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

• Main Authors: González-Irusta, José Manuel, Fauconnet, Laurence, Das, Diya, Catarino, Diana, Afonso, Pedro, Viegas, Cláudia Neto, Rodrigues, Luís, Menezes, Gui M, Rosa, Alexandra, Pinho, Mário Rui Rilhó, Silva, Hélder Marques da, Giacomello, Eva, Morato, Telmo
• Format: Dataset
• Language: English
• Published: PANGAEA 2022
• Subjects: Azores_EEZ; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); deep sea elasmobranchs; Deep-sea fisheries; File content; Generalized Additive Models; Mid-Atlantic Ridge; North Atlantic; species distribution modelling
• Online Access: https://doi.pangaea.de/10.1594/PANGAEA.940808; https://doi.org/10.1594/PANGAEA.940808

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 ...