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As global temperatures continue to rise, shallow coral reef bleaching has become more intense and widespread. Mesophotic coral ecosystems reside in deeper (30–150 m), cooler water and were thought to offer a refuge to shallow-water reefs. Studies now show that mesophotic coral ecosystems instead have limited connectivity with shallow corals but host diverse endemic communities. Given their extensive distribution and high biodiversity, understanding their susceptibility to warming oceans is imperative. In this multidisciplinary study of an atoll in the Chagos Archipelago in the central Indian Ocean, we show evidence of coral bleaching at 90 m, despite the absence of shallow-water bleaching. We also show that the bleaching was associated with sustained thermocline deepening driven by the Indian Ocean Dipole, which might be further enhanced by internal waves whose influence varied at a sub-atoll scale. Our results demonstrate the potential vulnerability of mesophotic coral ecosystems to thermal stress and highlight the need for oceanographic knowledge to predict bleaching susceptibility and heterogeneity. Rising global temperatures cause widespread bleaching of shallow coral reefs but mesophotic reefs at depths over 30 metres are thought to be sheltered by cooler waters. Here, at sites in the Chagos Archipelago, the authors show bleaching of corals at depths of 90 metres, which might be due to warm surface waters being pushed deeper by the ocean’s response to the Indian Ocean Dipole.

Anthropogenic litter is present in all marine habitats, from beaches to the most remote points in the oceans. On the seafloor, marine litter, particularly plastic, can accumulate in high densities with deleterious consequences for its inhabitants. Yet, because of the high cost involved with sampling the seafloor, no large-scale assessment of distribution patterns was available to date. Here, we present data on litter distribution and density collected during 588 video and trawl surveys across 32 sites in European waters. We found litter to be present in the deepest areas and at locations as remote from land as the Charlie-Gibbs Fracture Zone across the Mid-Atlantic Ridge. The highest litter density occurs in submarine canyons, whilst the lowest density can be found on continental shelves and on ocean ridges. Plastic was the most prevalent litter item found on the seafloor. Litter from fishing activities (derelict fishing lines and nets) was particularly common on seamounts, banks, mounds and ocean ridges. Our results highlight the extent of the problem and the need for action to prevent increasing accumulation of litter in marine environments.

In 2009 the NW and SE flanks of Anton Dohrn Seamount were surveyed using multibeam echosounder and video ground-truthing to characterise megabenthic biological assemblages (biotopes) and assess those which clearly adhere to the definition of Vulnerable Marine Ecosystems, for use in habitat mapping. A combination of multivariate analysis of still imagery and video ground-truthing defined 13 comprehensive descriptions of biotopes that function as mapping units in an applied context. The data reveals that the NW and SE sides of Anton Dohrn Seamount (ADS) are topographically complex and harbour diverse biological assemblages, some of which agree with current definitions of 'listed' habitats of conservation concern. Ten of these biotopes could easily be considered Vulnerable Marine Ecosystems; three coral gardens, four cold-water coral reefs, two xenophyophore communities and one sponge dominated community, with remaining biotopes requiring more detailed assessment. Coral gardens were only found on positive geomorphic features, namely parasitic cones and radial ridges, found both sides of the seamount over a depth of 1311-1740 m. Two cold-water coral reefs (equivalent to summit reef) were mapped on the NW side of the seamount; Lophelia pertusa reef associated with the cliff top mounds at a depth of 747-791 m and Solenosmilia variabilis reef on a radial ridge at a depth of 1318-1351 m. Xenophyophore communities were mapped from both sides of the seamount at a depth of 1099-1770 m and were either associated with geomorphic features or were in close proximity (< 100 m) to them. The sponge dominated community was found on the steep escarpment either side of the seamount over at a depth of 854-1345 m. Multivariate diversity revealed the xenophyophore biotopes to be the least diverse, and a hard substratum biotope characterised by serpulids and the sessile holothurian, Psolus squamatus, as the most diverse.

Fundamental ecological questions about the distribution of ocean life remain unanswered, hindering both the effective management of the ocean, and our comprehension of life on this planet. The benthic and pelagic realms are subject to different methods of study, and to understand where to best focus effort, a thorough understanding of existing information is required, allowing identification of critical knowledge gaps. Open-access data repositories provide a valuable means to identify such gaps; however, these repositories are subject to challenges in separating benthic from pelagic data. Here we demonstrate an automated data pipeline for extracting and separating benthic from pelagic data in open-access repositories. By stratifying data against essential ocean variables in a critical gap analysis, we show that large spatial and taxonomic biases exist in both the benthic and pelagic global datasets, favouring depths shallower than ~100 m, the northern hemisphere, and vertebrate species. The newly compiled, cleaned, and classified dataset is used to identify areas of chronic under sampling and high-priority regions for exploration. We argue that coordinated strategic prioritisation of sampling is needed to support modelling and prediction, enabling us to better manage our oceans and comprehend life on Earth. Large spatial and taxonomic biases exist in ocean ecological datasets, with shallow depths, the northern hemisphere, and vertebrate species being favoured, according to a critical gap analysis of open-access datasets.

Functional trait‐based approaches have enriched our understanding of key ecological processes such as species assembly and biodiversity loss. This focus on traits, rather than taxonomy, promotes comparability across spatial and organisational scales, further enabling the application of trait‐based methodologies to systems where species identity is difficult to recognise. Among other issues, however, the lack of standardisation is preventing trait‐based approaches from unlocking their true potential. Here, 407 published articles (peer‐reviewed and grey literature) are reviewed alongside the Biological Traits Information Catalogue (BIOTIC) to document inconsistencies in the understanding and use of trait terminology in the context of marine benthic ecosystems. Firstly, discrepancies in the operationalisation of key concepts are noted, each associated with six to ten separate definitions. Secondly, three distinct trait classification frameworks are identified, of which one presents considerable internal variation; within‐framework trait classification also emerges as inconsistent. Lastly, a total of 290 synonyms and associated modalities are noted with respect to 18 traits commonly implemented in benthic research, amounting to an average of 16 synonyms per trait. Researchers should be aware of such inconsistencies; to overcome them, we propose a set of guidelines aimed at standardising the reporting and classification of traits in benthic research for policy and management applications. As other standards may exist, we further present a ‘translation’ table intended for use by trait ecologists when reviewing existing literature that adheres to different trait classification frameworks than the ones we recommend. Standardising the reporting and storage of trait data will help align our understanding of the function of benthic assemblages, their role in delivering ecosystem services, and the impact of human activities on ecosystem function. Here, 407 published articles (peer‐reviewed and grey literature) are reviewed alongside the Biological Traits Information Catalogue (BIOTIC) to document inconsistencies in the understanding and use of trait terminology in the context of marine benthic ecosystems. Firstly, discrepancies in the operationalisation of key concepts are noted, each associated with six to ten separate definitions. Secondly, three distinct trait classification frameworks are identified, of which one presents considerable internal variation; within‐framework trait classification also emerges as inconsistent. Lastly, a total of 289 synonyms and associated modalities are noted with respect to 18 traits commonly implemented in benthic research, amounting to an average of 16 synonyms per trait.

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.

To support conservation efforts, accurate mapping of marine organism community’ distribution has become more critical than ever before. While previous mapping endeavours have primarily focused on easily accessible shallow‐water habitats, there remains limited knowledge about the ecosystems lying beyond SCUBA diving depths, such as mesophotic coral ecosystems (MCEs, ~30–150 m). MCEs are important habitats from an ecological and conservation perspective, yet little is known about the environmental factors that shape these ecosystems and their distribution, particularly in the Indian Ocean region. The goals of this study are to (1) predict the spatial distribution and extent of distinct benthic communities and MCEs in the Chagos Archipelago, central Indian Ocean, (2) test the effectiveness of a range of environmental and topography derived variables to predict the location of MCEs around Egmont Atoll and the Archipelago, and (3) independently validate the models produced. In addition, we compared the MCEs predicted extent in the Archipelago for the models derived from high‐resolution multibeam and low‐resolution GEBCO bathymetry data. Using maximum entropy modelling, all models resulted in excellent (> 0.9) performances, for AUC and threshold‐dependent metrics, predicting extensive and previously undocumented MCEs across the entire Archipelago with, however, differences in the predicted extent between the high‐ and low‐resolution models. Independent validation resulted in fair (> 0.7 AUC) and poor (> 0.6 AUC) performances for the high‐resolution and low‐resolution models, respectively. Photosynthetically active radiation (PAR), temperature, chlorophyll‐a, and topographically derived variables were identified as the most influential predictors. In conclusion, this study provides the first prediction of the distribution of MCEs and their distinct benthic communities in the Archipelago. It highlights their significance in terms of potential extent and response to various environmental factors, supporting decision making for prioritising future survey sites to study MCEs across the Archipelago and targeting ecologically important areas for conservation. This study aims to map and predict the distribution of mesophotic coral ecosystems (MCEs) in the Chagos Archipelago, Indian Ocean, using high‐ and low‐resolution bathymetry data. The models, developed using environmental and topographical variables, showed excellent predictive performance, revealing extensive, previously undocumented MCEs; though independent validation showed stronger results for high‐resolution data. The findings offer valuable insights for conservation efforts by highlighting the ecological importance of MCEs and guiding future research and conservation priorities in the region.

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.

Extractive activities in the ocean are expanding into the vast, poorly studied deep sea, with the consequence that environmental management decisions must be made for data-poor seafloor regions. Habitat classification can support marine spatial planning and inform decision-making processes in such areas. We present a regional, top–down, broad-scale, seafloor-habitat classification for the Clarion-Clipperton Fracture Zone (CCZ), an area targeted for future polymetallic nodule mining in abyssal waters in the equatorial Pacific Ocean. Our classification uses non-hierarchical, k-medoids clustering to combine environmental correlates of faunal distributions in the region. The classification uses topographic variables, particulate organic carbon flux to the seafloor, and is the first to use nodule abundance as a habitat variable. Twenty-four habitat classes are identified, with large expanses of abyssal plain and smaller classes with varying topography, food supply, and substrata. We then assess habitat representativity of the current network of protected areas (called Areas of Particular Environmental Interest) in the CCZ. Several habitat classes with high nodule abundance are common in mining exploration claims, but currently receive little to no protection in APEIs. There are several large unmanaged areas containing high nodule abundance on the periphery of the CCZ, as well as smaller unmanaged areas within the central CCZ, that could be considered for protection from mining to improve habitat representativity and safeguard regional biodiversity.

With ecosystem services (ES) vital for human wellbeing1, the protection of nature is a human rights matter. We outline how recent advances in international human rights law should inform a revamp of how precaution is applied within environmental decision-making. Critically, precautionary decision-making must evolve to make use of best-available evidence, including novel ES research approaches, to assess ‘foreseeable’ harms to all aspects of human wellbeing that are protected as human rights.