Description: Current European legislation such as the Marine Strategy Framework Directive (MSFD; 2008/56/EC) has highlighted the need for accurate maps on the geomorphology of Europe's maritime territory. Such information is notably essential for the production of habitat maps and cumulative impact assessments of human activities (Halpern et al., 2008) necessary for marine spatial planning initiatives (Gilliland and Laffoley, 2008) and assessments of the representativity/sufficiency of marine protected areas networks like Natura 2000. Broadscale satellite bathymetry presently allows the identification of all prominent geomorphic structures present on the seafloor with a high grade of accuracy. However, these datasets and maps still need to be more widely disseminated in the scientific community. In this contribution, we provide an inventory of some important datasets related to the physical characteristics of the seafloor surrounding the Azores Archipelago. The objective is to ensure that our compilation is readily available for any researchers interested in developing species distribution models, or for the management and conservation of natural resources in the region.

Description: Obtaining a comprehensive knowledge of the spatial and temporal variations of the environmental factors characterizing the Azores region is essential for conservation and management purposes. Although many studies are available for the region, there is a need for a general overview of the best available information. Here, we assembled a comprehensive collection of environmental data for this region. Data sources used in this study included remote sensing oceanographic data for 2003?2013 (sea surface temperature, chlorophyll-a concentration, particulate inorganic carbon (PIC), and particulate organic carbon (POC)), derived oceanographic data (primary productivity and North Atlantic oscillation index) for 2003?2013, and in situ data (temperature, salinity, oxygen, phosphate, nitrate and silicate) obtained from the World Ocean Atlas 2013.

Description: The first record of Antipathella subpinnata ( Ellis and Solander, 1786) for the Azores archipelago is presented based on bottom longline by-catch analysis and ROV seafloor surveys, extending the species western-most boundary of distribution in the NE Atlantic. The species was determined using classic taxonomy and molecular analysis targeting nuclear DNA. Although maximum spine height on Azorean colonies branchlets is slightly smaller than that reported from Mediterranean colonies (0.12 vs 0.16 mm), the analysis of partial 18S rDNA, complete ITS1, 5.8S, ITS2 and partial 28S rDNA suggests that the Azorean and Mediterranean specimens belong to the same species. Video surveys of an A. subpinnata garden detected near Pico Island are used to provide the first in situ description of the species habitat in the region and the first detailed description of a black coral garden in the NE Atlantic. With A. subpinnata being the only coral found between 150 and 196 m depths, this is the deepest black coral garden recorded in the NE Atlantic and the first one to be monospecific. The species exhibited a maximum density of 2.64 colonies/m**2 and occurred across a surface area estimated at 67,333 m**2, yielding a local population estimate of 50,500 colonies.

Description: We developed habitat suitability models for 14 vulnerable and foundation CWC taxa of the Azores employing an original combination of traditional and novel modelling techniques. We introduced the term ecoscape to identify a sensu stricto environmental filter that delimits the potential distribution of coexisting species. --- The published data include: 1. GAM and Maxent habitat suitability predictions classified as high (3), medium (2) or low (1) confidence. Confidence in habitat suitability prediction was estimated with a bootstrap process and depended on the frequency individual raster cells were classified as suitable based on sensitivity‐specificity sum maximization thresholds. Based on this process habitat suitability predictions were categorized as low [1-50%), medium [50-90%) or high [90-100%] confidence. 2. Combined Suitability Maps. GAM and Maxent predictions were combined and each raster cell predicted as suitable was classified based on local fuzzy matching and bootstrap frequencies as follow: value of 1.0 in .tif files: high confidence suitable cells, raster cells predicted as suitable with high confidence by GAM or Maxent, or both and with a local fuzzy similarity greater than 0.5; value of 0.5 in .tif files: medium confidence suitable cells, raster cells predicted as suitable with medium confidence by both GAM and Maxent OR raster cells predicted as suitable with high confidence by GAM or Maxent and with a local fuzzy similarity not equal to zero; value of 0.0 in .tif files: low confidence suitable cell, any other cell predicted as suitable by GAM or Maxent, or both. 3. Overlapping habitat suitability predictions. The .tif file shows the number of taxa predicted as suitable for each raster cell. 4. Regional ecoscapes. Ecoscapes were classified as shallow areas (1), upper slopes (2) and lower slopes (3). 5. Environmetal clusters used to define regional ecoscapes. Clusters were derived using the X-means algorithm.

Description: 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.

Description: Highlights • Marine Image Annotation Software (MIAS) are used to assist annotation of underwater imagery. • We compare 23 MIAS assisting human annotation including some that include automated annotation. • MIAS can run in real time (50%), allow posterior annotation (95%), and interact with databases and data flows (44%). • MIAS differ in data input/output and display, customization, image analysis and re-annotation. • We provide important considerations when selecting UIAS, and outline future trends. Abstract Given the need to describe, analyze and index large quantities of marine imagery data for exploration and monitoring activities, a range of specialized image annotation tools have been developed worldwide. Image annotation - the process of transposing objects or events represented in a video or still image to the semantic level, may involve human interactions and computer-assisted solutions. Marine image annotation software (MIAS) have enabled over 500 publications to date. We review the functioning, application trends and developments, by comparing general and advanced features of 23 different tools utilized in underwater image analysis. MIAS requiring human input are basically a graphical user interface, with a video player or image browser that recognizes a specific time code or image code, allowing to log events in a time-stamped (and/or geo-referenced) manner. MIAS differ from similar software by the capability of integrating data associated to video collection, the most simple being the position coordinates of the video recording platform. MIAS have three main characteristics: annotating events in real time, in posteriorly to annotation and interact with a database. These range from simple annotation interfaces, to full onboard data management systems, with a variety of toolboxes. Advanced packages allow to input and display of data from multiple sensors or multiple annotators via intranet or internet. Posterior human-mediated annotation often include tools for data display and image analysis, e.g. length, area, image segmentation, point count; and in a few cases the possibility of browsing and editing previous dive logs or to analyze annotation data. The interaction with a database allows the automatic integration of annotations from different surveys, repeated annotation and collaborative annotation of shared datasets, browsing and querying of data. Progress in the field of automated annotation is mostly in post processing, for stable platforms or still images. Integration into available MIAS is currently limited to semi-automated processes of pixel recognition through computer-vision modules that compile expert-based knowledge. Important topics aiding the choice of a specific software are outlined, the ideal software is discussed and future trends are presented.