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.

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.

Map showing all of Australia's Maritime Jurisdiction north of approx 25°S . This includes areas around Cocos (Keeling) Islands and areas west of 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 extended continental shelf approved by the Commission on the Limits of the Continental Shelf in April 2008, treaties and various maritime zones. 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.

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

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.

Australian estuaries and coastal waterways were classified into six subclasses according to the wave-, tide- and river-energies that shape them, and also according to their overall geomorphology. The geomorphic classification confirmed the energy classification. Within this framework: - 17% were classified as wave-dominated estuaries; - 11% were classified as tide-dominated estuaries; - 10% were classified as wave-dominated deltas; and - 9% were classified as tide-dominated deltas Therefore, only ~28% of Australian coastal waterways are actually estuaries. The remainder are delta's (19%), strandplains (~5%), or tidal creeks (~35%). A seventh subclass others (13%) includes: Drowned River Valleys, Embayments and Coastal Lakes/Lagoons/Creeks. Strandplains and Tidal Creeks are indicative of very low river-energy, and their joint dominance in the data set (~40%) reflects the fact that Australia is a dry continent, with relatively little river runoff by world standards.

This dataset provides the spatially continuous data of the seabed sand content (sediment fraction 63-2000 mm) 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 sand 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.

This review aims to provide some guidelines and suggestions in relation to the application of the methods to environmental data by comparing the features of the commonly applied methods that fall into three categories, namely: non-geostatistical interpolators, geostatistical interpolators and combined methods. Commonly used assessment measures are summarised and the criteria used to judge each measurement are also discussed. Two new measurements are proposed and a procedure is developed to compare the performance of the methods for different variables and from various disciplines. A total of 51 comparative studies on the performance of various methods in environmental sciences are summarised. The performance of 62 methods and sub-methods in the 51 comparative studies is compared. Several factors that affect the performance are discussed, including sampling design, sample spatial distribution, data quality, correlation between primary and secondary variable, and interaction among various factors. The impacts of sample density, variation in the data, sampling design and stratification on the estimations of the methods are quantified using data from 77 cases. A total of 26 methods are then classified based on their features to provide an overview of relationships among these methods. These features are quantified and a cluster analysis is conducted to show similarities among these spatial interpolators. A decision tree for selecting an appropriate method from these 26 methods is developed based on data availability and nature. Finally, a list of software packages for spatial interpolation is provided. Some important factors for spatial interpolation in marine environmental science are discussed, and recommendations are made for applying the methods to marine environmental data.

The OzEstuaries online GIS contains data for Australian estuaries (coastal waterways) and for oceans in the Australian region. Estuaries data include geomorphic habitat mapping, estuary condition, colour composite images (Landsat, MODIS and Quickbird satellite imagery and aerial photography), benthic classifications (from Landsat satellite imagery), bathymetry and population centres. Oceanic data include dissolved organic matter, chlorophyll concentration, suspended solids concentration and sea surface temperature (using MODIS satellite imagery) and bathymetry. The GIS provides facilities to search for and zoom to estuaries, integrate mapping and imagery datasets, and retrieve statistical information from the OzEstuaries database; allowing users to view spatial and statistical information. The oceanic imagery provides a regional context for coastal waterways. The GIS is part of Geoscience Australia's contribution to the Cooperative Research Centre for Coastal Zone, Estuary and Waterway Management (Coastal CRC). The geomorphic habitat mapping was conducted by Geoscience Australia for the National Land and Water Resources Audit, and is also part of Geoscience Australia's contribution to the Coastal CRC.

The Corporate Archive consists of deposited copies of all internal publications and documents of the agency and its predecessors: the Bureau of Mineral Resources, Australian Geological Survey Organisation and those which have merged with it over the years, such as AUSLIG. These include unique material such as field notebooks and a small quantity of manuscripts. Unrestricted items in this collection are currently being digitised to improve access