Continental shelf margin habitats are increasingly being recognized worldwide for conservation and protection from human activities due to their biodiversity value. Yet, quantitative data on the biodiversity of the epibenthic taxa found on these continental shelf margins are scant. Consequently, this paper quantified the diversity of epibenthic taxa on an exposed- and sediment-inundated reef system located on the continental shelf margin off southeastern Australia as part of a program developing deep reef monitoring protocols. The reef system harbored a rich epibenthic taxa, with a total of 55 taxa identified from the images captured by an autonomous underwater vehicle. A Cnidaria/Bryzoa/Hydroid matix dominated the assemblages recorded. Taxa richness, diversity and evenness declined with distance from exposed reef ledge features, a characteristic geomorphic feature of this region. Patterns of the epibenthic assemblages were characterized by (1) taxonomic turnover at scales of 5 to 10's m from exposed reef ledges, (2) 30 % of epibenthic taxa were recorded only once (i.e. singletons), and (3) generally low levels of abundance of the component epibenthic taxa. This suggests that the assemblages in this region contain a considerable number of locally rare taxa, and potentially represent a high level of endemism. This study also highlights the importance of exposed reef ledge features in this region as they provide a refuge against sediment scouring and inundation in sediment-dominated ecosystems. Consequently, from a perspective of conservation planning for continental shelf habitats, protecting a single, or just a few, areas of reef are unlikely to accurately represent the geomorphic diversity of cross-shelf habitats and the epibenthic diversity that responds to this. Likewise, sampling needs to be adaptive, and stratified to incorporate known or suspected patterns relating to such variability. In this context, the data collected here provides a regional baseline dataset on the epibenthic taxa that shape the overall community structure for the Flinders Commonwealth Marine Reserve, and a guideline for sand-inundated cross-shelf reefs in general. Similar studies are now required for other known categories of cross-shelf reefs, including relict coastlines and complex block features more typical of igneous rock types.

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 - 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and densitydriven circulation. Included in the parameters that were calculated to represent the magnitude of the bulk of the data are the quartiles of the distribution; Q25, Q50 and Q75 (i.e. the values for which 25, 50 and 75 percent of the observations fall below). Q75, or the 0.75 Quartile of the Geomacs output, represents the values for which 75% of the observations fall below (Hughes & Harris 2008).

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 - 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and densitydriven circulation. Included in the parameters that will be calculated to represent the magnitude of the bulk of the data are the quartiles of the distribution; Q25, Q50 and Q75 (i.e. the values for which 25, 50 and 75 percent of the observations fall below). The interquartile range, , of the GEOMACS output takes the observations from between Q25 and Q75 to provide an accurate representation of the spread of observations. The interquartile range was shown to provide a more robust representation of the observations than the standard deviation, which produced highly skewed observations (Hughes & Harris 2008).

This study aims to quantify sponge biodiversity of the Van Diemen Rise and eastern Joseph Bonaparte Gulf in northern Australia and to examine spatial and environmental patterns associated with differences in community structure of sponges.

As part of Geoscience Australia's commitment towards the National Environmental Programme's Marine Biodiversity Hub, we have developed a fully four-dimensional (3D x time) Lagrangian biophysical dispersal model to simulate the movement of marine larvae over large, topographically complex areas. The model operates by fusing the results of data-assimilative oceanographic models (e.g. BLUELink, HYCOM, ROMS) with individual-based particle behaviour. The model uses parallel processing on Australia's national supercomputer to handle large numbers of simulated larvae (on the order of several billion), and saves positional information as points within a relational database management system (RDBMS). The model was used to study Australia's northwest marine region, with specific attention given to connectivity patterns among Australia's north-western Commonwealth Marine Reserves and Key Ecological Features (KEFs). These KEFs include carbonate terraces, banks and reefs on the shelf that support diverse benthic assemblages of sponges and corals, and canyons that extend from the shelf edge to the continental slope and are potential biodiversity hotspots. We will show animations of larval movement near canyons within the Gascoyne CMR; larval dispersal probability clouds partitioned by depth and time; as well as matrices of connectivity values among features of interest. We demonstrate how the data can be used to identify connectivity corridors in marine environments, and how the matrices can be analysed to identify key connections within the network. Information from the model can be used to inform priorities for monitoring the performance of reserves through examining net contributions of different reserves (i.e. are they sources or sinks), and studying changes in connectivity structure through adding and removing reserve areas.

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_s3 is an ArcINFO grid of southern part of Jervis Bay survey area (south3 is part of Darling RD grid) produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

Discerning how marine ecosystems are linked through larval dispersal is essential for understanding demographic flow, investigating the development of population genetic structure, and for evaluating the potential responses of communities to climate change. This information is of critical importance when designing reserve networks, identifying key locations for restoration, controlling invasive species, and administering transboundary resources. As part of Geoscience Australia's commitment to the National Environmental Research Programme's Marine Biodiversity Hub, we have developed a fully four-dimensional (3D space x time) individual-based model that embeds artificially intelligent particles within real-world ocean flow fields, making it possible to examine expected dispersal patterns of marine larvae under a variety of conditions. The model fuses strategic biological behaviour with physical equations in a flexible manner through the use of object-oriented programming. We will discuss aspects of model development and testing, as well as practical issues relating to computing on the National Computing Infrastructure, and addressing large-scale data storage. We will also identify potential avenues for data analysis that can be used to inform environmental decision-making.

This dataset contains topographic position index data from seabed mapping surveys on the Van Diemen Rise in the eastern Joseph Bonaparte Gulf of the Timor Sea. The survey was conducted under a Memorandum of Understanding between Geoscience Australia (GA) and the Australian Institute of Marine Science (AIMS) in two consecutive years 2009 (GA survey number GA-0322 and AIMS survey number SOL4934) and 2010 (GA survey number GA-0325 and AIMS survey number SOL5117). The surveys obtained detailed geological (sedimentological, geochemical, geophysical) and biological data (macro-benthic and infaunal diversity, community structure) for the banks, channels and plains to investigate relationships between the physical environment and associated biota for biodiversity prediction. The surveys also provide Arafura-Timor Sea, and wider northern Australian marine region context for the benthic biodiversity of the Van Diemen Rise. Four study areas were investigated across the outer to inner shelf. Refer to the GA record 'Methodologies for seabed substrate characterisation using multibeam bathymetry, backscatter, and video data: A case study for the Eastern Joseph Bonaparte Gulf, Northern Australia' for further information on processing techniques applied (GeoCat: 74092; GA Record: 2013/11).

The accuracy of spatially continuous environmental data, usually generated from point samples using spatial prediction methods (SPMs), is crucial for evidence-informed environmental management and conservation. Improving the accuracy by identifying the most accurate methods is essential, but also challenging since the accuracy is often data specific and affected by multiple factors. Because of the high predictive accuracy of machine learning methods, especially random forest (RF), they were introduced into spatial statistics by combining them with existing SPMs, which resulted in new hybrid methods with improved accuracy. This development opened an alternative source of methods for spatial prediction. In this study, we introduced these hybrid methods, along with the modelling procedure adopted to develop the final predictive models. These methods were compared with the commonly used SPMs in R using cross-validation techniques based on both marine and terrestrial environmental data. We also addressed the following questions: 1) whether they are data-specific for marine environmental data, 2) whether input predictors affect their performance, and 3) whether they are equally applicable to terrestrial environmental data? This study provides suggestions and guidelines for the application of these hybrid methods to spatial predictive modelling not only in environmental sciences, but also in other relevant disciplines.

This resource contains species identifications of sponges from 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). Sponges were collected using an epibenthic sled towed for approximately 50 m at 21 stations across three survey areas. From these, a total of 339 specimens were retained for taxonomic identification and lodged at the Museum and Art Gallery of the Northern Territory (MAGNT) immediately after demobilisation. Species-level identifications were undertaken by Dr Belinda Alvarez de Glasby at the MAGNT. 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).