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Gorgonians play a fundamental role in the deep sea (below 200 m depth), composing three-dimensional habitats that are characterized by a high associated biodiversity and playing an important part in biogeochemical cycles. Here we describe the use of a benthic lander to monitoring polyps activity, used as a proxy of gorgonian feeding activity of three colonies of Placogorgia sp. Images cover a period of 22 days with a temporal resolution of 30 min. In addition, this seafloor observatory is instrumented with oceanographic sensors that allows continuous monitoring of the hydrographic conditions in the site. Deep-learning is used for automatic detection of the state of the polyps registered in the images. More than 1000 images of 3 large specimens of gorgonians are analyzed, annotating polyps as extended or retracted, using the semantic segmentation algorithm ConvNeXt. The segmentation results are used to describe the feeding patterns of this species. Placogorgia sp. shows a daily pattern of feeding conduct, depending on the hours of day and night. Using a Singular Spectrum Analysis approach, feeding activity is related to currents dynamics and Acoustic Doppler Current Profile (ADCP) return signal intensity, as proxy of suspended matter, achieving a linear correlation of 0.35 and 0.11 respectively. This is the first time that the behavior of the Placogorgia polyps, directly related to their feeding process, is described.

The deep‐sea environment remains one of the least explored and understood ecosystems on our planet. In this work, we present an underwater multiparametric observing platform, or lander, with a time‐lapse camera designed to study the dynamics and behaviour of deep‐sea species and its interactions with the environment variables over long time periods. Achieving this objective involves a technological challenge as it requires the development systems with numerous synchronized sensors that work at great depths and remain in operation for long periods. Moreover, reducing the costs will make it possible to have more sampling sites and increase our observation capability.

Macro and megafauna were studied in the Avile acute accent s Canyon System (ACS), southern Bay of Biscay (Cantabrian Sea), during several oceanographic cruises carried out from 2009 to 2017. The biodiversity of ACS is summarized and its description is herein updated after sampling surveys of several programmes (ECOMARG, INDEMARES, SponGES, INTEMARES) conducted by the Spanish Institute of Oceanography (IEO).This study has updated previous knowledge in the canyon area from past national and international projects, their reports and publications as well as data collected in the context of regional projects designed to gain new insight into the diversity of marine invertebrates and fishes from the ACS. Samples were taken using a range of sampling gears (Rock dredge, Beam trawl, Trawl gear GOC-73, Suprabenthic sledge, Box corer and Remoted operated vehicle), from 55 to 2291 m in depth. A total of 1015 species were identified at the ACS: 98 Porifera, 153 Cnidaria, 14 Brachiopoda, 22 Bryozoa, 97 Mollusca, 151 Annelida, 315 Arthropoda, 74 Echinodermata and 91 Chordata. New records for the Bay of Biscay fauna include 13 Porifera species, 17 Cnidaria, 7 Mollusca, Arthopoda, 3 Echinodermata and 4 Chordata. Also the bathymetric range of some species has been extended. As a result of the research projects carried out in the area in the last fifteen years, important information is now available which suggests that the ACS houses a large number of species with a high ecological value, that represents a biodiversity hotspot in terms of the presence of sponge aggregations and coral reefs in certain regions, and that it sustains important fisheries due to the abundance of comercial species. Given the relevance of the species and habitats occurring in the ACS, there is a need to implement a conservation and management plan of the area in order to maintain habitats in good state of preservation.

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. Pre

This work focuses on the study of habitats and communities of a high structural complexity area at different levels and scales. This gives us a better understanding of an area from an ecological point of view and at the same time provides us with tools that will facilitate management measures. It was developed in a complex circalittoral rocky platform of the central Cantabrian Sea (Bay of Biscay). The sampling was carried out using a towed photogrammetric vehicle and a rock dredge, which was used for the identification of the species. The first level of the study was the abiotic characterization of the area and the analysis of the communities. These analysis were developed using the unsupervised k-means classification method. For abiotic characterization we used the variables directly associated with the composition and morphology of the ground, such as backscatter, BPI (Bathymetric Position Index), roughness and slope. Depth was also included to discriminate between the circalittoral and bathyal zones. We obtained 5 different classes, which we related to the ground types observed by photogrammetry. In the analysis of the communities, the cluster was based on the sampling units extracted from the images (~10 m), from which 5 assemblages were obtained, providing information on the most abundant species of each class supplied by the abiotic study. The second level was carried out considering a management approach and was based on the modeling of the area at lower resolution, more suitable for the analysis of the habitat-fisheries interactions. Thus, the main habitat-forming species (HFS) of the entire circalittoral area were used to perform delta models based on GAMs (Generalize Additive Models). Obtaining the predictions of presence/absence and combining it with the predictions of densities, we got the zero inflated values density-based model. As all the identified habitats have vulnerable benthic species of a certain size settled on rocky bottoms, they can all be co