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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

20 pages, 12 figures, 3 tables, supplementary material https://www.frontiersin.org/articles/10.3389/fmars.2021.556313/full#supplementary-material.-- Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Some datasets presented in this article are not readily available because parts of the study are ongoing, and cannot be made public until they have been fully analysed. Requests to access the datasets should be directed to DP, d.m.price@soton.ac.uk and AW, a.wheeler@ucc.ie, Benthic fauna form spatial patterns which are the result of both biotic and abiotic processes, which can be quantified with a range of landscape ecology descriptors. Fine- to medium-scale spatial patterns (200 m2 were created and all organisms were geotagged in order to illustrate their point pattern. The pair correlation function was used to establish whether organisms demonstrated a clustered pattern (CP) at various scales. We further applied a point pattern modelling approach to identify four potential point patterns: complete spatial randomness (CSR), an inhomogeneous pattern influenced by environmental drivers, random clustered point pattern indicating biologically driven clustering and an inhomogeneous clustered point pattern driven by a combination of environmental drivers and biological effects. Reef framework presence and structural complexity determined inhabitant distribution with most organisms showing a departure from CSR. These CPs are likely caused by an affinity to local environmental drivers, growth patterns and restricted dispersion reproductive strategies within the habitat across a range of fine to medium scales. These data provide novel and detailed insights into fine-scale habitat heterogeneity, showing that non-random distributions are apparent and detectable at these fine scales in deep-sea habitats, These data were collected during the Querci expedition, funded by the Marine Institute under the Ship Time Programme of the National Development Plan. DP was funded by the Natural Environmental Research Council (grant number NE/N012070/1) and University of Southampton GSNOCS European Exchange Programme. VH was funded by the NERC CLASS project (grant number NE/R015953/1). AL and VH were supported by the iAtlantic project of the EU H2020 Research and Innovation Programme (grant number 818123). CLI was supported by the H2020 MSC Action HABISS (GA 890815), With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

A lack of data hinders effective marine management strategies for developing island states. This is a particularly acute problem for the Commonwealth of Dominica. Here we use publicly available remote sensing and model data to map their relatively unstudied waters. Two study areas were selected; a smaller area focussing on the nearshore marine environment, and a larger area to capture broader spatial patterns and context. Three broadscale landscape maps were created, using geophysical and oceanographic data to classify the marine environment based on its abiotic characteristics. Principal component analysis (PCA) was performed on each area, followed by K-means clustering. The larger area PCA revealed three eigenvalues > 1, and one eigenvalue of 0.980. Therefore, two maps were created for this area, to assess the significance of including the fourth principal component (PC). We demonstrate that including too many PCs could lead to an increase in the confusion index of final output maps. Overall, the marine landscape maps were used to assess the spatial characteristics of the benthic environment and to identify priority areas for future high-resolution study. Through defining and analysing existing conditions and highlighting important natural areas in the Dominican waters, these study results can be incorporated into the Marine Spatial Planning process.

Detailed knowledge of the shape of the seafloor is crucial to humankind. Bathymetry data is critical for safety of navigation and is used for many other applications. In an era of ongoing environmental degradation worldwide, bathymetry data (and the knowledge derived from it) play a pivotal role in using and managing the world’s oceans in a way that is in accordance with the United Nations Sustainable Development Goal 14 - conserve and sustainably use the oceans, seas and marine resources for sustainable development. However, the vast majority of our oceans is still virtually unmapped, unobserved, and unexplored. Only a small fraction of the seafloor has been systematically mapped by direct measurement. The remaining bathymetry is predicted from satellite altimeter data, providing only an approximate estimation of the shape of the seafloor. Several global and regional initiatives are underway to change this situation. This paper presents a selection of these initiatives as best practice examples for bathymetry data collection, compilation and open data sharing as well as the Nippon Foundation-GEBCO (The General Bathymetric Chart of the Oceans) Seabed 2030 Project that complements and leverages these initiatives and promotes international collaboration and partnership. Several non-traditional data collection opportunities are looked at that are currently gaining momentum as well as new and innovative technologies that can increase the efficiency of collecting bathymetric data. Finally, recommendations are given towards a possible way forward into the future of seafloor mapping and towards achieving the goal of a truly global ocean bathymetry.