Bioinspiration & Biomimetics

Known for their rapid water surface locomotion, whirligig beetles (Gyrinidae) are also fast divers. They can transition from the surface to underwater swimming in under 100 ms. Diving requires them to generate sufficient hydrodynamic torque to break the water surface and reorient the body. However, the mechanism by which whirligigs create the necessary pitch-down torque is unknown. Here, we show that their diving is achieved by bending the abdomen. The inertial hydrodynamic pressure on the flexed abdomen generates the torque necessary for surface breaking and body rotation. The torque is maximized at an abdominal angle of 45{degrees}, whereas for effective diving, whirligigs display a preferential abdominal angle of around 28{degrees}, closely aligning with the angle that maximizes the torque-to-drag ratio. This abdominal angle is optimized by balancing torque generation with the drag penalty. Our findings inspire a mechanism for surface-to-underwater transition, offering design principles for multimodal robots capable of locomotion across the air-water interface.

Spiders arrest their fall by abseiling on dragline silk, varying silk release rate to avoid excessive strains that could break the thread. To understand how this is achieved, we develop a minimal dynamical model of abseiling and formulate a time-optimal control problem subject to physical limits on silk strain and dragline spinning rate. We find that the optimal trajectory includes an interval over which the strain is constant, resulting in uniform deceleration consistent with published experiments. To test the generality of this strategy, we then turn to a distinct extreme motor behavior seen in jumping spiders: using a dragline anchored at the takeoff site to correct their orientation while jumping at speeds exceeding 100 body lengths per second. We show that the same constant-strain strategy suffices to counteract the initial body rotation and ensure an upright landing without significant loss of range. Together, our theoretical framework suggests how strain regulation may serve as a strategy for controlling extreme movements such as abseiling seen in many spiders and may be specialized to tasks such as aerial braking and reorientation in jumping spiders.

The Siamese fighting fish (Betta splendens) is well-known for its high levels of aggression and complex, stereotyped displays, making it an ideal model for testing deep learning-based tools for high-throughput, unbiased quantification of aggressive behaviour. A deep-learning object recognition model (BettaAI) was developed to detect the species' aggressive displays, including threat (opercula opening and fin distension), attack (bites) behaviours, as well as surface air breathing, a behaviour correlated with the frequency and intensity of aggression. Videos of males confronting their mirror image were acquired simultaneously with synchronised top and side cameras. Two separate models were trained, with some behaviours identified from top-view frames and others from side-view frames; outputs from both models were combined and post-processed for final quantification. BettaAI performed with high accuracy: F1 scores were 0.99 for the top-view model and 0.93 for the side-view model. Average Precision (AP) at 0.5 IoU exceeded 0.98 for all categories except bites, a rare behaviour in the dataset, which reached 0.865. Comparisons with a manually curated "ground-truth" dataset from videos of 10 males showed strong concordance between automated quantification and manual annotations by expert observers across all behaviours. A case study on 461 males reacting either to a mirror or to a video playback of a conspecific demonstrated the high-throughput and unbiased advantages of BettaAI, with threat displays being more frequent toward the mirror image and attack rates not differing between treatments. This work underscores the potential of BettaAI for automated, bias-free quantification of aggressive displays in B. splendens.

Marker-based optical motion capture remains the gold standard for kinematic analysis across biomechanical research. However, when tracking dynamic movements, severe occlusions often create missing data and noisy signals that require time-consuming manual cleaning. Existing automated methods usually rely on predefined skeleton models or need massive training datasets for machine learning solutions. This study presents the AMP: an automated marker based motion capture processing pipeline. The AMP is a fully analytical approach that uses hierarchical algorithms including 1) rigid-body constraint, 2) Kalman Filter state estimation and 3) kinematic pattern match to label markers and fill data gaps without manual human intervention. First, the algorithm identifies and removes mislabeling markers before any data scrubbing. During the data cleaning process, our hierarchical algorithm uses a translation-based step to track segments even when there are insufficient donor markers to form a full rigid body. To reconstruct missing marker trajectories, the pipeline initially uses Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation for short marker gaps. However, the PCHIP creates massive spatial errors during long occlusions, the system automatically switches to rigid-body tracking for gaps over 40 ms. Results showed the pipeline drastically reduced processing time compared to manual human expert methods by 73.5%. In addition, the AMP kept spatial errors within 10 mm of the manual ground truth. By using this combined hierarchical approach to match marker processing accuracy, the pipeline provides a fast, general-purpose tool that fully automates data cleaning and saves hours of manual effort.

Animal pose estimation typically requires manual annotation of body parts to train neural networks for each new recording system or environmental condition. Here we use a general-purpose vision-language model (VLM) to generate annotations in multi-animal recordings of fruit flies (Drosophila melanogaster). Initial annotations follow written anatomical descriptions and are reviewed by an independent VLM session using neighboring video frames. On 120 held-out fly images, AI annotations had a median distance of 2.28 pixels from human labels, with 84.0% agreement within eight pixels, averaged across landmark types. The comparison included 10 landmarks, with head and eyes analyzed separately because of systematic differences in placement. Review improved agreement primarily at the feet, with the largest remaining discrepancies at positions with limited visual evidence. Visual inspection revealed cases in which the AI annotations were more anatomically accurate than those of an experienced human annotator, particularly at the feet. A convolutional network trained using SLEAP on 300 AI-annotated fly images showed similar agreement with human labels and closely reproduced the AI annotations on held-out images, with a median distance of 1.56 pixels. We also applied the procedure to recordings from another laboratory, using new AI annotations to train a separate pose network. Although VLM annotation required minutes per fly, the trained networks enabled pose estimation throughout more than 700,000 frames. These results demonstrate that VLM annotations can support fast animal pose estimation without manually labeled training images.

Interception of moving objects is a universal pattern of animal behavior with many industrial applications. Inspired by the behavior of the characid fish Brycon guatemalensis, which catches fruits falling onto the surface of a river, we study one-shot (i.e., open-loop) interceptions using both stochastic optimal control theory and reinforcement learning. In the first instance, an agent actuates a control (wait time before launch and heading angle) to meet its target, given a noisy estimate of the target initial state. In a minimal setting, where launch velocity and actuated control are constants, we show that the optimal wait time is attenuated by noise in both the target measurement and control actuation, as well as agent energy conservation. We then derive an upper bound for the heading angle variance which scales as the ratio of the non-vanishing optimal interception error to the initial planar separation. Complementing our control framework, our deep reinforcement learning approach shows that the neural network representing the fish first learns the value function for the interception, then learns how to aim, and finally learns the optimal wait time. Beyond the specific problem at hand, our results may also have applications to problems with little time for feedback or corrections such as perching, landing, and docking.

1. For many animals, vision represents the primary sensory modality governing a vast diversity of behaviours. Creating 'virtual twins', or visually realistic digital reconstructions of real individuals interacting within their environments, offers a promising approach for understanding how visual information guides an animal's decisions, behaviours, and interactions. Such systems enable the creation of video scenes from any view, including the viewpoint of the animals themselves. This allows receiver information to then be analysed from the perspective of species-specific visual systems, or for the accurate reconstruction of a real animal's movement to provide social stimuli for behavioural experiments. 2. Despite advances in 3D modelling, photorealistic scene rendering, and animal tracking, efficiently creating realistic digital reconstructions of movement remains challenging for many species. In fishes, a continuously-deforming body due to the absence of discrete joints make it particularly difficult to map tracked body keypoints onto articulated digital models, often resulting in unnatural or erratic animations. 3. Here, we introduce DigiFish, an add-on for the open-source animating software Blender, that generates realistic fish animations from tracking data. By integrating anatomical keypoint tracking with spline-based body curvatures, DigiFish reconstructs biologically-realistic swimming postures and movements of fishes within customisable virtual aquatic environments. The system also supports virtual 'point-of-view' cameras that can be placed arbitrarily within a scene, or dynamically driven by eye-tracking data, enabling unlimited perspectives on how individuals, conspecifics, or landmarks appear against complex backgrounds and how these views change with realistic swimming and eye movements. 4. We provide the Blender add-on and user guide and use DigiFish to reconstruct two different contexts: staged predator-prey interactions in the laboratory and natural schooling of wild fish in the field. Using a further reconstruction of fish swimming in an arena, we also use an Elementary Motion Detector model to show that our virtual reconstructions successfully capture perceptually-realistic movements and postures of individual fish within a digitally reconstructed scene. 5. By integrating photo-realistic scenes with biologically-realistic, simulated motion, DigiFish offers a flexible and accessible platform for investigating visually guided behaviour, perception, and cognition in complex and dynamic environments.

Chemotaxis assays are widely used to quantify sensory-driven behavioral responses in Caenorhabditis elegans. Sodium azide is commonly used to immobilize animals at assay endpoints, although concerns have been raised that diffusion beyond the endpoint region could affect behavior before a choice is completed. We compared sodium azide-mediated immobilization with post-assay cooling using the repellent 1-octanol and attractant diacetyl. Wild-type animals showed robust avoidance of 30% 1-octanol and attraction to 0.5% diacetyl with either method, across multiple assay durations, and with or without worm food Escherichia coli OP50. These results support post-assay cooling as an alternative endpoint-scoring method.

Translocations are commonly used to reduce the risk of extinction of threatened species, and often to reintroduce animals to their former habitats. If extirpation occurred a long time ago, individuals may be placed into novel environments outside of conditions they have previously experienced, which can affect translocation success. To measure success of translocations, particularly those to novel habitats, it is important to monitor post-release spatial behaviour. In 2023, ten critically endangered k[a]k[a]p[o] (Strigops habroptilus) were released to form a new population at the fenced mainland island of Sanctuary Mountain Maungatautari (hereafter Maungatautari) in the North Island of New Zealand, an area from which they were extirpated approximately a century earlier. The spatial behaviour and habitat selection of these k[a]k[a]p[o] was analysed using dynamic space use and step selection functions with temporally dynamic responses applied to GPS logger data recorded over a seven month period. These showed three unexpected results: k[a]k[a]p[o] were highly mobile and used larger areas compared to other sites; there was synchronicity of movement, with most individuals simultaneously moving toward the perimeter fence in spring; and when near the fence k[a]k[a]p[o] selected for areas of pasture and exotic vegetation more than anticipated. While direct comparisons are difficult due to varying data availability, this unexpected spatial behaviour and habitat selection suggests that k[a]k[a]p[o] behavioural responses were different at Maungatautari compared to other sites. This may indicate that k[a]k[a]p[o] released at Maungatautari had a longer exploratory stage than elsewhere; that there were behavioural effects influenced by conspecific density or the availability of novel and exotic vegetation; or that there were specific breeding habitat requirements not met at the site during the study period. These results highlight the importance of high-resolution spatial monitoring to evaluate the success of translocations, particularly those to novel environments.

We investigated the hearing mechanism of Manduca sexta caterpillars to determine whether their behavioural responses to acoustic stimulation are caused by sound-induced substrate vibration or by direct detection of airborne sound. Behavioural response thresholds to direct substrate vibration and airborne sound were measured using pure-tone stimuli at 150 Hz and 2000 Hz. A frequency of 150 Hz was selected based on previous behavioural measurements showing the lowest sound detection thresholds between approximately 100 and 200 Hz, while 2000 Hz was selected based on laser Doppler vibrometry measurements that revealed a mechanical resonance near this frequency in a thoracic hair. At both test frequencies, the direct substrate vibration detection thresholds were more than 100 times greater, on average, than the sound-induced substrate vibrations measured at the behavioural sound detection thresholds. Thus, the behavioural responses to airborne sound cannot be explained by sound-induced substrate vibration. During airborne sound stimulation, caterpillars exhibited graded behavioural responses with increasing sound pressure level, progressing from freezing to twitching and finally to jump-startle. Together, these findings provide direct experimental evidence that the behavioural responses of M. sexta caterpillars to acoustic stimulation cannot be explained by sound-induced substrate vibration and instead support direct detection of airborne sound. Understanding how caterpillars respond to airborne sound may also provide biological inspiration for the development of bio-inspired acoustic sensors.

As animals explore their environment, self-generated motion can affect visual perception, while perceptual constraints shape movement decisions. Movement strategies should therefore balance the benefits of exploration against the potential perceptual costs of motion. Here, we test whether the movement strategies animals adopt can enhance information acquisition while accounting for the constraints that motion imposes on visual perception. We first quantified the visual perceptual abilities of freely moving fish in three dimensions by projecting virtual prey around three-spined sticklebacks (Gasterosteus aculeatus), determining how the likelihood of detecting prey varied across the fish's visual field as a function of their movement. Self-induced motion reduced the likelihood prey were detected, with increases in speed non-linearly reducing the perceptual abilities of the fish. Using agent-based simulations based on empirical models of visual perception, we demonstrate that when visual perception degrades non-linearly with speed, the strategy that maximizes prey detection is 'saltatory' locomotion, involving stationary pauses interspersed with movements. Moreover, experiments with freely swimming fish showed they adopted specific durations of stops and movements that improved prey detection given the way motion impacts their perception. Our results reveal that some movement strategies are more effective than others at gathering information during exploration.

Background: Articulated laparoscopic instruments require a reliable mechanical connection between the handle and shaft to maintain the intended instrument configuration during surgical manipulation. The handle shaft locking interface should resist unintended relative displacement under external loading. However, the appropriateness of a specific slip resistance threshold must be evaluated in relation to the instrument design and intended conditions of use. Objective: To evaluate the mechanical performance associated with an 8 N slip resistance specification for the locked handle shaft interface of an articulated laparoscopic instrument using a controlled bench model. Methods: A bench based mechanical evaluation method was designed using the instrument under test, mechanical fixtures, a calibrated force measurement device, and displacement measurement equipment. After engagement of the handle shaft locking mechanism, external forces were applied to evaluate resistance to relative displacement. Testing included axial loading, loading in relevant transverse directions, and repeated locking unlocking cycles. The primary outcome was the force corresponding to the onset of relative slippage at the locked interface. Secondary outcomes included displacement under load, complete locking failure, and changes in mechanical performance after repeated operation. Acceptance criteria should be established before confirmatory testing. Results: All 30 instruments from three production batches met the 8 N axial requirement (30/30). The axial slip onset force was 12.40 N . Under a sustained load of 8 N for 60 s, mean relative displacement increased from 0.06 mm to 0.10 mm, and mean residual displacement was 0.02 mm. After 1,000 cycles, the axial slip-onset force was 12.00 N . Conclusions: This study provides a bench-testing framework for evaluating an 8 N slip resistance specification for the handle shaft locking interface of an articulated laparoscopic instrument. Whether this specification provides adequate mechanical stability can be determined only by comparing the measured performance with engineering acceptance criteria established in advance on the basis of the instrument's intended use and foreseeable loading conditions.

Conventional clinical assessment of upper limb kinematics relies on goniometry or visual estimation, methods that provide limited information on multi-segment 3D kinematics and can have poor inter-rater reliability. Inertial measurement units (IMUs) represent a viable alternative; however, extracting anatomically meaningful joint angles from skin-mounted sensors remains challenging owing to sensor-to-segment misalignment, soft-tissue artefact, and suboptimal orientation estimation under dynamic conditions. This study developed and validated a multi-segment IMU-based framework for upper limb kinematic assessment, addressing three stages of the measurement pipeline: (i) sensor fusion gain optimisation as a function of task mean segment angular velocity; (ii) accuracy of an anatomical landmark registration-based alignment method; and (iii) comparative evaluation of landmark-based alignment against manual and neutral-pose alignment approaches. Twenty participants with typical shoulder function performed clinical and functional upper limb tasks, recorded simultaneously with an optoelectronic motion capture system and six IMUs tracking the thorax, clavicle, scapula, humerus, forearm, and hand. Orientation estimation used a gradient-descent complementary filter with velocity-dependent gain modulation. Sensor-to-segment alignment was achieved via manual, neutral-pose, and landmark registration methods. Mean sensor orientation errors ranged from 1.5 to 3 degrees across segments/tasks and increased with angular velocity. Landmark registration produced substantially lower joint angle errors than manual and neutral-pose alignment across all joints (root mean square error range: 3.7-7.8 degrees; manual: 8.3-24.2 degrees; neutral: 15.5-35.5 degrees). These results indicate that IMU-derived kinematic accuracy is context-dependent, varying with joint and movement type, suggesting that accuracy should be established for the specific movements and joints relevant to each clinical application.

Crank length affects the geometry of the bicycle-rider system and influences the motion of the lower limb throughout the pedal cycle. Conventional crank selection has largely relied on rider stature and mechanical power, yet mechanical power and cycling economy remain remarkably insensitive to crank length across a broad range. Yet, elite cyclists and triathletes increasingly adopt shorter cranks, particularly in disciplines where aerodynamic drag strongly affects cycling performance. However, the mechanisms that connect crank length to mechanical power and aerodynamic drag remain unclear. Here we show that crank length couples rider anthropometry, hip kinematics, and aerodynamic drag. Population anthropometry of 6,068 individuals reveals that stature alone poorly predicts personalized crank length, while geometric analysis identifies a nearly linear relationship between crank length and minimum lower hip angle: a 10-mm reduction in crank length increases hip-angle margin by approximately 1.42 degrees with little mechanical penalty. This geometric freedom permits more flexion of the torso and a more aerodynamic posture; the resulting aerodynamic advantage grows rapidly with cycling speed because aerodynamic power scales with velocity cubed. Data from 150 elite cyclists and triathletes support this shift toward shorter cranks, with triathletes adopting shorter relative crank lengths than cyclists. Our results establish crank length as a personalized design variable that connects lower-limb geometry to aerodynamics. These insights enable personalized crank selection, integrated rider-bicycle design, and performance optimization for the increased aerodynamic demands of high-speed cycling.

Tardigrades, key ecdysozoan organisms, have poorly characterized daily behavioral rhythms linked to their molting (ecdysial) cycle. We continuously tracked the locomotor activity of the model tardigrade Hypsibius exemplaris using infrared videography and deep learning-based automated tracking under three photic regimes: a light/dark cycle, constant darkness following entrainment, and constant darkness without prior entrainment. We identified a robust endogenous circadian-like rhythm in locomotor activity that persisted in constant darkness, independent of photic input. Although the light/dark cycle enhanced the rhythm amplitude and the overall animal activity, it was not required for rhythmicity. Individual phases varied widely, indicating a lack of population-level synchronization. Furthermore, we developed a behavioral classification system based on activity level, which enables categorizing distinct behaviors. Behavioral patterns were modulated by distinct ecdysial stages, with quiescence gating premolt and time-of-day dependent patterns in feeding and wandering. These findings provide evidence consistent with an endogenous, circadian-like timing system in tardigrades that integrates daily activity with molting cycles and establish H. exemplaris as a promising model for studying ecdysozoan chronobiology.

The mechanisms by which organisms extract relevant information from complex sensory signals and use it for decision-making constitute a fundamental issue in biological and cognitive sciences. In social animal societies, individual decision-making is profoundly shaped by information provided by conspecifics. Collective patch-foraging experiments in the laboratory provide a controlled setting in which social information use can be quantified. Here, we consider a go/no-go task in which groups choose between two patches differing in food reward probability. We use agent-based simulations, underpinned by an augmented collective drift-diffusion model, to investigate alternative mechanisms for representing and integrating social information. We consider two representations, continuous (counting representation) and discrete (pulsatile representation), and two integration mechanisms, biasing the decision threshold (threshold modulation) and modifying the accumulated belief (belief modulation), yielding four distinct social models, in addition to a non-interacting model. We first characterize the collective dynamics generated by these models across cognitive parameters and experimental conditions. The temporal dynamics of group accuracy provide informative signatures of the underlying mechanisms. We then assess model selection and parameter identifiability using Bayesian inference and Wasserstein distance minimization, applied to group-accuracy and departure-time distributions. Bayesian inference using distributions of group accuracy provides the most reliable identification across the conditions considered. Importantly, model selection and parameter identifiability are associated with different aspects of collective behavior: model selection is closely linked to the presence of oscillations in the temporal dynamics, whereas parameter identifiability is more closely related to the global accuracy value. Thus, the information available for distinguishing the underlying cognitive mechanisms is not necessarily the same as that required to recover their parameters. Our results further show that identification depends not only on model structure but also on the experimental conditions. Overall, this study provides practical guidelines for identifying socio-cognitive mechanisms in collective foraging experiments.

From the iconic V-formations of geese to the spectacular murmurations of starlings and communal roosting of millions of bramblings, bird flocks keep sparking wonder and scientific curiosity. In science, common themes include how flocking formation impacts the energy expenditure during migration, how it affects risks of predation, how it relates to information spread on food abundance, and how birds manoeuvre in dense flocks. Here, we report on observations of a very different type of flock formation: migrating terns over open sea flying side by side in a search party manner -- strikingly orthogonal to their heading -- with interbird distances up towards 300 m and flocks of around ten birds covering widths up to one kilometer. Such sparse formations of migrating birds stand in great contrast to the classes of bird flocks previously described in the literature. We present simultaneous field observations and ship-based radar measurements of flock characteristics and argue that this is a form of fly-and-forage migration that may be a common, but overlooked, network foraging strategy of surface feeding birds: while most migrants cannot combine migration and foraging, the situation is different for surface feeders like terns.

Devireddy et al. [1] previously showed that an enthalpy-based, macroscale model simulated tissue freezing histories are in close agreement with a coupled model incorporating cellular water transport and intracellular ice formation (IIF). That comparison, however, was performed for a limited set of tissue geometries, cooling conditions, and biophysical parameters. Here, we revisit that conclusion by reimplementing the published model from its governing equations and systematically expanding the parameter space to determine when microscale processes alter the predicted macroscale freezing response. We incorporate both surface catalyzed nucleation (SCN) and volume catalyzed nucleation (VCN) of intracellular ice with cellular osmotic dehydration and examine a broad range of tissue dimensions, cooling rates, convective boundary conditions, membrane permeabilities, cell sizes, activation energies, intracellular water fractions, and nucleation rates. Linearization of the cellular water transport equation about osmotic equilibrium yields an osmotic relaxation timethat can be compared with the local residence time of tissue undergoing phase change. Their ratio defines a dimensionless group, that organizes the transition between regimes in which the uncoupled enthalpy formulation and the coupled micro and macroscale formulation produce similar or substantially different results. Across the one-dimensional parameter space examined here, the two models remain within a prescribed thermal-error bound for the dimensionless group below approximately 0.1, whereas the discrepancy increases systematically above this threshold. Model divergence also depends on intracellular ice nucleation, i.e., when nucleation is sufficiently slow, cellular water transport can become rate limiting for latent heat release. Conversely, sufficiently rapid intracellular ice formation reduces the macroscopic discrepancy by providing an additional pathway for latent-heat release. Multidimensional simulations further show that geometry alone can move a nominally conventional freezing protocol into the coupled regime. In particular, multidirectional cooling can reduce the local residence time sufficiently to produce substantial differences in predicted frozen volume even when the corresponding thermal histories appear similar. Finally, we formulate an inverse approach for estimating IIF parameters from tissue scale thermal histories. Synthetic simulations indicate that parameter recovery is feasible within a finite range of cooling conditions, providing a testable strategy for future experimental measurements. Together, these results provide a mechanistic criterion for deciding when microscale biophysical processes must be retained in macroscale tissue-freezing models.

Healing of the gastrointestinal (GI) tract following surgical interventions is governed by coupled mechanobiological processes and may lead to severe complications such as perforation or stricture formation. A first comprehensive mechanobiological modeling framework of GI healing is herein proposed. The framework addresses two clinically relevant applications of bioprinting: endoscopic treatment of GI tissue defects and bioprinting-assisted surgical anastomosis. Tissue remodeling is based upon a hyperelastic material model with fiber dispersion coupling the spatiotemporal reaction diffusion dynamics of fibroblasts and cytokines with collagen deposition, reorientation, and plastic deformation. An active stress formulation drives fibroblasts and myofibroblast contraction while a multiplicative decomposition approach is adopted for collagen remodeling as a permanent change. The governing equations are discretised in time with an implicit Euler scheme, finite element in space and solved by a staggered algorithm implemented in the open-source platform FEniCS. Numerical simulations show that the initial collagen fibers content and orientation strongly influence fibroblast infiltration and scar contracture formation. The study quantifies the advantage of having a collagen bioprinted architecture aligned with tissue fibers, to reduce structural remodeling distortion. The model also predicts a strong correlation between collagen deposition and luminal burst pressure during the recovery of mechanical integrity in GI anastomosis.

Zoos and public aquariums serve as essential hubs for wildlife conservation, public education, and scientific research; however, the success of these missions depends upon maintaining high animal welfare standards despite constant human presence. A primary concern is the 'visitor effect': the alteration of animal behavior due to human presence. While this phenomenon is well-documented in captive mammals and has been explored in chondrichthyes, it remains largely unexamined in teleost fish, despite evidence of their sensitivity to human stimuli in natural environments. This study addresses this knowledge gap by investigating the behavioral responses of 13 teleost species to varying levels of crowd density, direct human solicitation, and ambient noise. Our multivariate analysis reveals that visitor density and visitor-induced disturbances are significant drivers of behavioral change, with species identity acting as the primary source of variance. We observed high inter-taxonomic variability: while several species (including Euxiphipops sextriatus, Dascyllus aruanus, and Zebrasoma flavescens) showed marked sensitivity through significant alterations in stress-coping, exploration, and aggression, others remained behaviorally stable. Furthermore, acoustic analysis indicates a complex soundscape where visitor density correlates with increased sound amplitude, particularly in the 100 Hz to 400 Hz range, a critical band for teleost hearing suggesting that visitor-induced noise is distinct from the aquarium's technical background noise. These findings underscore that the 'visitor effect' is not a uniform phenomenon but a complex interaction shaped by species-specific sensitivity and distinct anthropogenic stressors. Our results highlight the necessity of incorporating species-specific behavioral data and acoustic monitoring into welfare management protocols to better mitigate the impact of human presence on captive fish populations.