Predicting foraging dive outcomes in chinstrap penguins using biologging and animal-borne cameras. (9th July 2022)

• Record Type: Journal Article
• Authors: Manco, Fabrizio; Lang, Stephen D J; Trathan, Philip N
• Editors: Buston, Peter
• Is Part Of: Behavioral ecology. Volume 33:Number 5(2022)
• Page Start: 989
• Page End: 998
• Publication Date: 2022-07-09
• Subjects: biologging -- chinstrap penguins -- foraging ecology -- movement ecology -- Pygoscelis antarcticus -- random forest; Animal behavior -- Periodicals; Behavior evolution -- Periodicals; Ecology -- Periodicals; Psychology, Comparative -- Periodicals; 591.5
• DOI: 10.1093/beheco/arac066
• Languages: English
• ISSNs: 1045-2249

Abstract

Abstract: 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. Abstract : Animal-borne cameras offer a penguin's-eye view of foraging dives, allowing the prediction of successful prey encounter. Deploying both video cameras and tracking devices on the same individuals provides unique data about the foraging behavior of this marine predator. From the footage, we are able to use both the vertical and horizontal movement of the animals to train a machine-learning model that can accurately predict the outcome of each foraging dive.