Please note, we recommend using the new Map Viewer in ArcGIS Online. There is an issue in Map Viewer Classic with the display of grid cell values. The clickable area of each cell is shifted to the northwest. This can result in neighbouring pixel values being displayed. The underlying data is correct, and the values display correctly in the new Map Viewer and in ArcGIS Pro. The Australian population grid 2022 is a modelled 1 km x 1 km grid representation of the estimated resident population (ERP) of Australia from 30 June 2022. The population grid is created by reaggregating estimated resident population data from Statistical Areas Level 1 (SA1) to a 1 km x 1 km grid across Australia based on point data representing residential address points. The value of each grid cell represents the estimated population density (number of people per square kilometre) within each 1 km x 1 km grid cell.

SA1 boundaries are defined by the Australian Statistical Geography Standard (ASGS) Edition 3 (2021) and the 1 km x 1 km grid is based on the National Nested Grid.

Data considerations Caution must be taken when using the population grid as it presents modelled data only; it is not an exact measure of population across Australia. Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Regional population, 2022 Additional data input: ABS Address Register Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3, National Nested Grid Further information: Regional population methodology Source: Australian Bureau of Statistics (ABS)

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Australia: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

The ABS Australian population grid 2023 was created using 2023 Estimated Residential Population (ERP) data at the SA1 level. The SA1 level ERP data was then modelled down to a 1km x 1km grid across geographic Australia using various point layers that represent population. The value of each grid cell represents the population density (number of people per square kilometre) in that 1km x 1km cell. This is modelled data and caution must be used in its interpretation, as the population has NOT been measured at the 1km cell level. SA1s are defined by the Australian Statistical Geography Standard (ASGS) Edition 3 (2021) and the grid used is based on the National Nested Grid Standard.Data and geography notes:Source data publication: Regional population, 2022-23Geographic boundary information: Statistical Areas Level 1 (SA1)(2021) - Australian Statistical Geography Standard (ASGS) Edition 3, National Nested Grid StandardAdditional data inputs: ABS Address Register, Indigenous Community Points (ICP)Further information: Regional population methodologySource: Australian Bureau of Statistics (ABS) www.abs.gov.auProcessing steps:A subset of the Address Register was created to represent residential addresses as closely as possible. Indigenous Community Points were included where no AR point existed. SA1 centroid points were included where no AR or ICP point existed within an SA1. All these layers were combined into a single point layer (Allpoints).The Allpoints layer was overlaid with the ASGS 2021 SA1 boundaries to give every point an SA1 code. Points without an SA1 code (outside all SA1 regions) were dropped.Estimated Resident Population by SA1 (ERP) was averaged across all points within each SA1.Points were converted to raster using the National Nested grid as template. Point population values falling within each raster cell were summed.

Key FeaturesArcGIS Instant App (Atlas) created using the Population distribution by local government area webmap and the following widgets:BasemapMap LayerLegendMeasurementSketchSaveModification As needed, please refer to map for currency of data layers. Contact Digital Atlas of Australia

This is Version 1 of the Australian Soil Bulk Density - Whole Earth 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. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). 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 by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: Bulk Density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method; Units: g/cm3; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; 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); Variance explained (cross-validation): 0.4%; Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being;

1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).

Version 1 of the Australian Soil Property Maps combines mapping from the:

1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps.

These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.

Population density metrics for 2011 Statistical Area Level 2 (SA2) within 2011 Greater Capital City Statistical Areas (GCCSA), including SA2 Population-weighted density (PWD) for 2011 and 2014, PWD change 2011-2014, and ERP population counts by density classes. Selected Density Classes were based on the Australian Population Density Grid published by the ABS, December 2014 (cat. no. 1270.0.55.007). Corresponding population metrics for 2011 GCCSAs. PWD using standardised 1km grid cells provides a more comparable measure of the density in larger regions. It does this by weighting the density using the proportion of population living at that density. In this way the density measure reflects the density at which people actually live. This removes the effect of large unpopulated areas that may be within the regions being compared. In this way comparisons between regions are more valid.

The map service can be viewed at http://soe.terria.io/#share=s-AgXEN0N0Q95icRW7M9JIC9IYBdE

Downloadable spatial data also available below.

Map prepared by the ABS and presented as Figure BLT3 in Built environment theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au.

It should be noted that this data is now somwhat dated!

Human population density is a surrogate indicator of the extent of human pressures on the surrounding landscapes.

Areas with high population density are associated with higher levels of stream pollution and water diversion through sewers and drains. City and urban environments are substantially changed from their pre-European condition but a changed condition is not of itself necessarily poor by societal standards. It is the impacts such as polluted run-off to waterways, air pollution, sewage disposal, household water use and predation of wildlife by pets that confer impacts on catchment condition. Human population centres have an impact well beyond the built environment.

The impact of major population centres is well expressed in the AWRC map, but is best displayed in the 500 map. The main areas of impact are the major coastal and capital cities and suburbs, including popular beachside tourist destinations. Elsewhere, the impact of population density appears to be confined to the Murray and other major river valleys.

The Australian Bureau of Statistics compiles population statistics by sampling statistical local areas (SLAas) through the national census. These data can be converted to a per catchment basis.

Interpretation of the indicator is largely unequivocal, although there are land-uses/activities (e.g. mining) where population density is not a good indicator of the degree of habitat decline. This indicator has not been validated relative to habitat decline. This indicator is easy to understand.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

The exploration and management of mineral resources heavily rely on the availability of geoscientific data. However, the spatial distribution of these data can vary significantly across South Australia, creating challenges for comprehensive... The exploration and management of mineral resources heavily rely on the availability of geoscientific data. However, the spatial distribution of these data can vary significantly across South Australia, creating challenges for comprehensive geological analysis. Inspired by the Brazilian Geoscientific Knowledge Index (GKI) maps, this project aimed to develop a Data Density Index Map for South Australia. By visualising the distribution of critical geoscientific data, the map serves as a tool for identifying areas with high data concentrations, as well as regions that may benefit from additional data acquisition or exploration activities, ultimately facilitating decision-making and resource information management.

This is Version 2 of the Australian Soil Bulk Density - Whole Earth product of the Soil and Landscape Grid of Australia.

It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546EE212B0048

The map gives a modelled estimate of the spatial distribution of Bulk Density in soils across Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Attribute Definition: Bulk Density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method; Units: g/cm3; Period (temporal coverage; approximately): 1950-2021; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: Cloud Optimised GeoTIFF; Lineage: An attempt was made to update digital soil mapping of whole soil bulk density for Australia. This was an update of first attempt by Viscarra Rossel et al. (2014). Based on model evaluations using a dataset not included in any modelling, the updated version (2nd Version) represents a demonstrable improvement on the 1st version.

Since the first version, more measured site data has been made available and retrievable via the Australian SoilDataFederator. In 2014 there were 3776 sites with measured whole soil bulk density. For the new update, 6116 sites had measured data. Because of usually strong empirical relationships between bulk density, soil texture and soil carbon, the use of pedotransfer functions (to predict bulk density from soil texture and soil carbon) was performed with the intention of increasing data density and spatial coverage of data that would ultimately improve digital soil mapping prediction skill. This added a further 15735 sites after building a spatial pedotransfer function using a dataset of 12308 cases (3939 sites with bulk density, soil carbon and soil texture data).

The basic steps of the work entailed.

Use soil data federator to get pertinent soils observation data

Develop spatial pedotransfer function prediction whole soil bulk density using soil carbon and texture data.

Compile measured and inferred whole soil bulk density data (86306 cases), then setting aside a dataset of 7500 cases for external model evaluation.

Predictive models using random forest algorithm with 78806 data cases fitted. To account for uncertainties in pedotransfer function inferred data, Monte Carlo simulations were performed from the pedotransfer function model. Simulation was repeated 100 times.

Predictive model uncertainties quantified using UNEEC approach (Uncertainty Estimation based on local errors and Clustering).

Quantification of model extension limits derived using hybrid method involving multivariate convex hull analysis and count of observations.

Digital soil maps with quantified uncertainties (5th and 95th prediction interval limits) and assessment of model extrapolation risk were produced at 90m resolution for the following depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm.

All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

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

This database presents an index of the intensity, frequency and density of cyclone occurrence in the Australian region. It has been derived from data held in CSIRO CAMRIS database and originally collected by the Bureau of Meteorology from 1958 - 1990. The cyclone_density code in the coverage represents: 1 Australia, 2-23 the nominal index of cyclone density/intensity, as per the Bureau of Meteorology cyclones database.

Format: shapefile.

Quality - Scope: Dataset. Absolute External Positional Accuracy Check: +/- one degree. Non Quantitative accuracy: The attribute called nominal_index holds values 0-23, which represent the intensity and density of cyclone occurrence. The attribute called cyclone_density provides a subjective definition of the density of cyclone occurrence:

Nominal_Index : Cyclone_Density

0 : No cyclone occurrence. 1 : Australian Continent. 2 : Low. 3-8 : Medium 9-16 : High. 17-23 : Very high.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent and oceans. Lineage: The database shows an index of cyclone intensity and frequency from 1958-1990. The map was created from raw data provided by the Bureau of Meterology: 1. Data points represented each 6 hourly location of every cyclone. 2. Modelled the density of points to create a contour map by counting points which fell within a certain radius of each point. Weighted by distance as 1 to all points within 25 km of a cyclone eye, and a linearly decaying weight (with distance) of between 1 and 0 to all points between 25 and 50km away. This assumed that cyclones significantly affect areas less than 25km from the eye, and have a decreasing effect with distance away from the eye. 3. Values on the contour map were multiplied by an index derived from intensity (barometric depression) at cyclone eye. 4. Reclassed intensity - density distribution using a linear scale.

CAMRIS data were stored in VAX files, MS-DOS R-base files and as a microcomputer dataset accessible under the LUPIS (Land Use Planning Information System) land allocation package. A summary follows of data processing by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources. 2. BASE Table was generated incorporating specific fields. 3. SPANS environment: creating a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America). 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - selecting out specified fields, a desired window size (ie continental or continent and oceans) and resolution level (ie the quad tree level). 6. Rasterise maps specifying key parameters. 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid, and supplied to ERIN. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Identifying environmental characteristics that limit species’ distributions is important for contemporary conservation and inferring responses to future environmental change. The Tasmanian native hen is an island-endemic flightless rail and a survivor of a prehistoric extirpation event. Little is known about the regional-scale environmental characteristics influencing the distribution of native hens, or how their future distribution might be impacted by environmental shifts (e.g., climate change). Using a combination of local fieldwork and species distribution modelling, we assess environmental factors shaping the contemporary distribution of the native hen, and project future distribution changes under predicted climate change. We find 37.2% of Tasmania is currently suitable for the native hens, owing to low summer precipitation, low elevation, human-modified vegetation, and urban areas. Moreover, in unsuitable regions, urban areas can create ‘oases’ of habitat, able to support populations with high breeding activity by providing resources and buffering against environmental constraints. Under climate change predictions, native hens were predicted to lose only 5% of their occupied range by 2055. We conclude that the species is resilient to climate change and benefits overall from anthropogenic landscape modifications. As such, this constitutes a rare example of a flightless rail to have adapted to human activity. Methods Local-scale factors measurements (fieldwork) We selected geographically distant populations presenting different rainfall profiles during the late-autumn to spring period, April-November 2019, as rainfall is an important factor for native hens’ survival and reproduction (Ridpath, 1972a; Lévêque, 2022): ‘East’ (wukaluwikiwayna/Maria Island National park; 42°34'51"S 148°03'56"E), ‘North’ (Narawntapu National park; 41°08'53"S 146°36'52"E), and ‘West’ (adjacent to the town of Zeehan [712 inhabitants]; 41°53'03"S 145°19'56"E). The period April-November corresponds to the six-month period preceding the middle point of the breeding season, generally used for native hens’ surveys (Goldizen et al., 1998; Lévêque, 2022). All three populations were surveyed between the 10th and the 22nd of November 2019 (late spring, in the middle point of the breeding season) to determine population structure (total number of groups, group composition [number of adults and young], and breeding activity). Each population was monitored over two to five days, depending on habitat complexity and extent of the population area, until all native hens in the area had been surveyed, i.e., when the territories’ structure was found identical at least four times for populations with no previous data (‘North’ and ‘West’), and at least two times in well-known populations (‘East’; Lévêque, 2022), over two different half-day. To align with methods used by Lévêque (2022), we used territory mapping (Bibby et al., 2000; Gibbons & Gregory, 2006) as native-hens maintain year-round territories, and population sizes were measurable with our survey methodology. Territory mapping consists of establishing the location of birds over a number of visits to obtain distinct clusters representing each territory. Boundaries are determined by vocal disputes between neighbours, which are frequent in native hens. During each survey, a minimum of two observers conducted repeated group identification, based on location, neighbours’ location, and number of individuals per group (from two to five individuals per group in this study). The number of individuals and their age category (fledgling, juvenile, or adult) were recorded per territory. The total pasture area surveyed per population, and the total pasture area occupied by native hens were: North population: 2.0 km2 (1.3 km2 occupied); West population: 1.5 km2 (0.7 km2 occupied); East population: 1.5 km2 (0.6 km2 occupied). We measured environmental characteristics in the native-hens’ territory following methods established by Goldizen et al. (1998) to obtain quantitative measures of i) protection cover, ii) water availability, and iii) food availability; these parameters are important for native hen reproduction (Goldizen et al., 1998).

Protection cover was determined as the length (m) of the interface between dense patches of bushes and pasture, used by native hens for hiding and protecting chicks against predators (Lévêque, 2022). It is an important parameter for breeding success (Goldizen et al., 1998). We measured the total protection cover available to native hens in each population using satellite data from Google Maps (www.google.com/maps, accessed on 09/12/2019). For measures of food availability (grass) on territories, we selected random transects of a total length of 1 m across all territories (East: n = 15, North: n = 26, West: n = 22). Measurements of vegetation characteristics were measured and recorded every 2 cm along each transect, including the percentage of i) total vegetation cover, ii) green vegetation, iii) vegetation cover that was grass, iv) vegetation cover that was moss, and v) the grass height (average length of grass blades). The same observer (LL) recorded all measures. Water availability on territories was recorded as territories that had access to water (running or stagnant) at the time the surveys were undertaken. Rainfall data was collected from the Bureau of Meteorology (B.O.M.; www.bom.gov.au/climate/data) at the three population sites: North population at Port Sorell (Narawntapu National Park – 4km away from the population site), West population at Zeehan (West Coast Pioneers Museum), East population at Maria Island (Darlington). Rainfall was reported as the amount of rainwater that had accumulated i) during the six months prior to breeding season midpoint (31/10/2019); following Goldizen et al. (1998)) and ii) during summer [December-February]. Information on recent droughts (on a 3- to 11-month period prior to 31/10/2019) was assessed using values on rainfall percentile deficiency (below the 10th percentile) from B.O.M. (http://www.bom.gov.au/climate/drought/#tabs=Rainfall-tracker). The 6-, 7-, and 12- month-periods were not accessible. B.O.M. defines the category ‘Serious deficiency’ as rainfall that “lies above the lowest five percent of recorded rainfall but below the lowest ten percent (decile range 1) for the period in question”, and ‘severe deficiency’ as “rainfall is among the lowest five percent for the period in question”.

Species Distribution Modelling Data preparation We collected presence-point data for native hens across Tasmania from the Atlas of Living Australia (ALA: www.ala.org.au; accessed 19 February 2021). We additionally included data from BirdLife Tasmania, the Department of Primary Industries, Water and Environment (DPIPWE) reports, and our personal observations, resulting in a total of 23,923 occurrences. Our study area included the Tasmanian mainland and nearby islands, however a large area from the south-west of Tasmania was removed where native hen distribution is not well documented, however, they are thought to be rare or absent in this region due to large proportion of button grass vegetation creating unsuitable habitat (Fig. S2). All subsequent analyses were undertaken in Program R v4.0.4 (R Core Team, 2021). Duplicates were removed by converting presence points into grid presences at 1 km2 resolution and retaining one native hen observation per grid (n = 2447 grid points after this step). Occurrences were visually inspected for any potential errors/outliers from outside Tasmania and Tasmanian islands: this removed seven false occurrences on King and Flinders islands and two observations in freshwater inland lakes (Lake Crescent and Great Lake). As true-absence records were mostly unavailable, we generated pseudoabsences for sites where other land-bird species had been recorded (indicating observation effort at that point), but without native hen detections (Hanberry et al., 2012; Amin et al., 2021; Barlow et al., 2021). Native hens are large-bodied, ground-dwelling, active in the day, and have a loud, distinct call, all of which accounts for a high detectability, if present at a location. We extracted these data from ALA, with 780,499 possible observations on the Tasmanian mainland and all nearby islands. We then excluded all grid cells with a native hen presence and removed any records within 3 km of native hen records: this value was chosen because it is the dispersal distance under which a native hen can naturally move outside of its territory (Ridpath, 1972a). This process resulted in 3,222 pseudoabsence grid points. Citizen-science datasets offer unique opportunities to study a species distribution using ‘crowd-sourced’ effort, however, they tend to be access-biased and have non-random, clustered observations, leading to overrepresentation of certain regions and biases towards some environmental conditions (usually near urban areas; Steen et al., 2021). One way to reduce spatial autocorrelation is to selectively de-cluster occurrences in biased areas using a pre-defined (minimum linear) Nearest Minimum-neighbour Distance NMD (Pearson et al. 2007). As un-urbanised, sparsely populated areas have the least spatial point clustering (and hence spatial bias), the average number of observations in low human densities areas provides the threshold number of records that can be used to tune and select the optimal NMD (Amin et al., 2021). Therefore, we subdivided our data on a grid of 25 km2 cells to be relevant to the metric of human density and used the median of population density index (excluding cells < 1 human/km2) to define thresholds for low and high density. Population density was extracted from the ‘2011 Census of Population and Housing across Australia’ (bit.ly/3bth7W9). ‘Low density’ was defined as < 6 people/km2 and ‘High density’ as

Additional file 3: Supplementary Table 1. Predicted 2020 population, number of existing Australian 5 km parkrun events (at July 2021), and number of proposed parkrun events, in greater capital city and regional areas for each state. Supplementary Figure 1a. Map of current and proposed events for the greater capital city (Sydney) region of New South Wales. For this and all subsequent figures, numbered locations correspond to the order of selection by the location-allocation algorithm listed in Supplementary Table 2; the same population density scale has been used for all figures. Supplementary Figure 1b. Map of current and proposed events for regional New South Wales (NSW). Note for this and subsequent regional maps, event locations in the greater capital city are suppressed because of heavy clustering. Note also that the locations selected by the algorithm represent only the centroid of the SA2 area; sometimes this coincides with a regional town, but often the nearest town is visible as a darker purple area indicating higher population density. See the online interactive map for further details. Supplementary Figure 2. Map of current and proposed events for the Northern Territory (NT). Supplementary Figure 3. Map of current and proposed events for Queensland (QLD). Supplementary Figure 4. Map of current and proposed events for South Australia (SA). Supplementary Figure 5. Map of current and proposed events for Tasmania (TAS). Supplementary Figure 6a. Map of current and proposed events for the greater capital city (Melbourne) region of Victoria. Supplementary Figure 6b. Map of current and proposed events for regional Victoria (VIC). Supplementary Figure 7a. Map of current and proposed events for the greater capital city (Perth) region of Western Australia. Supplementary Figure 7b. Map of current and proposed events for regional Western Australia (WA). Supplementary Figure 8. Map of current events for the Australian Capital Territory (ACT). Note that no new events were proposed. Supplementary Table 2. Locations of new events selected by the location-allocation algorithm.

Aim: Greater understanding of the processes underlying biological invasions is required to determine and predict invasion risk. Two subspecies of olive (Olea europaea subsp. europaea and Olea europaea subsp. cuspidata) have been introduced into Australia from the Mediterranean Basin and southern Africa during the 19th century. Our aim was to determine to what extent the native environmental niches of these two olive subspecies explain the current spatial segregation of the subspecies in their non-native range. We also assessed whether niche shifts had occurred in the non-native range, and examined whether invasion was associated with increased or decreased occupancy of niche space in the non-native range relative to the native range.Location: South-eastern Australia, Mediterranean Basin and southern Africa.Methods: Ecological niche models (ENMs) were used to quantify the similarity of native and non-native realized niches. Niche shifts were characterized by the relative contribution of niche expansion, stability and contraction based on the relative occupancy of environmental space by the native and non-native populations.Results: Native ENMs indicated that the spatial segregation of the two subspecies in their non-native range was partly determined by differences in their native niches. However, we found that environmentally suitable niches were less occupied in the non-native range relative to the native range, indicating that niche shifts had occurred through a contraction of the native niches after invasion, for both subspecies.Main conclusions: The mapping of environmental factors associated with niche expansion, stability or contraction allowed us to identify areas of greater invasion risk. This study provides an example of successful invasions that are associated with niche shifts, illustrating that introduced plant species are sometimes readily able to establish in novel environments. In these situations the assumption of niche stasis during invasion, which is implicitly assumed by ENMs, may be unreasonable. Contains raster maps (ESRI ASCII format) for 29 environmental variables in three geographical regions (Mediterranean Basin, southern Africa and south-eastern Australia) at a 30-sec resolution.These maps were used for ecological niche modelling of native populations of Olea europaea subspecies europaea (Mediterranean Basin), native populations of Olea europaea subspecies cuspidata (southern Africa) along with the introduced populations of these two subspecies in south-western Australia.The bioclimatic variables considered comprise the 19 WORLDCLIM temperature and rainfall variables (denoted bio1 to bio19), 9 soil properties variables (Rooting capacity, Soil bulk density, Soil field capacity, Soil thermal capacity, Soil total nitrogen density, Oxygen availability, Soil profile available water capacity, Soil wilting point and Soil carbon density) and the mean normalized vegetative index (NDVI), averaged for months January and July from 2001 to 2011. See the original paper for details.

This record provides an overview of the scope and research output of NESP Marine Biodiversity Hub Project C5 - "Quantification of risk from shipping to large marine fauna across Australia". For specific data outputs from this project, please see child records associated with this metadata. Given the substantial increases in coastal/port development along the Australian coastline, and associated increase in recreational and commercial shipping, there is an increasing potential for adverse interactions with marine species. Two risks associated with these activities for large marine fauna are ship collisions and the impact of chronic ocean noise. Research is urgently needed to quantify these risks in both a spatial and temporal context to help develop and implement appropriate management strategies. This project aims to provide directed science (species- and area-specific) to inform decision-making by the Department of Environment in its application of the EPBC Act. Planned Outputs • Initial scoping report of ship strike risk summarising what is currently known about species that were tentatively nominated as being at-risk for ship strike, the data available, shipping size/type data needed and recommendations on what species to investigate further with a qualitative ranking from easiest to most difficult. • AIS data base for the Australian EZ and initial processing protocols. • Full Australia-wide fine-scale shipping density and average speed maps for 2012 – present including information such as vessel length, beam and draft. This data will directly feed into future noise mapping. • A national map of approximate density of small vessel distribution based on available proxies such as population density, boat registration data and boat ramp locations. • A suite of distribution and density surfaces for the various species nominated during Phase 1; • Spatial and temporal risk profiles for selected species. The risk maps will range from full fine-scale maps when data is present, to coarse-scale ‘regions of concern’ for species where distribution data is limited to approximate extent. • An updated version of a database of ship strikes (historical and recent) within the Australian EEZ Report on national ship strike risk to the limits of current data and knowledge. • Report on our ship strike risk methodological developments • Report on initial shipping noise mapping • Report on the recommendations and findings of the 2017 workshop on chronic noise in the marine environment.

Digital soil maps (DSMs) are prepared through quantitative modelling techniques that are based on relationships between soil attributes and the environment. DSMs are presented over NSW for a range of key soil properties, including soil organic carbon (SOC), pH, cation exchange capacity, sum-of-bases, available phosphorous, bulk density, clay, silt and sand (total and fine). The maps are at 100 m spatial resolution and cover ten soil depth intervals down to 2 m, consistent with widely used Australian and international systems. Random Forest decision tree modelling techniques were applied. Validation results for the maps indicate generally moderate to high performance and effectiveness. Maps of mean plus upper 95% and lower 5% prediction limits are available. The maps provide at least a useful first approximation of these soil properties across the State. The products are described more fully in the technical report: Gray (2023), Digital soil mapping of key soil properties over NSW, version 2.0 (76p). The report and raster layers can be downloaded through the NSW environmental data portal SEED (https://www.seed.nsw.gov.au/) and are also viewable through the DPE soil and landscape spatial viewer eSPADE (http://espade.environment.nsw.gov.au ). All maps, including prediction limits and intervals, are also available through the DPE data broker.

Spatial input data to parameterise the gully erosion module of the dSedNet model to simulate sediment generation and transport in the Western Port catchment for a 2018-19 study commissioned by Melbourne Water. Lineage: The 2003 gully map data were reprojected and spatially corrected. 'Active' gullies were determined through visual interpretation of aerial imagery from ESRI Base Layers (approx. 2013-2018) according to where the gully had sharply incised banks and/or presence of bare ground at base or edges. Some gullies were deleted where land use had changed and the gully was no longer visible, e.g. urban development, agriculture. New gullies were mapped where identified from recent aerial imagery. The workflow was executed within the ArcGIS (version 10.2) environment.

This map shows where youth populations are found throughout the world. Areas with more than 33% youth are highlighted with a dark red shading while a dot representation reveals the number of seniors and their distribution in bright red.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics