Metadata Factsheet

3. Goals And Targets Addressed

3a. Goal

Directly addresses Goal A: The integrity, connectivity and resilience of all ecosystems are maintained, enhanced, or restored, substantially increasing the area of natural ecosystems by 2050;

Human induced extinction of known threatened species is halted, and, by 2050, the extinction rate and risk of all species are reduced tenfold and the abundance of native wild species is increased to healthy and resilient levels;

The genetic diversity within populations of wild and domesticated species, is maintained, safeguarding their adaptive potential.

3b. Target

Indirectly addresses Target 1: Ensure that all areas are under participatory, integrated and biodiversity inclusive spatial planning and/or effective management processes addressing land- and sea-use change, to bring the loss of areas of high biodiversity importance, including ecosystems of high ecological integrity, close to zero by 2030, while respecting the rights of indigenous peoples and local communities.

Indirectly addresses Target 2: Ensure that by 2030 at least 30 per cent of areas of degraded terrestrial, inland water, and marine and coastal ecosystems are under effective restoration, in order to enhance biodiversity and ecosystem functions and services, ecological integrity and connectivity.

Indirectly addresses Target 3: Ensure and enable that by 2030 at least 30 per cent of terrestrial and inland water areas, and of marine and coastal areas, especially areas of particular importance for biodiversity and ecosystem functions and services, are effectively conserved and managed through ecologically representative, well-connected and equitably governed systems of protected areas and other effective area-based conservation measures, recognizing indigenous and traditional territories, where applicable, and integrated into wider landscapes, seascapes and the ocean, while ensuring that any sustainable use, where appropriate in such areas, is fully consistent with conservation outcomes, recognizing and respecting the rights of indigenous peoples and local communities, including over their traditional territories.

5. Definitions Concepts And Classifications

5a. Definition

The BHI is generated from two main inputs: a spatial grid indicating the present condition, or integrity, of the natural ecosystem associated with each cell across the spatial domain of interest (for the existing global implementation these are 30-arcsecond cells, approximately 900m wide at the equator); and pre-derived modelling of spatial variation in the species composition of ecological communities across the same grid. These two inputs are combined to assess the ‘effective proportion of habitat’ remaining within any spatial reporting units of interest (e.g. countries, ecoregions, biomes, ecosystem types, or the entire planet) – adjusting for the effects of the condition and functional connectivity of habitat, and of spatial variation in the species composition of ecological communities, across these units. This effective proportion of habitat can optionally be translated, through standard species-area analysis, into a prediction of the proportion of species expected to persist over the long term.

5b. Method Of Computation

BHI scores are initially calculated separately for each grid-cell in turn – the ‘focal cell’ – within the spatial domain of interest (for the existing global implementation these are 30-arcsecond cells, approximately 900m wide at the equator). For each focal cell this score is an estimate of the proportion of habitat remaining across all cells that are ecologically similar to this cell. This effective proportion of habitat remaining can optionally be translated into a prediction of the proportion of species, originally associated with this cell, which are expected to persist over the long term, anywhere within their range. This is achieved by raising the proportion of habitat remaining to the power of the exponent of a standard species-area relationship.

The BHI score for cell i is calculated as:

where s_ij is the ecological similarity between cell i and each other cell j in the grid, h_j is the condition of habitat within that cell (between 0 and 1), and z is the exponent of the species-area relationship (currently set to 0.25). The ‘ecological similarity’ between any pair of cells is the proportional overlap in species composition expected between those locations prior to any habitat transformation, ranging from 0 (for a pair of cells expected to have had no species in common) through to 1 (for a pair of cells expected to have supported exactly the same species). For the global implementation of BHI these ecological similarities are predicted as a function of the abiotic environmental attributes and geographical locations of the cells concerned, using a set of generalised dissimilarity models (GDMs; Ferrier et al 2007) previously fitted to occurrence records for more than 400,000 species of vascular plants, vertebrates and invertebrates worldwide. This set of models includes 183 separately fitted GDMs, one for each possible combination of three broad biological groups – plants, vertebrates and invertebrates – and 61 bio-realms (unique combinations of biomes and biogeographic realms, as per WWF’s ecoregionalisation; http://www.worldwildlife.org/biomes). Full details of these models are provided in Hoskins et al (2020).

As described under ‘Data sources’ the global implementation of the BHI currently employs a time series of change in the ‘condition of habitat’ derived through statistical land-use downscaling (undertaken by CSIRO), and calibration based on meta-analysis of local land-use impacts on biodiversity undertaken by the PREDICTS project, led by the Natural History Museum https://www.nhm.ac.uk/our-science/data/biodiversity-indicators.html.

In the latest version of the global BHI implementation, values of h_i in the above equation are first adjusted for the effects of habitat connectivity using the cost-benefit analysis (CBA) technique described by Drielsma et al (2007), as already employed in two other global biodiversity indicators developed by CSIRO – the PARC-connectedness index, and the Bioclimatic Ecosystem Resilience Index (BERI). This CBA technique was developed originally to analyse the connectedness of habitat for individual species, and is founded on well-established principles of metapopulation ecology. Using this technique, the connectedness of each cell is calculated as a weighted average of the habitat condition values of all cells in the surrounding landscape, with the contribution of each cell weighted by the probability of dispersal associated with the least-cost path between that cell and the focal cell of interest. This probability of dispersal is a function of the permeability values of cells along the least-cost path and an estimated median-dispersal parameter, assuming a negative-exponential relationship between distance and dispersal probability. Four different median-dispersal values are used – 200m, 2km, 20km, and 200km – and connectedness is therefore calculated four times and these results are then averaged to yield a single value for the focal cell.

The aggregate BHI score for any larger spatial reporting unit (e.g. a country) is derived as a weighted geometric mean of the individual scores of all cells falling within that unit:

where the contribution of each cell is weighted by the predicted overlap in species composition between this cell and all other cells, and therefore its ecological uniqueness:

The underpinning logic, and details, of this overall approach are described further by Ferrier et al (2004), Allnutt et al (2008), Di Marco et al (2019), Mokany et al (2019), and Ferrier et al (2024). The approach is underpinned by the same basic principle as that employed in approaches assessing the proportional loss (or, conversely, retention) of habitat within discrete ecological classes, such as ecoregions or broad ecosystem types. When discrete classes are used to assess habitat loss or retention, each location (cell) on the planet is viewed as belonging to a single class, and the proportional retention of habitat in the entire class is assigned to every cell within the class. In other words, all cells within a given class are assigned the same value. However, in the approach adopted here, each cell is viewed not as belonging to a homogeneous set of cells forming a discrete class, but rather as sitting within a continuum of ecological variation.

5c. Data Collection Method

All data used to derive this indicator are described below under ’Data sources’.

5d. Accessibility Of Methodology

All methods and underlying data used to derive the global implementation of the BHI are published in accessible peer-reviewed locations (see ‘Method of computation’ and ‘Data sources’ for details).

Options for deriving and reporting the BHI at national scale are described below under ‘National/regional indicator production’.

5e. Data Sources

The existing GDM models of ecological similarity used to derive the global implementation of the BHI were fitted using the following input data (see Hoskins et al 2020 for details):

• Global 30-arcsecond gridded environmental surfaces for: Min Monthly Min Temperature, Max Monthly Max Temperature, Max Diurnal Temperature Range, Annual Precipitation, Actual Evaporation, Potential Evaporation, Min Monthly Water Deficit, Max Monthly Water Deficit, Soil pH, Soil Clay Proportion, Soil Silt Proportion, Soil Bulk Density, Soil Depth, Ruggedness Index, Topographic Wetness Index (Sources: http://www.worldclim.org/ https://www.soilgrids.org/ http://www.earthenv.org/).
• Global occurrence records for all terrestrial species within the following taxa, extracted from data accessible through the Global Biodiversity Information Facility (GBIF http://www.gbif.org/): vascular plants, amphibians, reptiles, birds, mammals, ants, bees, beetles, bugs, butterflies, centipedes, dragonflies, flies, grasshoppers, millipedes, snails, moths, spiders, termites, wasps.

The global implementation of the BHI currently employs data on change in ecosystem (habitat) condition derived from a global time-series of land-use change generated by CSIRO using statistical downscaling of coarse-resolution land-use data using finer-resolution covariates, including remotely-sensed land cover and abiotic environmental attributes. This was achieved using the technique described by Hoskins et al (2016), extended by employing Version 2, in place of Version 1, of the Land Use Harmonisation product https://luh.umd.edu/, MODIS Vegetation Continuous Fields https://modis.gsfc.nasa.gov/data/dataprod/mod44.php as remote-sensing covariates in place of discrete land-cover classes, and constraints derived from ESA CCI land-cover mapping https://www.esa-landcover-cci.org/ (see also Di Marco et al 2019). Applying this downscaling approach across multiple years provides an effective means of translating observed changes in remotely-sensed land-cover covariates into estimated changes in the proportions of 12 land-use classes occurring in each and every 30-arcsecond terrestrial grid-cell on the planet.

These land-use proportions are then, in turn, translated into an estimate of ecosystem (habitat) condition, for any given cell in any given year, using coefficients derived from global meta-analyses of land-use impacts on local retention of species diversity undertaken by the PREDICTS project (led by the Natural History Museum). The same downscaled land-use dataset is also being used by the Natural History Museum to derive their Biodiversity Intactness Index (BII) globally https://www.nhm.ac.uk/our-science/data/biodiversity-indicators.html.

5f. Availability And Release Calendar

Global implementation available now.

Raw gridded results for the entire planet are accessible via the CSIRO Data Access Portal https://data.csiro.au/ and the UN Biodiversity Lab https://unbiodiversitylab.org/.

Aggregated country-level results are accessible via the Biodiversity Indicators Partnership Dashboard https://bipdashboard.natureserve.org/ and CSIRO’s Biodiversity Indicators Explorer https://shiny.csiro.au/BILBI-indicators/.

5g. Time Series

Time series available: 2000, 2005, 2010, 2015, 2020.

Next planned update: 2024.

5h. Data Providers

Organisations producing data used to derive this indicator are listed under ‘Data sources’.

5i. Data Compilers

CSIRO (Australia’s national science agency), in partnership with GEO BON, GBIF and the Natural History Museum.

5j. Gaps In Data Coverage

The global implementation of the BHI assesses only the terrestrial realm.

5k. Treatment Of Missing Values

All spatial data layers used to derive the BHI cover the entire terrestrial surface of the planet at 30-arcsecond grid resolution (see ‘Data sources’).

6. Scale

6a. Scale Of Use

Scale of application (please check all relevant boxes): Global, Regional, National

Scale of data disaggregation/aggregation: The existing global implementation is derived from data and models covering the entire terrestrial surface of the planet at 30-arcsecond grid resolution (these cells are approximately 900m wide at the equator).

Global/ regional scale indicator can be disaggregated to national level: Yes

National data is collated to form global indicator:

6b. National Regional Indicator Production

Three main options exist, or are anticipated, for countries to report national-level changes in the BHI. These options differ both in terms of ease of implementation, and in terms of the extent to which they mobilise national versus global data.

Option 1: In this option countries would simply extract the raw gridded BHI results (at 30-arcsecond grid resolution) for their country from the relevant globally-generated layers (for details of access to these results see ‘Availability and release calendar’ above). They could then use standard GIS processing to aggregate and report results by any desired set of spatial units – e.g. provinces, ecosystem types. This is the easiest option to implement, but results would be totally dependent on the quality of the global inputs employed, and would not benefit from incorporation of any better-quality national data.

Option 2: Countries would here make use of the existing global GDM modelling of spatial variation in species composition (the most challenging, and computationally demanding, component of the indicator’s workflow) but would replace the globally-generated mapping of change in ecosystem condition with best-available national data – e.g. ecosystem condition mapping generated by a country’s implementation of UN SEEA Ecosystem Accounts (see https://www.wavespartnership.org/en/planning-tool-peru for an example of the implementation of this option for a region in Peru). This option would also open up potential for countries to evaluate the contribution that alternative area-based actions might make to improving the present BHI score for their country, thereby providing a foundation for prioritising the implementation of such actions. While Option 2 would allow results for the BHI to reflect a country’s best understanding of changes in the condition of their ecosystems, the rigour of these results would still be constrained to some extent by the spatial resolution, and quality, of the global biodiversity modelling employed. CSIRO is currently exploring, with partner organisations, potential avenues for giving countries direct access to the analytical computing capability needed to implement this option.

Option 3: In this most demanding option countries would derive the BHI for their country from scratch, not only employing best-available national data on ecosystem condition, but also making use of best-available biological and environmental data to refine the modelling of spatial variation in species composition – potentially at a finer spatial resolution than that employed in the global implementation. As for Option 2, CSIRO is exploring potential avenues for giving countries direct access to the analytical computing capability needed to implement this option.

6c. Sources Of Differences Between Global And National Figures

Some modest differences between country-level results generated using the three options outlined above are to be expected, as a function of differences in the resolution and quality global versus national sources of data.

6d. Regional And Global Estimates And Data Collection For Global Monitoring

6d.1 Description Of The Methodology

Regional and global aggregates for the BHI should ideally be derived directly from raw gridded results across the region concerned, using the rigorous aggregation procedure described under ‘Method of computation’, rather than from pre-aggregated country values.

6d.2 Additional Methodological Details

6d.3 Description Of The Mechanism For Collecting Data From Countries

7. Other MEA And Processes And Organisations

7a. Other MEA And Processes

CBD Aichi Target 5 reporting.

IPBES Core Indicator, used in the IPBES Global Assessment (Chapters 2 and 3) and Regional Assessments.

7b. Biodiversity Indicator Partnership

Yes

10. Data Reporter

10a. Organisation

CSIRO (Australia’s national science agency)

10b. Contact Person

Simon Ferrier, simon.ferrier@csiro.au