Lauderdale, L. | bioRxiv Science

bioRxiv2022

Dynamics and Energetics of Bottlenose Dolphins (Tursiops truncatus) Fluke-and-Glide Gait

Intermittent locomotion composed of periods of active flapping/stroking followed by inactive gliding has been observed with species that inhabit both aerial and marine environments. However, studies on the energetic benefits of a fluke-and-glide (FG) gait during horizontal locomotion are limited for dolphins. This work presents a physics-based model of FG gait and analysis of the associated costs of transport of bottlenose dolphins (Tursiops truncatus). New estimates of gliding drag coefficients for the model were estimated experimentally from free-swimming bottlenose dolphins. The data-driven approach used kinematic measurement from 84 hours of biologging tag data collected from 3 animals to estimate the coefficients. A set of 532 qualified gliding events were automatically extracted for gliding drag coefficient estimation, and an additional 783 FG bouts were parameterized and used to inform the model-based dynamic analysis. Experimental results indicate that FG gait was preferred at speeds around 2.2 - 2.7 m/s. Observed FG bouts had an average duty factor of 0.45 and gliding duration of 5 sec. The average associated metabolic cost of transport (COT) and mechanical cost of transport (MECOT) of FG gait are 2.53 and 0.35 J {middle dot} m-1 {middle dot} kg-1 at the preferred speeds. This corresponded to an 18.9% and 27.1% reduction in cost when compared to modeled continuous fluking gait at the same reference speed. Average thrust was positively correlated with fluking frequency and amplitude as animals accelerated during the FG bouts. While fluking frequency and amplitude were negatively correlated for a given thrust range. These results support the supposition that FG gait enhances the horizontal swimming efficiency of bottlenose dolphins and provides new dynamical insights into the gait of these animals.

animal behavior and cognition↗

bioRxiv2021

Vision-based monitoring and measurement of bottlenose dolphins' daily habitat use and kinematics

This research presents a framework to enable computer-automated observation and monitoring of bottlenose dolphins (Tursiops truncatus) in a professionally managed environment. Results from this work provide insight into the dolphins movement patterns, kinematic diversity, and how changes in the environment affect their dynamics. Fixed overhead cameras were used to collect [~]100 hours of observations, recorded over multiple days including time both during and outside of formal training sessions. Animal locations were estimated using convolutional neural network (CNN) object detectors and Kalman filter post-processing. The resulting animal tracks were used to quantify habitat use and animal dynamics. Additionally, Kolmogorov-Smirnov analyses of the swimming kinematics were used for high-level behavioral mode classification. The detectors achieved a minimum Average Precision of 0.76. Performing detections and post-processing yielded 1.24x107 estimated dolphin locations. Animal kinematic diversity was found to be lowest in the morning and peaked immediately before noon. Regions of the habitat displaying the highest activity levels correlated to locations associated with animal care specialists, conspecifics, or enrichment. The work presented here demonstrates that CNN object detection is not only viable for large-scale marine mammal tracking, it also enables automated analyses of dynamics that provide new insight into animal movement and behavior.

animal behavior and cognition↗