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The science behind the Biodiversity Intactness Index
The Natural History Museum's Biodiversity Intactness Index (BII) has come about by using the PREDICTS data. The index has been evolving for decades, and we are always improving how we use data to estimate the BII.
In 2015, we published our first worldwide analysis of how numbers of species and individuals in ecological communities have been affected by land use change and intensification.
In 2016, responding to a knowledge gap in the planetary boundaries framework, we published the first estimates of BII from primary biodiversity data.
In 2021, we made it so users can access past and future BII estimates through the NHM Data Portal.
The PREDICTS team is a consortium of researchers at the Natural History Museum London, UN Environment Program World Conservation Monitoring Centre, University College London, Imperial College London, Swansea University and the University of Sussex.
More information
Assumptions
In our modelling, we assume the human pressures (land use change and intensification, human population growth and landscape simplification) have caused the differences we see in biodiversity within each study. However, these are not the only drivers of biodiversity change.
We assume that the species at sites with minimally disturbed plants are similar to species in a pristine area as truly untouched environments are rare.
We also assume that all species found in these minimally disturbed sites are naturally present. But this is not always true as some of these species may be invasive. Usually, the PREDICTS database can't identify which species present are native or invasive.
In our modelling, we only consider landscape structure and landscape history in very basic ways, meaning that we won't capture all aspects of how these factors reshape biodiversity. We can try to account for additional pressures affecting sites by including more variables in the models.
Because we lack representative long-term data, we do not have true baseline sites with which we can make biodiversity comparisons.
While our data are more geographically representative than other biodiversity databases, there are still some geographical gaps in the data used to calculate the BII.
Limitations
Although the mapping of land use change is continually advancing, there are still limitations. Filtering data within the Biodiversity Trends Explorer over broad areas and larger time periods will be more reliable than filtering for smaller areas and time periods.
In any model there is statistical and structural uncertainty. For example, when categorising each land use type there may be errors.
In the Biodiversity Trends Explorer, each country or region's BII value is the average across all its land with every square kilometre being equally important.
An alternative approach would be to weight each square kilometre by how ecologically active it is, so by its net primary productivity or how much life there is present and being produced. This would highlight that biodiversity intactness is more important in productive places such as rainforests than areas of low productivity such as deserts. Future releases of the BII will include weighted averages like this, as well as the area-weighted average in the current version.
Calculating uncertainty
Our estimates of BII for a country, region or the world are derived from statistical modelling of the whole data set.
To calculate the uncertainty of these estimates, we fitted the same statistical models ten times, leaving out the data from one major biome (broad natural habitat type) each time, and used these to project BII.
This technique, known as cross-validation, yielded ten estimates and we report the lowest and highest of these as our uncertainty margins at the end of each decade.
The range of estimates can be quite wide, partly because two of the biomes in particular - temperate broadleaf and mixed forests, and tropical and subtropical moist broadleaf forests - are particularly well represented in the data.
Cross-validation commonly leaves out a random subset of the data each time, but that approach underestimates the true uncertainty in parameter estimates.
In the future, we plan to also cross-validate by splitting the data up into major taxonomic groups.
External data sets
Multiple external data sets are used to calculate the BII.
Shared Socioeconomic Pathways
The Shared Socioeconomic Pathways (SSPs) are five socio-economic development scenarios that include global projections of wealth, population, education, technology and reliance on fossil fuels.
SSP1: Sustainable development
SSP2: Middle of the road development
SSP3: Regional rivalry
SSP4: Inequality
SSP5: Fossil-fuelled development
Read more about the SSPs in The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview (Riahi et all, 2017).
Land-use harmonization (LUH2)
The Land-Use Harmonization (LUH2) project has produced a harmonized set of land-use scenarios that smoothly connects the historical reconstructions of land-use with the future projections in the format required for Earth System Models.
Read more about LUH2 in Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6 (Hurtt et all, 2020).
Access the data
The 2021 data can be accessed through the data portal.
Published papers
De Palma, A., Hoskins, A., Gonzalez, R.E. et al. Annual changes in the Biodiversity Intactness Index in tropical and subtropical forest biomes, 2001–2012. Sci Rep 11, 20249 (2021).