Although methods for tracking animals underwater exist, they frequently involve costly infrastructure investment, or capture and manipulation of animals to affix or implant tags. These practical concerns limit the taxonomic coverage of aquatic movement ecology studies and implementation in areas where high infrastructure investment is impossible. Here we present a method based on deep-learning and structure-from-motion, with which we can accurately determine the 3D location of animals, the structure of the environment in which they are moving. Further behavioural decomposition of the body position and contour of animals subsequently allow quantifying the behavioural states of each interacting animal. This approach can be used with minimal infrastructure and without confining animals to to a fixed area, or capturing and interfering with them in any way. With this approach, we are able to track single individuals (Conger Eel, Conger oceanus), small heterospecific groups (Mullus surmuletus, Diplodus sp.), and schools of animals (Tanganyikan cichlids Lamprologus callipterus) in freshwater and marine systems, and in habitats ranging in environmental complexity. Positional information was highly accurate, with errors as low as 1.67% of body length. Tracking data was embedded in 3D environmental models that could be used to examine collective decision making, obstacle avoidance, and visual connectivity of groups. By analyzing body contour and position, we were also able to use unsupervised classification to quantify the kinematic behavioural states of each animal. The proposed framework allows us to understand animal behaviour in aquatic systems at an unprecedented resolution and a fraction of the cost of established methodologies, with minimal domain expertise at the data acquisition or analysis phase required. Implementing this method, research can be conducted in a wide range of field contexts to collect laboratory standard data, vastly expanding both the taxonomic and environmental coverage of quantitative animal movement analysis with a low-cost, open-source solution. 1/151 Understanding the movement and behaviour of animals in their natural habitats is the 2 ultimate goal of behavioural and movement ecology. By situating our studies in the 3 natural world, we have the potential to uncover the natural processes of selection acting 4 on the behaviour in natural populations, in a manner that cannot be achieved through 5 lab studies alone. The ongoing advance of animal tracking and biologging has the 6 potential to revolutionize not only the scale of data collected from wild systems, but 7 also the types of questions that can subsequently be answered. Incorporating 8 geographical data has already given insights, for example, into the homing behaviour of 9 reef fish, migratory patterns of birds, or the breeding site specificity of sea 10 turtles [7,17,46]. Great advances in systems biology have further been made through 11 the study of movement ecology, understanding migratory patterns of birds traversing 12 their phys...

Acquiring high resolution quantitative behavioural data underwater often involves installation of costly infrastructure, or capture and manipulation animals. Aquatic movement ecology can therefore be limited in scope of taxonomic and ecological coverage. Here we present a novel deep-learning based, multi-individual tracking approach, which incorporates Structure-from-Motion in order to determine the 3D location, body position and the visual environment of every recorded individual. The application is based on low-cost cameras and does not require the animals to be confined or handled in any way. Using this approach, single individuals, small heterospecific groups and schools of fish were tracked in freshwater and marine environments of varying complexity. Further, we established accuracy measures, resulting in positional tracking errors as low as 1.09 ± 0.47 cm (RSME) in underwater areas up to 500 m 2 . This cost-effective and open-source framework allows the analysis of animal behaviour in aquatic systems at an unprecedented resolution. Implementing this versatile approach, quantitative behavioural analysis can employed in a wide range of natural contexts, vastly expanding our potential for examining non-model systems and species.Background 1 Understanding the movement and behaviour of animals in their natural habitats is the 2 ultimate goal of behavioural and movement ecology. By situating our studies in the 3 natural world, we have the potential to uncover the processes of selection acting on the 4 behaviour in natural populations in a manner that cannot be achieved through lab 5 studies alone. The ongoing advance of animal tracking and biologging brings the 6 opportunity to revolutionize not only the scale of data collected from wild systems, but 7 also the types of questions that can subsequently be answered. Incorporating 8 geographical data has already given insights, for example, into the homing behaviour of 9 reef fish, migratory patterns of birds, or the breeding site specificity of sea 10 turtles [10,22,51]. Great advances in systems biology have further been made through 11 the study of movement ecology, understanding the decision-making processes at play 12 1/14 within primate groups manoeuvring through difficult terrain or the collective sensing of 13 birds traversing their physical environment [41,52]. Unravelling these aspects of animal 14 movement can also vastly improve management strategies [13,14], for example in the 15 creation of protected areas that incorporate bird migratory routes [48] or by reducing 16 by-catch with dynamic habitat usage models of marine turtles [37]. 17Yet the application of techniques that meet the challenges of working in naturally 18 complex environments is not straightforward, with practical, financial, and analytical 19 issues often limiting the resolution or coverage of data gathered. This is especially true 20 in aquatic ecosystems, where approaches such as Global Positioning System (GPS) tags 21 allow only sparse positioning of animals that surface more or less ...