Deep learning-assisted high resolution mapping of vulnerable habitats within the Capbreton Canyon System, Bay of Biscay

The Capbreton Canyon System is an area currently under study for its proposal as a Site of Community Importance under the EU Habitats Directive in the context of the LIFE IP INTEMARES project. Identifying and mapping benthic Vulnerable Marine Ecosystems (VMEs) plays a key role in this process. Although obtaining information on species distribution in deep sea rocky habitats has traditionally been a complicated task, the development of underwater remote sensing techniques resulted in a massive increase in the collection of digital imagery; however, processing all this information has led to another bottleneck due to the time-consuming nature of biota manual annotation. At this point, the use of computer vision and deep learning to automate image processing has substantial benefits but has rarely been adopted within the field of marine ecology. This study presents the integration of deep learning techniques for benthic fauna identification, high resolution multibeam echosounder (MBES) data and Species Distribution Models (SDMs), to map the potential habitat of the yellow coral Dendrophyllia cornigera, a representative species of the VME 1170 Reef habitat, on the circalitoral area of the Capbreton Canyon System. The localization and identification of the coral colonies was based on more than 7500 photographs taken during the INTEMARES-CapBreton 0619 and 0620 surveys using the photogrammetric ROTV Politolana. For the automatic annotation of the image set a deep learning based framework was developed by testing two different deep neural networks architectures; a FasterRCNN+Resnet101 model, accomplishing a precision of 100% over human expert annotation for presence/absence discrimination, was selected. Environmental data included different quantitative terrain attributes derived from high resolution MBES bathymetry data. A presence-only species distribution model, Maximum Entropy (MaxEnt), was used to infer the spatial distribution of D. cornigera over the study area. Predicted occurrences corresponded mainly to relevant topographic structures with significant slope, mainly associated to the edge of the continental shelf. These results are consistent with the ecological knowledge on the species and validate the use of deep learning tools to assist in the identification and mapping of VME for management and conservation purposes. This study provides a baseline for the protection of vulnerable habitats of the Capbreton Canyon System in the context of the Natura 2000 Network.The authors would like to thank the crew and scientific team aboard the R/V Ramón Margalef from the Spanish Institute of Oceanography and also appreciate the helpful assistance of the technicians of the ROTV Politolana for their skill in executing the dangerous visual transects very close to the rough bottoms of the study area. This research has been performed in the scope of the INTEMARES project. INTEMARES was partially funded by the European Commission LIFE + “Nature and Biodiversity” call (LIFE15 IPE ES 012). The Biodiversity Foundation of the Spanish Ministry for Ecological Transition, was the institution responsible for coordination this project. Deep-learning advances presented here are part of Deep-RAMP (Deep learning to improve the management of marine protected area network in the North Atlantic region) project funded in the frame of the Pleamar Program of the Biodiversity Foundation and is co-financed by the European Maritime and Fisheries Fund (EMFF)