Automated quantification of ecological interactions from video

Ecological interactions, such as predation, are fundamental events that underlie the flow and distribution of energy through food webs. Yet, directly measuring interaction rates in nature and how they vary across space and time remains a core challenge in ecology. To address this, we developed a machine learning pipeline that combines object detection, tracking, behavioral classification, and bias correction to quantify feeding rates of the planktivorous reef fish Chromis multilineata in situ . We show that the pipeline generates precise, unbiased estimates of planktivory at fine temporal and spatial scales, and use it to reveal how feeding rate changes in response to predator presence and proximity to refuge. While developed and tested in the context of planktivory, we expect this approach can be adapted to quantify a wider array of ecological interactions in situ .