Neil Brummitt

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.

Forests worldwide face intensifying natural and anthropogenic pressures that progressively erode their resilience, bringing them closer to abrupt, potentially irreversible shifts. Such loss of resilience can manifest as critical slowing down (CSD), a phenomenon characterized by slower recovery rates. It is often measured through statistical indicators, known as early warning signals, which are expected to rise in advance of a critical transition such as a tipping point. Earth Observation (EO) is well suited to monitoring CSD indicators at global scales. However, the reliability of these indicators depends strongly on the quality and consistency of the underlying satellite record given their derivation from optical biophysical proxies. This is particularly true for what is arguably one of the most useful EO dataset – the Landsat Collection 2 archive, given its extensive temporal coverage (1984-present) and 30-m spatial resolution. Here, we present a scalable and reproducible workflow for generating high-quality, long-term, cross-calibrated Landsat datasets specifically designed for the computation of statistical indicators of resilience over large extents of forest ecosystems. The workflow integrates robust imagery processing, consistent radiometric cross-calibration, and a multiscale grid-based implementation to improve the reliability of the Landsat imagery used for resilience analysis. Using an undisturbed boreal forest region in Quebec, Canada, as a case study, we demonstrate that the proposed workflow reduces inter-sensor discontinuities, enhances spatial consistency and preserves ecologically meaningful temporal dynamics across the 40-year Landsat record. Applied across ecoregions and biomes, this framework seeks to enable robust and comparable assessments of forest resilience, supporting large-scale ecosystem monitoring, early-warning assessments, and evidence-based conservation prioritisation.

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.

Mosses are an early lineage of the plant kingdom, with around 13,000 species. Although an important part of biodiversity, providing crucial ecosystem services, many species are threatened with extinction. However, only circa 300 species have so far had their extinction risk evaluated globally for the IUCN Red List. Functional traits are known to help predict the extinction risk of species in other plant groups. In this study, a matrix of 15 functional traits was produced for 723 moss species from around the world to evaluate the potential of such predictability. Binary generalized linear models showed that monoicous species were more likely to be threatened than dioicous species, and the presence of a sporophyte (sexual reproduction), vegetative reproduction and an erect (straight) capsule instead of a pendent (immersed) one lowers the risk of species extinction. A longer capsule, seta and stem length, as well as broader substrate breadth, are indicative of species with a lower risk of extinction. The best-performing models fitted with few traits were able to predict extinction risks of species with good accuracy. These models applied to Data Deficient (DD) species proved how useful they may be to speed up the IUCN Red List assessment process while reducing the number of listed DD species, by selecting species most in need of a full, detailed assessment. Some traits tested in this study are a novelty in conservation research on mosses, opening new possibilities for future studies. The traits studied and the models presented here are a significant contribution to the knowledge of mosses at risk of extinction and will help to improve conservation efforts.

Societal Impact Statement: Proposals to increase protected area networks to 30% of land area globally will, given habitat conversion, require ecosystem restoration. Trait‐based approaches provide tools for this and highlight priorities for protected area expansion—both where functional diversity has the highest values and where it is higher than expected given species richness. Maps of sampled angiosperm species from across Africa show where these diversity metrics deviate. These maps also show the 30% of land with greatest potential to support functional diversity at national and continental scales, of which less than a quarter is protected, demonstrating the need for coordinated trans‐national plant conservation efforts.

The UK government’s 25 Year Plan to Improve the Environment (25YEP), published in 2018, together with annual progress reports and subsequent periodic revisions, represent the most comprehensive and forward-looking single body of environmental legislation for the United Kingdom. The forthcoming update of the UK National Biodiversity Strategy and Action Plan (NBSAP) addressing targets for the Convention on Biological Diversity’s Global Biodiversity Framework to 2030 is also an opportunity to revisit the monitoring framework developed for the 25YEP. Here, we present an evaluation of the goals, targets and indicators of the 25YEP in light of gaps, synergies and opportunities for aligning with the CBD 2030 Framework. We make a number of recommendations for adopting indicators already developed in the context of CBD targets that can also measure progress towards environmental targets within the UK, with a view to more holistic monitoring of biodiversity status and trends. Landscape-scale interventions and, in particular, improvements to farming—exemplified by the new Environmental Land Management Schemes—and fishing policies have the potential for the greatest enhancement to the state of the UK environment and the plants and wildlife within it.

Geographic range size is the most commonly implemented criterion of species’ extinction risk used in IUCN Red List assessments, especially for poorly-recorded species. IUCN applies two contrasting range size measures to capture different facets of a species’ distribution: Extent of Occurrence (EOO; Criterion B1) is the area bounding all known occurrences and is a proxy for the spatial autocorrelation of risk, while the Area of Occupancy (AOO; Criterion B2) is the area occupied within this boundary and is related to population size at finer grains. Various methods have been proposed to measure both EOO and AOO. We evaluate the impact of applying four methods for each of Criterion B1 and of B2, as well as key parameter choices, on the Red List status of 227 poorly-recorded neotropical pteridophyte species. Between 2 and 100% of species would be considered threatened depending on methodology. The minimum convex polygon method of estimating EOO was relatively robust to sampling effort for all but the least-recorded species. The IUCN-recommended method for estimating AOO of summing occupied 2 × 2 km grid cells was very strongly correlated with the total number of records. It is likely that only a small fraction of species can be adequately assessed using this method, and we recommend caution applying the method to poorly-recorded species in particular, where models predicting occupancy in unsampled areas (e.g. species distribution models) may provide more accurate assessments. It is vital that methodological information is retained with assessments, and comparisons should only be made between assessments utilising equivalent methods.

Bioregionalisation partitions diversity so that similarity of the selected biological and ecological variables is higher within regions than it is outside those regions. The classic approach partitions an area based on species composition, whereas more recent methods based on remotely sensed data classify biogeographic regions on biophysical and structural variables of vegetation. Another, yet to be explored opportunity, is offered by identifying distinct ecological strategies of plants inhabiting a given area, that is, a functional trait‐based bioregionalisation. Here, we propose such a bioregionalisation using trait hypervolumes. We also compare the proposed functional bioregionalisation with established classifications based on species composition or on remotely sensed data to identify spatial congruence among them, and suggest possible reasons behind observed patterns. Natural history collections represent an underexploited resource, despite holding both trait and locality information and being taxonomically comprehensive. We compile values of traits (leaf size, plant height, seed number per fruit, seed volume) derived from natural history collections for a random sample of African angiosperm species (~1% of the continental flora) to estimate a trait hypervolume. We use hierarchical clustering to divide the hypervolume into four segments (each representing a distinct ecological strategy), whose spatial intersections produced 12 putative biogeographic regions, each containing one or more of these strategies. We spatially map the hypervolume segments onto the entire African continent and calculate the spatial congruence of the putative functional biogeographic regions with previous bioregionalisations. We identify values and combinations of traits that can be indicative of biogeographic regions. This functional bioregionalisation shows greater spatial congruence with that derived from species composition than from remote sensing. However, spatial congruence is low at the continent scale (19%–37%), and varies greatly among regions and in pairwise comparisons between bioregionalisations. Synthesis. Plant traits from natural history collections offer an underused source of information for biogeographic analyses. We demonstrate potential applications of trait hypervolumes in functional biogeography, and outline strengths and drawbacks of the different bioregionalisation methods. Finally, we suggest that key ecological strategies could be used in future models as proxies to anticipate shifts of species assemblages and biogeographic regions.