of wing kinematics, we found that over 85% of perturbation responses required active changes in motor input frequency. Unlike their robotic counterparts that explicitly abdicate frequency modulation in favor of energy efficiency, we find that wingstroke-to-wingstroke frequency modulation is an underappreciated control strategies that complements other strategies for maneuverability and stability in insect flight.

Dyadic social interactions evoke complex dynamics between two agents that while exchanging unequal levels of body autonomy and motor control, may find a fine balance to take turns and gradually build social rapport. To study the evolution of such complex interactions, we currently rely exclusively on subjective pencil and paper means. Here we complement this approach with objective biometrics of socio-motor behaviors conducive of socio-motor agency. Using a common clinical test as the backdrop of our study to probe social interactions between a child and a clinician, we demonstrate new ways to streamline the detection of social readiness potential in both typically developing and autistic children. We highlight differences between males and females and uncover a new data type amenable to generalize our results to any social settings. The new methods convert dyadic bodily biorhythmic activity into spike trains and demonstrates that in the context of dyadic behavioral analyses, they are well characterized by a continuous gamma process independent from corresponding binary spike rates. We offer a new framework that combines stochastic analyses, nonlinear dynamics, and information theory, to facilitate scaling the screening and tracking of social interactions with applications to autism.

Birds outperform engineered aircraft with exceptional maneuverability, achieved by continuously morphing their wings and tails in flight. Yet the coordination and control of these shape changes remain poorly understood. Using high-speed motion capture of Harris hawks, we analyzed 289,000 wing-tail configurations in over 2000 flights and identified four fundamental shape change patterns, or "morphing shape modes", that capture over 96% of wing and tail variation. Further modes reflect subtle but critical fine-tuning, in line with known morphing control mechanics. The hawks morphing flight is highly structured yet flexible, and we find adaptive strategies in response to obstacles, added weight, with maturity, while each individual shows unique morphing signatures. Our approach defines a shared kinematic morphospace for hawk flight, and more broadly a framework that enables future comparative biomechanics, bio-inspired design, and for interpreting high-dimensional natural motion.

Tracking the spread of emerging pathogens is critical to the design of timely and effective public health responses. Policymakers face the challenge of allocating finite resources for testing and surveillance across locations, with the goal of maximising the information obtained about the underlying trends in prevalence and incidence. We model this decision-making process as an iterative node classification problem on an undirected and unweighted graph, in which nodes represent locations and edges represent movement of infectious agents among them. To begin, a single node is randomly selected for testing and determined to be either infected or uninfected. Test feedback is then used to update estimates of the probability of unobserved nodes being infected and to inform the selection of nodes for testing at the next iterations, until a certain resource budget is exhausted. Using this framework we evaluate and compare the performance of previously developed Active Learning policies, including node-entropy and Bayesian Active Learning by Disagreement. We explore the performance of these policies under different outbreak scenarios using simulated outbreaks on both synthetic and empirical networks. Further, we propose a novel policy that considers the distance-weighted average entropy of infection predictions among the neighbours of each candidate node. Our proposed policy outperforms existing ones in most outbreak scenarios, leading to a reduction in the number of tests required to achieve a certain predictive accuracy. Our findings could inform the design of cost-effective surveillance strategies for emerging and endemic pathogens, and reduce the uncertainties associated with early risk assessments in resource-constrained situations.

The environments inhabited by flying insects demand a balance between flight efficiency and flight manoeuvrability. In structural oscillators such as the insect indirect flight motor, efficiency (arising from resonance) and manoeuvrability (arising from kinematic modulation) are typically quid pro quo, with modulation incurring penalties to efficiency. Band-type resonance is a phenomenon that offers, in theory, a strategy to lessen these penalties via careful navigation through a band of efficient kinematic states. However, identifying this band is challenging: no methods exist to identify the complete band in realistic motor models, involving elasticity distributed across thorax and wing. Nor are the effects of elasticity distribution on the band known. In this work, we address both open topics. We present a suite of numerical methods for identifying the complete resonance band in general systems. Applying them to models of the insect flight motor with distributed elasticity--thoracic and wing flexion--reveals that distributed elasticity is moderate-risk but high-reward morphological feature. Well-tuned distributions expand the resonance band over fourfold whereas poorly-tuned distributions completely extinguish the resonance band. These results indicate that distributing elasticity across the insect flight motor can have adaptive value, and motivate broader work identifying distributions across species.

Eusocial insects fascinate researchers with their sophisticated communication systems and sensory specialisations. Ants and termites have coexisted in a long-standing predator-prey arms race, offering insight into the interplay between ecology and evolution. The subterranean termite Coptotermes acinaciformis can detect the predatory ant Iridomyrmex purpureus through footstep-induced vibrations, triggering defensive responses. Ants produce noisier walking signatures than termites, while the inquiline termite Macrognathotermes sunteri walks more quietly than its host, suggesting species-specific vibroacoustic strategies. Using statistical analysis of video-tracked motion and footstep vibrations in confined arenas across six ant and ten termite species, we show that C. acinaciformis, despite its body size, moves more smoothly than ants, which alternate between directed and erratic paths. Inquiline termites, by contrast, displayed erratic movements. Ants consistently produced stronger vibrations closely linked to body mass, while Highly Comparative Time Series Analysis revealed termite motions approaching chaotic dynamics. Notably, while C. acinaciformis and I. purpureus produced distinct vibrational signatures, M. sunteri s signals overlapped with its host, consistent with vibroacoustic mimicry. Although the ecological nature of this association remains unresolved, our findings underscore the central role of vibrational cues in shaping interspecific dynamics and highlight vibroacoustic communication as an underappreciated driver of social insect ecology and evolution.

Buzz pollination is an important behavior in which bees use vibrations to extract pollen from poricidal anthers. However, the extent to which vibration frequency influences pollen release remains unclear. Here, we quantified pollen expulsion from Solanum sisymbriifolium anthers subjected to harmonic excitation over a broad frequency range encompassing the anthers first natural frequency. We excited anthers to expel pollen and measured anther kinematics and pollen release using high-speed videography. Particle tracking enabled continuous estimation of pollen release throughout each buzzing event, allowing both initial pollen flux and total pollen released to be quantified. Pollen release depended strongly on excitation frequency. Initial pollen flux, total pollen release, and anther kinematics peaked when excitation frequency approached the anthers natural frequency. Anther tip velocity amplitude exhibited the strongest correlation with total pollen release (r = 0.755) and initial pollen flux (r = 0.898). Experimental observations were compared with nonlinear and linear statistical models of pollen release. While both models captured trends in normalized pollen flux, they overpredicted total pollen release, suggesting that adhesive interactions play important roles during extended buzzing events. These findings demonstrate that anther structural dynamics influence pollen release and suggest that vibration amplification may improve the efficiency of buzz pollination.

Living systems have evolved cognitive complexity to reduce environmental uncertainty, enabling them to predict and prepare for future conditions. Anticipation, distinct from simple prediction, involves active adaptation before an event occurs and is a key feature of both neural and aneural biological agents. Building on the moving average convergence-divergence principle from financial trend analysis, we propose an implementation of anticipation through synthetic biology by designing and evaluating experimentally testable minimal genetic circuits capable of anticipating environmental trends. Through deterministic and stochastic analyses, we demonstrate that these motifs achieve robust anticipatory responses under a wide range of conditions. Our findings suggest that simple genetic circuits could be naturally exploited by cells to prepare for future events, providing a foundation for engineering predictive biological systems.

Predictive models for the spatial spread of infectious diseases has received much attention in recent years as tools for the management of infectious diseas outbreaks. Prominently, various versions of the so-called gravity model, borrowed from transportation theory, have been used. However, the original literature suggests that the model has some potential misspecifications inasmuch as it fails to capture higher-order interactions among population centers. The fields of economics, geography and network sciences holds alternative formulations for the spatial coupling within and among conurbations. These includes Stouffers rank model, Fotheringhams competing destinations model and the radiation model of Simini et al. Since the spread of infectious disease reflects mobility through the filter of age-specific susceptibility and infectivity and since, moreover, disease may alter spatial behavior, it is essential to confront with epidemiological data on spread. To study their relative merit we, accordingly, fit variants of these models to the uniquely detailed dataset of prevaccination measles in the 954 cities and towns of England and Wales over the years 1944-65 and compare them using a consistent likelihood framework. We find that while the gravity model is a reasonable first approximation, both Stouffers rank model, an extended version of the radiation model and the Fotheringham competing destinations model provide significantly better fits, Stouffers model being the best. Through a new method of spatially disaggregated likelihoods we identify areas of relatively poorer fit, and show that it is indeed in densely-populated conurbations that higher order spatial interactions are most important. Our main conclusion is that it is premature to narrow in on a single class of models for predicting spatial spread of infectious disease. The supplemental materials contain all code for reproducing the results and applying the methods to other data sets. Author summaryThe ability to predict how infectious disease will spread is of great importance in the face of the numerous emergent and re-emergent pathogens that currently threatening human well-being. We identified a variety of alternative models that predict human mobility as as a function of population distribution across a landscape. These consider some models that account for pair-wise interactions between population centers, as well as some that allow for higher-order interactions. We trained the models using a uniquely rich spatiotemporal data set on pre-vaccination measles in England and wales (1944-65), which comprises more than a million records from 954 cities and towns. Likelihood rankings of the different models reveal strong evidence for higher-order interactions in the form of competition among cities as destinations for travelers and, thus, dilution of spatial transmission. The currently most commonly used so-called gravity models were far from the best in capturing spatial disease dynamics.

Emergence of order in social insects is exemplified by their nesting architectures. Paper wasps construct hexagonal nests that, like crystals, harbour topological defects. In a paper wasp nest, we identify transient defects that migrate by local wall reorientation and vertex addition, akin to dislocation glide. Across growth phases, non-hexagonal polygons undergo short-range transformations in form and position. The transitions resemble alternating Y-{Delta} transformation-node release and release-and-straighten events, familiar from electrical networks. Burgers circuits show that passage through intermediates does not reduce the distortion in the nest, and all transitions conserve topological charge, thereby explaining the coupling of non-hexagonal cells. These dynamics propagate defect motion only to a finite extent, after which the defect stabilizes without global reorganization or functional gain. Our findings demonstrate that social insect nest building naturally realizes dynamic topological processes, shaped by material properties, construction rules, and geometric constraints.

Investigations of jamming in cancer have produced numerous phase diagrams intending to map fluidity across the epithelial-to-mesenchymal transition (EMT). Here, we use coalescence of homotypic and heterotypic multicellular spheroids to examine and carefully probe these phase spaces. Small changes in cellular EMT status result in full traversal of the solid-to-fluid continuum. We propose that stiff nuclei impede cell motion and spheroid coalescence and find that by softening nuclei, fluidization of an otherwise solid-like system occurs. Changes in fluidity during coalescence is fully captured by static cellular properties, such as internuclear spacing and nuclear shape, that can be assessed in individual non-interacting spheroids. We combine these quantities into an effective nuclear packing metric that depends on nuclear occupancy and nuclear elongation. Together, these findings reveal that nuclear morphology and packing act as important indicators and determinants of fluidity.

Epithelial tissues maintain mechanical integrity through a balance between cell-cell adhesion and cortical contractility. Disruption of E-cadherin-mediated adhesion is a hallmark of epithelial-mesenchymal transition and cancer progression; yet how local adhesion defects propagate to tissue-scale mechanical changes remains poorly understood. Here, we use a two-dimensional vertex model (varying mutant cell fraction, spatial arrangement, and initial tissue disorder) to investigate how adhesion-deficient cells regulate epithelial mechanics. We show that increasing the fraction of mutant cells drives the tissue towards geometric signatures associated with reduced mechanical rigidity, characterised by elevated cellular shape index and increased prevalence of non-hexagonal cells. Crucially, spatial organisation acts as an independent structural variable that modulates tissue mechanics beyond mutant fraction alone. For identical mutant fractions, randomly distributed mutants undergo rapid, spatially isolated T2-mediated removal events producing only transient shape-index perturbations. Clustered mutants, by contrast, undergo sequential boundary removal, delaying elimination and sustaining elevated shape index in the surrounding tissue. This persistent elevation induces topological disorder within the local neighbourhood that outlasts mutant clearance itself. Our results establish spatial organisation as a key determinant of epithelial rigidity transitions, with implications for understanding early-stage cancer progression.

Insects show diverse flight kinematics and morphologies reflecting their evolutionary histories and ecological adaptations. Many silkmoths utilizing low wingbeat frequencies and large wings to fly display body oscillations: Their bodies pitch and bob periodically - synchronized with their wing flapping cycle. Similar oscillations in butterflies augment weight support and thrust and reduce flight power requirements. However, how the instantaneous body and wing kinematics interact for these beneficial aerodynamic and power consequences is not well understood. We hypothesized that the body oscillations affect aerodynamic power requirements by influencing the wing rotation relative to the airflow. Using three-dimensional forward flight video recordings of four silkmoth species and a quasi-steady blade-element aerodynamic method, we found that the body pitch angle and the wing sweep angle maintain a narrow range of phase differences to enhance the angle of attack variation between each half-stroke due to enhanced wing rotation relative to the airflow. This redirects the aerodynamic force to increase upward and forward force during downstroke and upstroke respectively thus lowering the overall drag without compromising weight support and forward thrust. A reduction in energy expenditure is beneficial because adult silkmoths do not feed and rely on limited energy budgets.

The persistence of cell migration is a fundamental property of motile behavior, enabling cells to maintain directionality while adapting to fluctuations and external cues. This feature underlies essential processes such as development, immune responses, and cancer invasion. Classical mathematical models have offered key insights into directed migration, yet they often neglect temporal correlations arising from cellular mechanisms that stabilize polarity and protrusion dynamics, processes not well captured by simple white noise. Here, we introduce an agent-based model based on stochastic differential equations (SDEs) that integrates fractional Brownian motion (fBm) to explicitly incorporate translational autocorrelation in cell trajectories. We simulate migration as a function of angular reorientation (Dr) and the strength of correlated noise (H). In this framework, temporal correlation stabilizes trajectory features inherited from initial conditions, whereas angular reorientation introduces variability that enables transitions between erratic and directed motion. Our simulations show that, unlike models driven by white noise, positive correlation markedly enhances persistence even under strong angular reorientation. Moreover, the combination of Dr and H gives rise to emergent behaviors, particularly in the presence of taxis, where persistence and responsiveness are jointly tuned. These results identify correlated noise as a proxy for intrinsic cellular memory and provide a versatile computational framework to interpret the diversity and complexity of migratory behaviors. Significance StatementCell migration drives key biological processes such as immune surveillance, development, and cancer invasion. Most models reduce motility to random walks perturbed by white noise, overlooking temporal correlations that arise from intrinsic cellular memory. By integrating fractional Brownian motion into agent-based modeling, we show how correlated translational noise interacts with angular diffusion to produce emergent behaviors, including overshooting, exploratory loops, and persistent trajectories. Our framework unifies these outcomes under a single mechanistic description and highlights how intrinsic noise modulates taxis, exploration, and persistence. This approach provides mathematicians and cell biologists with a versatile tool to test how cells balance stability and adaptability in dynamic environments.

Vibrational signals produced during feeding are fundamental to termite behaviour, yet their function in regulating collective foraging remains unclear. In this study, we combine bioassays, micro-CT imaging, and elastic wave modelling to investigate how the subterranean termite Coptotermes acinaciformis evaluates wood through structural wave propagation. Using an axially excited Acoustic Black Hole (ABH), a tapered geometry that minimises wave reflections and effectively mimics an infinitely long food source, we show that termites preferentially attack longer wooden dowels and, remarkably, also lighter ABH-modified dowels. Micro-CT scans revealed feeding concentrated in the dowel core, coinciding with the region of maximum stress predicted by the models but where echo return was minimal. These results indicate that termites assess wood size through bite-induced echoes, analogous to echolocation in bats and dolphins, and preferentially exploit core regions of trunks and branches, thereby accounting for the tree-piping behaviour of termites. The reduction or absence of reflected waves may thus act as a cue that stimulates collective stigmergic foraging. From an applied perspective, ABH-inspired structures could form the basis of novel, chemical-free lures for termite management.

Integrated information theory (IIT) aims to account for the quality and quantity of consciousness in physical terms. According to IIT, a substrate of consciousness must be a system of units that is a maximum of intrinsic, irreducible cause-effect power, quantified by integrated information ({varphi}s). Moreover, the grain of each unit must be the one-- from micro (finer) to macro (coarser)--that maximizes the systems intrinsic irreducibility (i.e., maximizes{varphi} s). The units that maximize{varphi} s are called the intrinsic units of the system. This work extends the mathematical framework of IIT 4.0 to assess cause-effect power at different grains and thereby determine a systems intrinsic units. Using simple, simulated systems, we show that the cause-effect power of a system of macro units can be higher than the cause-effect power of the corresponding micro units. Two examples highlight specific kinds of macro units, and how each kind can increase cause-effect power. The implications of the framework are discussed in the broader context of IIT, including how it provides a foundation for tests and inferences about consciousness.

Nonlinearity plays a fundamental role in the performance of both natural and synthetic biological networks. Key functional motifs in living microbial systems, such as the emergence of bistability or oscillations, rely on nonlinear molecular dynamics. Despite its core importance, the rational design of nonlinearity remains an unmet challenge. This is largely due to a lack of mathematical modelling that accounts for the mechanistic basics of nonlinearity. We introduce a model for gene regulatory circuits that explicitly simulates protein dimerization--a well-known source of nonlinear dynamics. Specifically, our approach focusses on modelling co-translational dimerization: the formation of protein dimers during--and not after--translation. This is in contrast to the prevailing assumption that dimer generation is only viable between freely diffusing monomers (i.e., post-translational dimerization). We provide a method for fine-tuning nonlinearity on demand by balancing the impact of co- versus post-translational dimerization. Furthermore, we suggest design rules, such as protein length or physical separation between genes, that may be used to adjust dimerization dynamics in-vivo. The design, build and test of genetic circuits with on-demand nonlinear dynamics will greatly improve the programmability of synthetic biological systems.

Protein aggregation underlies the pathogenesis of many neurodegenerative diseases, and inhibitors are often assumed to elicit monotonic dose-responses. We ask whether simple aggregation pathways can intrinsically generate hormesis--a biphasic profile with low-dose stimulation and high-dose inhibition. We formulate a minimal mechanistic model in which a single inhibitor interacts sequentially with pathway intermediates. Analysis and simulation show a robust non-monotonic response: low inhibitor doses increase aggregate formation, whereas high doses suppress it. We prove that this profile is structural--arising from chemical network topology rather than tuned kinetic parameters. The mechanism rationalizes pro-aggregating effects at low doses and underscores the need for full-range dose-response evaluation in inhibitor screening.

For more than a billion years, microorganisms have evolved complex strategies for navigating aquatic habitats, despite the fundamental limitations and constraints imposed by their physical environment. A common theme across these strategies is the use of active slender appendages (cilia, flagella, archaella) to generate self-propulsion. Diverse selection pressures and evolutionary trajectories have driven the emergence of drastically different morphologies of biological microswimmers, each tailored for distinct functions ranging from motility to taxis to prey capture to feeding. Despite the biological and ecological significance of these intricate microscale processes, realistic computational modelling of these organisms and their behaviours is still in its infancy. Here, we present a comprehensive open-source simulation platform for motile microswimmers, that faithfully captures the universal hydrodynamic principles shared by such systems. We illustrate the predictive power and versatility of this approach to resolve and explore morphology-function relationships across different microswimmer species and provide new insights into the diversification of locomotion strategies in early eukaryotes.