Alex Hoi Hang Chan, Liu Jing Qi, Terry Burke, Will Pearse, Julia Schroeder

Measuring parental care behaviour in the wild is central to the study of animal life-history trade-offs, but is often labour and time-intensive. More efficient machine learning-based video processing tools have recently emerged that allow parental nest visit rates to be measured using video cameras and computer processing. Here, we used open-source software to detect movement events from videos taken at the nest box of a wild passerine bird population. We show that visit numbers from our automatic data collection pipeline strongly correlate with human observations and predicts an increase in brood fitness. Using a machine learning-assisted annotation approach on a subset of 18 videos, we show that the accuracy largely increased and cut annotation time by an average of 5.5x compared to that of a cohort of undergraduate students. Since our automatic pipeline collected biological-meaningful data that would have taken approximately 800 days by human observers, we encourage more researchers to apply existing open-source tools to assist data collection in animal behaviour studies.