The capacity to monitor shark biodiversity is vital for conservation. Baited Remote Underwater Video Systems (BRUVS) are an increasingly popular and cost-effective method to characterize their biodiversity, abundance, and distribution. However, BRUV monitoring remains hindered by extensive manual video annotation and a lack of accessible tools to generate datasets for ecological analysis.To overcome these barriers, we developed a suite of automatic tools for shark detection and taxonomic classification. We integrated an object detection model (SharkTrack) with hierarchical species classification (Shark Detector) trained on 264,712 images of 309 shark species, in two open platforms—an R package (sharkDetectoR) and a lightweight desktop application (SharkByte)—making the updated model package an accessible tool across computational environments and user expertise.Tested on 46,332 holdout images and 13.9 hours of BRUV footage from the Main Hawaiian Islands and Palau, these tools achieved 92% species-level accuracy as well as 94.7% detection accuracy, and reduced annotation time by up to 90%.These findings underscore the value of combining scalable artificial intelligence to advance shark biodiversity monitoring. By substantially reducing manual annotation demands while maintaining high detection and classification performance, this framework offers a practical solution to speed up BRUV post-processing and filtering of digital observations. At the same time, it demonstrates automated methods as crucial tools for conservation and management, especially to address species-specific data deficiencies in shark research.

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 ($T_{1^{st}}$), and Visit Duration ($T_{visit}$). 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.