Hard-bottom bathyal habitats and keystone epibenthic species on Le Danois Bank (Cantabrian Sea)

“El Cachucho” Marine Protected Area (MPA), which comprises Le Danois Bank and its intraslope basin, was included during 2008 in the Nature 2000 network mainly because of the presence of the habitat “1170 Reefs” according to the EU Habitat Directive. To review the effectiveness of existing management measures, several activities aimed at characterizing the most structurally complex hard-bottom habitats were planned and carried out during the ESMAREC 0514 survey. For identification of these habitats, several transects using the photogrammetric towed sled Politolana were carried out on Le Danois Bank, in the depth range between 427 and 1379 m, searching for the sea beds with higher values of slope and backscatter. Photogrammetric techniques were used for image scaling, so we could determine the surface areas of different substrata types (facies) and their species densities. A total area of 28,762 m2 was analyzed in the still images of 23 transects, verifying that 85% of the substrata of our study area are occupied by 4 different facies: Bedrock, bedrock with mixed sediments, mixed sediments with pebbles and boulders, and mixed sediments. Acoustic data and ground-truth visual data were combined to evaluate distinctive benthic scenarios. The relative abundances of the 123 epibenthic species identified by image analyses show that the most abundant are sponges (29%), cnidarians (26%), crustaceans (26%) and echinoderms (14%), i.e. mostly sessile species or those with low mobility. The keystone species of the “1170 Reefs” habitat are 3 cnidarians: Callogorgia verticillata, Paramuricea cf. placomus and Dendrophyllia cornigera, and 3 sponges, Asconema setubalense, Geodia msp.1 and Phakellia robusta. Eight new habitats (biotopes) have been identified on Le Danois Bank, six of which occur on the hard bottoms, with depth, substratum, BPI (Bathymetric Position Index) and slope as determining environmental variables that explain their spatial distributions.