Extinction risk predictions for the world's flowering plants to support their conservation.

More than 70% of all vascular plants lack conservation status assessments. We aimed to address this shortfall in knowledge of species extinction risk by using the World Checklist of Vascular Plants to generate the first comprehensive set of predictions for a large clade: angiosperms (flowering plants, c. 330 000 species). We used Bayesian Additive Regression Trees (BART) to predict the extinction risk of all angiosperms using predictors relating to range size, human footprint, climate, and evolutionary history and applied a novel approach to estimate uncertainty of individual species-level predictions. From our model predictions, we estimate 45.1% of angiosperm species are potentially threatened with a lower bound of 44.5% and upper bound of 45.7%. Our species-level predictions, with associated uncertainty estimates, do not replace full global, or regional Red List assessments, but can be used to prioritise predicted threatened species for full Red List assessment and fast-track predicted non-threatened species for Least Concern assessments. Our predictions and uncertainty estimates can also guide fieldwork, inform systematic conservation planning and support global plant conservation efforts and targets.

Re-evaluating the importance of threatened species in maintaining global phytoregions.

Anthropogenic introductions are known to be changing the structure of global phytogeographical regions (phytoregions), but previous studies have been limited by incomplete or biased data sets that are likely to underestimate the importance of threatened species. In this work, we analyse a comprehensive data set of all known species and their occurrences (at botanical country resolution) to quantify the impact of potential future extinction scenarios. We used Infomap, a network-based community detection algorithm, to generate phytoregional delineations for six species-distribution scenarios (native, introduced and extinctions of species that are either documented as threatened or likely to be threatened, as well as combinations thereof). We compared the numbers and sizes of phytoregions to characterise the amount and spatial distribution of changes in global phytoregions under each scenario. Extinctions of species that are predicted to be threatened had a greater homogenising effect on phytoregions than introductions, and there was some evidence that introductions may even mitigate the homogenisation caused by extinctions, though this interaction is complex. This research provides the first evidence that the loss of threatened species would have significant ramifications for global phytoregions and demonstrates the need to consider extinction processes in studies of anthropogenic effects on biodiversity patterns.

rWCVP: a companion R package for the World Checklist of Vascular Plants.

The World Checklist of Vascular Plants (WCVP) is an extremely valuable resource that is being used to address many fundamental and applied questions in plant science, conservation, ecology and evolution. However, databases of this size require data manipulation skills that pose a barrier to many potential users. Here, we present rWCVP, an open-source R package that aims to facilitate the use of the WCVP by providing clear, intuitive functions to execute many common tasks. These functions include taxonomic name reconciliation, geospatial integration, mapping and generation of multiple different summaries of the WCVP in both data and report format. We have included extensive documentation and tutorials, providing step-by-step guides that are accessible even to users with minimal programming experience. rWCVP is available on cran and GitHub.

Evidence-based guidelines for automated conservation assessments of plant species.

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.

Plants used traditionally as antimalarials in Latin America: Mining the tree of life for potential new medicines.

ETHNOPHARMACOLOGICAL RELEVANCE: Malaria remains a serious and challenging disease. Traditional antimalarial medicines are largely based on plants, and ethnopharmacological research has inspired the development of antimalarial pharmaceuticals such as artemisinin. Antimalarial drug resistance is an increasing problem in Plasmodium species, and new therapeutic strategies to combat malaria are needed. Although the number of malaria cases has been decreasing in Latin America, malaria remains a significant threat in many regions. Local people in Latin America have been using numerous plant species to treat malaria, some of which have been scientifically studied, but many others have not. AIM OF THE STUDY: Our principal objective is to harness ethnobotanical data on species used traditionally to treat malaria, combined with phylogenetic approaches, to understand how ethnobotany could help identify plant genera as potential sources of new medicines. MATERIALS AND METHODS: Plants used to treat malaria in Latin America were compiled from published and grey literature, unpublished data, and herbarium specimens. Initial assessment of potentially important species/genera/families included compiling the number of species used within the genus, the number of use reports per genus and species, and the geographic distribution of their use. The analysis of taxonomic distribution of species reported as antimalarial in Latin America (excluding the Southern Cone) was conducted, to determine which genera and families with reputed antimalarial properties are over-represented, and phylogenetic analyses were performed to identify if there was evidence for antimalarial species being dispersed/clustered throughout the tree or at its tips. This approach enabled 'hot-nodes' in certain families to be identified, to predict new genera with potential antimalarial properties. RESULTS: Over 1000 plant species have been used to treat malaria in Latin America, of which over 600 species were cited only once. The genera with the highest number of antimalarial species were Aspidosperma, Solanum, Piper, Croton and Aristolochia. In terms of geographic distribution, the most widely used genera were Aspidosperma, Momordica, Cinchona, Senna and Stachytarpheta. Significant phylogenetic signal was detected in the distribution of native species used for malaria, analysed in a genus-level phylogenetic framework. The eudicot and magnoliidae lineages were over-represented, while monocots were not. CONCLUSION: Analysis of ethnobotanical use reports in a phylogenetic framework reveals the existence of hot nodes for malaria across the Latin American flora. We demonstrate how species and genera currently lacking such reports could be pinpointed as of potential interest based on their evolutionary history. Extending this approach to other regions of the world and other diseases could accelerate the discovery of novel medicines and enhance healthcare in areas where new therapeutic strategies are needed.

Botanical Monography in the Anthropocene.

Unprecedented changes in the Earth's biota are prompting urgent efforts to describe and conserve plant diversity. For centuries, botanical monographs - comprehensive systematic treatments of a family or genus - have been the gold standard for disseminating scientific information to accelerate research. The lack of a monograph compounds the risk that undiscovered species become extinct before they can be studied and conserved. Progress towards estimating the Tree of Life and digital information resources now bring even the most ambitious monographs within reach. Here, we recommend best practices to complete monographs urgently, especially for tropical plant groups under imminent threat or with expected socioeconomic benefits. We also highlight the renewed relevance and potential impact of monographies for the understanding, sustainable use, and conservation of biodiversity.

Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.

The mobilization of large-scale datasets of specimen images and metadata through herbarium digitization provide a rich environment for the application and development of machine learning techniques. However, limited access to computational resources and uneven progress in digitization, especially for small herbaria, still present barriers to the wide adoption of these new technologies. Using deep learning to extract representations of herbarium specimens useful for a wide variety of applications, so-called "representation learning," could help remove these barriers. Despite its recent popularity for camera trap and natural world images, representation learning is not yet as popular for herbarium specimen images. We investigated the potential of representation learning with specimen images by building three neural networks using a publicly available dataset of over 2 million specimen images spanning multiple continents and institutions. We compared the extracted representations and tested their performance in application tasks relevant to research carried out with herbarium specimens. We found a triplet network, a type of neural network that learns distances between images, produced representations that transferred the best across all applications investigated. Our results demonstrate that it is possible to learn representations of specimen images useful in different applications, and we identify some further steps that we believe are necessary for representation learning to harness the rich information held in the worlds' herbaria.

Harnessing the potential of integrated systematics for conservation of taxonomically complex, megadiverse plant groups.

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.

Empleo del Potencial de la Sistemática Integrada para la Conservación de Grupos Botánicos Complejos y Megadiversos Resumen Cada vez se reconoce más el valor que tienen las colecciones de historia natural para la investigación dentro de la ciencia de la conservación, a pesar de las limitaciones en la documentación adecuada de la cobertura taxonómica, geográfica y temporal. Los análisis con base en especímenes son de particular importancia en los grupos de plantas tropicales, para los cuales las observaciones en el campo son escasas y potencialmente de poca confianza debido la gran cantidad de retos de identificación que amplifican la diversidad. Las bases de datos de especímenes que son curadas por especialistas son fuentes abundantes de datos identificados autoritativamente y de distribución georeferenciada, por lo que es urgente la necesidad de dichos datos para géneros extensos. Comparamos las entradas en una base de datos monográfica para el género neotropical extenso Myrcia en 2007 y en 2017. Clasificamos y cuantificamos las diferencias en los registros de especímenes durante esta década y determinamos el impacto potencial de estos cambios sobre las evaluaciones de conservación. Separamos las identificaciones erróneas de los cambios causados por la remodelación taxonómica y consideramos los efectos de la adición de especímenes y de georeferencias. Calculamos el impacto potencial de cada cambio sobre las estimaciones de la extensión de la distribución (EOO, en inglés), la medida que se utiliza con mayor frecuencia en las valoraciones del riesgo de extinción de las plantas tropicales. Examinamos si los cambios en un espécimen particular estuvieron asociados con especies para las cuales los cambios en la EOO durante la década fueron lo suficientemente amplios para cambiarles la categoría de conservación. Las correcciones hechas a especímenes previamente mal identificados o carentes de georeferencias estuvieron sobre-representadas en dichas especies, mientras que los cambios asociados con la remodelación taxonómica (agrupamiento y separación) estuvieron sub-representados. Entre las especies presentes en ambos años, las transiciones hacia un estado de conservación con menor amenaza superaron en cantidad a aquellas hacia un estado de mayor amenaza (8% vs 3%, respectivamente). Las especies que con anterioridad se clasificaban como deficientes de datos tuvieron más transiciones hacia el estado de amenazadas que hacia el estado de no amenazadas (10% vs 7%, respectivamente). Los científicos de la conservación corren el riesgo de llegar a conclusiones poco confiables si utilizan bases de datos de especímenes que no sean actualizadas para reflejar el conocimiento cambiante.

The use and misuse of herbarium specimens in evaluating plant extinction risks.

Herbarium specimens provide verifiable and citable evidence of the occurrence of particular plants at particular points in space and time, and are vital resources for assessing extinction risk in the tropics, where plant diversity and threats to plants are greatest. We reviewed approaches to assessing extinction risk in response to the Convention on Biological Diversity's Global Strategy for Plant Conservation Target 2: an assessment of the conservation status of all known plant species by 2020. We tested five alternative approaches, using herbarium-derived data for trees, shrubs and herbs in five different plant groups from temperate and tropical regions. All species were previously fully assessed for the IUCN Red List. We found significant variation in the accuracy with which different approaches classified species as threatened or not threatened. Accuracy was highest for the machine learning model (90%) but the least data-intensive approach also performed well (82%). Despite concerns about spatial, temporal and taxonomic biases and uncertainties in herbarium data, when specimens represent the best available evidence for particular species, their use as a basis for extinction risk assessment is appropriate, necessary and urgent. Resourcing herbaria to maintain, increase and disseminate their specimen data is essential to guide and focus conservation action.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.