The power of few: Using representative sampling to predict global plant biodiversity patterns

Achieving the Kunming-Montreal Global Biodiversity Framework’s 30x30 target requires accurate, high-resolution biodiversity maps. However, for plants, distribution data remains spatially sparse and taxonomically biased, limiting our ability to create representative maps of global plant diversity for conservation prioritisation. To address this, we developed a scalable workflow that uses optimised representative sampling of species to generate robust global plant diversity maps.

Using global plant taxonomic checklists as a baseline, our approach employs a genetic algorithm to maximise geographic and compositional representativeness of the selected species for modelling relative to established coarse-scale global diversity patterns. This ensures that a comparatively small number of species are able to capture broader biodiversity patterns. We then apply ensemble species distribution modelling techniques, such as MaxEnt-based binary predictor ensembles and multi-algorithm ensemble models, to model species distributions at fine scales. By iteratively modelling multiple optimised samples across a range of sample sizes, we are able to determine sample size thresholds beyond which additional species no longer affect predicted diversity hotspots, allowing us to define a minimum number of species needed for stable and reliable diversity mapping at different scales. We use the results to create a high-resolution (10km²) global plant diversity map that explicitly incorporates uncertainty by deriving predicted diversity intervals for each cell from the repeated samples.  

The resulting high-resolution global plant diversity map explicitly accounts for sampling biases, geographic gaps, and predictive uncertainty. Importantly, we demonstrate that robust biodiversity prediction is achievable using only a fraction of all species when using a representative sample, offering a pragmatic solution where comprehensive modelling remains unfeasible. This methodology provides a data-efficient foundation for spatial conservation planning and priority-setting aligned with global targets such as 30x30.

Our framework is transferable across taxa and scales, offering a generalisable path forward for biodiversity assessment under data limitations. By strategically leveraging representative samples, this approach supports more inclusive and globally consistent conservation planning.