Fine-scale structural complexity created by reef-building coral in shallow-water environments is influential on biodiversity, species assemblage and functional trait expression. Cold-water coral reefs are also hotspots of biodiversity, often attributed to the hard surface and structural complexity provided by the coral. However, that complexity has seldom been quantified on a centimetric scale in cold-water coral reefs, unlike their shallow-water counterparts, and has therefore never been linked in a similar way to the reef inhabitant community. Structure from motion techniques which create high-resolution 3D models of habitats from sequences of photographs is being increasingly utilised, in tandem with 3D spatial analysis to create useful 3D metrics, such as rugosity. Here, we demonstrate the use of ROV video transect data for 3D reconstructions of cold-water coral reefs at depths of nearly 1000 m in the Explorer Canyon, a tributary of Whittard Canyon, NE Atlantic. We constructed 40 3D models of approximately 25-m-length video transects using Agisoft Photoscan software, resulting in sub-centimetre resolution reconstructions. Digital elevation models were utilised to derive rugosity metrics, and orthomosaics were used for coral coverage assessment. We found rugosity values comparable to shallow-water tropical coral reef rugosity. Reef and nearby non-reef communities differed in assemblage composition, which was driven by depth and rugosity. Species richness, epifauna abundance and fish abundance increased with structural complexity, being attributed to an increase in niches, food, shelter and alteration of physical water movement. Biodiversity plateaued at higher rugosity, illustrating the establishment of a specific reef community supported by more than 30% coral cover. The proportion of dead coral to live coral had limited influence on the community structure; instead, within-reef patterns were explained by depth and rugosity, though our results were confounded to a certain extent by multi-collinearity. Fine-scale structural complexity appeared to be integral to local-scale ecological patterns in cold-water coral reef communities.

Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle—UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R² = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R² = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km² of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m² resolution to underpin evidence-based management and conservation of seagrass.

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 m²were 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.

Accurate knowledge of the spatial extent of seagrass habitats is essential for monitoring and management purposes given their ecological and economic significance. Extent data are typically presented in binary (presence/absence) or arbitrary, semi-quantitative density bands derived from low-resolution satellite imagery, which cannot resolve fine-scale features and intra-habitat variability. Recent advances in consumer-grade unoccupied aerial vehicles (UAVs) have advanced our ability to survey large areas at higher resolution and at lower cost. This has improved the accessibility of mapping technologies to developing coastal nations, where a large proportion of the world’s seagrass habitats are found. Here, we present the application of UAV-gathered imagery to determine seagrass habitat extent and percent of canopy cover. Four contrasting sites were surveyed in the Turneffe Atoll Marine Reserve, Belize, and seagrass canopy cover was ground truthed from in situ quadrats. Orthomosaic images were created for each site from the UAV-gathered imagery. Three modelling techniques were tested to extrapolate the findings from quadrats to spatial information, producing binary (random forest) and canopy cover (random forest regression and beta regression) habitat maps. The most robust model (random forest regression) had an average absolute error of 6.8–11.9% (SE of 8.2–14), building upon previous attempts at mapping seagrass density from satellite imagery, which achieved errors between 15–20% approximately. The resulting maps exhibited great intra-habitat heterogeneity and different levels of patchiness, which were attributed to site energetics and, possibly, species composition. The extra information in the canopy cover maps provides greater detail and information for key management decisions and provides the basis for future spatial studies and monitoring programmes.

Hydrothermal vent fields found at mid-ocean ridges emit hydrothermal fluids that disperse as neutrally buoyant plumes. From these fluids seafloor massive sulfides (SMS) deposits are formed, which are being explored as possible new mining sites for (trace) metals and rare earth elements (REEs). It has been suggested that during mining activities large amounts of suspended matter will appear in the water column due to excavation processes and discharge of mining waste from the surface vessel. Understanding how hydrothermal plumes can be characterised by means of geochemistry and microbiology as they spread away from their source and how they affect their surrounding environment may help in characterising the behaviour of the dilute distal part of chemically enriched mining plumes. This study on the extensive Rainbow hydrothermal plume, observed up to 25 km downstream from the vent site, enabled us to investigate how microbial communities and (trace) metal composition change in a natural plume with distance. The (trace) metal and REE content of suspended particulate matter (SPM) was determined using sector field inductively coupled plasma mass spectrometry (SF-ICP-MS) with high resolution (HR), and the microbial communities of the neutrally buoyant plume, above-plume, below-plume, and near-bottom water and sediment were characterised by using 16S rRNA amplicon sequencing methods. Both vertically in the water column and horizontally along the neutrally buoyant plume, geochemical and biological changes were evident, as the neutrally buoyant plume stood out by its enrichments in (trace) metals and REEs, as, for example, Fe, Cu, V, Mn and REEs were enriched by factors of up to ∼80, ∼90, ∼52, ∼2.5 and ∼40, respectively, compared to above-plume water samples taken at 1000 m water depth. The concentrations of these elements changed as the plume aged, shown by the decrease in element ∕ Fe molar ratios of chalcophile elements (Cu, Co, Zn), indicative of rapid removal from the hydrothermal plume or removal from the solid phase. Conversely, increasing REE ∕ Fe molar ratios imply uptake of REEs from the ambient seawater onto Fe-oxyhydroxides. This was also reflected in the background pelagic system, as Epsilonproteobacteria started to dominate and univariate microbial biodiversity declined with distance away from the Rainbow hydrothermal vent field. The Rainbow hydrothermal plume provides a geochemically enriched natural environment, which is a heterogeneous, dynamic habitat that is conducive to ecological changes in a short time span. This study of a hydrothermal plume provides a baseline study to characterise the natural plume before the interference of deep-sea mining.

Management of deep-sea fisheries in areas beyond national jurisdiction by Regional Fisheries Management Organizations/Arrangements (RFMO/As) requires identification of areas with Vulnerable Marine Ecosystems (VMEs). Currently, fisheries data, including trawl and longline bycatch data, are used by many RFMO/As to inform the identification of VMEs. However, the collection of such data creates impacts and there is a need to collect non-invasive data for VME identification and monitoring purposes. Imagery data from scientific surveys satisfies this requirement, but there currently is no established framework for identifying VMEs from images. Thus, the goal of this study was to bring together a large international team to determine current VME assessment protocols and establish preliminary global consensus guidelines for identifying VMEs from images. An initial assessment showed a lack of consistency among RFMO/A regions regarding what is considered a VME indicator taxon, and hence variability in how VMEs might be defined. In certain cases, experts agreed that a VME could be identified from a single image, most often in areas of scleractinian reefs, dense octocoral gardens, multiple VME species’ co-occurrence, and chemosynthetic ecosystems. A decision flow chart is presented that gives practical interpretation of the FAO criteria for single images. To further evaluate steps of the flow chart related to density, data were compiled to assess whether scientists perceived similar density thresholds across regions. The range of observed densities and the density values considered to be VMEs varied considerably by taxon, but in many cases, there was a statistical difference in what experts considered to be a VME compared to images not considered a VME. Further work is required to develop an areal extent index, to include a measure of confidence, and to increase our understanding of what levels of density and diversity correspond to key ecosystem functions for VME indicator taxa. Based on our results, the following recommendations are made: 1. There is a need to establish a global consensus on which taxa are VME indicators. 2. RFMO/As should consider adopting guidelines that use imagery surveys as an alternative (or complement) to using bycatch and trawl surveys for designating VMEs. 3. Imagery surveys should also be included in Impact Assessments. And 4. All industries that impact the seafloor, not just fisheries, should use imagery surveys to detect and identify VMEs.