Combining Deep Learning and Ecological Monitoring for BRUV Coral Reef Megafauna Assessment

Abstract

Coral reef ecosystems are increasingly threatened by climate change, pollution, and overfishing, causing major declines in marine megafauna and high-trophic-level fishes. Monitoring these species is vital for conservation, yet traditional survey methods are slow and resource intensive. This study presents a semi-automated monitoring pipeline that integrates deep learning (DL) with human-in-the-loop validation to streamline Baited Remote Underwater Video (BRUV) analyses in the Gita Nada Marine Protected Area (MPA), Indonesia. A total of 244 BRUV deployments from SORCE’s long-term monitoring program in the Gita Nada MPA, comprising 328 h of footage, collected 2023–2025 under Indonesian research oversight through Yayasan SORCE Konservasi Indonesia, were processed using a DL workflow. To address long-tailed species distributions, focal taxa were grouped into six Morphological Groups and detected using a YOLOv12x model trained via transfer learning from the Community Fish Detector. A custom temporal-tracking framework extracted ecological metrics including N, Time to First Visit (T1st), and Visit Duration (Tvisit). The pipeline achieved moderate to high detection and tracking performance for several Morphological Groups, achieving object detection F1-scores of up to 0.873 and an overall tracker recall and precision of 0.80 and 0.76, respectively, although performance varied substantially among groups and was substantially limited for data-deficient taxa. As a proof-of-concept, we applied the framework to assess ecological shifts in Cheloniidae and Carangidae across coral-cover gradients. Overall, this semi-automated approach reduces BRUV processing effort and provides a scalable foundation for generating the large datasets needed to detect subtle ecological change.