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: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Levin, L. A., Bett, B. J., Gates, A. R., Heimbach, P., Howe, B. M., Janssen, F., McCurdy, A., Ruhl, H. A., Snelgrove, P., Stocks, K., I., Bailey, D., Baumann-Pickering, S., Beaverson, C., Benfield, M. C., Booth, D. J., Carreiro-Silva, M., Colaco, A., Eble, M. C., Fowler, A. M., Gjerde, K. M., Jones, D. O. B., Katsumata, K., Kelley, D., Le Bris, N., Leonardi, A. P., Lejzerowicz, F., Macreadie, P., I., McLean, D., Meitz, F., Morato, T., Netburn, A., Pawlowski, J., Smith, C. R., Sun, S., Uchida, H., Vardaro, M. F., Venkatesan, R., & Weller, R. A. Global observing needs in the deep ocean. Frontiers in Marine Science, 6, (2019):241, doi: 10.3389/fmars.2019.00241.

Description: The deep ocean below 200 m water depth is the least observed, but largest habitat on our planet by volume and area. Over 150 years of exploration has revealed that this dynamic system provides critical climate regulation, houses a wealth of energy, mineral, and biological resources, and represents a vast repository of biological diversity. A long history of deep-ocean exploration and observation led to the initial concept for the Deep-Ocean Observing Strategy (DOOS), under the auspices of the Global Ocean Observing System (GOOS). Here we discuss the scientific need for globally integrated deep-ocean observing, its status, and the key scientific questions and societal mandates driving observing requirements over the next decade. We consider the Essential Ocean Variables (EOVs) needed to address deep-ocean challenges within the physical, biogeochemical, and biological/ecosystem sciences according to the Framework for Ocean Observing (FOO), and map these onto scientific questions. Opportunities for new and expanded synergies among deep-ocean stakeholders are discussed, including academic-industry partnerships with the oil and gas, mining, cable and fishing industries, the ocean exploration and mapping community, and biodiversity conservation initiatives. Future deep-ocean observing will benefit from the greater integration across traditional disciplines and sectors, achieved through demonstration projects and facilitated reuse and repurposing of existing deep-sea data efforts. We highlight examples of existing and emerging deep-sea methods and technologies, noting key challenges associated with data volume, preservation, standardization, and accessibility. Emerging technologies relevant to deep-ocean sustainability and the blue economy include novel genomics approaches, imaging technologies, and ultra-deep hydrographic measurements. Capacity building will be necessary to integrate capabilities into programs and projects at a global scale. Progress can be facilitated by Open Science and Findable, Accessible, Interoperable, Reusable (FAIR) data principles and converge on agreed to data standards, practices, vocabularies, and registries. We envision expansion of the deep-ocean observing community to embrace the participation of academia, industry, NGOs, national governments, international governmental organizations, and the public at large in order to unlock critical knowledge contained in the deep ocean over coming decades, and to realize the mutual benefits of thoughtful deep-ocean observing for all elements of a sustainable ocean.

Description: Preparation of this manuscript was supported by NNX16AJ87A (NASA) Consortium for Ocean Leadership, Sub-Award No. SA16-33. AC was supported by FCT-Investigador contract (IF/00029/2014/CP1230/CT0002). LL was supported by a NASA subaward from the Consortium for Ocean Leadership. AG and HR were supported by Horizon 2020, EU Project “EMSO Link” grant ID 731036. AG, BB, DJ, and HR contributions were supported by the UK Natural Environment Research Council Climate Linked Atlantic Section Science project (NE/R015953/1). JP was funded by the Swiss Network for International Studies, and the Swiss National Science Foundation (grant 31003A_179125). TM was supported by Program Investigador FCT (IF/01194/2013), IFCT Exploratory Project (IF/01194/2013/CP1199/CT0002), H2020 Atlas project (GA 678760), and the H2020 MERCES project (GA 689518). This is PMEL contribution number 4965.