Description: Connectivity is a fundamental process driving the persistence of marine populations and their adaptation potential in response to environmental change. In this study, we analysed the population genetics of two morphologically highly similar deep-sea sponge clades (Phakellia hirondellei and the 'Topsentia-and-Petromica (TaP)' clade) at three locations in the Cantabrian Sea. Sponge taxonomy was assessed by spicule analyses, as well as by 18S sequencing and COI sequencing. The corresponding host microbiome was analysed by 16S rRNA gene sequencing. In addition we set up an oceanographic modelling framework, for which we used seawater flow cytometry data (derived from bottom depths of CTD casts) as ground-truthing data.

Description: This data set presents the results of an automated cluster analysis using Gaussian mixture models of the entire Atlantic seafloor environment. The analysis was based on eight global datasets and their derivatives: Bathymetry, slope, terrain ruggedness index, topographic position index, sediment thickness, POC flux, salinity, dissolved oxygen, temperature, current velocity, and phytoplankton abundance in surface waters along with seasonal variabilities (see Source data set). We obtained nine seabed areas (SBAs) that portray the Atlantic seafloor that are shown as polygons in the data set. The attribute table holds short descriptions of each SBA as well as about the colours used in the accompanying paper publication. Data sets like this can be used for further analysis like e.g. for landscape ecology metrics to identify regions of interest. The compressed file further contains a style file that can be used to directly load the correct style in the QGIS software package.

Description: A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date.

Description: These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.

Description: In highly fragmented and relatively stable cold-seep ecosystems, species are expected to exhibit high migration rates and long-distance dispersal of long-lived pelagic larvae to maintain genetic integrity over their range. Accordingly, several species inhabiting cold seeps are widely distributed across the whole Atlantic Ocean, with low genetic divergence between metapopulations on both sides of the Atlantic Equatorial Belt (AEB, i.e. Barbados and African/European margins). Two hypotheses may explain such patterns: (i) the occurrence of present-day gene flow or (ii) incomplete lineage sorting due to large population sizes and low mutation rates. Here, we evaluated the first hypothesis using the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined COI barcoding of 763 individuals with VIKING20X larval dispersal modelling at a large spatial scale not previously investigated. Population genetics supported the parallel evolution of Gigantidas and Bathymodiolus genera in the Atlantic Ocean and the occurrence of a 1-3 Million-year-old vicariance effect that isolated populations across the Caribbean Sea. Both population genetics and larval dispersal modelling suggested that contemporary gene flow and larval exchanges are possible across the AEB and the Caribbean Sea, although probably rare. When occurring, larval flow was eastward (AEB - only for B. boomerang) or northward (Caribbean Sea - only for G. mauritanicus). Caution is nevertheless required since we focused on only one mitochondrial gene, which may underestimate gene flow if a genetic barrier exists. Non-negligible genetic differentiation occurred between Barbados and African populations, so we could not discount the incomplete lineage sorting hypothesis. Larval dispersal modelling simulations supported the genetic findings along the American coast with high amounts of larval flow between the Gulf of Mexico (GoM) and the US Atlantic Margin, although the Blake Ridge population of B. heckerae appeared genetically differentiated. Overall, our results suggest that additional studies using nuclear genetic markers and population genomics approaches are needed to clarify the evolutionary history of the Atlantic bathymodioline mussels and to distinguish between ongoing and past processes.

Description: Landscape maps based on multivariate cluster analyses provide an objective and comprehensive view on the (marine) environment. They can hence support decision making regarding sustainable ocean resource handling and protection schemes. Across a large number of scales, input parameters and classification methods, numerous studies categorize the ocean into seascapes, hydro-morphological provinces or clusters. Many of them are regional, however, while only a few are on a basin scale. This study presents an automated cluster analysis of the entire Atlantic seafloor environment, based on eight global datasets and their derivatives: Bathymetry, slope, terrain ruggedness index, topographic position index, sediment thickness, POC flux, salinity, dissolved oxygen, temperature, current velocity, and phytoplankton abundance in surface waters along with seasonal variabilities. As a result, we obtained nine seabed areas (SBAs) that portray the Atlantic seafloor. Some SBAs have a clear geological and geomorphological nature, while others are defined by a mixture of terrain and water body characteristics. The majority of the SBAs, especially those covering the deep ocean areas, are coherent and show little seasonal and hydrographic variation, whereas other, nearshore SBAs, are smaller sized and dominated by high seasonal changes. To demonstrate the potential use of the marine landscape map for marine spatial planning purposes, we mapped out local SBA diversity using the patch richness index developed in landscape ecology. It identifies areas of high landscape diversity, and is a practical way of defining potential areas of interest, e.g. for designation as protected areas, or for further research. Clustering probabilities are highest (100%) in the center of SBA patches and decrease towards the edges (〈 98%). On the SBA point cloud which was reduced for probabilities 〈98%, we ran a diversity analysis to identify and highlight regions that have a high number of different SBAs per area, indicating the use of such analyses to automatically find potentially delicate areas. We found that some of the highlights are already within existing EBSAs, but the majority is yet unexplored.

Description: Earth System Sciences have been generating increasingly larger amounts of heterogeneous data in recent years. We identify the need to combine Earth System Sciences with Data Sciences, and give our perspective on how this could be accomplished within the sub-field of Marine Sciences. Marine data hold abundant information and insights that Data Science techniques can reveal. There is high demand and potential to combine skills and knowledge from Marine and Data Sciences to best take advantage of the vast amount of marine data. This can be accomplished by establishing Marine Data Science as a new research discipline. Marine Data Science is an interface science that applies Data Science tools to extract information, knowledge, and insights from the exponentially increasing body of marine data. Marine Data Scientists need to be trained Data Scientists with a broad basic understanding of Marine Sciences and expertise in knowledge transfer. Marine Data Science doctoral researchers need targeted training for these specific skills, a crucial component of which is co-supervision from both parental sciences. They also might face challenges of scientific recognition and lack of an established academic career path. In this paper, we, Marine and Data Scientists at different stages of their academic career, present perspectives to define Marine Data Science as a distinct discipline. We draw on experiences of a Doctoral Research School, MarDATA, dedicated to training a cohort of early career Marine Data Scientists. We characterize the methods of Marine Data Science as a toolbox including skills from their two parental sciences. All of these aim to analyze and interpret marine data, which build the foundation of Marine Data Science.

Description: Marine Heatwaves (MHWs) are ocean extreme events, characterized by anomalously high temperatures, which can have significant ecological impacts. The Northeast U.S. continental shelf is of great economical importance as it is home to a highly productive ecosystem. Local warming rates exceed the global average and the region experienced multiple MHWs in the last decade with severe consequences for regional fisheries. Due to the lack of subsurface observations, the depth-extent of MHWs is not well-known, which hampers the assessment of impacts on pelagic and benthic ecosystems. This study utilizes a global ocean circulation model with a high-resolution (1/20°) nest in the Atlantic to investigate the depth structure of MHWs and associated drivers on the Northeast U.S. continental shelf. It is shown that MHWs exhibit varying spatial extents, with some only occurring at depth. The highest intensities are found around 100 m depth with temperatures exceeding the climatological mean by up to 7°C, while surface intensities are typically smaller (around 3°C). Distinct vertical structures are associated with different spatial MHW patterns and drivers. Investigation of the co-variability of temperature and salinity reveals that over 80% of MHWs at depth (〉50 m) coincide with extreme salinity anomalies. Two case studies provide insight into opposing MHW patterns at the surface and at depth, being forced by anomalous air-sea heat fluxes and Gulf Stream warm core ring interaction, respectively. The results highlight the importance of local ocean dynamics and the need to realistically represent them in climate models.