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Refreshed and salty from its travels along the Ningaloo Reef, our favourite underwater robot Starbug X has been shining a light on some of the secrets beneath the world-heritage waters.

After analysing more than 2000 hours of coastal shark sightings, researchers from The University of Western Australia and other collaborators have warned that habitats important for juvenile shark survival are not adequately protected.

BoBBLE drones scour the oceans for data.

Meet the WA scientists racing to stop fatal shark attacks.

Indian Ocean science mission recovers key underwater drone.

The aim of this study was to document the composition and distribution of deep-water fishes associated with a submarine canyon-valley feature. A work-class Remotely Operated Vehicle (ROV) fitted with stereo-video cameras was used to record fish abundance and assemblage composition along transects at water depths between 300 and 900 metres. Three areas (A, B, C) were sampled along a submarine canyon-valley feature on the continental slope of tropical north-western Australia. Water conductivity/salinity, temperature, and depth were also collected using an ROV mounted Conductivity Temperature and Depth (CTD) instrument. Multivariate analyses were used to investigate fish assemblage composition, and species distribution models were fitted using boosted regression trees. These models were used to generate predictive maps of the occurrence of four abundant taxa over the survey areas. CTD data identified three water masses, tropical surface water, South Indian Central Water (centred ∼200 m depth), and a lower salinity Antarctic Intermediate Water (AAIW) ∼550 m depth. Distinct fish assemblages were found among areas and between canyon-valley and non-canyon habitats. The canyon-valley habitats supported more fish and taxa than non-canyon habitats. The fish assemblages of the deeper location (∼700-900 m, Area A) were different to that of the shallower locations (∼400-700 m, Areas B and C). Deep-water habitats were characterised by a Paraliparis (snail fish) species, while shallower habitats were characterised by the family Macrouridae (rat tails). Species distribution models highlighted the fine-scale environmental niche associations of the four most abundant taxa. The survey area had a high diversity of fish taxa and was dominated by the family Macrouridae. The deepest habitat had a different fish fauna to the shallower areas. This faunal break can be attributed to the influence of AAIW. ROVs provide a platform on which multiple instruments can be mounted and com

Imagery has become a key tool for assessing deep-sea megafaunal biodiversity, historically based on physical sampling using fishing gears. Image datasets provide quantitative and repeatable estimates, small-scale spatial patterns and habitat descriptions. However, taxon identification from images is challenging and often relies on morphotypes without considering a taxonomic framework. Taxon identification is particularly challenging in regions where the fauna is poorly known and/or highly diverse. Furthermore, the efficiency of imagery and physical sampling may vary among habitat types. Here, we compared biodiversity metrics (alpha and gamma diversity, composition) based on physical sampling (dredging and trawling) and towed-camera still images (1) along the upper continental slope of Papua New Guinea (sedimented slope with wood-falls, a canyon and cold seeps), and (2) on the outer slopes of the volcanic islands of Mayotte, dominated by hard bottoms. The comparison was done on selected taxa (Pisces, Crustacea, Echinoidea, and Asteroidea), which are good candidates for identification from images. Taxonomic identification ranks obtained for the images varied among these taxa (e.g., family/order for fishes, genus for echinoderms). At these ranks, imagery provided a higher taxonomic richness for hard-bottom and complex habitats, partially explained by the poor performance of trawling on these rough substrates. For the same reason, the gamma diversity of Pisces and Crustacea was also higher from images, but no difference was observed for echinoderms. On soft bottoms, physical sampling provided higher alpha and gamma diversity for fishes and crustaceans, but these differences tended to decrease for crustaceans identified to the species/morphospecies level from images. Physical sampling and imagery were selective against some taxa (e.g., according to size or behavior), therefore providing different facets of biodiversity. In addition, specimens collected at a larger scale