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 below the surface. However, biologging and
tracking devices in particular have provided very detailed information
about how various species use their habitat. From these indirect
observations, researchers have tried to infer foraging and prey catching
events for a more accurate definition of these species’ ecological niches.
In this study, we deployed video cameras in addition to GPS and time-depth
recorders on chinstrap penguins during the brood phase of the 2018-19
breeding season at various colonies on the Gourlay peninsula (South Orkney
Islands). More than 57 hours of footage from 16 birds covering 770 dives
were scrutinized by two independent observers. The outcome of each dive
was classified as unsuccessful, individual krill encounter or krill swarm
encounter. In addition, the number of prey items caught was recorded for
successful dives. We then used various predicting variables derived from
the other logging devices or from the environment 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 as there was a high agreement from both annotators.
We also demonstrate that it was possible to accurately predict the outcome
of each dive from basic dive patterns and horizontal movement
characteristics that have not been used for penguins previously. Finally,
we discuss how video footage can help build more accurate habitat models
and gain wider knowledge about predator behavior or prey distribution.