Although marine reserves are becoming increasingly important as anthropogenic impacts on the marine environment continue to increase, we have little baseline information for most marine environments. In this study, we focus on the Oceanic Shoals Commonwealth Marine Reserve (CMR) in northern Australia, particularly the carbonate banks and terraces of the Sahul Shelf and Van Diemen Rise which have been designated a Key Ecological Feature (KEF). We use a species-level inventory compiled from three marine surveys to the CMR to address several questions relevant to marine management: 1) Are carbonate banks and other raised geomorphic features associated with biodiversity hotspots? 2) Are there environmental or biogeographic variables that can help explain local and regional differences in community structure? 3) How do sponge communities differ between individual raised geomorphic features? Approximately 750 sponge specimens were collected in the Oceanic Shoals CMR and assigned to 348 species, of which only 18% included taxonomically described species. Between the eastern and western CMR, there was no difference between sponge species richness or assemblages on raised geomorphic features. Within individual raised geomorphic features, sponge assemblages were significantly different (ANOSIM: Global R = 0.328, p < 0.001), but species richness was not. There were no environmental factors related to sponge species richness, although sponge assemblages were weakly but significantly related to several environmental variables (mean depth, mean backscatter, mean slope). These patterns of sponge diversity are considered in the context of marine reserve management in order to explore how such information may help support the future management of this region.

Understanding the distribution and abundance of sponges and their associated benthic habitats is of paramount importance for the establishment and monitoring of marine reserves. Benthic sleds or trawls can collect specimens for taxonomic and genetic research, but these sampling methods can be too qualititative for many ecological analyses and too destructive for monitoring purposes. Advances in the use of underwater videography and still imagery for biodiversity habitat mapping and modelling have been used within Geoscience Australia to extract data related to sponge biodiversity patterns across three regions. In the new Oceanic Shoals Commonwealth Marine Reserve, sponge morphologies were characterized from still images to locate areas in which biodiversity may be high due to habitat-forming taxa. In the Carnarvon Shelf abundance of a target sponge (Cinachyrella sp.) was quantified from video to investigate relationships between biology and sediment characteristics. Around Lord Howe Island, benthic habitats are being analysed to the national standard of classification using both video and still images. Importantly specialists within ecology, geophysics and spatial statistics work together to integrate biological and physical data to provide unique and meaningful maps of predicted distributions and habitat suitability for key ecological benthic habitats.

Geoscience Australia carried out marine surveys in Jervis Bay (NSW) in 2007, 2008 and 2009 (GA303, GA305, GA309, GA312) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments (for textural and biogeochemical analysis) and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wavegenerated currents. Data and samples were acquired using the Defence Science and Technology Organisation (DSTO) Research Vessel Kimbla. Bathymetric mapping, sampling and tide/wave measurement were concentrated in a 3x5 km survey grid (named Darling Road Grid, DRG) within the southern part of the Jervis Bay, incorporating the bay entrance. Additional sampling and stills photography plus bathymetric mapping along transits was undertaken at representative habitat types outside the DRG. entrance_3m is an ArcINFO grid of entrance of Jervis Bay survey area produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

The Leeuwin Current has significant ecological impact on the coastal and marine ecosystem of south-western Australia. This study investigated the spatial and temporal dynamics of the Leeuwin Current using monthly MODIS SST dataset between July 2002 and December 2012. Topographic Position Index layers were derived from the SST data for the mapping of the spatial structure of the Leeuwin Current. The semi-automatic classification process involves segmentation, 'seeds' growing and manual editing. The mapping results enabled us to quantitatively examine the current's spatial and temporal dynamics in structure, strength, cross-shelf movement and chlorophyll a characteristic. It was found that the Leeuwin Current exhibits complex spatial structure, with a number of meanders, offshoots and eddies developed from the current core along its flowing path. The Leeuwin Current has a clear seasonal cycle. During austral winter, the current locates closer to the coast (near shelf break), becomes stronger in strength and has higher chlorophyll a concentrations. While, during austral summer, the current moves offshore, reduces its strength and chlorophyll a concentrations. The Leeuwin Current also has notable inter-annual variation due to ENSO events. In El Niño years the current is likely to reduce strength, move further inshore and increase its chlorophyll a concentrations. The opposite occurs during the La Niña years. In addition, this study also demonstrated that the Leeuwin Current has a significantly positive influence over the regional nutrient characteristics during the winter and autumn seasons.

A defining characteristic of the seabed is the proportion that is hard, or immobile. For marine ecosystems, hard seabed provides the solid substrate needed to support sessile benthic communities, often forming 'hotspots' of biodiversity such as coral and sponge gardens. For the offshore resource and energy industry, knowledge of the distribution of hard versus soft seabed is important for planning infrastructure (pipelines, wells) and to managing risk posed by geo-hazards such as migrating sand waves or mass movements on steep banks. Maps that delineate areas of hard and soft seabed are therefore a key product to the informed management and use of Australia's vast marine estate. As part of the Australian Government's Offshore Energy Security Program (2007-2011) and continuing under the National CO2 Infrastructure Plan (2011-2015), Geoscience Australia has been developing integrated seabed mapping methods to better map and predict seabed hardness using acoustic data (multibeam sonar), integrated with information from biological and physical samples. The first method used was a two-stage, classification-based clustering method. This method uses acoustic backscatter angular response curves to derive a substrate type map. The angular response curve is the backscatter value as a function of the incidence angle, where this angle lies between the incident acoustic signal from the normal. The second method was a prediction-based classification, using a machine learning method called random forest. This method was based on bathymetry, backscatter data and their derivatives, as well as underwater video and sediment data. The techniques developed by Geoscience Australia offer a fast and inexpensive assessment of the seabed that can be used where intensive seabed sampling is not feasible. Moreover, these techniques can be applied to areas where only multibeam acoustic data are available. Importantly, the identification of seabed substrate types in spatially continuous maps provides valuable baseline information for effective marine conservation management and infrastructure development.

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of continental shelf (~20 - 300 m depth) bed shear stress (unit of measure: Pascal, Pa) on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and densitydriven circulation. Included in the parameters that will be calculated to represent the magnitude of the bulk of the data are the quartiles of the distribution; Q25, Q50 and Q75 (i.e. the values for which 25, 50 and 75 percent of the observations fall below). The interquartile range, , of the GEOMACS output takes the observations from between Q25 and Q75 to provide an accurate representation of the spread of observations. The interquartile range was shown to provide a more robust representation of the observations than the standard deviation, which produced highly skewed observations (Hughes & Harris 2008).

Geoscience Australia carried out marine surveys in Jervis Bay (NSW) in 2007, 2008 and 2009 (GA303, GA305, GA309, GA312) to map seabed bathymetry and characterise benthic environments through colocated sampling of surface sediments (for textural and biogeochemical analysis) and infauna, observation of benthic habitats using underwater towed video and stills photography, and measurement of ocean tides and wavegenerated currents. Data and samples were acquired using the Defence Science and Technology Organisation (DSTO) Research Vessel Kimbla. Bathymetric mapping, sampling and tide/wave measurement were concentrated in a 3x5 km survey grid (named Darling Road Grid, DRG) within the southern part of the Jervis Bay, incorporating the bay entrance. Additional sampling and stills photography plus bathymetric mapping along transits was undertaken at representative habitat types outside the DRG. jb_s1 is an ArcINFO grid of southern part of Jervis Bay survey area (south1 is part of Darling RD grid) produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

This dataset contains species-level identifications of polychaetes collected during survey SOL5117 (R.V. Solander 30 July - 27 August, 2010). Animals were collected from the Joseph Bonaparte Gulf with a Smith McIntyre grab, with a few specimens from a benthic sled. Species-level identifications were undertaken by Chris Glasby and Charlotte Watson at the Museum and Art Gallery of the Northern Territory (MAGNT) and were delivered to Geoscience Australia on the 6 June 2013. See GA Record 2011/08 for further details on survey methods and specimen acquisition. Data is presented here as delivered by the taxonomist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications. The data file contains two spreadsheets: - 'species list' includes all polychaete species as identified at the MAGNT, including family, abundance, and comments from the taxonomists. It also contains phyla-level identifications for non-polychaete specimens that were mistakenly sent to the MAGNT with the polychaete samples. CG = Chris Glasby; CW = Charlotte Watson - 'Stations' includes location and depth for each station at which grabs and sleds were deployed.

Seabed hardness is an important character of seabed substrate as it may influence the nature of attachment of an organism to the seabed. Hence spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is usually inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage or directly measured at limited locations. It can also be predicted based on two-class hardness data using environmental predictors, but no study has been undertaken for predicting multiple-class hardness data. In this study, we classified the seabed hardness into four classes based on underwater video images that were extracted from the underwater video footage. We developed an optimal predictive model to predict the spatial distribution of seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam bathymetry, backscatter and other derived predictors. A novel model selection method that is the averaged variable importance (AVI) was used based on predictive accuracy that was acquired from averaging the results of 100 times replication of 10-fold cross validation. Finally, the spatial predictions generated using the most accurate model was visually examined and analyzed in comparison with previously published predictions based on two-class hardness data. This study confirmed that: 1) seabed hardness of four classes can be predicted into a spatially continuous layer with a high degree of accuracy; 2) model selection for RF is essential for identifying an optimal predictive model in environmental sciences and AVI select the most accurate predictive model(s) instead of the most parsimonious ones, and is recommended for future studies; 3) the typical approach used in pre-selecting predictors by excluding correlated variables (i.e. r 0.95 or the inflation factor 20) needs to be re-examined for identifying predictive models using machine learning methods, at least for the application of random forest in marine environmental sciences; 4) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to `small p and large n problems in the environmental sciences; and 5) the spatial predictions for four-class hardness data were similar with the predictions based on two hardness classes, with a high match rates. RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies. In summary, AVI shows its effectiveness in searching for the most accurate predictive models and are recommended for future studies. This study further confirms the superior performance of RF in marine environmental sciences. RF is an effective modelling method with high predictive accuracy not only for presence/absence data and but also for multi-level categorical data. RF and AVI are recommended for generating spatially continuous predictions of categorical variables in future studies.

Multibeam sonar data incorporates a wide range of metrics of physical seabed properties that can be utilised to generate substrate maps for marine habitat mapping. In particular, statistical descriptors of seabed form and texture can be derived to maximise the information provided by multibeam data. This study investigates the full potential of multibeam data for mapping seabed properties for an area of geomorphically complex seabed on the continental shelf offshore from Point Cloates, Western Australia. In 2008, as part of a collaborative survey within the Commonwealth Environmental Research Facilities (CERF) Marine Biodiversity Hub, Geoscience Australia acquired high resolution multibeam data and sediment samples across a 280 km2 area of the shelf, using a Kongsberg EM 3002 (300 kHz) system. Using this data, a two stage analysis was developed to: (i) separate 'hard seabed (e.g., reefs, ridges and mounds) from 'soft' sediments, and; (ii) predict textural properties for seabed sediments, including %Gravel, %Sand, %Mud, mean grain size and sorting. For a mapping tool, we chose the Random Forest Decision Tree technique. This entailed using ten combinations of input datasets as explanatory variables, including morphometric variables derived from bathymetry, and angular response curves and related statistics derived from backscatter mosaics. The training dataset was derived by combining sediment data from grab samples with locations of hard substrate inferred from bathymetry data. The predictive mapping of 'hard' and 'soft' seabed types resulted in predictions with very strong confidence levels, especially when bathymetry information was combined with backscatter data (i.e., cross-validated Area Under Curve = 0.99). The five sediment properties were predicted with moderate to good cross-validation accuracies (Figure 1). The highest accuracies were achieved for %Mud and Sorting, (R2s equal 0.73 and 0.68, respectively).