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  • 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).

  • 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

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Australia is increasingly recognised as a global hotspot for sponge biodiversity, with sponges playing key roles in habitat provision, water quality, bioerosion, and biodiscovery. Despite the intense focus on marine resource management in northern Australia, there is a large knowledge gap about sponge communities in this region. This study focuses on shelf environments of the Timor Sea, in particular the Van Diemen Rise and Londonderry Rise which are characterised by extensive carbonate terraces, banks and reefs, separated by soft sediment plains and deeply incised valleys. These carbonate terraces and banks are recognised as a Key Ecological Feature (KEF) in the marine region plans for northern Australia (North and Northwest Marine Regions) and are in part incorporated into the Oceanic Shoals Commonwealth Marine Reserve. To support the management of this marine reserve and its associated KEF, we use new datasets to investigate regional patterns in sponge assemblages and their relationships to seabed geomorphology. To do this, we use sponge assemblage data and multibeam-derived variables (depth, backscatter, slope, geomorphic feature) from seven survey areas located on the Van Diemen Rise (four sites) and Londonderry Rise (three sites), spanning approximately 320 km in an east-west direction. The dataset was collected during three collaborative surveys undertaken in 2009, 2010 and 2012 by Geoscience Australia, the Australian Institute of Marine Science and the Museum and Art Gallery of the Northern Territory as part of the Australian Government's Offshore Energy Security Initiative and the National Environmental Research Program Marine Biodiversity Hub. All surveys returned geophysical, biological, geochemical, and sedimentological data. Benthic biota were collected with a benthic sled across a range of geomorphic features (bank, terrace, ridge, plain, valley) identified from high-resolution multibeam sonar. Sponges were then taxonomically identified to 350 species, with the species accumulation curve indicating there may be over 900 sponge species in the region. Sponge assemblages were different between the Van Diemen Rise and Londonderry Rise, as well as between individual banks in the same area, indicating that different suites of species occurred at regional (east-west) and local (between banks) scales. Relationships between sponges and other multibeam-derived variables are more complex and warrant further research. The current study will help: i) facilitate integrated marine management by providing a baseline species inventory; ii) support the listing of carbonate banks of the Timor Sea shelf as a Key Ecological Feature, and; iii) inform future monitoring of marine protected area performance, particularly for areas of complex seabed geomorphology.