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Video and image data are regularly used in the field of benthic ecology to document biodiversity. However, their use is subject to a number of challenges, principally the identification of taxa within the images without associated physical specimens. The challenge of applying traditional taxonomic keys to the identification of fauna from images has led to the development of personal, group, or institution level reference image catalogues of operational taxonomic units (OTUs) or morphospecies. Lack of standardisation among these reference catalogues has led to problems with observer bias and the inability to combine datasets across studies. In addition, lack of a common reference standard is stifling efforts in the application of artificial intelligence to taxon identification. Using the North Atlantic deep sea as a case study, we propose a database structure to facilitate standardisation of morphospecies image catalogues between research groups and support future use in multiple front-end applications. We also propose a framework for coordination of international efforts to develop reference guides for the identification of marine species from images. The proposed structure maps to the Darwin Core standard to allow integration with existing databases. We suggest a management framework where high-level taxonomic groups are curated by a regional team, consisting of both end users and taxonomic experts. We identify a mechanism by which overall quality of data within a common reference guide could be raised over the next decade. Finally, we discuss the role of a common reference standard in advancing marine ecology and supporting sustainable use of this ecosystem.

Population connectivity refers to the exchange of individuals among populations: it affects gene flow, regulates population size and function, and mitigates recovery from natural or anthropogenic disturbances. Many populations in the deep sea are spatially fragmented, and will become more so with increasing resource exploitation. Understanding population connectivity is critical for spatial management. For most benthic species, connectivity is achieved by the planktonic larval stage, and larval dispersal is, in turn, regulated by complex interactions between biological and oceanographic processes. Coupled biophysical models, incorporating ocean circulation and biological traits, such as planktonic larval duration (PLD), have been used to estimate population connectivity and generate spatial management plans in coastal and shallow waters. In the deep sea, knowledge gaps in both the physical and biological components are delaying the effective use of this approach. Here, we review the current efforts in conservation in the deep sea and evaluate (1) the relevance of using larval dispersal in the design of marine protected areas and (2) the application of biophysical models in the study of population connectivity. Within biophysical models, PLD can be used to estimate dispersal distance. We propose that a PLD that guarantees a minimum dispersal distance for a wide range of species should be used in the planning of marine protected areas in the deep sea. Based on a review of data on species found at depths > 200 m, a PLD of 35 and 69 days ensures a minimum distance for 50% and 75%, respectively, of eurybathic and deep-sea species. We note that more data are required to enhance accuracy and address the high variability in PLD between and within taxonomic groups, limiting generalizations that are often appealing to decision-makers. Given the imminent expansion of resource exploitation in the deep sea, data relevant to spatial management are needed urgently.

Modelling approaches have the potential to significantly contribute to the spatial management of the deep-sea ecosystem in a cost effective manner. However, we currently have little understanding of the accuracy of such models, developed using limited data, of varying resolution. The aim of this study was to investigate the performance of predictive models constructed using non-simulated (real world) data of different resolution. Predicted distribution maps for three deep-sea habitats were constructed using MaxEnt modelling methods using high resolution multibeam bathymetric data and associated terrain derived variables as predictors. Model performance was evaluated using repeated 75/25 training/test data partitions using AUC and threshold-dependent assessment methods. The overall extent and distribution of each habitat, and the percentage contained within an existing MPA network were quantified and compared to results from low resolution GEBCO models. Predicted spatial extent for scleractinian coral reef and Syringammina fragilissima aggregations decreased with an increase in model resolution, whereas Pheronema carpenteri total suitable area increased. Distinct differences in predicted habitat distribution were observed for all three habitats. Estimates of habitat extent contained within the MPA network all increased when modelled at fine scale. High resolution models performed better than low resolution models according to threshold-dependent evaluation. We recommend the use of high resolution multibeam bathymetry data over low resolution bathymetry data for use in modelling approaches. We do not recommend the use of predictive models to produce absolute values of habitat extent, but likely areas of suitable habitat. Assessments of MPA network effectiveness based on calculations of percentage area protection (policy driven conservation targets) from low resolution models are likely to be fit for purpose.

Seamounts and oceanic islands rise from the seafloor and provide suitable habitat for a diverse range of biological assemblages including Vulnerable Marine Ecosystems (VMEs). Whilst they have been the focus of some work globally, there has been little description of the biological and physical environments of seamounts in the South Atlantic Ocean. In this study, we characterized benthic assemblage composition from 13 seamounts and oceanic islands spanning 8–40°S within the exclusive economic zones (EEZs) of Ascension Island, Saint Helena and Tristan da Cunha. Drop camera imagery was collected between 170 and 1000 m. All fauna present in images were identified and quantified, and multivariate statistics were used to describe biological assemblages and identify their environmental drivers. Benthic communities of temperate regions (Tristan da Cunha archipelago) were shown to be distinct from those found in the tropics, with latitude and depth identified as key environmental drivers of assemblage composition. Our results are consistent with the current understanding of the biogeography of the South Atlantic, both in terms of the distinction between tropical and temperate regions, and the influence of depth and water mass structure on assemblage distribution. Faunal assemblages are similar to those observed in the North Atlantic in terms of functional groups. VMEs are present within the EEZs of all three territories and are potentially protected from some threats by large marine protected areas (MPAs). Our imagery, data and analyses provide a baseline for south Atlantic seamounts so that future monitoring can establish whether existing protected status is sufficient to conserve both unique biodiversity and considerable potential for vital ecosystem services.

Larval dispersal is an important ecological process of great interest to conservation and the establishment of marine protected areas. Increasing numbers of studies are turning to biophysical models to simulate dispersal patterns, including in the deep-sea, but for many ecologists unassisted by a physical oceanographer, a model can present as a black box. Sensitivity testing offers a means to test the models' abilities and limitations and is a starting point for all modelling efforts. The aim of this study is to illustrate a sensitivity testing process for the unassisted ecologist, through a deep-sea case study example, and demonstrate how sensitivity testing can be used to determine optimal model settings, assess model adequacy, and inform ecological interpretation of model outputs. Five input parameters are tested (timestep of particle simulator (TS), horizontal (HS) and vertical separation (VS) of release points, release frequency (RF), and temporal range (TR) of simulations) using a commonly employed pairing of models. The procedures used are relevant to all marine larval dispersal models. It is shown how the results of these tests can inform the future set up and interpretation of ecological studies in this area. For example, an optimal arrangement of release locations spanning a release area could be deduced; the increased depth range spanned in deep-sea studies may necessitate the stratification of dispersal simulations with different numbers of release locations at different depths; no fewer than 52 releases per year should be used unless biologically informed; three years of simulations chosen based on climatic extremes may provide results with 90% similarity to five years of simulation; and this model setup is not appropriate for simulating rare dispersal events. A step-by-step process, summarising advice on the sensitivity testing procedure, is provided to inform all future unassisted ecologists looking to run a larval dispersal simulation.

The use of species occurrence as a proxy for habitat type is widespread, probably because it allows the use of species distribution modeling (SDM) to cost-effectively map the distribution of e.g., vulnerable marine ecosystems. We have modeled the distribution of epibenthic megafaunal taxa typical of soft-bottom, Deep-Sea Sponge Aggregations (DSSAs), i.e., “indicators,” to discover where in the Barents Sea region this habitat is likely to occur. The following taxa were collectively modeled: Hexadella cf. dedritifera, Geodia spp., Steletta sp., Stryphnus sp. The data were extracted from MarVid, the video database for the Marine AREAl database for NOrwegian waters (MAREANO). We ask whether modeling density data may be more beneficial than presence/absence data, and whether using this list of indicator species is enough to locate the target habitat. We use conditional inference forests to make predictions of probability of presence of any of the target sponges, and total density of all target sponges, for an area covering a large portion of the Norwegian Barents Sea and well beyond the data’s spatial range. The density models explain <31% of the variance, and the probability models have high classificatory power (AUC > 0.88), depending on the variables/samples used to train the model. The predicted surfaces were then classified on the basis of a probability threshold (0.75) and a density threshold (13 n/100 m2) to obtain polygons of “core area” and “hotspots” respectively (zones). The DSSA core area comprises two main regions: the Egga shelf break/Tromsøflaket area, and the shelf break southwest of Røst bank in the Træna trench. Four hotspots are detected within this core area. Zones are evaluated in the light of whole-community data which have been summarized as taxon richness and density of all megafauna. Total megafaunal density was significantly higher inside the hotspots relative to the background. Richness was not different between zones. Hotspots appeared different to one another in their richness and species composition although no tests were possible. We make the case that the effectiveness of the indicator species approach for conservation planning rests on the availability of density data on the target species, and data on co-occurring species.

In this paper, we describe the species composition of biotopes occurring in a wide range of environments and present their geographic distribution based on results from quantitative analyses of video-records collected as part of the Norwegian seabed mapping program MAREANO. We present results from an analysis of annotated video records at 757 stations from an area exceeding 100,000 km2 in the Barents Sea and Norwegian Sea. A two-way indicator species analyses (TWINSPAN) was used to identify sample groups and species assemblages for biotope classification. Environmental conditions were compared for the station groups identified at different similarity levels to detect environmental drivers behind each division and hence biotopes indicated by the analysis. In total, 27 groups were identified as potential biotopes in the study area giving a geographic resolution suitable for management needs and subsequent predictive modeling. The faunal composition was mainly correlated with water masses (temperature and salinity). The most distinct biotope identified occurred on Spitsbergenbanken, a shallow area (<50 m) with strong bottom currents. The other biotopes formed two main groups characterized by different oceanographic properties: (1) Atlantic Water and Arctic Intermediate Water associated with higher temperatures and stronger current speed and (2) Arctic Water, Atlantic Water, and Norwegian Sea Deep Water (NSDW) associated with both lower temperatures and slower current speeds. The cold-water species occurred both in the shallow (<200 m) Artic Water in the north-eastern part of the study area, and the deep (>600 m) NSDW, separating into two TWINSPAN groups. Further divisions of these groups reflected variations in sediment and terrain attributes. Ten biotopes were characterized by indicators species of vulnerable marine ecosystems (e.g., coral gardens, sea pen communities, and sponge aggregations). Knowledge about megafauna composition and biotopes is poor for deep-water benthic habitats in the Arctic region, and better classification of benthic biotopes will be valuable for management purposes such as design of monitoring program for documenting the effects of climate change on ecosystems.

Background Under the threat of climate change populations can disperse, acclimatise or evolve in order to avoid fitness loss. In light of this, it is important to understand neutral gene flow patterns as a measure of dispersal potential, but also adaptive genetic variation as a measure of evolutionary potential. In order to assess genetic variation and how this relates to environment in the honeycomb worm (Sabellaria alveolata (L.)), a reef-building polychaete that supports high biodiversity, we carried out RAD sequencing using individuals from along its complete latitudinal range. Patterns of neutral population genetic structure were compared to larval dispersal as predicted by ocean circulation modelling, and outlier analyses and genotype-environment association tests were used to attempt to identify loci under selection in relation to local temperature data. Results We genotyped 482 filtered SNPs, from 68 individuals across nine sites, 27 of which were identified as outliers using BAYESCAN and ARLEQUIN. All outlier loci were potentially under balancing selection, despite previous evidence of local adaptation in the system. Limited gene flow was observed among reef-sites (FST = 0.28 ± 0.10), in line with the low dispersal potential identified by the larval dispersal models. The North Atlantic reef emerged as a distinct population and this was linked to high local larval retention and the effect of the North Atlantic Current on dispersal. Conclusions As an isolated population, with limited potential for natural genetic or demographic augmentation from other reefs, the North Atlantic site warrants conservation attention in order to preserve not only this species, but above all the crucial functional ecological roles that are associated with their bioconstructions. Our study highlights the utility of using seascape genomics to identify populations of conservation concern.

Habitat suitability models are being used worldwide to help map and manage marine areas of conservation importance and scientific interest. With groundtruthing, these models may be found to successfully predict patches of occurrence, but whether all patches are part of a larger interbreeding metapopulation is much harder to assert. Here we use a North Atlantic deep-sea case study to demonstrate how dispersal models may help to complete the picture. Pheronema carpenteri is a deep-sea sponge that, in aggregation, forms a vulnerable marine ecosystem in the Atlantic Ocean. Published predictive distribution models from United Kingdom and Irish waters have now gained some support from targeted groundtruthing, but known aggregations are distantly fragmented with little predicted habitat available in-between. Dispersal models were used to provide spatial predictions of the potential connectivity between these patches. As little is known of P. carpenteri’s reproductive methods, twenty-four model set-ups with different dispersal assumptions were simulated to present a large range of potential dispersal patterns. The results suggest that up to 53.1% of the total predicted habitat may be reachable in one generation of dispersal from known populations. Yet, even in the most dispersive scenario, the known populations in the North (Hatton-Rockall Basin) and the South (Porcupine Sea Bight) are predicted to be unconnected, resulting in the relative isolation of these patches across multiple generations. This has implications for Ireland’s future conservation efforts as they may have to conserve patches from more than one metapopulation. This means that conserving one patch may not demographically support the other, requiring additional attentions to ensure that marine protected areas are ecologically coherent and sustainable. This example serves as a demonstration of a combined modeling approach where the comparison between predicted distribution and dispersal maps can highlight areas with higher conservation needs.

Larval dispersal data are increasingly sought after in ecology and marine conservation, the latter often requiring information under time limited circumstances. Basic estimates of dispersal [based on average current speeds and planktonic larval duration (PLD)] are often used in these situations, usually acknowledging their oversimplified nature, but rarely with an understanding of how oversimplified those assumptions are. Larval dispersal models (LDMs) are becoming more accessible and may produce “better” dispersal predictions than estimates, but the uncertainty introduced by choosing one underlying hydrodynamic model over another is rarely discussed. This case study uses theoretical and simplified deep-sea LDMs to compare the passive predictions of dispersal as driven by two different hydrodynamic models (HYCOM and POLCOMS) and a range of informed basic estimates (based on average current speeds of 0.05, 0.1, and 0.2 m/s). The aim is to provide generalizable insight into the predictive variability introduced by (a) choosing a model over an estimate, and (b) one hydrodynamic over another. LDMs were found to be up to an order of magnitude more conservative in dispersal distance predictions than even the slowest tested estimate (0.05 m/s). The difference increased with PLD which may result in a bigger disparity for deep-sea species predictions. Although the LDMs were more spatially targeted than the estimates, the two LDM predictions were also significantly different from each other. This means that choosing one hydrodynamic model over another could result in contrasting ecological interpretations or advice for marine conservation. These results show a greater potential for hydrodynamic model variability than previously appreciated by larval dispersal ecologists and strongly advocates groundtruthing predictions before use in management. Advice is offered for improved model selection and interpretation of predictions.