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

This study explored the full potential of high-resolution multibeam data for an automatic and accurate mapping of complex seabed under a predictive modelling framework. Despite of the extremely complex distributions of various hard substrata at the inner-shelf of the study area, we achieved a nearly perfect prediction of 'hard vs soft' classification with an AUC close to 1.0. The predictions were also satisfactory for four out of five sediment properties, with R2 values range from 0.55 to 0.73. In general, this study demonstrated that both bathymetry and backscatter information (from the multibeam data) should be fully utilised to maximise the accuracy of seabed mapping. From the modelled relationships between sediment properties and multibeam data, we found that coarser sediment generally generates stronger backscatter return and that deeper water with its low energy favours the deposition of mud content. Sorting was also found to be a better sediment composite property than mean grain size. In addition, the results proved one again the advantages of applying proper feature extraction approaches over original backscatter angular response curves.

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.

Marine benthic biodiversity can be quantified using a range of sampling methods, including benthic sleds or trawls, grabs, and imaging systems, each of which targets a particular community or habitat. Research studies often incorporate only one of these sampling methods in published results, and the generality of marine biodiversity patterns identified among different sampling methods remains unknown. In this study we use three biological collections obtained during a collaborative survey between Geoscience Australian and the Australian Institute of Marine Science to the Van Diemen Rise in northern Australia: 1) Infauna sampled from a Smith-McIntyre grab, 2) Epifauna sampled from a benthic sled, and 3) Biological communities identified from video. For each dataset, we investigated potential patterns of species richness and community structure in relation to depth, geomorphology, and study area, as well as the relationships between datasets. No gear type yielded data that was strongly correlated with depth, but different patterns were evident among gear types based on study area and geomorphology. Comparisons among datasets indicate that species richness from sleds and grabs are more strongly correlated with each other than with richness from video. Further research is planned to incorporate datasets from other regions and habitats in order to provide a general assessment of sampling methods used in the quantification of benthic marine biodiversity in Australasia.

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.

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.

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.

Submerged relict reef systems and modern coral communities discovered around the Balls Pyramid shelf are presented as new evidence of extensive carbonate production at the boundary of reef-forming seas. Balls Pyramid is the southernmost island in a chain of island-reefs in the southwest Pacific Ocean, 24 km south of the southernmost known coral reef in the Pacific Ocean at Lord Howe Island. This paper explores the detailed geomorphic structure of the shelf through the production of a high resolution bathymetric model from multibeam echosounder data and depth estimates from satellite imagery. Key seafloor features identified include a large, mid shelf reef dominating the shelf landscape in 20 - 60 m water depth, mid shelf basins and channels, and shelf margin terrace sequences in 50 - 100 m depth. Sub-bottom profiles, backscatter, drill core and vibro-core data are used to investigate the seafloor composition. Drill cores extracted from the submerged reef surface confirm coral, coralline algae and cemented sands composition, and vibro-core material extracted from unconsolidated areas demonstrate substantial accumulation of carbonates shed from the reef surface. Underwater video imagery reveals abundant modern mesophotic reef communities, including hard corals, colonising the relict reef surface. This paper reveals prolific past reef growth and abundant modern coral growth on what was previously considered to be a planated volcanic shelf outside of reef-forming seas, thus extending understanding of reef evolution at, and beyond, the limits of growth.

We undertook a biological data acquisition program as part of the transit of the R.V. Southern Surveyor between Darwin and Cairns 15-24 October 2012. The overarching aim of this program was to use an ROV and benthic sled to collect benthic marine information and specimens for biodiversity and biodiscovery research in areas previously mapped by Geoscience Australia during survey GA-276, including a bank (Area I) and terrace/hole feature within the proposed Wessel Islands CMR (Area II). This study focuses on sessile invertebrates such as sponges and octocorals due to their ecological importance as habitat providers and their chemical importance as sources of marine natural products and medicines. In less than 24 hours of sampling effort, survey SS2012/t07 resulted in 261 voucher specimens which will be used for biodiversity and natural products research. A total of 49 samples are to be lodged at the ABL, and samples with weights larger than 300 g will be sent to the NCI for screening of active compounds against cancer and HIV. Sponges were the most abundant group collected based on both biomass (~ 139 kg) and number of voucher specimens (93), followed by cnidarians (30 kg, 73 vouchers), particularly hard corals (23 kg, 11 vouchers). As expected the top of the bank in Area I had a seemingly diverse and abundant sessile invertebrate community, with consistent patchy occurrence of sponges, octocorals, and hard corals. The terrace at in Area II supports moderate densities of sponges and octocorals, while the adjacent deep hole at ~ 100 m seems to be covered with muddy gravel and supports scattered mobile and sedentary invertebrates, of which crinoids dominate, as well as skates and numerous small demersal fish.