Spatial layers are provided representing predicted community-level biodiversity patterns for several taxonomic groups (birds, reptiles, fungi) across Australia at 9s resolution (~250 m). Species richness (α-diversity) and pairwise compositional dissimilarity (β-diversity) were modelled by combining spatial environment layers with species occurrence observations and survey data. The models were projected spatially to produce spatial layers (maps) of predicted diversity. Lineage: Methods The methods used to generate these data are described in full in the supporting file provided (‘DiversityLayers_Methods’). In summary, spatial environment layers that could potentially help to predict patterns of community level diversity across Australia were obtained from a variety of sources and aligned to a common 9s resolution (~250 m) spatial grid for Australia. Biological records for birds and reptiles were obtained from the Atlas of Living Australia, aggregated to spatial grid cells and those grid cells with adequate number of species recorded to be considered a ‘community sample’ used to model community diversity patterns. For fungi, surveyed compositional data were used from Bisset et al. (2016).

To model species richness, we used generalised additive modelling (GAM), with environmental predictor variables selected through interactive backward elimination variable selection process. The final models of species richness for each taxonomic group were projected spatially across Australia.

To model pairwise community compositional dissimilarity we used generalised dissimilarity modelling (GDM), with environmental predictor variables selected through interactive backward elimination variable selection process. The final models of compositional dissimilarity for each taxonomic group were projected spatially across Australia, creating a model transformed layer for each predictor variable.

Data products The spatial layers are provided in a separate folder for each taxonomic group. Within each folder, a species richness prediction grid is provided (‘Taxa_Richness’), and GDM transformed predictor layers are provided, one for each predictor variable used in the model (‘Taxa_GDM_tran…’). See Mokany et al. (2022) for a description of how these GDM transformed predictor layers are generated and how they can be used to predict the compositional dissimilarity between any pair of grid cells.

All spatial layers are in GDA94 geographic projection (EPSG:4283) and geotiff format, with no-data values set to -9999.

References Mokany, K., et al. 2022. A working guide to harnessing generalized dissimilarity modelling for biodiversity analysis and conservation assessment. - Global Ecology and Biogeography 31: 802-821.

Plant functional trait maps for AustraliaThese data are associated with a manuscript by Samuel C. Andrew, Irene Martín-Forés, Greg R. Guerin, David Coleman, Daniel S. Falster, Elizabeth Wenk, Ian J. Wright, & Rachael V. Gallagher (Mapping plant functional traits using gap-filled datasets to inform management and modelling, in review). All included files can be used to run the Rmarkdown file "Biogeography_Rcode_241010.Rmd " for the main steps of data prep and analyses. Data objects are uploaded in the zip folder “Data_objects_1_to_9.zip”Inputs“Supplementary_methods.zip”: Data and scripts for using AusTraits data to calculate species mean values. The code for calculating mean species values with the method described in the paper is in the “1_build_Austraits_2.R” R code files. Please contact David Coleman (dave.r.coleman@gmail.com) for further information."1_gap_filled_traits_all_natives_species.csv": This file contains the species level trait data for native Australian species. Gap filling estimates were done by grouping varieties and subspecies to the species taxonomic level. The columns with the “_var” suffix have the variance for each estimated values from the trait gap filling workflow. Species with a value in the "_var" columns had estimated values for that trait and can have the trait values converted to NA for a ungap filled dataset (see output 7)."2_Grid_cell_cliamte_data.csv": Includes a list of equal area grid cells (10 x 10 km) for Australia. The latitude and longitude coordinates are given in the second and third columns. Equal areas coordinates are given in the fourth and fifth columns ("x", "y"). The remaining columns have climate data; "AnnMeanTemp" - Mean Annual Temperature (MAT), "AnnPrecip" – Mean Annual Precipitation (MAP), "maxTemp" – Average maximum temperature of the warmest month, "minTemp" - Average minimum temperature of the coldest month."3_species_cell_id_200306.rds": A R .rds file (loaded with the readRDS() function) that includes the stacked species distribution data. The species expected to occur in each grid cell are listed. The plant herbarium occurrence data from the Atlas of Living Australia (ALA) were used for species distribution modelling, see description in Andrew et al., (2021. Journal of Vegetation Science, 32(2), e13018. https://doi.org/10.1111/jvs.13018). The ALA occurrence data were downloaded in December 2019 (see https://doi.org/10.6084/m9.figshare.24503893.v2 for data) but a fresh download of ALA occurrence data is probably recommended for future studies."4_Austraits_taxa_data_230627.csv": taxonomic data from AusTraits that was used to update species names in distribution data ("3_species_cell_id_200306.rds") to binomial names."5_Unweighted_ausplots_Trait_data.csv": Estimates of trait means and variance for AusPlots species inventories.“6_SDM_press_base_raster_10km.tif”: Raster mask of Australia used to plot grid cell values for maps. Projection "Lambert Azimuthal Equal Area", crs = "+proj=laea +lat_0=-25.2744 +lon_0=133.7751 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".Outputs"7_EA_SDM_10km_GC_Trait_layers.rds": This file can be loaded in R with the load() function and contains the outputs of the first chunk of Rmarkdown code in the “Biogeography_Rcode_241010.Rmd” Rmarkdown file. Includes a data frame for species level trait data (“Trait_data”) and a data frame for grid cell climate and community trait summaries (Aus_grid_cell_data). For the grid cell data each of the four traits have mean (“mean_” prefix), standard deviation(“SD_” prefix), variability (“var_” prefix), maximum (“max_” prefix), minimum (“min_” prefix), number of species with trait data (“species_” prefix), and the percentage of species with trait data ( “_PerCent” suffix). For estimates with gap filled trait data the column names have the “_gap” suffix."8_EA_SDM_10km_GC_models_240422.rds": GAM model outputs used to make plotting figures and variance partitioning analyses faster to run."9_Bootstrap_data_240422.rds": Data and outputs from running the models with 100 random subsets of 10% of the grid cells.Results“Biogeography_Rcode_241010.Rmd”: Rmarkdown file with scripts to use inputs and outputs 1 to 9 and the csv files from “Results_tables.zip”. The script should run as is with the data objects and csv files in the same directory as the Rmarkdown file.“Results_tables.zip”: includes the csv files for Tables 1, 2 and S1 ("Table_1_modes_240422.csv", "Table_2_partitioning_240422.csv", “Table_S1_plot_models_240422.csv”). Used to report model results neatly in the Knit file.“Biogeography_Knit_241010.html”: The output from the Rmarkdown file that shows the figures and tables.“Trait_Maps.zip”: zipped folder of main trait maps as raster layers. Projection "Lambert Azimuthal Equal Area", crs = "+proj=laea +lat_0=-25.2744 +lon_0=133.7751 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".

Map showing the distribution of phylogenetic diversity for mammals in Australia. Underpinning data sourced from the Australian Natural Heritage Assessment Tool (ANHAT), Australian Government Department of the Environment and Energy. For further information on ANHAT see:\r http://www.environment.gov.au/heritage/publications/australian-natural-heritage-assessment-tool\r \r Map prepared by the Department of Environment and Energy in order to produce Figure BIO7 (b) (map 1 of 2) in the Biodiversity theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au\r \r The map service can be viewed at: http://soe.terria.io/#share=s-5khshYMXnojKickVXlhwbtHfz2y\r \r Downloadable spatial data also available below.\r

Shannon's diversity index raster Victoria, AustraliaInput file used to model the species distributions of 40 reptile species in Victoria, Australia.Cell size - 250 x 250Original map of land cover types for Victoria obtained DataVic website. The original layer included 15 land cover classes. These were reclassified into five classes - cropping, grazing pasture, native vegetation, plantation forests and other.FRAGSTATS (v4.2, McGarigal et al 2012) was used to perform moving window analysis on the edited file to calculate Shannon's Diversity Index. Further details of methods used to generate the input files and perform modelling are outlined in the methods section of the publication.Original dataset - Victorian Land Cover Mapping 2016https://metashare.maps.vic.gov.au/geonetwork/srv/api/records/45fb10e4-866a-50a2-902d-e4d0728f0caf/formatters/sdm-html?root=html&output=htmlDOI - 10.26279/5b98592d6b27d

The Biodiversity Values Map (BV Map) identifies land with high biodiversity value that is particularly sensitive to impacts from development and clearing. The BV Map is one of the triggers for determining whether the Biodiversity Offset Scheme (BOS) applies to a clearing or development proposal. The BV Map has been prepared by the Department of Planning and Environment (DPE) under Part 7 of the Biodiversity Conservation Act 2016 (BC Act). A range of mapping layers are included in the BV map. These mapping layers have been developed and are maintained by a range of agencies and councils. The inclusion of these layers on the BV map requires the approval of the Environment Agency Head or delegate. The BV Map shows areas that have been added in the last 90 days as the BOS does not apply to development proposals lodged within this time period. Areas that no longer meet one of the criteria for being included on the BV map will also be removed in map updates. It is planned to update the BV Map quarterly, however users of the BV Map are strongly encouraged to visit the BMAT website and BMAT Tool viewer regularly to be up to date with the latest version and other related information. The spatial data for this version is available from the Web Service (see link below). The latest version of the BV map can be viewed in the Biodiversity Values Map and Threshold (BMAT) Tool (see URL link below).

This is Version 1 of the Soil Bacteria and Fungi Beta Diversity product of the Soil and Landscape Grid of Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. These products provide estimates of the Beta Diversity of soil fungi and bacteria. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated using Digital Soil Mapping methods

Attribute Definition: Soil Bacteria and Fungi Beta Diversity Units: NA; Period (temporal coverage; approximately): 1950-2022; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 6; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Format: Cloud Optimised GeoTIFF.

Lineage: Soil microorganisms mediate a wide range of key processes and ecosystem services on which humans depend. In this study, we report on the biogeography and spatial pattern of soil biota for the Australian continent. We used as basis the DNA sequences from the Biome of Australia Soil Environments (BASE) which were collected over a range of different sites across Australia.

We calculated the beta diversity of abundant taxa of soil bacteria and fungi, treating representative sequence data (OTUs) as individual taxa. Two ordination methods were applied to investigate the dissimilarities in microbial community composition, non-metric multidimensional scaling (NMDS) and Uniform Manifold Approximation and Projection (UMAP) for dimension reduction. The NMDS and UMAP used the weighted UniFrac distance for bacteria and Bray-Curtis dissimilarity for fungi on taxa relative abundance. The results of the NMDS for bacteria indicated that the structure of the data was captured fairly well, with a stress of 0.09. However, the stress of the fungi NMDS was 0.16, indicating that the fungi community composition was moderately well explained.

We further collected a large set of environmental covariates that control the biogeography of soil biota, such as soil properties terrain attributes of vegetation indices, and of which maps are available. We fitted a quantile regression forest machine learning model to exploit the quantitative relationship between point-estimated values of beta diversity and environmental covariates, and used to model to predict beta diversity across Australia along with an estimate of uncertainty.

Soil property and vegetation are the dominant controls of soil biota. The resulting maps also reveal the pattern of soil biota which can further be used for regional assessment of soil biodiversity and from which degradation induced by global changes can be monitored.

Code - https://github.com/AusSoilsDSM/SLGA Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html

Map showing the distribution of phylogenetic diversity for vascular plants in Australia. Underpinning data sourced from the Australian Natural Heritage Assessment Tool (ANHAT), Australian Government Department of the Environment and Energy. For further information on ANHAT see:\r http://www.environment.gov.au/heritage/publications/australian-natural-heritage-assessment-tool\r \r Map prepared by the Department of Environment and Energy in order to produce Figure BIO7 (a) (map 1 of 2) in the Biodiversity theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au\r \r The map service can be viewed at: http://soe.terria.io/#share=s-stuTwUO1lqM3x71ulqHRW4Zv1Of\r \r Downloadable spatial data also available below.\r

Map showing the distribution of phylogenetic diversity for hylid frogs in Australia. Underpinning data sourced from the Australian Natural Heritage Assessment Tool (ANHAT), Australian Government Department of the Environment and Energy. For further information on ANHAT see:

http://www.environment.gov.au/heritage/publications/australian-natural-heritage-assessment-tool

Map prepared by the Department of Environment and Energy in order to produce Figure BIO7 (a) (map 1 of 2) in the Biodiversity theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au

The map service can be viewed at: http://soe.terria.io/#share=s-xO5fCh67EKxEVlSVUwwul1Hrv18

Downloadable spatial data also available below.

No description is available. Visit https://dataone.org/datasets/b72b3269153f1069edbc839e66da2924 for complete metadata about this dataset.

Map showing the distribution of phylogenetic diversity for passerine birds in Australia. Underpinning data sourced from the Australian Natural Heritage Assessment Tool (ANHAT), Australian Government Department of the Environment and Energy. For further information on ANHAT see: http://www.environment.gov.au/heritage/publications/australian-natural-heritage-assessment-tool

Map prepared by the Department of Environment and Energy in order to produce Figure BIO7 (c) (map 1 of 2) in the Biodiversity theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au

The map service can be viewed at: http://soe.terria.io/#share=s-3mnGTPLjSAKwZnsIJxXeAjhkObb

Downloadable spatial data also available below.

Map showing the distribution of phylogenetic diversity for myobatrachid frogs in Australia. Underpinning data sourced from the Australian Natural Heritage Assessment Tool (ANHAT), Australian Government Department of the Environment and Energy. For further information on ANHAT see: http://www.environment.gov.au/heritage/publications/australian-natural-heritage-assessment-tool

Map prepared by the Department of Environment and Energy in order to produce Figure BIO7 (e) (map 1 of 2) in the Biodiversity theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au

The map service can be viewed at: http://soe.terria.io/#share=s-weqLO2SfSDfQDKLNneMXILMrvJ5

Downloadable spatial data also available below.

Abstract The Catchment Scale Land Use of Australia – Update December 2023 dataset is the national compilation of catchment scale land use data available for Australia (CLUM), as of December 2023. It replaces the Catchment Scale Land Use of Australia – Update December 2020. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies). Land use is classified according to the Australian Land Use and Management Classification version 8. It has been compiled from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources, through the Australian Collaborative Land Use and Management Program. Catchment scale land use data was produced by combining land tenure and other types of land use information including, fine-scale satellite data, ancillary datasets, and information collected in the field. The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary, reflecting the source data, capture date and scale. Date and scale of mapping are provided in supporting datasets.

Currency Date modified: December 2023 Publication Date: June 2024 Modification frequency: As needed (approximately annual) Data Extent Coordinate reference: WGS84 / Mercator Auxiliary Sphere Spatial Extent North: -9.995 South: -44.005 East: 154.004 West: 112.505 Source information Data, Metadata, Maps and Interactive views are available from Catchment Scale Land Use of Australia - Update 2023 Catchment Scale Land Use of Australia - Update 2023 – Descriptive metadata The data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). ABARES is providing this data to the public under a Creative Commons Attribution 4.0 license. Lineage statement This catchment scale land use dataset provides the latest compilation of land use mapping information for Australia’s regions as at December 2023. It is used by the Department of Agriculture, Fisheries and Forestry, state agencies and regional natural resource management groups to address issues such as agricultural productivity and sustainability, biodiversity conservation, biosecurity, land use planning, natural disaster management and natural resource monitoring and investment. The data vary in date of mapping (2008 to 2023) and scale (1:5,000 to 1:250,000). 2023 updates include more current data and/or reclassification of existing data. The following areas have updated data since the December 2020 version:

New South Wales (2017 v1.5 from v1.2). Northern Territory (2022 from 2020). Tasmania (2021 from 2019). Victoria (2021 from 2017). Data were also added from the Great Barrier Reef Natural Resource Management (NRM) regions in Queensland (2021 from a variety of dates 2009 to 2017). the Australian Tree Crops. Australian Protected Cropping Structures and Queensland Soybean Crops maps as downloaded on 30 November 2023. The capital city of Adelaide was updated using 2021 mesh block information from the Australian Bureau of Statistics. Minor reclassifications were made for Western Australia and mining area within mining tenements more accurately delineated in South Australia.

Links to land use mapping datasets and metadata are available at the ACLUMP data download page at agriculture.gov.au. State and territory vector catchment scale land use data were produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field, as outlined in 'Guidelines for land use mapping in Australia: principles, procedures and definitions, 4th edition' (ABARES 2011). The Northern Territory, Queensland, South Australia, Tasmania, Victoria and Western Australia were mapped to version 8 of the ALUM classification (‘The Australian Land Use and Management Classification Version 8’, ABARES 2016). The Australian Capital Territory was mapped to version 7 of the ALUM classification and converted to version 8 using a look-up table based on Appendix 1 of ABARES (2016). Purpose for which the material was obtained: This catchment scale land use dataset provides the latest compilation of land use mapping information for Australia’s regions as at December 2023. It is used by the Department of Agriculture, Fisheries and Forestry, state agencies and regional natural resource management groups to address issues such as agricultural productivity and sustainability, biodiversity conservation, biosecurity, land use planning, natural disaster management and natural resource monitoring and investment. The data vary in date of mapping (2008 to 2023) and scale (1:5,000 to 1:250,000). Do not use this data to:

Derive national statistics. The Land use of Australia data series should be used for this purpose. Calculate land use change. The Land use of Australia data series should be used for this purpose.

It is not possible to calculate land use change statistics between annual CLUM national compilations as not all regions are updated each year; land use mapping methodologies, precision, accuracy and source data and satellite imagery have improved over the years; and the land use classification has changed over time. It is only possible to calculate change when earlier land use datasets have been revised and corrected to ensure that changes detected are real change and not an artefact of the mapping process. Note: The Digital Atlas of Australia downloaded and created a copy of the source data in October 2024 that was suitable to be hosted through ArcGIS Image Server & Image Dedicated. A copy of the raster was created with RGB fields as a colour map with Geoprocessing tools in ArcPro. Note: The Digital Atlas of Australia downloaded and created a copy of the source data in February 2025 that was suitable to be hosted through ArcGIS Image Server & Image Dedicated. A copy of the raster dataset was created with RGB fields as a colour map with Geoprocessing tools in ArcPro, and the raster dataset was re-projected from 1994 Australia Albers to WGS 1984 Web Mercator (Auxiliary Sphere). Data dictionary

Field name DField description Code values

OID Internal feature number that uniquely identifies each row Integer

Service Pixel value (Date) The year for which land use was mapped in the vector data provided by state and territory agencies or others, Date Range: 2008 to 2023 Integer

Count Count of the number of raster cells in each class of VALUE Integer

Label Reflecting the Date of the source data ranges from 2008 to 2023 Text

Contact Department of Agriculture, Fisheries and Forestry (ABARES), info.ABARES@aff.gov.au

Map showing the distribution of phylogenetic diversity for snakes and lizards in Australia. Underpinning data sourced from the Australian Natural Heritage Assessment Tool (ANHAT), Australian Government Department of the Environment and Energy. For further information on ANHAT see:\r http://www.environment.gov.au/heritage/publications/australian-natural-heritage-assessment-tool\r \r Map prepared by the Department of Environment and Energy in order to produce Figure BIO7 (d) (map 1 of 2) in the Biodiversity theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au\r \r The map service can be viewed at: http://soe.terria.io/#share=s-4YY0REOlYwRfw4Ietv7bbkmMGew\r \r Downloadable spatial data also available below.\r

Across the world’s oceans, our knowledge of the habitats on the seabed is limited. Increasingly, video/imagery data from remotely operated underwater vehicles (ROVs) and towed and drop cameras, deployed from vessels, are providing critical new information to map unexplored benthic (seabed) habitats. However, these vessel-based surveys involve considerable time and personnel, are costly, require favorable weather conditions, and are difficult to conduct in remote, offshore, and deep marine habitats, which makes mapping and surveying large areas of the benthos challenging. In this study, we present a novel and efficient method for mapping diverse benthic habitats on the continental shelf, using animal-borne video and movement data from a benthic predator, the Australian sea lion (Neophoca cinerea). Six benthic habitats (between 5-110m depth) were identified from data collected by eight Australian sea lions from two colonies in South Australia. These habitats were macroalgae reef, macroalgae meadow, bare sand, sponge/sand, invertebrate reef and invertebrate boulder habitats. Percent cover of benthic habitats differed on the foraging paths of sea lions from both colonies. The distributions of these benthic habitats were combined with oceanographic data to build Random Forest models for predicting benthic habitats on the continental shelf. Random forest models performed well (validated models had a >98% accuracy), predicting large areas of macroalgae reef, bare sand, sponge/sand and invertebrate reef habitats on the continental shelf in southern Australia. Modelling of benthic habitats from animal-borne video data provides an effective approach for mapping extensive areas of the continental shelf. These data provide valuable new information on the seabed and complement traditional methods of mapping and surveying benthic habitats. Better understanding and preserving these habitats is crucial, amid increasing human impacts on benthic environments around the world.

The term "Smartline" refers to a GIS line map format which can allow rapid capture of diverse coastal data into a single consistently classified map, which in turn can be readily analysed for many purposes. This format has been used to create a detailed nationally-consistent coastal geomorphic map of Australia, which is currently being used for the National Coastal Vulnerability Assessment (NCVA) as part of the underpinning information for understanding the vulnerability to sea level rise and other climate change influenced hazards such as storm surge. The utility of the Smartline format results from application of a number of key principles. A hierarchical form- and fabric-based (rather than morpho-dynamic) geomorphic classification is used to classify coastal landforms in shore-parallel tidal zones relating to but not necessarily co-incident with the GIS line itself. Together with the use of broad but geomorphically-meaningful classes, this allows Smartline to readily import coastal data from a diversity of differently-classified prior sources into one consistent map. The resulting map can be as spatially detailed as the available data sources allow, and can be used in at least two key ways: Firstly, Smartline can work as a source of consistently classified information which has been distilled out of a diversity of data sources and presented in a simple format from which required information can be rapidly extracted using queries. Given the practical difficulty many coastal planners and managers face in accessing and using the vast amount of primary coastal data now available in Australia, Smartline can provide the means to assimilate and synthesise all this data into more usable forms.

Satellite image map of Charybdis Glacier, Mac. Robertson Land, Antarctica. This map is part (c) in a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:500000, and was produced from Landsat TM and Landsat MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows traverses/routes/foot/tracks, stations/bases, and glaciers/ice shelves. The map has only geographical co-ordinates.

This file set includes:Two raster datasets of marine ecosystems in Spencer Gulf produced for a cumulative impact assessment. There is one raster for the benthic ecosystems and one for the pelagic ecosystem. For each of the rasters there is an associated projection file with the same name.Two tiff files of the ecosystem maps (illustrating what they look like when plotted)A metadata text file with details of the spatial data layers and their projection - as well as sources of further information.

Satellite image map of Edward VIII Gulf, Kemp Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1993. The map is at a scale of 1:100000, and was produced from a Landsat TM (WRS 139-107) scene (bands 2,3 and 4). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and penguin colonies, and gives some historical text information. The map has both geographical and UTM co-ordinates.

The Oceanic Shoals survey (SOL5650, GA survey 339) was conducted on the R.V. Solander in collaboration with Geoscience Australia, the Australian Institute of Marine Science (AIMS), University of Western Australia and the Museum and Art Gallery of the Northern Territory between 12 September - 5 October, 2012. This dataset comprises an interpreted geomorphic map. Interpreted local-scale geomorphic maps were produced for each survey area in the Oceanic Shoals Commonwealth Marine Reserve (CMR) using multibeam bathymetry and backscatter grids at 2 m resolution and bathymetric derivatives (e.g. slope; 1-m contours). Six geomorphic units; bank, depression, mound, plain, scarp and terrace were identified and mapped using definitions suitable for interpretation at the local scale (nominally 1:10 000). Maps and polygons were manual digitised in ArcGIS using the spatial analyst and 3D analyst toolboxes. For further information on the geomorphic mapping methods please refer to Appendix N of the post-survey report, published as Geoscience Australia Record 2013/38: Nichol, S.L., Howard, F.J.F., Kool, J., Stowar, M., Bouchet, P., Radke, L., Siwabessy, J., Przeslawski, R., Picard, K., Alvarez de Glasby, B., Colquhoun, J., Letessier, T. & Heyward, A. 2013. Oceanic Shoals Commonwealth Marine Reserve (Timor Sea) Biodiversity Survey: GA0339/SOL5650 Post Survey Report. Record 2013/38. Geoscience Australia: Canberra. (GEOCAT #76658).

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

Satellite image map of Stibbs Bay, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1992. The map is at a scale of 1:100000, and was produced from Landsat TM scene WRS 137-107. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, penguin colonies, refuge/depots, Specially Protected Areas (SPA), and gives some historical text information. The map has both geographical and UTM co-ordinates.