摘要The study of parental food provisioning is essential for understanding the breeding ecology of birds.We con-ducted the first study using accelerometry to detect food provisioning in birds,using Support Vector Machine(SVM)models to identify when adults feed chicks of three different age classes.Accelerometers were attached to the head of adult female Imperial Shags(Leucocarbo atriceps),and various attributes derived from the acceler-ation signals were used to train SVM models for each chick age class.Model performance improved with chick age class,with SVM models achieving high overall accuracy(＞88％)and highest sensitivity in older chick categories(＞91％).However,precision values,especially for younger chicks,remained relatively low(between 26％and 45％).The application of a time filter based on the minimum duration of the observed food provisioning behaviours for each chick age category,improved model performance by reducing false provisioning behaviours,particularly in the model for older chicks,which showed the highest precision(72.4％).This study highlights the effectiveness of accelerometry and machine learning in studying parental food provisioning in birds,providing a rapid and accurate data collection method to complement traditional techniques.The described methodology can be applied to any bird species that exhibits distinctive movements while feeding its offspring and has suitable characteristics for attaching an accelerometer to the body part that best captures this movement.Finally,it is hoped that the results of this study will contribute to future research on key questions in parental investment theory and reproductive strategies in birds.