A spatial-temporal framework for species abundance modelling and scenario assessment in the UK

Projecting species abundance under future environmental conditions remains a critical challenge in biodiversity modelling. Exceedingly, abundance frameworks are retrospective or taxonomically limited, lacking integration with high-resolution environmental data and their corresponding future projections. Recent research highlights that forward-looking predictions linking abundance to environmental drivers are crucial for effective conservation planning under global change.

Prescribed by the Global Biodiversity Framework, the United Kingdom’s legally binding targets of halting and reversing species abundance decline present unique modelling opportunities and challenges. The UK benefits from exceptionally rich, long-term citizen science and statutory monitoring schemes across multiple taxonomic groups yet integrating these into unified abundance projections remains technically challenging. Combining these datasets with comprehensive environmental covariates enables the development of national-scale abundance projections. Here, we present a spatial-temporal framework that harmonises data on hundreds of species across taxonomic groups (e.g. birds, plants, butterflies, moths) with high-resolution environmental covariates covering climate, soils and land cover, and their scenario-based projections (RCPs, SSPs).

Using inlabru for Bayesian approximation, we develop spatially explicit models at 1 km resolution across the UK. Carefully formulated spatial-temporal random effects capture residual dependencies whilst remaining computationally efficient at national scale. This framework enables abundance predictions at a notable combination of spatial extent (national), resolution (1 km) and taxonomic breadth.  

This framework explicitly links abundance to environmental covariates and their future projections, enabling predictions under novel climate and land use conditions rather than simple temporal extrapolation. We estimate a baseline abundance and project changes under alternative scenarios, quantifying uncertainty throughout. This hierarchical approach provides policy-ready outputs at multiple biological scales; from individual species through taxonomic groups to community-level measures.

Model outputs provide 1 km resolution gridded abundance estimates with associated uncertainties. The integration of comprehensive environmental datasets with robust spatial-temporal modelling at national scale represents significant advances in abundance projections. This scalable framework can be adapted by other nations working towards Global Biodiversity Framework targets; with such forward-looking modelling approaches essential for tracking progress and identifying interventions to halt and reverse biodiversity decline.