A fully four-dimensional (3D x time) object-oriented biophysical dispersal model was developed to simulate the movement of marine larvae over semi-continuous surfaces. The model is capable of handling massive numbers of simulated larvae, can accommodate diverse life history patterns and distributions of characteristics, and saves point-level information to a relational database management system.

At the Australian Marine Sciences Association conference held in Canberra in July 2014, a group of scientists and managers engaged in a roundtable discussion to identify areas where linkages could be improved between researchers working on marine population connectivity and managers of marine areas. Population connectivity is the degree of demographic connectedness between populations, indicating the degree to which populations are linked through dispersal and recruitment of organisms, or through gene flow. Connectivity allows organisms and genes to move among different habitats, helping to ensure survival of species by providing increased habitat and reproduction options, and helping to maintain genetic variability. Although connectivity science was used as the focal point of the discussion, the issues discussed are applicable to other topics at the interface of science and management. Here we summarise the key themes and outcomes/recommendations from the discussion.

As the oceans simultaneously warm and acidify, prospects for marine biota are of growing concern. In addition to these global change stressors, marine organisms are also exposed to many other anthropogenic stressors with likely interactive effects, including synergisms in which the combined effects of multiple stressors are greater than the sum of individual effects. Early life stages of marine organisms have been recognised as potentially vulnerable to the stressors associated with global change, but identifying patterns across studies, species and a range of response variables is challenging. In this study, we identify knowledge gaps in research on multiple abiotic stressors and early life stages (embryo to larvae), and we perform a meta-analysis to quantify stressor interactions on early life history stages of marine invertebrates, specifically between temperature, salinity and pH as these are the best studied. Temperature was the most common stressor (91% of studies), while the most common combination of stressors was temperature and salinity (66%), followed by temperature and pH (17.5%). All studies were conducted in the laboratory although four studies also undertook field experiments. Synergistic interactions (68% of individual tests) were more common than additive (16%) or antagonistic (16%) interactions. The meta-analysis yielded several key results: 1) Temperature and salinity synergistically interacted to negatively affect marine embryos and larvae, while temperature and pH antagonistically interact. 2) Embryos are more vulnerable than larvae to thermal and salinity stress but not to pH stress. 3) Survival is less likely to be affected than sub-lethal responses in stress treatments incorporating pH, but there is no discernible pattern in temperature, salinity, and temperature/salinity treatments. 4) Interaction types vary among stressors, phyla, ontogenetic stages, and biological responses. 5) Elevated temperature is generally not a greater stressor than ocean acidification and salinity, but this depends on ontogenetic stage and phylum 6) Ocean acidification is a greater stressor for calcifying than non-calcifying embryos and larvae. We use these results to identify organisms that may be particularly vulnerable or robust to stress associated with temperature, pH, and salinity. Although several clear patterns have emerged from this review and meta-analysis, the challenge now is to develop recommendations for stress ecology experiments in order to facilitate inter-study comparisons, as well as to translate these results to the field.

Imagery collected by still and video cameras is an increasingly important tool for minimal impact, repeatable observations in the marine environment. Data collection from imagery is based on identification, annotation and enumeration of biological subjects and environmental features within an image. For such annotation data to be long-lived and useful beyond their project specific initial purpose, they need to be widely understood. A standardized annotation vocabulary is needed in order to generate regional, national or even global data sets from multiple sources to address broad-scale conservation and ecosystem-based management questions, and also for the development of computer algorithms to automate annotation. This need was addressed, within the Australian context, through the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) project (www.catami.org). The CATAMI classification scheme (CCS) is designed to annotate benthic substrates and biota in marine imagery. It is the first nationally standardised classification based on combinations of coarse-level taxonomy and morphology. The CCS is a flexible, hierarchical classification that bridges the gap between habitat or biotope classifications and taxonomic classifications, allowing for limitations in identifying biological taxa specific to imagery. The CCS is well described, documented, and maintained through web-based data-bases (www.catami.org and http://www.cmar.csiro.au/caab/), and it can be applied across benthic image collection methods, annotation platforms and scoring methods. The CCS was released in 2013 and has already been taken up by on-going Australian marine monitoring programs and by industry environmental consultants. Its incorporation into newly developed on-line image annotation tools further strengthens its continued use and development.

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.

A biophysical dispersal model was used to simulate hydrodynamic connectivity among canyons located within Australia's South-west marine region. The results show that exchange among canyons in this area is greatly influenced by the Leeuwin current, transporting larvae in a unidirectional manner around Cape Leeuwin, and continuing eastwards along the Great Australian Bight. Larvae within canyons tend to remain within them, however if they are transported above the canyon walls, they then have the opportunity to be transported significant distances (thousands of kilometres). Analysis of the variability in connectivity patterns reveals concentrated flow near the shelf break, with increasing levels of variability leading offshore from the canyons. While the average potential flow distance and duration between canyons were approximately 550 kilometres and 33 days respectively, the average realized flow distance and duration were approximately 30 kilometres and 6 days respectively. This study provides the first consideration of connectivity among submarine canyons and will help improve management of these features by providing a better understanding of larval movement, transboundary exchange and the potential spread of invasive species.

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.

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.

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

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 will be 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). The interquartile range, , of the GEOMACS output takes the observations from between Q25 and Q75 to provide an accurate representation of the spread of observations. The interquartile range was shown to provide a more robust representation of the observations than the standard deviation, which produced highly skewed observations (Hughes & Harris 2008).