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Visible phenotypes based on locomotion and posture have played a critical role in understanding the molecular basis of behavior and development in Caenorhabditis elegans and other model organisms. However, it is not known whether these human-defined features capture the most important aspects of behavior for phenotypic comparison or whether they are sufficient to discover new behaviors. Here we show that four basic shapes, or eigenworms, previously described for wild-type worms, also capture mutant shapes, and that this representation can be used to build a dictionary of repetitive behavioral motifs in an unbiased way. By measuring the distance between each individual's behavior and the elements in the motif dictionary, we create a fingerprint that can be used to compare mutants to wild type and to each other. This analysis has revealed phenotypes not previously detected by real-time observation and has allowed clustering of mutants into related groups. Behavioral motifs provide a compact and intuitive representation of behavioral phenotypes.

An ethogram is a catalogue of discrete behaviors typically employed by a species. Traditionally animal behavior has been recorded by observing study individuals directly. However, this approach is difficult, often impossible, in the case of behaviors which occur in remote areas and/or at great depth or altitude. The recent development of increasingly sophisticated, animal-borne data loggers, has started to overcome this problem. Accelerometers are particularly useful in this respect because they can record the dynamic motion of a body in e.g. flight, walking, or swimming. However, classifying behavior using body acceleration characteristics typically requires prior knowledge of the behavior of free-ranging animals. Here, we demonstrate an automated procedure to categorize behavior from body acceleration, together with the release of a user-friendly computer application, \"Ethographer\". We evaluated its performance using longitudinal acceleration data collected from a foot-propelled diving seabird, the European shag, Phalacrocorax aristotelis. The time series data were converted into a spectrum by continuous wavelet transformation. Then, each second of the spectrum was categorized into one of 20 behavior groups by unsupervised cluster analysis, using k-means methods. The typical behaviors extracted were characterized by the periodicities of body acceleration. Each categorized behavior was assumed to correspond to when the bird was on land, in flight, on the sea surface, diving and so on. The behaviors classified by the procedures accorded well with those independently defined from depth profiles. Because our approach is performed by unsupervised computation of the data, it has the potential to detect previously unknown types of behavior and unknown sequences of some behaviors.

Despite increasing interest, animal personality is still a puzzling phenomenon. Several theoretical models have been proposed to explain intraindividual consistency and interindividual variation in behaviour, which have been primarily supported by qualitative data and simulations. Using an empirical approach, I tested predictions of one main life-history hypothesis, which posits that consistent individual differences in behaviour are favoured by a trade-off between current and future reproduction. Data on life-history were collected for individuals of a natural population of grey mouse lemurs (Microcebus murinus). Using open-field and novel-object tests, I quantified variation in activity, exploration and boldness for 117 individuals over 3 years. I found systematic variation in boldness between individuals of different residual reproductive value. Young males with low current but high expected future fitness were less bold than older males with high current fecundity, and males might increase in boldness with age. Females have low variation in assets and in boldness with age. Body condition was not related to boldness and only explained marginal variation in exploration. Overall, these data indicate that a trade-off between current and future reproduction might maintain personality variation in mouse lemurs, and thus provide empirical support of this life-history trade-off hypothesis.

Bemisia tabaci (whitefly) infestations and the subsequent transfer of viruses are the cause of severe losses in crop production and horticultural practice. To improve biological control of B. tabaci, we investigated repellent properties of plant-produced semiochemicals. The mix of headspace volatiles, collected from naturally repellent wild tomato accessions, influenced B. tabaci initial choice behavior, indicating a role for plant semiochemicals in locating host plants. A collection of wild tomato accessions and introgression lines (Solanum pennellii LA716 x Solanum lycopersicum 'Moneyberg') were extensively screened for attractiveness to B. tabaci, and their headspace profiles were determined by means of gas chromatography-mass spectrometry. Correlation analysis revealed that several terpenoids were putatively involved in tomato-whitefly interactions. Several of these candidate compounds conferred repellence to otherwise attractive tomato plants when applied to the plant's branches on paper cards. The sesquiterpenes zingiberene and curcumene and the monoterpenes p-cymene, α-terpinene, and α-phellandrene had the strongest effects in free-choice bioassays. These terpenes also elicited a response of receptors on the insect's antennae as determined by electroantennography. Conversely, the monoterpene β-myrcene showed no activity in both assays. B. tabaci apparently uses, besides visual cues, specific plant volatile cues for the initial selection of a host. Altering whitefly choice behavior by manipulation of the terpenoid composition of the host headspace may therefore be feasible.

The objectives of this study were to 1) evaluate the validity of automated monitoring systems as assessment method for the behavioral activity of dairy cows compared with video recording, and 2) determine the sampling intervals required to obtain reliable estimates of the daily behavior. To determine lying, standing, and walking, 12 cows were equipped with automatic recording devices (IceTag = 12 cows, HOBO Pendant G = 5 cows), and their behavior was simultaneously recorded using a video recording system. The correspondence between the IceTag, HOBO logger, and video recording data was analyzed using 2 × 2 contingency tables, and we determined the sensitivity, specificity, and predictive value (positive and negative). Both types of loggers demonstrated high sensitivity (Sen ≥ 0.961) and specificity (Sp ≥ 0.951) for lying and standing behaviors with predictive values near 1.00. The HOBO logger can accurately describe the laterality of lying behavior, whereas the IceTag device inadequately recorded walking, with probability predictive values ≤ 0.303. Daily behaviors of the dairy cows were compared for 10 different sampling intervals (1 s, and 1, 2, 3, 4, 5, 10, 15, 30, and 60 min) collected by the IceTag, using linear regression. A strong relationship (R(2) ≥ 0.978) was found between the total lying times from data on a per-second basis and estimates obtained by 1, 2, 3, 4, 5, 10, and 15 min sampling intervals. The sampling intervals of 1 and 2 min were comparable for all aspects of lying behavior (R(2) ≥ 0.813; P > 0.05 for slope = 1, intercept = 0). Long sampling intervals (30 and 60 min) showed positive relationship for estimating time spent lying and standing (R(2) ≥ 0.774), but were inappropriate for predicting these behaviors, because they lacked accuracy and precision. Both the IceTag and HOBO logger accurately measured all aspects of lying and standing behavior. Reliable estimates of lying and standing time can be generated using relatively short interval lengths (e.g., 3, 4, 5, 10, or 15 min). Shorter sampling intervals (≤ 2 min) are required to accurately measure aspects of lying behavior such as number of lying bouts per day. The automated monitoring systems are time- and labor-saving tools that can be used by research or on farm to assess cow comfort related to lying behavior.

Constructing activity budgets for marine animals when they are at sea and cannot be directly observed is challenging, but recent advances in bio-logging technology offer solutions to this problem. Accelerometers can potentially identify a wide range of behaviours for animals based on unique patterns of acceleration. However, when analysing data derived from accelerometers, there are many statistical techniques available which when applied to different data sets produce different classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions). We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis accelerometers. From video we identified 26 behaviours that could be grouped into one of four categories (foraging, resting, travelling and grooming) representing key behaviour states for wild seals. We used data from 10 seals to train four predictive classification models: stochastic gradient boosting (GBM), random forests, support vector machine using four different kernels and a baseline model: penalised logistic regression. We then took the best parameters from each model and cross-validated the results on the two seals unseen so far. We also investigated the influence of feature statistics (describing some characteristic of the seal), testing the models both with and without these. Cross-validation accuracies were lower than training accuracy, but the SVM with a polynomial kernel was still able to classify seal behaviour with high accuracy (>70%). Adding feature statistics improved accuracies across all models tested. Most categories of behaviour -resting, grooming and feeding-were all predicted with reasonable accuracy (52-81%) by the SVM while travelling was poorly categorised (31-41%). These results show that model selection is important when classifying behaviour and that by using animal characteristics we can strengthen the overall accuracy.

In the wild, behaviors are often expressed over long time periods in complex and dynamic environments, and many behaviors include direct interaction with the environment itself. However, measuring behavior in naturalistic settings is difficult, and this has limited progress in understanding the mechanisms underlying many naturally evolved behaviors that are critical for survival and reproduction. Here we describe an automated system for measuring long-term bower construction behaviors in Lake Malawi cichlid fishes, in which males use their mouths to sculpt sand into large species-specific structures for courtship and mating. We integrate two orthogonal methods, depth sensing and action recognition, to simultaneously track the developing bower structure and the thousands of individual sand manipulation behaviors performed throughout construction. By registering these two data streams, we show that behaviors can be topographically mapped onto a dynamic 3D sand surface through time. The system runs reliably in multiple species, across many aquariums simultaneously, and for up to weeks at a time. Using this system, we show strong differences in construction behavior and bower form that reflect species differences in nature, and we gain new insights into spatial, temporal, social dimensions of bower construction, feeding, and quivering behaviors. Taken together, our work highlights how low-cost tools can automatically quantify behavior in naturalistic and social environments over long timescales in the lab.

Classifying movement behaviour of marine predators in relation to anthropogenic activity and environmental conditions is important to guide marine conservation. We studied the relationship between grey seal ( Halichoerus grypus ) behaviour and environmental variability in the southwestern Baltic Sea where seal-fishery conflicts are increasing. We used multiple environmental covariates and proximity to active fishing nets within a multivariate hidden Markov model (HMM) to quantify changes in movement behaviour of grey seals while at sea. Dive depth, dive duration, surface duration, horizontal displacement, and turning angle were used to identify travelling, resting and foraging states. The likelihood of seals foraging increased in deeper, colder, more saline waters, which are sites with increased primary productivity and possibly prey densities. Proximity to active fishing net also had a pronounced effect on state occupancy. The probability of seals foraging was highest <5 km from active fishing nets (51%) and decreased as distance to nets increased. However, seals used sites <5 km from active fishing nets only 3% of their time at sea highlighting an important temporal dimension in seal-fishery interactions. By coupling high-resolution oceanographic, fisheries, and grey seal movement data, our study provides a scientific basis for designing management strategies that satisfy ecological and socioeconomic demands on marine ecosystems.

Increasingly, animal behavior studies are enhanced through the use of accelerometry. To allow translation of raw accelerometer data to animal behaviors requires the development of classifiers. Here, we present the “rabc” (r for animal behavior classification) package to assist researchers with the interactive development of such animal behavior classifiers in a supervised classification approach. The package uses datasets consisting of accelerometer data with their corresponding animal behaviors (e.g., for triaxial accelerometer data along the x, y and z axes arranged as “x, y, z, x, y, z,…, behavior”). Using an example dataset collected on white stork (Ciconia ciconia), we illustrate the workflow of this package, including accelerometer data visualization, feature calculation, feature selection, feature visualization, extreme gradient boost model training, validation, and, finally, a demonstration of the behavior classification results. Other than the serial functions to turn raw accelerometer data into behaviors, this package also provides interactive visualization tools to assist in handling and interpreting the accelerometer input data, deciding on appropriate behavior categories for classification and understanding the classification results. In brief, this package promotes the integration of the user's expert knowledge on their own research system in developing advanced behavior classification models.

Background Automated behavior monitoring is increasingly important in precision dairy farming, supporting early disease detection, welfare assessment, and productivity optimization. Although accelerometers effectively detect postural changes, they have limited capacity to capture rotational or transitional movements. Gyroscopes provide complementary angular velocity data that may enhance classification of complex behaviors. However, their combined use remains underexplored, particularly at the individual cow level. This study aims to evaluate the performance of accelerometer, gyroscope, and combined sensors models for classifying four key cow behaviors: lying, standing, eating, and walking at the individual animal level. Results Over 780,000 labeled observations were obtained from seven dairy cows monitored over 90 days. Lying behavior consistently produced low, stable signals across all axes of accelerometer and gyroscope, while eating showed the greatest variability, particularly along the X and Y axes. Significant axis-specific and behavior-specific differences were observed ( p  < 0.05), with GyroY and GyroZ capturing the highest rotational activity during eating and walking. Signal vector magnitudes effectively distinguished behaviors, with lying showing the lowest values and eating the highest. Random Forest models combining accelerometer and gyroscope data consistently outperformed single-sensor approaches, particularly for classifying lying and standing behaviors. Although eating and walking exhibited lower sensitivity, sensor fusion improved classification robustness across individuals. Conclusion The integration of accelerometer and gyroscope data enhanced classification accuracy, particularly for static behaviors. Axis-specific signal patterns and individualized modeling revealed critical insights into behavior differentiation and cow-specific variability. These findings support the development of scalable, sensor-based monitoring systems tailored to precision livestock management.