This paper presents the assessment results of 125 species of bryophytes in continental Africa, from a sample of 1500 species randomly selected from around the world. This is part of a contribution towards one of the headline indicators of the UN Convention on Biological Diversity, the Red List Index, which measures trends in extinction risk of biodiversity. Assessments of the selected species followed the IUCN Red List Categories and Criteria and represent a significant further step in the evaluation of threatened African bryophytes. Of these 125 species, 33 species are found to be threatened with extinction (either Vulnerable, Endangered or Critically Endangered) or are Near Threatened under IUCN Red List criteria, of which 27 species of these are formally assessed as threatened, across 20 African countries. One further species has a range below the Criterion B threshold for Vulnerable but is not facing any significant threats, while four poorly known species are here provisionally assessed as being Data Deficient. The other 84 species included in this sample of African species and presented in Table 1 are Least Concern, due to their large geographic ranges, therefore suggesting that 21.6% of African bryophytes overall are threatened with extinction. The most recurrent threat is ongoing, relentless, small-scale deforestation, followed by land conversion to agriculture and urban expansion, and most threatened bryophytes are those localised endemics known only from single or very few locations. A further 41 bryophyte species endemic to Madagascar and/or on other islands of the western Indian Ocean, including 5 that are also found in Africa, have already been treated in a separate publication.

BACKGROUND AND AIMS:Fewer than one percent of the World's plant genera have >500 species, yet these big genera collectively account for >25% of plant species. It remains unclear how these specific big genera achieved their present-day global distributions and if they share characteristics that may have contributed to their success. We examined the distributions of big plant genera to determine: (i) if the diversity patterns of big genera are representative of overall plant diversity patterns; and (ii) if there are groups of big genera with similar geographic distributions that may help explain their success. METHODS:We mapped the distribution of each flowering plant species at the botanical country scale using data from the World Checklist of Vascular Plants and investigated the proportion of species in big genera in each botanical country across latitudes and climate zones. We used hierarchical clustering to determine whether big genera could be grouped based upon their distributions in botanical countries, aggregated into floristic realms. KEY RESULTS:Big plant genera are not distributed evenly relative to global flowering plant diversity but are particularly well-represented in continental and polar regions of the Northern Hemisphere. Big plant genera can be grouped into five clusters based upon their shared distributions, each centred around one floristic realm. Individual big genera, however, tend to occur across multiple floristic realms, with >92% occurring in two or more floristic realms and ∼33% occurring across all realms. CONCLUSIONS:We propose that pre-adaptions, ecological opportunity, long-distance dispersal, and key innovations have played a central role in the geographical evolution of big genera, and contributed to their exceptional size and distribution. Collectively, these factors have resulted in repeated radiations among different clades across big plant genera and have ultimately led to the accumulation of species diversity in these groups.

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.

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.

Although most plant species have yet to have their extinction risk evaluated for the IUCN Red List, current knowledge of plant diversity suggests that tens of thousands (8%-38% of species) are likely to remain assessed as Data Deficient (DD). This impacts evaluations of the overall proportion of threatened species, as well as on setting appropriate priorities for biodiversity conservation. One of the principle causes of Data Deficiency is taxonomic uncertainty: data permitting an IUCN Red List assessment is often lacking, because species that are poorly known are also taxonomically uncertain. Establishing the taxonomic status of DD species thus assists in determining whether confusion over this is causing their evaluation as DD. In this paper, three separate numerical methods - hierarchical clustering, ordination and phylogenetic ordination - were applied to three independent datasets of standardized traits for plant species from China in order to identify, for each DD species, morphologically similar Non-DD species. Semi-automated analysis of morphological disparity quickly identifies which species are in need of further taxonomic research, as shared traits can indicate either potential synonymy or, otherwise, successful adaptations of distinct species to similar ecological conditions. Extinction risk assessments of morphologically and phylogenetically distinct DD species could then be informed by data on habitat preferences, geographic range or population size from Non-DD species with the most similar traits. Ultimately, determination of the morphological distance between DD species and their most similar Non-DD species, and therefore of differences in the trait space occupied by DD and Non-DD species, could be the basis of future research for improved inference of conservation priorities, and help distinct DD species with similar ecological traits as other taxonomically accepted, threatened species to be identified as being threatened themselves.

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.

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.

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.

In this paper, assessments of extinction risk for 41 species of bryophytes found on Madagascar and/or adjacent islands of the western Indian Ocean are presented, which are taken from assessments of a sample of 1500 bryophyte species selected at random from around the world. This is itself a contribution towards the IUCN Red List and the Red List Index, which measures trends in extinction risk of biodiversity and is one of the headline indicators of the UN Convention on Biological Diversity. Assessments of the selected species followed the IUCN Red List Categories and Criteria and represent a significant further step in the evaluation of threatened Indian Ocean bryophytes. There are 35 species endemic to one or other Indian Ocean island, while six are shared with the African continent. Of these 41 species, all but one species is listed here as being either threatened with extinction (classified as Vulnerable, Endangered or Critically Endangered) or Near Threatened under IUCN Red List criteria. From this we estimate that more than one third (>37%) of Indian Ocean bryophytes overall and more than half (>54%) of bryophytes found in Madagascar are threatened with extinction, after accounting for additional widespread species in the sample that are considered to be of Least Concern. The principal threat is overwhelmingly from relentless, small-scale deforestation and conversion of natural habitats for subsistence agriculture, and a large majority of the threatened bryophytes assessed in this paper are localised endemics often known from historical collections in single or very few locations.

Species differ in their biological susceptibility to extinction, but the set of traits determining susceptibility varies across taxa. It is yet unclear which patterns are common to all taxa, and which are taxon-specific, with consequences to conservation practice. In this study we analysed the generality of trait-based prediction of extinction risk across terrestrial (including freshwater) vertebrates, invertebrates and plants at a global scale. For each group, we selected five representative taxa and within each group we explored whether risk can be related to any of 10 potential predictors. We then synthesized outcomes across taxa using a meta-analytic approach. High habitat specificity was a consistent predictor across vertebrates, invertebrates and plants, being a universal predictor of risk. Slow life-history traits – large relative offspring size, low fecundity, long generation length –, and narrow altitudinal range were also found to be good predictors across most taxa, but their universality needs to be supported with additional data. Poor dispersal ability was a common predictor of extinction risk among invertebrate and plant taxa, but not consistently among vertebrates. The remaining traits (body size, microhabitat verticality, trophic level, and diet breadth) were useful to predict extinction risk but only at lower taxonomical levels. Our study shows that despite the idiosyncrasies among taxa, universal susceptibility to extinction exists and several traits might influence extinction risk for most taxa. Informing conservation prioritization at lower taxonomic scales should however include taxon-specific trait-based predictors of extinction risk.

Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade-offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species-targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much-needed guide through the ever-increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.

As the United Nations develops a post-2020 global biodiversity framework for the Convention on Biological Diversity, attention is focusing on how new goals and targets for ecosystem conservation might serve its vision of 'living in harmony with nature'1,2. Advancing dual imperatives to conserve biodiversity and sustain ecosystem services requires reliable and resilient generalizations and predictions about ecosystem responses to environmental change and management3. Ecosystems vary in their biota4, service provision5 and relative exposure to risks6, yet there is no globally consistent classification of ecosystems that reflects functional responses to change and management. This hampers progress on developing conservation targets and sustainability goals. Here we present the International Union for Conservation of Nature (IUCN) Global Ecosystem Typology, a conceptually robust, scalable, spatially explicit approach for generalizations and predictions about functions, biota, risks and management remedies across the entire biosphere. The outcome of a major cross-disciplinary collaboration, this novel framework places all of Earth's ecosystems into a unifying theoretical context to guide the transformation of ecosystem policy and management from global to local scales. This new information infrastructure will support knowledge transfer for ecosystem-specific management and restoration, globally standardized ecosystem risk assessments, natural capital accounting and progress on the post-2020 global biodiversity framework.

Aim The environmental preferences of species are an important facet of their response to changing conditions, and these have long been thought to exhibit phylogenetic conservatism. However, these bioclimatic envelopes have not previously been imputed from climate records at the date and location of occurrence, and the strength of their phylogenetic signal has not been studied at a broad scale. Here, we combine records from global climate reconstructions with contemporaneous plant occurrences for all available terrestrial plant species and test for phylogenetic niche conservatism in plant climatic traits. Location Global. Time period 1901-2018. Major taxa studied Terrestrial plants. Methods We used >100 million plant records from the Global Biodiversity Information Facility (GBIF) to produce distributions of bioclimatic envelopes for >200,000 species, using a range of climate variables. We matched species observations to historical climate reconstructions from the European Centre for Medium-Range Weather Forecasting (ECMWF) and compared this with WorldClim climate averages. We tested for phylogenetic signal in a supertree of plants using Pagel's lambda. Finally, to investigate how well bioclimatic envelopes could be inferred for poorly known and rare species, we performed cross-validation by removing occurrence records for some common species to test how accurately their bioclimatic envelopes were estimated. Results We found extremely strong phylogenetic signals (lambda > 0.9 in some cases) for climate variables from both climate datasets, including temperature, soil temperature, solar radiation and precipitation. We were also able to impute missing bioclimatic envelopes for artificially removed species, having a correlation with observed data of .7. Main conclusions We reconstructed plant climatic tolerances for >200,000 plant species historically recorded on GBIF using a technique that could be applied to any comparable biodiversity dataset. Although global information on most species is sparse, we explored methods for bias correction and data imputation, with positive results for both.

Herbaria are renowned as collections of specimens for research in plant taxonomy, plant identification and more recently in plant phylogenetics. The production of Floras and monographs in herbaria is fundamental to the understanding of plant taxonomy and plant biogeography. Herbaria have played an important role in providing the raw geographic data behind plant species distributions which form the basis of the most commonly used biodiversity metric: species richness. Less well recognised is the potential for using comprehensive species checklists, produced by herbaria, as a sampling frame when projecting biodiversity metrics. Functional diversity metrics derived from plant trait values are growing in importance in biodiversity monitoring; however, it is unclear whether the trait-based functional attributes are responsive to changes in species richness in all geographic areas. Modelling of the spatial distribution of trait values is one way to investigate the limits of biodiversity monitoring reliant on trait values. The research outputs of herbaria are arguably an untapped resource of such trait data. Greater digitisation of published Flora treatments as well as continuing digitisation of herbarium specimens is increasingly making these resources more available. With appropriate methods to ameliorate known biases in the species locality and plant trait data held by herbaria, these institutions can play an important role in building spatial models of plant trait distributions. Such models help to establish the relationships between species richness and plant functional diversity metrics in different biomes required for trait-led biodiversity monitoring. Here we present a six-step method to allow data held by herbaria to be used to establish a spatial model of functional diversity metrics at a continental scale.

Societal Impact StatementIdentifying regions of the world that are rich in plant species will enable conservation efforts to be more effectively targeted. We present a review of global studies of plant diversity, including novel analyses from our own work, and highlight areas of the world that are consistently identified by multiple studies utilizing varied data sets as being particularly rich in plant species. This will be of interest to botanical professionals and conservationists seeking to identify and conserve priority species‐rich environments, including those working to progress international conservation targets, and to all those interested in the global distribution of biodiversity and its conservation.SummaryAreas of high diversity for vascular plants, both for numbers of species and of endemic species, are by now well established and in agreement across a variety of studies using a wide range of data from different sources. Here we review the current state of knowledge of geographical patterns of plant diversity around the world, compare this with our knowledge of vertebrate taxonomic groups, and reflect on next steps for better characterizing patterns of diversity in order to achieve effective conservation prioritization. We illustrate this with analyses of geographical patterns of plant diversity from three different data types with differing degrees of geographical and ecological resolution. At broad spatial scales these analyses are largely congruent with each other and with areas of high diversity and endemism for species of terrestrial vertebrates.

David A. Keith, Jose R. Ferrer, Emily Nicholson, Melanie J. Bishop, Beth A. Polidoro, Eva RamirezLlodra, Mark G. Tozer, Jeanne L. Nel, Ralph Mac Nally, Edward J. Gregr, Kate E. Watermeyer, Franz Essl, Don Faber-Langendoen, Janet Franklin, Caroline E. R. Lehmann, Andres Etter, Dirk J. Roux, Jonathan S. Stark, Jessica A. Rowland, Neil A. Brummitt, Ulla C. Fernandez-Arcaya, Iain M. Suthers, Susan K. Wiser, Ian Donohue, Leland J. Jackson, R. Toby Pennington, Nathalie Pettorelli, Angela Andrade, Tytti Kontula, Arild Lindgaard, Teemu Tahvanainan, Aleks Terauds, Oscar Venter, James E. M. Watson, Michael A Chadwick, Nicholas J. Murray, Justin Moat, Patricio Pliscoff, Irene Zager, Richard T. Kingsford

Summary Biodiversity is eroding at unprecedented rates due to human activity 1 . Species’ trajectories towards extinction are shaped by multiple factors, including life-history traits 2 as well as human pressures 3 . Previous studies linking these factors to extinction risk have been narrow in their taxonomic and geographic scope 4 , thus limiting the ability for identifying global predictors. We studied the relation between 12 traits and the extinction risk of almost 900 species representing 15 groups across the tree of life (vertebrates, invertebrates and plants) at a global scale. We show that threatened species share narrow habitat breadth, poor dispersal ability, low fecundity, small altitudinal range, and are affected by a large human footprint. Other traits either show contrasting responses among groups (body size, offspring size, and change in human footprint), or relations were found for only a limited number of taxa (generation length, diet breadth, microhabitat). Our study suggests that in the absence of data on the precise distribution and population trends of species, traits can be used as predictors of extinction risk and thus help guide future research, monitoring and conservation efforts.

Ecosystems are governed by dynamic processes such as competition for resources, reproduction and dispersal. These shape their biodiversity and how the system responds to change. Current approaches to modelling ecosystems, especially plants, focus on either describing fine-scale processes for individual species or broad-scale patterns for limited groups of plant functional types. Digitisation of herbarium and other plant records has unlocked a wealth of information that can be used to drive models of plant communities and make predictions for their future under different scenarios of climate change. The advent of increased computational capacity and fast, high level programming languages allows for simulation of such landscapes at unprecedented scales. Here, we demonstrate a tool for Ecosystem Simulation through Integrated Species Trait-Environment Modelling (EcoSISTEM), which models plant species across multiple ecosystem sizes, from patches and small islands to regions and entire continents. These simulated ecosystems support the ability to generate many different types of habitat, as well as reproducing different disturbance scenarios such as climate change, habitat loss and invasion. EcoSISTEM also reproduces examples of real-world species distributions by integrating plant occurrence records and global climate reconstructions to simulate plant species throughout the continent of Africa for the past century. EcoSISTEM allows us to flexibly explore the dynamics of tens of thousands of species interacting across a continent. The code parallelises efficiently across multiple nodes on high performance computing platforms, and has been scaled up to run on over 1000 cores. It allows us to study the impact of changes to climate, resources and habitat and investigate real-life mechanisms surrounding climate change and biodiversity loss.

Aim The Area of Occupancy (AOO) of a species is often utilized to assess extinction risk for determining IUCN Red List status. However, the recommended raw-counts method of summing occupied grid cells likely reflects only sampling effort, as the majority of species have not been sampled across their entire range at the fine grains required by IUCN. More accurate measurements can be generated at coarser grains (so-called atlas data) as false absences are reduced. If we fit the occupancy-area relationship to these data, we can extrapolate the relationship down to estimate occupancy at finer grains. Numerous models have been proposed to carry out such occupancy downscaling, but have only been tested on a limited range of species. Methods We test the ability of downscaling models to recover fine grain AOO against the raw-counts method for 28,900 virtual species with a wide range of prevalence and aggregation characteristics, subsampled to reflect common spatial biases in sampling effort. We address several questions for ensuring accurate downscaling: How to generate accurate atlas data? How far can we accurately extrapolate the occupancy-area relationship given perfect data? Can occupancy downscaling overcome false absences at fine grain sizes? And how does sampling bias and coverage affect accuracy? Results Downscaling was more accurate than the raw-counts method in all scenarios except where sampling coverage was very high and/or the sampling bias was positively related to the species distribution. However, if atlas data contained many false absences, then even downscaling under-estimated actual occupancy. Main conclusions Occupancy downscaling has the potential to be a useful tool for estimating AOO for IUCN Red List assessments, especially when sampling coverage is low and the currently recommended method is ineffective. However, its application should be tailored to the species' characteristics, as well as the sampling coverage and bias of the species' records.

In the face of biodiversity loss, we rely upon measures of diversity to describe the health of ecosystems and to direct policymakers and conservation efforts. However, there are many complexities in natural systems that can easily confound biodiversity measures, giving misleading interpretations of the system status and, as a result, there is yet to be a consistent framework by which to measure this biodiversity loss. Ecosystems are governed by dynamic processes, such as reproduction, dispersal and competition for resources, that both shape their biodiversity and how the system responds to change. Here, we incorporate these processes into simulations of habitat and environmental change, in order to understand how well we can identify signals of biodiversity loss against the background inherent variability these processes introduce. We developed a tool for Ecosystem Simulation through Integrated Species Trait-Environment Modelling (EcoSISTEM), which models on the species-level for several sizes of ecosystem, from small islands and patches through to entire regions, and several different types of habitat. We tested a suite of traditionally-used and new biodiversity measures on simulated ecosystems against a range of different scenarios of population decline, invasion and habitat loss. We found that the response of biodiversity measures was generally stronger in larger, more heterogeneous habitats than in smaller or homogeneous habitats. We were also able to detect signals of increasing homogenisation in climate change scenarios, which contradicted the signal of increased heterogeneity and distinctiveness through habitat loss.