Big-data approaches lead to an increased understanding of the ecology of animal movement

Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal “movement ecology” (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences. Description Animal tracking in a big data world So-called “big-data” approaches have revolutionized fields of research from astronomy to genetics. Such approaches are not limited to fields that seem inherently technical, because the combination of rapid data collection and advanced analytical techniques could be applied to almost any scientific question. Nathan et al. reviewed how these modern approaches are being applied to the very old field of animal tracking and monitoring. Large-scale data collection can reveal details about how animals use their environment and interact with each other that were impossible to explore previously. Such methodological shifts will open new avenues of research—and conservation—across species. —SNV A review suggests that modern “big-data” techniques are vastly increasing our understanding of animal movement and its ecology. BACKGROUND Movement is ubiquitous across the natural world. All organisms move, actively or passively, regularly or during specific life stages, as a result of varied proximate drivers including energetic demands, social interactions, competition or predation. Movement closely interacts with individual fitness, affects a myriad of ecological processes, and is crucial for animals’ ability to cope with human-induced rapid environmental changes. Driven by advances in analytical methods and technologies for tracking mammals, birds, fish, and other free-ranging vertebrates (hereafter, wildlife), movement ecology is rapidly transforming into a data-rich discipline, following previous developments in fields such as genomics and environmental monitoring. This ongoing revolution is facilitated by cost-effective automated high-throughput wildlife tracking systems that generate massive high-resolution datasets across scales relevant to the ecological context in which animals perceive, interact with, and respond to their environment. ADVANCES Modern tracking technologies efficiently generate copious, accurate information on the movements of multiple individual animals in the wild. Reverse-GPS technologies, which primarily use acoustic signals under water and radio signals over land, are automated high-throughput systems that are highly cost- and power-effective and capable of simultaneous tracking of multiple small animals (e.g., 20-g birds) at high spatiotemporal resolution (e.g., 1-s interval, a few meters) for months, but they require system installation and are usually limited to regional scales (≤100 km wide). GPS-based systems are, by contrast, readily available, longer term, and cover nearly global scales, but are similarly spatially accurate and periodically capable of high-resolution tracking at regional scales. However, they are more cost- and power-demanding, limited to larger animals, and cannot be applied under water. Two other tracking technologies, radar and computer vision, permit high-resolution snapshots of the movement of multiple individuals and can noninvasively track nontagged animals, but are less cost-effective, usually limited to smaller scales, and make individual identification challenging. Combined, these high-throughput technologies enable groundbreaking research in animal behavior, cognitive sciences, evolution, and ecology, facilitating previously infeasible investigation of animal movement ecology. Big movement data can help link interindividual variation in movement to individual behavior, traits, cognition and physiology; divulge fine-scale interactions within or among species; improve evidence-based management of human-wildlife interactions; and elucidate behavioral changes across spatiotemporal scales. OUTLOOK High-throughput wildlife tracking technologies are opening new research frontiers in biology and ecology. Their advantages, however, come with typical big-data costs such as computational load, intensive data management and processing, and challenging statistical analyses. Enlisting fields with a longer history of big data offers new prospects to address these challenges. Progress will arise from combining observational and experimental movement ecology and data-rich studies revealing behavioral shifts across individuals, species, scales, ecosystems, and life stages. High-resolution wildlife tracking is currently infeasible at large to global scales, a key limitation that can be addressed by combining low- and high-rate sampling, increasing interoperability between technologies, standardizing and sharing data, and promoting multidisciplinary international collaboration. Coupling movement and environmental big data could help determine impacts of major environmental and climate changes on animal–environment interactions, whereas real-time movement data could uniquely inform biodiversity conservation and ecosystem management. Why do high-throughput movement data matter? Big movement data are essential for addressing key ecological questions, as conclusions based on traditional lower-resolution data could differ markedly from the correct conclusions. We illustrate several examples for contrasting conclusions derived from lower- versus higher-resolution data of the same tracks from the same number of animals. Higher-resolution data can reveal that bolder birds visit more sites across the landscape and that bird tracks frequently cross each other, suggesting high potential for disease transmission, and that fish avoid fisheries and frequently search locally within small patches. None of these conclusions, however, could have been drawn from lower-resolution data. See also movies S1 to S5.