Behaviourally mediated predation avoidance in penguin prey: in situ evidence from animal-borne camera loggers

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.

Identifying prey capture events of a free-ranging marine predator using bio-logger data and deep learning

Marine predators are integral to the functioning of marine ecosystems, and their consumption requirements should be integrated into ecosystem-based management policies. However, estimating prey consumption in diving marine predators requires innovative methods as predator-prey interactions are rarely observable. We developed a novel method, validated by animal-borne video, that uses tri-axial acceleration and depth data to quantify prey capture rates in chinstrap penguins (Pygoscelis antarctica). These penguins are important consumers of Antarctic krill (Euphausia superba), a commercially harvested crustacean central to the Southern Ocean food web. We collected a large data set (n = 41 individuals) comprising overlapping video, accelerometer and depth data from foraging penguins. Prey captures were manually identified in videos, and those observations were used in supervised training of two deep learning neural networks (convolutional neural network (CNN) and V-Net). Although the CNN and V-Net architectures and input data pipelines differed, both trained models were able to predict prey captures from new acceleration and depth data (linear regression slope of predictions against video-observed prey captures = 1.13; R 2 ≈ 0.86). Our results illustrate that deep learning algorithms offer a means to process the large quantities of data generated by contemporary bio-logging sensors to robustly estimate prey capture events in diving marine predators.

Mechanisms of group-hunting in vertebrates

Group-hunting is ubiquitous across animal taxa and has received considerable attention in the context of its functions. By contrast much less is known about the mechanisms by which grouping predators hunt their prey. This is primarily due to a lack of experimental manipulation alongside logistical difficulties quantifying the behaviour of multiple predators at high spatiotemporal resolution as they search, select, and capture wild prey. However, the use of new remote-sensing technologies and a broadening of the focal taxa beyond apex predators provides researchers with a great opportunity to discern accurately how multiple predators hunt together and not just whether doing so provides hunters with a per capita benefit. We incorporate many ideas from collective behaviour and locomotion throughout this review to make testable predictions for future researchers and pay particular attention to the role that computer simulation can play in a feedback loop with empirical data collection. Our review of the literature showed that the breadth of predator:prey size ratios among the taxa that can be considered to hunt as a group is very large (<100 to >102 ). We therefore synthesised the literature with respect to these predator:prey ratios and found that they promoted different hunting mechanisms. Additionally, these different hunting mechanisms are also related to particular stages of the hunt (search, selection, capture) and thus we structure our review in accordance with these two factors (stage of the hunt and predator:prey size ratio). We identify several novel group-hunting mechanisms which are largely untested, particularly under field conditions, and we also highlight a range of potential study organisms that are amenable to experimental testing of these mechanisms in connection with tracking technology. We believe that a combination of new hypotheses, study systems and methodological approaches should help push the field of group-hunting in new directions.

Experimental analysis of the sweepback angle effect on the thrust generation of a robotic penguin wing

Penguins have evolved excellent swimming skills as diving birds, benefiting from their agile wings. This paper experimentally analyzes the effects of the wing sweepback angle on thrust generation using a robotic penguin wing. A developed wing mechanism that can realize penguin-like flapping and feathering motion was used for actuating five alternative wing models, with different sweepback angles ranging from 0° to 50°. Force measurements under a steady water flow were conducted for both fixed and flapping states for all wing models. The results showed that small sweepback angles of 30° or less in the fixed state caused a steep lift curve and a moderate sweepback angle of 30° produced the largest lift-to-drag ratio. In the flapping state, the smaller sweepback wings generated a larger net thrust for the same wing motion, whereas the larger-sweepback wings produced more thrust under the same Strouhal number. The findings also revealed that larger sweepback wings more easily achieve the maximum net thrust in terms of less angle-of-attack control. In contrast, the hydrodynamic efficiency was not greatly affected by the sweepback. Regardless of the sweepback, the trend of the efficiency increasing with increasing flow speed indicates that the penguin wings can be more suitable for high-speed locomotion for higher hydrodynamic efficiency.

Variability in prey field structure drives inter-annual differences in prey encounter by a marine predator, the little penguin

Understanding how marine predators encounter prey across patchy landscapes remains challenging due to difficulties in measuring the three-dimensional structure of pelagic prey fields at scales relevant to animal movement. We measured at-sea behaviour of a central-place forager, the little penguin (Eudyptula minor), over 5 years (2015-2019) using GPS and dive loggers. We made contemporaneous measurements of the prey field within the penguins' foraging range via boat-based acoustic surveys. We developed a prey encounter index by comparing estimates of acoustic prey density encountered along actual penguin tracks to those encountered along simulated penguin tracks with the same characteristics as real tracks but that moved randomly through the prey field. In most years, penguin tracks encountered prey better than simulated random movements greater than 99% of the time, and penguin dive depths matched peaks in the vertical distribution of prey. However, when prey was unusually sparse and/or deep, penguins had worse than random prey encounter indices, exhibited dives that mismatched depth of maximum prey density, and females had abnormally low body mass (5.3% lower than average). Reductions in prey encounters owing to decreases in the density or accessibility of prey may ultimately lead to reduced fitness and population declines in central-place foraging marine predators.

Fluid drag reduction by penguin-mimetic laser-ablated riblets with yaw angles

The bodies of penguins, which swim underwater to forage, are densely covered with feathers, in which the barbs are oriented in the longitudinal direction. We hypothesize that these barbs act as riblets and reduce friction drag during swimming. Considering various real-world swim conditions, the drag reduction effect is expected to be robust against changes in the flow speed and yaw angle relative to the flow. To test this hypothesis, we created trapezoidal riblets based on the morphology of these barbs and measured the drag of flat plates with these fabricated riblets in a water tunnel. The spacing, width, and height of the barbs were found to be approximately 100, 60, and 30 μm, respectively. This spacing resulted in a nondimensional spacings⁺of 5.5 for a typical penguin swimming speed of 1.4 m s^-1. We fabricated four types of riblets on polyimide films by ultraviolet laser ablation. The first was a small-scale riblet for which the spacing was decreased to 41 μm to simulate the surface flow condition of the usual and slower swim behaviors in our water tunnel. The other three were manufactured to the actual scale of real barbs (spacing of 100 μm) with three different rib ridge widths: 10, 25, and 50 μm. Yaw angles of 0°, 15°, 30°, and 45° were also tested with the actual-scale riblets. The drag reduction rate of the small-scale riblet was maximized to 1.97% by the smallests⁺of 1.59. For all three actual-scale riblets, increasing the yaw angle from zero to 15° enhanced the drag reduction rate for the full range ofs⁺up to 13.5. The narrow-ridge riblet reduced drag at an even higher yaw angle of 45°, but the drag increased with zero yaw angle. Overall, the medium-ridge riblet, which was representative of the barbs, was well-balanced.

Multi-predator assemblages, dive type, bathymetry and sex influence foraging success and efficiency in African penguins

Marine predators adapt their hunting techniques to locate and capture prey in response to their surrounding environment. However, little is known about how certain strategies influence foraging success and efficiency. Due to the miniaturisation of animal tracking technologies, a single individual can be equipped with multiple data loggers to obtain multi-scale tracking information. With the addition of animal-borne video data loggers, it is possible to provide context-specific information for movement data obtained over the video recording periods. Through a combination of video data loggers, accelerometers, GPS and depth recorders, this study investigated the influence of habitat, sex and the presence of other predators on the foraging success and efficiency of the endangered African penguin, Spheniscus demersus, from two colonies in Algoa Bay, South Africa. Due to limitations in the battery life of video data loggers, a machine learning model was developed to detect prey captures across full foraging trips. The model was validated using prey capture signals detected in concurrently recording accelerometers and animal-borne cameras and was then applied to detect prey captures throughout the full foraging trip of each individual. Using GPS and bathymetry information to inform the position of dives, individuals were observed to perform both pelagic and benthic diving behaviour. Females were generally more successful on pelagic dives than males, suggesting a trade-off between manoeuvrability and physiological diving capacity. By contrast, males were more successful in benthic dives, at least for Bird Island (BI) birds, possibly due to their larger size compared to females, allowing them to exploit habitat deeper and for longer durations. Both males at BI and both sexes at St Croix (SC) exhibited similar benthic success rates. This may be due to the comparatively shallower seafloor around SC, which could increase the likelihood of females capturing prey on benthic dives. Observation of camera data indicated individuals regularly foraged with a range of other predators including penguins and other seabirds, predatory fish (sharks and tuna) and whales. The presence of other seabirds increased individual foraging success, while predatory fish reduced it, indicating competitive exclusion by larger heterospecifics. This study highlights novel benthic foraging strategies in African penguins and suggests that individuals could buffer the effects of changes to prey availability in response to climate change. Furthermore, although group foraging was prevalent in the present study, its influence on foraging success depends largely on the type of heterospecifics present.

First evidence of underwater vocalisations in hunting penguins

Seabirds are highly vocal on land where acoustic communication plays a crucial role in reproduction. Yet, seabirds spend most of their life at sea. They have developed a number of morphological, physiological and behavioural adaptations to forage in the marine environment. The use of acoustic signals at sea could potentially enhance seabirds’ foraging success, but remains largely unexplored. Penguins emit vocalisations from the sea surface when commuting, a behaviour possibly associated with group formation at sea. Still, they are unique in their exceptional diving abilities and feed entirely underwater. Other air-breathing marine predators that feed under water, like cetaceans, pinnipeds and marine turtles, are known to emit sound underwater, but such behaviour has not yet been described in seabirds. We aimed to assess the potential prevalence and diversity of vocalisations emitted underwater by penguins. We chose three study species from three different genera, and equipped foraging adults with video cameras with built-in microphones. We recorded a total of 203 underwater vocalisation from all three species during 4 h 43 min of underwater footage. Vocalisations were very short in duration (0.06 s on average), with a frequency of maximum amplitude averaging 998 Hz, 1097 Hz and 680 Hz for King, Gentoo and Macaroni penguins, respectively. All vocalisations were emitted during feeding dives and more than 50% of them were directly associated with hunting behaviour, preceeded by an acceleration (by 2.2 s on average) and/or followed by a prey capture attempt (after 0.12 s on average). The function of these vocalisations remain speculative. Although it seems to be related to hunting behaviour, these novel observations warrant further investigation.