Outputs of predictive distribution models of deep-sea elasmobranchs in the Azores EEZ (down to 2,000m depth) using Generalized Additive Models

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 x 1.12 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). 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; Centroselachus crepidater; Centroscymnus coelolepis; Etmopterus princeps.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; Centroselachus crepidater; Centroscymnus coelolepis; Etmopterus princeps.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; Centroselachus crepidater; Centroscymnus coelolepis; Etmopterus princeps.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; Centroselachus crepidater; Centroscymnus coelolepis; Etmopterus princeps.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 32.678347 LineageOccurrence data was collected from two types of surveys: 38 fishery-independent surveys on board of R/V Arquipélago and on-board observer data surveys from PNRD collection, CoralFISH and Discardless/MERCES/SPONGES programme. Environmental data: Bathymetric Position Index derived from Benthic Terrain Model 3.0 tool in ArcGIS 10.1 (Walbridge et al., 2018) using multibeam data for the Azores EEZ and bathymetry data extracted from EMODNET. Nitrates, phosphates, and silicates concentration (μmol⋅L− 1), dissolved oxygen (ml⋅L− 1) and percentage of oxygen saturation (%) near the ocean bottom were extracted from Amorim et al. (2017), which were rescaled from an original resolution of 0.008 degrees. The near-bottom temperature (◦C) and near-bottom current speed (m⋅s−1) average values were based on a MOHID hydrodynamic model application (Viegas et al., 2018).  Spearman’s coefficient of correlation and the Variation Inflation Factors (VIFs) were used to evaluate collinearity. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862428 (MISSION ATLANTIC).