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Nevertheless, in marine spatial planning or MPA network designation processes, for example, it might be extremely useful to consult more than one classification to illuminate several aspects of the same area e. Lecours et al. The use of ocean landscape maps with regard to conservation targeted decision making is pointed out by Lecours as being somewhat like the classic problem of comparing apples and pears: There is a lack of uniformity concerning input data selection, standardised clustering techniques and algorithms.

Furthermore, quality assessment widely differs and there is no method yet to combine the uncertainties and errors that occur during a mapping process into one ultimate uncertainty estimation. Visualising mapping results in e. It is difficult to state whether the environmental clusters identified here contain distinct species assemblages: in addition to physical conditions, life-history traits and biological interactions will influence biogeographic patterns. Even if the physical environment is similar, species and assemblages may differ.

While individuals may not be the same, species with similar traits and functional behaviour could populate areas with comparable physical environments e. McGill et al. From a biodiversity management perspective, such spatially explicit delineation of potential ecosystem functions and therefore services is of high value even if the exact species occupying the particular environment are not known.

Quantitative metrics like the patch richness index to calculate diversity can discover those regions automatically and objectively. Morato et al. Nevertheless, to effectively predict and relate species to environmental conditions, classifications and data at a finer scale must be available Lim et al. The combination of high-resolution classifications and SDMs or HSMs is a very promising task, capable of supporting marine area-based management and spatial planning work Lim et al.

At the same time, it is crucial to keep in mind that the map presented here is based on the current environmental conditions. As a result of climate change, seabed environments will change e. The SBAs may change in shape and extent, or in characteristics.

A next step may be to create similar marine landscape maps based on future predictions of the seafloor environment under different climate scenarios, in order to provide policy-makers with a forward look in addition to the comprehensive description of the present-day situation presented here. The results of such patch richness analyses ought to make it easier to identify potential areas of interest. Because they are based on a multivariate cluster analysis, they combine complex ensembles of multiple influencing parameters, a process which is challenging for a human brain but simple for machine algorithms.

The highlights shall draw our attention to areas where special conditions prevail, and which might be worth to have a closer look at. In the case of the Falkland region Figure 6 , the Falkland plateau seems to be a barrier between two areas that are under the influence of different variables as shown by the altered composition of neighbouring SBAs. The highlights in Figure 5 mostly cover seamounts or other subsea features, which, with regard to the Walvis Ridge where highlights and EBSA largely correspond, confirms that this is an area of significance.

From Figures 4 — 6 , we see that landscape diversity hotspots are often found on and around seamounts or regions of strongly varying topography. This is perhaps not surprising as they are the regions where the physico-chemical conditions in the ocean are known to change significantly over small spatio-temporal extents.

It is for this reason that research has often been concentrated there e. Over the Atlantic basin, the identified regions of high landscape diversity correspond in parts with the latest EBSAs. As EBSAs need a certain amount of ground-truth data for their definition, our landscape diversity map could be useful to concentrate research in presently unstudied areas which may harbour significant landscape diversity. EBSAs are defined on a solid data basis which is why there may be a bias towards well investigated regions.

This may be the fact in the Walvis ridge region Figure 5 — with Walvis ridge itself being significantly more examined than the surrounding environment.

The EBSA patch covers the entire Walvis ridge and the area around Cape basin but only some of the nearby seamounts — besides other reasons probably due to insufficient existing data. It is not easy to find proof for this assumption. A search in Google scholar in April however yields over hits for publications containing Walvis Ridge in their title, over containing Cape basin and 25 for Vema seamount, but none for e.

Malloy seamount. This could be an indicator for unbalanced data distribution and research effort between these areas. Furthermore, EBSA definition is based on multiple criteria where biological diversity is just one of them. In addition to the sea floor, the water column is taken into consideration, too. This might be due to a lack of ground truth data needed for EBSA designation. It may also be because some regions we highlighted fail on other EBSA criteria. Nevertheless, the highlights of diversity in combination with the SBA map pinpoint towards regions of interest and can help finding and defining new research areas, e.

They may be able to support entire stages of EBSA identification without introducing too much subjectivity. Despite the fact that multivariate clustering techniques are more objective than hierarchical methods, unsupervised analyses can still bear error sources that may not be visible at first sight but must be considered when using them. Although a density estimation and model-based clustering approach seems suitable for this kind of high dimensional and complex data, it is the input data quality that needs to be looked at.

The most prominent quality-reducing factors are differences in resolution, especially when dealing with multiple data sources. This holds true for vertical as well as for horizontal resolution. Hence, they only give a very rough approximation of the conditions prevailing in those depths or at the seafloor.

Local small-scale vertical variations e. Given that ground truth seafloor data are scarce in the deep sea, we considered for our analysis the last depth level as defined by CMEMS to be representing seafloor conditions.

This induces a huge vertical uncertainty, which cannot be resolved with the present data and models. Bathymetry, on the other hand, is a seafloor layer by nature, and can have a much higher vertical resolution. Tozer et al. Hence in places, we are aligning data from nominally different depths: those directly at the seafloor e.

Horizontal resolution is another constraint and mainly attributed to limited data availability. However, we dismissed this option, as the information loss would have been intolerably high considering the fact that oxygen and phytoplankton are the only data of this low resolution. Local hydrodynamic and morphologic conditions are important drivers for food flux and organic matter transport to the seabed. These processes however typically operate at the scale of an offshore bank or seamount, i.

Both downscaling from low to high resolution as well as the reverse can be critical; this is because high-resolution data naturally inherit more parameter variance that is passed on when resampled to coarser resolution than data that was collected at a coarse resolution in the first place, thus affecting the analysis.

To at least partially accommodate this in our analysis, we scaled the data and chose model-based clustering, as it is robust towards different variances e. An approach to obviate these deficiencies could be using nested classifications, running multiple cluster algorithms on the existing classes as performed in Hogg et al. This would refine the original clusters and split them into smaller parts, but would, of course, not change the initial data resolution.

Such nesting of classifications, on an ocean basin scale, would however result in complex clusters with multiple hierarchical levels which would be unwieldy to analyse. Higher resolution ocean models e. To date, such models usually have a very fine resolution at the sea surface which also becomes coarse towards the seafloor.

In our approach we preferred the CMEMS product, even though it has the same limited vertical resolution at depth. However, because CMEMS used an assimilation towards observational data despite the fact that these are sparse at depth we aimed at a more realistic representation of the hydrography.

Another limitation which may influence the classification result is the predictor variable selection itself. This issue has been widely discussed e. In this study, we focussed on morphological and hydrographical parameters, largely leaving out biologic measures, as our aim was to define submarine landscapes e. Pearman et al. However, the ocean and its inhabitants form a coherent system and likewise, human impacts e.

Hence, in the future data selection will have to be expanded to encompass the full range of factors that affect the seafloor habitat. A more holistic approach, also with respect to marine protected area designation, would not only be to include a larger span of environmental data, but also information on natural resources abundances, fishing grounds, etc. Visalli et al. For the whole-Atlantic approach targeted here, the data layers necessary for this extended type of analysis are, sadly, simply not available at present.

This work presents a marine landscape map of the Atlantic seafloor based on an unsupervised, multivariate statistics cluster analysis. We found nine seabed areas in total, each of them being unique and differently defined by oceanographic and morphologic determinants. Unsupervised cluster analyses have the advantage of providing an objective view on the ocean environment, stepping away from human-defined hierarchical categorisations towards an unbiased understanding of seafloor ecosystem coherence.

Generally, depending on the clustering technique applied and the selection of input parameters, the results can be very different, highlighting the complexity and variability of the ocean.

Hence, depending on the purpose, a combination of several existing models may be more useful than one single classification. Automated landscape analyses can help to understand the classifications better, and subsequent quantitative metrics will help to identify biodiversity hotspots and vulnerable habitats by pointing out new complex regions of interest.

Studies like this and in combination with other, also smaller-scaled classifications can be used e. A valuable future task would be to assess whether species distribution patterns can be further related to the SBAs we found. Also collating more ground truth data and a detailed assessment of the diversity highlighted regions shall support decisions about protection objectives.

Publicly available datasets were analyzed in this study. MS prepared the figures and wrote the first draft of the manuscript. VH and CD wrote sections of the manuscript. AB gave input to oceanic models and error analysis and CD gave input to the use of the analysis. All authors contributed to manuscript revision, read, and approved the submitted version.

The results are hosted on the iAtlantic Geonode geonode. Special thanks to Iason Gazis from Geomar for their help during the process of clustering model selection. We would also like to thank the reviewers for their valuable and friendly comments and their support. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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