Geoscience Australia marine reconnaissance survey TAN0713 to the Lord Howe Rise offshore eastern Australia was completed as part of the Federal Government's Offshore Energy Security Program between 7 October and 22 November 2007 using the New Zealand Government's research vessel Tangaroa. The survey was designed to sample key, deep-sea environments on the east Australian margin (a relatively poorly-studied shelf region in terms of sedimentology and benthic habitats) to better define the Capel and Faust basins, which are two major sedimentary basins beneath the Lord Howe Rise. Samples recovered on the survey contribute to a better understanding of the geology of the basins and assist with an appraisal of their petroleum potential. They also add to the inventory of baseline data on deep-sea sediments in Australia. The principal scientific objectives of the survey were to: (1) characterise the physical properties of the seabed associated with the Capel and Faust basins and Gifford Guyot; (2) investigate the geological history of the Capel and Faust basins from a geophysical and geological perspective; and (3) characterise the abiotic and biotic relationships on an offshore submerged plateau, a seamount, and locations where fluid escape features were evident. This dataset comprises chlorin indices measured on seabed sediments (0-2 cm). Some relevant publications which pertain to these datasets include: 1. Heap, A.D., Hughes, M., Anderson, T., Nichol, S., Hashimoto, T., Daniell, J., Przeslawski, R., Payne, D., Radke, L., and Shipboard Party, (2009). Seabed Environments and Subsurface Geology of the Capel and Faust basins and Gifford Guyot, Eastern Australia - post survey report. Geoscience Australia, Record 2009/22, 166pp. 2. Radke, L.C. Heap, A.D., Douglas, G., Nichol, S., Trafford, J., Li, J., and Przeslawski, R. 2011. A geochemical characterization of deep-sea floor sediments of the northern Lord Howe Rise. Deep Sea Research II 58: 909-921

The Petrel Sub-basin Marine Environmental Survey GA-0335, (SOL5463) was undertaken by the RV Solander during May 2012 as part of the Commonwealth Government's National Low Emission Coal Initiative (NLECI). The survey was undertaken as a collaboration between the Australian Institute of Marine Science (AIMS) and GA. The purpose was to acquire geophysical and biophysical data on shallow (less then 100m water depth) seabed environments within two targeted areas in the Petrel Sub-basin to support investigation for CO2 storage potential in these areas. This dataset comprises an interpreted geomorphic map. Interpreted local-scale geomorphic maps were produced for each survey area in the Petrel Sub-basin using multibeam bathymetry and backscatter grids at 2 m resolution and bathymetric derivatives (e.g. slope; 1-m contours). Five geomorphic units; bank, plain, ridge, terrace and valley, 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.

Geoscience Australia undertook a marine survey of the Vlaming Sub-basin in March and April 2012 to provide seabed and shallow geological information to support an assessment of the CO2 storage potential of this sedimentary basin. The survey was undertaken under the Australian Government's National CO2 Infrastructure Plan (NCIP) to help identify sites suitable for the long term storage of CO2 within reasonable distances of major sources of CO2 emissions. The Vlaming Sub-basin is located offshore from Perth, Western Australia, and was previously identified by the Carbon Storage Taskforce (2009) as potentially highly suitable for CO2 storage. The principal aim of the Vlaming Sub-basin marine survey (GA survey number GA334) was to look for evidence of any past or current gas or fluid seepage at the seabed, and to determine whether these features are related to structures (e.g. faults) in the Vlaming Sub-basin that may extend up to the seabed. The survey also mapped seabed habitats and biota in the areas of interest to provide information on communities and biophysical features that may be associated with seepage. This research addresses key questions on the potential for containment of CO2 in the Early Cretaceous Gage Sandstone (the basin's proposed CO2 storage unit) and the regional integrity of the South Perth Shale (the seal unit that overlies the Gage Sandstone). This dataset comprises an interpreted geomorphic map.

Population connectivity science involves investigating how populations are related to one another through biological dispersal. Here, we review tools, techniques and analyses used by connectivity researchers, and place them in the context of how they can be used by marine managers and policy-makers to enhance their decision-making capabilities. Specific examples of developing technologies include: advances in mark and recapture techniques, underwater imaging systems, population genetic analyses, as well as four-dimensional dispersal simulations (3D space x time). These data can then be analysed using a wide array of analyses, including matrix analysis, graph theory, and various GIS-based routines. The results can be used to identify key source and sink areas, critical linkages (keystones), natural clusters and groups, levels of accuracy, precision and variability, as well as areas of asymmetric exchange. In turn, this information can be used to help identify natural management units, to target critical conservation areas, to develop efficient sampling strategies through power analysis, and to negotiate equitable allocation of resources to upstream management in cases where downstream benefits are significant. Through a better understanding of how connectivity science can assist decision-making, we hope to encourage increased uptake of these kinds of information into institutional planning processes.

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) biophysical dispersal model to simulate the movement of marine larvae over large, topographically complex areas. 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.

This dataset contains species identifications of molluscs collected during survey SOL4934 (R.V. Solander, 27 August - 24 September, 2009). Animals were collected from the Joseph Bonaparte Gulf with a benthic sled. Specimens were lodged at Northern Territory Museum on the 8 February 2010. Species-level identifications were undertaken by Richard Willan at the Northern Territory Museum and were delivered to Geoscience Australia on the 15 March 2010. See GA Record 2010/09 for further details on survey methods and specimen acquisition. Data is presented here exactly as delivered by the taxonomist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications.<p><p>This dataset is not to be used for navigational purposes.

Geoscience Australia carried out a marine survey on Carnarvon shelf (WA) in 2008 (SOL4769) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wave generated currents. Data and samples were acquired using the Australian Institute of Marine Science (AIMS) Research Vessel Solander. Bathymetric mapping, sampling and video transects were completed in three survey areas that extended seaward from Ningaloo Reef to the shelf edge, including: Mandu Creek (80 sq km); Point Cloates (281 sq km), and; Gnaraloo (321 sq km). Additional bathymetric mapping (but no sampling or video) was completed between Mandu creek and Point Cloates, covering 277 sq km and north of Mandu Creek, covering 79 sq km. Two oceanographic moorings were deployed in the Point Cloates survey area. The survey also mapped and sampled an area to the northeast of the Muiron Islands covering 52 sq km. Sample diversity indices calculated in PRIMER (version 6) using the species level data from Carnarvon_infauna(26_Oct_2010).xls

Geoscience Australia carried out marine surveys in south-east Tasmania in 2008 and 2009 (GA0315) to map seabed bathymetry and characterise benthic environments through observation of habitats using underwater towed video. Data was acquired using the Tasmania Aquaculture and Fisheries Institute (TAFI) Research Vessel Challenger. Bathymetric mapping was undertaken in seven survey areas, including: Freycinet Pensinula (83 sq km, east coast and shelf); Tasman Peninsula (117 sq km, east coast and shelf); Port Arthur and adjacent open coast (17 sq km); The Friars (41 sq km, south of Bruny Island); lower Huon River estuary (39 sq km); D Entrecastreaux Channel (7 sq km, at Tinderbox north of Bruny Island), and; Maria Island (3 sq km, western side). Video characterisations of the seabed concentrated on areas of bedrock reef and adjacent seabed in all mapped areas, except for D Entrecastreaux Channel and Maria Island. The GA0315_SETasi folder contains video footage; the excel file is the video characterisation data. Underwater video footage represents raw data. Video characterisation dataset include percent cover of substate.

In the past two decades, multibeam sonar systems have become the preferred seabed mapping tool. Many users have assumed that multibeam bathymetry data is highly accurate in spatial position. In reality, both vertical and horizontal uncertainties exist in every data point. These uncertainties are often represented as one single measure of Total Propagated Uncertainty (TPU). TPU is important to understand because it affects the quality of products generated from multibeam bathymetry data. To account for the magnitude and spatial distribution of this influence, an objective uncertainty analysis is required. Randomisation is the key process in such an uncertainty analysis. This study compared two randomisation methods, restricted spatial randomness (RSR) and complete spatial randomness (CSR), in an uncertainty analysis of a slope gradient dataset derived from multibeam bathymetry data. CSR regards data error in every grid cell as independent and assumes that the data error varies within a known statistical distribution without any neighbourhood effect. RSR assumes spatial structure and thus spatial auto-correlation in the data. We present a case study from a survey of the Oceanic Shoals Commonwealth Marine Reserve in the Timor Sea, conducted in 2012 by the Marine Biodiversity Hub through the Australian Government National Environmental Research Program. The survey area is characterised by steep-sided carbonate banks and terraces with abrupt breaks in slope of limited spatial extent. As habitats, the carbonate banks and terraces are important because they provide hardground for diverse epibenthic assemblages of sponges and corals, with their steep sides marking the environmental transition to deeper water, soft sediment habitats. In this analysis, the data errors in the multibeam bathymetry data were assumed to follow a Gaussian distribution with a mean of zero and a standard deviation represented by the TPU. The CSR and RSR methods were each implemented using a Monte Carlo procedure with 500 iterations. After about 300 iterations, the Monte Carlo procedure converged for both methods. Results for the study area are compared against pre-processed slope data (Figure 1a). The averaged slope gradient from the CSR method is 4.5 degree greater than the original slope layer, whereas for the RSR method this value is 0.03 degree. Moreover, the slope layer from the CSR method resolves noticeably less detail than the original slope layer and is an over-simplification of the true bathymetry (Figure 1b). In contrast, the RSR method maintained the spatial pattern and detail observed in the original slope layer (Figure 1c). This study demonstrates that although the uncertainty in multibeam bathymetry data should not be ignored, its impact on the subsequent derivative analysis may be limited. The selection of appropriate randomisation method is important for the uncertainty analysis. When the data errors exhibit spatial structure, we recommend using the RSR method.

This dataset contains species identifications of echinoderms collected during survey GA2476 (R.V. Solander, 12 August - 15 September 2008). Animals were collected from the Western Australian Margin with a BODO sediment grab or rock dredge. Specimens were lodged at Museum of Victoria on the 10 March 2009. Species-level identifications were undertaken by Tim O'Hara at the Museum of Victoria and were delivered to Geoscience Australia on the 24 April 2009. See GA Record 2009/02 for further details on survey methods and specimen acquisition. Data is presented here exactly as delivered by the taxonomist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications.