Geoscience Australia carried out marine surveys in Jervis Bay (NSW) in 2007, 2008 and 2009 (GA303, GA305, GA309, GA312) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments (for textural and biogeochemical analysis) and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wavegenerated currents. Data and samples were acquired using the Defence Science and Technology Organisation (DSTO) Research Vessel Kimbla. Bathymetric mapping, sampling and tide/wave measurement were concentrated in a 3x5 km survey grid (named Darling Road Grid, DRG) within the southern part of the Jervis Bay, incorporating the bay entrance. Additional sampling and stills photography plus bathymetric mapping along transits was undertaken at representative habitat types outside the DRG. jb_s4 is an ArcINFO grid of southern part of Jervis Bay survey area (south4 is part of Darling RD grid) produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

In November 2012, the Australian Government finalised a national network of Commonwealth Marine Reserves (CMR) covering 3.1 million km2 and representing the full range of large scale benthic habitats known to exist around mainland Australia. This network was designed using the best available regional-scale information, including maps of seabed geomorphic features and associated Key Ecological Features. To support the management objectives of the marine reserves, new site-specific information is required to improve our understanding of biodiversity patterns and ecosystem processes across a range of spatial scales. In this context, the Marine Biodiversity Hub (funded through the National Environmental Research Program) recently completed a collaborative 'voyage of discovery' to the Oceanic Shoals CMR in the Timor Sea. This area was chosen because it hosts globally significant levels of biodiversity (including endemic sponge and coral taxa), faces rapidly increasing pressures from human activities (offshore energy industry, fishing) yet is recognised as one of the most poorly known regions of Northern Australia. Undertaken in September 2012 on board RV Solander, the survey acquired biophysical data on the shallow seabed environments for targeted areas within the Oceanic Shoals CMR, with a focus on the carbonate banks that characterise this tropical shelf and are recognised as a Key Ecological Feature. Data collected included 500 km2 of high resolution (300 kHz) multibeam sonar bathymetry and acoustic backscatter across four grids, plus seabed sediment samples, underwater tow-video transects (~1 km length), pelagic and demersal baited underwater video, epifaunal and infaunal samples and water column profiles at pre-determined stations. Station locations were designed to provide a random but spatially balanced distribution of sample sites, with weighting toward the banks. This design also facilitated observations of patterns of benthic biodiversity at local to feature-scale and transitions associated with depth-gradients and exposure to tidal currents. Results reveal the banks are broadly circular to elliptical with steep sides, mantled by muddy sand and gravel with areas of hard ground. Rising to water depths of 50-70 m, the banks support benthic assemblages of sponges and corals (including hard corals at shallower sites) which in turn support other marine invertebrates. In strong contrast, the surrounding seabed is characterised by barren, mud-dominated sediments in 70-100 m water depth, although infaunal samples reveal diverse biological communities beneath the seafloor. While the bank assemblages are locally isolated, the potential exists for connectivity between shoals via tide-driven larval dispersal. Ongoing work is aimed at identifying species to determine overlap between bank communities, as well as modelling the sources, pathways and sinks for larvae as a proxy for understanding the physical processes controlling the patterns of biodiversity across the Oceanic Shoals CMR at multiple scales.

Marine benthic biodiversity can be quantified using a range of sampling methods, including benthic sleds or trawls, grabs, and imaging systems, each of which targets a particular community or habitat. Research studies often incorporate only one of these sampling methods in published results, and the generality of marine biodiversity patterns identified among different sampling methods remains unknown. In this study we use three biological collections obtained during a collaborative survey between Geoscience Australian and the Australian Institute of Marine Science to the Van Diemen Rise in northern Australia: 1) Infauna sampled from a Smith-McIntyre grab, 2) Epifauna sampled from a benthic sled, and 3) Biological communities identified from video. For each dataset, we investigated potential patterns of species richness and community structure in relation to depth, geomorphology, and study area, as well as the relationships between datasets. No gear type yielded data that was strongly correlated with depth, but different patterns were evident among gear types based on study area and geomorphology. Comparisons among datasets indicate that species richness from sleds and grabs are more strongly correlated with each other than with richness from video. Further research is planned to incorporate datasets from other regions and habitats in order to provide a general assessment of sampling methods used in the quantification of benthic marine biodiversity in Australasia.

Seabed hardness is an important character of seabed substrate as it may influence the nature of attachment of an organism to the seabed. Hence spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is usually inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage or directly measured at limited locations. It can also be predicted based on two-class hardness data using environmental predictors, but no study has been undertaken for predicting multiple-class hardness data. In this study, we classified the seabed hardness into four classes based on underwater video images that were extracted from the underwater video footage. We developed an optimal predictive model to predict the spatial distribution of seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam bathymetry, backscatter and other derived predictors. A novel model selection method that is the averaged variable importance (AVI) was used based on predictive accuracy that was acquired from averaging the results of 100 times replication of 10-fold cross validation. Finally, the spatial predictions generated using the most accurate model was visually examined and analyzed in comparison with previously published predictions based on two-class hardness data. This study confirmed that: 1) seabed hardness of four classes can be predicted into a spatially continuous layer with a high degree of accuracy; 2) model selection for RF is essential for identifying an optimal predictive model in environmental sciences and AVI select the most accurate predictive model(s) instead of the most parsimonious ones, and is recommended for future studies; 3) the typical approach used in pre-selecting predictors by excluding correlated variables (i.e. r 0.95 or the inflation factor 20) needs to be re-examined for identifying predictive models using machine learning methods, at least for the application of random forest in marine environmental sciences; 4) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to `small p and large n problems in the environmental sciences; and 5) the spatial predictions for four-class hardness data were similar with the predictions based on two hardness classes, with a high match rates. RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies. In summary, AVI shows its effectiveness in searching for the most accurate predictive models and are recommended for future studies. This study further confirms the superior performance of RF in marine environmental sciences. RF is an effective modelling method with high predictive accuracy not only for presence/absence data and but also for multi-level categorical data. RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies.

Benthic marine invertebrates and their planktonic life stages live in a multistressor world where stressor levels are, and will continue to be, exacerbated by global change. Global warming and increased atmospheric CO2 are causing the oceans to warm, decrease in pH and become hypercapnic. These concurrent stressors have strong impacts on biological processes, but little is known about their combined effects on marine invertebrate development. Increasing temperature has a pervasive stimulatory effect on metabolism until lethal levels are reached, whereas hypercapnia can depress metabolism. Ocean acidification is a major threat to calcifying life stages because it decreases carbonate mineral saturation and also exerts a direct pH effect on physiology. Ocean pH, pCO2 and CaCO3 covary and will change simultaneously with temperature, challenging our ability to predict future outcomes for marine biota. The need to consider both ocean warming and acidification is reflected in the recent increase in multifactorial studies of these stressors on development of marine invertebrates. The outcomes and trends in these studies are synthesized here. Different sensitivities of life history stages and species have implications for persistence and community function in a changing ocean. Some species are more resilient than others and may be potential 'winners' in the climate change stakes. For echinoderms where multistressor studies span across life stages, the impacts of pH/pCO2 and warming on benthic-pelagic life cycle phases are assessed. As the ocean will change more gradually over coming decades than in 'future shock' experiments, it is likely that some species may be able to tolerate near future ocean change through acclimatization or adaption.

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy, can be inferred based on underwater video footage at limited locations. It can also be predicted to two classes. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e. hard90 and hard70) for seabed video footage by. We developed optimal predictive models to predict the spatial distribution of seabed hardness using random forest (RF) based on point data of hardness classes and spatially continuous multibeam backscatter data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), the combined, Boruta, and RRF were tested. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were also examined. Finally, the most accurate models were used to predict the spatial distribution of the hardness classes and the predictions were visually examined and compared with the predictions based on two-class hardness classification. This study confirms that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness can be predicted into a spatially continuous layer with a high degree of accuracy; 3) the typical approach used to pre-select predictors by excluding highly correlated predictors needs to be re-examined when using machine learning methods, at least, for RF, in the environmental sciences; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving the predictive models; 5) FS is essential for identifying an optimal RF predictive model and the RF methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data, can be applied to `small p and large n problems in environmental sciences, and is recommended for future studies. In addition, automated computational programs for AVI need be developed to improve its computational efficiency and caution should be taken when applying filter FS method in selecting predictive models in future studies.

Submerged relict reef systems and modern coral communities discovered around the Balls Pyramid shelf are presented as new evidence of extensive carbonate production at the boundary of reef-forming seas. Balls Pyramid is the southernmost island in a chain of island-reefs in the southwest Pacific Ocean, 24 km south of the southernmost known coral reef in the Pacific Ocean at Lord Howe Island. This paper explores the detailed geomorphic structure of the shelf through the production of a high resolution bathymetric model from multibeam echosounder data and depth estimates from satellite imagery. Key seafloor features identified include a large, mid shelf reef dominating the shelf landscape in 20 - 60 m water depth, mid shelf basins and channels, and shelf margin terrace sequences in 50 - 100 m depth. Sub-bottom profiles, backscatter, drill core and vibro-core data are used to investigate the seafloor composition. Drill cores extracted from the submerged reef surface confirm coral, coralline algae and cemented sands composition, and vibro-core material extracted from unconsolidated areas demonstrate substantial accumulation of carbonates shed from the reef surface. Underwater video imagery reveals abundant modern mesophotic reef communities, including hard corals, colonising the relict reef surface. This paper reveals prolific past reef growth and abundant modern coral growth on what was previously considered to be a planated volcanic shelf outside of reef-forming seas, thus extending understanding of reef evolution at, and beyond, the limits of growth.

This resource contains geochemistry data for the Oceanic Shoals Commonwealth Marine Reserve (CMR) in the Timor Sea collected by Geoscience Australia during September and October 2012 on RV Solander (survey GA0339/SOL5650). This dataset comprises inorganic element data from the fine fraction (Mud: <63um) of the upper ~2cm of seabed sediment. The Oceanic Shoals Commonwealth Marine Reserve survey was undertaken as an activity within the Australian Government's National Environmental Research Program Marine Biodiversity Hub and was the key component of Research Theme 4 - Regional Biodiversity Discovery to Support Marine Bioregional Plans. Hub partners involved in the survey included the Australian Institute of Marine Science, Geoscience Australia, the University of Western Australia, Museum Victoria and the Museum and Art Gallery of the Northern Territory. Data acquired during the survey included: multibeam sonar bathymetry and acoustic backscatter; sub-bottom acoustic profiles; physical samples of seabed sediments, infauna and epibenthic biota; towed underwater video and still camera observations of seabed habitats; baited video observations of demersal and pelagic fish, and; oceanographic measurements of the water column from CTD (conductivity, temperature, depth) casts and from deployment of sea surface drifters. Further information on the survey is available in the post-survey report published as Geoscience Australia Record 2013/38 (Nichol et al. 2013).

The Oceanic Shoals survey (SOL5650, GA survey 339) was conducted on the R.V. Solander in collaboration with Geoscience Australia, the Australian Institute of Marine Science (AIMS), University of Western Australia and the Museum and Art Gallery of the Northern Territory between 12 September - 5 October, 2012. This dataset comprises an interpreted geomorphic map. Interpreted local-scale geomorphic maps were produced for each survey area in the Oceanic Shoals Commonwealth Marine Reserve (CMR) using multibeam bathymetry and backscatter grids at 2 m resolution and bathymetric derivatives (e.g. slope; 1-m contours). Six geomorphic units; bank, depression, mound, plain, scarp and terrace were identified and mapped using definitions suitable for interpretation at the local scale (nominally 1:10 000). Maps and polygons were manual digitised in ArcGIS using the spatial analyst and 3D analyst toolboxes. For further information on the geomorphic mapping methods please refer to Appendix N of the post-survey report, published as Geoscience Australia Record 2013/38: Nichol, S.L., Howard, F.J.F., Kool, J., Stowar, M., Bouchet, P., Radke, L., Siwabessy, J., Przeslawski, R., Picard, K., Alvarez de Glasby, B., Colquhoun, J., Letessier, T. & Heyward, A. 2013. Oceanic Shoals Commonwealth Marine Reserve (Timor Sea) Biodiversity Survey: GA0339/SOL5650 Post Survey Report. Record 2013/38. Geoscience Australia: Canberra. (GEOCAT #76658).

Marine benthic biodiversity can be measured using a range of sampling methods, including benthic sleds or trawls, grabs, and imaging systems, each of which targets a particular community or habitat. Due to the high cost and logistics of benthic sampling, particularly in the deep sea, studies are often limited to only one or two biological sampling methods. Results of biodiversity studies are used for a range of purposes, including species inventories, environmental impact assessments, and predictive modelling, all of which underpin appropriate marine resource management. However, the generality of marine biodiversity patterns identified among different sampling methods is unknown, as are the associated impacts on management decisions. This report reviews studies that have used two or more sampling methods in order to determine the consistency of their results among gear types, as well as the optimum combination of gear types. In addition, we directly analyse data that were acquired using multiple gear types to examine the consistency of biodiversity patterns among different gear types. These data represent two regions: 1) Joseph Bonaparte Gulf (JBG) in northern Australia, and 2) Icelandic waters as part of the Benthic Invertebrates of Icelandic Waters (BIOICE) program. For each dataset, we investigate potential patterns of biodiversity (measured by species richness, diversity indices, abundance, and community structure) in relation to environmental variables such as depth, geomorphology, and substrate. The availability of worldwide data from benthic marine biodiversity surveys reporting the results of two or more gear types is generally poor. Surveys were concentrated in the coastal regions of UK, Norway and Australia, with limited or no studies elsewhere and only 13% including the slope or deep sea. Between different gear groups, our review and analysis of datasets from two regions (northern Australia and Iceland) demonstrates there is little consistency in marine biodiversity trends, with only one study yielding consistent ecological patterns between sampling gear groups (imagery and epifaunal). This indicates that ideal gear combinations are not easily able to be generalised among studies and regions. In addition, the lack of consistency between sampling gear groups highlights the need to analyse gear-specific data and avoid amalgamation. Even among gear that yielded relatively consistent ecological relationships, results varied across biological or environmental factors. Within a gear group, there are more consistencies in ecological relationships, with only two out of the eight studies compiled showing inconsistent ecological relationships A lack of gear-specific studies precluded the determination of the optimal combination of gear types for a particular regions or environments. Nevertheless, based on our findings, we provide preliminary recommendations and inform further research: 1) If general biodiversity patterns are to be investigated, sampling for marine benthic surveys should be carried out using multiple gear types that are concurrently deployed; 2) Target measures of biodiversity need to be decided a priori and appropriate gear used; 3) Preliminary data will help determine the optimal combination of gear types used to sample that region and address a given hypothesis; and 4) If only two gear types are able to be deployed, a grab or corer should be one of them, as this sampling gear type samples a different habitat than other gear groups.