Williams et al. (2009) report on new multibeam sonar bathymetry and underwater video data collected from submarine canyons and seamounts on Australia's southeast continental margin to 'investigate the degree to which geomorphic features act as surrogates for benthic megafaunal biodiversity' (p. 214). The authors describe what they view as deficiencies in the design of the Marine Protected Areas (MPAs) in the southeast region of Australia, in which geomorphology information was employed as a surrogate to infer regional-scale patterns of benthic biodiversity. This comment is designed to support and underscore the importance of evaluating MPA designs and the validity of using abiotic surrogates such as geomorphology to infer biodiversity patterns, and seeks to clarify some of the discrepancies in geomorphic terminologies and approaches used between the original study and the Williams et al. (2009) evaluation. It is our opinion that the MPA design criteria used by the Australian Government are incorrectly reported by Williams et al. (2009). In particular, we emphasise the necessity for consistent terminology and approaches when undertaking comparative analyses of geomorphic features. We show that the MPA selection criteria used by the Australian Government addressed the issues of false homogeneity described by Williams et al. (2009), but that final placement of MPAs was based on additional stakeholder considerations. Finally, we argue that although the Williams et al. (2009) study provides valuable information on biological distributions within seamounts and canyons, the hypothesis that geomorphic features (particularly seamounts and submarine canyons) are surrogates for benthic biodiversity is not tested explicitly by their study.

This service has been created specifically for display in the National Map and the chosen symbology may not suit other mapping applications. The Australian Topographic web map service is seamless national dataset coverage for the whole of Australia. These data are best suited to graphical applications. These data may vary greatly in quality depending on the method of capture and digitising specifications in place at the time of capture. The web map service portrays detailed graphic representation of features that appear on the Earth's surface. These features include the administration boundaries from the Geoscience Australia 250K Topographic Data, including state forest and reserves.

<div>The Abbot Point to Hydrographers Passage bathymetry survey was acquired for the Australian Hydrographic Office (AHO) onboard the RV Escape during the period 6 Oct 2020 – 16 Mar 2021. This was a contracted survey conducted for the Australian Hydrographic Office by iXblue Pty Ltd as part of the Hydroscheme Industry Partnership Program. The survey area encompases a section of Two-Way Route from Abbot Point through Hydrographers Passage QLD. Bathymetry data was acquired using a Kongsberg EM 2040, and processed using QPS QINSy. The dataset was then exported as a 30m resolution, 32 bit floating point GeoTIFF grid of the survey area.</div><div>This dataset is not to be used for navigational purposes.</div>

Legacy product - no abstract available

Chart of Australia's maritime jurisdiction developed for Customs. This chart is an updated version of GeoCat 65339 differing from it with the depiction of the new ECS. Simplified B&W Version produced by A. Hatfield 3 July 2008, for inclusion in a Customs Cabsub. Not for general release.

Spatially continuous information of seabed sediments provides important information for seabed mapping and characterisation, prediction of marine biodiversity, and marine environmental planning. Seabed sediment data is available at sampled point locations, so spatially continuous data can only be predicted from the point data. The accuracy of the predicted data is crucial to evidence-based decision making in marine environmental management and conservation. Improving the predictive accuracy is essential, but also challenging, since the accuracy is often data-specific and affected by many factors (Li & Heap 2011 and 2014). Because of their high predictive accuracy, machine learning methods were introduced by Geoscience Australia into spatial statistics by combining them with existing spatial interpolation methods (SIMs). This resulted in new hybrid methods, with the hybrids of random forest (RF) with inverse distance weighting (IDW) or ordinary kriging (OK) (i.e. RFIDW or RFOK) displaying high predictive capacity (Li & Heap 2014, and Li et al. 2011). However, their applications to environmental variables are still rare. Model selection for RF and the hybrid methods has proven to be necessary and further testing is required. Moreover, model averaging was argued to be able to improve predictive accuracy, but no consistent findings have been observed in previous studies. In this study, we aim to identify the most accurate methods for spatial prediction of seabed sediments in the Petrel sub-basin, northwest Australia. We experimentally examined: 1) the effects of input secondary variables on the performance of RFOK and RFIDW; 2) whether the performances of RF, SIMs and their hybrid methods are data-specific; 3) the effects of model averaging on predictive accuracy of these methods; and 4) whether additional samples improve spatial predictions of seabed sediments. For RF and the hybrid methods, up to 21 variables were used as predictors. The predictive accuracy was assessed in terms of relative mean absolute error (RMAE) and relative root mean squared error (RRMSE) based on the results of 100 iterations of 10-fold cross-validation. This study found that: - the predictive errors changes with the input secondary variables; - the most accurate model could be missed during the model selection; - RFOK proved to be the most accurate method; - the methods are not data specific, but their models are, so the best model needs to be identified; - model averaging is clearly data specific; and - contribution of additional samples to the spatial predictions is not apparent. In summary, model selection is important for identifying the most optimal models for RF and the hybrid methods. Effects of model averaging should also be examined for individual studies. The hybrid methods displayed substantial potential for predicting environmental properties and are recommended for spatial prediction not only in environmental sciences but also in other relevant disciplines. Guidelines are provided in this study for improving spatial predictions of biophysical variables in both marine and terrestrial environments.

Australian Offshore Mineral Locations (AMSIS) is a graphical representation in the AMSIS web mapping application of the Australian Offshore Mineral Locations data. The Australian Offshore Mineral Locations data is derived as part of a joint project to compile the first offshore minerals map of Australia's marine jurisdiction. It is being undertaken by Geoscience Australia, CSIRO's Wealth from Oceans National Research Flagship and Division of Exploration and Mining, and all seven state/territory Geological Surveys. Drivers for this initiative include: (1) Australia's having one of the largest marine jurisdictions in the world, if its recent lodgement with the UN Commission on the Limits of the Continental Shelf is agreed; (2) currently poorly known marine mineral potential in the New Australia for strategic and longer term resource planning; (3) anticipated increasing interest by the minerals industry in marine resources. The preparation of this data is a dynamic process and will develop over time. For the First Edition of the Australian Offshore Minerals Map, the data has been collated from sources including all of the State/Territory Geological Surveys, cruise reports, exploration reports and journal articles relating to the specific mineral occurrences. For each point the 'Offshore Minerals' attribute table includes ID number, commodity type, commodity mineralogy, decimal degree location and reference of each sample. For Points found outside state waters the 'Offshore Minerals' dataset also includes the Cruise, Station, Sample names and the percent of Mn, Fe, Cu, Co, Ni of the points in the attribute table.

This dataset contains some of the interpreted basement and crustal features of the Otway Basin, southeastern Australia. Such features include faults, crustal fracture zones and basement terrains. These features were digitised from images that were scanned and georeferenced from the literature. The purpose for producing this data set was to compare some of the existing interpretations with each other and with the potential field data. These files are contained within the "faults_and_structure" directory of the CD-ROM. Files have been named according to the principle author and the year of publication and are listed below. cooper_1995_faults.shp - Map showing the location of the Sorell-Purrumbete Trend, Woorndoo Fault and Sorell Fault Zone as defined by Cooper 1995. Attributes include the name of the geological feature. finlayson_1993.shp - Map showing the major oceanic fracture systems and the area of extended Otway continental lithosphere as defined by Finlayson et al., 1993. Attributes include the name of the geological feature. foster_1992_faults.shp - Map showing the location of the Woorndoo fault zone, Mortlake discontinuity, Stavely Belt, Avoca Fault and Sorell Fault as defined by Foster and Gleadow, 1992. Attributes include the name of the geological feature. miller_2002_faults.shp - Map showing some of the faults interpreted by Miller et al., 2002. Major faults include the Moyston Fault, Avoca Fault, Mortlake discontinuity and the Sorell-Purrumbete trend. Attributes include the type of fault and name if known. woollands_2001_structural_zones.shp - Map showing the Palaeozoic basement structural zones of Victoria as defined in Wollands and Wong, 2001. Attributes include the name of the structural zone and sub-zone. woollands_2001_faults.shp - Map showing the faults that delineate the Palaeozoic basement structural zones of Victoria as defined in Wollands and Wong, 2001. Attributes include the name of the fault.

Legacy product - no abstract available

Abiotic surrogates for marine biodiversity have been identified across multiple ecosystems and vary according to spatial scale, region, habitat, and biodiversity measures. Compared to other regions, our knowledge of the relationships between abiotic and biotic factors in northern Australian waters is limited. As part of the Australian Government's program of collecting pre-competitive regional information on seabed habitats, Geoscience Australia recently collaborated with the Australian Institute of Marine Science to conduct a survey along a representative channel of the Van Diemen Rise in the Joseph Bonaparte Gulf (50 - 250 km off the coast of Darwin). We used a range of methods to collect physical and biological data including multibeam sonar, towed underwater video, oceanographic moorings, sediment sampling, and epibenthic sampling. Depth was a major driver in epibenthic biomass and richness. Sponge and octocoral gardens were common on almost all banks surveyed but rarely found on other geomorphic features, suggesting that biodiversity of epifauna is linked to geomorphology and depth. Infaunal assemblages were extremely diverse in soft sediment plains and correlated to some geochemical factors. Species-level identifications will show whether these biological communities are different across sites and thereby reveal potentially unique habitats in the region. Results from this survey will identify key environmental drivers of biological assemblages in a representative region of the Van Diemen Rise to produce regional-scale information on seabed habitats in northern Australia for resource management purposes.