We developed habitat suitability models for 14 vulnerable and foundation cold-water coral (CWC) taxa of the Azores (NE Atlantic) using GAM and MAXENT models. The modelled taxa are: Acanthogorgia spp., Callogorgia verticillata, Coralliidae spp., Dentomuricea aff. meteor, Desmophyllum pertusum, Errina dabneyi, Leiopathes cf. expansa, Madrepora oculata, Narella bellissima, Narella versluysi, Paracalyptrophora josephinae, Paragorgia johnsoni, Solenosmilia variabilis and Viminella flagellum. Models were built using a model grid having a cell size of a 1.13 x 1.11 km (i.e. about 0.01° in the UTM zone 26N projection). This resolution was considered a good compromise between the original resolution of occurrence and environmental data and our capacity to resolve suitable and unsuitable areas within the same geomorphological feature using model predictions. Study area and model background were limited to depths shallower than 2000 m where most of the sampling events took place. Predictors variables included bathymetric position indexes (5 km and 20 km radii), slope, particulate organic carbon flux, seawater chemistry (principal component of dissolved near-seafloor nutrient concentration and calcite/aragonite saturation levels) and near seafloor values of current speed, oxygen saturation and temperature. Presence records were obtained from two different sources: species annotations from underwater imagery (76%) and longline and handline bycatch records (24 %).The published data include: 1. Binary GAM and Maxent habitat suitability predictions. A bootstrap process (n = 100) evaluated the local confidence of model predictions. Each bootstrap iteration sampled occurrence data with replacement, fitted HSMs models and produced binary suitability maps based on sensitivity‐specificity sum maximization thresholds. Depending on the number of times individual raster cells were predicted as suitable they were classified as: low [1-30%), medium [30-70%) or high [70-100%] confidence suitable cells. This process was repeated independently for GAM and Maxent models. In raster layers: (3) identifies high-confidence suitable cells, (2) medium-confidence suitable cells, (1) low-confidence suitable cells and NAs unsuitable cells. 2. Local fuzzy matching of GAM and Maxent habitat suitability predictions. The level of similarity between the spatial distribution of GAM and Maxent binary predictions (low, medium and high confidence suitable cells) at a local (i.e. cell) level was measured considering two membership functions: category similarity, which assumed that some categories were more similar than others; distance decay, which defined the fuzzy similarity of two cells as (i) identical if they matched perfectly, (ii) linearly decreasing with distance if the matching category was found within a 2-cell radius (~2 km) or (iii) totally different when no matching category was found within a 2-cell radius. After combining the two membership functions similarity scores ranged from 0 (totally different) to 1 (identical). Values of similarity greater than 0.5 indicate raster cells that are more similar than different.3. Combined habitat suitability maps. Suitable raster cells of combined habitat suitability maps were classified as follows: (i) high confidence suitable cell (3 in raster layers), raster cell predicted as suitable with high-confidence by both GAM and Maxent models; (ii) medium confidence suitable cell (2 in raster layers), raster cell predicted as suitable with medium or high confidence by GAM, Maxent or both and with a local fuzzy similarity greater than 0.5; (iii) low confidence suitable cell (1 in raster layers), any other cell predicted as suitable by GAM and/or Maxent.4. Cold water coral richness based on habitat suitability predictions. The .tif file shows the number of taxa predicted as suitable for each raster cell. Note that only high confidence suitable cells of combined habitat suitability maps are considered.

Version 2, 2023-02-02: This version replaces Version 1. doi:10.1594/PANGAEA.921282