Search

Research on predator–prey interactions between sharks and cetaceans remain limited. Here, we report on a video of a shortfin mako shark circling a finless porpoise with a damaged caudal fin in the Seto Inland Sea, Japan. The finless porpoise was neither emaciated nor inactive, but unable to swim effectively due to the complete lack of a caudal fin. Some circumstantial evidence, including a bite mark on the porpoise's head, strongly suggests that the mako shark attacked it. Furthermore, the possible time difference between the two injuries the porpoise sustained may reflect the shark's hunting tactics. While mako sharks primarily feed on small fish and cephalopods, this observation suggests they also may prey on live cetaceans more often than previously thought. [ABSTRACT FROM AUTHOR]

The way goal-oriented birds adjust their travel direction and route in response to wind significantly affects their travel costs. This is expected to be particularly pronounced in pelagic seabirds, which utilize a wind-dependent flight style called dynamic soaring. Dynamic soaring seabirds in situations without a definite goal, e.g. searching for prey, are known to preferentially fly with crosswinds or quartering-tailwinds to increase the speed and search area, and reduce travel costs. However, little is known about their reaction to wind when heading to a definite goal, such as homing. Homing tracks of wandering albatrosses (Diomedea exulans) vary from beelines to zigzags, which are similar to those of sailboats. Here, given that both albatrosses and sailboats travel slower in headwinds and tailwinds, we tested whether the time-minimizing strategies used by yacht racers can be compared to the locomotion patterns of wandering albatrosses. We predicted that when the goal is located upwind or downwind, albatrosses should deviate their travel directions from the goal on the mesoscale and increase the number of turns on the macroscale. Both hypotheses were supported by track data from albatrosses and racing yachts in the Southern Ocean confirming that albatrosses qualitatively employ the same strategy as yacht racers. Nevertheless, albatrosses did not strictly minimize their travel time, likely making their flight robust against wind fluctuations to reduce flight costs. Our study provides empirical evidence of tacking in albatrosses and demonstrates that man-made movement strategies provide a new perspective on the laws underlying wildlife movement. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Leg rings are frequently used to mark aquatic birds in order to identify individuals, and study population dynamics and migration patterns, with the proviso being that the rings should not affect the birds. The effects of tags and rings are of particular interest in diving birds because any change in body shape could impact swimming efficiency and costs, as water is almost a thousand times denser than air. We attached tri‐axial accelerometers to both ringed and unringed breeding Imperial Shags Leucocarbo atriceps to assess dive performance based on descent angle, descent rate, power stroke rate, power stroke peak acceleration amplitude and Vectorial Dynamic Body Acceleration (VeDBA) as a proxy for energy expenditure. Ringed birds, especially females, had a higher foot‐stroke amplitude than unringed animals. In addition, the overall efficiency of the ringed individuals, as expressed by the descent rate per unit VeDBA, was compromised (by 3.5% in females and 4.3% in males) compared with unringed birds. We conclude that leg rings change the diving performance of Imperial Shags, although the effect is small and may not affect reproductive success. However, given that birds are typically ringed for life, we urge researchers to be particularly careful about the potential cumulative effect of attaching leg rings to foot‐propelled diving species. [ABSTRACT FROM AUTHOR]

The bio-logging technique is extensively used in the fields of ecology and ethology, wherein a data logger, such as a sensor or camera, is attached to the target animal's body to collect the required data. In this method, the efficiency of recovery of the data logger is not ideal. In this study, we proposed a new recovery method, with the aim of addressing the aforementioned problem in bio-logging. The authors previously fabricated a data-logger separator, which weighed approximately 10 g, and was targeted at small seabirds. Because there were some problems associated with the circuit board and the separation performance of this device, we modified the device to overcome the previous drawbacks. We fabricated a flexible printed circuit to improve the operation of the mounted actuator and wireless microcomputer, and improve the efficiency of the fabrication process. We conducted an experiment to determine the proper length and position at which the actuator is attached, in order to achieve a stable motion. We thus fabricated a new prototype with these improvements and performed an operational test at low temperatures from a particular distance, simulating actual usage in a natural environment. The results demonstrated that separation occurred without failure, thus indicating that the separator can be efficiently used in practical environment. [ABSTRACT FROM AUTHOR]

In ecology and ethology, a technique called 'bio-logging' has been used to record the behaviour of wild animals by attaching a small logger, which includes a global positioning system (GPS) or acceleration sensor, to the animal of interest. This method's disadvantage is that the recovery of the logger is inefficient. In this study, we propose a data logger separator, which solves this problem by focussing on the logger recovery method. The device, which weighs ∼10 g, is intended for use on a shallow-diving seabird such as gulls. It comprises a separation mechanism based on a rotary key to achieve quiet separation and avoid causing stress to the seabird and the surrounding individuals. Moreover, the device is equipped with a wireless microcomputer that can be separated via a remote control from a distance of ∼100 m. For separation experiments on stuffed birds, the separation performance was confirmed to be sufficient. Because of experimentally testing the device in a real environment with low temperatures and wet conditions, it was considered that the separator's performance was sufficient to allow it to be used practically in the wild. [ABSTRACT FROM AUTHOR]

Animal behavior is the final and integrated output of brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient prior knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed. Here, we report a versatile method using a hybrid supervised/unsupervised machine learning approach for behavioral st ate e stimation and f eature ex tr action (STEFTR) only from low-content animal trajectory data. To demonstrate the effectiveness of the proposed method, we analyzed trajectory data of worms, fruit flies, rats, and bats in the laboratories, and penguins and flying seabirds in the wild, which were recorded with various methods and span a wide range of spatiotemporal scales—from mm to 1,000 km in space and from sub-seconds to days in time. We successfully estimated several states during behavior and comprehensively extracted characteristic features from a behavioral state and/or a specific experimental condition. Physiological and genetic experiments in worms revealed that the extracted behavioral features reflected specific neural or gene activities. Thus, our method provides a versatile and unbiased way to extract behavioral features from simple trajectory data to understand brain function. [ABSTRACT FROM AUTHOR]

This review summarizes the advances in bio-logging technology that enables us to monitor foraging behavior, movement, behavioral performance, physiological performance, and sociality in a wide range of bird species, as well as their habitat. Subsequently, navigation is discussed, using long-distance movements in streaked shearwaters as a case study. Moreover, challenges and future research directions in bio-logging science are presented, with focus on: multimodal recording, big data analysis, feedback logging, low-power consumption and power generation systems, logger effects, and capture-recapture methods. [ABSTRACT FROM AUTHOR]

Behaviour could be expressed as a set of specific movement patterns in time. An animal's movement or trajectory could characterise its behaviours and provide information about its internal states. Recent advances in GPS-based sensor technologies led to drastic increase in volume of the data collected from animals' movements which enables researchers to analyse and model their behaviours using data-driven methods. However, having compact, discriminative, semantical and independent numerical representations of trajectories as features, is essential for employing the most of available off-the-shelf machine learning and deep learning techniques. Inspired by language processing, the approach presented in this study utilizes Skip-gram model to create contextual vector embeddings or representations of key-points in animal trajectories to be used as input features. Here, a key-point is defined as a location which represents a trajectory segment. It is assumed that these key-points encapsulate contextual information which is attributed to a certain behaviour or specific group of animals with similar behavioural features. So, the vector embeddings could be interpreted as contextual semantical representations of trajectory key-points independent of their spatial coordinates. With these representations, it would be possible to predict likelihood of preceding or subsequent key-points given a context or an internal state, or vice versa. To test this hypothesis, an experiment was conducted on birds' trajectories logged from a seabird species, Streaked Shearwater (Calonectris leucomelas). In this experiment, vector representations of the key-points in birds' trajectories were constructed and optimized using candidate sampling. The experimental results showcased the utility of these vector embeddings in both exploration of Streaked Shearwater trajectory data and improvement of gender-based trajectory classification. In summary, the proposed method provided a novel approach for numerical representation of animal trajectories and, it was illustrated to be semantically more explanatory for analysis as well as being more informative as features for modelling of animal movement data. [ABSTRACT FROM AUTHOR]

Recent advances in robotics and measurement technologies have enabled biologists to record the trajectories created by animal movements. In this paper, we convert time series of animal trajectories into sequences of finite symbols, and then propose a machine learning method for gaining biological insight from the trajectory data in the form of symbol sequences. The proposed method is used for training a classifier which differentiates between the trajectories of two groups of animals such as male and female. The classifier is represented in the form of a sparse linear combination of subsequence patterns, and we call the classifier an S3P-classifier. The trained S3P-classifier is easy to interpret because each coefficient represents the specificity of the subsequence patterns in either of the two classes of animal trajectories. However, fitting an S3P-classifier is computationally challenging because the number of subsequence patterns is extremely large. The main technical contribution in this paper is the development of a novel algorithm for overcoming this computational difficulty by combining a sequential mining technique with a recently developed convex optimization technique called safe screening. We demonstrate the effectiveness of the proposed method by applying it to three animal trajectory data analysis tasks. [ABSTRACT FROM AUTHOR]