Abstract
BackgroundAcquiring high resolution quantitative behavioural data underwater often involves installation of costly infrastructure, or capture and manipulation of animals. Aquatic movement ecology can therefore be limited in taxonomic range and ecological coverage.MethodsHere 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, manipulated, or handled in any way.ResultsUsing this approach, single individuals, small heterospecific groups and schools of fish were tracked in freshwater and marine environments of varying complexity. Positional tracking errors as low as 1.09 ± 0.47 cm (RSME) in underwater areas up to 500 m2 were recorded.ConclusionsThis 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 be employed in a wide range of natural contexts, vastly expanding our potential for examining non-model systems and species.
Highlights
Acquiring high resolution quantitative behavioural data underwater often involves installation of costly infrastructure, or capture and manipulation of animals
The application of techniques that meet the challenges of working in naturally complex environments is not straightforward, with practical, financial, and analytical issues often limiting the resolution or coverage of data gathered. This is especially true in aquatic ecosystems, where approaches such as Global Positioning System (GPS) tags allow only sparse positioning of animals that surface intermittently, or Pop-up Satellite Archival Tags (PSATs) which integrate surface positions with logged gyroscope and accelerometer data to estimate movement of larger aquatic animals [10, 11]
Object annotation and detection to the final triangulation of animal trajectories, we provide a set of open-source utilities and scripts
Summary
Acquiring high resolution quantitative behavioural data underwater often involves installation of costly infrastructure, or capture and manipulation of animals. The application of techniques that meet the challenges of working in naturally complex environments is not straightforward, with practical, financial, and analytical issues often limiting the resolution or coverage of data gathered This is especially true in aquatic ecosystems, where approaches such as Global Positioning System (GPS) tags allow only sparse positioning of animals that surface intermittently, or Pop-up Satellite Archival Tags (PSATs) which integrate surface positions with logged gyroscope and accelerometer data to estimate movement of larger aquatic animals [10, 11]. Does the spatial resolution of respective tracking systems, e.g. currently 4.9 m for GPS, limit the possibilities of behavioural analyses on a fine scale, and excludes almost all animals below a certain size class [12] These methods require animals to be captured and equipped with tags that should not exceed 5% of the animals weight [13], further limiting current generation GPS and PSATs to larger animals. Approaches that facilitate collection of behavioural data in smaller animals, those in large groups, and those in varied aquatic habitats, are still lacking
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