Cold-water corals are habitat-forming species that are also classified as indicators of vulnerable marine ecosystems (VMEs) due to the threat of various anthropogenic impacts, e.g., fisheries and oil/mineral exploration. To best protect VMEs, knowledge of their habitat requirements and distribution is essential. However, comprehensive sampling of the deep sea is difficult due to access and cost constraints, so species distribution modeling (SDM) is often used to predict overall distributions and ecological preferences of species based on limited data. We used Maximum Entropy (Maxent) modeling to predict the probability of presence of the reef-building scleractinian Lophelia pertusa and the octocorals Paragorgia arborea and Primnoa resedaeformis using a total of 2149 coral presence points and 15 environmental predictor variables. The environmental variables used in the analysis were processed to 176 m resolution and included bathymetry, depth, geomorphometric characteristics [slope, aspect, and bathymetric position index (BPI)], oceanography (temperature, salinity, current directions, and speed), surface chlorophyll a concentration, sediment type, and marine landscape type. Comparing presence points with environmental data showed that the temperature and depth range for Lophelia was narrower compared to the gorgonians, and it occurred in shallower, warmer water. Observations showed that Lophelia had a broad, bimodal response to Broad BPI, while the predicted model indicated a more narrow response. Paragorgia tolerated the greatest range of sloping according to the model. All three species were observed with a bimodal pattern along a wide range of mean current speed, while the models indicated a high response to faster current speed. Jackknife tests showed that sediment type was an important predictor for gorgonian corals, while BPI and minimum temperature were more important for Lophelia. The spatial precision of the models could be further increased by applying environmental layers with a higher and uniform spatial resolution. The predicted distribution of corals and their relation to environmental variables provides an important background for prioritizing areas for detailed mapping surveys and will aid in the conservation efforts for these VMEs in Norwegian waters and beyond.
Accurate seafloor maps serve as a critical component for understanding marine ecosystems and guiding informed ocean management decisions. From 2004 to 2015, the Atlantic Ocean continental margin offshore of the United States has been systematically mapped using multibeam sonars. This work was done in support of the U.S. Extended Continental Shelf (ECS) Project and for baseline characterization of the Atlantic canyons, but the question remains as to the relevance of these margin-wide data sets for conservation and management decisions pertaining to these areas. This study utilized an automatic segmentation approach to initially identify landform features from the bathymetry of the region, then translated these results into complete coverage geomorphology maps of the region utilizing the coastal and marine ecological classification standard (CMECS) to define geoforms. Abyssal flats make up more than half of the area (53%), with the continental slope flat class making up another 30% of the total area. Flats of any geoform class (including continental shelf flats and guyot flats) make up 83.06% of the study area. Slopes of any geoform class make up a cumulative total of 13.26% of the study region (8.27% abyssal slopes, 3.73% continental slopes, and 1.25% seamount slopes). While ridge features comprise only 1.82% of the total study area (1.03% abyssal ridges, 0.63 continental slope ridge, and 0.16% seamount ridges). Key benefits of the study’s semi-automated approach include computational efficiency for large datasets, and the ability to apply the same methods to large regions with consistent results.
Frontiers in Psychology
Novel Approaches for Studying Creativity in Problem-Solving and Artistic Performance