The science of PREDICTS
PREDICTS pulls together species abundance data from studies around the world. These studies cover a broad range of taxa, including plants, fungi, invertebrates and vertebrates, to model how human activities impact biodiversity.
A representative data set
Over 500 researchers contributed their raw biodiversity data to the PREDICTS database that can be downloaded from the Museum's data portal. This data set is now taxonomically and geographically extensive and fairly representative.
This data set is openly available for anyone to use.
The PREDICTS database holds data from across all major terrestrial animal, plant and fungal groups. The database contains over 2% of all species named by science, with good coverage across most major groups.
Most of the data in the PREDICTS database are from arthropods, particularly insects, which is appropriate as insects are the most species known to science.
Most of the rest of the data are from plants, especially vascular plants (tracheophytes); this reflects their high diversity (over 400,000 species, many times more than the number of terrestrial vertebrate species) and crucial role as the base of food chains.
Vertebrates are over-represented relative to their diversity, but this bias is much reduced compared with other global data sets and indicators. We don't have as many data sets on fungi and non-arthropod invertebrates as we would like, but they are at least represented.
This broad representative coverage really matters, because different taxonomic groups don't respond the same way to human pressures.
Most other biodiversity indicators are based on a single taxonomic group such as vertebrates. Basing an indicator on only one group risks giving a misleading picture of the true state of nature.
Calculating multiple measures of biodiversity
The PREDICTS database is full of raw biodiversity data. For most studies, we know the number of individuals found of each species at each site.
This rich data means that we can calculate many different measures of biodiversity, including the total number of species (species richness) and the compositional similarity (how similar each site's ecological community is to near-undisturbed sites).
Combining models of abundance and compositional similarity lets us estimate the Biodiversity Intactness Index, an indicator of how much natural biodiversity remains, on average, across a region.
Find out more about how we calculate the Biodiversity Intactness Index.
The PREDICTS models
To analyse the data from the PREDICTS database statistically, it is essential to account for the fact that the underlying data come from such a broad range of sources, focusing on different species groups and using different methods to sample them.
We do this using mixed-effects models to account for the hierarchical structure of the data. This is a robust, flexible method and allows us to assess different biodiversity metrics in response to different pressures.
This walkthrough lets you follow the technical steps we use to estimate the Biodiversity Intactness Index.
Focus: PREDICTS uses data on local biodiversity around the world to model how human activities affect biological communities.
Hudson et al. (2016) The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecology and Evolution
Newbold T., Hudson L.N., Purvis A. (2015) Global effects of land use on local terrestrial biodiversity, Nature
Newbold et al. (2016) Global patterns of terrestrial assemblage turnover within and among land uses. Ecography
De Palma et al. (2017) Dimensions of biodiversity loss: Spatial mismatch in land-use impacts on species, functional and phylogenetic diversity of European bees. Diversity and Distributions
Newbold T., Hudson L. N. and Purvis A. (2016) Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science