Ecological inference using data from accelerometers needs careful protocols

Accelerometers in animal-attached tags are powerful tools in behavioural ecology, they can be used to determine behaviour and provide proxies for movement-based energy expenditure. Researchers are collecting and archiving data across systems, seasons and device types. However, using data repositories to draw ecological inference requires a good understanding of the error introduced according to sensor type and position on the study animal and protocols for error assessment and minimization.Using laboratory trials, we examine the absolute accuracy of tri-axial accelerometers and determine how inaccuracies impact measurements of dynamic body acceleration (DBA), a proxy for energy expenditure, in human participants. We then examine how tag type and placement affect the acceleration signal in birds, using pigeons Columba livia flying in a wind tunnel, with tags mounted simultaneously in two positions, and back- and tail-mounted tags deployed on wild kittiwakes Rissa tridactyla. Finally, we present a case study where two generations of tag were deployed using different attachment procedures on red-tailed tropicbirds Phaethon rubricauda foraging in different seasons.Bench tests showed that individual acceleration axes required a two-level correction to eliminate measurement error. This resulted in DBA differences of up to 5% between calibrated and uncalibrated tags for humans walking at a range of speeds. Device position was associated with greater variation in DBA, with upper- and lower back-mounted tags varying by 9% in pigeons, and tail- and back-mounted tags varying by 13% in kittiwakes. The tropicbird study highlighted the difficulties of attributing changes in signal amplitude to a single factor when confounding influences tend to covary, as DBA varied by 25% between seasons.Accelerometer accuracy, tag placement and attachment critically affect the signal amplitude and thereby the ability of the system to detect biologically meaningful phenomena. We propose a simple method to calibrate accelerometers that can be executed under field conditions. This should be used prior to deployments and archived with resulting data. We also suggest a way that researchers can assess accuracy in previously collected data, and caution that variable tag placement and attachment can increase sensor noise and even generate trends that have no biological meaning

Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers

Otsuka R., Yoshimura N., Tanigaki K., et al. Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers. Methods in Ecology and Evolution 15, 716 (2024); https://doi.org/10.1111/2041-210X.14294.Machine learning-based behaviour classification using acceleration data is a powerful tool in bio-logging research. Deep learning architectures such as convolutional neural networks (CNN), long short-term memory (LSTM) and self-attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration-based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal-specific behaviours, which may have limited the application of deep learning techniques in this area. To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre-training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state-of-the-art deep learning model architectures. Data augmentation improved the overall model performance when one of the various techniques (none, scaling, jittering, permutation, time-warping and rotation) was randomly applied to each data during mini-batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre-training with unlabelled data did not improve model performance. The state-of-the-art deep learning models, including a model consisting of four CNN layers, an LSTM layer and a multi-head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features. Our experiments showed that deep learning techniques are promising for acceleration-based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning and self-supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time-series sensor data

Ecological inference using data from accelerometers needs careful protocols

1. Accelerometers in animal-attached tags are powerful tools in behavioural ecology, they can be used to determine behaviour and provide proxies for movement-based energy expenditure. Researchers are collecting and archiving data across systems, seasons and device types. However, using data repositories to draw ecological inference requires a good understanding of the error introduced according to sensor type and position on the study animal and protocols for error assessment and minimisation. 2. Using laboratory trials, we examine the absolute accuracy of tri-axial accelerometers and determine how inaccuracies impact measurements of dynamic body acceleration (DBA), a proxy for energy expenditure, in human participants. We then examine how tag type and placement affect the acceleration signal in birds, using pigeons Columba livia flying in a wind tunnel, with tags mounted simultaneously in two positions, and back- and tail-mounted tags deployed on wild kittiwakes Rissa tridactyla. Finally, we present a case study where two generations of tag were deployed using different attachment procedures on red-tailed tropicbirds Phaethon rubricauda foraging in different seasons. 3. Bench tests showed that individual acceleration axes required a two-level correction to eliminate measurement error. This resulted in DBA differences of up to 5% between calibrated and uncalibrated tags for humans walking at a range of speeds. Device position was associated with greater variation in DBA, with upper and lower back-mounted tags varying by 9% in pigeons, and tail- and back-mounted tags varying by 13% in kittiwakes. The tropicbird study highlighted the difficulties of attributing changes in signal amplitude to a single factor when confounding influences tend to covary, as DBA varied by 25% between seasons. 4. Accelerometer accuracy, tag placement and attachment critically affect the signal amplitude and thereby the ability of the system to detect biologically meaningful phenomena. We propose a simple method to calibrate accelerometers that can be executed under field conditions. This should be used prior to deployments and archived with resulting data. We also suggest a way that researchers can assess accuracy in previously collected data, and caution that variable tag placement and attachment can increase sensor noise and even generate trends that have no biological meaning

Testing the efficacy of unsupervised machine learning techniques to infer shark behaviour from accelerometry data

Biologging is becoming a powerful tool in the study of free-ranging animal behaviour. Accelerometers play an important role particularly for cryptic aquatic species by facilitating the measurement of animal body movement and thus, behaviour. However, our ability to collect large and complex data sets is surpassing our ability to analyse them, prompting a need to develop methodologies for automated behavioural classification. Unsupervised machine learning is particularly useful for behavioural classification where direct observations to link patterns of acceleration to animal behaviour are not always attainable. We tested the ability of unsupervised machine learning to classify shark behaviour by applying two common unsupervised approaches, K-means clustering and Hidden Markov models (HMM), to ground-truthed accelerometry data collected from captive juvenile lemon sharks (Negaprion brevirostris). Although K-means clustering demonstrated low classification performance, the HMM performed well in distinguishing broad categories in behaviour (resting vs swimming), but generally had poor performance in rare and more complex behaviours (e.g. prey handling or burst swimming). This study is one of the first to validate the use of common unsupervised machine learning algorithms and lends further support to their use in the study of behaviour in free-ranging animals, while also showing limitations in their ability to discern complex behaviours

Remotely sensing motion: the use of multiple biologging technologies to detect fine-scale, at-sea behaviour of breeding seabirds in a variable Southern Ocean environment

The at-sea behaviour of seabirds, such as albatrosses and petrels (order Procellariiformes), is difficult to study because they spend most of their time on the ocean and have extremely large ranges. In the early 2000s, behavioural studies of seabirds were dominated by diving patterns of diving birds or spatial studies from satellite telemetry. Recent advances in biologging technologies have opened up new avenues for studying the at-sea behaviour of farranging seabirds in their natural environment. Bio-logging devices are now small enough to be attached to flying seabirds where multiple sensors record data at infrasecond sampling rates. These data can be used to infer, inter alia, body posture, activity (e.g. flapping, takeoff, landing, etc.), magnetic heading and spatial distribution at a resolution that was not previously possible. Bio-logging devices are battery powered and a tradeoff exists between the length of deployments and sampling frequencies, however not a lot of study has been done on what the effect of coarse sampling rates are on data quality. Together with the masses of data that are generated by bio-logging devices, analytical tools have also become available to extract useful metrics from the data. This thesis utilized some of the latest bio-logging technology to study the at-sea behaviour of several procellariiforms, breeding on Marion, Gough and Nightingale Islands, from finescale data. After describing the loggers used and the methods of deployment in Chapter 2, I assess the effect that sampling rates have on metrics derived from GPS loggers in Chapter 3. This was done by sub-sampling GPS tracks recorded at 1-s sampling intervals, showing the effect that different sampling intervals have on metrics, including the total distance travelled and behavioural states derived from path length and turning angles. I show that for larger sampling intervals, the total distance travelled will be underestimated at varying degrees depending on flight sinuosity. By varying sampling rates when estimating behavioural states, I show that moderate (10–30 min) sampling intervals may produce better results. I explore the limitations of low-cost GPS loggers for fine-scale analyses and conclude that specialized loggers are most likely required when sampling at intervals < 1 s. In Chapter 4 I use specialized loggers in the form of tri-axial magnetometer, and video loggers and describe two novel methods to extract roll angles of albatrosses during dynamic soaring flight. Animal body angles are normally extracted by using tri-axial accelerometer data, but their dynamic soaring flight mode inhibits the use of these methods. I show how magnetometer data are independent of dynamic movement and can be used to estimate roll angles of flying seabirds. This method is validated from bird-borne video footage where the horizon is used as a proxy for the bird's roll angle and I describe a method to automatically extract such angles using computer vision techniques. These new methods are then applied to data collected from Wandering Albatrosses Diomedea exulans in Chapter 5, showing how the birds vary their roll angle in response to changing winds. Additionally, flapping flight was identified from patterns in the vertical axis (heave) of a tri-axial accelerometer and I show how Wandering Albatrosses may be flapping more than expected. By coupling flapping and roll angles I show that flapping, on occasion, occur at the upper turn of the dynamic soaring cycle, a period previous thought devoid of flaps. These results also suggest possible sexual differences, where males seem to flap more often than females and limit their take-offs to favourable wind conditions. Lastly, in Chapter 6 I use the same methods as in the previous two chapters to compare the fine-scale flight of six Procellariiformes species breeding on Marion, Gough and Nightingale Islands. I show how these species have varied flight patterns where they respond differently to wind patterns, most likely driving their distribution and eventual foraging areas. As expected, smaller species seem to be more manoeuvrable allowing them to rapidly roll at extreme angles in strong winds while tolerating light winds by increasing the amount of time spent flapping. Breeding location also played a role as birds from the Tristan da Cunha archipelago flapped more often and flew in lighter winds than Marion Island birds. In summary, Chapter 7 discusses how, using a multisensor approach, bio-logging technology can be effectively used to study the fine-scale behaviour of flying seabirds. Each of the loggers have their own limitations and it is important to take these into account when analyzing the data. I describe two new methods for extracting roll angles from dynamic soaring seabirds and show how individuals from several species vary roll angle and flapping flight in response to changing winds. This thesis highlights the varying behavioural strategies that flying seabirds use in the Southern Ocean, showing that individual species and populations may respond differently to changing environmental conditions

Fine-scale changes in flight effort revealed by animal-borne loggers

The movements of the air are central to the life of flying birds, because they can determine whether the costs of flight are closer to resting or sprinting, and whether birds are able to reach their destination. Yet for species relying mainly on flapping flight, studies about the effects of weather on flight effort have mainly focussed on wind, with other atmospheric factors receiving less attention. In addition, with the development of new technologies to measure flight effort, it has become clear that some methods need standardisation and further verification. The goal of this PhD is to provide insight into how atmospheric conditions affect flight costs more broadly and study the extent to which birds prioritise energy expenditure over other currencies, such as time and risk. I used high-frequency data-loggers to explore the combined effects of wind and thermals, as well as air density, on flight effort over fine scales, as well as how birds adjust their behaviour to these factors. Results showed that pigeons (Columba livia), which are not limited by energy expenditure, prioritise speed over energy savings, and use a very costly flight style which could serve as a predator-avoidance strategy. I also found that wind support was a strong predictor of whether chick-rearing tropicbirds (Phaethon rubricauda) use thermal soaring to save energy during foraging trips, suggesting that birds were weighing up the trade-off between energy and time, and chose to save energy only when this would not cost them too much time. Comparison of air density between seasons also revealed that the flapping flight of tropicbirds was more costly during summer, when air density was lower. This finding shows that the effect of seasonal changes in air density on flight costs is significant, outweighing the influence of both wind and thermal availability. It also sheds new light on how flight costs (particularly those in tropical birds) might be affected by global change. Finally, the analysis of the accelerometer data showed that the type of tag used, as well as differences in the longitudinal position and attachment method, affected the amplitude of the signal, which has implications for the robustness of acceleration-based proxies for flight effort. Nonetheless, the adoption of standardized calibrations should facilitate the comparison of these metrics between study sites and through time, improving the prospect that they can be used to study the effect of a changing climate on flight costs and avian ecology

A review of the effects of wind on the movement, behavior, energetics, and life history of seabirds

For decades, studies have highlighted links between wind patterns and the behavior, ecology, distribution, energetics and life history of seabirds. However, only relatively recently have advancements in tracking technologies and improvements in the resolution of globally available wind data allowed wind impacts on seabirds to be quantified across multiple spatiotemporal scales. Here, we review and synthesize current knowledge of the effects of wind on seabirds. We first describe global patterns of wind circulation and relevant atmospheric processes and discuss the relationship between seabird morphology, flight performance and behavior relative to wind. We then develop a conceptual model linking seabird movement strategies to wind, morphology, flight capabilities and central-place constraint. Finally, we examine how wind influences seabird populations via effects on flight efficiency and energetics, and wind impacts associated with climate variability and severe weather. We conclude by highlighting research priorities for advancing our understanding of the effects of wind on seabird ecology and behavior; these include assessing how and to what extent seabirds use ocean waves for efficient flight, understanding how seabirds sense and anticipate wind patterns, and examining how wind has shaped seabird evolution. Future research should also focus on assessing how wind modulates habitat accessibility, and how this knowledge could be incorporated into theory of seabird habitat use. Moreover, approaches that focus on mechanistic links between climate, wind and demography are needed to assess population-level effects, and will be imperative to understanding how seabirds may be impacted by climate-driven changes to wind patterns

Causes and consequences of individual behavioural variability in a pelagic seabird

Individual differences in behaviour among wild animals have received increasing attention in the last few decades, accelerated by the development of higher-resolution, lighter and cheaper biotelemetry methods that provide unprecedented insights into individuals’ behavioural tendencies. This thesis builds on earlier research to investigate the phenomenon of individual consistency in foraging behaviour in a pelagic seabird, the Manx shearwater (Puffinus puffinus), using biotelemetry data collected from five UK colonies by the Oxford Navigation Group since 2008. Chapter 1 introduces key terms and definitions, and reviews the literatures of individuality in animal behaviour, particularly the causes and consequences of variability in space use and foraging, its implications for the role of learning and memory, and empirical support for the widespread occurrence of individual foraging site fidelity in seabirds. It concludes by outlining the analysis and predictions for Manx shearwaters for the following chapters. Chapter 2 use simple characteristics of foraging trips extracted from GPS data collected at five UK colonies to describe the typical variability in foraging behaviour with respect to breeding stage and colony differences, as well as variation with age. Hypotheses as to the nature of variation between breeding stages and with age, informed by life history and cognitive expectations respectively, are explored, alongside commentary on possible explanations for colony differences. Chapter 3 focusses on the Copeland dataset to investigate variability in foraging behaviour using the same metrics across individuals, and to quantify individual consistency in site use. We use a behavioural state classification algorithm and randomised site comparison method to quantify the extent of individual foraging site fidelity, a composite measure of within-individual consistency and between-individual variability. We then investigate potential mechanisms relating to resource tracking, experience and physical constraints. Chapter 4 discusses the overall conclusions, including the contribution of this dataset to understanding breeding stage variation in foraging behaviour, colony differences, individual repeatability and foraging site variability, and the likely role of learning and memory in foraging in this species, before outlining potential future research directions

Circles within spirals, wheels within wheels; Body rotation facilitates critical insights into animal behavioural ecology

How animals behave is fundamental to enhancing their lifetime fitness, so defining how animals move in space and time relates to many ecological questions, including resource selection, activity budgets and animal movement networks. Historically, animal behaviour and movement has been defined by direct observation, however recent advancements in biotelemetry have revolutionised how we now assess behaviour, particularly allowing animals to be monitored when they cannot be seen. Studies now pair ‘convectional’ radio telemetries with motion sensors to facilitate more detailed investigations of animal space-use. Motion sensitive tags (containing e.g., accelerometers and magnetometers) provide precise data on body movements which characterise behaviour, and this has been exemplified in extensive studies using accelerometery data, which has been linked to space-use defined by GPS. Conversely, consideration of body rotation (particularly change in yaw) is virtually absent within the biologging literature, even though various scales of yaw rotation can reveal important patterns in behaviour and movement, with animal heading being a fundamental component characterising space-use. This thesis explores animal body angles, particularly about the yaw axis, for elucidating animal movement ecology. I used five model species (a reptile, a mammal and three birds) to demonstrate the value of assessing body rotation for investigating fine-scale movement-specific behaviours. As part of this, I advanced the ‘dead-reckoning’ method, where fine-scale animal movement between temporally poorly resolved GPS fixes can be deduced using heading vectors and speed. I addressed many issues with this protocol, highlighting errors and potential solutions but was able to show how this approach leads to insights into many difficult-to-study animal behaviours. These ranged from elucidating how and where lions cross supposedly impermeable man-made barriers to examining how penguins react to tidal currents and then navigate their way to their nests far from the sea in colonies enclosed within thick vegetation