Ludwig Baldaszti

Societal Impact Statement: The IUCN Red List is the main global authority for determining species extinction risk. For vascular plants, fewer than 22% of all species have been globally assessed. Here, we evaluate whether those species assessed are representative of all known vascular plant species. There are significant biases in the IUCN Red List in terms of the distribution of species assessed, with herbaceous species and geographical regions such as temperate Asia under‐represented. The Sampled Red List Index for plants provides less biased extinction risk estimates, although biases remain. We hope that our findings facilitate effective prioritisation for future Red Listing projects.

Incomplete information on distributions for a high proportion of the world’s plant species together with biases in global biodiversity data mean that current estimates of plant diversity patterns are skewed. A key issue is that current predictions rely on a subset of species that is not representative of all plant species. Here we tested the feasibility of a representative sampling approach for mapping global vascular plant diversity at the finest scale where comprehensive data is available. Using the World Checklist of Vascular Plants as a reference, we generate random samples of species with increasing sample sizes from the global species pool. We compare the diversity patterns retrieved from the samples against the patterns of the reference dataset using spatially weighted correlation coefficients and four different diversity metrics. We find that at the botanical country scale, representative global maps of species and phylogenetic diversity can be created with small numbers of species (∼1% [0.2% and 0.4%, respectively]) at the botanical country scale. For effective growth form and family diversity sample sizes encompassing ∼20% [19.2% and 19.5%, respectively] of all species are needed. Random samples require markedly fewer species to reach high correlations than when restricting the pool of species to single plant families or genera. We show that when representative samples are used robust inferences of plant diversity patterns can be made from only a small proportion of species.

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.

The Afromontane region harbors ancient grasslands with high levels of endemism, now under threat from land-use change, biological invasions and encroachment, and climate warming. As part of an international Plant Functional Traits Course we collected comprehensive trait data in five sites along an elevation gradient from 2,000–2,800 m a.s.l. and in a climate warming experiment at 3,064 m a.s.l. in the Maloti-Drakensberg, South Africa. We sampled 24,405 aboveground and 94 root trait measurements from 171 vascular plant taxa paired with 11 other datasets reflecting vegetation and structure, leaf and ecosystem carbon and water fluxes, leaf hyperspectral reflectance, and microclimatic and environmental data. Our data provide the first recorded trait data for 47 vascular plant species and more than double the trait data coverage from the Maloti-Drakensberg (106% increase). This study offers insights into plant and ecosystem functioning, provides a baseline for assessing impacts of environmental change, builds local competence, and aligns with similar data from China, Svalbard, Peru, and Norway.

In 2004, David Frodin published a landmark review of the history and concepts of big plant genera. Two decades of taxonomic activity have taken place since, coinciding with a revolution in phylogenetics and taxonomic bioinformatics. Here we use data from the World Flora Online (WFO) to provide an updated list of big (more than 500 species) and megadiverse (more than 1000 species) flowering plant genera and highlight changes since 2004. The number of big genera has increased from 57 to 86; today one of every four plant species is classified as a member of a big genus, with 14% in just 28 megadiverse genera. Most (71%) of the growth in big genera since 2000 is the result of new species description, not generic re-circumscription. More than 15% of all currently accepted flowering plant species described in the last two decades are in big genera, suggesting that groups previously considered intractable are now being actively studied taxonomically. Despite this rapid growth in big genera, they remain a significant yet understudied proportion of plant diversity. They represent a significant proportion of global plant diversity and should remain a priority not only for taxonomy but for understanding global diversity patterns and plant evolution in general.