Animals are assumed to obtain/conserve energy effectively to maximise their fitness, which manifests itself in a variety of behavioral strategies. For marine animals, however, these behavioral strategies are generally unknown due to the lack of high-resolution monitoring techniques in marine habitats. As large marine herbivores, immature green turtles do not need to allocate energy to reproduction but are at risk of shark predation, although it is a rare occurrence. They are therefore assumed to select/use feeding and resting sites that maximise their fitness in terms of somatic growth, while avoiding predation. We investigated fine-scale behavioral patterns (feeding, resting and other behaviors), microhabitat use and time spent on each behavior for eight immature green turtles using data loggers including: depth, global positioning system, head acceleration, speed and video sensors. Immature green turtles at Iriomote Island, Japan, spent an average of 4.8 h feeding on seagrass each day, with two peaks, between 5∶00 and 9∶00, and between 17∶00 and 20∶00. This feeding pattern appeared to be restricted by gut capacity, and thus maximised energy acquisition. Meanwhile, most of the remaining time was spent resting at locations close to feeding grounds, which allowed turtles to conserve energy spent travelling and reduced the duration of periods exposed to predation. These behavioral patterns and time allocations allow immature green turtles to effectively obtain/conserve energy for growth, thus maximising their fitness.

Air-breathing divers are assumed to have evolved to apportion their time between surface and underwater periods to maximize the benefit gained from diving activities. However, whether they change their time allocation depending on the aim of the dive is still unknown. This may be particularly crucial for 'surfacers' because they dive for various purposes in addition to foraging. In this study, we counted breath events at the surface and estimated oxygen consumption during resting, foraging and other dives in 11 green turtles (Chelonia mydas) in the wild. Breath events were counted by a headmounted acceleration logger or direct observation based on an animal-borne video logger, and oxygen consumption was estimated by measuring overall dynamic body acceleration. Our results indicate that green turtles maximized their submerged time, following this with five to seven breaths to replenish oxygen for resting dives. However, they changed their dive tactic during foraging and other dives; they surfaced without depleting their estimated stores of oxygen, followed by only a few breaths for effective foraging and locomotion. These dichotomous surfacing tactics would be the result of behavioural modifications by turtles depending on the aim of each dive.

Background: An animal-borne video recording system has recently been developed to study the behavior of freeranging animals. In contrast to other types of sensor data (i.e., acceleration), video images offer the advantage of directly acquiring information without analysis. However, most previous findings have only been obtained through visual observation of image data. Here, we demonstrate a new method of data analysis for animal-borne videos using a computer vision technique referred to as template matching. As a case study, we tracked the horizontal head movements of green turtles (Chelonia mydas) to investigate how they move their heads to look around the underwater environment. Results:Template matching allowed tracking of head movements with high accuracy (0.34 ± 0.12 % and 0.52 ± 0.29 % of the root-mean-square error on the x-and y-coordinates, respectively), high true (86.2 ± 8.1 %), and low false extraction rates (6.6 ± 8.4 %). However the program sometimes failed because the turtle's head would move out of range of the video. During cruising swimming, green turtles did not significantly move their heads to one side, moving with a ratio of 50.5:49.5 (left: right). Green turtles moved their heads from side to side more widely and more slowly before (12.0 ± 4.6 point and 0.25 ± 0.03 Hz, respectively) and after taking a breath (27.5 ± 2.9 point and 0.27 ± 0.03 Hz) compared to during cruising swimming (8.4 ± 3.8 point and 0.32 ± 0.01 Hz). Before feeding, turtles moved their heads slowly (0.23 ± 0.03 Hz) and narrowly (9.3 ± 3.6 point). Our combined approach using video and gyro loggers revealed that when making a turn, turtles always turned their heads to the side 1.38 ± 0.77 s before turning their body. Conclusions:Our method enables researchers to quantitatively extract information regarding vision cognition and behavioral responses in green turtles in the wild that could not otherwise be obtained from other sensors used in previous studies. This new method using a combination of computer vision and bio-logging (e.g., gyroscope) can serve as a powerful tool in animal behavior and ecological studies.

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