Biodiversity Habitat Index
The Biodiversity Habitat Index (BHI) estimates the impacts of habitat loss and degradation on the retention of terrestrial biodiversity. It integrates information from remotely sensed land-cover and land-use change datasets with modeled fine-scaled spatial variation in biodiversity composition.
This indicator ranges from 0-1. Values closer to 1 for a given spatial reporting unit (e.g. a particular country, hydro-basin or region) indicate that finely-mapped environments supporting relatively distinct assemblages of species within that unit are, on average, well covered by intact natural habitat. Values closer to 0 indicate that very little natural habitat remains across most of the distinctive environments occurring within a given reporting unit.
The BHI is derived by combining a habitat-condition surface for the year of interest with fine-scaled modelling and mapping of spatial turnover in biodiversity composition, or 'ecological similarity' - i.e. the proportion of species that any two locations (grid cells) would be predicted to share if they were both covered by intact habitat. (See Tables 1 & 2 for details on ecological similarity and habitat-condition surface).
Please note that, unlike previous results for the BHI presented on the dashboard, which were generated globally for forest biomes only, using data on forest loss from the Global Forest Change dataset, the results presented here have been expanded to cover all terrestrial biomes across the planet. As detailed in Table 2, this has been achieved through use of CSIRO's statistically downscaled land-use dataset.
The score assigned to a given 'focal cell' is calculated as the average condition of all ecologically-similar cells, with the contribution any other cell makes to this calculation weighted according to its predicted level of similarity with the focal cell (Ferrier et al. 2004; Allnutt et al. 2008; Williams et al. 2016). The BHI for any given spatial reporting unit (e.g. basin, country) is then derived as a weighted geometric mean (Buckland et al. 2005) of the scores obtained for all cells within that unit, with the contribution of each cell weighted according to its ecological uniqueness - i.e. the inverse of the summed ecological similarity of this cell to all other cells (for further details see Ferrier et al. 2004, Allnutt et al. 2008, Williams et al. 2016). This aggregate BHI score therefore indicates the proportional retention of habitat across the full range of environments, supporting relatively distinct assemblages of species, within the reporting unit of interest.
The BHI score for a given reporting unit can optionally be translated into an estimate of the proportion of native species expected to persist over the longer term within that unit, by invoking a standard species-area relationship - i.e. by raising this score to the power of 0.25 (see Ferrier et al. 2004 and Di Marco et al. 2019 for details).
Table 1. Ecological similarity models (Ferrier et al. 2007; Hoskins et al. 2019).
| Description | Data Types | Components | Sources |
| The fitted generalized dissimilarity models (GDMs) represent spatial turnover in species composition as a function of environmental variables, the geographical distance between records, and the identity of WWF ecoregions within which they occur. "Ecological similarity" equals the predicted proportional overlap in species composition between any given pair of locations (grid cells) - i.e. the mean proportion of species occurring at one of the locations that would be expected to also occur at the other location (in the absence of habitat degradation at both locations). | Abiotic Environmental Surfaces | Min Monthly Min Temperature | WorldClim, WorldGrids |
| Max Monthly Max Temperature | |||
| Max Diurnal Temperature Range | |||
| Annual Precipitation | |||
| Actual Evaporation | |||
| Potential Evaporation | |||
| Min Monthly Water Deficit | |||
| Max Monthly Water Deficit | |||
| Soil pH | SoilGrids | ||
| Soil Clay Proportion | |||
| Soil Silt Proportion | |||
| Soil Bulk Density | |||
| Soil Depth | |||
| Ruggedness Index | EarthEnv | ||
| Topographic Wetlands Index | |||
| Global Occurrence Records for Terrestrial Species | Amphibians, Birds, Mammals | Map of Life | |
| Vascular plants, Reptiles, Ants, Bees, Beetles, Bugs, Butterflies, Centipedes, Dragonflies, Flies, Grasshoppers, Millipedes, Snails, Moths, Spiders, Termites, Wasps | Global Biodiversity Information Facility (GBIF) | ||
| Bio-realms | N/A | WWF |
Table 2. Change in habitat condition.
| Description | Data Types | Sources |
| Change in condition was estimated through an extension of CSIRO's statistical downscaling of coarse-resolution land-use data using 1km-resolution environmental and remotely-sensed land-cover covariates (Hoskins et al. 2016). This recent work has adapted Hoskins et al's approach to employ Version 2, in place of Version 1, of the Land Use Harmonization product, thereby generating downscaled estimates of 12, rather than the original five, land-use classes, and MODIS Vegetation Continuous Fields as remote-sensing covariatesin place of discrete land-cover classes (Di Marco et al. 2019). Applying this downscaling approach across multiple years provides an effective means of translating observed changes in remote-sensing covariates into estimated changes in the proportions of land-use classes occurring in each and every 1km terrestrial grid-cell on the planet. These proportions are then, in turn, translated into an estimate of 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 (Newbold et al. 2016; https://www.predicts.org.uk/), augmented and harmonized with coefficients derived independently by Chaudhary and Brooks (2018). These harmonized coefficients scale condition in terms of the proportion of native species (i.e. alpha diversity), originally associated with a given cell, which are expected to still be present at that location. | Land Use | Land Use Harmonization, Version 2 |
| Vegetation | MODIS Vegetation Continuous Fields |
Results presented here for the BHI indicator are based on vascular plants only, and are presented for just three time points: 2005, 2010 and 2015. In the future these results will be extended to cover all three broad biological groups for which modelling has been undertaken (i.e. plants, invertebrates and vertebrates) and to include additional time points.
More information and further resources are available in the indicator factsheet here.
Allnutt, T.F., Ferrier, S., Manion, G., Powell, G.V.N., Ricketts, T.H., Fisher, B.L., Harper, G.J., Kremen, C., Labat, J., Lees, D.C., Pearce, T.A., Irwin, M.E. and Rakotondrainibe, F. (2008). A method for quantifying biodiversity loss and its application to a 50-year record of deforestation across Madagascar. Conservation Letters, 1, 173-181.
Buckland, S. T., Magurran, a E., Green, R. E., Fewster, R. M. (2005). Monitoring change in biodiversity through composite indices. Philosophical Transaction: Biological Sciences, 360(1454), 243-254.
Chaudhary, A., Brooks, T.M. (2018) Land use intensity-specific global characterization factors to assess product biodiversity footprints. Environmental Science & Technology 52, 5094-5104
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.
Ferrier, S., Powell, G.V.N., Richardson, K.S., Manion, G., Overton, J.M., Allnutt, T.F., Cameron, S.E., Mantle, K., Burgess, N.D., Faith, D.P., Lamoreux, J.F., Kier, G., Hijmans, R.J., Funk, V.A., Cassis, G.A., Fisher, B.L., Flemons, P., Lees, D., Lovett, J.C., van Rompaey, R.S.A.R (2004). Mapping more of terrestrial biodiversity for global conservation assessment. BioScience, 54, 1101-1109.
Ferrier, S., Manion, G., Elith, J. and Richardson, K. (2007) Using generalised dissimilarity modelling to analyse and predict patterns of beta-diversity in regional biodiversity assessment. Diversity and Distributions, 13, 252-264.
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.
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., Robertson, T., Ferrier, S., 2019. Supporting global biodiversity assessment through high-resolution macroecological modelling: Methodological underpinnings of the BILBI framework. BioRxiv https://www.biorxiv.org/content/10.1101/309377v3.
Newbold, T., Hudson, L.N., Arnell, A.P., Contu, S., De Palma, A., Ferrier, S., Hill, S.L.L., Hoskins, A.J., Lysenko, I., Phillips, H.R.P., Burton, V.J., Chng, C.W.T., Emerson, S., Gao, D., Pask-Hale, G., Hutton, J., Jung, M., Sanchez-Ortiz, K., Simmons, B.I., Whitmee, S., Zhang, H., Scharlemann, J.P.W., Purvis, A., 2016. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288-291.
Williams, K.J., Harwood, T.D., Ferrier, S. (2016). Assessing the ecological representativeness of Australia's terrestrial National Reserve System: A community-level modelling approach. Publication Number EP163634. CSIRO Land and Water, Canberra, Australia. https://publications.csiro.au/rpr/pub?pid=csiro:EP163634