Report | December 31, 2025
Artificial intelligence as a tool for bionomic transects: the case of Isidella elongata (Esper, 1788) forests in the Western Mediterranean
Abstract
Deep-sea ecosystems are among the least explored on Earth, and traditional sampling methods often underestimate fragile megabenthic species such as gorgonians. As a result, the use of underwater vehicles for visual surveys has increased considerably. This study combines underwater video surveys and artificial intelligence (AI) to characterize the distribution and density of the bamboo coral Isidella elongata, a habitat forming species in the southeastern Iberian margin. Ten video transects obtained with a remotely operated towed vehicle (ROTV) between 300 and 725 m depth were analyzed using a YOLOv8-based detection algorithm trained on 983 manually annotated frames. Manual counting identified 2,237 colonies (2,347 ind/ha on average), revealing dense aggregations in the Cartagena Canyon and Seco de Palos seamount. AI-based detection achieved overall satisfactory performance, reproducing spatial patterns and colony size structure. However, it tended to overestimate large colonies and underestimate small ones, depending on image quality, specimen size, and environmental complexity. Although this methodology is not perfect, it provides a robust exploration tool for rapid localization and quantification of I. elongata meadows, greatly reducing video processing time. The integration of automated detection algorithms and standardized image databases is expected to enhance future deep-sea monitoring efforts. This work provides the first quantitative assessment of I. elongata populations in the southeastern Spanish margin, highlighting the potential of AI for the study and conservation of Vulnerable Marine Ecosystems (VMEs) in the Mediterranean Sea.
