High-resolution, non-invasive animal tracking and reconstruction of local environment in aquatic ecosystems

Abstract

Abstract Background Acquiring 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. Methods 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, manipulated, or handled in any way. Results Using 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. Conclusions 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 be employed in a wide range of natural contexts, vastly expanding our potential for examining non-model systems and species.

Keywords

• 3D tracking
• Collective behaviour
• Aquatic ecosystems
• Computer vision
• Structure from motion
• Machine learning