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High-resolution multibeam bathymetry data were collected during the 3D seismic survey (https://doi.pangaea.de/10.1594/PANGAEA.943506 https://doi.pangaea.de/10.1594/PANGAEA.943523). In 2006 onboard RRS Charles Darwin the EM-120 (13 kHz, 191 beams) was used. The data was processed to a 5x5 m grid.The data is fully processed and no additional data exist. The data is projected to UTM WGS 1984 31N. These data should not be used for navigational purposes.

This data set includes n.4 files (NetCDF format) containing observational data and related metadata from two mooring sites, sites BB and FF, located in the Southern Adriatic Sea from the period from March 2012 to June 2020. The stand-alone moorings are equipped with an ADCP-RDI system which measures currents along the last 100 meters of the water column and a CTD probe located approximatively 10 m above the bottom. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic submarine observatory of EMSO-ERIC European Consortium. The data are described in data paper Paladini et al., (In prep): Deep water hydrodynamic observations of two moorings sites on the continental slope of the Southern Adriatic Sea (Mediterranean Sea).

This is the English version of the informed consent that has been used for staekholder interactions. Similar forms have been used for focus groups and workshops.

Data layers producedProbPresence: This dataset contains the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function, through the implementation gam in the package mgcv. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.ProbPresence_Error: This dataset contains the standard error associated with the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function, through the implementation gam in the package mgcv. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.BinPresence_Kappa: This dataset contains the binary maps of the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function and a threshold that maximizes Kappa. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.BinPresence_MSS: This dataset contains the binary maps of the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function and the maximization of the sum of sensitivity and specificity (MSS) threshold, which minimizes misclassification likelihoods of false negatives and false positives. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.PredAbundance: This dataset contains the predicted abundance (Pa) of 6 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with negative binomial distributions and a log link, through the implementation of gam in the package mgcv. Etmopterus spinax; Deania profundorum; Raja clavata; Etmopterus pusillus; Deania calcea; Galeorhinus galeus.PredAbundance_Error: This dataset contains the standard error associated with the predicted abundance (Pa) of 6 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) the Azores, using a Generalized Additive Models (GAM) approach with negative binomial distributions and a log link, through the implementation gam in the package mgcv. Etmopterus spinax; Deania profundorum; Raja clavata; Etmopterus pusillus; Deania calcea; Galeorhinus galeus.FinalAbundance: This dataset contains the final predicted abundance (Fpa) of 6 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Delta Generalized Additive Models (GAM) approach recommended for zero-inflated data. This approach involves using the Probability of presence and the presence-only data to predict species abundances (Pa) (as described in other datasets). The final predicted abundance values (Fpa) were computed by multiplying the Pp by the Pa. Etmopterus spinax; Deania profundorum; Raja clavata; Etmopterus pusillus; Deania calcea; Galeorhinus galeus.Extent: West -37.479533; East -18.832939; North 44.355782; South 32.678347Spatial Reference:Type: ProjectedGeographic coordinate reference: GCS_WGS_1984Projection: WGS_1984_UTM_Zone_26NPoint of Contact: Luis Rodrigues; Ocean Sciences Institute - Okeanos, University of the Azores, Rua Professor Doutor Frederico Machado 4, 9901-862 Horta, Portugal. lmcrod@gmail.com Description: We developed predictive distribution models of deep-sea elasmobranchs for up to 2000 m depth in the Azores EEZ and neighboring seamounts, from approximately 33°N to 43°N and 20°W to 36°W. Georeferenced presence, absence, and abundance data were obtained from scientific surveys and commercial operations reporting at least one deep-sea elasmobranch capture. A 20-year 'survey dataset' (1996-2017) was compiled from annual scientific demersal surveys using two types of bottom longlines (types LLA and LLB), and an 'observer dataset' (2004-2018) from observer programs covering commercial fisheries operations using bottom longline (similar to type LLA) and vertical handline ('gorazeira'). We used the most ecologically relevant candidate environmental predictors for explaining the spatial distribution of deep-sea elasmobranch in the Azores: depth, slope, northness, eastness, Bathymetric Position Index (BPI), nitrates, and near bottom currents. We merged existing multibeam data for the Azores EEZ with bathymetry data extracted from EMODNET (EMODnet Bathymetry Consortium 2018) to calculate depth values (down to 2000m). All variables were projected with the Albers equal-area conical projection centered in the middle of the study area and were rescaled using bilinear interpolation to a final grid cell resolution of 1.12 x1.12 km (i.e., 0.012°). Slope, northness, and eastness were computed from the depth raster using the function terrain in the R package raster. BPI was derived from the rescaled depth with an inner radius of 3 and an outer radius of 25 grid cells using the Benthic Terrain Model 3.0 tool in ArcGIS 10.1. Nitrates were extracted from Amorim et al. (2017). Near-bottom current speed (m·s-1) average values were based on a MOHID hydrodynamic model application (Viegas et al., 2018) with an original resolution of 0.054°. Besides the environmental variables, we also included three operational predictors in the analysis: year, fishing effort (number of hooks) and gear type (longline LLA and LLB, and gorazeira).