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_s4 is an ArcINFO grid of southern part of Jervis Bay survey area (south4 is part of Darling RD grid) 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.

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'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 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). Q25, or the 0.25 Quartile of the Geomacs output, represents the values for which 25% of the observations fall below (Hughes & Harris 2008).

Submarine canyons have been recognised as areas of significant ecological and conservation value. In Australia, 713 canyons were mapped and classified in terms of their geomorphic properties. Many of them are identified as Key Ecological Features (KEFs) and protected by Commonwealth Marine Reserves (CMRs) using expert opinion based on limit physical and ecological information. The effectiveness of these KEFs and CMRs to include ecologically significant submarine canyons as prioritised conservation areas needs to be objectively examined. This study used two local-based spatial statistical techniques, Local Moran's I (LMI) and the Gi* statistic, to identify hotspots of Australian canyons (or unique canyons) for conservation priority. The hotspot analysis identified 29 unique canyons according to their physical attributes that have ecological relevance. Most of these unique physical canyons are distributed on the southern margins. Twenty-four of the 29 canyons are enclosed by the existing KEFs and protected by CMRs to varied extents. In addition, the hotspot analysis identified 79 unique canyons according to their chlorophyll a concentrations, all of which are located in the South-east marine planning region. The findings can be used to update or revise the profile descriptions for some existing KEFs. In future, if the boundaries of these KEFs are deemed necessary to be reviewed, the new information and knowledge could also be used to enhance the conservation priorities of these KEFs.

Geoscience Australia completed an underwater towed video survey (GA survey 0338) of the Shelf Rocky Reefs Key Ecological Feature (KEF) in the vicinity of the Solitary Islands in collaboration with the New South Wales Office of Environment and Heritage on the R.V. Bombora between 7 - 16 August 2012. The aim of the survey was to characterize benthic habitat in areas of the KEF, and to compare and contrast the effectiveness of different methods for capturing visual representations of biological communities. The survey collected forward-facing mono video, forward-facing stereo video, and downward facing stills along 12 transects, each of 2 km length. The geographic position of the vessel was determined using a GPS system, and the location of the towed camera body was recorded using a USBL system. The KEF survey was part of the National Marine Biodiversity Hub's National Monitoring Evaluation and Reporting Theme. The aim of this theme is to develop a blueprint for the sustained monitoring of the Commonwealth Marine Reserve Network, specifically; 1) to contribute to an inventory of demersal and epibenthic conservation values in the KEF and; 2) to test methodologies and deployment strategies in order to inform future survey design efforts. Embargo statement: Resource embargoed pending completion of NERP research. Release date 31 December 2014 Attribution statement: Users of NERP Marine Biodiversity Hub data are required to clearly acknowledge the source of the material in the format: "Data was sourced from the NERP Marine Biodiversity Hub" the Marine Biodiversity Hub is supported through funding from the Australian Government's National Environmental Research Program (NERP), administered by the Department of Sustainability, Environment, Water, Population and Communities (DSEWPaC)." Dataset name: National Environmental Research Program (NERP) Marine Biodiversity Hub, 2012, Flinders Commonwealth Marine Reserve Shelf Backscatter

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

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.

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.

Baseline information on biodiversity and habitats is required to manage Australia's northern tropical marine estate. This study aims to develop an improved understanding of seafloor environments of the Timor Sea. Clustering methods were applied to a large dataset comprising physical and geochemical variables which describe organic matter (OM) reactivity/quantity/source and geochemical processes. Infauna data were used to assess different groupings. Clusters based on physical/geochemical data discriminated infauna better than geomorphic features. Major variations amongst clusters included grainsize and a cross-shelf transition in from authigenic-Mn /As enrichments (inner shelf) to authigenic-P enrichment (outer shelf). Groups comprising raised features had the highest reactive OM concentrations (e.g. low chlorin indices and C:N-ratios, and high k) and benthic algal '13C signatures. Surface area normalised OM concentrations higher than continental shelf norms were observed in association with: (i) low -15N, inferring Trichodesmium input; and (ii) pockmarks, which impart bottom-up controls on seabed chemistry and cause inconsistencies between bulk and pigment OM pools. Low Shannon-Wiener diversity occurred in association with low redox and porewater pH and evidence for high energy. Highest beta-diversity was observed at euphotic depths. Geochemical data and clustering methods used here provide insight into ecosystem processes influencing biodiversity patterns in the region.