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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. We use a species-level inventory compiled from three marine surveys to the Oceanic Shoals Commonwealth Marine Reserve (CMR) in northern Australia 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 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, 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. 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 at multiple spatial scales.
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Discerning how marine ecosystems are linked through larval dispersal is essential for understanding demographic flow, investigating the development of population genetic structure, and for evaluating the potential responses of communities to climate change. This information is of critical importance when designing reserve networks, identifying key locations for restoration, controlling invasive species, and administering transboundary resources. As part of Geoscience Australia's commitment to the National Environmental Research Programme's Marine Biodiversity Hub, we have developed a fully four-dimensional (3D space x time) individual-based model that embeds artificially intelligent particles within real-world ocean flow fields, making it possible to examine expected dispersal patterns of marine larvae under a variety of conditions. The model fuses strategic biological behaviour with physical equations in a flexible manner through the use of object-oriented programming. We will discuss aspects of model development and testing, as well as practical issues relating to computing on the National Computing Infrastructure, and addressing large-scale data storage. We will also identify potential avenues for data analysis that can be used to inform environmental decision-making.
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Australia is increasingly recognised as a global hotspot for sponge biodiversity, but there is a large knowledge gap about sponge communities in northern Australia. Such information is particularly important to manage and monitor the Commonwealth Marine Reserves network finalised by the Australian government in November 2012. This study aims to quantify sponge biodiversity of the Van Diemen Rise and eastern Joseph Bonaparte Gulf in northern Australia and to examine spatial and environmental patterns associated with differences in community structure of sponges. Two collaborative surveys were undertaken in 2009 and 2010 as part of the Australian Government's Offshore Energy Security Initiative. Sponges were collected with a benthic sled from 65 sites across a range of geomorphic features (bank, terrace, ridge, plain, valley) and environmental variables (depth, distance offshore, substrate hardness, slope). Approximately 700 sponge specimens were collected and assigned to 283 species, representing three classes, 53 families and 117 genera. Results showed that sponges were positively and significantly related to other taxa in respect to richness and biomass, thus highlighting their important role in habitat provision. Distance offshore and geomorphic features affected community structure, species richness and biomass. In general sponge diversity was highest further offshore and on raised geomorphic features. Sponge assemblages collected from the same bank were more similar than those collected from different banks. There were no strong relationships between sponges and other environmental factors. The current study will help facilitate integrated marine management by providing a baseline species inventory, supporting the listing of carbonate banks of the Van Diemen Rise as a key ecological feature, and highlighting the importance of sponges as habitat providers and potential biological surrogates for monitoring activities.
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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.
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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.
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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
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This dataset contains species identifications of molluscs collected during survey SOL5650 (R.V. Solander, 12 September - 5 October, 2012). Animals were collected from the Joseph Bonaparte Gulf with a benthic sled. Specimens were lodged at Northern Territory Museum immediately after the survey. Species-level identifications were undertaken by Dr Richard Willan at the Northern Territory Museum and were delivered to Geoscience Australia on the 17 January 2013. See associated post-survey report for further details on survey methods and specimen acquisition. Data is presented here exactly as delivered by the taxonomist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications.
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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.
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This resource contains species identifications of sponges from the Oceanic Shoals Commonwealth Marine Reserve (CMR) in the Timor Sea collected by Geoscience Australia during September and October 2012 on RV Solander (survey GA0339/SOL5650). Sponges were collected using an epibenthic sled towed for approximately 50 m at 21 stations across three survey areas. From these, a total of 339 specimens were retained for taxonomic identification and lodged at the Museum and Art Gallery of the Northern Territory (MAGNT) immediately after demobilisation. Species-level identifications were undertaken by Dr Belinda Alvarez de Glasby at the MAGNT. The Oceanic Shoals Commonwealth Marine Reserve survey was undertaken as an activity within the Australian Government's National Environmental Research Program Marine Biodiversity Hub and was the key component of Research Theme 4 - Regional Biodiversity Discovery to Support Marine Bioregional Plans. Hub partners involved in the survey included the Australian Institute of Marine Science, Geoscience Australia, the University of Western Australia, Museum Victoria and the Museum and Art Gallery of the Northern Territory. Data acquired during the survey included: multibeam sonar bathymetry and acoustic backscatter; sub-bottom acoustic profiles; physical samples of seabed sediments, infauna and epibenthic biota; towed underwater video and still camera observations of seabed habitats; baited video observations of demersal and pelagic fish, and; oceanographic measurements of the water column from CTD (conductivity, temperature, depth) casts and from deployment of sea surface drifters. Further information on the survey is available in the post-survey report published as Geoscience Australia Record 2013/38 (Nichol et al. 2013).
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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. Ecological data collected from Torres Strait suggests that bed shear stresses exceeding 0.4 Pa are important in determining the species present (Long, Bode, & Pitcher 1997). Although this data may not be representative of other regions or benthic communities, it has been utilised to calculate two parameters for determining the relationship between shear bed stress and the benthic community. One of the parameters is the total percentage of time the bed shear stress exceeds 0.4 Pa, and this is denoted (Hughes & Harris 2008).