Abstract
How environmental features (e.g., people, enrichment, or other animals) affect movement is an important element for the study of animal behavior, biomechanics, and welfare. Here we present a stationary overhead camera-based persistent monitoring framework for the investigation of bottlenose dolphin (Tursiops truncatus) response to environmental stimuli. Mask R-CNN, a convolutional neural network architecture, was trained to automatically detect 3 object types in the environment: dolphins, people, and enrichment floats that were introduced to stimulate and engage the animals. Detected objects within each video frame were linked together to create track segments across frames. The animals’ tracks were used to parameterize their response to the presence of environmental stimuli. We collected and analyzed data from 24 sessions from bottlenose dolphins in a managed lagoon environment. The seasons had an average duration of 1 h and around half of them had enrichment (42%) while the rest (58%) did not. People were visible in the environment for 18.8% of the total time (∼4.5 h), more often when enrichment was present (∼3 h) than without (∼1.5 h). When neither enrichment nor people were present, the animals swam at an average speed of 1.2 m/s. When enrichment was added to the lagoon, average swimming speed decreased to 1.0 m/s and the animals spent more time moving at slow speeds around the enrichment. Animals’ engagement with the enrichment also decreased over time. These results indicate that the presence of enrichment and people in, or around, the environment attracts the animals, influencing habitat use and movement patterns as a result. This work demonstrates the ability of the proposed framework for the quantification and persistent monitoring of bottlenose dolphins’ movement, and will enable new studies to investigate individual and group animal locomotion and behavior.
Highlights
Features in an animal’s environment have the potential to influence behavior, biomechanics and movement patterns during daily life
The pattern of movement does vary between conditions, with a less defined center of rotation when enrichment or people were present
Mean speed during the baseline condition (1.2 m/s) was higher than when either enrichment or people were present in the environment
Summary
Features in an animal’s environment have the potential to influence behavior, biomechanics and movement patterns during daily life. For animals in managed settings, monitoring movement patterns to inform welfare practices is essential [1,2,3]. Behavior and movement patterns in response to common or long-term events in the surrounding environment can be predictable and identifiable [4,5,6]. Have shown that fixed feeding times during the day can result in stable and predictable behavior in stump-tailed macaques under human care. Previous animal studies have investigated how enrichment and human interaction affect dolphin behavior in managed settings with trained observers monitoring and recording behavior [1,8]. Trained researchers directly observe and manually record animal behavior, but this approach can be time consuming and is limited to when the animals are visible to the observer [1,8]
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