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Assessing species' extinction risk is vital to setting conservation priorities. However, assessment endeavors, such as those used to produce the IUCN Red List of Threatened Species, have significant gaps in taxonomic coverage. Automated assessment (AA) methods are gaining popularity to fill these gaps. Choices made in developing, using, and reporting results of AA methods could hinder their successful adoption or lead to poor allocation of conservation resources. We explored how choice of data cleaning type and level, taxonomic group, training sample, and automation method affect performance of threat status predictions for plant species. We used occurrences from the Global Biodiversity Information Facility (GBIF) to generate assessments for species in 3 taxonomic groups based on 6 different occurrence-based AA methods. We measured each method's performance and coverage following increasingly stringent occurrence cleaning. Automatically cleaned data from GBIF performed comparably to occurrence records cleaned manually by experts. However, all types of data cleaning limited the coverage of AAs. Overall, machine-learning-based methods performed well across taxa, even with minimal data cleaning. Results suggest a machine-learning-based method applied to minimally cleaned data offers the best compromise between performance and species coverage. However, optimal data cleaning, training sample, and automation methods depend on the study group, intended applications, and expertise.La valoración del riesgo de extinción de las especies es vital para el establecimiento de prioridades de conservación. Sin embargo, los esfuerzos de valoración, como los que se usan para generar la Lista Roja de Especies Amenazadas de la UICN, tienen brechas importantes en la cobertura taxonómica. Los métodos de valoración automatizada (VA) están ganando popularidad como reductores de estas brechas. Las elecciones realizadas en el desarrollo, uso y reporte de resultados de los métodos de VA podrían obstaculizar su adopción exitosa o derivar en una asignación deficiente de recursos para la conservación. Exploramos cómo la selección del tipo de limpieza de datos y el nivel, grupo taxonómico, muestra de entrenamiento y el método de automatización afectan el desempeño de las predicciones del estado de amenaza de las especies de plantas. Usamos los registros de la Global Biodiversity Information Facility (GBIF) para generar las valoraciones de las especies de tres grupos taxonómicos con base en seis métodos diferentes de VA basados en la presencia de las especies. Medimos el desempeño de cada método y cobertura después de una limpieza de presencia cada vez más estricta. La información de la GBIF limpiada automáticamente tuvo un desempeño comparable con los registros de presencia limpiados manualmente por expertos. Sin embargo, todos los tipos de limpieza de datos limitaron la cobertura de las valoraciones automatizadas. En general, los métodos basados en el aprendizaje automático tuvieron un buen desempeño en todos los taxones, incluso con una limpieza mínima de datos. Los resultados sugieren que un método basado en el aprendizaje automático aplicado a información con la mínima limpieza ofrece el mejor equilibrio entre el desempeño y la cobertura de la especie. A pesar de esto, la limpieza óptima de datos, la muestra de entrenamiento y los métodos de automatización dependen del grupo de estudio, las aplicaciones deseadas y la experiencia.

Herbarium specimens are increasingly recognized as an important resource for conservation science and virtual herbaria are making specimens freely available to a wider range of users than ever before. Few virtual herbaria are designed with conservation use as a primary driver. Exceptionally, Brazil's Reflora Virtual Herbarium (RVH) was created to increase knowledge and conservation of the Brazilian flora. The RVH is closely integrated with the Flora of Brazil 2020 platform on which Brazil's new national Flora is under construction. Both resources are accessible via the Reflora home page and thousands of users move seamlessly between these Reflora resources. To understand how the Reflora resources are currently used and their impact on conservation science, we conducted a literature review and an online survey. We searched for publications of studies in which Reflora resources were used and publications resulting from Brazilian researchers who were part of Reflora's research and mobility program. The survey contained multiple choice questions and questions that required a written response. We targeted Reflora webpage visitors with the survey to capture a wider range of Reflora users than the literature review. Reflora resources were used for a variety of conservation‐relevant purposes. Half the 806 scientific publications in which Reflora was cited and 81% of the 1069 survey respondents accessing Reflora resources mentioned conservation‐relevant research outputs. Most conservation‐relevant uses of the Reflora resources in scientific publications were research rather than implementation focused. The survey of Reflora users showed conservation uses and impacts of virtual herbaria were more numerous and diverse than the uses captured in the literature review. Virtual herbaria are vital resources for conservation science, but they must document use and impacts more comprehensively to ensure sustainability., Article impact statement: Virtual herbaria, vital resources for taxonomy, conservation and ecology, must showcase their relevance to gain ongoing support.

The value of natural history collections for conservation science research is increasingly recognized, despite their well‐documented limitations in terms of taxonomic, geographic, and temporal coverage. Specimen‐based analyses are particularly important for tropical plant groups for which field observations are scarce and potentially unreliable due to high levels of diversity‐amplifying identification challenges. Specimen databases curated by specialists are rich sources of authoritatively identified, georeferenced occurrence data, and such data are urgently needed for large genera. We compared entries in a monographic database for the large Neotropical genus Myrcia in 2007 and 2017. We classified and quantified differences in specimen records over this decade and determined the potential impact of these changes on conservation assessments. We distinguished misidentifications from changes due to taxonomic remodeling and considered the effects of adding specimens and georeferences. We calculated the potential impact of each change on estimates of extent of occurrence (EOO), the most frequently used metric in extinction‐risk assessments of tropical plants. We examined whether particular specimen changes were associated with species for which changes in EOO over the decade were large enough to change their conservation category. Corrections to specimens previously misidentified or lacking georeferences were overrepresented in such species, whereas changes associated with taxonomic remodeling (lumping and splitting) were underrepresented. Among species present in both years, transitions to less threatened status outnumbered those to more threatened (8% vs 3%, respectively). Species previously deemed data deficient transitioned to threatened status more often than to not threatened (10% vs 7%, respectively). Conservation scientists risk reaching unreliable conclusions if they use specimen databases that are not actively curated to reflect changing knowledge., Article impact statement: Digitized natural history collections must be updated as taxonomic knowledge grows so as to maximize their utility for conservation science.

Societal Impact Statement Research and publication of the planet's remaining plant and fungal species as yet unknown to science is essential if we are to address the United Nations Sustainable Development Goal (SDG) 15 “Life on Land” which includes the protection of terrestrial ecosystems and halting of biodiversity loss. If species are not known to science, they cannot be assessed on the International Union for Conservation of Nature (IUCN) Red List of Threatened Species and so the possibility to protect them from extinction is reduced. Furthermore, until species are known to science they cannot be fully scientifically evaluated for their potential as new foods, medicines, and products which would help address SDGs 1,2,3, and 8. Summary Scientific discovery, including naming new taxa, is important because without a scientific name, a species is invisible to science and the possibilities of researching its ecology, applications and threats, and conserving it, are greatly reduced. We review new scientific discoveries in the plant and fungal kingdoms, based largely on new names of taxa published in 2019 and indexed in the International Plant Names Index and Index Fungorum. Numbers of new species in both kingdoms were similar with 1942 new species of plant published and 1882 species of fungi. However, while >50% of plant species have likely been discovered, >90% of fungi remain unknown. This gulf likely explains the greater number of higher order taxa for fungi published in 2019: three classes, 18 orders, 48 families and 214 genera versus one new family and 87 new genera for plants. We compare the kingdoms in terms of rates of scientific discovery, globally and in different taxonomic groups and geographic areas, and with regard to the use of DNA in discovery. We review species new to science, especially those of interest to humanity as new products, and also by life‐form. We consider where future such discoveries can be expected. We recommend an urgent increase in investment in scientific discovery of plant and fungal species, while they still survive. Priorities include more investment in training taxonomists, in building and equipping collections‐based research centers for them, especially in species‐rich, income‐poor countries where the bulk of species as yet unknown to science are thought to occur.

The IUCN Red List of Threatened SpeciesTM (hereafter the Red List) is an important global resource for conservation that supports conservation planning, safeguarding critical habitat and monitoring biodiversity change (Rodrigues et al. 2006). However, a major shortcoming of the Red List is that most of the world's described species have not yet been assessed and published on the Red List (Bachman et al. 2019Eisenhauer et al. 2019). Conservation efforts can be better supported if the Red List is expanded to achieve greater coverage of mega-diverse groups of organisms such as plants, fungi and invertebrates. There is, therefore, an urgent need to speed up the Red List assessment and documentation workflow. One reason for this lack of species coverage is that a manual and relatively time-consuming procedure is usually employed to assess and document species. A recent update of Red List documentation standards (IUCN 2013) reduced the data requirements for publishing non-threatened or 'Least Concern' species on the Red List. The majority of the required fields for Least Concern plant species can be found in existing open-access data sources or can be easily calculated. There is an opportunity to consolidate these data and analyses into a simple application to fast-track the publication of Least Concern assessments for plants. There could be as many as 250,000 species of plants (60%) likely to be categorised as Least Concern (Bachman et al. 2019), for which automatically generated assessments could considerably reduce the outlay of time and valuable resources for Red Listing, allowing attention and resources to be dedicated to the assessment of those species most likely to be threatened. We present a web application, Rapid Least Concern, that addresses the challenge of accelerating the generation and documentation of Least Concern Red List assessments. Rapid Least Concern utilises open-source datasets, such as the Global Biodiversity Information Facility (GBIF) and Plants of the World Online (POWO) through a simple web interface. Initially, the application is intended for use on plants, but it could be extended to other groups, depending on the availability of equivalent datasets for these groups. Rapid Least Concern users can assess a single species or upload a list of species that are assessed in a batch operation. The batch operation can either utilise georeferenced occurrence data from GBIF or occurrence data provided by the user. The output includes a series of CSV files and a point map file that meet the minimum data requirements for a Least Concern Red List assessment (IUCN 2013). The CSV files are compliant with the IUCN Red List SIS Connect system that transfers the data files to the IUCN database and, pending quality control checks and review, publication on the Red List. We outline the knowledge gap this application aims to fill and describe how the application works. We demonstrate a use-case for Rapid Least Concern as part of an ongoing initiative to complete a global Red List assessment of all native species for the United Kingdom Overseas Territory of Bermuda.

Societal Impact Statement There is increasing awareness that plants and fungi, as natural solutions, can play an important role in tackling ongoing global environmental challenges. We illustrate how understanding current and projected threats to plants and fungi is necessary to manage and mitigate risks, while building awareness of gaps and bias in current assessment coverage is essential to adequately prioritize conservation efforts. We highlight the state of the art in conservation science and point to current methods of assessment and future studies needed to mitigate species extinction. Summary Plant and fungal biodiversity underpin life on earth and merit careful stewardship in an increasingly uncertain environment. However, gaps and biases in documented extinction risks to plant and fungal species impede effective management. Formal extinction risk assessments help avoid extinctions, through engagement, financial, or legal mechanisms, but most plant and fungal species lack assessments. Available global assessments cover c. 30% of plant species (ThreatSearch). Red List coverage overrepresents woody perennials and useful plants, but underrepresents single‐country endemics. Fungal assessments overrepresent well‐known species and are too few to infer global status or trends. Proportions of assessed vascular plant species considered threatened vary between global assessment datasets: 37% (ThreatSearch), and 44% (International Union for Conservation of Nature Red List of Threatened Species). Our predictions, correcting for several quantifiable biases, suggest that 39% of all vascular plant species are threatened with extinction. However, other biases remain unquantified, and may affect our estimate. Preliminary trend data show plants moving toward extinction. Quantitative estimates based on plant extinction risk assessments may understate likely biodiversity loss: they do not fully capture the impacts of climate change, slow‐acting threats, or clustering of extinction risk, which could amplify loss of evolutionary potential. The importance of extinction risk estimation to support existing and emerging conservation initiatives is likely to grow as threats to biodiversity intensify. This necessitates urgent and strategic expansion of efforts toward comprehensive and ongoing assessment of plant and fungal extinction risk.

This work was supported by CNPq project (455505/2014-4) to all authors; The MPEG/ITVDS/FADESP Term of Agreement (01205.000250/2014-10); DCZ – CNPq productivity grant; NFOM - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (BR- 88887/130640/2016-00). In order to establish effective conservation strategy, drivers of local and regional patterns of biodiversity need to be understood. The composition of local biodiversity is dependent on a number of factors including evolution and redistribution of lineages through dispersal and environmental heterogeneity. Brazilian canga is characterised by a ferrugineous substrate, found both in the Iron Quadrangle of Minas Gerais and in the Carajás mountains in Amazonia. Canga is one of several specialised habitat types comprising Brazilian campo rupestre, a montane vegetation found within or adjacent to several major Brazilian bioregions, including the Atlantic Forest and Amazonia, with exceptionally high levels of diversity and endemism arising from both history of dispersal and environmental variation. In order to inform biodiversity conservation for canga, and more broadly for campo rupestre, we performed floristic and phylogenetic analyses investigating affinities between 28 sites on different substrates (canga and quartzite) and geographic locations (Carajás, Pará [Amazonia]; Cadeia do Espinhaço, Minas Gerais; Chapada Diamantina, Bahia). Through analysis of 11204 occurrences of 4705 species of angiosperms, we found that Amazonian Carajás canga plant communities formed a cohesive group, distinct from species assemblages found in Eastern Brazil (Minas Gerais, Bahia), either on canga or quartzite. The phylogenetic megatree of species across all sites investigated shows associations between certain clades and Amazonian canga, with few shared species between the Amazonian Carajás and Eastern Brazil sites, while the floristic comparison shows high levels of heterogeneity between sites. The need for reserves for Amazonian Carajás canga has been recognized and addressed by the creation of a national park. However, current sampling does not provide sufficient reassurance that the canga areas now benefitting from full legal protection adequately represent the regional canga flora. Publisher PDF