Biology subjects

Taylor, G. K.

Publications and source records attributed to Taylor, G. K..

18 recordsLinked to original sources

bioRxiv2025

Signatures of Motion: Decomposition of Adaptive Morphing Flight in Harris' Hawks

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.

animal behavior and cognition↗

bioRxiv2024

Motion vision is tuned to maximize sensorimotor energy transfer in blowfly flight

Insects achieve agile flight using a sensor-rich control architecture whose embodiment eliminates the need for complex computation. For example, their visual systems are tuned to detect the optic flow associated with specific self-motions, but what functional principle does this tuning embed and how does it facilitate motor control? Here we test the hypothesis that evolution co-tunes physics and physiology by aligning an insects sensors to its dynamically-significant modes of self-motion. Specifically, we show that the tuning of the blowfly motion vision system maximizes the flow of signal energy from gust disturbances and control inputs to sensor outputs, jointly optimizing observability and controllability. This evolutionary principle differs from the conventional engineering-design paradigm of optimizing state estimation, with implications for novel robotic systems combining high performance with low power-consumption.

neuroscience↗

bioRxiv2023

A robust and flexible deep-learning workflow for animal tracking

Developments in automated animal behavioural analysis software are increasing the efficiency of data collection and improving the standardization of behavioural measurements. There are now several open-source tools for tracking laboratory animals, but often these are only accurate under limited conditions (e.g. uniform lighting and background, uncluttered scenes, unobstructed focal animal). Tracking fish presents a particular challenge for these tools because movement at the waters surface introduces significant noise. Partial occlusion of the focal animal can also be troublesome, particularly when tracking the whole organism. But identifying the position of an animal is only part of the task - analysing the movement of the animal relative to their environment and experimental context is often what provides information about their behaviour. Therefore, the automated detection of physical objects and boundaries would also be beneficial, but this feature is not commonly incorporated into existing tracking software. Here we describe a video processing method that uses a range of computer vision algorithms (e.g. object detector and tracker, optical flow, parallel plane homology) and computational geometry techniques (e.g. Voronoi tessellation) to analyse the movement behaviour of fish in response to experimental stimuli. A behavioural experiment, which involved tracking a fishs trajectory through a field of obstacles, motivated our development of a set of tools that: (1) measure an animals trajectory, (2) record obstacle position, and (3) detect when the fish passed through virtual gates between adjacent obstacles and/or the aquarium wall. We have introduced a novel Detect+Track approach that significantly enhances the accuracy and robustness of animal tracking, overcoming some of the limitations of existing tools and providing a more reliable solution for complex experimental conditions. Our workflow is divided into several discrete steps, and provides a set of modular software building blocks that can be adapted to analyse other experimental designs. A detailed tutorial is provided, together with all the data and code required to reproduce our results.

animal behavior and cognition↗

bioRxiv2023

Ultra-fast micro-CT of an unrestrained live insect

Micro-CT has revolutionized functional morphology by enabling volumetric reconstruction of biological specimens at micrometre scales, but its accuracy is compromised by fixation artefacts. State-of-the-art in vivo imaging avoids this, but still requires subjects to be tethered, anaesthetized, or stained. Here we use ultra-fast synchrotron-based micro-CT to produce the first 3D scan of an unrestrained living organism at micrometre resolution, demonstrating the potential of this method in physiology, behaviour, and biomechanics.

zoology↗

bioRxiv2023

Zebra finches use brightness cues for gap selection in flight

Flying animals that are adapted to life in cluttered environments require effective and robust guidance mechanisms to avoid collisions. Birds and insects use optic flow cues to avoid obstacles when flying, but these are only generated by self-motion and are likely to be used in conjunction with other cues such as visual contrast between foreground and background objects. Orchid bees use a strategy based on relative brightness to find clear paths through dense environments. To investigate the possibility that birds use a similar strategy, we presented zebra finches Taeniopygia guttata with symmetric or asymmetric brightness distributions behind the aperture of a tunnel and recorded their flight through it. The background brightness conditions influenced both the birds decision to enter the tunnel and their flight direction upon exit. Zebra finches were more likely to initiate flight through the tunnel if they could see a bright background through its aperture and were more likely to fly to the bright side on exiting the tunnel. We found no evidence of a centring response during gap negotiation; instead, the zebra finches entered the tunnel by turning tightly around its near edge. These results hint at a possible pre-planning of the trajectories before the onset of flight.

animal behavior and cognition↗

bioRxiv2023

Hawks pursuing targets through clutter avoid obstacles by applying open loop steering corrections during closed loop pursuit

Collision avoidance [1-4] and target pursuit [5-8] are challenging flight behaviors for any animal or autonomous vehicle, but their interaction is even more so [9-11]. For predators adapted to hunting in clutter, the demands of these two tasks may conflict, requiring effective reconciliation to avoid a hazardous collision or loss of target. Technical approaches to obstacle avoidance rely mainly on path-planning algorithms [12], but these are unlikely to be effective during closed-loop pursuit of a maneuvering target, so collision avoidance must instead be implemented reactively during prey pursuit. For example, the pursuit-avoidance behavior of predatory flies has been successfully modelled by combining feedback on target motion with feedback on obstacle looming [13]. It is unclear, however, whether this mechanism will generalize to complex environments with many looming obstacles, and it remains unknown how aerial predators reconcile the conflict between obstacle avoidance and prey pursuit in clutter. Here we use high-speed motion capture data to show how Harris hawks Parabuteo unicinctus avoid collisions by making open-loop steering corrections during closed-loop pursuit. We find that hawks combine continuous feedback on target motion with a discrete feedforward steering correction aimed at clearing an upcoming obstacle as closely as possible at maximum span. By biasing the hawks flight direction, this guidance law provides an effective means of prioritizing obstacle avoidance whilst remaining locked-on to the target. We anticipate that a similar mechanism may be used in terrestrial and aquatic pursuit. The same biased guidance law could be used for obstacle avoidance in drones designed to intercept other drones in clutter, or in drones using closed-loop guidance to navigate between fixed waypoints in urban environments.

zoology↗

bioRxiv2022

Visual versus visual-inertial guidance in hawks pursuing terrestrial targets

The flight behaviour of predatory birds is well modelled by a guidance law called proportional navigation, which commands steering in proportion to the angular rate of the line-of-sight from predator to prey. The line-of-sight rate is defined with respect to an inertial frame of reference, so proportional navigation is necessarily implemented using visual-inertial sensor fusion. In Harris hawks, pursuit of terrestrial targets may be even better modelled by assuming that visual-inertial information on the line-of-sight rate is combined with visual information on the deviation angle between the attackers velocity and the line-of-sight. Here we ask whether a new variant of this mixed guidance law can model Harris hawk pursuit behaviour successfully using visual information alone. We use high-speed motion capture to record n=228 attack fights from N=4 Harris hawks, and confirm that proportional navigation and mixed guidance using visual-inertial information both model the trajectory data well. Moreover, the mixed guidance law still models the data closely if visual-inertial information on the line-of-sight rate is replaced with purely visual information on the apparent motion of the target relative to the background. Although the original form of the mixed guidance law provides the closest fit, all three models provide an adequate phenomenological model of the behavioural data, whilst each making different predictions on the physiological pathways involved.

animal behavior and cognition↗

bioRxiv2022

Gap selection and steering during obstacle avoidance in pigeons

The ability of birds to fly through cluttered environments has inspired biologists interested in understanding its underlying mechanisms, and engineers interested in applying its underpinning principles. To analyse this problem empirically, we break it down into two distinct, but related, questions: How do birds select which gaps to aim for? And how do they steer through them? We answered these questions by using a combined experimental and modelling approach, in which we released pigeons (Columbia livia domestica) inside a large hall whose open exit was separated from the release point by a curtain creating two vertical gaps--one of which was obstructed by an obstacle. We tracked the birds using a high-speed motion capture system, and found that their gap choice seemed to be biased by their intrinsic handedness, rather than determined by extrinsic cues such as the size of the gap or its alignment with the destination. We modelled the pigeons steering behaviour algorithmically by simulating their flight trajectories under a set of six candidate guidance laws, including those used previously to model targeted flight behaviours in birds. We found that their flights were best modelled by delayed proportional navigation commanding turning in proportion to the angular rate of the line-of-sight from the pigeon to the midpoint of the gap. Our results are consistent with this being a two-phase behaviour, in which the pigeon first heads forward from the release point before steering towards the midpoint of whichever gap it chooses to aim for under closed-loop guidance. Our findings have implications for the sensorimotor mechanisms that underlie clutter negotiation in birds, uniting this with other kinds of target-oriented behaviours including aerial pursuit.

animal behavior and cognition↗

bioRxiv2022

Optimization of dynamic soaring in a flap-gliding seabird and its impacts on large-scale distribution at sea

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.

animal behavior and cognition↗

bioRxiv2022

Steering clear of the confusion effect: aerial predators target fixed points in dense prey aggregations

Collective behaviours are widely assumed to confuse predators, but empirical support for a confusion effect is often lacking, and its importance must depend on the predators targeting mechanism. Here we show that raptors steer attacks on swarming bats by turning towards a fixed point in the swarm, rather than by using closed-loop pursuit of an individual. Any prey that the predator is on a collision course with will appear on a constant bearing, so target selection emerges naturally from the geometry of a collision. Our results show how predators can simplify the demands on their sensory system by decoupling steering from targeting when grabbing prey from a dense swarm. We anticipate that the same tactic will be used against flocks and schools across a wide range of taxa, in which case a confusion effect is paradoxically more likely to occur in attacks on sparse groups, for which steering and targeting cannot be decoupled.

animal behavior and cognition↗

bioRxiv2021

BioThings SDK: a toolkit for building high-performance data APIs in biomedical research

SummaryTo meet the increased need of making biomedical resources more accessible and reusable, Web APIs or web services have become a common way to disseminate knowledge sources. The BioThings APIs are a collection of high-performance, scalable, annotation as a service APIs that automate the integration of biological annotations from disparate data sources. This collection of APIs currently includes MyGene.info, MyVariant.info, and MyChem.info for integrating annotations on genes, variants, and chemical compounds, respectively. These APIs are used by both individual researchers and application developers to simplify the process of annotation retrieval and identifier mapping. Here, we describe the BioThings Software Development Kit (SDK), a generalizable and reusable toolkit for integrating data from multiple disparate data sources and creating high-performance APIs. This toolkit allows users to easily create their own BioThings APIs for any data type of interest to them, as well as keep APIs up-to-date with their underlying data sources. Availability and implementationThe BioThings SDK is built in Python and released via PyPI (https://pypi.org/project/biothings/). Its source code is hosted at its github repository (https://github.com/biothings/biothings.api).

bioinformatics↗

bioRxiv2021

Optimisation of unsteady flight in learned avian perching manoeuvres

Perching at speed is amongst the most challenging flight behaviours that birds perform1,2, and beyond the capability of current autonomous vehicles. Smaller birds may touchdown by hovering3-8, but larger birds typically swoop upward to perch1,2 - presumably because the adverse scaling of their power margin prohibits slow flapping flight9, and because swooping transfers excess kinetic to potential energy1,2,10. Perching is risky in larger birds6,11, demanding precise control of velocity and pose12-15, but it is unknown how they optimize this challenging manoeuvre. More generally, whereas cruising flight behaviours such as migration and commuting are adapted to minimize cost-of-transport or time-of-flight16, the optimization of unsteady flight manoeuvres remains largely unexplored7,17. Here we show that swooping minimizes neither the time nor energy required to perch safely in Harris hawks Parabuteo unicinctus, but instead minimizes the distance flown under hazardous post-stall conditions. By combining motion capture data from 1,563 flights with flight dynamics modelling, we found that the birds choice of where to transition from powered dive to unpowered climb minimizes the distance from the landing perch over which very high lift coefficients are required. Time and energy are therefore invested to maintain the control authority needed to execute a safe landing, rather than being minimized continuously as in technical applications of autonomous perching under nonlinear feedback control13 and deep reinforcement learning18,19. Naive birds learn this behaviour on-the-fly, so our findings suggest an alternative reward function for reinforcement learning of autonomous perching in air vehicles.

animal behavior and cognition↗

bioRxiv2021

Through hawks' eyes: reconstructing a bird's visual field in flight to study gaze strategy and attention during perching and obstacle avoidance

We present a method to analyse visual attention of a bird in flight, that combines motion capture data with renderings from virtual cameras. We applied it to a small subset of a larger dataset of perching and obstacle avoidance manoeuvres, and studied visual field stabilisation and gaze shifts. Our approach allows us to synthesise visual cues available to the bird during flight, such as depth information and optic flow, which can lead to novel insights into the birds gaze strategy in flight. This preliminary work demonstrates the method and suggests several new hypotheses to investigate with the full dataset.

animal behavior and cognition↗

bioRxiv2020

Attack behaviour in naïve Gyrfalcons is modelled by the same guidance law as in Peregrines, but at a lower guidance gain

The aerial hunting behaviours of birds are strongly influenced by their flight morphology and ecology, but little is known of how this variation relates to the behavioural algorithms guiding flight. Here we use onboard GPS loggers to record the attack trajectories of captive-bred Gyrfalcons (Falco rusticolus) during their maiden flights against robotic aerial targets, which we compare to existing flight data from Peregrines (Falco peregrinus) The attack trajectories of both species are modelled most economically by a proportional navigation guidance law, which commands turning in proportion to the angular rate of the line-of-sight to target, at a guidance gain N. However, Gyrfalcons operate at significantly lower values of N than Peregrines, producing slower turning and a longer path to intercept. Gyrfalcons are less agile and less manoeuvrable than Peregrines, but this physical constraint is insufficient to explain their lower guidance gain. On the other hand, lower values of N promote the tail-chasing behaviour that is typical of wild Gyrfalcons, and which apparently serves to tire their prey in a prolonged high-speed pursuit. Moreover, during close pursuit of fast evasive prey such as Ptarmigan (Lagopus spp.), proportional navigation will be less prone to being thrown off by erratic target manoeuvres if N is low. The fact that low-gain proportional navigation successfully models the maiden attack flights of Gyrfalcons suggests that this behavioural algorithm is embedded in a hardwired guidance loop, which we hypothesise is ancestral to the clade containing Gyrfalcons and Peregrines. SUMMARY STATEMENTNaive Gyrfalcons attacking aerial targets are modelled by the same proportional navigation guidance law as Peregrines, but with a lower navigation constant that promotes tail-chasing rather than efficient interception.

zoology↗

bioRxiv2020

Birds versus bats: attack strategies of bat-hunting hawks, and the dilution effect of swarming

Aggregation behaviors can often reduce predation risk, whether through dilution, confusion, or vigilance effects, but these effects are challenging to measure under natural conditions, involving strong interactions between the behaviors of predators and prey. Here we study aerial predation of massive swarms of Brazilian free-tailed bats Tadarida brasiliensis by Swainsons hawks Buteo swainsoni, testing how the behavioral strategies of predator and prey influence catch success and predation risk. The hawks achieved high overall catch success, but they were no more successful against lone bats than against bats flying in column formation. There was therefore no evidence of any net vigilance or confusion effect, and hawks attacking the column benefitted from the opportunity to make several attempted grabs. Even so, the bats overall risk of predation was an order of magnitude higher when flying alone. Attacks on lone bats ([~]10% of attacks) were greatly overrepresented relative to the proportion of bats classified as flying alone ([~]0.2%), so dilution is both necessary and sufficient to explain the higher survival rates of bats flying in the column. From the hawks perspective, their odds of catching a bat more than trebled if the attack involved a stoop rather than level flight, or a rolling rather than pitching grab maneuver. These behavioral tactics were independently deployed in nearly three-quarters of all attacks. Hence, whereas the survival rate of a bat depends principally on whether it flies alone or in a group, the catch success of a hawk depends principally on how it maneuvers to attack. Lay summaryBats emerging by daylight from a massive desert roost are able to minimise their predation risk by maintaining tight column formation, because the hawks that attack them target stragglers disproportionately often. Whereas the predation risk of a bat therefore depends on how it maintains its position within the swarm, the catch success of a hawk depends on how it maneuvers to attack. Catch success is maximised by executing a stooping dive or a rolling grab.

animal behavior and cognition↗

bioRxiv2020

A semi-empirical aerodynamic model of insect flight forces

Blade element modelling provides a quick analytical method for estimating the aerodynamic forces produced during insect flight, but such models have yet to be tested rigorously using kinematic data recorded from free-flying insects. This is largely because of the paucity of detailed free-flight kinematic data, but also because analytical limitations in existing blade element models mean that they cannot incorporate the complex three-dimensional movements of the wings and body that occur during insect flight. Here, we present a blade element model with empirically-fitted aerodynamic force coefficients that incorporates the full three-dimensional wing kinematics of manoeuvring Eristalis hoverflies, including torsional deformation of their wings. The two free parameters were fitted to a large free-flight dataset comprising N = 26, 541 wingbeats, and the fitted model captured approximately 80% of the variation in the stroke-averaged forces in the sagittal plane. We tested the robustness of the model by subsampling the data, and found little variation in the parameter estimates across subsamples comprising 10% of the flight sequences. The simplicity and generality of the model that we present is such that it can be readily applied to kinematic datasets from other insects, and also used for the study of insect flight dynamics.

biophysics↗

bioRxiv2019

Motor output and control input in flapping flight: a compact model of the deforming wing kinematics of manoeuvring hoverflies

Insects are conventionally modelled as controlling flight by varying a few summary kinematic parameters that are defined on a per-wingbeat basis, such as the stroke amplitude, mean stroke angle, and mean wing pitch angle. Nevertheless, as insects have tens of flight muscles and vary their kinematics continuously, the true dimension of their control input subspace is likely to be much higher. Here we present a compact description of the deforming wing kinematics of 36 manoeuvring Eristalis hoverflies, applying functional principal components analysis to Fourier series fits of the wingtip position and wing twist measured over 26,541 wingbeats. This analysis offers a high degree of data reduction, in addition to insight into the natural kinematic couplings. We used statistical resampling techniques to verify that the principal components were repeatable features of the data, and analysed their coefficient vectors to provide insight into the form of these natural couplings. Conceptually, the dominant principal components provide a natural set of control input variables that span the control input subspace of this species, but they can also be thought of as output states of the flight motor. This functional description of the wing kinematics is appropriate to modelling insect flight as a form of limit cycle control.

animal behavior and cognition↗

bioRxiv2018

Birds invest wingbeats to keep a steady head and reap the ultimate benefits of flocking

Flapping flight is the most energetically demanding form of sustained forwards locomotion that vertebrates perform. Flock dynamics therefore have significant implications for energy expenditure. Despite this, no studies have quantified the biomechanical consequences of flying in a cluster flock relative to flying solo. Here, we compared the flight characteristics of homing pigeons (Columba livia) flying solo and in pairs, using high-precision 5 Hz GPS and 200 Hz tri-axial accelerometer biologgers. Paired flight increased route accuracy by ~7%, but, was accompanied by an increase in wingbeat frequency of ~18%. As expected, paired individuals benefitted from improved homing route accuracy, which reduced flight distance by ~7% and time by ~9%. However, realising these navigational gains involved substantial changes in flight kinematics and energetics. Both individuals in a pair increased their wingbeat frequency by c.18%, by decreasing the duration of their upstroke. This sharp increase in wingbeat frequency caused just a 3% increase in airspeed, but reduced the oscillatory displacement of the body by ~22%, which we hypothesise relates to an increased requirement for visual stability and manoeuvrability when flocking. Overall, the shorter flight distances and increased wingbeat frequency in a pair resulted in a net increase in the aerodynamic cost of returning home, which we estimate was ~14%. Our results demonstrate that flocking costs have been underestimated by an order of magnitude and force reinterpretation of their mechanistic origin. We show that, for pigeons, two heads are better than one, but keeping a steady head necessitates energetically costly kinematics.

animal behavior and cognition↗