National Ecosystem Accounts, experimental estimates methodology

Concepts

Terrestrial realm

The Terrestrial realm includes all dry land, its vegetation cover, proximate atmosphere and substrate (soils, rocks) to the rooting depth of plants, and associated animals and microbes (IUCN GET). Terrestrial realm EFGs are listed in Table 3.

Transitional realm

Transitional realms represent areas where the core realms (Terrestrial, Freshwater, Marine, and Subterranean) meet and interact. The Terrestrial realm transitions to Palustrine wetlands, Shoreline and supralittoral, and Brackish tidal systems. For the National Ecosystem Accounts, only the Brackish tidal biome is reported as part of the Coastal realm, please refer to Coastal Realm. Transitional realm EFGs are listed in Table 4.

Data Sources

CSIRO MFT Extent

Marine-Freshwater-Terrestrial-Transitional realm ecosystem spatial extent (MFT extent) data were compiled at IUCN GET Ecosystem Functional Group level by CSIRO for the NEA. It uses:

• NVIS version 7.0 Extant Theme (DCCEEW) to define natural EFGs
• NVIS version 7.0 Pre-1750 Theme (DCCEEW) to define natural EFGs not in the extant dataset
• Geofabric version 3.3 (BoM) to define Lakes and wetlands biome EFGs
• OzCoasts (GA) to define Semi-confined transitional waters biome EFGs
• Land Use of Australia (ABARES) to define Intensive land-use biome EFGs
• DEA Land Cover (GA) to define annual changes between Land Use of Australia reporting years
• Interim Biogeographic Regionalisation for Australia Version 7 (DCCEEW) to identify natural EFGs across Terrestrial and Transitional Realms (Terrestrial-Freshwater, Terrestrial-Freshwater-Marine)
• Note: while the extent of freshwater ecosystems was calculated alongside the terrestrial extent, the results are published separately in the freshwater section.

Output specifications:

• annual rasters for pre-1750 and 2010–11 to 2023–24 (a)
• 100 m grid spatial resolution.

(a) Note that Terrestrial and Transitional Realm extent were only published until 2020–21.

Methods

Spatial extent rasters classified at IUCN GET EFG were combined with geography rasters to derive the geographical extent for each EFG. The area of each EFG category was calculated for each region by counting the pixels present for each category in each region and multiplying by the area of the pixel. Each 100 m pixel represents a 1 ha area.

Unclassified treatment

A small number of EFGs are not spatially distinct in the IUCN GET level 3 classification. The Permanently open riverine estuaries and bays EFG in the Freshwater-Marine realm, spatially and conceptually overlaps a number of EFGs from the Marine shelf biome of the Marine realm. This results in approximately 3.2 million hectares of Terrestrial, Freshwater or Transitional (excluding coastal) ecosystems overlapping with other EFGs. The impact is minor (approximately 0.22% of the area within the outer boundary of Australia's contiguous EEZ is reported as Terrestrial, Freshwater or Transitional (excluding Coastal) EFGs and also in another realm). Terrestrial and Freshwater extent data cubes contain the following caveat: ‘Some areas reported as Terrestrial, Freshwater or Transitional Ecosystem Functional Groups (EFGs) are also reported within other realms. The sum of extent across realms will exceed the area within the outer boundary of Australia’s contiguous Exclusive Economic Zone (EEZ), see methodology.‘ 

Concepts

Remote sensing data were used to derive biotic and abiotic ecosystem condition variables. The variables were selected to represent a diverse set of condition characteristics to populate the ecosystem condition accounts following the SEEA ECT. The remote sensing variables included in the accounts are shown in Table 5.

Data Sources

The terrestrial condition variables were compiled by CSIRO as part of their Habitat Condition Assessment System (HCAS) Version 3.3 dataset. HCAS provides a set of 14 condition variables, five of which were selected to provide a representative subset typology classes from the SEEA ECT. This data combines inputs from a variety of sources:

• reference sites that represent the most intact examples of Australia’s ecosystems (CSIRO)
• environmental covariates (Terrestrial Ecosystem Research Network (TERN))
• Digital Earth Australia Surface Reflectance NBART Landsat Analysis Ready Data Collection 3 (GA).

Methods

These condition variable data are represented in 3-year calendar year time periods (epochs). The epoch with the end year that matched the end year of the financial year was used to represent the account years of interest. For example, the 2010–11 account year was used to represent the 2009 to 2011 epoch year. Each condition metric was summarised by EFG and output geographic region using area-weighted averages.

Concepts

Grazed biomass provisioning services are the ecosystems’ contributions to the growth of grazed biomass, as an input to the growth of cultivated livestock. This service excludes the ecosystems’ contributions to the growth of crops used to produce fodder for livestock (e.g. hay and soybean meal). This is a final ecosystem service.

In some intensive livestock production (e.g. feedlots), there may be some disconnect between ecosystems and the production of livestock and livestock products. Where the livestock production process does not involve direct connection with an ecosystem, no ecosystem service exists.

Data Sources

Livestock numbers

Data on livestock numbers for cattle and calves, sheep and lambs by pasture type that feeds into the Agricultural Commodities, Australia (ABS) was provided by the ABS Agricultural Statistics team since this data is not published at this level of detail. Data was provided for each state and territory, and nationally for 2010–11, 2015–16 and 2020–21 financial years.

Herd estimates

Data on beef cattle herd and sheep flock estimates was obtained from the Australian Agriculture: Livestock (ABS) publication to benchmark estimates reported in the Agricultural Census. Data was used for each state and territory for 2010–11 to 2020–21 financial years.

Livestock products

Data on slaughtered livestock numbers and red meat produced was collated from the latest quarterly release of Livestock Products (ABS). Additionally, data split by bulls and cows was provided by the Agricultural Statistics team since data is not published by these splits. Data provided was for each state and territory and nationally for 2010–11 to 2020–21 financial years.

Dressing proportions

Information on dressing weight percentage matrix used in calculating live weight from carcass weight as reported in the ABS Livestock Products publication was sourced from the Meat & Livestock Australia (MLA, 2017) Cattle/Sheep Assessment Manual (Meat & Livestock Australia).

Methods

In the National Ecosystem Accounts, grazed biomass service is attributed to two ecosystem categories: improved pastures or other grazed lands. Table 6 describes the IUCN GET ecosystems that correspond to these categories.

• Improved pastures include Sown livestock pastures and Derived semi-natural pastures and old fields from the Intensive land-use biome as per the IUCN GET.
• Other grazed lands include natural pastures/grasslands, rangelands, woodland/shrubland, forested areas and swamps/wetlands covering many different biomes.

Grazed biomass service is reported as forage for either beef cattle and calves or sheep and lambs. This is based on herd numbers as at the end of financial year (30 June) and does not include slaughtered livestock. Dairy cows are excluded as they are highly dependent on high-input production systems where ecosystem service contribution is more limited.

Where joint production occurs, for example in the case of improved pastures, where land is cultivated for grazing purposes (i.e. crops planted, water and fertiliser applied) no attempt was made to split out the ecosystem’s contribution, due to lack of data. Similarly, livestock kept in feedlots for accelerated weight gain (i.e. grain fed) prior to slaughter have been excluded (based on counts from the Agricultural Census) as it is not possible at this stage to estimate ecosystem contribution (i.e. time livestock spent on pasture before entering feedlot system).

Physical

Physical quantity of the grazed biomass service was estimated using information on daily dry matter intake rate, live weight, number of livestock and days on pasture.

Dry matter (DM) intake rate is the amount of feed a livestock animal consumes per day on a moisture-free basis (i.e. weight of grass without moisture component). This is represented as a percentage of the livestock’s live weight. DM intake is assumed to be 2% of live weight per day for cattle, sheep and lamb. For more information, see Calculating Dry Matter Intakes for Various Classes of Stock.

Live weight (weight of livestock prior to slaughter) is estimated from slaughter data (from Livestock Products, Australia). Average carcass weight is calculated from the total weight of red meat and number of animals slaughtered. Live weight is estimated by dividing the carcass weight by a dressing proportion. Dressing proportions represent the ratio between live weight and carcass weight (see Cattle Assessment Manual and Sheep Assessment Manual (MLA 2017)). Dressing weight proportions used to calculate live weight were selected based on livestock sex (bulls and cows) and or age (cows and calves; sheep and lamb).

Number of livestock refers to number of cattle (bulls/cows/calves), sheep and lamb by pasture type reported in the Agricultural Census as at 30 June. Beef cattle and sheep estimates from the census were benchmarked to estimates from the Australian Agriculture: Livestock publication. This includes experimental estimates of the number of cattle and sheep in Australia.

Benchmarked values for 2010–11, 2015–16 and 2020–21 were used to derive proportional splits of livestock type and grazing ecosystem type. These splits were carried forward and applied to livestock totals from the Australian Agriculture: Livestock publication for the years between these time points (e.g. 2010–11 splits were applied to 2011–12 through to 2014–15) to produce estimates for a full time series (2010–11 to 2022–23).

Number of days on pasture refers to number of days a livestock animal spent grazing on improved or other grazed lands. This was based on animal age as defined in Table 7.

Current data sources will not support future updates and the methodology for estimating grazed biomass provisioning services will need to be changed to reflect new data sources.

Monetary

As experimental statistics, components may be refined, added or removed over time. In this release, monetary estimates of grazed biomass provisioning have been removed pending further methodological development work. Physical estimates for grazed biomass provisioning remain unaffected.

Concepts

Global climate regulation

Global climate regulation services are the ecosystems’ contributions to reducing concentrations of greenhouse gases (GHG) in the atmosphere through the removal (sequestration) of carbon from the atmosphere and the retention (storage) of carbon in ecosystems. These services support the regulation of the chemical composition of the atmosphere and oceans to stabilise the climate, in turn avoiding damages that arise due to climate change. This is a final ecosystem service.

Carbon sequestration

The carbon sequestration component of the service reflects the ability of ecosystems to remove carbon from the atmosphere. Carbon sequestration is not currently published in the NEA.

Carbon retention

The carbon retention component of global climate regulation reflects the ability of ecosystems to accumulate and retain the stock of carbon – i.e. ecosystems supply a service through the avoided emission of carbon to the atmosphere. This is the component that is currently in the accounts, reflecting availability of data sources.

Data Sources

FullCAM

DCCEEW’s Full Carbon Accounting Model (FullCAM) is used to estimate emissions and removals for the LULUCF sector (Land use, land-use change and forestry as included in the IPCC 2006 Guidelines) as a part of National Inventory Reporting of carbon emissions nationally (National Inventory Report 2022, Volume 1). FullCAM spatially models the carbon retained by ecosystems above and below ground and incorporates multiple datasets on vegetation cover and climate. Derived estimates are totalled by ecosystem and source and aggregated nationally and by state in kilotonnes of carbon for 1989–90 to 2021–22.

Carbon Price

The price of carbon is sourced from the Clean Energy Regulator’s Australian Carbon Credit Units Scheme auctions (Auction results | Clean Energy Regulator) reflecting the institutional settings at the time.

Methods

Physical

Carbon retention estimates have been collated for select land-based ecosystems. FullCAM ecosystem classifications are concorded to the corresponding IUCN GET classification(s) based on definitions and area extent coherence at a state level to ensure appropriate selection as outlined in Table 8. Production native forests, environmental plantings, plantations softwood/hardwood, wetlands, perennial woody crops, settlements, sparse grasslands or wetlands converted to grassland were excluded due to inconsistencies in classifications and extent.

State and national-level carbon retention estimates are taken directly from aggregated fields and compiled into the appropriate classifications.

Monetary

The value of carbon retention is in mitigating the release of stored carbon into the atmosphere. Based on the work by Edens (2021), the carbon retention value can be represented by an annual yield that reflects a nominal return to asset holders in maintaining the stored stock of carbon on behalf of society. In the estimation of the carbon retention value we (a) estimate the carbon stock in ecosystem assets, (b) multiply this by a suitable carbon price and (c) convert this into an annual service flow by multiplying this value by a nominal suitable rate of return to generate an annual yield. 

The price of carbon is sourced from the Clean Energy Regulator’s Australian Carbon Credit Units Scheme auctions, reflecting the institutional settings at the time. It is acknowledged that carbon markets in Australia are not yet considered 'mature', yet prices such as those set through the Clean Energy Regulator’s auction can be considered suitable for estimates of climate regulation, as they reflect a market-based outcome.

The rate of return reflects a yield comprising a real discount rate of 4% plus the relevant nominal price level, in line with the national accounts. 

Valuation approaches have not yet been internationally adopted as a standard, as practitioners seek to test ways to best reflect carbon retention values. Monetary estimates for carbon retention were not available for 2010–11 and 2011–12 as there was no institutional arrangement that placed a value on carbon emissions at that time.

Concepts

The Freshwater Realm includes all permanent and temporary freshwater bodies as well as saline water bodies that are not directly connected to the oceans, and are characterised by water regimes defined by frequency, duration, flow velocity, depth and extent of inundation (IUCN GET). Freshwater realm EFGs are listed in Table 9.

Freshwater ecosystems are reported by state, drainage division and river region. Drainage divisions and river regions are defined according to connected water flows and catchments, making them useful geographies to compare freshwater condition.

Data Sources

• Australian Hydrological Geospatial Fabric (Geofabric) v3.3 (BoM) was used to define hydrological features such as watercourses, water bodies and catchments.
• Geofabric v2.1.1 (BoM) was used to define upland and lowland categories, and additional variables including the Strahler stream order.
• Episodic arid rivers (F1.6) were defined using 4 categories from the paper Hydrological characteristics of Australia: national catchment classification and regional relationships (Jaffrés).
• Freeze thaw streams (F1.3) were sourced from the MFT extent (CSIRO), refer to Terrestrial and Transitional realm data sources. 

Methods

Rivers and streams biome

River lines from BoM Geofabric were classified according to the six IUCN GET Rivers and streams biome ecosystem functional groups (refer to Table 10).

Perennial/non-perennial categories were from Geofabric v3.3.

Geofabric 2.1 catchments with Strahler numbers greater than 3 were used to create a lowland mask. 

Episodic arid rivers (F1.6) were defined using the categories:

• Cluster 3 – central Australia – dry riverbeds
• Cluster 4 – flat central Australia – infrequent, slow flows
• Cluster 5 – spatially disjointed, large catchments – slow-moving flows
• Cluster 6 – southern inland Australia – arid.

Lakes and wetlands biome

As the Lakes biome and Wetlands biome data inputs are included together with the CSIRO MFT extent dataset, the numbers have been compiled together. Please refer to the methodology described in the Terrestrial and Transitional realm.

Freshwater ecosystem condition is influenced by a variety of factors such as the adjacent land use, biotic composition and inundation frequency. Table 11 provides a summary of published freshwater condition measures.

Concepts

Land use

The Australian Bureau of Agricultural and Resource Economics (ABARES) collects information on how land is used and managed and organises this information according to the Australian Land Use and Management (ALUM) Classification (version 8). The ALUM classification has a hierarchical, three-tiered structure, and groups primary land uses categories based on the potential degree of modification on the natural native land cover.

Land-use scores

Land-use scores were assigned to Secondary and Tertiary ALUM Classes to reflect the pressure expected to affect surrounding freshwater bodies, following the method described by Stein et al. (2002). This produces a more granular definition of land-use intensity than the ordinal Primary Classes. Water (Primary Category 6) was split into its Tertiary Categories so that different land-use scores could be assigned to different water classes.

Fractional cover variables

Fractional cover variables measure the cover of:

• Photosynthetic vegetation (PV): green vegetation (leaves, grass, growing crops)
• Non-photosynthetic vegetation (NPV): non-green vegetation (branches, dry grass/hay, and dead leaf litter)
• Bare soil (BS): bare soil (ground or rock).

These are reported as a fraction of total land area covered. For example, a PV fractional cover of 0.2 indicates that 20 percent of the area in question is covered with photosynthetic vegetation.

Fringing zone photosynthetic vegetation cover

Fringing zone photosynthetic vegetation cover includes a set of condition measures compiled by CSIRO for NEAP. These measures aggregate the PV fractional cover variables created by DEA to provide a measure of the green vegetation fringing (surrounding) freshwater bodies. The fringing zone is 100 m for rivers and streams and 200 m for lakes and wetlands. These reflect the widths required to maintain ecological condition, e.g. protects water temperature, water quality and aquatic biodiversity.

Data Sources

Land-use intensity

Land-use intensity was compiled by CSIRO for the NEA. It uses:

• annual extent of freshwater realm ecosystems at IUCN GET-level (CSIRO)
• Rivers and streams network at IUCN GET EFG-level (ABS)
• national scale land use data (Land use of Australia)
• Land-use scores per ALUM Tertiary Classification as defined by Stein et al. (2002), refer to Table 12
• water catchments and subcatchments (HydroBASINS v1 Level 10).

Outputs published from this series include:

• Land-use intensity indicator.

Fractional Cover

Fractional cover measures were compiled by CSIRO for the NEA. It uses:

• annual extent of freshwater realm ecosystems at IUCN GET-level (CSIRO)
• Rivers and streams network at IUCN GET EFG-level (ABS)
• annual fractional cover percentiles data (Digital Earth Australia (DEA) Fractional Cover Percentiles (Landsat)).

Outputs published from this series include:

• Photosynthetic vegetation minimum cover (PV10) variable
• Photosynthetic vegetation annual range (PV Range) variable.

Methods

Land-use intensity (LUI)

Freshwater ecological condition, especially that of rivers, is known to be strongly influenced by its adjacent land use. Land-use intensity, as defined by the land use score (see Table 12) is used as a proxy to infer the condition of freshwater ecosystems. The LUI variable was transformed to an indicator, by inverting (rescaling) the values so that 1 represents an ideal reference condition and 0 represents poor condition. The LUI indicator was then summarised as an area-weighted average over the containing and upstream catchments, and aggregated by output ecosystems and geographical boundaries. Table 12 provides the land use classes and the associated land-use score.

Fringing zone fractional cover

The photosynthetic vegetation fractional cover measures the proportion of each pixel that is covered by green vegetation throughout a year. A single fractional cover value is calculated for each waterbody by averaging the values from pixels in the fringing zone around it.

PV10 represents the lowest 10th percentile of cover of green vegetation in the fringing zone of the waterbodies recorded in a year. The minimum fractional cover is a conservative measure of vegetation cover. It ignores any isolated high cover values, reporting only where areas have been consistently vegetated. This reduces errors associated with signal noise and other measurement complications.

PV Range represents the difference between the 90th and 10th percentiles of vegetation cover, providing a measure of vegetation variability within a year.

Each condition metric was summarised by EFG and geographic region using area-weighted averages for lakes and wetlands and length-weighted averages for rivers and streams.

Quality indicator: Missing data

There are data gaps for both fringing zone fractional cover and land-use intensity variables. Cloud cover and regional variability in satellite coverage has led to gaps in the fractional cover variables for some study years. Missing data occurs for LUI where Geofabric catchments were not available to overlay and average LUI values before applying them to freshwater ecosystems.

A missing values indicator was created to determine the quality of the final condition metrics. The extent of observed condition values was taken for every year, in km for rivers and streams and ha for lakes and wetlands. This extent was subtracted from, then divided by the total extent for the same year to determine the proportion of missing values. Finally, the maximum of these missing values was determined across all years for each variable, ecosystem, geography combination. A missing values maximum (%) of 8.72 for photosynthetic vegetation cover, small permanent freshwater lakes in Tasmania means that, across all study years, a maximum of 8.72% of values could not be observed for this combination. In other words, one can expect a minimum of 91.28% coverage in any year.

Classifications

Common International Classification of Ecosystem Services (CICES)

The Common International Classification of Ecosystem Services (CICES) is a standardised hierarchical classification framework developed to categorise ecosystem services into provisioning, regulation, and cultural benefits to include both biotic and abiotic aspects. CICES is focused on the final ecosystem services in which the user of the service is an economic unit. CICES v5.2 subcategories have been used to split Water Supply.

Concepts

Water supply service reflects the combined ecosystem contributions of water flow regulation services, water purification services, and other ecosystem services to provide water of appropriate quality to economic units (businesses, households and government), for consumption or production processes. This is considered a final ecosystem service. The scope of this account is limited to surface water only (i.e. ground water is not in scope).

Water provisioning services is broken down into 3 categories:

1. surface water for drinking
2. surface water used as a material
3. surface water used for energy.

Surface water for drinking

Water supplied by ecosystems that is used as drinking water. Due to the lack of data distinguishing potable from non-potable water and water used solely for drinking, water supplied to households is used as a proxy. This water serves multiple purposes, including drinking, gardening, and other household activities.

Surface water used as a material

Water supplied by ecosystems that is used as a material or for cooling. This includes all water used as intermediate consumption throughout the economy. It encompasses self-extracted water by industry as well as the water distributed by the water supply industry to other industries. This water usage spans productive activities like agriculture, mining, and manufacturing, as well as general consumption, including potable water use in office buildings and retail outlets.

Surface water used for energy – abiotic flow

The flow of water on land that can be converted to electrical or mechanical energy, providing hydraulic potential.

Data Sources

Industry water use

Data for water use by industry were collated from the Water Account, Australia (ABS) 'Physical Supply and Use Tables, by Water Type' for each state and territory and nationally from the Water Account, Australia and associated input data sources.

Unit level water used for energy production

Unit-level data were delivered by the Water Accounts team for total non-consumptive water used in the generation of hydroelectricity. 

Allocation trading

Data on allocation trading is downloaded from the Bureau of Meteorology (BoM) Water Market Dashboard. The data includes:

1. All reported trades for surface water and groundwater.
2. Trades are reported with variables such as state, origin water system, destination water system, drainage division, volume trade in megalitres (ML), Net price, price per ML, and date of trade.
3. Data in dashboard is provided by the States and Territories, and by irrigation infrastructure operators (IIOs) acting on their behalf.

Methods

Physical

Water provisioning service estimates are derived primarily from Water Account, Australia and the Water Supply and Sewerage Services Survey (WSSS). The WSSS is a comprehensive census of water utilities, ensuring full coverage without the need for sampling methodologies.

Consistent with the scope of the National Ecosystem Accounts, only surface water ecosystems are considered in scope for freshwater provisioning services. As a result, only surface water supplied to the economy contributes to the ecosystem service estimates presented in this account. Groundwater is excluded.

Data for 2010–11 were excluded due to the unavailability of sufficiently reliable and granular source data.

Surface water for drinking

In the absence of data distinguishing potable from non‑potable water, or water used solely for drinking, water supplied to households is used as a proxy for surface water used for drinking. This water supports multiple household uses, including drinking, gardening and other domestic activities.

To quantify the surface water component of water extracted and distributed by the Water supply industry to households, ratios based on Water supply industry self‑extracted water types (surface water, groundwater and seawater for desalination) are calculated and applied to total household water supply volumes.

Surface water used as a material

This category includes self‑extracted surface water by industry as well as surface water distributed by the Water supply industry to other industries. It covers water used in productive activities such as agriculture, mining and manufacturing, as well as general consumption, including potable water use in office buildings and retail outlets.

To quantify the surface water component of water extracted and distributed by the Water supply industry to industries, ratios based on Water supply industry self‑extracted water types (surface water, groundwater and seawater for desalination) are calculated and applied to industry supply totals.

Agriculture – irrigation (2022–23 only)

For the 2022–23 reference year only, irrigated agricultural water use is estimated using an evapotranspiration (ET)‑based method, replacing earlier survey‑based irrigation estimates. This method provides nationally coherent estimates of total irrigated agricultural water use.

An endorsed surface water / groundwater allocation method is applied to the ET‑derived irrigation totals to distinguish water sourced from surface water and groundwater. Consistent with the surface water ecosystem scope of the National Ecosystem Accounts, only the surface water component of irrigated agricultural water use contributes to the freshwater provisioning ecosystem service. Irrigation sourced from groundwater is excluded.

For all earlier reference years, agricultural surface water use is derived using existing methods from the Water Account, Australia, and remains unchanged.

Surface water used as an energy source

Surface water used as an energy source is derived from Water Account, Australia estimates of water used by the Electricity and Gas Supply industry. While these estimates include water use associated with gas supply, such volumes are negligible compared with hydroelectricity.

In line with the System of Environmental‑Economic Accounting – Ecosystem Accounting (SEEA EA), water used for hydroelectricity is treated as an abiotic flow. Although ecosystems contribute to hydroelectricity through regulating services (such as flow regulation), the water volume itself is not supplied as a material input, as it passes through turbines and is returned to the river system. Consequently, surface water used for energy is published for transparency but excluded from freshwater provisioning ecosystem service totals.

Monetary

Freshwater provisioning services supplied as a material and for drinking are valued using a prices‑from‑similar‑markets approach, based on observed prices from tradable surface water allocation markets. Water allocations represent specific volumes of water assigned to water access entitlements and can be traded separately from both land and entitlements, allowing market‑based valuation of water services.

On average, approximately 1,750 GL of surface water allocations are traded annually, representing around 15–20% of the physical volume of surface water used as a material and for drinking. Approximately 90% of priced surface water allocation trading occurs in New South Wales and Victoria, reflecting the scale and maturity of water markets in the Murray–Darling Basin.

Water allocation prices reflect a range of supply and demand factors, including rainfall, storage levels, allocation carryover, agricultural demand, institutional arrangements and climatic conditions.

To value water provisioning services, ecosystem service prices are multiplied by the corresponding physical quantities, which are sourced from the Physical Supply and Use Tables (PSUTs).

Water system‑level median prices are first estimated using data from the Bureau of Meteorology’s Water Market Dashboard.These prices are then aggregated to state and territory‑level ecosystem service prices using volume‑weighted averages based on traded volumes. The Australian total price is derived as a volume‑weighted median across states and territories.

For states and territories without observed prices, the median price of all other states and territories is applied. The Australian Capital Territory does not operate a surface water allocation market; therefore, New South Wales prices are used as a suitable proxy. No water quality adjustments have been made for the valuation of water used for drinking.

Monetary estimates are not produced for surface water used as an energy source, as these flows are classified as abiotic and excluded from ecosystem service valuation.

Concepts

Coastal Realm

The Coastal realm includes the Brackish tidal biome which is associated with the shorelines at the interface of Terrestrial, Freshwater, and Marine realms. These ecosystems range from coastal riverways and terrestrial-dominated coastal saltmarshes to marine-dominated intertidal forests. (IUCN GET). Coastal realm EFGs are listed in Table 13.

Data Sources

Geoscience Australia’s (GA) DEA Coastal Ecosystems dataset, derived from Sentinel-2 imagery, provides extent data for different coastal ecosystems at 10 metre resolution, and was used to define extent for Saltmarsh and Mangroves as the most accurate dataset available. This data was released in December 2025. It is important to note that due to the probabilistic model used to assign ecosystems in the DEA Coastal Ecosystems product, extent metrics are highly sensitive to small changes. Metrics could potentially note the loss (or gain) of extent particularly for ecosystems in transition that may move across probability thresholds year to year. Therefore the comparison between reference years presented in this publication should be interpreted with caution and accordingly, no change matrix is presented. See the Caveats and Limitations section of the DEA Coastal Ecosystems documentation for more information.

Methods

Spatial extent data has been aggregated from GA’s DEA Coastal Ecosystems dataset using the 2025 ABS Marine Reporting Area’s (AMRA) and SA2 geographies.

Mangroves fall within MFT1.2 Intertidal forests and shrublands, and Saltmarsh fall within the MFT1.3 Coastal saltmarshes and reedbeds (IUCN GET). MFT1.2 and MFT1.3 were provided as part of CSIRO’s MFT Extent and were replaced in the Extent accounts in with the higher resolution DEA Coastal ecosystems dataset. Due to this, they have been labelled according to the GA dataset scope, i.e. Mangroves and Saltmarsh.

It is acknowledged that this approach results in an overestimate of the total area where GA Mangrove and Saltmarsh overlap other EFGs from CSIRO data. Additionally, any CSIRO MFT1.2 (mangrove) and MFT1.3 (saltmarsh) regions outside of GA’s Coastal Ecosystems data and outside CSIRO’s benthic marine extent data will not be captured. This method results in approximately 1.2 million hectares of Mangrove and Saltmarsh ecosystems overlapping with other EFGs. Coastal extent data cubes contain the following caveat: Some areas reported as Coastal Ecosystem Functional Groups (EFGs) are also reported within other realms. The sum of extent across realms will exceed the area within the outer boundary of Australia’s contiguous Exclusive Economic Zone (EEZ), see methodology. The impact is minor (Approximately 0.08% of the area within the outer boundary of Australia's contiguous EEZ is reported as Coastal EFGs and also in another realm).

Concepts

Mangrove canopy cover

Mangrove canopy cover is a suitable measure of mangrove condition as it provides an indication of the overall health and productivity of mangrove forests. Monitoring changes to mangrove canopy cover can help describe how mangrove forests are responding to environmental changes as reduction in canopy cover is typical when mangroves experience ecosystem disturbance.

Data Sources

GA’s DEA Mangroves (Landsat) dataset consists of a sequence of 30 m resolution maps (for each year from 1987) that are produced with satellite imagery data collected by the Landsat satellites. The data provides information about the extent and canopy density of mangroves for the Australian coastlines. 

Methods

Selected time points are presented for this condition metric (2010–11, 2015–16, 2020–21 and 2021–22). The 2020–21 condition dataset was constrained (i.e. spatially clipped) to the extent that was observed in the GA 10 m DEA Coastal Ecosystems dataset for 2020–21, while 2021–22 condition was constrained to the 2021–22 extent to ensure coherence of extent across the different accounts. Due to gaps in extent coverage for years 2010–11, 2015–16, the nearest comparable extent time point, 2020–21, was used.

The GA mangrove canopy cover dataset has 3 classifications, with each class having a unique canopy cover percentage associated with it as described in Table 14. Any region without a classification was allocated as Unclassified.

Mangrove canopy cover spatial extent rasters were combined with geography rasters to derive the geographical extent for each level of cover and expressed as a percentage of the regional Mangrove extent.

Concepts

Saltmarsh ecosystems are impacted by land use in surrounding catchments. Catchments are areas within natural boundaries over which water flows and drains to an end point. Catchment land use impacts saltmarsh through factors such as nutrient pollution, increased sediment loads and altered hydrology. Therefore, land-use intensity (LUI) of catchments containing saltmarsh is a suitable measure to represent Saltmarsh condition.

Data Sources

Land-use intensity

The Australian Bureau of Agricultural and Resource Economics’ (ABARES) Land Use of Australia 2010–11 to 2020–21 data classifies Australia’s land use by Australian Land Use and Management (ALUM) Classification (version 8) classes. The ALUM primary classes provide information on land-use intensity (LUI), they are:

• LUI 1: Conservation and natural environments includes Nature conservation, Managed resource protection, and Other minimal uses.
• LUI 2: Production from relatively natural environments includes Grazing native vegetation and Production native forests.
• LUI 3: Production from dryland agriculture and plantations includes Grazing modified pastures, Plantation forests, Dryland cropping, and Dryland horticulture.
• LUI 4: Production from irrigated agriculture and plantations includes Irrigated pastures, Irrigated cropping, and Irrigated horticulture.
• LUI 5: Intensive uses include Urban residential, Intensive horticulture and animal production, Rural residential and farm infrastructure, Mining and waste, and Other intensive uses.

Catchments

The Australian Hydrological Geospatial Fabric (Geofabric) Surface Catchments data is used to define the catchment boundaries within which land use impacts the saltmarsh present. These surface catchments define base-level catchments for stream segments, sinks and coastal draining areas.

Methods

Saltmarsh condition is estimated from the proportion of land-use intensity (LUI) classes in the catchment surrounding Saltmarsh. LUI 6 (water) is excluded from these proportions as this class does not represent a land-use pressure to saltmarsh. The area (of Saltmarsh) associated with each LUI class was calculated and summarised to national and state levels. Results are expressed as hectares of each of the LUI classes. Saltmarsh present outside catchments, or in catchments containing only LUI 6 or null values are allocated to the unclassified class.

Concepts

Coastal protection services

Coastal protection services are the ecosystem contributions of linear elements along the shoreline and the seascape to protecting assets from the impacts of tidal or storm surges on local communities. These ecosystems may include coral reefs, sand banks, dunes or mangrove ecosystems. This is a final ecosystem service. Measurements of coastal protection services included in this account are the count of dwellings and number of residents protected by Mangroves and Saltmarsh in the event of a tidal or storm surge.

Storm surge

Storm surge is an abnormal rise in sea level over and above the normal (astronomical) tide levels. It can be thought of as the change in the water level due to the presence of a storm. These powerful ocean movements are caused by strong winds and atmospheric lows piling water up against the coast, such as when a cyclone approaches. Large waves can also be generated by winds, increasing the risk of the storm surge in coastal areas. Erosion and damage to infrastructure, including loss of private property, can be caused by wave overtopping of coastal defences during extreme high water levels associated with severe events.

Mangrove and Saltmarsh ecosystems reduce damage to dwellings during a storm surge by attenuating and dissipating wave and wind energy. 

Dwellings

For the dwelling count, a dwelling was defined as a structure which is intended to have people live in it, and which was habitable on Census night. Unoccupied residences of owners, managers or caretakers of caravan parks, marinas and manufactured home estates are also counted, but other unoccupied dwellings in such establishments are not counted, please refer to Census dictionary: dwelling.

Address Register Dwellings

The ABS Address Register is an up-to-date, comprehensive list of all known physical addresses within Australia and excludes non-physical addresses such as post office boxes and email addresses. For more information, please refer to: ABS Address Register, Users' Guide.

People

The counts of people are extracted from the 2021 Census and are geocoded to Mesh Block level based on their usual place of residence (PURP). Population counts are perturbed to avoid the release of identifiable data.

Data Sources

Mangrove extent

Geoscience Australia’s DEA Coastal Ecosystems, derived from Sentinel-2 imagery, which provides extent data for different coastal ecosystems at 10 m resolution and was used to define extent for Saltmarsh and Mangroves as the basis for coastal protection estimates.

Coastline

Coastline data was sourced from the Geoscience Australia (GA) Digital Earth Australia (DEA) coastlines product, which includes the mainland and all islands. The 2021 and 2022 coastlines were used to match the extent time periods.

Coastal Connectivity

Geoscience Australia has shared with the ABS an unpublished set of ocean connectivity layer prototypes. These are based on different implementations of topography, tide, and mangrove data. A combination Digital Elevation Model coupled with Highest Astronomical Tide (HAT) layer was used to identify areas where risk of inundation and damage was highest.

Population and dwelling counts

Population and dwelling data was sourced from the ABS Census Mesh Block counts. The usual resident population and total dwelling count per mesh block were both obtained from the 2021 Census.

Dwelling location

Dwelling spatial data was sourced from the ABS Residential Address Register Common Frame which is a dataset containing location-based points for all residences within Australia. The November 2021 and November 2022 frames were utilised to best align with the mangrove extent timepoints.

Methods

Physical

The areas of protection are calculated based on the location of Mangroves and Saltmarsh which meet certain criteria within the scope of inundation (coastal connectivity layer), with a minimum belt width of at least 100 m for Mangroves and 40 m for Saltmarsh and meeting a connectivity threshold which approximates a 1-in-100-year coastal inundation event. 

A distance accumulation method assumes the Mangrove and Saltmarsh act as a barrier, effectively increasing the distance to the coastline. The 'No DEA mangroves + highest astronomical tide' connectivity layer was chosen to incorporate information on distance and elevation connectivity to ocean water into the coastal protection model. An inundation depth approximating the lower bound of a 1-in-100-year coastal inundation event has been implemented. 

The scope of inundation is overlaid with the Address Register Common Frame and Census Mesh Block Counts (2021). Distance accumulation and inundation potential values are then used to derive dwellings and residents protected by calculating the proportion of Address Register Common Frame dwellings per Census Mesh Block that are protected. Results were aggregated to state and national-level based on State and Territory boundaries defined within the Australian Statistical Geography Standard (ASGS).

This method builds upon that used in the 2025 release of the National Ecosystem Account as well as the first attempt used in the experimental National Ocean Account. The new estimates of coastal protection are significantly higher than the previously published numbers due to improved methodology, considering a much more realistic scope of potential inundation by storm surge.

Mangroves and Saltmarsh can provide protection to the same dwellings or residents, so the total protection would be less than the sum of the two. 

Mangrove and Saltmarsh extent data was only available for 2020–21 and 2021–22 therefore estimates for coastal protection were only produced for these years.

Monetary

As experimental statistics, components may be refined, added or removed over time. In this release, monetary estimates of coastal protection service have been removed pending further methodological development work. Physical estimates of the number of people and dwellings protected by Coastal ecosystems are unaffected by this decision.

Concepts

The Marine realm includes all connected saline ocean waters characterised by waves, tides and currents (IUCN GET). Within the Marine realm, the Marine shelf and Deep sea floors biomes represent the benthic ecosystems while the Pelagic ocean waters biome represents the water column above the benthic ecosystems.

Each EFG within the Pelagic ocean waters biome represents a depth layer as follows:

• Epipelagic ocean waters (0 to 200 m depth)
• Mesopelagic ocean waters (200 to 1000 m depth)
• Bathypelagic ocean waters (1000 to 3000 m depth)
• Abyssopelagic ocean waters (3000 m to 6000 m depth).

Marine realm EFGs are listed in Table 15.

Data Sources

CSIRO’s Spatial Extent of IUCN ecosystem functional groups data provides the spatial extent of the Marine realm at the IUCN GET EFG level for Australia. Some EFGs in the IUCN GET are not included or have been combined in this dataset. Marine Pelagic ocean waters data was derived from the AusBathyTopo 250 m (Australia) 2023 Grid. Data for the Marine shelf and Deep sea floors biomes are derived from the Natural Values Ecosystems map as described in Methods for developing extent of IUCN Global Ecosystem Typology ecosystem functional groups across the marine, freshwater and terrestrial realms in Australia: pre-1750 and 2010–11 to 2023–24. The Natural Values Ecosystems map provides broad-scale categorisation of Australia’s marine ecosystems and combines several input data sources and interpolates data gaps. The map was created with a series of sequential steps initially assigning cells to broad sediment-based ecosystems based on depth ranges then adding ecosystem data (e.g. reefs, seagrass) as described in Designing a targeted monitoring program to support evidence-based management of Australian Marine Parks: National Implementation.

The Marine shelf and Deep sea floors biomes are provided in one raster with a reference period of 2021–22 and the Pelagic ocean waters biome is provided in a separate raster with a reference period of 2022–23. Both are 250 m resolution rasters.

Methods

Marine benthic and pelagic extent rasters were combined with geography rasters to calculate the area of each EFG by region. For both benthic and pelagic marine extent, the Integrated Marine and Coastal Regionalisation of Australia (IMCRA) geography was used to define the boundary between marine and non-marine ecosystems. 

The use of separate extent rasters for benthic marine ecosystems and Terrestrial, Freshwater and Transitional ecosystems results in some areas being counted as multiple ecosystems where benthic marine EFGs overlap with transitional EFGs (e.g. Permanently open riverine estuaries and bays). Benthic marine EFGs can also overlap with coastal EFGs. Approximately 2.2 million hectares of benthic marine EFGs overlap with other EFGs. Benthic marine extent data cubes contain the following caveat: Some areas reported as benthic marine EFGs are also reported within other realms. The sum of extent across realms will exceed the area within the outer boundary of Australia’s contiguous Exclusive Economic Zone (EEZ), see methodology. The impact is minor (approximately 0.15% of the area within the outer boundary of Australia's contiguous EEZ is reported as benthic marine EFGs and also in another realm).

Concepts

Wild fish provisioning services represent the contribution of ecosystems to the provision of fish and other seafoods. This is a final ecosystem service used by the fishing industry for the purposes of generating an economic return. The contribution of the ecosystem occurs up to the point of harvest, and both the supply and use of ecosystem services are recorded in the location of harvest. 

Data Sources

Physical and monetary estimates of wild fish provisioning services were provided by CSIRO’s National data for Australia's ecosystem services: fisheries biomass provisioning services (2000-01 to 2022-23). Spatial State and Territory fishery data was supplied for each fishery from the Blue Economy CRC. Commonwealth fishery data was from the Australian Fisheries Management Authority (AFMA).

Methods

Physical

Detailed methodology has been outlined in Methods for developing a national dataset for Australia’s ecosystem services: fisheries biomass provisioning services (2000-01 to 2022-23). The Australian Fisheries Management Authority (AFMA) provided detailed data on Commonwealth-managed fisheries to CSIRO from 2000 to 2023. State-managed fisheries data were provided at fishery-level and are aggregated across various temporal intervals. Where specific combinations of EFGs and Provincial Bioregions could potentially identify individual fishers, those records were reassigned to an 'Unallocated' Provincial Bioregion category. State and Territory catch data was allocated based on the proportion of fishery area falling within each EFG and IMCRA Provincial Bioregion. To maintain confidentiality, data provided to ABS was at an aggregated level.

Both Commonwealth and State and Territory catch data was rescaled using the Australian Bureau of Agricultural and Resource Economics (ABARES) Fishery Statistics to address species-level data gaps, with the assumption that any underestimation was proportionally distributed across IMCRA Provincial Bioregions and EFGs. Catch from ABARES 'other fisheries' were excluded as they were outside the EEZ.

For each financial year, the catch of each taxonomic group (e.g. finfish, crustaceans, molluscs and other) within EFG and IMCRA Provincial Bioregion were summed.

Monetary

The estimation of the monetary value of wild fish provisioning services comprises (a) the quota or licence fees for single species fisheries where available or (b) a rent revenue share for multi-species fisheries, based on the market value of fisheries, to distribute a quota fee across different wild fish catch. 

Quota and licence prices established in the market reflect the expected resource rent generated by their use. However, not all fisheries have quota or license fee data and many fishing operations occur in multi-species fisheries where not all fish species caught have a quota or license fee. 

The rent revenue share approach divides the estimated rent (annualised total quota sales value or licence asset value) at the aggregate fishery level by the total revenue of the entire fishery to produce a rent revenue share. This share is applied to the price of each species caught in the fishery to produce an monetary value of ecosystem services. 

Monetary estimates of wild fish provisioning services are only provided for 2010–11 to 2021–22 as this year contains greater data availability of recorded quota and license fees consistent with a market-based outcome for deriving monetary value of ecosystem services, following SNA exchange values principle. Due to data availability, some States and Territories report data from 2017–18 onwards only.

Many animal and plant species have been introduced to Australia over time from overseas, and some of these have developed into feral animals or weeds.

Concepts

Feral animals

The term ‘feral’ denotes exotic species introduced to Australia which have escaped confinement and established wild, self-sustaining populations (see feral animals).

Feral birds are defined as ‘those species that have been transported to an area in which they do not normally occur. These do not include summer migrants [who] have been regular visitors over a long period of time but do not make Australia their permanent home’ (Birdlife Australia).

Weeds

Weeds are plants which have a negative impact on the environment, agriculture, society or the economy, with introduced plants forming the majority of weeds in Australia. The Australian Weed Strategy 2017–2027 is a national strategy focused on 4 stages of weed management: prevention, eradication, containment and asset protection.

Data Sources

Feral animals

The feral animal species list was compiled from multiple sources as there is not a single, national, authoritative list available for feral animal species in Australia. The species list of feral animals is not comprehensive but is intended to capture the key national feral species from each taxonomic group. Table 16 to Table 20 show the lists of feral animals that are included in the accounts.

The following data sources were used to compile lists of feral animals:

• Introduced birds (Birdlife Australia)
• Feral animals in Australia (DCCEEW)
• Introduced insects and other invertebrates in Australia (DCCEEW)
• PestSmart (Centre for Invasive Species Solutions)
• FeralScan (Centre for Invasive Species Solutions)
• Freshwater pest fish in New South Wales (NSW Department of Primary Industries).

In consultation with the ALA, there are now approved species lists on the ALA website available for use in this publication and beyond, the feral species list can be found here (ABS Invasive Animals List). 

Weeds

The impacts of weeds have been recognised at a national level – Weeds Australia (part of the Centre for Invasive Species Solutions) publishes nationally significant weeds on the Weeds of National Significance (WoNS). Table 21 shows the list of weed species that are included in the accounts.

For more information, please visit: 

• Weed profiles (Weeds Australia)
• Weeds in Australia (DCCEEW).

In consultation with the ALA, there are now approved species lists on the ALA website available for use in this publication and beyond, the weeds list can be found here (ABS Invasive Weeds List).

Observational data

The Atlas of Living Australia (ALA) provided spatial and temporal observational data on key weeds and feral animal species. The ALA is the Australian node of the Global Biodiversity Information Facility (GBIF) and is ‘a collaborative, digital, open infrastructure that pulls together Australian biodiversity data from multiple sources, making it accessible and reusable’.

Methods

After establishing the feral animal and weed species lists with the ALA, these ALA-approved lists were then downloaded from the ALA via the ALA ‘galah’ R package. For each account year, direct observational records for each species were downloaded from the ALA database up to and including the years of interest for each account period, i.e. a cumulative record of feral animals and weeds. Calendar year was adopted for biodiversity data. ALA records from Cocos, Christmas, Norfolk, Macquarie, Heard and McDonald Islands, as well as Antarctica, were excluded to match the scope of the National Ecosystem Accounts. 

Species counts for plants (weeds) and birds, mammals, amphibians, freshwater fish and invertebrates (feral animals) were aggregated by state and territory as well as nationally.

Concepts

Threatened species

Threatened species refers to species of conservation concern listed under the national Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act). The definition of a species under the EPBC Act includes sub-species and distinct populations that the Minister for the Environment and Water has determined to be species for the purposes of the Act. New nominations for species are assessed under the EPBC Act by the Threatened Species Scientific Committee according to the criteria for each threat category (Conservation dependent, Vulnerable, Endangered, Critically Endangered, Extinct in the wild, and Extinct) and listed accordingly. 

The threat status of a species may change over time. This is assessed by expert groups under a status review assessment which may result in a transfer to a higher or lower threat level under the Act. If a species sufficiently recovers such that it no longer meets any of the criteria for listing, it may be delisted and removed from the Threatened Species list. Changes may also be the result of taxonomic reclassifications.

The number and trend of these lists can be used as a simple indication of the health of Australia’s biodiversity.

Data Sources

The Species Profile and Threats (SPRAT) database provides information on species listed as threatened under the EPBC Act. For more information, please visit:

• EPBC Act List of Threatened Fauna
• EPBC Act List of Threatened Flora
• Listings since commencement of EPBC Act
• Species and Communities Removed from the Threatened List.

Methods

The Threatened Species Status table is intended to provide a snapshot of threatened species status across different taxonomic groups (Plants, Birds, Mammals, Reptiles, Frogs, Fish, Other animals), and how this is tracking over time. Lists of threatened species are available from the EPBC Act SPRAT database, including current threat status (Conservation dependent, Vulnerable, Endangered, Critically Endangered, Extinct in the wild, and Extinct) and historic movements such as new listings, transfers and removals.

For opening and closing year stocks, the number of species in each threat category was summarised by taxonomic group using the most recent status for each year of interest. The gross and net change in threat status, including new listings, transfers between categories and de-listings, were calculated for each taxonomic group over the account period of interest (5 years).

Concepts

The Threatened Species Index (TSX) shows the average change in population size of threatened species compared with a reference year, which is assigned a score of 1.0. It shows relative change and not population numbers themselves. A score below 1.0 reflects a decrease in population size compared with the base year. For example, a score of 0.5 indicates an average 50% reduction in population size.

Data Sources

Threatened Species Index data was sourced from the Threatened Species Index (TSX). The TSX is a tool that tracks the abundance of Australia’s threatened and near-threatened species at national, state and regional levels. The Threatened Species Index shows the average change in population size of threatened species compared with a reference year.

Methods

The TSX shows the average change in populations compared to a reference year for threatened plants, birds, and mammals individually and combined. The TSX database was filtered to include: threatened species (Vulnerable, Endangered and Critically Endangered) listed under the EPBC Act; all species groups (marine and terrestrial); and both 'actively managed' and 'no known management sites' are included. A reference year of 2000 was used.

Threatened species index values were extracted and summarised by birds, mammals, and plants for years of interest at national and state levels.

Concepts

Original range

Original range refers to a species’ estimated distribution in 1750. The year 1750 is used as a reference point to describe Australian ecosystems prior to European colonisation, recognising that these landscapes had been shaped and managed for tens of thousands of years by Aboriginal and Torres Strait Islander peoples through enduring land management practices and knowledge systems. This reference point supports analysis of how colonisation and subsequent land use changes have altered ecosystem condition over time and enables the best available estimates of species distributions and ecosystem condition prior to these changes. This historical perspective informs understanding of long‑term impacts on biodiversity and supports the development of effective conservation strategies.

Threatened species habitat (TSH)

Threatened species habitat (TSH) can be measured in two ways; as habitat provision for nationally listed threatened species (expressed as a quantity: species hectares), or as the proportion of habitat for nationally listed threatened species remaining since 1750.

Data Sources

Threatened species habitat measures were compiled by CSIRO and provided to the ABS in the form of 100m grid raster datasets. It uses a habitat-based approach which combines species distribution data with a range of ecosystem condition information. It integrates the following datasets:

• species lists from the Environment Protection and Biodiversity Conservation (EPBC) Act 1999
• species observations from the Atlas of Living Australia (ALA 2024)
• species distribution maps from Species of National Environmental Significance (SNES) (DCCEEW 2025)
• pre-1750 spatial distributions for all threatened species (CSIRO)
• time series of annual of ecosystem condition (CSIRO)
• pre-1750 vegetation mapping (DCCEEW 2021)
• IBRA bioclimatic subregions (DCCEEW 2020)
• elevations (Hutchinson et al. 2008).

Outputs include:

• Annual rasters for threatened species habitat, with units as species hectares

Methods

For the full methodology, please refer to the paper Methods for developing national biodiversity data for ecosystem accounting in Australia 1990-2024.

The data was summarised by EFG and geographic region using area-weighted averages, and was published:

• for calendar years 2010 to 2023
• at national, state and territory, and ASGS SA2.

Concepts

Original range

Original range refers to a species’ estimated distribution in 1750. The year 1750 is used as a reference point to describe Australian ecosystems prior to European colonisation, recognising that these landscapes had been shaped and managed for tens of thousands of years by Aboriginal and Torres Strait Islander peoples through enduring land management practices and knowledge systems. This reference point supports analysis of how colonisation and subsequent land use changes have altered ecosystem condition over time and enables the best available estimates of species distributions and ecosystem condition prior to these changes. This historical perspective informs understanding of long‑term impacts on biodiversity and supports the development of effective conservation strategies.

Species persistence

Species persistence provides the number and proportion of originally occurring native species in an ecosystem accounting area expected to persist in the long term across their 1750 range.

Data Sources

Species persistence measures were compiled by CSIRO and provided to the ABS. It combines information on underlying biodiversity patterns with changes in ecosystem condition over time. It integrates the following inputs:

• time series of annual of ecosystem condition (CSIRO)
• a model that predicts compositional similarity between 2 locations under reference ecosystem condition
• expected species richness under reference condition (CSIRO)
• a spatially defined ecosystem accounting area.

Data for the four taxonomic groups were derived from the following: 

For vascular plants: 

• Spatial biodiversity models were derived using plant community composition data; spatial layers were used as predictors for plant species richness and community compositional similarity.
• Species observations from the ALA were used to estimate the pre-1750 numbers of native species. 

For reptiles: 

• Spatial biodiversity models were derived using species observation data from the ALA; spatial layers were used as predictors for reptile species richness and community compositional similarity.
• Species observations from the ALA were used to estimate the pre-1750 numbers of native species. 

For birds: 

• Spatial biodiversity models were derived using species observations from the ALA; spatial layers were used as predictors for bird species richness and community compositional similarity.
• Species observations from the ALA were used to estimate the pre-1750 numbers of native species. 

For fungi: 

• Spatial biodiversity models were derived using data obtained from the Biomes of Australian Soil Environments (BASE) project; spatial layers were used as predictors for fungi species richness and community compositional similarity.
• Species observations from the ALA were used to estimate the pre-1750 numbers of native species. 

For more information, please visit:

• Methods for developing national biodiversity data for ecosystem accounting in Australia: 1990–2024
• National biodiversity data for ecosystem accounting in Australia: 1990-2024.

Methods

For the full methodology, please refer to the paper Methods for developing national biodiversity data for ecosystem accounting in Australia 1900-2024. 

The data was used as provided, and published: 

• for calendar years 2010 to 2023
• for reptiles, birds, vascular plants and fungi
• as number of species and proportion,
• at national, state and territory, and ASGS Statistical Area 2.

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