Foraging conditions for breeding penguins improve with distance from colony and progression of the breeding season at the South Orkney Islands

Contrasting environmental conditions precluded lower availability of Antarctic krill affecting breeding chinstrap penguins in the Antarctic Peninsula

Dramatic decreases of chinstrap penguin populations across the Antarctic Peninsula (AP) are thought to be influenced by climate-driven changes affecting its main prey, the Antarctic krill, however, empirical evidence supporting such hypotheses are scarce. By coupling data on breeding chinstrap penguins, environmental remote sensing and estimates of krill acoustic density, we were able to demonstrate that penguins substantially increased their foraging effort in a year of low krill availability, with consequent reduction in breeding success. A winter of low sea ice cover followed by a summer/spring with stronger wind and lower marine productivity explained the lower and deeper krill availability. Our results highlight the importance of environmental variability on penguin populations, as variability is expected to increase under climate change, affecting foraging behaviour responses.

Predicting foraging dive outcomes in chinstrap penguins using biologging and animal-borne cameras

Direct observation of foraging behavior is not always possible, especially for marine species that hunt underwater. However, biologging and tracking devices have provided detailed information about how various species use their habitat. From these indirect observations, researchers have inferred behaviors to address a variety of research questions, including the definition of ecological niches. In this study, we deployed video cameras with GPS and time-depth recorders on 16 chinstrap penguins (Pygoscelis antarcticus) during the brood phase of the 2018–2019 breeding season on Signy (South Orkney Islands). More than 57 h of footage covering 770 dives were scrutinized by two observers. The outcome of each dive was classified as either no krill encounter, individual krill or krill swarm encounter and the number of prey items caught per dive was estimated. Other variables derived from the logging devices or from the environment were used to train a machine-learning algorithm to predict the outcome of each dive. Our results show that despite some limitations, the data collected from the footage was reliable. We also demonstrate that it was possible to accurately predict the outcome of each dive from dive and horizontal movement variables in a manner that has not been used for penguins previously. For example, our models show that a fast dive ascent rate and a high density of dives are good indicators of krill and especially of swarm encounter. Finally, we discuss how video footage can help build accurate habitat models to provide wider knowledge about predator behavior or prey distribution.