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

In November 2012, the Australian Government finalised a national network of Commonwealth Marine Reserves (CMR) covering 3.1 million km2 and representing the full range of large scale benthic habitats known to exist around mainland Australia. This network was designed using the best available regional-scale information, including maps of seabed geomorphic features and associated Key Ecological Features. To support the management objectives of the marine reserves, new site-specific information is required to improve our understanding of biodiversity patterns and ecosystem processes across a range of spatial scales. In this context, the Marine Biodiversity Hub (funded through the National Environmental Research Program) recently completed a collaborative 'voyage of discovery' to the Oceanic Shoals CMR in the Timor Sea. This area was chosen because it hosts globally significant levels of biodiversity (including endemic sponge and coral taxa), faces rapidly increasing pressures from human activities (offshore energy industry, fishing) yet is recognised as one of the most poorly known regions of Northern Australia. Undertaken in September 2012 on board RV Solander, the survey acquired biophysical data on the shallow seabed environments for targeted areas within the Oceanic Shoals CMR, with a focus on the carbonate banks that characterise this tropical shelf and are recognised as a Key Ecological Feature. Data collected included 500 km2 of high resolution (300 kHz) multibeam sonar bathymetry and acoustic backscatter across four grids, plus seabed sediment samples, underwater tow-video transects (~1 km length), pelagic and demersal baited underwater video, epifaunal and infaunal samples and water column profiles at pre-determined stations. Station locations were designed to provide a random but spatially balanced distribution of sample sites, with weighting toward the banks. This design also facilitated observations of patterns of benthic biodiversity at local to feature-scale and transitions associated with depth-gradients and exposure to tidal currents. Results reveal the banks are broadly circular to elliptical with steep sides, mantled by muddy sand and gravel with areas of hard ground. Rising to water depths of 50-70 m, the banks support benthic assemblages of sponges and corals (including hard corals at shallower sites) which in turn support other marine invertebrates. In strong contrast, the surrounding seabed is characterised by barren, mud-dominated sediments in 70-100 m water depth, although infaunal samples reveal diverse biological communities beneath the seafloor. While the bank assemblages are locally isolated, the potential exists for connectivity between shoals via tide-driven larval dispersal. Ongoing work is aimed at identifying species to determine overlap between bank communities, as well as modelling the sources, pathways and sinks for larvae as a proxy for understanding the physical processes controlling the patterns of biodiversity across the Oceanic Shoals CMR at multiple scales.

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 were 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). Q75, or the 0.75 Quartile of the Geomacs output, represents the values for which 75% of the observations fall below (Hughes & Harris 2008).

Benthic marine invertebrates and their planktonic life stages live in a multistressor world where stressor levels are, and will continue to be, exacerbated by global change. Global warming and increased atmospheric CO2 are causing the oceans to warm, decrease in pH and become hypercapnic. These concurrent stressors have strong impacts on biological processes, but little is known about their combined effects on marine invertebrate development. Increasing temperature has a pervasive stimulatory effect on metabolism until lethal levels are reached, whereas hypercapnia can depress metabolism. Ocean acidification is a major threat to calcifying life stages because it decreases carbonate mineral saturation and also exerts a direct pH effect on physiology. Ocean pH, pCO2 and CaCO3 covary and will change simultaneously with temperature, challenging our ability to predict future outcomes for marine biota. The need to consider both ocean warming and acidification is reflected in the recent increase in multifactorial studies of these stressors on development of marine invertebrates. The outcomes and trends in these studies are synthesized here. Different sensitivities of life history stages and species have implications for persistence and community function in a changing ocean. Some species are more resilient than others and may be potential 'winners' in the climate change stakes. For echinoderms where multistressor studies span across life stages, the impacts of pH/pCO2 and warming on benthic-pelagic life cycle phases are assessed. As the ocean will change more gradually over coming decades than in 'future shock' experiments, it is likely that some species may be able to tolerate near future ocean change through acclimatization or adaption.

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy, can be inferred based on underwater video footage at limited locations. It can also be predicted to two classes. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e. hard90 and hard70) for seabed video footage by. We developed optimal predictive models to predict the spatial distribution of seabed hardness using random forest (RF) based on point data of hardness classes and spatially continuous multibeam backscatter data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), the combined, Boruta, and RRF were tested. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were also examined. Finally, the most accurate models were used to predict the spatial distribution of the hardness classes and the predictions were visually examined and compared with the predictions based on two-class hardness classification. This study confirms that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness can be predicted into a spatially continuous layer with a high degree of accuracy; 3) the typical approach used to pre-select predictors by excluding highly correlated predictors needs to be re-examined when using machine learning methods, at least, for RF, in the environmental sciences; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving the predictive models; 5) FS is essential for identifying an optimal RF predictive model and the RF methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data, can be applied to `small p and large n problems in environmental sciences, and is recommended for future studies. In addition, automated computational programs for AVI need be developed to improve its computational efficiency and caution should be taken when applying filter FS method in selecting predictive models in future studies.

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_s2 is an ArcINFO grid of southern part of Jervis Bay survey area (south2 is part of Darling RD grid) produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software

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.

Northern Australia has been the focus of recent marine biodiversity research to support resource management for both industry and conservation. Much of this research has targeted habitat-forming sessile invertebrates and charismatic megafauna, but smaller macrofauna and infauna must also be considered due to their important roles in ecosystem functions. In this study, a Smith-McIntyre grab was used during two surveys in 2009 and 2010 to the Joseph Bonaparte Gulf to collect sediment samples which were then elutriated over a 500µm sieve. The associated polychaetes were identified to species-level. A total of 2224 individual polychaetes were collected from 133 grabs and represent 43 families, including several new species, at least one new genus (Pilargidae) and many new distribution records. Biodiversity patterns were also analysed according to environmental and spatial factors (grain-size, carbonate, total organic content, depth, distance offshore) in order to inform predictive models and further our understanding of ecosystem processes in the region. These patterns differ from those of larger epifauna collected on the same surveys, highlighting the need to consider small macrofauna in biodiversity research and associated marine management.

Submarine canyons are highly energetic and dynamic environment. Owing to their abrupt and complex topographies that are contrast to the adjacent shelf and slope, they can generate intense mixing, both horizontally through internal tides and waves and vertically through upwelling and downwelling. Complex hydrodynamic processes and increased food supply in sediment and water column result in elevated primary and secondary production which would favour the development of a highly productive and temporally dynamic food web over the canyons. Consequently, many submarine canyons, especially those incise into continental shelf, are considered as biodiversity hotspots. To better understand the ecosystem functions and ecological processes of marine environment, identification and classification of submarine canyons are needed. This study developed a national-scale submarine canyon classification system for Australian ocean based on canyon's physical characteristics. A hierarchical classification scheme was proposed. At the top level, the submarine canyons were classified into shelf-incising canyons and confined-to-slope canyons. At the lower levels, the canyons were classified on their morphometry, shape and location characteristics separately. Accurate identification of submarine canyons was the critical first step for the success of the proposed canyon classification system. A national bathymetry data at a spatial resolution of 250 metres and a completed set of multibeam bathymetry data at a spatial resolution of 50 metres from all previous multibeam surveys, both published by Geoscience Australia, were used. Hill-shaded layers were generated from which most submarine canyons could be easily identified. The extents of individual canyons, from wall to wall, were manually digitised as a GIS polygon layer. The initial number of canyons was then filtered using the following criteria: - Depth of the canyon head is less than 4000 m, - Depth range between the canyon's head and foot is greater than 600 m, and - Incision of the canyon head is greater than 100 m. At the lower levels, the following metrics were calculated as the inputs to the canyon classifications: - Morphometry metrics: incision depth of the canyon head, standard deviation of the slope gradient (within all cells in a canyon), slope gradient between the canyon head and the canyon foot, and canyon overall rugosity. - Shape metrics: canyon area, number of branches, length/width ratio of the smallest bounding rectangle, border index, compactness and canyon volume. - Location metrics: depth of the canyon head, depth range between the canyon's head and foot, canyon density, distance to coast, distance to the shelf break, incision depth (shelf-incising canyons only), and incision area (shelf-incising canyons only). The hierarchal agglomerative clustering technique was used for the unsupervised classifications. After the filtering, a total of 708 submarine canyons were identified for the entire Australian EEZ. Among these 708 canyons, 134 of them incise into continental shelf; the rest are confined in continental slope. For the shelf-incising canyons, the morphometry, shape and location based classifications all resulted in three classes. Combining the three lower level classifications yielded 15 classes. For the slope-confined canyons, the morphometry, shape and location based classifications resulted in three, four and four classes, respectively. Combining the three lower level classifications yielded 37 classes. GeoHab 2013

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_s3 is an ArcINFO grid of southern part of Jervis Bay survey area (south3 is part of Darling RD grid) produced from the processed EM3002 bathymetry data using the CARIS HIPS and SIPS software