Understanding Of Animal Behavior Research Articles

Motion parameters measurement of user-defined key points using 3D pose estimation

Motion parameters measurement is essential for understanding animal behavior, exploring the laws of object motion, and studying control methods. Nowadays, advanced computer vision based on machine learning technology supports markerless object tracking in 2D videos. However, due to the fact that all objects move in three-dimensional space, this paper introduces a method of measuring motion parameters using 3D pose estimation. First, an enhanced iterative bundle adjustment algorithm is proposed for multi-camera calibration in a multi-camera vision system by adding two control parameters, which dramatically reduces the reprojection error of multi-camera calibration and lays the foundation for high-precision triangulation. Then, a new spatiotemporal loss function is proposed, which considers the relationship between key points that do not constitute limbs, thereby improving triangulation accuracy. The new multi-camera calibration algorithm is evaluated on ChArUco and 3D pose estimation for metronome, planet pendulum, human hand, Koi, and cheetah. The experimental results show that: (1) the two hyper-parameters in the enhanced iterative bundle adjustment algorithm effectively suppress the influence of noise and play a good role in reducing the reprojection error of multi-camera calibration; (2) the spatiotemporal loss function has a strong constraining ability, the time loss can stabilize high frame rate video triangulation to maintain accuracy, while the space loss can improve the accuracy of triangulation for more complex structures; (3) multi-view data fusion is also conducive to improving the accuracy of triangulation. Moreover, the method was successfully applied to some actual measurement scenes: (1) the accurate measurement of the frequency of a metronome; and (2) the success measurement of the movement of a Koi, which conforms to the basic model of fish swimming. Some dynamic measurement results are displayed at https://github.com/wux024/AdamPose.

Worker task organization in incipient bumble bee nests

Bumble bees (Bombus: Apidae) are a long-standing model system for understanding animal behaviour, ecology and evolution. However, how workers in this system are organized to perform fundamental tasks related to brood feeding and food collection remains unclear. Bumble bees undergo dramatically different life stages, across which the social environment, and therefore task organization, changes over time. Queens initiate nests solitarily, and when the first cohort of workers emerge, they help the queen carry out brood-feeding and food collection tasks, whereas the queen transitions to primarily egg laying. Although task organization has been studied in mature colonies, few studies to date have explored how these tasks are organized in young, incipient nests. Here, we explored how food-related tasks, including brood feeding and food collection, are organized by workers in incipient colonies. We found that food-related tasks were nested, in that the majority of workers fed brood, a subset also collected nectar and a smaller subset also collected pollen. These patterns suggest that brood feeding is a task shared by most workers, and that the distinction between pollen collecting versus nonpollen collecting might be the most important axis of division of labour in bumble bee nests, at least at the early nesting stage.

Anipose: A toolkit for robust markerless 3D pose estimation.

SUMMARYQuantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method Deep-LabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. To help users get started with 3D tracking, we provide tutorials and documentation at http://anipose.org/.

Working Dog Training for the Twenty-First Century.

Dogs are trained for a variety of working roles including assistance, protection, and detection work. Many canine working roles, in their modern iterations, were developed at the turn of the 20th century and training practices have since largely been passed down from trainer to trainer. In parallel, research in psychology has advanced our understanding of animal behavior, and specifically canine learning and cognition, over the last 20 years; however, this field has had little focus or practical impact on working dog training. The aims of this narrative review are to (1) orient the reader to key advances in animal behavior that we view as having important implications for working dog training, (2) highlight where such information is already implemented, and (3) indicate areas for future collaborative research bridging the gap between research and practice. Through a selective review of research on canine learning and behavior and training of working dogs, we hope to combine advances from scientists and practitioners to lead to better, more targeted, and functional research for working dogs.

Nesting attempts and success of Arctic-breeding geese can be derived with high precision from accelerometry and GPS-tracking

Sensors, such as accelerometers, in tracking devices allow for detailed bio-logging to understand animal behaviour, even in remote places where direct observation is difficult. To study breeding in birds remotely, one needs to understand how to recognise a breeding event from tracking data, and ideally validate this by direct observation. We tagged 49 adult female pink-footed geese (Anser brachyrhynchus) with transmitter neckbands in Finland in spring of 2018 and 2019, and in Svalbard in summer 2018, and validated inferences from tracking by field observations of nesting sites and family status in 2018–2020 (54 spring–summer tracks). We estimated nesting locations by taking the median coordinates of GPS-fixes at which the goose was motionless (overall dynamic body acceleration, ODBA < 1) on days with a daily median ODBA < 1, which approached the real nesting locations closely (within 1.6–3.7 m, n = 6). The start of nesting was defined as the first day on which the goose spent > 75% of time within 50 m of the nest, because nest site attendances steeply increased within one day to above this threshold. Nesting duration (number of consecutive days with > 75% nest site attendance) ranged between 3 and 44 days (n = 28), but was 30–34 days in confirmed successful nests (n = 9). The prolonged nesting of 39–44 days (n = 3) suggested incubation on unhatchable egg(s). Nest losses before hatching time occurred mostly in day 3–10 and 23–29 of nesting, periods with an increased frequency of nest site recesses. As alternative method, allowing for non-simultaneous GPS and accelerometer data, we show that nesting days were classified with 98.6% success by two general characteristics of breeding: low body motion (daily median ODBA) and low geographic mobility (daily SD of latitude). Median coordinates on nesting days approached real nest sites closely (within 0.8–3.6 m, n = 6). When considering only geographic mobility (allowing for GPS data only) nesting locations were similarly accurate, but some short nesting attempts were undetected and non-breeding tracks misclassified. We show that nesting attempts, as short as 3 days, and nesting success can be detected remotely with good precision using GPS-tracking and accelerometry. Our method may be generalised to other (precocial) bird species with similar incubation behaviour.

Model selection for 24/7 pig position and posture detection by 2D camera imaging and deep learning

Continuous monitoring of pig posture is important for better understanding animal behavior. Previous studies focused on day recordings and did not investigate how deep learning models could be applied during longer periods including night recordings under near-infrared light from several pens. Therefore, the objective of this research was to study how a suitable deep learning model for continuous 24/7 pig posture detection could be achieved. We selected a deep learning model from over 150 different model configurations covering experiments concerning 3 detection heads, 4 base networks, 5 transfer datasets and 12 data augmentations. For this purpose, we test and validate our models using 4690 annotations of randomly drawn images from 24/7 video recordings covering 2 fattening periods from 10 pens. Our results indicate that pig position and posture was detected on the test set with 84% mAP@0.50 (49% mAP@[0.50:0.05:0.95]) for day recordings and for night recordings 58% mAP@0.50 (29% mAP@[0.50:0.05:0.95]) was achieved. The main reason for lower mAP during night recordings was degraded near-infrared image quality. Our work reports important findings concerning the applicability of deep learning models on night near-infrared recordings for posture detection. The dataset is publicly available for further research and industrial applications.

A random walk model that accounts for space occupation and movements of a large herbivore

Animal movement has been identified as a key feature in understanding animal behavior, distribution and habitat use and foraging strategies among others. Large datasets of invididual locations often remain unused or used only in part due to the lack of practical models that can directly infer the desired features from raw GPS locations and the complexity of existing approaches. Some of them being disputed for their lack of biological justifications in their design. We propose a simple model of individual movement with explicit parameters, based on a two-dimensional biased and correlated random walk with three forces related to advection (correlation), attraction (bias) and immobility of the animal. These forces can be directly estimated using individual data. We demonstrate the approach by using GPS data of 5 red deer with a high frequency sampling. The results show that a simple random walk template can account for the spatial complexity of wild animals. The practical design of the model is also verified for detecting spatial feature abnormalities and for providing estimates of density and abundance of wild animals. Integrating even more additional features of animal movement, such as individuals’ interactions or environmental repellents, could help to better understand the spatial behavior of wild animals.

Burying of dead cubs by a brown bear in Greece: Food caching or ‘grief’ behavior?

Understanding animal behavior is essential for the conservation of endangered species. We document the case of a female brown bear (Ursus arctos) burying her 2 cubs-of-the-year, following their death in a car accident in May 2020 in the Prefecture of Kastoria, northern Greece. Both cubs were partially buried, half-covered with earth in an open field. We explore the circumstances and causes of this unusual behavior in relation to 2 potential explanations: caching of food and ‘grieving.’ Although the evidence collected is not conclusive enough to accept either of the 2 explanations, we report this observation to draw attention to this type of behavior in brown bears in the wild and hope that it will serve as a source for motivating future research.

Digital Twins in Livestock Farming

Simple SummaryA digital twin can be described as a digital replica of a real-world entity. It simulates the physical state and maybe the biological state and behavior of the real-world entity based on input data. It helps in predicting, optimizing, and improving decision making. It has revolutionized the industrial world, particularly the manufacturing industry, construction and healthcare sector, smart cities, and energy industry. In this perspectives paper, we explore the development and implementation of the digital twin in modern animal farming. In addition to showcasing potential applications, this review provides in-depth insights about the potential implementation and characterization of digital twins in modern animal farming.Artificial intelligence (AI), machine learning (ML) and big data are consistently called upon to analyze and comprehend many facets of modern daily life. AI and ML in particular are widely used in animal husbandry to monitor both the animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and effective business decisions for the farmer. One particularly promising area that advances upon AI is digital twin technology, which is currently used to improve efficiencies and reduce costs across multiple industries and sectors. In contrast to a model, a digital twin is a digital replica of a real-world entity that is kept current with a constant influx of data. The application of digital twins within the livestock farming sector is the next frontier and has the potential to be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. The mental and emotional states of animals can be monitored using recognition technology that examines facial features, such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers to build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis of individual farms. Our goal in this review is to assess the progress toward the use of digital twin technology in livestock farming, with the goal of revolutionizing animal husbandry in the future.

Tri-axial accelerometry shows differences in energy expenditure and parental effort throughout the breeding season in long-lived raptors.

Cutting-edge technologies are extremely useful to develop new workflows in studying ecological data, particularly to understand animal behavior and movement trajectories at the individual level. Although parental care is a well-studied phenomenon, most studies have been focused on direct observational or video recording data, as well as experimental manipulation. Therefore, what happens out of our sight still remains unknown. Using high-frequency GPS/GSM dataloggers and tri-axial accelerometers we monitored 25 Bonelli’s eagles Aquila fasciata during the breeding season to understand parental activities from a broader perspective. We used recursive data, measured as number of visits and residence time, to reveal nest attendance patterns of biparental care with role specialization between sexes. Accelerometry data interpreted as the overall dynamic body acceleration, a proxy of energy expenditure, showed strong differences in parental effort throughout the breeding season and between sexes. Thereby, males increased substantially their energetic requirements, due to the increased workload, while females spent most of the time on the nest. Furthermore, during critical phases of the breeding season, a low percentage of suitable hunting spots in eagles’ territories led them to increase their ranging behavior in order to find food, with important consequences in energy consumption and mortality risk. Our results highlight the crucial role of males in raptor species exhibiting biparental care. Finally, we exemplify how biologging technologies are an adequate and objective method to study parental care in raptors as well as to get deeper insight into breeding ecology of birds in general.

Road visibility influences habitat selection by grizzly bears (Ursus arctos horribilis)

Anthropogenic disturbances, including roads, are known to influence animal habitat selection and mortality. In this study, we consider the role of sensory perception in understanding why and how animals respond to disturbances. Our goal was to investigate the effect of visual perception (visibility) around roads on grizzly bear (Ursus arctos horribilis Ord, 1815) habitat selection and mortality in Alberta, Canada. We used detailed topographic and vegetation data from airborne light detection and ranging (lidar) to estimate visibility around roads. We modelled habitat selection as a function of road visibility and environmental variables using GPS telemetry data from 39 grizzly bears and integrated step selection analysis (iSSA). Finally, we assessed mortality risk in visible areas by comparing habitat selection between grizzly bears that died and those that survived. We found that grizzly bears were less likely to select visible areas when moving slowly or resting, but were more likely to select visible areas when travelling. We found that grizzly bears that survived selected for areas farther from roads than grizzly bears that died. However, no difference in selection for visible areas was observed. An exploratory analysis showed that grizzly bear mortalities commonly occurred in visible areas. Our findings highlight the importance of sensory perception in understanding animal behaviour.

Conservation and people: Towards an ethical code of conduct for the use of camera traps in wildlife research

Abstract1. Camera trapping is a widely employed tool in wildlife research, used to estimate animal abundances, understand animal movement, assess species richness and understand animal behaviour. In addition to images of wild animals, research cameras often record human images, inadvertently capturing behaviours ranging from innocuous actions to potentially serious crimes.2. With the increasing use of camera traps, there is an urgent need to reflect on how researchers should deal with human images caught on cameras. On the one hand, it is important to respect the privacy of individuals caught on cameras, while, on the other hand, there is a larger public duty to report illegal activity. This creates ethical dilemmas for researchers.3. Here, based on our camera‐trap research on snow leopards Panthera uncia, we outline a general code of conduct to help improve the practice of camera trap based research and help researchers better navigate the ethical‐legal tightrope of this important research tool.

Understanding Katydid Communication and Its Ecological Significance

Katydids use species-specific sound signals for courtship and long-distance communication. These sound signals are the product of intense selection pressures, both natural and sexual, while simultaneously being affected by physiological and environmental limitations. Their unique communication patterns and high sensitivity to ecological disturbances make them an important system to understand animal behavior, as well as ecosystem changes. This article introduces readers to acoustic communication in katydids and the significance of sound in their lives.

The representation of animal behaviour in the fossil record

Paleontologists and biologists generally utilize different lines of evidence and approaches to study, conceptualize and categorize animal behaviour, which has led to a divide between the two disciplines and lack of integration of their data. Numerous filters impact the preservation of behaviour in the fossil record. Here, we utilize 13 broad categories of behaviour representing the range of modern studies of animal behaviour and assess how they are represented in the fossil record. The data summarized in an existing compilation of ‘fossil behaviours’ is used to estimate the relative occurrence of each category. We also examine the taxonomic coverage of fossil animal behaviour using the same source. We found that feeding and habitat selection-related behaviours were disproportionately represented. Examples of other behavioural categories, especially social ones, such as mating, communication or parenting were considerably rarer, whereas personality or play were not represented at all. Arthropods tended to be most well represented, including insects in amber, followed by vertebrates. Taxonomic coverage was overall fairly diverse. A broader literature search was used to identify the oldest paleontological evidence of each behavioural category. We found that most categories probably appeared by the end of the Cambrian radiation. Finally, we suggest that the preservation of behaviour in fossils can be estimated from first principles, such as the amount of time animals engage in a behaviour or which behaviours impact a substrate. Reconstructing the evolution of behaviour in deep time requires understanding how the fossil record captures behavioural information. We strongly encourage paleontologists and biologists studying behaviour to work together to help complete our understanding of animal behaviour across the entirety of its evolutionary history. • The fossil record of behaviour in deep time richly complements the modern. • A diversity of behaviours is represented in the paleontological literature. • Surveying this record can reveal how behaviours originated and evolved over time. • Understanding how behavioural evidence preserves is important for this goal.

A Bayesian Markov Model with Pólya-Gamma Sampling for Estimating Individual Behavior Transition Probabilities from Accelerometer Classifications

The use of accelerometers in wildlife tracking provides a fine-scale data source for understanding animal behavior and decision-making. Current methods in movement ecology focus on behavior as a driver of movement mechanisms. Our Markov model is a flexible and efficient method for inference related to effects on behavior that considers dependence between current and past behaviors. We applied this model to behavior data from six greater white-fronted geese (Anser albifrons frontalis) during spring migration in mid-continent North America and considered likely drivers of behavior, including habitat, weather and time of day effects. We modeled the transitions between flying, feeding, stationary and walking behavior states using a first-order Bayesian Markov model. We introduced P\'olya-Gamma latent variables for automatic sampling of the covariate coefficients from the posterior distribution and we calculated the odds ratios from the posterior samples. Our model provides a unifying framework for including both acceleration and Global Positioning System data. We found significant differences in behavioral transition rates among habitat types, diurnal behavior and behavioral changes due to weather. Our model provides straightforward inference of behavioral time allocation across used habitats, which is not amenable in activity budget or resource selection frameworks.

Animal movement models for multiple individuals

AbstractStatistical models for animal movement provide tools that help ecologists and biologists learn how animals interact with their environment and each other. Efforts to develop increasingly realistic, implementable, and scientifically valuable methods for analyzing remotely observed trajectories have provided practitioners with a wide selection of models to help them understand animal behavior. Increasingly, researchers are interested in studying multiple animals jointly, which requires methods that can account for dependence across individuals. Dependence can arise for many reasons, including shared behavioral tendencies, familial relationships, and direct interactions on the landscape. We provide a synopsis of recent statistical methods for animal movement data applicable to settings in which inference is desired across multiple individuals. Highlights of these approaches include the ability to infer shared behavioral traits across a group of individuals and the ability to infer unobserved social networks summarizing dynamic relationships that manifest themselves in movement decisions.This article is categorized under: Statistical Models &gt; Bayesian Models Data: Types and Structure &gt; Time Series, Stochastic Processes, and Functional Data Data: Types and Structure &gt; Social Networks

ThermalTracker-3D: A thermal stereo vision system for quantifying bird and bat activity at offshore wind energy sites

We present a new, efficient method for extracting three-dimensional animal motion trajectories from thermal stereo video data. Understanding animal behavior in the wild or other unconstrained environments is often based on animal movements. The technology described here is for understanding how bird and bat behavior is affected by the presence of wind turbines, specifically offshore wind turbines which are challenging to monitor. There is a need for both baseline data prior to wind farm construction and post-construction data when the turbines are operating. Thermal cameras were chosen because they are equally effective both night and day. In previous work, we developed a method for generating two-dimensional images of animal motion using thermal video from a single camera. The motion track image is formed by combining a sequence of video frames into a single composite image that shows the entire flight trajectory. Here we demonstrate that the composite motion track images from a stereo pair of thermal cameras can be used directly to generate three-dimensional tracks in real time without the need for an explicit tracking algorithm. The method was evaluated using an unmanned aerial system (UAS) equipped with GPS. The UAS flew both straight and curving trajectories at distances between 50 and 325 m from the camera system. The ThermalTracker-3D estimated positions were within ±10 m of the GPS-derived positions in the x and y (flight height) dimensions, and within ±20 m in the z (range) dimension for 90% of the data points.The range estimates were within the bounds of the achievable accuracy of the cameras and camera arrangement used. The results demonstrate the practical usefulness of the method for assessing collision risk to seabirds at proposed offshore wind energy sites and for quantifying avoidance behavior at operating offshore wind farms.

Scrutiny of Methods for Image Detection and Recognition of Different Species of Animals

Animal detection-based study is useful in many real-life applications. Techniques involved in animal detection are useful in observing the locomotive behavior of the engaged animal and in result it prevent harmful interruption of animals in residential areas. There are some branches of research in animal detection. Some of these branches will therefore be discussed in this journal. Humans have developed many algorithms and techniques to gain a better understanding of animal behaviour. Therefore, for early preventive measures, these technologies can also serve as a warning system for humans from encroachment of dangerous wild animals. Such tasks can be reduced to three main branches, namely animal detection, tracking and recognition. Through these papers, new approaches for study and a variety of technologies/algorithms implemented in the past are identified and appropriate ways for solving the research gaps are suggested to fill the gap.

New formulation of the logistic-Gaussian process to analyze trajectory tracking data

Improved communication systems, shrinking battery sizes and the price drop of tracking devices have led to an increasing availability of trajectory tracking data. These data are often analyzed to understand animal behavior. In this work, we propose a new model for interpreting the animal movent as a mixture of characteristic patterns, that we interpret as different behaviors. The probability that the animal is behaving according to a specific pattern, at each time instant, is nonparametrically estimated using the Logistic-Gaussian process. Owing to a new formalization and the way we specify the coregionalization matrix of the associated multivariate Gaussian process, our model is invariant with respect to the choice of the reference element and of the ordering of the probability vector components. We fit the model under a Bayesian framework, and show that the Markov chain Monte Carlo algorithm we propose is straightforward to implement. We perform a simulation study with the aim of showing the ability of the estimation procedure to retrieve the model parameters. We also test the performance of the information criterion we used to select the number of behaviors. The model is then applied to a real dataset where a wolf has been observed before and after procreation. The results are easy to interpret, and clear differences emerge in the two phases.

An Adaptive-Threshold Mechanism for Odor Sensation and Animal Navigation

Identifying the environmental information and computations that drive sensory detection is key for understanding animal behavior. Using experimental and theoretical analysis of AWCON, a well-described olfactory neuron in C.elegans, here we derive a general and broadly useful model that matches stimulus history to odor sensation and behavioral responses. We show that AWCON sensory activity is regulated by an absolute signal threshold that continuously adapts to odor history, allowing animals to compare present and past odor concentrations. The model predicts sensory activity and probabilistic behavior during animal navigation in different odor gradients and across a broad stimulus regime. Genetic studies demonstrate that the cGMP-dependent protein kinase EGL-4 determines the timescale of threshold adaptation, defining a molecular basis for a critical model feature. The adaptive threshold model efficiently filters stimulus noise, allowing reliable sensation in fluctuating environments, and represents a feedforward sensory mechanism with implications for other sensory systems.