Submarine canyons are spectacular topographical features that intersect the continental margins of the world's oceans. Canyons comprise unique habitats in terms of complexity, instability, material processing, and hydrodynamics, and they may support diverse assemblages of larger epibenthos. Yet, quantitative data on the biodiversity of the megabenthos in canyons are scant. Consequently, we quantified the diversity of sponges (a key and dominant group of the megabenthos) in 5 canyons located on the continental margin off southeastern Australia at depths from 114 to 612 m. The canyons harboured a rich sponge fauna, with a total of 165 species, belonging to 65 genera, 41 families, 10 orders, and 2 classes in 14 sled samples. Species richness declined with depth, but was positively linked to spatial heterogeneity of bottom types. Areas comprised of a broader range of bottom types (e.g. mixed rocky and sandy/muddy bottoms) contained more species than areas with more uniform substratum properties. Spatial patterns of the sponge assemblages were characterized by (1) high species turnover both between sites in individual canyons and between different canyons, and (2) low levels of site occupancy of the component species, with most species recorded from single canyons only. Variations in depth, substratum type and topographic relief resulted in heterogeneous environmental conditions of benthic habitats in canyons that corresponded to changes in the assemblage structure of sponges. A broad comparison with other abrupt topographical features in the bathyal zone of the region suggests that canyon assemblages may rival the diversity of sponges on seamounts. Site-to-site variation in diversity and species composition within individual canyons suggests that biological patterns may be finer-grained than the spatial scale of conventional geomorphological units. Consequently, from a perspective of conservation planning, a single or a few canyons are unlikely to accurately represent the regional faunal diversity, because of the strong biotic separation of communities between canyons and the limited distributional ranges of the component species.

Williams, A., Bax, N. J., Kloser, R. J., Althaus, F., Barker, B., and Keith G. 2009. Australia’s deep-water reserve network: implications of false homogeneity for classifying abiotic surrogates of biodiversity. – ICES Journal of Marine Science, 66: 214–224. Australia’s southeast network of deep-water marine reserves, declared in July 2007, was designed using a hierarchy that represented the distribution of marine biodiversity as a nested set of bioregions. In this hierarchy, geomorphic units, individual or aggregations of seabed geomorphic features, are the finest scale used in the design process. We evaluated the interaction between two hierarchical levels (depth and geomorphic features), using video survey data on seamounts and submarine canyons. False within-class homogeneity indicated that depth, size, complexity, configuration, and anthropogenic impact need to be added as modifiers to allow geomorphic features to act as surrogates for biodiversity distribution. A consequence of using unmodified geomorphic surrogates, and of not correctly nesting geomorphic features within depth, is the diminished recognition of the importance and comparative rarity of megafaunal biodiversity of the continental margin (<1500-m depths). We call this area the zone of importance, because it is where targeted marine impacts coincide with the greatest megafaunal biodiversity. Refining the geomorphic classification is desirable for future biodiversity characterization, but an alternative approach is to define patterns in biodiversity and abiotic variables jointly, and to utilize finer scale information and provide a classification that preserves the maximum information of both datasets.

Submarine canyons increase seascape diversity on continental margins and harbour diverse and abundant biota vulnerable to fishing. Because many canyons are fished, there is an increasing emphasis on including them in conservation areas on continental margins. Here we report on sponge diversity and bottom cover in three canyons of South‐eastern Australia, test the performance of biological and abiotic surrogates, and evaluate how biological data from detailed seabed surveys can be used in conservation planning in these habitats. The biological data on sponge assemblage structure and species richness were obtained from 576 seafloor images taken between 148 and 472 m depth, yielding 65 morphospecies. Seafloor characteristics were similar within and between canyons, being almost exclusively composed of sediments with very few rocky substrates of higher relief. This environmental homogeneity did not, however, translate into biological uniformity of the megabenthos, and environmental factors were consequently poor predictors of biological features. By contrast, total bottom cover of sponges was highly correlated with species richness and served as a good proxy for species‐level data in this situation. Design strategies that employ information on cover or richness of sponges provided a large dividend in conservation effort by dramatically reducing the number of spatial units required to achieve a specified conservation target of 50–90% of species to be included in reserves. This demonstrates that image‐derived data are useful for the design of reserves in the deep sea, particularly where extractive sampling is not warranted. Using biological data on the sponge megabenthos to identify conservation units can also minimise socio‐economic costs to fisheries because of a smaller geographic and bathymetric ambit of conservation areas.

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Protecting deep‐sea coral‐based vulnerable marine ecosystems (VMEs) from human impacts, particularly bottom trawling, is a major conservation challenge in world oceans. Management processes for these ecosystems are weakened by key uncertainties that could be substantially addressed by having much greater volumes of quantitative image‐derived data that detail the distribution and abundance of coral reefs and the nature of impacts upon them. Considerably greater volumes of data could be available if the resource costs of image annotation are reduced. In this paper we propose a solution: a deep learning system capable of automatically identifying reef‐building stony corals amongst other seabed substrata in much larger volumes of seabed imagery than was previously possible. Using a previously annotated dataset, we trained a convolutional neural network on approximately 70,000 classified images (‘snips’) comprising six benthic substrate classes, including reef‐building stony coral—‘coral matrix’. Model performance improvements, chiefly by dataset cleaning, transfer learning and hyperparameter optimisation, resulted in the final trained model achieving validation accuracy of 98.19%. The classification was robust: benthic substrate types were accurately differentiated, and in some cases more consistently than was achieved by human annotators. Synthesis and applications. The availability of much larger volumes of automatically annotated image‐derived data will improve spatial management of impacts on coral‐based VMEs in the deep sea by (1) improved cross‐validation and performance of spatial models required to predict coral distribution and abundance over the large scales of managed areas, and (2) establishing empirical relationships between coral abundance on the seabed and coral bycatch landed during fishing operations.