Animal-borne soundscape logger as a system for edge classification of sound sources and data transmission for monitoring near-real-time underwater soundscape.

The underwater environment is filled with various sounds, with its soundscape composed of biological, geographical, and anthropological sounds. Our work focused on developing a novel method to observe and classify these sounds, enriching our understanding of the underwater ecosystem. We constructed a biologging system allowing near-real-time observation of underwater soundscapes. Utilizing deep-learning-based edge processing, this system classifies the sources of sounds, and upon the tagged animal surfacing, it transmits positional data, results of sound source classification, and sensor readings such as depth and temperature. To test the system, we attached the logger to sea turtles (Chelonia mydas) and collected data through a cellular network. The data provided information on the location-specific sounds detected by the sea turtles, suggesting the possibility to infer the distribution of specific species of organisms over time. The data showed that not only biological sounds but also geographical and anthropological sounds can be classified, highlighting the potential for conducting multi-point and long-term observations to monitor the distribution patterns of various sound sources. This system, which can be considered an autonomous mobile platform for oceanographic observations, including soundscapes, has significant potential to enhance our understanding of acoustic diversity.

Migration, residency and habitat utilisation by wild and cultured Japanese eels (Anguilla japonica) in a shallow brackish lagoon and inflowing rivers using acoustic telemetry.

This study monitored post-release movements of 20 wild Japanese eels (Anguilla japonica) [mean ± S.D. 520.8 ± 92.3 mm total length (TL), 217.9 ± 146.3 g body mass (BM)] in a brackish water lagoon in northeastern Japan using acoustic telemetry to elucidate how wild Japanese eels use different river, estuary and marine environments. In addition, 12 cultured Japanese eels (TL = 578.9 ± 18.0 mm, BM = 344.9 ± 25.5 g) were released to understand the comparative behaviours of wild and cultured eels. Both types of eels were simultaneously released in the southern inner part of the lagoon in September 2016 where there are freshwater influences from a river. Following release, eight of the wild eels (40%) were largely sedentary near the released point (river mouth) and stayed at the site for overwinter. Nonetheless, several individuals showed behavioural plasticity of habitat use: three wild eels moved towards the northern part of the lagoon with stronger influence from the sea during May-July 2017. Two wild eels showed clear repeated movements from the lagoon to a river at night and returned to the lagoon by dawn for more than a week every day, and one wild eel migrated upstream for overwintering. Signals from 55% of the wild eels could be detected for more than 6 months, whereas those from all of the cultured eels were lost by December 2016, indicating a short resident time of large cultured eels (BM > 200 g) released in a brackish water area. One wild silver eel migrated to the outer sea during the ebb tide at night in November 2016, probably triggered by the decrease in water temperature (from c. 20°C to c. 13°C), and seven cultured eels similarly moved to the outer sea during October-November 2016. The results revealed the similarities (e.g., nocturnal movements) and differences (e.g., stay period and seasonal movements) in the behavioural characteristics of wild and cultured eels and indicated that habitat connectivity among river, estuary and coastal waters is crucial for enabling eels to efficiently utilise these productive habitats through their behavioural plasticity.

The influence of body size on the intermittent locomotion of a pelagic schooling fish.

There is a potential trade-off between grouping and the optimizing of the energetic efficiency of individual locomotion. Although intermittent locomotion, e.g. glide and upward swimming (GAU), can reduce the cost of locomotion at the individual level, the link between the optimization of individual intermittent locomotion and the behavioural synchronization in a group, especially among members with different sizes, is unknown. Here, we continuously monitored the schooling behaviour of a negatively buoyant fish, Pacific bluefin tuna (N = 10; 21.0 ∼ 24.5 cm), for 24 h in an open-sea net cage using accelerometry. All the fish repeated GAU during the recording periods. Although the GAU synchrony was maintained at high levels (overall mean = 0.62 for the cross-correlation coefficient of the GAU timings), larger fish glided for a longer duration per glide and more frequently than smaller fish. Similar-sized pairs showed significantly higher GAU synchrony than differently sized pairs. Our accelerometry results and the simulation based on hydrodynamic theory indicated that the advantage of intermittent locomotion in energy savings may not be fully optimized for smaller animals in a group when faced with the maintenance of group cohesion, suggesting that size assortative shoaling would be advantageous.

Not So Fast: Swimming Behavior of Sailfish during Predator-Prey Interactions using High-Speed Video and Accelerometry.

Billfishes are considered among the fastest swimmers in the oceans. Despite early estimates of extremely high speeds, more recent work showed that these predators (e.g., blue marlin) spend most of their time swimming slowly, rarely exceeding 2 m s(-1). Predator-prey interactions provide a context within which one may expect maximal speeds both by predators and prey. Beyond speed, however, an important component determining the outcome of predator-prey encounters is unsteady swimming (i.e., turning and accelerating). Although large predators are faster than their small prey, the latter show higher performance in unsteady swimming. To contrast the evading behaviors of their highly maneuverable prey, sailfish and other large aquatic predators possess morphological adaptations, such as elongated bills, which can be moved more rapidly than the whole body itself, facilitating capture of the prey. Therefore, it is an open question whether such supposedly very fast swimmers do use high-speed bursts when feeding on evasive prey, in addition to using their bill for slashing prey. Here, we measured the swimming behavior of sailfish by using high-frequency accelerometry and high-speed video observations during predator-prey interactions. These measurements allowed analyses of tail beat frequencies to estimate swimming speeds. Our results suggest that sailfish burst at speeds of about 7 m s(-1) and do not exceed swimming speeds of 10 m s(-1) during predator-prey interactions. These speeds are much lower than previous estimates. In addition, the oscillations of the bill during swimming with, and without, extension of the dorsal fin (i.e., the sail) were measured. We suggest that extension of the dorsal fin may allow sailfish to improve the control of the bill and minimize its yaw, hence preventing disturbance of the prey. Therefore, sailfish, like other large predators, may rely mainly on accuracy of movement and the use of the extensions of their bodies, rather than resorting to top speeds when hunting evasive prey.

Use of a gyroscope/accelerometer data logger to identify alternative feeding behaviours in fish.

We examined whether we could identify the feeding behaviours of the trophic generalist fish Epinephelus ongus on different prey types (crabs and fish) using a data logger that incorporated a three-axis gyroscope and a three-axis accelerometer. Feeding behaviours and other burst behaviours, including escape responses, intraspecific interactions and routine movements, were recorded from six E. ongus individuals using data loggers sampling at 200 Hz, and were validated by simultaneously recorded video images. For each data-logger record, we extracted 5 s of data when any of the three-axis accelerations exceeded absolute 2.0 g, to capture all feeding behaviours and other burst behaviours. Each feeding behaviour was then identified using a combination of parameters that were derived from the extracted data. Using decision trees with the parameters, high true identification rates (87.5% for both feeding behaviours) with low false identification rates (5% for crab-eating and 6.3% for fish-eating) were achieved for both feeding behaviours.

Monitoring escape and feeding behaviours of cruiser fish by inertial and magnetic sensors.

A method was developed and applied for monitoring two types of fast-start locomotion (feeding and escape) of a cruiser fish, Japanese amberjacks Seriola quinqueradiata. A data logger, which incorporated a 3-axis gyroscope, a 3-axis accelerometer and a 3-axis magnetometer, was attached to the five fish. The escape, feeding and routine movements of the fish, which were triggered in tank experiments, were then recorded by the data logger and video cameras. The locomotor variables, calculated based on the high resolution measurements by the data logger (500 Hz), were investigated to accurately detect and classify the types of fast-track behaviour. The results show that fast-start locomotion can be detected with a high precision (0.97) and recall rate (0.96) from the routine movements. Two types of fast-start movements were classified with high accuracy (0.84). Accuracy was greater if the data were obtained from the data logger, which combined an accelerometer, a gyroscope and a magnetometer, than if only an accelerometer (0.80) or a gyroscope (0.66) was used.

Ethogram of Immature Green Turtles: Behavioral Strategies for Somatic Growth in Large Marine Herbivores.

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.

Accelerometer tags: detecting and identifying activities in fish and the effect of sampling frequency.

Monitoring and measuring the behaviour and movement of aquatic animals in the wild is typically challenging, though micro-accelerometer (archival or telemetry) tags now provide the means to remotely identify and quantify behavioural states and rates such as resting, swimming and migrating, and to estimate activity and energy budgets. Most studies use low-frequency (≤32 Hz) accelerometer sampling because of battery and data-archiving constraints. In this study we assessed the effect of sampling frequency (aliasing) on activity detection probability using the great sculpin (Myoxocephalus polyacanthoceaphalus) as a model species. Feeding strikes and escape responses (fast-start activities) and spontaneous movements among seven different great sculpin were triggered, observed and recorded using video records and a tri-axial accelerometer sampling at 100 Hz. We demonstrate that multiple parameters in the time and probability domains can statistically differentiate between activities with high detection (90%) and identification (80%) probabilities. Detection probability for feeding and escape activities decreased by 50% when sampling at <10 Hz. Our analyses illustrate additional problems associated with aliasing and how activity and energy-budget estimates can be compromised and misinterpreted. We recommend that high-frequency (>30 Hz) accelerometer sampling be used in similar laboratory and field studies. If battery and/or data storage is limited, we also recommend archiving the events via an on-board algorithm that determines the highest likelihood and subsequent archiving of the various event classes of interest.