The Spatial Data Dictionary is a specification for the capture of geoscientific spatial data. It describes fields for each feature type in a database, containing the themes currently created from Geoscience Australia's databases. It forms a foundation for the production of geoscientific spatial data by specifying rules regarding the structure of such data. The dictionary covers such matters as allowable coverage names, feature types, and attribute values. A theme is a set of spatial objects. Some of the themes in this data dictionary have associated look-up tables. Look-up tables store an additional array of attributes that may be linked to the primary attribute table of a theme. Object type, feature definition, field type, attribute case, compulsion for data entry, a list of valid values and any rules or comments regarding the feature are also given in this data dictionary. The Data Dictionary consists of four modules: • Module 1: Definitions, Rules and Terminology • Module 2: Geology, Geophysical, Geochemistry and Geochronology Themes • Module 3: Mineral Deposits and Mineral Potential Assessment Themes, Surveys and Field Observations Themes • Module 4: Urban Infrastructure Themes, Terrain Physiography Themes, Cartographic Themes

<div>This data product contains geospatial seabed morphology and geomorphology information for the Beagle Marine Park and is intended for use by marine park managers, regulators, the general public and other stakeholders. A nationally consistent two-part (two-step) seabed geomorphology classification system was used to map and classify the distribution of key seabed features. </div><div><br></div><div>In step 1, semi-automated GIS mapping tools (GA-SaMMT; Huang et al., 2022; eCat Record 146832) were applied to bathymetry digital elevation models (DEM) in a GIS environment (ESRI ArcGIS Pro) to map polygon extents (topographic high, low, and planar) and quantitatively characterise their geometries. The geometric attributes were then used to classify each shape into discrete Morphology Feature types (Part 1: Dove et al., 2020; eCat Record 144305). In step 2, the seabed geomorphology was interpreted by applying additional datasets and domain knowledge to inform their geomorphic characterisation (Part 2: Nanson et al., 2023; eCat Record 147818). Where available, backscatter intensity, seabed imagery, seabed sediment samples and sub-bottom profiles supplemented the bathymetry DEM and morphology classifications to inform the geomorphic interpretations.</div><div><br></div><div>The Beagle Marine Park seabed morphology and geomorphology features were informed by a post survey report (Barrett et al., 2021). Seabed units were classified at multiple resolutions that were informed by the underlying bathymetry: </div><div><br></div><div>· A broad scale layer represents features that were derived from a 30 m horizontal resolution compilation DEM (Beaman et al 2022; eCat Record 147043). </div><div>· A series of medium and fine scale feature layers were derived from individual 1 m horizontal resolution DEMs (Nichol et al., 2019; eCat Record 130301). </div><div><br></div><div>The data product and application schema are fully described in the accompanying Data Product Specification. </div><div><br></div><div><em>Barrett, N, Monk, J., Nichol, S., Falster, G., Carroll, A., Siwabessy, J., Deane, A., Nanson, R., Picard, K., Dando, N., Hulls, J., and Evans, H. (2021). Beagle Marine Park Post Survey Report: South-east Marine Parks Network. Report to the National Environmental Science Program, Marine Biodiversity Hub. University of Tasmania.</em></div><div><br></div><div><em>Beaman, R.J. (2022). High-resolution depth model for the Bass Strait -30 m. <a href=https://dx.doi.org/10.26186/147043>https://dx.doi.org/10.26186/147043</a>, GA eCat Record 147043.&nbsp;</em></div><div><br></div><div><em>Dove, D., Nanson, R., Bjarnadóttir, L. R., Guinan, J., Gafeira, J., Post, A., Dolan, Margaret F.J., Stewart, H., Arosio, R., Scott, G. (2020). A two-part seabed geomorphology classification scheme (v.2); Part 1: morphology features glossary. Zenodo. <a href=https://doi.org/10.5281/zenodo.40752483>https://doi.org/10.5281/zenodo.4075248</a>; GA eCat Record 144305&nbsp;</em></div><div><br></div><div><em>Huang, Z., Nanson, R. and Nichol, S. (2022). Geoscience Australia's Semi-automated Morphological Mapping Tools (GA-SaMMT) for Seabed Characterisation. Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/146832>https://dx.doi.org/10.26186/146832</a>; GA eCat Record 146832 </em></div><div><em>&nbsp;</em></div><div><em>Nanson, R., Arosio, R., Gafeira, J., McNeil, M., Dove, D., Bjarnadóttir, L., Dolan, M., Guinan, J., Post, A., Webb, J., Nichol, S. (2023). A two-part seabed geomorphology classification scheme; Part 2: Geomorphology classification framework and glossary (Version 1.0) (1.0). Zenodo.<a href=https://doi.org/10.5281/zenodo.7804019>https://doi.org/10.5281/zenodo.7804019</a>; GA eCat Record 147818&nbsp;</em></div>

<div>This data product contains geospatial seabed morphology and geomorphology information for Flinders Reefs and Cairns Seamount (Coral Sea Marine Park). These maps are intended for use by marine park managers, regulators, the general public and other stakeholders. A nationally consistent two-part (two-step) seabed geomorphology classification system was used to map and classify the distribution of key seabed features. </div><div><br></div><div>In step 1, semi-automated GIS mapping tools (GA-SaMMT; Huang et al., 2022; eCat Record 146832) were applied to a bathymetry digital elevation model (DEM) in a GIS environment (ESRI ArcGIS Pro) to map polygon extents (topographic high, low, and planar) and to quantitatively characterise their geometries. Their geometric attributes were then used to classify each shape into discrete Morphology Feature types (Part 1: Dove et al., 2020; eCat Record 144305). In step 2, the seabed geomorphology was interpreted by applying additional datasets and domain knowledge to inform their geomorphic characterisation (Part 2: Nanson et al., 2023; eCat Record 147818). Where available, backscatter intensity, seabed imagery, seabed sediment samples and sub-bottom profiles supplemented the bathymetry DEM and morphology classifications to inform the geomorphic interpretations.</div><div><br></div><div>The Flinders Reefs seabed morphology and geomorphology maps were derived from an 8 m horizontal resolution bathymetry DEM compiled from multibeam surveys (FK200429/GA4861: Beaman et al., 2020; FK200802/GA0365: Brooke et al, 2020), Laser Airborne Depth Sounder (LADS), Light Detection and Ranging (LiDAR) and bathymetry supplied by the Australian Hydrographic Office.</div><div><br></div><div>A subset of the FK200802/GA0365 multibeam survey was gridded at 1 m horizontal resolution to derive the key morphology and geomorphology features at the top of Cairns Seamount (-35 to -66 m; within the upper mesophotic zone).</div><div><br></div><div>The data product and application schema are fully described in the accompanying Data Product Specification. </div><div><br></div><div><em>Beaman, R., Duncan, P., Smith, D., Rais, K., Siwabessy, P.J.W., Spinoccia, M. 2020. Visioning the Coral Sea Marine Park bathymetry survey (FK200429/GA4861). Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/140048>https://dx.doi.org/10.26186/140048</a>; GA eCat record 140048</em></div><div><br></div><div><em>Brooke, B., Nichol, S., Beaman, R. 2020. Seamounts, Canyons and Reefs of the Coral Sea bathymetry survey (FK200802/GA0365). Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/144385>https://dx.doi.org/10.26186/144385</a>; GA eCat record 144385</em></div><div><br></div><div><em>Dove, D., Nanson, R., Bjarnadóttir, L. R., Guinan, J., Gafeira, J., Post, A., Dolan, Margaret F.J., Stewart, H., Arosio, R., Scott, G. (2020). A two-part seabed geomorphology classification scheme (v.2); Part 1: morphology features glossary. Zenodo. <a href=https://doi.org/10.5281/zenodo.4075248>https://doi.org/10.5281/zenodo.4075248</a>; GA eCat Record 144305 </em></div><div><br></div><div><em>Huang, Z., Nanson, R. and Nichol, S. (2022). Geoscience Australia's Semi-automated Morphological Mapping Tools (GA-SaMMT) for Seabed Characterisation. Geoscience Australia, Canberra. <a href=https://dx.doi.org/10.26186/146832>https://dx.doi.org/10.26186/146832</a>; GA eCat Record 146832</em></div><div><br></div><div><em>Nanson, R., Arosio, R., Gafeira, J., McNeil, M., Dove, D., Bjarnadóttir, L., Dolan, M., Guinan, J., Post, A., Webb, J., Nichol, S. (2023). A two-part seabed geomorphology classification scheme; Part 2: Geomorphology classification framework and glossary (Version 1.0) (1.0). Zenodo. <a href=https://doi.org/10.5281/zenodo.7804019>https://doi.org/10.5281/zenodo.7804019</a>; GA eCat Record 147818 </em></div>

This data product contains seabed morphology and geomorphology information for a subset area of Zeehan Marine Park. A nationally consistent seabed geomorphology classification scheme was used to map and classify the distribution of key seabed features. The Zeehan Marine Park seabed morphology and geomorphology maps were derived from a 2 m horizontal resolution bathymetry DEM compiled from a multibeam survey undertaken for Parks Australia by the University of Tasmania. Semi-automated GIS mapping tools (GA-SaMMT); (Huang et. al., 2022; eCat Record 146832) were applied to a bathymetry digital elevation model (DEM) in a GIS environment (ESRI ArcGIS Pro) to map polygon extents (topographic high, low, and planar) and to quantitatively characterise polygon geometries. Geometric attributes were then used to classify each shape into discrete Morphology Feature types (Dove et. al., 2020; eCat Record 144305). Seabed geomorphology features were interpreted by applying additional datasets and domain knowledge to inform their geomorphic characterisation (Nanson et. al., 2023; eCat Record 147818). Where available, backscatter intensity, seabed imagery, and survey reports supplemented the bathymetry DEM and morphology classifications to inform the geomorphic interpretations. The data product and classification schema are fully described in the accompanying Data Product Specification. Dove, D., Nanson, R., Bjarnadóttir, L. R., Guinan, J., Gafeira, J., Post, A., Dolan, Margaret F.J., Stewart, H., Arosio, R., Scott, G. (2020). A two-part seabed geomorphology classification scheme (v.2); Part 1: morphology features glossary. Zenodo. https://doi.org/10.5281/zenodo.4075248; Huang, Z., Nanson, R., Nichol, S. 2022. Geoscience Australia's Semi-automated Morphological Mapping Tools (GA-SaMMT) for Seabed Characterisation. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/146832 Nanson, R., Arosio, R., Gafeira, J., McNeil, M., Dove, D., Bjarnadóttir, L., Dolan, M., Guinan, J., Post, A., Webb, J., Nichol, S. (2023). A two-part seabed geomorphology classification scheme; Part 2: Geomorphology classification framework and glossary (Version 1.0) (1.0). Zenodo. https://doi.org/10.5281/zenodo.7804019

The seascape of the vast Australian continental margin is characterised by numerous submarine canyons that represent an equally vast array of geomorphic and oceanographic heterogeneity. Theoretically, this heterogeneity translates into habitats that may vary equally widely in their ecological characteristics. Here we describe the methodology to develop a framework to broadly derive estimates of potential habitat condition (¿suitability¿ sensu lato) for pelagic and epibenthic megafauna (including demersal fishes), and benthic infauna in all of Australia¿s known submarine canyons. We find that the high geomorphic and oceanographic diversity of submarine canyons creates a multitude of potential habitat types. In general, it appears that canyons may be particularly high-quality for benthic species. Canyons that incise the shelf tend to score higher in habitat potential than those confined to the slope. Canyons with particularly high habitat potential are located mainly off the Great Barrier Reef, the NSW coast, the eastern margin of Tasmania and Bass Strait, and on the southern margin. Many of these canyons have complex bottom topography, are likely to be productive, and have less intense sediment disturbance regimes. The framework presented here can be relevant ¿ once refined and comprehensively validated with ecological data - in a management and conservation context to identify canyons (or groups of canyons) that are likely to represent high-value habitat along a vast continental margin where marine planning decisions may require spatial prioritization decisions. <b>Citation:</b> Zhi Huang, Thomas A. Schlacher, Scott Nichol, Alan Williams, Franziska Althaus, Rudy Kloser, A conceptual surrogacy framework to evaluate the habitat potential of submarine canyons, <i>Progress in Oceanography</i>, Volume 169, 2018, Pages 199-213, ISSN 0079-6611, https://doi.org/10.1016/j.pocean.2017.11.007

The East Australian Current (EAC) onshore encroachment drives coastal upwelling and shelf circulations, changes slope-shelf bio-physical dynamics, and consequently exerts significant influence on coastal marine ecosystem along the south-eastern Australian margin. The EAC is a highly dynamic eddy-current system which exhibits high-frequency intrinsic fluctuations and eddy shedding. As a result, low-frequency variability in the EAC is usually overshadowed and rarely detectable. For decades, despite many efforts into the ocean current observations, the seasonality of EAC’s shoreward intrusion remains highly disputable. In this study, for the first time we use a long-term (26 years) remotely sensed AVHRR Sea Surface Temperature (SST) dataset spanning 1992-2018 to map the EAC off the coast of northern New South Wales (NSW), between 28°S - 32.5°S. A Topographic Position Index (TPI) image processing technique was applied to conduct the quantitative mapping. The mapping products have enabled direct measurement (area and distance) of the EAC’s shoreward intrusion. Subsequent spatial and temporal analyses have shown that the EAC move closer to the coast in austral summer and autumn than in austral winter, with the mean distance-to-coast ~6 km shorter and occupying the shelf area ~12% larger. This provides quantitative and direct evidence of the seasonality of the EAC’s shoreward intrusion. Such seasonal migration pattern of the EAC thus provides new insights into the seasonal upwelling and shelf circulations previously observed in this region. As a result, we were able to confirm that the EAC is a driving force of the seasonal ocean dynamics for the northern NSW coast.

Rapid, efficient, and accurate prediction of mineral occurrence that takes uncertainty into 20 account is essential to optimise defining exploration targets. Traditional approaches to mineral 21 potential mapping often fail to fully appreciate spatial uncertainties of input predictors and their 22 spatial cross-correlation. In this study a stochastic technique based on multivariate 23 geostatistical simulations and ensemble tree-based learners is introduced for predicting and 24 uncertainty quantification of mineral exploration targets. The technique is tested on a synthetic 25 case inspired by the characteristics of a hydrothermal mineral system model and a real-world 26 dataset from the Yilgarn Craton in Western Australia. Results from the two cases proved the 27 superior performance and robustness of the proposed stochastic technique, especially when 28 dealing with high dimensional and large data sets. <b>Citation:</b> Talebi, H., Mueller, U., Peeters, L.J.M. et al. Stochastic Modelling of Mineral Exploration Targets. <i>Math Geosci </i>54, 593–621 (2022). https://doi.org/10.1007/s11004-021-09989-z

1. A robust scientific conclusion is the result of a rigorous scientific process. In observational ecology, this process involves making inferences about a population from 20 a sample. The sample is crucial, and is the result of implementing a survey design. A good survey design ensures that the data from the survey is capable of answering the research question. An even better design, such as spatially balanced designs, will also aim to reduce uncertainty as far as budgets will allow. 2. In many study areas, there are `legacy sites', that already have a time-series observed, and return visits to these sites are beneficial to enhance examination of temporal variability. We propose a method to incorporate these legacy sites into the survey effort whilst also maintaining spatial balance. This is the first formal method to perform this task. 3. Simulation experiments indicate that incorporating the spatial location of legacy sites increases spatial balance and decreases uncertainty in inferences (smaller standard errors in mean estimates). We illustrate the process using a proposed survey of a large marine reserve in South-Eastern Australia, where quantification of the reserve's biodiversity is required. 4. Our approach allows for integration of legacy sites into a new spatially-balanced 35 design, increasing efficiency. Scientists, managers and funders alike will benefit from this methodology { it provides a tool to provide efficient survey designs around established ones. In this way, it can aid integrated monitoring programs. An R-package that implements these methods, called MBHdesign, is available from CRAN. <b>Citation:</b> Foster, S.D., Hosack, G.R., Lawrence, E., Przeslawski, R., Hedge, P., Caley, M.J., Barrett, N.S., Williams, A., Li, J., Lynch, T., Dambacher, J.M., Sweatman, H.P.A. and Hayes, K.R. (2017), Spatially balanced designs that incorporate legacy sites. <i>Methods Ecol Evol</i>, 8: 1433-1442. https://doi.org/10.1111/2041-210X.12782