Australia's near-pristine estuaries are some of our most valuable natural assets, with many natural and cultural heritage values. They are important as undisturbed habitat for native plants and animals, for biodiversity conservation, as Indigenous lands and for tourism. They also support near-shore fisheries. In addition, by studying near-pristine estuaries, scientists can learn more about the way humans have changed natural systems. This information then feeds into natural resource management because it constitutes benchmark or baseline information against which similar information from more modified estuaries can be compared.

Selected geomorphic features and sedimentary facies were mapped in 283 of Australia's wave- and tide-dominated estuaries and deltas to quantitatively evaluate established evolutionary facies models that depict the evolution of estuaries into deltas during stable sea level conditions. While diagnostic facies for wave- and tide-dominated estuaries and deltas approximate those specified by the models, statistical analyses of the data also reveal two additional insights regarding the evolution of estuaries to deltas. First, there is an offshore shift in the locus of sand accumulation between tide-dominated estuaries and deltas, associated with the onset of delta development. Second, the mean surface area of intertidal environments (i.e., intertidal flats, mangroves/melaleuca, saltmarsh/salt flat facies) is greater in wave-dominated deltas than in wave-dominated estuaries. Tidal penetration associated with the river establishing a more direct and permanent connection to the sea during late-stage development presents a natural impediment to continued formation of an alluvial plain and full development of the 'classic' wave-dominated delta morphology. A notional evolutionary pathway for wave-dominated estuaries is developed from the distribution of facies that predicts the rate and susceptibility of geomorphic and habitat changes. The 'classic' deltaic geomorphology may be unattainable for wave-dominated systems, except those with significant terrigenous sediment inputs. Our study is the first published example of geomorphic and sedimentary data assembled from a large number of wave- and tide-dominated estuaries and deltas across an entire continent.

Report to the National Oceans Office on the production of a consistent, high-quality bathymetric data grid and definition and description of geomorphic units for part of Australia's marine jurisdiction.

This dataset provides the spatially continuous data of seabed gravel (sediment fraction >2000 µm), mud (sediment fraction < 63 µm) and sand content (sediment fraction 63-2000 µm) expressed as a weight percentage ranging from 0 to 100%, presented in 0.0025 decimal degree (dd) resolution raster grids format and ascii text file. The dataset covers the Vlaming sub-basin in the Australian continental EEZ. This dataset supersedes previous predictions of sediment gravel, mud and sand content for the basin 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 the basin scale. The dataset may not be appropriate for use at smaller 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.

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.

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.

The collection consists of seabed samples collected by Geoscience Australia and other organizations since the 1950s. Samples consist of various shallow cores types, rocks derived from dredging, and sea bed sediments collected by grab and dredge methods. A large proportion of samples are refrigerated.

In order to design a representative network of high seas marine protected areas (MPAs), an acceptable scheme is required to classify the benthic bioregions of the oceans. Given the lack of sufficient biological information to accomplish this task, we used a multivariate statistical method with 6 biophysical variables (depth, seabed slope, sediment thickness, primary production, bottom water dissolved oxygen and bottom temperature) to objectively classify the ocean floor into 11 different categories, comprised of 53,713 separate polygons, that we have termed "seascapes". Validation of the seascape classification was carried out by comparing the seascapes with an existing map of seafloor geomorphology, and by GIS analysis of the number of separate polygons and perimeter/area ratio. We conclude that seascapes, derived using a multivariate statistical approach, are biophysically meaningful subdivisions of the ocean floor and can be expected to contain different biological associations, in as much as different geomorphological units do the same. Our study illustrates how the identification of potential sites for high seas marine protected areas can be accomplished by GIS analysis of seafloor geomorphic and seascape classification maps. Using this approach, maps of seascape and geomorphic heterogeneity were generated in which heterogeneity hot-spots identify themselves as MPA candidates. The use of computer-aided mapping tools removes subjectivity in the MPA design process and provides greater confidence to stakeholders that an unbiased result has been achieved.

This is a compilation of Seabed and Habitat Mapping Publications 2008 - 2010: GA Record 2008_20.pdf Vlaming Sub-Basin and Mentelle Basin: Environmental Summary GA Record 2008_23.pdf A Review of Spatial Interpolation Methods for Environmental Scientists GA Record 2009_02.pdf Carnarvon Shelf Survey Post-Survey Report GA Record 2009_09.pdf Ceduna Sub-basin: Environmental Summary GA Record 2009_10.pdf Mapping and characterising soft sediment habitats, and evaluating physical variables as surrogates of biodiversity in Jervis Bay, NSW GA Record 2009_12.pdf Temporal and fine-scale variation in the biogeochemistry of Jervis Bay GA Record 2009_13.pdf Review of Ten Key Ecological Features (KEFs) in the Northwest Marine Region GA Record 2009_22.pdf Seabed Environments and Subsurface Geology of the Capel and Faust basins and Gifford Guyot,Eastern Australia GA Record 2009_26.pdf Deep Sea Lebensspuren: Biological Features on the Seafloor of the Eastern and Western Australian Margin GA Record 2009_38.pdf Frontier basins of the west Australian continental margin: post-survey report of marine reconnaissance and geological sampling survey GA2476 GA Record 2009_42.pdf A Review of Surrogates for Marine Benthic Biodiversity GA Record 2009_43.pdf Southeast Tasmania Temperate Reef Survey Post-Survey Report GA Record 2010_09.pdf Seabed Environments of the Eastern Joseph Bonaparte Gulf, Northern Australia