Metadata Factsheet

PDF Generated On: Fri Nov 22 2024 02:38:43 GMT+0000 (Coordinated Universal Time)

1. Indicator Name

Bioclimatic Ecosystem Resilience Index (BERI)

2. Date Of Metadata Update

2024-10-15 12:00:00 UTC

3. Goals And Targets Addressed

3a. Goal

Indirectly 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

Directly addresses Target 8: Minimize the impact of climate change and ocean acidification on biodiversity and increase its resilience through mitigation, adaptation, and disaster risk reduction actions, including through nature-based solutions and/or ecosystem-based approaches, while minimizing negative and fostering positive impacts of climate action on biodiversity.

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.

4. Rationale

The Bioclimatic Ecosystem Resilience Index (BERI) measures the capacity of landscapes to retain species diversity in the face of climate change, as a function of the area, integrity and connectivity of natural ecosystems across those landscapes. The indicator assesses the extent to which any given spatial configuration of natural habitat will promote or hinder climate-induced shifts in biological distributions. It does this by analyzing the functional connectivity of each grid-cell of natural habitat to areas of habitat in the surrounding landscape which are projected to support a similar assemblage of species under climate change to that currently associated with the cell of interest.

The BERI directly addresses Target 8 by providing a rigorous, yet straightforward, measure of the extent to which cumulative changes in the area, integrity and connectivity of natural ecosystems are helping or hindering efforts to “minimize the impact of climate change … on biodiversity and increase its resilience”. It can therefore be used to monitor and report past-to-present trends in the capacity of landscapes to retain species diversity in the face of ongoing climate change by repeatedly recalculating the indicator using best-available mapping of ecosystem condition or integrity observed at multiple points in time, e.g. for different years. A wide variety of data sources can be used for this purpose, spanning spatial scales from global to subnational, and including data assembled by countries for deriving ecosystem condition accounts under the UN SEEA Ecosystem Accounting framework. The indicator can then be aggregated and reported by any desired spatial unit – e.g. a country, a biome, an ecosystem type, or the entire planet.

The BERI offers an effective means of linking actions under Target 8 to both ecosystem-level and species-level outcomes under Goal A. As for the closely related Biodiversity Habitat Index (BHI), the BERI effectively integrates three of the key ecosystem attributes addressed in Goal A – i.e. area, integrity and connectivity – and the combined effect that changes in these attributes are expected to have on the retention of species diversity. However, it differs from the BHI in giving much stronger consideration to the potential impacts of climate change, and particularly to the major role that functional habitat connectivity will play in facilitating climate-induced shifts in the distribution of species and ecological communities.

The BERI can also serve as a leading indicator for assessing the contribution that proposed or implemented area-based actions under Targets 1, 2 and 3 are expected to make to enhancing the present capacity of landscapes to retain species diversity in the face of climate change. This would allow actions under these targets to be better linked both to the achievement of Target 8, and to achieving outcomes under Goal A, thereby providing a stronger foundation for strategic prioritisation of such actions by member countries.

5. Definitions Concepts And Classifications

5a. Definition

As described by Ferrier et al (2020) the BERI 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, and of potential shifts in species composition over time under a plausible range of climate scenarios. These two inputs are combined to assess the extent to which each cell in the grid is functionally connected (through least-cost-path analysis) to areas of natural habitat in the surrounding landscape which are projected to support a similar assemblage of species under climate change to that currently associated with the cell of interest. To derive the cell’s BERI value, the effective amount of this connected habitat expected under climate change is then expressed as a proportion of the maximum possible amount of connected habitat if the cell were surrounded by a continuous expanse of intact natural vegetation, and were not subjected to any change in climate. The aggregate value of the BERI for any larger spatial reporting unit of interest (e.g. a country, an ecoregion, a biome, an ecosystem type, or the entire planet) can then be derived as a weighted average of the values of all cells within that unit (with the contribution of individual cells weighted by ecological uniqueness).

5b. Method Of Computation

Full details of the method of computation for the BERI are provided by Ferrier et al (2020), and methodological refinements subsequently incorporated into the global implementation are described by Harwood et al (2022).

BERI scores are initially derived 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). The amount of connected (accessible) habitat in the surrounding landscape which is projected to support a similar assemblage of species under climate- change scenario k to that currently associated with focal cell i is calculated as:


where n is the number of cells in a circular neighbourhood (500km radius in the current global implementation of BERI) around the focal cell (see Ferrier et al 2020 and Harwood et al 2022 for radial-segmentation approach to optimising neighbourhood calculations); λ is the median distance that species in the biological group of interest are expected to be able to disperse through intact habitat within the time period of the scenario, used to scale probability of dispersal as a negative exponential function of effective distance; ℎ0 is the habitat condition of cell j; and 𝑠"0# is the compositional similarity predicted between the focal cell under present-day climate, and cells j under climate scenario k, assuming hypothetically that the habitat in both these cells are in perfect condition.

As described under ‘Data sources’ the global implementation of the BERI currently employs a time series of change in the ‘habitat condition’ 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.

The ‘compositional similarity’ between any pair of cells is the proportional overlap in species composition expected between those locations (assuming hypothetically that both cells are in perfect condition), ranging from 0 (for a pair of cells expected to have no species in common) through to 1 (for a pair of cells expected to have support exactly the same species). For the global implementation of the BERI these compositional 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). The fitted GDMs are used to project shifting patterns of compositional similarity between grid-cells under climate change using standard space-for-time substitution (Blois et al 2013).

For each focal cell the calculation of cik (see equation above) is repeated using four different median- dispersal values – 200m, 2km, 20km, and 200km – and these results are then averaged to yield a single cik value for the cell.

Again for each focal cell, the cik values calculated under six plausible climate scenarios (see ‘Data sources’), along with that obtained if no future change in climate is assumed (a total of m=7 scenarios in the equation below), are summarised into a single robust metric using the Limited Degree of Confidence approach. The BERI index for the focal cell is then expressed as the ratio between this summary metric of the amount of connected compositionally-similar habitat expected under climate change and ci0, the maximum possible value of the metric that would be obtained if the focal cell were completely surrounded by a continuous expanse of habitat in perfect condition, with no change in climate:


The aggregate BERI score for any larger spatial reporting unit (e.g. a country) is derived as a weighted 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 (in the absence of habitat degradation and climate change), and therefore the cell’s ecological uniqueness:

Further explanation of this approach to aggregation is provided by Harwood et al (2022).

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 BERI are published in accessible peer-reviewed locations (see ‘Method of computation’ and ‘Data sources’ for details).

Options for deriving and reporting the BERI 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 BERI 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
  • Global occurrence records for all terrestrial species within the following taxa, extracted from data accessible through the Global Biodiversity Information Facility

(Sources: http://www.worldclim.org/ https://www.soilgrids.org/http://www.earthenv.org/).

(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 BERI 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.ph... 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.

Downscaled CMIP5 climate projections used in deriving the current global implementation of the BERI were sourced from WorldClim (http://www.worldclim.org/CMIP5v1). Six climate scenarios were used, all projected to 2050. Each of these scenarios combines a particular climate model (GCM) with a particular level of greenhouse gas concentration (RCP). Four of the scenarios employ the IPSL- CM5A-LR GCM, combined with RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 respectively. To account for potential differences between climate models, RCP 8.5 (the highest greenhouse-gas concentration) was further combined with two alternative GCMs: ACCESS1-0 and GFDL-CM3 (for further details see Ferrier et al 2020).

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 BERI assesses only the terrestrial realm.

5k. Treatment Of Missing Values

All spatial data layers used to derive the BERI 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

Scale of application: 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 BERI. 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 BERI 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. This option would also open up potential for countries to evaluate the contribution that alternative area-based actions might make to improving the present BERI score for their country, thereby providing a foundation for prioritising the implementation of such actions. While Option 2 would allow results for the BERI 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 BERI 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 (see Harwood et al 2022 for an example of this option implemented for a state of Australia). 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 BERI 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 15 reporting.

7b. Biodiversity Indicator Partnership

Yes

8. Disaggregation

Global results for this indicator can potentially be reported by any desired set of spatial units at, or above, the resolution of 30-arcsecond grid-cells – e.g. by countries, biomes, ecosystem types, or the entire planet. They can also be reported separately for each of three biological groups – plants, invertebrates, vertebrates – or as a combined average across the groups.

9. Related Goals Targets And Indicators

Many of the datasets, models and analytical techniques used in the global implementation of the BERI are also used in deriving two other indicators developed by CSIRO – the Biodiversity Habitat Index (BHI) and the Protected Area Representativeness & Connectedness (PARC) indices.

As described under ‘Data sources’ the global implementation of the BERI currently employs a time series of change in ecosystem condition 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). 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.

The continuous nature of the ecosystem-condition time series underpinning the global BERI implementation confers an important advantage relative to indicators underpinned by observed changes in the distribution of discrete land-cover classes. The BERI can reflect changes in condition resulting from disturbance or management of an ecosystem represented by a single mapped land- cover type, e.g. “forest”, not just changes in the extent of that type.

The BERI can also be derived from any other gridded dataset for which local ecosystem condition or integrity is estimated on a continuous scale for each cell. Good potential therefore exists to derive this indicator from, and thereby add considerable value to, a number of other component and complementary indicators addressing ecosystem integrity in the draft GBF monitoring framework. For example, a recent refinement of the closely-related BHI across all forests globally, based on 300m grid-resolution mapping of the Forest Landscape Integrity Index (Ferrier et al 2024), could be readily extended to generate a refined version of the BERI. Similar potential exists to derive the BERI from the Ecosystem Intactness Index, Ecosystem Integrity Index, or Mean Species Abundance index.

10. Data Reporter

10a. Organisation

CSIRO (Australia’s national science agency)

10b. Contact Person

Simon Ferrier, simon.ferrier@csiro.au

11. References

Blois, J.L., Williams, J.W., Fitzpatrick, M.C., Ferrier, S. (2013) Space can substitute for time in predicting climate-change effects on biodiversity. PNAS 110: 9374-9379. https://www.pnas.org/doi/full/10.1073/pnas.1220228110

Di Marco, M., Harwood, T.D., Hoskins, A.J., Ware, C., Hill, S.L.L., Ferrier, S. (2019) Projecting impacts of global climate and land-use scenarios on plant biodiversity using compositional-turnover modelling. Global Change Biology 25: 2763-2778. https://onlinelibrary.wiley.com/doi/full/10.1111/g...

Drielsma, M., Ferrier, S., Manion, G. (2007) A raster-based technique for analysing habitat configuration: the cost-benefit approach. Ecological Modelling 202: 324-332. https://www.sciencedirect.com/science/article/pii/S0304380006005291?via%3Dihub

Ferrier, S., Harwood, T.D., Ware, C., Hoskins, A.J. (2020) A globally applicable indicator of the capacity of terrestrial ecosystems to retain biological diversity under climate change: the Bioclimatic Ecosystem Resilience Index. Ecological Indicators 117: 106554. https://www.sciencedirect.com/science/article/pii/S1470160X2030491X

Ferrier, S., Ware, C., Austin, J.M., Grantham, H.S., Harwood, T.D., Watson, J.E.M. (2024) Ecosystem extent is a necessary but not sufficient indicator of the state of global forest biodiversity. Conservation Letters e13045https://conbio.onlinelibrary.wiley.com/doi/full/10.1111/conl.13045

Hansen, A.J., Noble, B.P., Veneros, J., East, A., Goetz, S.J., Supples, C., Watson, J.E.M., Jantz, P.A., Pillay, R., Jetz, W., Ferrier, S., Grantham, H.S., Evans, T.D., Ervin, J., Venter, O., Virnig, A.L.S. (2021) Toward monitoring forest ecosystem integrity within the post-2020 Global Biodiversity Framework. Conservation Letters e12822. https://conbio.onlinelibrary.wiley.com/doi/full/10...

Harwood, T., Love, J., Drielsma, M., Brandon, C., Ferrier, S. (2022) Staying connected: assessing the capacity of landscapes to retain biodiversity in a changing climate. Landscape Ecology. https://link.springer.com/article/10.1007/s10980-0...

Hoskins, A.J., Bush, A., Gilmore, J., Harwood, T., Hudson, L.N., Ware, C., Williams, K.J., Ferrier, S. (2016) Downscaling land-use data to provide global 30” estimates of five land-use classes. Ecology and Evolution 6: 3040-3055. https://onlinelibrary.wiley.com/doi/10.1002/ece3.2...

Hoskins, A.J., Harwood, T.D., Ware, C., Williams, K.J., Perry, J.J., Ota, N., Croft, J.R., Yeates, D.K., Jetz, W., Golebiewski, M., Purvis, A., Ferrier, S. (2020) BILBI: Supporting global biodiversity assessment through high-resolution macroecological modelling. Environmental Modelling & Software 132: 104806. https://www.sciencedirect.com/science/article/pii/S1364815220301225

12. Graphs And Diagrams

1. Indicator Name

Bioclimatic Ecosystem Resilience Index (BERI)

2. Date Of Metadata Update

2024-10-15 12:00:00 UTC

3. Goals And Targets Addressed

3a. Goal

Indirectly 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

Directly addresses Target 8: Minimize the impact of climate change and ocean acidification on biodiversity and increase its resilience through mitigation, adaptation, and disaster risk reduction actions, including through nature-based solutions and/or ecosystem-based approaches, while minimizing negative and fostering positive impacts of climate action on biodiversity.

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.

4. Rationale

The Bioclimatic Ecosystem Resilience Index (BERI) measures the capacity of landscapes to retain species diversity in the face of climate change, as a function of the area, integrity and connectivity of natural ecosystems across those landscapes. The indicator assesses the extent to which any given spatial configuration of natural habitat will promote or hinder climate-induced shifts in biological distributions. It does this by analyzing the functional connectivity of each grid-cell of natural habitat to areas of habitat in the surrounding landscape which are projected to support a similar assemblage of species under climate change to that currently associated with the cell of interest.

The BERI directly addresses Target 8 by providing a rigorous, yet straightforward, measure of the extent to which cumulative changes in the area, integrity and connectivity of natural ecosystems are helping or hindering efforts to “minimize the impact of climate change … on biodiversity and increase its resilience”. It can therefore be used to monitor and report past-to-present trends in the capacity of landscapes to retain species diversity in the face of ongoing climate change by repeatedly recalculating the indicator using best-available mapping of ecosystem condition or integrity observed at multiple points in time, e.g. for different years. A wide variety of data sources can be used for this purpose, spanning spatial scales from global to subnational, and including data assembled by countries for deriving ecosystem condition accounts under the UN SEEA Ecosystem Accounting framework. The indicator can then be aggregated and reported by any desired spatial unit – e.g. a country, a biome, an ecosystem type, or the entire planet.

The BERI offers an effective means of linking actions under Target 8 to both ecosystem-level and species-level outcomes under Goal A. As for the closely related Biodiversity Habitat Index (BHI), the BERI effectively integrates three of the key ecosystem attributes addressed in Goal A – i.e. area, integrity and connectivity – and the combined effect that changes in these attributes are expected to have on the retention of species diversity. However, it differs from the BHI in giving much stronger consideration to the potential impacts of climate change, and particularly to the major role that functional habitat connectivity will play in facilitating climate-induced shifts in the distribution of species and ecological communities.

The BERI can also serve as a leading indicator for assessing the contribution that proposed or implemented area-based actions under Targets 1, 2 and 3 are expected to make to enhancing the present capacity of landscapes to retain species diversity in the face of climate change. This would allow actions under these targets to be better linked both to the achievement of Target 8, and to achieving outcomes under Goal A, thereby providing a stronger foundation for strategic prioritisation of such actions by member countries.

5. Definitions Concepts And Classifications

5a. Definition

As described by Ferrier et al (2020) the BERI 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, and of potential shifts in species composition over time under a plausible range of climate scenarios. These two inputs are combined to assess the extent to which each cell in the grid is functionally connected (through least-cost-path analysis) to areas of natural habitat in the surrounding landscape which are projected to support a similar assemblage of species under climate change to that currently associated with the cell of interest. To derive the cell’s BERI value, the effective amount of this connected habitat expected under climate change is then expressed as a proportion of the maximum possible amount of connected habitat if the cell were surrounded by a continuous expanse of intact natural vegetation, and were not subjected to any change in climate. The aggregate value of the BERI for any larger spatial reporting unit of interest (e.g. a country, an ecoregion, a biome, an ecosystem type, or the entire planet) can then be derived as a weighted average of the values of all cells within that unit (with the contribution of individual cells weighted by ecological uniqueness).

5b. Method Of Computation

Full details of the method of computation for the BERI are provided by Ferrier et al (2020), and methodological refinements subsequently incorporated into the global implementation are described by Harwood et al (2022).

BERI scores are initially derived 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). The amount of connected (accessible) habitat in the surrounding landscape which is projected to support a similar assemblage of species under climate- change scenario k to that currently associated with focal cell i is calculated as:


where n is the number of cells in a circular neighbourhood (500km radius in the current global implementation of BERI) around the focal cell (see Ferrier et al 2020 and Harwood et al 2022 for radial-segmentation approach to optimising neighbourhood calculations); λ is the median distance that species in the biological group of interest are expected to be able to disperse through intact habitat within the time period of the scenario, used to scale probability of dispersal as a negative exponential function of effective distance; ℎ0 is the habitat condition of cell j; and 𝑠"0# is the compositional similarity predicted between the focal cell under present-day climate, and cells j under climate scenario k, assuming hypothetically that the habitat in both these cells are in perfect condition.

As described under ‘Data sources’ the global implementation of the BERI currently employs a time series of change in the ‘habitat condition’ 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.

The ‘compositional similarity’ between any pair of cells is the proportional overlap in species composition expected between those locations (assuming hypothetically that both cells are in perfect condition), ranging from 0 (for a pair of cells expected to have no species in common) through to 1 (for a pair of cells expected to have support exactly the same species). For the global implementation of the BERI these compositional 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). The fitted GDMs are used to project shifting patterns of compositional similarity between grid-cells under climate change using standard space-for-time substitution (Blois et al 2013).

For each focal cell the calculation of cik (see equation above) is repeated using four different median- dispersal values – 200m, 2km, 20km, and 200km – and these results are then averaged to yield a single cik value for the cell.

Again for each focal cell, the cik values calculated under six plausible climate scenarios (see ‘Data sources’), along with that obtained if no future change in climate is assumed (a total of m=7 scenarios in the equation below), are summarised into a single robust metric using the Limited Degree of Confidence approach. The BERI index for the focal cell is then expressed as the ratio between this summary metric of the amount of connected compositionally-similar habitat expected under climate change and ci0, the maximum possible value of the metric that would be obtained if the focal cell were completely surrounded by a continuous expanse of habitat in perfect condition, with no change in climate:


The aggregate BERI score for any larger spatial reporting unit (e.g. a country) is derived as a weighted 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 (in the absence of habitat degradation and climate change), and therefore the cell’s ecological uniqueness:

Further explanation of this approach to aggregation is provided by Harwood et al (2022).

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 BERI are published in accessible peer-reviewed locations (see ‘Method of computation’ and ‘Data sources’ for details).

Options for deriving and reporting the BERI 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 BERI 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
  • Global occurrence records for all terrestrial species within the following taxa, extracted from data accessible through the Global Biodiversity Information Facility

(Sources: http://www.worldclim.org/ https://www.soilgrids.org/http://www.earthenv.org/).

(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 BERI 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.ph... 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.

Downscaled CMIP5 climate projections used in deriving the current global implementation of the BERI were sourced from WorldClim (http://www.worldclim.org/CMIP5v1). Six climate scenarios were used, all projected to 2050. Each of these scenarios combines a particular climate model (GCM) with a particular level of greenhouse gas concentration (RCP). Four of the scenarios employ the IPSL- CM5A-LR GCM, combined with RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 respectively. To account for potential differences between climate models, RCP 8.5 (the highest greenhouse-gas concentration) was further combined with two alternative GCMs: ACCESS1-0 and GFDL-CM3 (for further details see Ferrier et al 2020).

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 BERI assesses only the terrestrial realm.

5k. Treatment Of Missing Values

All spatial data layers used to derive the BERI 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

Scale of application: 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 BERI. 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 BERI 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. This option would also open up potential for countries to evaluate the contribution that alternative area-based actions might make to improving the present BERI score for their country, thereby providing a foundation for prioritising the implementation of such actions. While Option 2 would allow results for the BERI 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 BERI 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 (see Harwood et al 2022 for an example of this option implemented for a state of Australia). 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 BERI 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 15 reporting.

7b. Biodiversity Indicator Partnership

Yes

8. Disaggregation

Global results for this indicator can potentially be reported by any desired set of spatial units at, or above, the resolution of 30-arcsecond grid-cells – e.g. by countries, biomes, ecosystem types, or the entire planet. They can also be reported separately for each of three biological groups – plants, invertebrates, vertebrates – or as a combined average across the groups.

9. Related Goals Targets And Indicators

Many of the datasets, models and analytical techniques used in the global implementation of the BERI are also used in deriving two other indicators developed by CSIRO – the Biodiversity Habitat Index (BHI) and the Protected Area Representativeness & Connectedness (PARC) indices.

As described under ‘Data sources’ the global implementation of the BERI currently employs a time series of change in ecosystem condition 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). 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.

The continuous nature of the ecosystem-condition time series underpinning the global BERI implementation confers an important advantage relative to indicators underpinned by observed changes in the distribution of discrete land-cover classes. The BERI can reflect changes in condition resulting from disturbance or management of an ecosystem represented by a single mapped land- cover type, e.g. “forest”, not just changes in the extent of that type.

The BERI can also be derived from any other gridded dataset for which local ecosystem condition or integrity is estimated on a continuous scale for each cell. Good potential therefore exists to derive this indicator from, and thereby add considerable value to, a number of other component and complementary indicators addressing ecosystem integrity in the draft GBF monitoring framework. For example, a recent refinement of the closely-related BHI across all forests globally, based on 300m grid-resolution mapping of the Forest Landscape Integrity Index (Ferrier et al 2024), could be readily extended to generate a refined version of the BERI. Similar potential exists to derive the BERI from the Ecosystem Intactness Index, Ecosystem Integrity Index, or Mean Species Abundance index.

10. Data Reporter

10a. Organisation

CSIRO (Australia’s national science agency)

10b. Contact Person

Simon Ferrier, simon.ferrier@csiro.au

11. References

Blois, J.L., Williams, J.W., Fitzpatrick, M.C., Ferrier, S. (2013) Space can substitute for time in predicting climate-change effects on biodiversity. PNAS 110: 9374-9379. https://www.pnas.org/doi/full/10.1073/pnas.1220228110

Di Marco, M., Harwood, T.D., Hoskins, A.J., Ware, C., Hill, S.L.L., Ferrier, S. (2019) Projecting impacts of global climate and land-use scenarios on plant biodiversity using compositional-turnover modelling. Global Change Biology 25: 2763-2778. https://onlinelibrary.wiley.com/doi/full/10.1111/g...

Drielsma, M., Ferrier, S., Manion, G. (2007) A raster-based technique for analysing habitat configuration: the cost-benefit approach. Ecological Modelling 202: 324-332. https://www.sciencedirect.com/science/article/pii/S0304380006005291?via%3Dihub

Ferrier, S., Harwood, T.D., Ware, C., Hoskins, A.J. (2020) A globally applicable indicator of the capacity of terrestrial ecosystems to retain biological diversity under climate change: the Bioclimatic Ecosystem Resilience Index. Ecological Indicators 117: 106554. https://www.sciencedirect.com/science/article/pii/S1470160X2030491X

Ferrier, S., Ware, C., Austin, J.M., Grantham, H.S., Harwood, T.D., Watson, J.E.M. (2024) Ecosystem extent is a necessary but not sufficient indicator of the state of global forest biodiversity. Conservation Letters e13045https://conbio.onlinelibrary.wiley.com/doi/full/10.1111/conl.13045

Hansen, A.J., Noble, B.P., Veneros, J., East, A., Goetz, S.J., Supples, C., Watson, J.E.M., Jantz, P.A., Pillay, R., Jetz, W., Ferrier, S., Grantham, H.S., Evans, T.D., Ervin, J., Venter, O., Virnig, A.L.S. (2021) Toward monitoring forest ecosystem integrity within the post-2020 Global Biodiversity Framework. Conservation Letters e12822. https://conbio.onlinelibrary.wiley.com/doi/full/10...

Harwood, T., Love, J., Drielsma, M., Brandon, C., Ferrier, S. (2022) Staying connected: assessing the capacity of landscapes to retain biodiversity in a changing climate. Landscape Ecology. https://link.springer.com/article/10.1007/s10980-0...

Hoskins, A.J., Bush, A., Gilmore, J., Harwood, T., Hudson, L.N., Ware, C., Williams, K.J., Ferrier, S. (2016) Downscaling land-use data to provide global 30” estimates of five land-use classes. Ecology and Evolution 6: 3040-3055. https://onlinelibrary.wiley.com/doi/10.1002/ece3.2...

Hoskins, A.J., Harwood, T.D., Ware, C., Williams, K.J., Perry, J.J., Ota, N., Croft, J.R., Yeates, D.K., Jetz, W., Golebiewski, M., Purvis, A., Ferrier, S. (2020) BILBI: Supporting global biodiversity assessment through high-resolution macroecological modelling. Environmental Modelling & Software 132: 104806. https://www.sciencedirect.com/science/article/pii/S1364815220301225

12. Graphs And Diagrams


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