This includes collection of core from sonic drilling and soil and water samples from boreholes and surface water. The Core is stored in plastic in core trays (4 x 1m). The water samples are disposed of once analysed.

The dataset provides the spatially continuous data of the seabed gravel content (sediment fraction >2000 µm) expressed as a weight percentage ranging from 0 to 100%, presented in 0.01 decimal degree resolution raster format. The dataset covers the Australian continental EEZ, including seabed surrounding Tasmania. It does not include areas surrounding Macquarie Island, and the Australian Territories of Norfolk Island, Christmas Island, and Cocos (Keeling) Islands or Australia's marine jurisdiction off of the Territory of Heard and McDonald Islands and the Australian Antarctic Territory. This dataset supersedes previous predictions of sediment gravel content for the Australian Margin with demonstrated improvements in accuracy. Accuracy of predictions varies based on density of underlying data and level of seabed complexity. Artefacts occur in this dataset as a result of insufficient samples in relevant regions. This dataset is intended for use at national and regional scales. The dataset may not be appropriate for use at local scales in areas where sample density is insufficient to detect local variation in sediment properties. To obtain the most accurate interpretation of sediment distribution in these areas, it is recommended that additional samples be collected and interpolations updated.

GEOMA T is a geological-oceanographic computer modelling project which aims to enhance our understanding of the processes controlling sediment mobilisation on the Australian continental shelf. This report describes tidal and surface ocean swell-wave models and their application to studies of shelf sediment mobilisation. The work has been carried out over the past 2 years by a team of collaborators from AGSO, the University of Tasmania, the Australian Bureau of Meteorology and Kort & Matrikelstyrelsen, Geodetic Division in Denmark. Our models predict that swell wave energy is sufficient to mobilise fine sand (0.1 mm diameter), on at least one occasion during the year March 1997 to February 1998, over 63.5% of the Australian continental shelf. The largest and most powerful waves were able to mobilise fine sand up to a water depth of 148 m in the Great Australian Bight. Tidal currents are capable of mobilising fine sand at least once per semi-lunar cycle (ie. -2 weeks) over about 56.4% of the shelf. Overlaying the wave and tide threshold exceedence maps demonstrates that there are areas on the shelf where one process dominates, some areas where tides and waves are of relatively equal importance and still other areas where neither process is significant. We defined 6 shelf regions of relative wave and tidal energy: zero (no-mobility); waves-only, wave-dominated, mixed, tide-dominated and tides-only. The relative distribution of these regions varies with grain size. Inclusion of estimated mean grain size is being undertaken at the present time and this will enhance the usefulness of the regionalisations. GEOMA T provides a predictive, process-based understanding of the shelf sedimentary system. It helps to explain the distribution patterns of surficial sediments and will probably be useful for mapping biological habitats and communities, although further work is needed to better define these relationships. GEOMA T provides a useful tool that will assist with marine environmental management in general, and with the National Ocean's Office regional marine planning process in particular. It has demonstrated applications to marine engineering projects where shelf sediment mobilisation is of concern and to regional studies of pollution dispersal and accumulation.

In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of seabed mud content across the Australian Exclusive Economic Zone (AEEZ), which indicated that machine learning methods are generally among the most accurate methods. In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK) and inverse distance squared (IDS). We aim to identify the most accurate methods for spatial interpolation of seabed mud content in three regions (i.e., N, NE and SW) in AEEZ using samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods and their combinations with OK or IDS) is compared using a simulation experiment. The prediction accuracy changes with the methods, inclusion and exclusion of slope, search window size, model averaging and the study region. The combination of RF and OK (RFOK) and the combination of RF and IDS (RFIDS) are, on average, more accurate than the other methods based on the prediction accuracy and visual examination of prediction maps in all three regions when slope is included and when their searching widow size is 12 and 7, respectively. Averaging the predictions of these two most accurate methods could be an alternative for spatial interpolation. The methods identified in this study reduce the prediction error by up to 19% and their predictions depict the transitional zones between geomorphic features in comparison with the control. This study confirmed the effectiveness of combining machine learning methods with OK or IDS and produced an alternative source of methods for spatial interpolation. Procedures employed in this study for selecting the most accurate prediction methods provide guidance for future studies.

This report details the keystroke methodology used to create the seascape maps for planning areas of the Australian margin.

This report describes the iterative methods used to create the seascapes, including a detailed appendix documenting the different datasets used in the different planning zones. Creating the seascapes is necessarily an iterative process whereby the available datasets are combined in different combinations, or added as they become available, using an unsupervised 'crisp' ISOClass classification in ERMapper. In each classification only biophysical properties that have consistent and definable relationships with the benthic biota and are known in sufficient detail across Australia's entire marine region are used to create the seascapes. An initial validation of the classification technique has been undertaken on a subset of the data for the shelf surrounding Tasmania using an alternative unsupervised 'fuzzy' classification. Results of this validation indicate that the unsupervised classification methodology provides consistent and reliable classes for defining the seascapes.

Map showing all of Australia's Maritime Jurisdiction north of approx 25°S. Updated in June 2014 from "Australia's Maritime Jurisdiction North of 25°S" (GeoCat 71985) to conform with "Australian Maritime Boundaries 2014" data by Geoscience Australia. This includes areas around Cocos (Keeling) Islands and areas around Christmas Island as well as those contiguous to the continent in the north. Included as one of the now 28 constituent maps of the "Australia's Maritime Jurisdiction Map Series" (GeoCat 71789). Depicting Australia's continental shelf as proclaimed in the "Seas and Submerged Lands (Limits of Continental Shelf) Proclamation 2012" established under the "Seas and Submerged Lands Act 1973". Background bathymetry image is derived from a combination of the 2009 9 arc second bathymetry and topographic grid by Geoscience Australia and a grid by W.H.F. Smith and D.T. Sandwell, 1997. Background land imagery derived from Blue Marble, NASA's Earth Observatory. 3277mm x 1050mm (for 42" plotter) sized .pdf downloadable from the web.

A growing need to manage marine biodiversity sustainably at local, regional and global scales cannot be met by applying the limited existing biological data. Abiotic surrogates of biodiversity are thus increasingly valuable in filling the gaps in our knowledge of biodiversity patterns, especially identification of hotspots, habitats needed by endangered or commercially valuable species and systems or processes important to the sustained provision of ecosystem services. This review examines the use of abiotic variables as surrogates for patterns in benthic assemblages with particular regard to how variables are tied to processes affecting biodiversity and how easily those variables can be measured at scales relevant to resource management decisions.

In this study, we conducted a simulation experiment to identify robust spatial interpolation methods using samples of seabed mud content in the Geoscience Australian Marine Samples database. Due to data noise associated with the samples, criteria are developed and applied for data quality control. Five factors that affect the accuracy of spatial interpolation were considered: 1) regions; 2) statistical methods; 3) sample densities; 4) searching neighbourhoods; and 5) sample stratification. Bathymetry, distance-to-coast and slope were used as secondary variables. Ten-fold cross-validation was used to assess the prediction accuracy measured using mean absolute error, root mean square error, relative mean absolute error (RMAE) and relative root mean square error. The effects of these factors on the prediction accuracy were analysed using generalised linear models. The prediction accuracy depends on the methods, sample density, sample stratification, search window size, data variation and the study region. No single method performed always superior in all scenarios. Three sub-methods were more accurate than the control (inverse distance squared) in the north and northeast regions respectively; and 12 sub-methods in the southwest region. A combined method, random forest and ordinary kriging (RKrf), is the most robust method based on the accuracy and the visual examination of prediction maps. This method is novel, with a relative mean absolute error (RMAE) up to 17% less than that of the control. The RMAE of the best method is 15% lower in two regions and 30% lower in the remaining region than that of the best methods in the previously published studies, further highlighting the robustness of the methods developed. The outcomes of this study can be applied to the modelling of a wide range of physical properties for improved marine biodiversity prediction. The limitations of this study are discussed. A number of suggestions are provided for further studies.

Map showing all of Australia's Maritime Jurisdiction including external Territories and the AAT. One of the 27 constituent maps of the "Australia's Maritime Jurisdiction Map Series" (GeoCat 71789). Depicting Australia's extended continental shelf approved by the Commission on the Limits of the Continental Shelf in April 2008. Background bathymetry image is derived from a combination of the 2009 9 arc second bathymetry and topographic grid by Geoscience Australia and a grid by W.H.F. Smith and D.T. Sandwell, 1997. Background land imagery derived from Blue Marble, NASA's Earth Observatory. A0 sized .pdf downloadable from the web.