A growing need to manage marine biodiversity at local, regional and global scales cannot be met by applying the limited existing biological data sets. Abiotic surrogacy is increasingly valuable in filling the gaps in our knowledge of biodiversity 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 utility of abiotic surrogates across spatial scales with particular regard to how abiotic variables are tied to processes which affect biodiversity and how easily those variables can be measured at scales relevant to resource management decisions.

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.

An important aim of the comparative geomorphology of estuaries project was to increase understanding of the environmental characteristics of near-pristine estuaries and provide a reference dataset for quantifying changes in habitat patterns in modified systems. It was anticipated that this aim would be fulfilled by identifying key geomorphic characteristics of the near-pristine systems that may be used to benchmark the current condition of, and quantify change within, 'modified' waterways. Here we provide examples of some very promising results obtained from our preliminary analyses of the geomorphic habitat area information contained within the GIS maps available on OzEstuaries.

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.

This map produced in June 2014, is an updated and modified version of "Northern Region (Map for Australian Customs and Border Protection Service)" GeoCat 77778, October 2013. Map showing all of Australia's Maritime Jurisdiction north of approx 25CS . This includes areas around Cocos (Keeling) Islands and areas west of Christmas Island as well as those contiguous to the continent in the north. Map derived from one of the "Australia's Maritime Jurisdiction Map Series" (GeoCat 71985). Depicting Australian Maritime Boundaries sourced from Australia's Maritime Boundaries data, by Geoscience Australia, 2014. Supplemented with the 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". International Boundaries are sourced from the most reliable publicly available sources. Some international boundaries remain in dispute. 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. Map size 3m x 2m for Department of Immigration and Border Protection. (for internal use only - not for publication)

This is a joint product developed by GA and Skyring Environment Entetrprises. It is an animated CDROM developed specifically in Authoware software for state of the art visual presentation.

Seascapes describing a layer of ecologically meaningful biophysical variable that spatially represent potential seabed habitats have been derived for the Australian margin and adjacent seabed in a new analysis of existing biophysical data. A total of 13 seascapes were derived for the continental shelf and nine seascapes for regions beyond the continental shelf using the unsupervised ISOCLASS classification in the software package ERMapper. The ecological significance of the seascapes is assessed at the national, regional and local scale using existing biological data. Options and avenues for future development are also described.

The benthic (sea floor) component of the National Marine Bioregionalisation covers the 80% of Australia's Exclusive Economic Zone that lies beyond the continental shelf break. It provides a description of patterns of biological distributions and physical habitats on the seafloor. The Benthic Bioregionalisation Report is a technical document describing how the benthic bioregions were created. It includes descriptions of all the datasets used, details of each bioregion, and examples of how the physical data may be used to sub-divide the marine bioregions for management. An evaluation of the benthic bioregionalisation including strengths, weaknesses and future work is also contained in the report.

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.

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.