Bioinspiration & Biomimetics

Take-off is a fast and energy-efficient strategy for bipedal animals, such as birds, to achieve rapid movement; however, how muscle physiology scales to govern this universal behavior remains unresolved. Research in other species physiologies is not readily applicable. As a result, important questions, whether theropod dinosaurs such as Tyrannosaurus rex were capable of jumping, remain unanswered. In this article, we coupled Lagrangian dynamics with Hills muscle equations and developed new experimental methods to quantify joint rotational stiffness and damping, thereby enabling a systematic description of lower-limb mechanics. The approach establishes a novel kinetic framework that links muscle contractile properties to lower-limb performance without invoking control optimization. Animal observations and tabletop mechanisms validate the framework. The mechanics model reveals that the take-off time of about 0.1 s across body masses of 0.003 to 90 kg is achievable, as heavier birds generate proportionally higher reaction forces. Additionally, Tyrannosaurus rex should be capable of jumping, based on the available physiology data. Beyond evolutionary insights, our framework provides a new methodology for analyzing the mechanical properties of biological joints and informing the design of scalable bio-inspired robots.

The queen bee is the central individual responsible for colony establishment, growth, and survival. Reliable confirmation of successful mating, continued queen presence, and normal reproductive performance is essential for effective colony management. We present a queen bee detection system based on an array of Hall-effect sensors and a miniature magnetic tag attached to the queen. The system is designed for continuous operation and real-time monitoring. A prototype was developed, constructed, and evaluated under both laboratory and field conditions. Field experiments conducted in an apiary demonstrated that the system can reliably detect queen bee passages through the hive entrance, enabling the identification of activities associated with mating flights. The results confirm the feasibility of Hall-effect sensing for automated, non-invasive queen bee monitoring and establish magnetic sensing as a promising new measurement modality for precision apiculture.

ObjectiveRobotic-assisted needle insertions lack haptic feedback, a key sensory cue for detecting tissue transitions and regulating puncture force. Modeling this feedback requires an understanding of soft-tissue biomechanics during insertion. Vibroacoustic signals generated by needle-tissue interactions may provide an additional sensing modality, but their interpretation requires validation against anatomical ground truth. MethodsA multimodal framework was developed to correlate vibroacoustic signals with high-resolution post-puncture microCT ({micro}CT) imaging in Manduca sexta, an insect model containing interconnected soft-tissue layers. A custom clip-on prototype recorded vibroacoustic signals during manual needle insertions. Three trajectory-marking strategies were evaluated to determine 3D coordinates of soft-tissue layer crossings and to assess correlations between acoustic events and anatomical transitions. Distances between layer crossings and needle displacement were used for spatiotemporal alignment of vibroacoustic and {micro}CT data. ResultsA {micro}CT-compatible nylon string preserved puncture trajectories without artifacts and enabled high-resolution 3D reconstruction of anatomy and needle paths. Fusion of vibroacoustic and imaging data allowed identification of acoustic events associated with tissue entry, exit, and transitions. ConclusionBy integrating high-resolution {micro}CT imaging with vibroacoustic sensing, this study establishes a biologically grounded framework for validating the relationship between vibroacoustic signals and anatomical tissue transitions during needle insertion, providing a basis for future quantitative analyses. SignificanceThis work provides initial evidence for correlating vibroacoustic signals recorded during needle insertion with corresponding {micro}CT-identified tissue barriers. Because vibroacoustics offers substantially higher temporal and spatial resolution than most imaging modalities, it has the potential to improve tissue sensing and procedural accuracy in future needle-based interventions.

Understanding how biological shape and movement interact with surrounding fluids represents a fundamental challenge at the intersection of biology, physics, and engineering. Fish locomotion exemplifies this challenge: body morphology and swimming kinematics together determine the hydrodynamic forces and flow structures that enable efficient propulsion and maneuverability. Whereas biologists have long sought to connect morphological variation to swimming performance, traditional morphometric approaches provide limited insight into the fluid mechanical consequences of shape differences. Similarly, although computational fluid dynamics can reveal detailed flow physics, simulating hydrodynamics across diverse and dynamic morphologies remains prohibitively expensive for systematic investigation. To bridge this gap, we introduce a data-driven framework that connects fish body shape dynamics to hydro-dynamic performance through compact morphospace parameterization and reduced-order modeling. Using CFD simulations of 15 fish species from the Digital Life Project database (www.digitallife3d.org/3d-model), we generate hydrodynamic datasets capturing the shape-flow relationship. Principal Component Analysis (PCA) extracts four dominant shape parameters from dorsal body profiles, which are then integrated into an Inverse-Design with Dynamic Mode Decomposition (ID-DMD) framework to model the resulting fluid dynamics. The resulting modal analysis suggests that locomotion strategies emerge from specific shape-flow interactions. We further demonstrate the frameworks utility through single- and multi-objective shape optimization, showing how it enables efficient exploration of the morphology-hydrodynamics relationship. This approach offers a novel analysis and design tool for understanding how biological form and motion interact with fluid mechanics, with applications ranging from bio-inspired vehicle development to evolutionary biomechanics.

The flying fox bats roost in large colonies, suspended upside-down with minimal grip efforts from tree branches that are exposed to environmental disturbances. In this study, we investigate the oscillation dynamics of bats hanging from tree branches under natural conditions with wind. Bats modulate their grips to control the oscillation during wind disturbances and actively transform their postures. Using field observations, we analyze the angular deformation, speed, and phase of individual and collective bats swaying motions in response to environmental perturbations. We observed the mechanical coupling-based synchronization of collective bat oscillations on a tree branch. To rationalize this new phenomenon of bats synchronization behavior, we perform a table-top experiment of a physical model using active oscillators and passive systems. This work could inform the design of bio-inspired suspension systems and contribute to our understanding of animal balance and collective behavior in unsteady and complex environments.

We present three quantitative methods: 1) estimation of exoskeleton mechanical power and energy ratio from published data, 2) a systematic review of the exoskeleton literature on reported energy ratios, and 3) timing correction analysis of the replication experiment, to address concerns raised by Collins et al. (2026) about Luo et al. (2024). Together, these analyses support the reported metabolic reductions and the validity of exoskeleton control via learning in simulation. The critique rests on an unsupported premise: that exoskeleton energy ratios above 4 are physiologically implausible. This premise of Collins et al. (2026) is not supported by the cited evidence, and the error originates in their own cited source. Sawicki and Ferris (2009), the paper they invoke as authority for the limit of 4, state explicitly that "reported values of the muscular efficiency range from 0.10 to 0.34, with many sources assuming an average of [~]0.25." The value of 4 corresponds to this average, it is not a physiological ceiling. Treating an average as a physiological upper limit is a fundamental error. The published exoskeleton literature further contradicts the claim, including work by the authors of the critique themselves (Collins et al., 2015: 4.3; Young et al., 2017: 5.0) and independent work (Malcolm et al., 2013: 4.8; Seo et al., 2017: 6.7). In contrast, our walking energy ratio is 2.4, calculated directly from Fig. 4 of our paper. Our device delivers higher peak torque (14.1 Nm vs. 10.9 Nm, Lim et al., 2019) and achieves a slightly larger metabolic reduction (24.3% vs. 21%). Independent groups have since demonstrated meaningful metabolic reductions using learning-in-simulation frameworks, including Barati et al. (2026, 15.2% mean and 22.5% maximum) and Zhou et al. (2025, [~]20% during running). The claim of Collins et al. (2026) that this problem "remains unsolved" is directly contradicted by these independent results. The experiment in the critique is not a valid replication of our method. Our controller is a neural network with [~]10,000 parameters learned through deep reinforcement learning in musculoskeletal simulation; the critique instead applies a pre-programmed fixed torque curve with no learnable parameters. Beyond this, the replication contains three methodological errors: 1) a heel-strike timing assumption producing offsets up to 30% of the gait cycle; 2) an averaged torque profile that discards subject-specific control; and 3) a device [~]50% heavier than ours (4.8 kg vs. 3.2 kg) without measuring the metabolic penalty of the added weight. The critique also misreports Samsung data, with reported values approximately double those in the original publication, errors that directly underpin their physiological limit argument.

Animals navigating complex vertical environments must secure stable footholds, a challenge for species without feet. While arboreal climbing has evolved repeatedly in snakes, the physical mechanisms they use to scale broad, nearly flat surfaces remain poorly understood. By measuring three-dimensional body kinematics and per-contact forces on a smooth vertical wall with protruding posts, we show that cornsnakes climb by dynamically balancing forces across a highly redundant network of 5 to 16 simultaneous contacts--far exceeding the three contacts minimally required for physical stability. Using a computational model and a robotic climber, we demonstrate that while simple body undulations and passive friction are mechanically sufficient to climb this terrain, snakes systematically deviate from this passive baseline. While downward climbing relies primarily on friction, ascending snakes actively generate positive mechanical work at their contacts to propel themselves. Furthermore, we found that whenever a snake engages a new contact, it triggers a stereotyped, system-wide redistribution of force that seamlessly integrates the new foothold without disrupting whole-body balance. These results reveal how a continuous, flexible body can transform sparse environmental features into a robust, fault-tolerant network. This mechanism provides a biomechanical framework for understanding the repeated evolution of limbless climbing and offers physical principles for designing agile robots for unstructured terrain.

Many people which are affected by drop foot syndrome, have to face difficulty while walking which leads to pathological gait. This type of syndrome is treated by means of an external artificial stimulation known as functional electric stimulator (FES). In this paper we are designing an online feedback control system which optimize the strength of a FES given to paretic muscle which results in correction of pathological gait of the patient in a tolerable domain. Different phases of gait are identified using inertial measurement unit (IMU) as a feedback sensor mounted on the foot. Data is collected form 8 different healthy subjects and average of collected data is used as a reference template. Different trajectories of drop foot patients are simulated (due to unavailability of patients) and corrected according to the reference template.

AO_SCPLOWBSTRACTC_SCPLOWMuscle efficiency decreases with increasing size, largely due to a relative decrease in its mechanical output. Muscle mechanical output depends on its activation, strain, and strain rate and thus varies between different muscles within a limb during locomotion. Distinct muscle coordination patterns are required for efficient cycling, and so we would expect that the coordination patterns for efficient cycling or indeed locomotion would change across animal sizes. We tested whether muscle coordination would change with muscle size using data derived from human cycling: this paradigm allowed for controlled changes in both crank torque and cadence, allowing the multifactorial problem of muscle power output to be decomposed. We used kinematic and pedal data from 12 cyclists undergoing steady pedalling at cadences from 80 to 140 r.p.m. and generated musculoskeletal simulations of their movements. We introduced novel multisegment muscle models in the simulation that incorporated the internal muscle mass and thus accounted for the scaling effects of muscle tissue inertia. We solved the simulations for the muscle activity that was required to minimise the metabolic cost during cycling for each condition. The masses of the muscle models were scaled across five orders of magnitude. The predicted muscle activations were classified by Principal Component analysis to identify whether the coordination of muscle activity was modulated across models with different sized muscles. Analysis of variance revealed significant changes in coordination at the large-scale factors. This study shows how the coordination of muscle activity during locomotion will likely change across a range of body sizes due to the non-linear effects of the inertial mass within the muscle tissues.

Predator-prey interactions shape the evolution of escape behavior in prey, including different combinations of evasive movements, that may enhance unpredictability in fleeing directions and trajectories. So-called protean motion can enhance survival of flying prey in the wild, but quantifying such behaviors under natural conditions remains challenging. Here we used stereoscopic high-speed videography to record the escape flight behavior of wild males of the butterfly species Morpho menelaus in the Amazonian rainforest, and reconstructed 3D flight trajectories using artificial-neural-network-based tracking. During the experiments, we used a lure to attract freely patrolling male butterflies and elicited escape flights by intercepting their trajectory with a looming insect net swing. We then compared the escape flight kinematics to the pre-attack patrolling behavior. Attacks first induced a rapid upward maneuvering, directly followed by an unpredictable horizontal turn. The following escape flight trajectories showed increased horizontal erraticity and greater intra-individual heading variability, as compared to the pre-attack flight. Surprisingly, the mean speed decreased in the escape phase, notably in the horizontal plane. A significant negative association between horizontal trajectory complexity and flight speed was detected, indicating a speed-erraticity trade-off. These results show that wild Morpho butterflies respond to attacks by combining a climbing maneuver with an unpredictable heading change, followed by a protean escape flight; this increased escape erraticity comes at the expense of reduced escape flight speed. Because these large and relatively slow-flying butterflies display bright iridescent blue coloration on their dorsal wing side, erraticity during flight might enhance the dynamic flash coloration, likely limiting accurate targeting by predators.

Human-in-the-loop optimization (HILO) is an established method for identifying subject-specific optimal controllers for performance augmentation. For HILO algorithms to be useful in rehabilitation, however, the optimization algorithm may need to account for how the human response changes over time in response to assistance. In this study, we tested a modified version of Bayesian optimization (BO), dynamic Bayesian optimization (DBO), in a three-parameter optimization problem that sought to identify participant-specific optimal solutions for increasing walking speed. As opposed to BO, DBO accounts for the non-stationarity of human responses. Sixteen healthy participants received bilateral hip torque pulses delivered by a hip exoskeleton. The exoskeleton torque parameters were determined using HILO with either DBO or BO. Validation iterations were introduced to objectively compare performance across optimizers at different time points of HILO. The results showed that both DBO and BO significantly increased walking speed compared to baseline. When comparing performance between DBO and BO, DBO emerged as an improvement over BO both in terms of efficacy, modeling accuracy, and personalization. DBO induced changes in walking speed relative to baseline that exceeded those induced by BO at three of the four validation iterations. DBO outperformed BO in modeling accuracy in later validation iterations. DBO personalization induced changes in walking speed that were significantly greater than those induced by previously identified assistive solutions, while this was not the case of BO. Overall, our results indicate that DBO outperformed BO due to its greater ability to account for non-stationary aspects of the human response.

Studies on active and sedated fish passing through turbines and pumps show different mortality and injury rates for both cases. Consequently, fish behavior appears to play a substantial role in these outcomes. However, direct behavioral observations in hydraulic machines using quantitative parameters to draw conclusions about the underlying mechanisms are hardly possible and remain understudied. In this study, we examined the behavior of adult brown trout (Salmo trutta) in an experimental flume under hydraulic conditions characterized by strong flow acceleration and high velocities typical of turbine and pump intakes. Fish movement behavior was analyzed based on a quantitative approach to enable the analysis of swimming behavior even in flow velocities exceeding the sprint swimming speed of fish. The application of Hidden Markov Models (HMM) to analyze activity states and movement modes of fish from video tracking data demonstrated significant effects of the spatial velocity gradient (SVG) and flow velocity on fish behavior. Notably, SVG emerged as the primary trigger for avoidance reactions when exceeding a threshold of [Formula]. Fish exhibited distinct movement patterns under dark and daylight conditions, with more avoidance reactions in darkness. Whereas a considerable proportion of fish in daylight increased their swimming activity in the zone were flow velocity exceeded sprint swimming speed, in dark conditions no activity peak occurred in the same zone. The results illustrate how hydraulic conditions and lighting influence fish behavior. Integrating the behavioral rules identified in this study into numerical mortality-risk models could substantially improve their predictive accuracy. Thus, the findings allow for the development of less fish harming engineering solutions for hydropower facilities and pumping stations.

Continuous Psychophysics, which couples a continuous stimulus with a continuous response, is a promising tool to break out of the confines of traditional designs based on discrete trials. In this pre-registered study, we explore to what extent this paradigm is useful in the study of multisensory integration. We expand on Tonelli et al.s (2025) seminal study by additionally examining the role of eye-movements, using a Kalman filter to estimate the sensory noise underlying behavioral tracking parameters and employing a virtual reality set-up. We immersed two cohorts of participants (n = 30 each) in a virtual meadow environment and asked them to continuously track a drone (Experiment 1) or a swarm of flies (Experiment 2) with a controller, while simultaneously recording their eye movements. We manipulated the reliability of visual cues using four levels of fog (from a completely clear view to impenetrable fog where no visual cues to the targets position were available) as well as the presence of sound cues emitted from the object (sound present/absent). The maximum correlation between stimulus and response was higher when sound was present in some conditions, particularly when visual uncertainty was high, while the tracking delay remained unaffected across all fog levels. Using a Kalman filter to estimate the underlying sensory noise, we found strong evidence that sensory noise was lower when sound was present than when sound was absent both for manual and for ocular tracking, particularly for those conditions with higher visual uncertainty. In exploratory analyses, we further show strong correlations between manual and ocular tracking in all measures (maximum correlation, tracking delay, sensory precision). However, when isolating the multisensory advantage, these correlations all but disappeared for maximum correlation and tracking delay, while remaining substantial for sensory precision. Similarly, behavioral tracking correlated generally strongly with underlying sensory noise, but much less so when it came to the advantage conferred by added sound cues. Our results show that continuous psychophysics is well-suited for the study of multisensory integration, particularly when a Kalman filter analysis is used to estimate sensory uncertainty from behavioral data.

Locating, tracking, and intercepting objects is a fundamental behavior for many organisms. For instance, predators must track and capture erratically moving prey for their survival. Using the echolocating bat as a model species, we investigate how short-term changes in target motion predictability affect longer-term motor plans when tracking a prey item. We used a paradigm where prey motion is under experimental control, and then applied computational methods to characterize how target motion predictability influences short- and long-term behavioral control. We find that target motion predictability during the tracking phase of insect capture influences both short-term changes in sonar call control, as well as longer-term behavioral control for transitioning between hunting phases. For changes in immediate behavioral control, bats produce more bursts of calls at a higher rate when tracking unpredictable moving prey, an indication that the bat is collecting more information about the targets motion for unpredictable than predictable trials. In terms of longer-term behavioral control, target motion unpredictability delays the transition from tracking to capture phase behaviors. We suggest that the bat does this to collect more information about target motion to time the transition from tracking to capture behaviors for hunting success. Additionally, we find the effects of target motion unpredictability are first seen as changes in the vocal motor plan and then the auditory motor plan (ear motion), hinting at a sequencing of motor changes that warrant further investigation. SummaryWhen presented with a more challenging hunting task, bats will increase their production of bursts of calls at a higher rate and delay their transition into capture behaviors.

Biomechanical simulations of complex hand motions remain scarce, due to challenges that span computation and data acquisition. Using a computer vision-based motion capture approach, a 23-degree of freedom musculoskeletal model, and direct collocation optimization, we performed muscle-driven simulations to track hand kinematics from 7 participants performing American Sign Language gestures. While proximal joints were tracked accurately, interphalangeal joint tracking was significantly worse, with a consistent flexion bias. Modifications to finger extensor muscle paths that incorporated the dual-inserting nature of the extensors improved accuracy, suggesting better representation of extensor force distribution across distal joints may be necessary for accurate hand simulations.

Background and AimsActive capsule endoscopy could advance gastrointestinal diagnostics by enabling controlled navigation beyond passive peristalsis. However, current systems are often limited by inefficient propulsion, high power demands, or reliance on external actuation. Herein, we designed, developed and evaluated a novel electromagnetic impact-actuated capsule endoscope incorporating a ferromagnetic rail-enhanced locomotion mechanism. MethodsThe capsule employed an internal electromagnetic actuator comprising a movable coil-armature assembly guided along a ferromagnetic rail and surrounded by permanent magnets. Controlled current pulses generated reciprocating motion and propulsion through momentum transfer. Bench-top testing using a deformable intestinal model assessed locomotion and power consumption. Ex-vivo experiments were subsequently performed in porcine intestine under dry and physiologically simulated wet conditions. Transit speed, power consumption, and system stability were recorded. ResultsBench-top testing demonstrated stable propulsion at speeds up to 8.5 mm/s with a mean power consumption of 84 mW. During ex-vivo evaluation, mean capsule velocities were 1.95 mm/s and 7.2 mm/s under dry and wet conditions, respectively. Average power consumption was 96 mW and 193 mW. The actuator maintained reliable locomotion while preserving a compact system volume of [~]6.19 cm3. Lubricated conditions, representative of the intestinal environment, resulted in enhanced propulsion efficiency despite a concomitant increase in instantaneous power consumption. ConclusionThe electromagnetic impact-actuated capsule demonstrated reliable locomotion in biologically relevant ex-vivo environments while maintaining compact dimensions and moderate power requirements. Ferromagnetic rail-enhanced flux concentration offers a promising propulsion strategy for future actively navigated and therapeutic capsule endoscopy platforms.

The mass-rearing of black soldier fly (Hermetia illucens) larvae (BSFL) is a promising solution for converting organic waste into high-quality insect protein, but preventing larval escape from open-top rearing containers remains a major management challenge. Conventional escape-control methods are often unreliable or impractical. To address this, we developed and evaluated a novel physical barrier, the anti-climbing tape, featuring regularly arranged macroscale protrusions designed to disrupt larval locomotion on vertical surfaces. We conducted a series of experiments to examine the design parameters of the anti-climbing tapes, including the gap size between protrusions and the number of protrusion rows. Our results demonstrate that the anti-climbing tape prevents escape via a dual mechanism: (1) physical obstruction, in which gaps narrower than the larval body width block larvae from passing through, and (2) adhesion reduction, in which the elevated protrusion array decreases the effective contact area for wet adhesion while increasing gravitational torque acting on the larval body. The effectiveness of these mechanisms was dependent on larval size. A design featuring 0.5-mm gaps and a 15-row protrusion array completely prevented the escape of later-instar larvae (>10 mm) in a 20-day large-scale trial, whereas the method was less effective for smaller larvae. In conclusion, the anti-climbing tape provides a robust and chemical-free approach to BSFL escape in mass rearing. To ensure reliable performance, its design parameters, both gap size and array width must be optimised to suppress the mechanical and adhesive components of larval climbing according to the target larval size. Conflict of interestS. Jang and M. Shimoda are inventors on a Japanese patent application (No. 2022-172252, filed November 27, 2022) related to the method described in this manuscript. FundingThis study was supported by Korea-Japan Joint Government Scholarship Program for the Students in Science and Engineering Departments, the Korean Scholarship Foundation, and the University of Tokyo Foundations Support Fund for International Students.

Computational Hill-type muscle models are widely used to simulate muscle force production because of their efficiency and physiological interpretability. However, their formulation relies on limiting assumptions, including debated multiscale simplifications, a simplified excitation-activation dynamics and an inability to capture slow and fast fibres. Moreover, existing Hill-type models remain insufficiently validated across physiological scales, fibre types, and contraction modes. We addressed these limitations by developing a multiscale fibre-type specific Hill-type neuromuscular actuator with mechanistic excitation-activation dynamics and systematically validated it against comprehensive experimental benchmarks. The model built upon a previously proposed motoneuron-driven actuator incorporating calcium-kinetics-based activation dynamics. The excitation-activation formulation was further refined to strengthen its physiological basis, while the contraction dynamics was extended by including an activation- and length-dependent force-velocity relationship, elastic tendon, passive elastic element, and the fibre-type-specific effects of yielding and sag. Validation was performed against four benchmark datasets spanning motor-unit and whole-muscle scales, including slow and fast fibres under both isometric and dynamic conditions. Experimental force traces were obtained from six muscles of rats and cats using a broad range of stimulation frequencies, muscle lengths, and imposed length changes, combining previous literature datasets with experiments performed ad hoc for this study. Overall, the model reproduced forces across all benchmark conditions, with mean absolute errors typically below 15% of the maximum isometric force, although larger errors were observed in specific submaximal and dynamic trials. The inclusion of physiologically based excitation-activation dynamics, together with yielding and sag, improved model performance under submaximal activation conditions. This study presents the first systematic validation of a single multiscale Hill-type neuromuscular actuator against comprehensive experimental motor unit and muscle force data, providing a benchmark framework for the development and assessment of future models. Author summarySkeletal muscles generate force through a complex sequence of events that links neural signals to muscle contraction. Because direct measurements are difficult to obtain, researchers often rely on computer models to investigate neuromuscular function and estimate muscle forces. However, most modelling approaches rely on simplifying assumptions about how force is generated across different biological scales, how muscles are activated, and how slow and fast muscle fibres behave. Moreover, they have not been validated against comprehensive experimental data. As a result, it remains unclear how accurately these models can reproduce muscle force across different physiological conditions. In this study, we established the first comprehensive set of experimental benchmarks spanning both motor-unit and whole-muscle scales, including slow and fast muscles under isometric and dynamic conditions. We used these benchmarks to validate a newly developed multiscale muscle model that explicitly represents the physiological pathway from neural stimulation to force production. The model incorporates experimentally based descriptions of calcium dynamics, activation, tendon elasticity, and fibre-type-specific contractile properties. We then compared simulated and experimental force responses across a wide range of stimulation frequencies, muscle lengths, and length-change conditions.

Animals rely on a wide range of environmental signals, including celestial and terrestrial cues for navigation. While celestial cues, such as the sun, play a major role in maintaining a constant heading during long-distance migration and dispersal, terrestrial cues provide an animal with a short-range navigation system, ideal to pinpoint highly specific locations. In Monarch butterflies, the simulation of a terrestrial landmark, i.e. a vertical stripe, induces an attraction behavior (all animals head toward the stimulus) while a small green light spot, simulating the sun, elicits menotactic orientation (animals adopt individual-specific headings relative to the stimulus). However, the mechanisms underlying how the animal distinguishes between a stimulus as a terrestrial landmark versus a celestial cue remains unclear. To explore this, we tested non-migratory Monarch butterflies (Danaus plexippus) in a flight simulator. The inner surface of simulator was equipped with an area of LEDs, allowing to present different visual stimuli to the butterflies during tethered flight. By systematically manipulating the stimulus width, height, brightness, and elevation we found that Monarch butterflies exhibited attraction behavior to high contrast areas, like stripe edges. Menotactic behavior was not achieved by solely decreasing the stimulus to a small light spot but also required for the stimulus to be presented at higher elevation to be interpreted as a sun stimulus. These findings suggest that multiple parameters, inherently set by the butterflys navigation system, are critical to interpret a visual stimulus as celestial cue or terrestrial landmark, producing dynamic switches between different orientation strategies during navigation.

Observer effects are a frequent problem in animal behaviour studies, particularly when assessing responses to human disturbance. Automated Behavioural Response (ABR) systems, which combine camera traps with automated sound playbacks, offer a solution but have been primarily used on large terrestrial mammals. Here, we demonstrate their use in a small ([~]110g) arboreal primate, the eastern pygmy marmoset (Cebuella niveiventris). We conducted two playback experiments to test the risk-disturbance and distracted prey hypotheses. The marmosets exhibited strong anti-predator responses to avian predator calls, including increased fleeing and vocalisations. Human speech elicited similar but weaker responses, indicating that pygmy marmosets do not perceive raptors and humans as equivalent threats. Embedding predator calls into anthropogenic noise reduced vocal responses, suggesting that anthropogenic noise interferes with responses to predation cues. Across five weeks, we generated 128 successful experimental trials, demonstrating that ABRs can rapidly produce sample sizes sufficient for hypothesis testing in the field.