AI, Precision Monitoring, and Statistical Approaches to Animal Behavior

The study of animal behavior has long been constrained by a fundamental trade off: detailed behavioral observation is labor intensive and short lived, while long term monitoring sacrifices granularity. Traditional ethological methods, i.e., direct observation, manual coding of video recordings, and field note-taking, yield rich qualitative insights but are inherently subjective, unscalable, and ill suited for capturing behavioral dynamics across extended periods.Recent advances in artificial intelligence, computer vision, bioacoustic analysis, precision animal monitoring technologies, and statistical modeling are rapidly transforming this landscape. Machine learning algorithms can now detect, track, and classify animal movements, postures, vocalizations, and social interactions with unprecedented accuracy and scale, while precision monitoring technologies including accelerometers, biologgers, GPS, RFID systems, thermal imaging, and other wearable or remote sensing platforms enable continuous and minimally invasive assessment of behavioral and physiological responses. In parallel, modern statistical frameworks provide essential tools across the entire discipline of ethology to model complex behavioral processes, quantify uncertainty, test hypotheses, and draw robust inferences across diverse behavioral domains, ranging from individual movement, foraging, and habitat use to temporal dynamics, physiological correlates, and social structures. These technologies enable researchers to move beyond snapshot observations toward continuous, high-resolution behavioral phenotyping across laboratory, farm, captive wildlife, and free-ranging settings, opening new frontiers in our understanding of the developmental, mechanistic, ecological, and phylogenetic influences on animal behavioral systems.This Research Topic aims to bring together cutting-edge contributions that advance the application of AI-assisted and precision monitoring technologies focused on animal social behavior and communication, alongside statistical methods and quantitative modeling applied to any aspect of animal behavior. Despite rapid progress, significant challenges remain in translating computational and statistical tools into robust ethological research practice. These include: the development of generalizable behavioral classification frameworks that work across species and contexts; the integration of multimodal data streams (video, audio, accelerometry, GPS, RFID, thermal imaging, proximity sensing, and physiological sensors) into unified analytical pipelines; the need for open-source tools and benchmark datasets to accelerate methodological progress; the validation of sensor-derived behavioral indicators across species, environments, and management conditions; the advancement of statistical methodologies, inferential frameworks, and validation approaches to evaluate, interpret, and model all forms of behavioral data; and critical questions about how both automated and traditional behavioral metrics map onto biologically meaningful constructs such as individual fitness, behavioral syndromes, communication complexity, and social dynamics. By addressing these challenges, we can unlock the full potential of computational and quantitative approaches to test longstanding hypotheses in animal behavior research. We invite contributions that demonstrate novel applications, validate computational methods against traditional behavioral measures, or advance innovative statistical and quantitative methodologies addressing any aspect of animal behavior. Applications across livestock, companion animals, captive wildlife, and free-ranging populations are particularly encouraged, including studies linking automated and precision behavioral monitoring with animal welfare, health, social organization, and responses to environmental or management conditions.We welcome interdisciplinary submissions that bridge ethology, computational science, statistics, and engineering. We explicitly encourage submissions focused on statistical modeling, methodological innovation, and quantitative analysis across all areas of animal behavior, in addition to AI- and sensor-focused submissions centered on social monitoring. Specific themes include but are not limited to:1. Automated detection and fine-grained classification of social behaviors and interactive sequences;2. Deep learning approaches to individual recognition and social network analysis;3. Multimodal integration of video, audio, and movement data for behavior recognition;4. Systematic validation studies comparing automated and manual behavioral coding, including statistical approaches to assessing agreement and reliability;5. Open-source tool development and benchmark dataset contributions;6. AI-assisted long-term behavioral monitoring in wild populations;7. Precision monitoring and sensor-based approaches for continuous assessment of animal behavior, including wearable sensors, RFID, biologgers, thermal imaging, and remote sensing;8. Automated and precision behavioral monitoring in livestock, companion animals, captive wildlife, and free-ranging populations;9. Statistical modeling, quantitative methods, and inferential frameworks applied to any aspect of animal behavior (e.g., movement ecology, foraging strategies, time-budget analyses, survival/mark-recapture behavioral models, and behavioral state space models);10. Novel statistical approaches for analyzing behavioral time-series, longitudinal data, and complex behavioral repertoires;11. Ethical considerations in automated surveillance of animals.All manuscripts should provide detailed, in-depth knowledge about factors influencing animal behavior, individual behavioral variation, or social interactions and communication, and their implications for individual and species fitness outcomes. Topic editors: Leonardo Castilho, Axiu MAO, Rogério Ribeiro Vicentini Source: https://www.frontiersin.org/research-topics/86611/ai-precision-monitoring-and-statistical-approaches-to-animal-behavior