ALBERT

Description: This data is showing the outcomes of the analysis done by ATLAS researchers on the environmental status of nine deep-sea areas in the northeast Atlantic. These results are part of the ATLAS work facilitating the implementation of the European Commission's Marine Strategy Framework Directive in the deep waters of the North Atlantic. The nine study areas that were examined are: 1) LoVe Ocean Observatory, 2) Faroe-Shetland Channel, 3) Reykjanes Ridge, 4) Rockall Bank, 5) Mingulay Reef Complex, 6) Porcupine Seabight, 7) Bay of Biscay, 8) Azores, 9) Gulf of Cádiz. The analyses were carried out using the Nested Environmental status Assessment Tool (NEAT). The environmental status outcomes are shown for the total study area, the designated spatial assessment units (SAUs), the ecosystem components ("Benthic invertebrates", "Fish", "Benthos") and the habitats ("Aggregations of L. pertusa & M. oculata on soft sediments", "Aggregations of sea pens & alcyonaceans on soft sediments", "Aggregations of L. pertusa & M. oculata on hard substrates", "Aggregations of Antipatharians and alcyonaceans on hard substrates", "Benthic", "Rocky", "Sedimentary").

Description: Trait-based approaches that complement taxonomic-based studies have increased in popularity among the scientific community over the last decades. The collection of biological and ecological characteristics of species (i.e., traits) provides insight into species and ecosystem vulnerability to environmental and anthropogenic changes, as well as ecosystem functioning. While most of the available trait databases to date contain essential information to understand the functional diversity of a taxonomic group or functional group based on size, the FUN Azores trait database has an ecosystem-based approach that provides a comprehensive assessment of diverse fauna (meio-, macro-, and megafauna) from benthic and pelagic environments in the Azores Marine Park; including ridges, seamounts, and hydrothermal vents. We used a collaborative approach involving 30 researchers with different expertise to develop the trait database; which contains compiled data on 14 traits representing morphological, behavioral, and life history characteristics for 1210 species, across 10 phyla.

Description: 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).