Description: We used environmental niche modelling along with the best available species occurrence data and environmental parameters to model habitat suitability for key cold-water coral and commercially important deep-sea fish species under present-day (1951-2000) environmental conditions and to forecast changes under severe, high emissions future (2081-2100) climate projections (RCP8.5 scenario) for the North Atlantic Ocean (from 18°N to 76°N and 36°E to 98°W). This dataset contains a set of terrain (static in time) and environmental (dynamic in time) variables were used as candidate predictors of present-day (1951-2000) distribution and to forecast future (2081-2100) changes. All predictor variables were projected with the Albers equal-area conical projection centred in the middle of the study area. The terrain variable depth was extracted from a bathymetry grid built from two data sources: the EMODnet Digital Terrain Model (EMODnet, 2018) and the General Bathymetric Chart of the Oceans (GEBCO 2014; Weatherall et al., 2015). Slope (in degrees) was derived from the final bathymetry grid using the Raster package in R (Hijmans, 2016) and the Bathymetric Position Index (BPI) was computed using the Benthic Terrain Model 3.0 tool in ArcGIS 10.1 with an inner radius of 3 and an outer radius of 25 grid cells. In order to avoid extreme values, BPI was standardized using the scale function from the Raster package. Environmental variables of present-day and future conditions, including particulate organic carbon (POC) flux at 100-m depth (epc100, mg C m-2 d-1), bottom water dissolved oxygen concentration (µmol kg-1), pH, and potential temperature (°K) were downloaded from the Earth System Grid Federation (ESGF) Peer-to-Peer (P2P) enterprise system. The epc100 was converted to export POC flux at the seafloor using the Martin curve (Martin, Knauer, Karl, & Broenkow, 1987) following the equation: epc = epc100*(water depth/export depth)-0.858, and setting the export depth to 100 m. Near seafloor aragonite (Ωar) and calcite (Ωcal) saturation were also used as candidate predictors for habitat suitability of cold-water coral species. These saturation states were computed by dividing the bottom water carbonate ion concentration (mol m-3) by the bottom water carbonate ion concentration (mol m-3) for seawater in equilibrium with pure aragonite and calcite. Yearly means of these parameters were calculated for the periods 1951-2000 (historical simulation) and 2081-2100 (RCP8.5 or business-as-usual scenario) using the average values obtained from the Geophysical Fluid Dynamics Laboratory's ESM 2G model (GFDL-ESM-2G; Dunne et al., 2012), the Institut Pierre Simon Laplace's CM6-MR model (IPSL-CM5A-MR; Dufresne et al., 2013) and Max Planck Institute's ESM-MR model (MPI-ESM-MR; Giorgetta et al., 2013) within the Coupled Models Intercomparison Project Phase 5 (CMIP5) for each grid cell of the present study area.

Description: We used environmental niche modelling along with the best available species occurrence data and environmental parameters to model habitat suitability for key cold-water coral and commercially important deep-sea fish species under present-day (1951-2000) environmental conditions and to forecast changes under severe, high emissions future (2081-2100) climate projections (RCP8.5 scenario) for the North Atlantic Ocean (from 18°N to 76°N and 36°E to 98°W). The VME indicator taxa included Lophelia pertusa , Madrepora oculata, Desmophyllum dianthus, Acanela arbuscula, Acanthogorgia armata, and Paragorgia arborea. The six deep-sea fish species selected were: Coryphaenoides rupestris, Gadus morhua, blackbelly Helicolenus dactylopterus, Hippoglossoides platessoides, Reinhardtius hippoglossoides, and Sebastes mentella. We used an ensemble modelling approach employing three widely-used modelling methods: the Maxent maximum entropy model, Generalized Additive Models, and Random Forest. This dataset contains: 1) Predicted habitat suitability index under present-day (1951-2000) and future (2081-2100; RCP8.5) environmental conditions for twelve deep-sea species in the North Atlantic Ocean, using an ensemble modelling approach.  2) Climate-induced changes in the suitable habitat of twelve deep-sea species in the North Atlantic Ocean, as determined by binary maps built with an ensemble modelling approach and the 10-percentile training presence logistic (10th percentile) threshold. 3) Forecasted present-day suitable habitat loss (value=-1), gain (value=1), and acting as climate refugia (value=2) areas under future (2081-2100; RCP8.5) environmental conditions for twelve deep-sea species in the North Atlantic Ocean. Areas were identified from binary maps built with an ensemble modelling approach and two thresholds: 10-percentile training presence logistic threshold (10th percentile) and maximum sensitivity and specificity (MSS). Refugia areas are those areas predicted as suitable both under present-day and future conditions. All predictions were projected with the Albers equal-area conical projection centred in the middle of the study area. The grid cell resolution is of 3x3 km.

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).

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: 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: The Marine Strategy Framework Directive (MSFD) requires Member States to achieve good environmental status (GES) across their marine waters. The EU have requested ICES to advise on methods for assessing adverse effects on seabed habitats, through selection of relevant indicators for the assessment of benthic habitats and seafloor integrity and associated threshold values for GES in relation to Descriptor 6 – Seabed integrity under the MFSD. Two sets of criteria were developed to evaluate indicators and thresholds respectively for evaluation of suitability for assessing GES. 16 indicator and 12 threshold criteria were compiled and weighted by importance. The criteria were designed for evaluation at a subregional or regional level. The scoring for these criteria is meant as a guidance when choosing indicators and thresholds, so failure to meet one criterion will not necessarily prevent the use of the indicator or thresh-old in an assessment. The framework was evaluated for 6 indicators and for 11 methods for setting thresholds. The criteria were found to be useful for evaluation both indicators and thresholds. The process works most consistently when there are experts in the group on both the criteria themselves and on the indicators and thresholds. The MFSD Descriptor 6 determination of GES needs both a quality threshold (when are seabed habitats in a good state in a specific location) and an extent threshold (proportion of the assessment area that needs to have seabed habitats in good state). Eleven different methods for setting thresholds were identified, of which more are suitable for setting quality than for extent thresholds. Preferred methods identified an ecologically-motivated difference between a good and de-graded state, rather than another transition. Quality thresholds based on the lower boundary of the range of natural variation were considered most promising. This approach can be used for most, but not all, indicators. The WK collated a standardized dataset to test the specificity, sensitivity and/or responsiveness of sampling-based benthic indicators to pressure gradients for evaluation by WKBENTH3. Risk-based methods will be evaluated as maps and by scored sensitivity and impact score per MSFD habitat type and subdivision. Participants provided input into the selection of indicators for the compilation of indicators. A template was developed for documenting the characteristics of each indicator to facilitate the evaluation of the indicators.

Description: ICES

Description: Published

Description: Non Refereed