Leave or stay? Video-logger revealed foraging efficiency of humpback whales under temporal change in prey density

A method to estimate prey density from single-camera images: A case study with chinstrap penguins and Antarctic krill

Estimating the densities of marine prey observed in animal-borne video loggers when encountered by foraging predators represents an important challenge for understanding predator-prey interactions in the marine environment. We used video images collected during the foraging trip of one chinstrap penguin (Pygoscelis antarcticus) from Cape Shirreff, Livingston Island, Antarctica to develop a novel approach for estimating the density of Antarctic krill (Euphausia superba) encountered during foraging activities. Using the open-source Video and Image Analytics for a Marine Environment (VIAME), we trained a neural network model to identify video frames containing krill. Our image classifier has an overall accuracy of 73%, with a positive predictive value of 83% for prediction of frames containing krill. We then developed a method to estimate the volume of water imaged, thus the density (N·m-3) of krill, in the 2-dimensional images. The method is based on the maximum range from the camera where krill remain visibly resolvable and assumes that mean krill length is known, and that the distribution of orientation angles of krill is uniform. From 1,932 images identified as containing krill, we manually identified a subset of 124 images from across the video record that contained resolvable and unresolvable krill necessary to estimate the resolvable range and imaged volume for the video sensor. Krill swarm density encountered by the penguins ranged from 2 to 307 krill·m-3 and mean density of krill was 48 krill·m-3 (sd = 61 krill·m-3). Mean krill biomass density was 25 g·m-3. Our frame-level image classifier model and krill density estimation method provide a new approach to efficiently process video-logger data and estimate krill density from 2D imagery, providing key information on prey aggregations that may affect predator foraging performance. The approach should be directly applicable to other marine predators feeding on aggregations of prey.

Quantity over quality? Prey-field characteristics influence the foraging decisions of little penguins (Eudyptula minor)

Quantifying prey characteristics is important for understanding the foraging behaviour of predators, which ultimately influence the structure and function of entire ecosystems. However, information available on prey is often at magnitudes which cannot be used to infer the fine-scale behaviour of predators, especially so in marine environments where direct observation of predator-prey interactions is rarely possible. In the present study, animal-borne video data loggers were used to determine the influence of prey type and patch density on the foraging behaviour of the little penguin (Eudyptula minor), an important predator in southeastern Australia. We found that numerical density positively influenced time spent foraging at a patch. However, when accounting for calorific value in density estimates, individuals spent longer at dense patches of low-quality prey. This may reflect a trade-off between capture effort and calorific gain as lower quality prey were captured at higher rates. During the breeding season, foraging trip distance and duration is constrained by the need to return to the colony each day to feed offspring. The results of the study suggest that, under these spatio-temporal constraints, little penguins maximize foraging performance by concentrating efforts at larger quantities of prey, irrespective of their calorific quality.

Using an omnidirectional video logger to observe the underwater life of marine animals: Humpback whale resting behaviour

Animal-borne video loggers are powerful tools for investigating animal behaviour because they directly record immediate and extended peripheral animal activities; however, typical video loggers capture only a limited area on one side of an animal being monitored owing to their narrow field of view. Here, we investigated the resting behaviour of humpback whales using an animal-borne omnidirectional video camera combined with a behavioural data logger. In the video logger footage, two non-tagged resting individuals, which did not spread their flippers or move their flukes, were observed above a tagged animal, representing an apparent bout of group resting. During the video logger recording, the swim speed was relatively slow (0.75 m s^-1), and the tagged animal made only a few strokes of very low amplitude during drift diving. We report the drift dives as resting behaviour specific to baleen whales as same as seals, sperm whales and loggerhead turtles. Overall, our study shows that an omnidirectional video logger is a valuable tool for interpreting animal ecology with improved accuracy owing to its ability to record a wide field of view.