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: deep sea elasmobranchs; Deep-sea fisheries; Generalized Additive Models; Mid-Atlantic Ridge; North Atlantic; species distribution modelling; File content; Binary Object; Binary Object MD5 Hash; Binary Object Media Type; Binary Object File Size; Gam; Dipturus batis; Leucoraja fullonica
• Online Access: https://dx.doi.org/10.1594/pangaea.940808; https://doi.pangaea.de/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 ... : 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 ...